{
  
    
        "api-strategy-api-development-leveraging-ai-for-a-better-api-strategy": {
          "title": "Leveraging AI For a Better API Strategy",
          "content"	 : "“API strategy” is a term prominently established in the ecosystem and heavily discussed, implemented, and followed by organizations. The term is more relevant now since API strategy has become, for the most part, AI strategy, since AI agents and services are now consuming APIs and tools to work towards business-specific goals under human tutelage. So the longstanding definition and scope of API strategy must take into account AI consumers.Then comes the actual process of building the API strategy. To no one’s surprise, here we can use AI too. So this article will be about how we can use AI to assemble and put into action better API strategies.                Build Smarter API Strategy with Moesif              14 day free trial. No credit card required.              Try for Free        What is API strategyAPI strategy thoroughly outlines, in a high-level, how to use application programming interfaces (APIs) to drive meaningful business outcomes. It aligns APIs with revenue, reliability, and long-term growth.The first obvious benefit of this premeditation is that your API program, since its inception or conceptualization, has zero misalignment with the business requisites, and close to zero chances of such in the future.Look at modern SaaS or enterprise environments: there exists internal platforms, partner ecosystems, and integrations shouldered by APIs, even more so with organizations’ oftentimes aggressive AI adoption. APIs have therefore become business infrastructure; API strategy guarantees that you intentionally, measurably, and sustainably maintain those interfaces.An efficacious API strategy answers these questions:  Why does this API exist? It must support one or more business objectives.  Who consumes it? The consumer demographic may consist of external developers, partners, internal teams, or monetized customers.  How does it evolve safely? Versioning, deprecation, and compatibility policies must exist.  How is it measured? Usage, performance, and revenue signals must inform decisions.APIs slip into mere technical assets if you fail to answer these questions, since no strategic direction exists. You will ship endpoints, monitor them, but flounder to connect API behavior to business impact. And things deteriorate as you scale.AI can help build and solidify your API strategy, but it can only do so when teams have established a lucid definition of the strategy itself and a shared understanding of that strategy.The limits of traditional API strategyModern APIs generate millions of requests, across regions, tenants, and use cases. The static style guides, rigid review processes, and manual approval gates will work for a portfolio of ten APIs, but unquestionably break down at enterprise scale. Without the force-multiplying capability of AI, the traditional way of strategizing APIs around your business aims will only cost you capital, by stifling innovation and introducing risks.Governance and decisionsTraditionally, maintaining quality requires human intervention. So your architects will look at documents and resources like OpenAPI specs for consistency, security, and standards. However, as the number of APIs and their integrations grow, review teams get overwhelmed. It also becomes more impractical to not have automated processes in place that are principled and controlled. You will be waiting weeks for approval or the situation will likely force your hand to sidestep governance entirely, precipitating shadow APIs, which are even more precarious now in the AI-saturated market.And with growth, strategic decisions also get harder. For example:  Pricing tiers set without deep behavioral segmentation  Security policies applied uniformly across different risk profiles  Roadmap priorities based on anecdotal customer feedbackThese decisions often rely on partial signals, but not the entirety of the context. When in small scale, you can intuitively make that deficiency work, but not in large scale.SpecificationsAI-assisted software development means code changes even faster now than documentation. So how feasible is the static strategy that relies on that documentation being the source of truth?If you don’t have automated, intelligent feedback loops, the discrepancies between the designed API (the spec) and the running API (production) only exacerbates.Forward-looking analyticsWhat insights does your API analytics give you?  Request volume against an endpoint  Average latency  Tenants generating the most trafficThese are, undoubtedly, valuable. But they don’t explain why behavior changed nor predict. You might suddenly discover that usage has dropped for a major customer; you have the dashboards to verify that. But what is the churn risk? An endpoint might have low volume but high strategic dependency, like having many high-value accounts depend on it. You need AI-powered analytics tools like Moesif to keep tabs on such scenarios.Scale changes the problemThroughout the expansion of the API ecosystem, more customers integrate, more partners build on top, and more internal services depend on shared contracts. But every new integration also contributes to the complexity.Human-driven analysis doesn’t scale linearly with complexity; the surface area grows faster than the team’s amplitude. As a result, you inevitably face problems like this:  Slower incident response  Missed monetization opportunities  Reactive roadmap adjustmentsModern, AI-powered API ecosystems will exceed the assumption that you can interpret all relevant signals by yourself for a perceptive, cogent API strategy. You need to incorporate AI to keep in pace with the scale of modern software systems and the competitive innovation in building such systems.Where AI improves API strategyAI improves API strategy at two moments: when you first define it and as you review, manage, and update it over time. In both cases, AI can help ground strategic conversations in observable patterns as opposed to assumptions.API design and governanceDecisions behind API standards, versioning rules, and design conventions often rely on documentation and review processes that vary across teams. For example, producers focus on delivery, governance focuses on policies, and product concentrates on time-to-market.By integrating AI, you can review OpenAPI contracts at scale, detect schema inconsistencies, identify and escalate potential breaking changes, and highlight divergence from naming or structural standards. You will know about compatibility risks before release.Being able to evaluate measurable drift is better than debating whether or not standards are being followed, and friction between producers and governance roles.Development confidence and flexible strategyYour confidence in any strategic change results from delivery confidence. Teams, for good reasons, will hesitate to evolve APIs if the test coverage and protocols is weak or contract validation is inconsistent.AI can help generate tests, discover edge cases, and validate contracts, all of which contribute to increased confidence in iterative development. You can simulate scenarios and verify compatibility faster.For API-early teams, this creates a disciplined foundation. And API-first organizations can more quickly adapt their strategy to business requisites.Documentation and developer experienceOne of the most common strategy gaps appears in adoption. APIs are technically sound but can be difficult to understand or integrate. AI can generate documentation drafts from contracts, summarize version changes, and analyze support tickets to identify recurring motifs that thwart customers from achieving their goals. Later on, they are automatically added to the documentation to help troubleshoot future issues.AI can also help generate SDKs and their documentation, examples, onboarding flows, guides, and tutorials, as well as keep them updated. Changes in each release and their impact internally and on customers, technical and business, can be efficiently and apropos communicated.Through an intelligent, automated, and controlled process, documentation and related resources evolve and accommodate the product as well as its customers. The end result is a materially improved and forward-looking developer experience.Lifecycle decisionsHow do you definitively know whether a version is safe to deprecate or whether migration is complete? It is one example of a lifecycle decision that has debates and disagreements around.Using AI-powered platforms like Moesif, you can get insights about adoption patterns across accounts and environments. You can identify where migration readiness is strong and where risky. AI helps with lifecycle decisions by making data-backed insights more accessible, thereby making those decisions evidence-driven as opposed to only being a matter of timelines.For example, the following time series looks at API adoption for a version across customers:We can then select Ask AI to quickly get some insights into migration and deprecation posture of the API version:Aligning API behavior with business outcomesPeople have differing incentives. A developer cares about the code, but a product manager cares about adoption. If you don’t understand the incentives of the person you are talking to, empathize with them, you won’t make progress.AI can help correlate usage with retention signals, expansion behavior, support load, and revenue exposure. You can leverage it to establish a shared analytics layer across roles, irrespective of the varying technical expertise of the individuals. This allows leadership to evaluate aspects that materially influence business outcomes.Moesif’s AI Explain makes analytics accessible to every team member; they can ask questions about the real-time analytics data to quickly garner insights that they can start acting on in no time.How to introduce AI into your API strategyLet’s go back to another one of Kin Lane’s posts that opines that starting with an API strategy must follow a thorough understanding of where one is in their API journey. We can follow that same line of thought when introducing AI in the process.So if your organization is API-early, before anything else, make sure API contracts are standardized and usage data is structured. You want your current state of affairs as lucid as possible: a very promising aspect where you can leverage AI.If yours is an API-aware but wrestling with governance alignment, introduce AI at points of disagreement. Governance reviews is another great place to leverage AI, for example dependency management.For API-first organizations operating at scale, focus on business alignment. Use AI-powered analytics tools like Moesif to correlate API usage with revenue and retention. You can also analyze support cost, and even consider using AI-powered chatbot for real-time support. You can use AI to model your roadmaps and discuss pricing to precisely control the economic impact of your endeavors.Regardless of maturity in terms of where you are in the API journey, the foundation remains the same: data discipline. You must consistently enrich and tag requests with contextual information like tenant, version, and plan data. Make contracts machine-readable. Same goes for support-related analysis.It’s also important to introduce AI within the existing cadence of your strategic activities, for example, quarterly roadmap reviews, lifecycle planning sessions, and pricing evaluations. Irrespective of whether or not AI is there, leadership must remain accountable for decisions.A sound approach is to start with one recurring tension in your API strategy. Then observe the outcome that follows after you make the decision with the help of AI. Based on the result, if you are confident, you can branch out into more.ConclusionIntroducing AI into API strategy is an incremental enhancement to how you review and refine strategy. The proliferation of AI and its democratization across disciplines are showing us how powerful and critical the roles of APIs are. AI-assisted API strategy offers meaningful benefits there as we build and deliver more complex software through more proactive, automated, accessible, and fast processes in API lifecycles.                Align API Usage with Business Outcomes using AI              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Enhance Your API Strategy with AI.            Use Moesif’s AI-powered, real-time analytics to understand your product and customers and drive business growth.            Try for Free            No credit card required            ",
          "url": " /api-strategy/api-development/Leveraging-AI-For-a-Better-API-Strategy/",
          "author": "Sakib",
          "categories": "api-strategy, api-development"
        }
      
    ,
  
    
        "api-strategy-the-5-best-mixpanel-alternatives-of-2025": {
          "title": "The 5 Best Mixpanel Alternatives of 2025",
          "content"	 : "On November 9, 2025, Mixpanel suffered a security breach when an attacker gained unauthorized access to their systems through a smishing (SMS phishing) campaign. The breach resulted in customer data being exported, including names, email addresses, approximate locations, and analytics information. The fallout has been swift—OpenAI, one of Mixpanel’s high-profile customers, has already terminated their relationship with the platform and is conducting expanded security reviews across their entire vendor ecosystem.While the breach specifically affected analytics data rather than core product systems, it’s a stark reminder that the tools we use to understand our users also become custodians of sensitive information. Trust, as OpenAI noted in their response, is foundational to the customer experience —and once it’s shaken, teams start looking for alternatives.Even without this latest news as a factor, there are many other product analytics platforms available that go beyond the limitations and use cases of Mixpanel. While Mixpanel has become a popular tool for good reason—the platform is quite solid for the bulk of use cases—there are some advanced and specific use cases that lend themselves better to other platforms. For example, at Moesif we focus heavily on helping developer tools and platforms keep track of customer behavior beyond just what happens in the UI, looking at system-level events like API calls. For applications that rely on stuff outside of the UI to paint the full picture of a user’s journey, Mixpanel often comes up short.In this post we’ll go over the top 5 Mixpanel alternatives for a wide assortment of use cases. Let’s get started by looking a bit further at Mixpanel, its key features, and where it excels before digging into other options.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        What is Mixpanel?Mixpanel is a powerful product analytics tool that allows companies to track user behavior within web and mobile applications. Unlike traditional analytics tools that focus on page views (like Google Analytics used to), Mixpanel pioneered the event-based tracking model. This means instead of just knowing someone visited a page, you know they “clicked a button,” “watched a video,” or “added an item to cart.” Having this level of insight is critical for modern applications and websites where knowing deeper insights on how users are interacting and converting matters.As one of the early platforms in the space, Mixpanel has been a go-to solution for product managers and marketers for years because of its ability to create complex funnels, retention cohorts, and segmentation reports. It helps teams answer questions like, “Do users who watch the onboarding video retain longer than those who don’t?” or “Where are users dropping off in the signup flow?”—areas where earlier generations of tools were not able to really give much insight.Understanding Mixpanel LimitationsWhile Mixpanel is a giant in the industry, it isn’t without its flaws and limitations. Although many teams get up and running quickly on the platform, a few items get teams looking for an alternative when the quantitative and qualitative data offered by Mixpanel isn’t quite enough. Here are the main structural and functional limitations that drive teams to look beyond Mixpanel:Developer Blind SpotsMixpanel is great for UI interactions, but it often misses the “invisible” events that happen at the API or system level. For developer-first products, the real story happens in the code, not just the clicks. When a developer integrates your API, Mixpanel might show you they visited your docs and clicked around your dashboard. But did they successfully make their first API call? How long did it take? What errors did they hit? These questions go unanswered.Manual Event Tracking Creates Technical DebtManual tracking implementation is perhaps Mixpanel’s most significant structural limitation. Upon implementation, you need to make your best guess about which user events matter the most, then have an engineer configure it all. If you change your mind or want to track additional events later, you’ll have to set them up again and risk ending up with inconsistent (and thus difficult to use) data.The practical impact is that you can’t access historical data on events you didn’t set up to track from the beginning. By the time you discover that a particular action is important, it’s too late to collect data on it. Any historical insight is lost forever.Funnel Report InconsistenciesUsers frequently report that Mixpanel’s funnel reports can give inconsistent starting values—the same event showing different numbers in a funnel report versus a metrics report. This leads to data discrepancies that erode trust in the insights you’re generating. When your team starts questioning whether the numbers are accurate, the tool stops being useful for decision-making.Scalability ConstraintsMixpanel becomes less effective as organizations scale, largely because of how data gets accessed. Since collecting event data is manual, data often becomes siloed and only accessed by the individual who set it up. The lack of a centralized place to process, store, and maintain governance makes it harder for teams to work together on large-scale data projects. What starts as a single source of truth becomes fragmented tribal knowledge.Cost at ScaleMixpanel’s pricing model is based on Monthly Tracked Users (MTUs) or event volume. As your product grows, costs can increase significantly. This creates an uncomfortable dynamic where success is punished—the more users you have, the more you pay, often at rates that outpace the additional value you’re extracting from the platform.Data Governance and PrivacyAs highlighted by the November 2025 breach, trusting a third-party vendor with deep, identifiable user data requires robust security assurance. For organizations with strict data residency requirements (like GDPR in Europe) or compliance needs (HIPAA, SOC 2), relying on a US-centric cloud platform introduces complexity. Some alternatives offer self-hosting options that eliminate this concern entirely.What to Look for in a Mixpanel AlternativeWhen shopping for an alternative, you shouldn’t just look for a clone. You should look for platforms that solve the specific problems Mixpanel creates or ignores. Here’s what separates the best modern alternatives from the pack:Autocapture vs. Manual TaggingSome tools record every user interaction automatically from day one. You don’t need to define events upfront; you can define them retroactively when you need the data. This eliminates the “I wish we’d been tracking that” problem entirely, especially for non technical users .API and System VisibilityFor SaaS and developer platforms, you need to see API usage, latency, and errors alongside user behavior. If your product has significant functionality that happens outside the browser—through APIs, SDKs, or background processes—UI-only analytics will always give you an incomplete picture. API analytics provides the visibility that traditional product analytics tools miss.Self-Hosting and Open Source OptionsFor strict compliance needs, the ability to host the analytics stack on your own servers is a game-changer. It’s also a hedge against exactly the kind of third-party breach that just affected Mixpanel’s customers.Integrated ActionabilityThe best tools don’t just show you data; they let you act on it. This might mean triggering an email when a user hits a specific error, alerting sales when a customer’s usage suggests they’re ready to upgrade, or even connecting usage data directly to billing systems.Reasonable Pricing at ScaleLook for pricing models that scale predictably with your growth. Some platforms offer volume discounts that actually make the per-event cost decrease as you scale, rather than punishing you for success.Measuring Success with AnalyticsSwitching tools is also a great time to audit how you measure success. A common trap is tracking “vanity metrics”. These are numbers that look good in dashboards but don’t actually correlate with business outcomes. Total page views, raw session counts, and feature usage without context all fall into this category.Instead, focus on metrics that align with your specific business model:Retention Rate: Are users coming back after Day 1, Day 7, or Day 30? Retention is the clearest signal of whether your product delivers ongoing value.Time to Value (TTV): How quickly does a new user get to their first “Aha!” moment? For developer products, this is often called “Time to First Hello World”—the interval between signup and successful first API call.Feature Adoption: Are people actually using that new feature you spent months building? And more importantly, do users who adopt that feature retain better than those who don’t?API Usage and Health: For developer tools, success might look like a high volume of successful API calls, low error rates, and acceptable latency. These API metrics are invisible to UI-focused analytics tools.Revenue Attribution: Can you connect specific user behaviors to revenue outcomes? This is where analytics moves from interesting to actionable.The focus should be on which metrics really matter to your core business. If you’re a blog site that gets paid for views then maybe high-level metrics like page visits are okay, but if your website is there to drive leads for your product, you’ll obviously want to look much deeper.Common Mistakes in AnalyticsAt Moesif, we’ve seen almost everything when it comes to analytics, including mistakes that many teams make when implementing and analyzing them. Before diving into specific tools, here are a few pitfalls to avoid regardless of which platform you choose:Tracking Everything Immediately: Don’t try to instrument every possible interaction on day one. You’ll end up with a cluttered dashboard that no one uses and event names that are inconsistent. Start with your core KPIs and expand thoughtfully.Ignoring Data Governance: Bad data in means bad insights out. Establish naming conventions for events early and enforce them ruthlessly. Mixing “Sign Up” with “signup_clicked” with “user_signup_complete” makes analysis painful. Consistency is key.Siloing Analytics Data: Analytics shouldn’t live in a vacuum. Your product data should ideally connect to your CRM, marketing tools, and support desk to give a 360-degree view of the customer. Look for platforms with support for these types of integrations.Confusing Correlation with Causation: Just because users who use Feature X retain longer doesn’t mean Feature X causes retention. It might just be that power users tend to use every feature. Be careful about determining causality, especially when making product decisions based on analytics insights.This covers the main areas where we tend to see folks struggle when implementing and leveraging analytics. Luckily, many of the best platforms help to enforce best practices and make many of these challenges obsolete (or close to it). Next, let’s look at some of the best Mixpanel alternatives for you to check out.Top 5 Mixpanel AlternativesNow that we’ve established what to look for, let’s dig into the specific platforms. Each of these serves different use cases and team profiles, so the “best” choice depends heavily on what you’re building and who your users are.MoesifBest for: API-first products, developer platforms, AI/ML companies, and B2B SaaS where value extends beyond the UI.Moesif takes a fundamentally different approach than Mixpanel. While Mixpanel and most alternatives focus on what happens in your UI—button clicks, page views, form submissions—Moesif focuses on what happens at the infrastructure level. For developer tools, AI platforms, and API-first products, this is the difference between seeing shadows on the wall and understanding actual system behavior.Why API-Level Visibility MattersConsider a developer integrating your API. In Mixpanel, you might see they visited your docs, maybe clicked around your dashboard, perhaps downloaded an SDK. But the critical questions remain unanswered: Did they successfully make their first API call? How long did integration take? What errors did they encounter? What endpoints are they actually using versus what you expected?This is the “Time to First Hello World” problem—the single most important metric for developer products, and one that UI-focused tools simply cannot capture. Moesif tracks the complete developer journey: from first documentation visit, through authentication setup, to first successful API call, and onward to how usage patterns evolve over time.Payload-Level AnalyticsMoesif can inspect API request and response bodies (with appropriate privacy controls), enabling analysis that’s impossible with standard UI tracking. Want to know how many tokens your AI customers are consuming? Which specific fields in your API response are actually being used? Whether customers are sending malformed requests that your documentation doesn’t address?This payload visibility lets you answer product questions that would require custom instrumentation and engineering work in any UI-focused tool. You can segment and aggregate by URI, HTTP headers, body fields, or customer demographics—giving you the full picture of how your API is actually being used in the wild. Moesif integrates with major API gateways including Kong, AWS API Gateway, Azure APIM, and NGINX, so you can get visibility regardless of your infrastructure choices.Monetization Built InFor API businesses moving to usage-based pricing (and in 2025, that’s most of them), Moesif connects usage data directly to billing. You can meter any usage metric—API transactions, feature utilization, unique users, even custom outcomes—and implement prepaid, postpaid, or pay-as-you-go models with a few clicks. Companies like You.com have used Moesif to launch usage-based billing for their AI APIs in days rather than months.The platform integrates with billing providers like Stripe for automatic invoicing, which means usage-based pricing doesn’t require building custom billing infrastructure. For AI companies charging by token, API platforms charging by call volume, or SaaS products implementing consumption-based models, this is transformative.Debug and Support Use CasesWhen a customer reports an issue, your support team can see their exact API call history, including the specific errors they encountered, the payloads they sent, and the responses they received. This makes Moesif valuable beyond product management—engineering and support teams use it as a debugging and observability tool.You can also set up alerts for specific error conditions, identify customers who are struggling with integration, and automate outreach based on usage patterns. The platform bridges the gap between observability (what’s happening in your system) and customer success (what should we do about it).Key Features  API and Payload Analytics: Inspect request/response bodies to track specific usage metrics impossible with UI tracking  Developer Journey Tracking: See the complete path from docs visit to first API call to scaled usage  Usage-Based Billing: Connect usage data directly to Stripe, Chargebee, or Zuora for automated invoicing  Time to First Hello World: Track the metric that matters most for developer products  Customer Health Dashboards: Identify at-risk customers, upsell opportunities, and integration problems  Self-Serve Reporting: Empower product, engineering, and support teams without bottlenecking on BIConsiderationsMoesif is purpose-built for API-first products. If your entire user experience happens in a browser UI with no meaningful API or system-level activity, traditional product analytics tools may serve you better. The platform’s strength is its depth in infrastructure-level visibility—if that’s not where your product’s value is delivered, you likely won’t fully leverage what Moesif offers.PricingMoesif offers a free tier, with paid plans based on event volume. Pricing scales with committed volume (higher tiers get better per-event rates), and all paid plans include 1 year of data retention. Enterprise plans offer custom retention and additional security features.AmplitudeBest for: Large consumer (B2C) products and traditional product management teams who want the most direct Mixpanel replacement.Amplitude is frequently cited as the most direct competitor to Mixpanel, and for good reason. There’s substantial overlap in capabilities—funnels, retention charts, path analysis, cohort comparisons. If you’re comfortable with Mixpanel’s paradigm but frustrated with execution issues, Amplitude is the obvious evaluation.Where Amplitude ExcelsAmplitude shines with advanced visualization capabilities and its “Compass” feature, which helps identify behaviors that correlate with long-term retention. For product teams trying to answer “what actions predict whether a user will stick around?”, Compass automates the correlation analysis that would otherwise require manual hypothesis testing.The platform also excels at cross-platform tracking—stitching together web and mobile user journeys into a unified view. For consumer apps where users bounce between devices, this continuity matters. Amplitude handles the identity resolution challenges better than most alternatives.Amplitude has also invested heavily in AI capabilities and feature management tools that Mixpanel lacks. You can get predictive analytics about user behavior and manage feature rollouts from the same platform, reducing tool sprawl.The Same Core LimitationHere’s the honest truth: Amplitude improves on Mixpanel’s execution but doesn’t fundamentally change the paradigm. You’ll still need to decide which events to track upfront, and you’ll still need engineering resources to configure that tracking. If you change your mind later, you’ll still have gaps in historical data.The platform offers more flexibility in visualization and analysis than Mixpanel, but the underlying architecture—manual event definition, no autocapture, no retroactive analysis—remains the same. Amplitude is a better tool for the same approach, not a fundamentally different approach.Some non-analyst users also find Amplitude frustrating despite its self-serve positioning. While there are plenty of options for slicing and dicing data, the sheer number of features can overwhelm teams who just want straightforward answers.Key Features  Product Analytics: Funnels, retention, path analysis with more visualization flexibility than Mixpanel  Compass: Automated correlation analysis to identify behaviors that predict retention  Cross-Platform Tracking: Unified view of users across web, mobile, and other touchpoints  AI-Powered Insights: Predictive analytics and anomaly detection  Feature Management: Built-in feature flags and experimentation (newer addition)  Self-Serve Reporting: Designed for product teams to explore data independentlyConsiderationsAmplitude’s manual event tracking means you’ll face the same “I wish we’d tracked that” moments as Mixpanel. The platform also offers limited connectivity with some data warehouses compared to alternatives, which can be a constraint if your analytics strategy depends on data warehouse integration.For teams that need API-level visibility, system event tracking, or usage-based monetization, Amplitude won’t fill those gaps—it’s focused squarely on UI-level product analytics.PricingAmplitude offers a generous free tier (up to 50,000 monthly tracked users), which is often enough for early-stage startups. The Plus tier starts at $49/month for up to 300,000 users. Growth and Enterprise tiers have custom pricing with additional features and support.PostHogBest for: Engineering-led teams, companies with strict data privacy requirements, and those wanting an all-in-one open-source solution.PostHog has gained massive traction by taking a fundamentally different approach to the analytics market: open source first, self-hosting as a core option, and bundling multiple tools into a single platform. In the wake of the Mixpanel breach, PostHog’s model looks increasingly attractive.The Open Source AdvantagePostHog’s code is open source and can be self-hosted on your own infrastructure. This isn’t just a philosophical stance—it has practical implications for security and compliance. When you self-host, your analytics data never leaves your systems. There’s no third-party breach risk because there’s no third party holding your data.For companies with strict data privacy requirements (HIPAA, GDPR, SOC 2), self-hosting eliminates an entire category of compliance complexity. You’re not evaluating whether your analytics vendor’s security posture meets your requirements; you’re applying your own security posture to your own infrastructure.Even if you use PostHog’s cloud offering, the transparency of open-source code means you can audit exactly what data is being collected and how it’s being processed. There’s no black box.The All-in-One BundlePostHog isn’t just analytics—it bundles session recording, feature flags, A/B testing, and surveys into a single platform. This reduces tool sprawl and creates tighter integration between capabilities. Want to see session recordings for users who dropped off at a specific funnel step? You can do that without exporting data between tools.Feature flags in particular are well-integrated. You can target experiments to specific cohorts identified through your analytics data, measure the impact directly in the same platform, and roll out successful variants without context-switching.Developer-Friendly ImplementationPostHog is designed for technical teams. The documentation is excellent, the API is well-designed, and the community (largely composed of engineers) provides solid support. If your team is comfortable working with code and infrastructure, PostHog’s implementation will feel natural.The platform also captures some events automatically out of the box, reducing (though not eliminating) the manual tagging burden.Key Features  Product Analytics: Event tracking, funnels, retention, paths, trends—the core product analytics toolkit  Session Recording: Watch how users interact with your product without a separate tool  Feature Flags: Roll out features gradually and target specific user segments  A/B Testing: Built-in experimentation framework connected to your analytics data  Surveys: Collect qualitative feedback alongside quantitative data  Self-Hosting: Run PostHog on your own infrastructure for complete data controlConsiderationsThe breadth of PostHog’s feature set creates a learning curve. Users consistently mention that the platform can feel overwhelming initially, especially for less technical team members. You’ll want dedicated time for onboarding and setup.The free tier also has limited data retention (1 month in some modules), which constrains historical analysis if you’re not on a paid plan. And while PostHog is more developer-friendly than alternatives, it still requires technical resources to fully implement and maintain.For API-level analytics and usage-based monetization, PostHog doesn’t offer the same depth as Moesif. Its strength is in UI-level product analytics with the added benefits of open source and self-hosting, making it a robust analytics solution .PricingPostHog offers a generous free tier with usage-based pricing beyond that. The pricing is transparent and published—no “contact sales for pricing” opaqueness. Self-hosted deployments can reduce costs further since you’re paying only for infrastructure rather than per-event fees.HeapBest for: Non-technical product teams who need autocapture simplicity and the ability to analyze events retroactively.Heap’s core selling point directly addresses Mixpanel’s most painful limitation: manual event tracking. With Heap, you don’t decide which events to track upfront. The platform records everything—every click, swipe, form submission, and page view—automatically from the moment you install it.The Power of Retroactive AnalysisThis autocapture approach enables something genuinely transformative: retroactive analysis. Suppose your team decides, six months into using Heap, that you want to understand how users interact with a specific button that was never tagged in your Mixpanel setup. In Mixpanel, that data doesn’t exist. In Heap, you define the event now and instantly see six months of historical data for it.The practical impact is huge. Product teams can explore hypotheses about user behavior without waiting for engineering to instrument new events. Questions that would require “let’s add tracking and wait a few weeks for data” become immediately answerable. This changes the speed at which you can learn from your users.Lower Engineering BurdenBecause Heap captures everything automatically, the implementation burden shifts dramatically. You’re not maintaining a complex tagging plan, not filing tickets for engineering to add new events, not discovering months later that someone misspelled an event name. The platform handles data collection; your team focuses on analysis.This makes Heap particularly attractive for product and marketing teams who don’t have dedicated engineering resources for analytics instrumentation. You install a snippet, wait for data to accumulate, and start analyzing.The Scale Trade-offHeap’s autocapture approach has a downside: at high scale, it can capture more data than you need, making it harder to find signals in the noise. Sites with millions of weekly visitors may need to configure limits on data capture to avoid overwhelming the system and the analysts trying to use it.There’s also a discoverability challenge. When everything is captured but nothing is explicitly defined, finding the specific interactions you care about requires more exploration than in a well-organized manual tagging system. Some teams find this liberating; others find it chaotic.Key Features  Autocapture: Every user interaction recorded automatically from day one  Retroactive Analysis: Define events now, analyze historical data immediately  Segmentation: Create cohorts based on behavior and integrate with marketing tools  Session Replay: Built-in session recording to see exactly how users interact  AI Features: Heap’s CoPilot can answer questions about your data in natural language  Integrations: Connect to Salesforce, Marketo, Shopify, and major data warehousesConsiderationsAutocapture is a different philosophy, not an objectively better one. Some teams prefer the discipline of explicit event definitions—it forces clarity about what you’re measuring and why. Heap’s approach can lead to data sprawl if you’re not thoughtful about how you organize and name your retroactively-defined events.For API-level visibility and system events, Heap won’t help—it’s focused entirely on UI interactions. And like most alternatives, it doesn’t offer the usage-based monetization capabilities that API businesses need.PricingHeap offers four plans with pricing available on request: Free, Growth, Pro, and Premier. The free tier provides a starting point, but access to advanced features and longer data retention requires paid plans.Google Analytics (GA4)Best for: Marketing teams focused on acquisition, traffic analysis, and ad attribution. Complement to (not replacement for) product analytics.Google Analytics is ubiquitous—most websites have it installed by default as it is a core component in most marketing analytics stacks. But it’s important to understand what GA4 is and isn’t, especially in the context of customer journey analytics . It’s primarily a web analytics platform focused on acquisition: where visitors come from, how they find you, and top-level engagement metrics. It is not a product analytics tool in the same sense as Mixpanel.Where GA4 ExcelsFor understanding traffic sources, GA4 is unbeatable. Which campaigns are driving visitors? Which organic keywords are performing? How does paid versus organic acquisition compare? These are GA4’s core strengths, and the integration with Google Ads makes attribution for paid campaigns seamless.GA4 has moved toward an event-based model (a significant change from Universal Analytics), which brings it closer to product analytics territory. You can track custom events and create conversion goals. For marketing websites and content sites where the primary goal is lead generation, GA4 often provides enough insight without additional tools.It’s also free, which matters. For early-stage companies or marketing teams with limited budgets, GA4 provides solid foundational data at no cost.Where GA4 Falls ShortGoogle Analytics users usually find out quickly that GA4 struggles with user identity. Linking visits from different devices and sessions to a single person requires workarounds, and the platform is built around aggregate analysis rather than individual user journeys. If your goal is understanding how specific users move through your product, GA4 will frustrate you.Custom event tracking requires Google Tag Manager, adding implementation complexity. The data model, while improved, still feels designed for websites rather than applications. And the interface, while familiar, can be confusing—Google has a history of sunsetting and restructuring Analytics products, which creates learning curve churn.Perhaps most importantly for this comparison: GA4 has no API-level visibility, no autocapture beyond basic page views, limited session replay capabilities, and no built-in experimentation. It’s a different tool for a different purpose.Key Features  Traffic Analysis: Detailed acquisition metrics, source/medium tracking, campaign attribution  Google Ads Integration: Seamless connection between ad spend and on-site behavior  Event Tracking: Custom events via Google Tag Manager  Audience Segmentation: Create audiences for remarketing and analysis  Free Tier: Full-featured analytics at no cost  Real-Time Data: See active users and their behavior as it happensConsiderationsData privacy is a concern with GA4. Google’s data practices have raised compliance questions in Europe, and using GA4 typically requires cookie consent banners that can disrupt user experience. For companies where GDPR compliance is critical, this adds friction.The paid version (GA360) starts at $50,000/year—far more expensive than dedicated product analytics tools that offer deeper capabilities. At that price point, you’d get significantly more value from a platform built for product analytics rather than an enterprise version of a marketing tool.PositioningGA4 is best understood as a complement to product analytics, not a replacement. Use GA4 for acquisition metrics, traffic analysis, and marketing attribution. Use a dedicated product analytics tool for understanding how users engage with your product after they arrive.Most mature analytics stacks include both.Comparison Summary            Platform      Best For      Key Differentiator      Limitation                  Moesif      API-first products, developer platforms      API/payload-level analytics, usage-based monetization      Less suited for pure UI products              Amplitude      Traditional product analytics      Direct Mixpanel competitor with better execution      Still requires manual event tracking              PostHog      Engineering teams, privacy-conscious orgs      Open source, self-hosting option, all-in-one bundle      Learning curve, technical implementation              Heap      Non-technical teams      Autocapture, retroactive analysis      Can be overwhelming at scale              Google Analytics      Marketing teams      Free, excellent traffic/acquisition analysis      Not true product analytics      ConclusionThe recent Mixpanel breach is a reminder that the tools we rely on aren’t infallible. Whether you’re evaluating alternatives due to security concerns, cost pressures, or because you’ve outgrown Mixpanel’s UI-centric focus, 2025 offers strong options: Amplitude and Heap for traditional product analytics, PostHog for open-source flexibility and self-hosting, and Google Analytics for acquisition metrics.But if you’re building an API platform, AI service, or developer tool—where the real user journey happens in code rather than clicks—UI-focused analytics will always leave you blind to what matters most. First API call, integration completion, error resolution, usage scaling: these moments happen outside the browser.Start a free trial with Moesif to see what API-level visibility looks like. No credit card required.                See What Mixpanel Misses: Track the Full API Journey              14 day free trial. No credit card required.              Try for Free                    ×                                                                            The Mixpanel Alternative for API & AI Products.            Go deep into API data and AI context for actionable insights that drive your business value.            Try for Free            No credit card required            ",
          "url": " /api-strategy/The-5-Best-Mixpanel-Alternatives-of-2025/",
          "author": "Matthew",
          "categories": "API-Strategy"
        }
      
    ,
  
    
        "technical-api-development-how-to-leverage-moesif-effectively-for-api-observability": {
          "title": "How to Leverage Moesif Effectively for API Observability",
          "content"	 : "You can make your API observability posture more powerful and beneficial by treating Moesif as an engineering implement. The platform automatically captures API traffic out-of-the-box and provides actionable analytics and visualizations. However, the degrees to which they precisely and empirically illustrate the data, depend on where and how you’ve integrated Moesif.This article discusses the deliberate setups and best practices that we suggest you pay attention to as you integrate and utilize Moesif.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        Choose Where to Integrate MoesifDo not install Moesif too early or too late in the request lifecycle; the integration point determines what data becomes available to Moesif. Ideally, you want to capture traffic after authentication resolves but before business logic executes. As a result, Moesif can tie each event to a valid user session while still recording every error or payload mismatch that occurs in downstream logic.For example, here’s a minimal configuration that integrates Moesif in an Express-based system:var express = require('express');var app = express();var moesif = require('moesif-nodejs');/* snip */const moesifMiddleware = moesif({  applicationId: 'YOUR_MOESIF_APPLICATION_ID',  logBody: true,  getSessionToken: (req, res) => req.headers['Authorization'],})// Use Moesif after authentication middlewareapp.use(authMiddleware);app.use(moesifMiddleware);/* snip */Another example: if you have middleware for parsing HTTP bodies, place Moesif after that as well.The preceding example intercepts requests once user context is available but before the request enters business logic. Capturing at this level makes certain that Moesif records every inbound and outbound traffic details, including status codes, latency data, and headers.You may have a distributed or multi-gateway environment, like WSO2 or Kong; then integrate Moesif at the edge layer where requests first reach your network. That way, you corroborate uniform coverage across APIs; it also eliminates blind spots that internal routing and microservice fan-out precipitates.Enrich Events with ContextRaw telemetry grows into utility when you correlate it to the customers and systems driving it. With Moesif, you have different ways to corroborate identification and attribution in analytics data:  Customer identification for users and companies  Custom metadata          Event-level custom metadata      User and company metadata      The following diagram illustrates how these components relate in Moesif’s data model:Using HTTP headers, you can inject this information at the API gateway or application layer. The custom metadata fields allow you to add identifiers like version numbers, LLM models, environment tags, and so on. From there, you define the proper filters in Moesif, and observe and utilize API calls pertinent to specific entities. API requests, no longer anonymous, become actionable observability data.See a server integration documentation for more information on how to implement the identification and attribution layer you want for your setup.Let’s get into more detail about enriching Moesif events with identifiers relevant to your architecture:Identify Users and CompaniesEach event can include a unique user ID, and, where applicable, a company ID. These identifiers allow fine-grained segmentation and behavioral analytics in Moesif; you can filter by account or customer in addition to endpoints and API-specific dimensions. This also sets up important components by which you can create and receive alerts.Here’s a FastAPI example:def identify_user(request, response):    # Implement your custom logic which reads user id from your request context    # For example, you can extract the claim from a JWT in the Authorization header    return "12345"def identify_company(request, response):    # Implement your custom logic which reads company id from your request context    # For example, you can extract the claim from a JWT in the Authorization header    return "67890"moesif_settings = {    'APPLICATION_ID': 'Your Moesif Application Id',    'LOG_BODY': True,    'DEBUG': True,    'IDENTIFY_USER': identify_user,    'IDENTIFY_COMPANY': identify_company,}app = FastAPI()app.add_middleware(MoesifMiddleware, settings=moesif_settings)In conjunction with exhaustive API traffic data, customer identification makes possible deeper and more potent troubleshooting and debugging.Consider some real-world issues:  Which customers are observing increased 4xx errors after deployment?  Has the new SDK release improved onboarding flow?The following time series breaks down 4xx errors across companies. It also enables Percent Breakdown to easily understand what percentage of the traffic has incurred those errors:You can then interact with a company ID value to go straight to the company profile for more information about the customer.Tag By Environment and VersionIf you have multi-tenant or polyglot systems, consider tagging requests with version and environment identifiers: for example, explicit API versions and tags for staging and production environments. It also allows you to detect inconsistency in deployment states; for example, you may find that your staging behaves correctly but production exhibits higher latency and degraded performance.These context fields also make it possible to filter or compare across versions to evaluate new releases. Engineers can intuitively visualize latency or error-rate differentials between API versions in Moesif UI, without custom queries, to validate performance before and after releases.For example, the following time series analyzes error distributions for an API version:Capture Custom EventsMoesif can track both incoming and outgoing HTTP requests. However, many workflows and activities happen asynchronously and in ways that never appear as HTTP requests. For example, webhook deliveries, queue processing, AI pipelines, or downstream handoffs don’t fit the criteria of an HTTP traffic component. Oversights of tracking these events leave consequential blind spots in observability.To that end, Moesif supports custom actions. Actions are custom events or activities that occur based on user interactions or as side effects of other occurrences, for example:  UI interactions like someone signing up or logging in  System actions like internal retries or user-driven milestones  Backend activitiesYou can log these custom actions programmatically using Moesif’s Actions API directly. You can also use event streams like AWS Firehose and Logstash.An Action looks like this:{  "action_name": "Finished Data Processing Job",  "request": {    "time": "2025-01-28T04:45:42.914"  },  "company_id": "12345",  "metadata": {    "total_rows": 1024,    "found_rows": 999,    "consumed_input_tokens": 54322,    "time_seconds": 66.3  }}You can correlate these events with API usage to, for example, understand failure cascades or measure workflow reliability. If a webhook retry event precedes 5xx error responses and you observe an increase in such responses, it might point to an integration hitch. You become conscious of a potential issue before it impacts your customers and any support tickets.In Moesif, you can filter by actions-type events and action names, For example, the following Live Event Log shows custom action events like users visiting landing page, signing-in, and so on:On top of that, consider if you have HTTP events that delineate a specific outcome or action; you can attach a meaningful and recognizable name for it programmatically using the action_name field.Here’s another example that breaks data export operations across customers by filtering the relevant action’s name:We recommend that you use custom actions selectively. Prioritize flows that delineate critical business or reliability events, from where silent or non-obvious failures can originate:  Webhook delivery attempts and acknowledgments  User actions that indicate successful onboarding, provisioning, or purchased planIntegrate Moesif into Engineering WorkflowsLastly, route the insights Moesif gives you into systems your engineers already work: Slack, PagerDuty, emails, or custom internal webhooks for fine-grained automation.Alerts in Moesif are highly customizable and can serve assorted use cases. In place of opening an analysis in Moesif reactively, your defined alert rules can automatically notify teams when specific conditions meet:  Trigger an alert when a single account produces over 50 401 Unauthorized errors in a small amount of time.  Send a PagerDuty incident if a critical endpoint’s 5xx error rate exceeds a specific percentage.  Notify a Slack channel if usage for a key account drops by more than 70% over a small time window.Moesif requires you to specify a channel to dispatch alert notifications to. You can, for example, use a Slack channel to observe transient issues, while PagerDuty supports production-impacting incident response.For more information, see Real-Time Rolling Alerts and Calendar-Based Alerts that demonstrate how you can monitor real-time incidents and long-term anomalies and trends.As an example, the following static alert rule monitors the P90 latency for a GenAI API. It sets a static threshold of 500 milliseconds and evaluates the latency metric each calendar month.Embed DashboardsMoesif supports securely embedding your analysis metrics into external workflows and third-party solutions. It can strengthen your internal observability by making it continuous, seamless, and more accessible, through familiar day-to-day tools. Real-time, flexible, and fast access to API metrics and customer behavior makes sure that you can observe them alongside system and infrastructure metrics; consequently, both technical and customer context substantiate your incident responses and initiatives.Safeguard Privacy and Data HygieneMoesif provides field-level redaction through configuration options to mask sensitive resources, especially in systems handling personally identifiable information (PII) or financial data. You can therefore make sure replace or remove such resources before they leave your environment,Here’s a Node.js sample that uses the maskContent option to remove two fields from the event model before sending to Moesif:import _ from 'lodash';var options = {  maskContent: function(event) {    const newEvent = _.omit(event, ['request.headers.Authorization', 'event.response.body.sensitive_field'])    return newEvent;  }};We also recommend that you regularly carry out audits to make sure that event payloads are lightweight and focused, and no unnecessary fields exist. Effective API observability should prioritize relevance of data over volume.Wrapping UpConsider how AI is taking over industries and systems becoming more distributed; more and more contracts exist among APIs, clients, and integrations. They not only serve end users, but also internal customers, and engineers that work on these systems as well. As a result, there is more room for silent failures and missed technical and business opportunities.The efforts you put behind capturing and enriching the data through Moesif pay off every time you encounter something unanticipated. Moesif encourages thoughtful instrumentation, consistent enrichment, and the discipline to be mindful about the data you emit; explicit intent and control will give you more precise insights and thereby more utility.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design, Build, and Deliver Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/How-to-Leverage-Moesif-Effectively-for-API-Observability/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "api-strategy-api-monetization-how-to-build-an-internal-chargeback-model-for-your-api-and-ai-usage-using-moesif": {
          "title": "How to Build an Internal Chargeback Model for Your API and AI Usage Using Moesif",
          "content"	 : "API and AI services now sit at the heart of modern products. However, the more we use them, the harder it seems to become to account for the budget.Launching an AI product often leads to massive end-of-period bills. This requires attributing costs to the key internal power users and consumption drivers. The challenge is identifying the departments, products, or projects responsible for the consumption, and the extent to which they contribute. Vanity metrics alone, like API calls or token counts, don’t explain growth drivers. You need deeper, more attributable metrics to effectively manage and understand the spend.This post discusses how an internal chargeback model provides a framework for gaining clarity and an actionable strategy when launching a new AI API program.                Eliminate the Black Box of Shared AI Costs              14 day free trial. No credit card required.              Start Free        The Problem of Unaccounted API and AI UsageOpaque Costs in Distributed SystemsAPIs and AI workloads stand across multiple services, gateways, and cloud providers. When a request goes through this stack, the compute and network costs quietly accumulate across systems, like load balancers, AI models, databases, and storage layers. By the time these activities appear in invoices, they’ve lost important business context:  Which department generated the usage?  Which product drove the associated costs?  Which team should own the bill?Metrics Lacking OwnershipMost organizations can collect usage data: API call counts, latency distributions, or token consumption metrics. But how many of those connect to identity and ownership? Many organizations lack fine-grained tooling to translate usage into a coherent strategy. For example, consider an endpoint that logs millions of calls each day, but without consistent tagging through entities like headers or metadata. Those events remain anonymous from a cost-allocation outlook, and thus engender attribution gaps. Finance sees aggregate usage, while engineering sees operational activity, and neither can reconcile the two.The Complexity of AI Cost AttributionIn production AI systems, one request often results in a distributed chain of model and data operations across different infrastructure layers. Consider an enterprise search assistant. It can answer employee questions using internal data sources and APIs. To satisfy a user query, one AI request may trigger:  An embedding model  A vector DB lookup  An LLM inferenceEach of these can have disparate billing semantics:  Embedding might charge per token  Database can charge per compute unit  The inference model can charge by input and output tokensAnd when overall costs spike, the root cause is hard to ascertain:  New user adoption  Inefficient prompts  Recursive bugsLack of end-to-end attribution makes it challenging to link costs to actual consumption. It also obscures responsibility and optimization opportunities.Unaccounted use, while an accounting inconvenience, also skews engineering and product decisions. With no team directly responsible for resource consumption, overuse feels complimentary. It exacerbates overprovisioned services and development environment sprawl; shared APIs and AI decline into shared liabilities.Chargeback models can solve this problem as it reintroduces causality between action and cost by treating resource usage as a billable, attributable event. Therefore, as an organization, before any pricing model or automation, establish the foundation for chargeback; every request, task, or AI invocation must carry consistent identifiers that link to the owning team or project. Then use Moesif to track, meter, and report the usage to measure accountability.The Case for Internal ChargebacksIn most organizations, API and AI usage starts as a few shared services for internal teams. But it quickly becomes more complex with data pipelines, inference jobs, and model integrations. Each draws on shared compute, storage, and vendor-specific costs. To promote responsible consumption, you must allocate costs accurately to each team or department based on their actual usage. Without a structured budget allocation, every department benefits from the infrastructure, but none feel responsible for managing cost efficiency. An internal chargeback creates stewardship to design incentives for teams to stay on top of their monthly spend.What is an Internal Chargeback Model?An internal chargeback model assigns the cost of technology resources to the internal teams or products that consume them. Chargebacks create an internal transparency framework among teams, the platform no longer being a monolithic cost center. The model doesn’t change how APIs run; it changes how their costs are recognized and optimized.A strong chargeback model requires three components:  Measured Usage  Clear units: requests, tokens, GB processed  Unit Cost  The financial rate tied to each unit  Ownership Mapping  Which department, product, or team generated the usageMoesif provides the foundation for all three by converting raw API and AI events into billable, attributable metrics.Why API and AI Workloads Require ChargebacksAPI and AI systems have distinct cost dynamics in comparison to traditional apps. Their costs scale horizontally, as each new interaction, workflow, or user interaction adds measurable consumption. AI especially institutes variability: inference costs fluctuate by model type, prompt complexity, and token amounts. Without a chargeback model, these expenses blend into a shared cloud budget, hiding inefficient usage and encouraging overconsumption.Chargebacks as ControlChargebacks demonstrate real costs to the teams that generate them, and in doing so, encourage better architectural decisions. Developers learn to assess performance trade-offs against economic impact. Product owners can predict budgets grounded in actual resource usage and not ballpark figures. Platform teams and department heads, in turn, reap the benefits of data-driven decisions when rationalizing infrastructure investments or cost optimizations.In Moesif, you can repurpose usage data already collected for analytics to distribute costs, using product catalog features in tandem with billing meters and custom webhooks; we will provide a demonstration in the later section.Aligning Engineering and FinanceReliable, near–real-time data on technology spend gives teams autonomy to track and manage their own consumption. Instead of budget surprises, discussions shift toward measurable efficiency improvements. As AI and API ecosystems grow more complex, with token pricing, multi-model pipelines, and vendor-specific charges, you can’t retrofit this alignment later. Financial clarity must be designed into the architecture from day one.How to Implement an Internal Chargeback System With MoesifLet’s look at the general steps involved in an internal chargeback implementation using Moesif. We will start by making sure reliable data exists, with consistent identifiers; that makes it possible to accurately extract quantifiable usage metrics using event-level analytics. Then, through Moesif’s automated metering system, usage data becomes financial data that your internal system utilizes to calculate and effectuate the chargeback.Step 1: Identify and Attribute UsageEvery API or AI request must entail adequate context to determine who has generated it. Moesif offers various means to inject identification and attribution:  Customer identification for users and companies  HTTP headers at gateway or app layer  Custom metadata          Event-level custom metadata      User and company metadata      Then it’s only a matter of defining the appropriate filters in Moesif to observe and make use of API calls associated with a particular entity.See a server integration documentation for more information on how to implement the identification and attribution layer you want for your setup.The Live Event Log in Moesif shows real-time API traffic; from here you can verify whether or not the events contain appropriate identification and attribution data.Step 2: Create Plan and Optional PriceA billing meter in Moesif must have an associated plan and price to attribute the usage to. Use Moesif’s Product Catalog to define the financial model.  The plan describes your internal pricing plan  The price defines the usage rate; it can be flat or per-unit—for example, US$0.01 per 1,000 tokensEven if your chargeback feeds into a custom billing system, defining plans and prices in Moesif helps maintain clean mapping.Create a Plan  Select Create New and then select Plan.  Enter a name for the plan.  Select Custom as the billing provider.  In the External Plan Id field, you can enter the external plan ID from your custom billing solution.  Choose a reporting schedule. It dictates how Moesif reports usage to your webhook.  Enter an optional description and plan metadata.  Select Create.The following example shows a plan with calendar-aligned billing:Create a Price (Optional)  Select Create New and then select Price.  Enter a name for the price.  Link to the custom plan you created in the preceding step.  Optionally, add price metadata.  Define the pricing details:          You can choose between usage-based and flat-rate pricing models.      For a usage-based model, specify the charged amount and the usage quantity.      Define the usage calculation and aggregation method. For example, you may choose to sum up all consumed units in a month.        Select Create.Here’s an example price that charges 0.01 USD for each 1000 units of consumption:Step 3: Create Billing MeterAfter the preceding steps, you have instituted ownership and the cost semantics it entails. A billing meter connects these two by allowing you to define, track, and measure usage.Moesif’s billing meters can define precise units of billable metrics, along dimensions that capture real cost drivers. For example:  Total LLM tokens  Input and output tokens  Requests to premium models  Compute-heavy endpoints  Only successful 2xx responsesMeters let you combine:  Filters  Custom metrics  Scripted fields  Multipliers—for example, US$0.01 per 1,000 tokensThis turns raw AI usage into financial quantity.To create a billing meter, select Create New and then select Billing Meter. Then follow these steps:Specify Billing Details  Enter a name for the meter.  Select Custom as the billing provider.  Select your webhook. To create a new webhook, select Add New Webhook from the dropdown menu.          Enter webhook name and URL.      Select POST as the request method.      (Recommended) Add a request header to authenticate requests coming from Moesif.      Select Save.        Select the custom plan and optional price for chargeback you created in the first two steps.  Set the usage multiplier to match the number of units you’re charging for. Moesif multiplies the raw metered usage by this multiplier before calculating the billable quantity. The billing meter tracks every event. But since our example price charges for every 1000 units, we must set the usage multiplier to 0.001.  Optionally, set which direction Moesif should round the usage too to handle fractional or decimal usage values when usage multiplier is less than one.  Select the subscription statuses the billing meter should track usage for.Define Event Filters and Billable Metrics  Define event filters in the Filters pane to specify which events to track and meter. For example, you may want to meter events for a specific API or endpoint and disregard error responses.  In the Metrics pane, define the metric to charge for. In addition to predefined metric types, you can create custom metrics. If a field does not exist in your event data that you want to bill on, you can compute and create the field using Scripted Fields.  Select Create to finish creating the meter.Here’s an example of a billing meter. It defines a custom metric that charges for total token consumption for successful requests to two API endpoints.Step 4: Create SubscriptionTo send usage data through the webhook, you must create a subscription for your internal customers, as in, the different teams of your organization. The following example shows a cURL command to Moesif’s /subscriptions API endpoint:curl --request POST --url 'https://api.moesif.net/v1/subscriptions' --header 'X-Moesif-Application-Id: YOUR_COLLECTOR_APPLICATION_ID' --header 'Content-Type: application/json' --data '{  "subscription_id": "UNIQUE_SUBSCRIPTION_ID",  "company_id": "COMPANY_ID",  "current_period_start": "2025-10-22T20:13:00.001Z",  "current_period_end": "2026-10-21T20:13:00.001Z",  "status": "active",  "items": [    {      "plan_id": "MOESIF_PLAN_ID" <-- the GUID of the Moesif Plan    }  ]}'For more information about the subscription data, see the custom webhook setup and managing subscriptions docs.Step 5: Connect with Internal SystemsMoesif sends a payload containing usage data to the webhook that looks like this:{  "idempotency_key":"KG5LxwFBepaKHyUD",  "company_id":"J7F3-R9K1-T5B8",  "subscription_id":"sub_a9e4bde6606e",  "plan_id":"68f789d3596caf0b2f514306",  "billing_meter_id":"68f8d856596caf0b2f51b123",  "quantity":2,  "start_time":"2025-10-01T00:00:00.000Z",  "end_time":"2025-10-31T23:59:59.999Z"}The webhook can be a Lambda or internal microservice. It can then convert the payload into the internal schema and post to your billing system or internal APIs to carry out the rest of the chargeback process. Each record should include these details at the minimum:  Department ID or equivalent. This maps to the webhook payload’s company_id.  Billing period, mapping to the webhook payload’s start_time and end_time.  Usage quantity.Finance systems can then aggregate and report these costs at the business-unit level. The teams use the same reports for budgeting.ValidateHere are some validation steps to make sure everything functions as intended:  Test your meter.  Conduct a test billing cycle and observe the following:          Usage data the webhook payload reports      Observe usage statistics with corresponding revenue in the Billing Meter’s Synced Usage pane. From that pane, you can also view the events associated with the usage.            Make sure your internal systems correctly receive the data.  Reconcile reported costs with vendor invoices.ConclusionAs organizations grow their AI and API footprint, shared workloads become a significant, but often poorly understood cost center. Without attributing costs directly, budgets get strained, optimization stalls, and cost overruns become inevitable. Notwithstanding the business dimension, the infrastructure ceases to be a managed resource.An internal chargeback model administers financial discipline so that:  Usage becomes accountable  Costs become explainable  Teams become responsible consumers  Finance and engineering align on real-time cost dataUsing Moesif, you can implement this model with precision, leveraging the analytics you already collect to drive financial clarity, operational efficiency, and healthier organizational culture.                The Infrastructure for AI Cost Attribution              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Stop Guessing Who Spent the AI Budget            Accurately attribute usage to teams and automate internal chargebacks with Moesif.            Try for Free            No credit card required            ",
          "url": " /api-strategy/api-monetization/How-to-Build-an-Internal-Chargeback-Model-for-Your-API-and-AI-Usage-Using-Moesif/",
          "author": "Sakib",
          "categories": "API-Strategy, API-Monetization"
        }
      
    ,
  
    
        "technical-api-development-introducing-moesif-basic-insights-for-wso2-api-manager-apim-and-bijira": {
          "title": "Introducing Moesif Basic Insights for WSO2 API Manager (APIM) and Bijira",
          "content"	 : "One of the principals of Moesif’s efforts has been making API analytics more accessible and easier to use for everyone. We have helped enterprises of different sizes and use cases grow and succeed with their API and AI products through data-backed insights. With Moesif, teams of assorted expertise feel empowered to gain deeper understanding of their product, customers, and the overarching market; consequently, every stakeholder can contribute to shared business goals through more effective teamworks.As the next step towards realizing that vision, we are launching Moesif Basic Insights—a limited version of Moesif available to all WSO2 API Manager and Bijira customers.                Monitor and Analyze Your WSO2 APIs with Moesif                            Start Free        What is Moesif Basic Insights?Moesif Basic Insights is a free, lightweight version of the Moesif platform. It is included as part of a valid WSO2 subscription to the following WSO2 products:  WSO2 API Manager  BijiraYou can access Moesif Basic Insights for Bijira in the standalone web app at https://moesif.com/moesif-basic. If you’re using WSO2 API Manager, you can access it from the left sidebar.Why We Have Built Moesif Basic Insights for WSO2An API ecosystem built with WSO2 API Manager or Bijira possesses APIs, apps, and customers that generate millions of data points. Now WSO2 customers can effectively leverage that data through Moesif. Moesif Basic Insights provides a frictionless and integrated API analytics experience that comes included with your WSO2 API Manager and Bijira plan, requiring no complex setup or instrumentation.What You Can Achieve: Available FeaturesMoesif Basic Insights resembles the WSO2 analytics architecture and organizes all the features into these components:  Inbound Analytics: Analytics for requests and data between the user and API Proxy  Outbound Analytics: Analytics for requests and data between the API Proxy and the backend service  AI Analytics: Outbound analytics purpose-built for AI APIs  Alerting: Alert history and creation of new alert rulesThe following sections go over the features and analytics available in these components in WSO2 API Manager and Bijira across your APIs and applications.Analytics OverviewThe Overview panel in Inbound Analytics, Outbound Analytics, and AI Analytics contains a rundown of your API’s usage across different criteria:Inbound and Outbound Analytics  Total traffic  Error request count  Average error rate  95th percentile latency  Time series visualization of request count and error count ratio with latencyAI Analytics  Total traffic  Error request count  Average error rate  95th percentile latency  Token usage statistics:          Total token      Prompt token      Completion token        Time series visualization of request count and error count ratio with latencyUnderstanding API TrafficThe Traffic panel contains API traffic statistics:Inbound Analytics  API usage over time  API usage by Application  API usage by Target, where Target is the the target endpoint for an API proxy  API resource usage summaryOutbound Analytics  Target usage over time  Application usage over time  Target usage summaryAI Analytics  Vendor usage over time  Application usage over time  Token usage distribution across total, prompt, and completion tokens usage  Token usage over time, classifiable by total, prompt, and completion token types  Summary of average token usage by vendor  Vendor usage summaryThese metrics provide insights like:  APIs and applications driving the most traffic  Peak usage hours  Most popular API resources, AI features, and vendorsIdentifying and Troubleshooting API ErrorsThe Errors panel in Inbound, Outbound, and AI analytics shows API error statistics across existing APIs. It gives you an immediate, high-level observation of your API’s health. You can break errors down and visualize them by API, categories such as authentication and throttling, and status code.The following Inbound Analytics illustrates errors by status codes :Identifying and Investigating Performance BottlenecksThe Latency panel in Inbound, Outbound, and AI analytics contains insights about your API’s performance by calculating 95th percentile latencies. It allows you to identify services that are performing poorly and provides breakdown of latency in different criteria.Here’s an example of various latency statistics in Inbound Analytics:Identifying Top Platforms and DevicesThe Devices panel in Inbound Analytics illustrates the top platforms and user agents so you can keep abreast of the technology your customers use. It helps you make informed product decisions, like whether to build new SDKs or developer tools.AlertsAlerts help you stay ahead of issues and proactively take measures for better customer experiences. The Alerts panel in Alerting shows details about existing alert rules as well as the history of triggered alerts. The preserved history helps track incident patterns over time.You can also create and configure new alerts in the Alert Rules tab.Alerts notify automatically when API exceeds a specified threshold of a metric; for example, too many 400 Bad Request errors. You can also specify where alert notifications should be dispatched to.Usage ReportsTo help demonstrate the value and health of your API program and keep everyone informed, the Usage Reports panel allows you to download usage reports:  The Monthly Reports tab allows you to download monthly reports.  In the Report Generator tab, you can specify the following criteria to generate and download reports:          The API      The applications or API consumers      The time period.      The Custom Reports panel allows further customization for generating reports where you can define the categories and metrics to plot. For example, the following custom report breaks down backend latency across applications:Usage reports frees teams from the inconvenience of manually creating reports or spreadsheets, making communication easier among stakeholders and justifying future investments.Geographic HeatmapsGeographic heatmaps can supplement insights into your product, customers, and how both correlate when geographic location plays an important role in your product and analytics requirements. Using the Geo Map panel in Inbound Analytics, you can analyze API usage by location through two heatmap types:  Density heatmap  Metric heatmapGet StartedTo get started with Moesif Basic Insights in WSO2 API Manager and Bijira, follow these steps:  Go to Moesif Basic Insights web app and sign up. Then follow along the onboarding process.  Follow the installation instructions for WSO2 API Manager and Bijira in the Quick Install screen. For WSO2 API Manager, update the deployment.toml file of your WSO2 API Manager instance accordingly. For more information about the installation process, see the integration docs for WSO2 API Manager and Bijira.    After successful integration, Moesif starts receiving API traffic from API Manager and Bijira. The installation screen shows a confirmation banner that it has started receiving event data.  Optionally, set up customer identification.  Invite team members to collaborate in the last step.You can configure your installation, import customer data, and set up customer identification anytime by following these steps:  Select the account icon to access your organization and app settings.  Select Installation.Access More of the Moesif PlatformAs your API program matures and becomes more critical to your business, you will require more clarity as your questions evolve from what your API usage is to why it is so. Upgrading to a paid plan empowers you to go deeper and thereby refine your strategy to grow.By upgrading to Moesif Enterprise, you can gain access to more features, including:  Advanced API and customer behavior analysis  API monetization  Configurable workspaces and dashboards to organize analytics  Advanced and custom API monitoring and alerting with anomaly detection  AI–powered insights through AI Explain  Embeddable metricsGo Beyond Basic Metrics to Deep Behavioral AnalysisWhile Basic Insights contributes high-level trends, Moesif Enterprise allows you to dig into the complete user journey to understand both the traffic and the behavior behind it. That means having definitive answers to business questions that directly impact your product strategy:  Which specific customer segments are adopting the new AI features?  What API usage patterns correlate with customer retention versus churn?  How can we link specific API errors to individual user actions for faster, more effective troubleshooting?Configurable workspaces can keep everything organized across different analysis dimensions and business units. Federating that with advanced alerting and AI-powered insights allows you to adopt a proactive, data-driven strategy.Turn Your APIs and AI Usage Into Revenue StreamsOne of the most powerful capabilities of Moesif Enterprise is the tooling to monetize your APIs. You can create new revenue sources based on actual product usage, thanks to the strong foundation of Moesif’s high-dimensional, high-cardinality analytics. With industry-proven and AI-ready API monetization features, you can:  Monetize your AI and API products:          Implement usage-based and outcome-based billing      Monetize MCP servers      Implement usage-based pricing for AI agents        Create, manage, and experiment with complex pricing plans with different tiers and features  Provide customers with self-service developer portal and embeddable analytics workspaces to track their own usage and costs, improving transparency and user experiencesHow to UpgradeTo add the complete Moesif Enterprise feature suite to your existing WSO2 subscription, reach out to your WSO2 account manager.                The Data-Driven Advantage for WSO2 Users                            Start Free                    ×                                                                            Design, Build, and Deliver Better API and AI products.            Augment WSO2's best-in-class API management platform with actionable insights and revenue tools.            Get Started Free            No credit card required            ",
          "url": " /technical/api-development/Introducing-Moesif-Basic-Insights-for-WSO2-API-Manager-APIM-and-Bijira/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "api-monetization-api-strategy-how-to-best-plan-usage-based-pricing-for-ai-agents": {
          "title": "How to Best Plan Usage-Based Pricing For AI Agents",
          "content"	 : "The rise of AI agents has reshaped software economics; businesses have been increasingly adopting them for efficiency, scale, and delivering values faster. However, pricing them has remained a hard problem.By the established norms, you would tie cost to headcount or access, but that doesn’t fit; traditional methods misalign with how agents deliver value. And newer approaches often create more confusion than clarity. If you are a buyer, your concern might be fairness, and vendors worry about protecting their margins, while both sides are trying to define what “value” actually means.Irrespective of your industry and customer demographic, the best pricing model should tie cost to real, observable activity while staying flexible as the market matures. So this article will try to examine why AI agents are uniquely difficult to price, and propose some strategic guidelines to best implement usage-based schemes using Moesif.                Implement Predictable Pricing for AI Agents              14 day free trial. No credit card required.              Try for Free        What are AI Agents?AI agents are software systems that can autonomously plan and execute tasks with minimal human intervention. They are powered by large language models (LLMs) and have integrations with external tools or APIs. These agents autonomously decompose goals into smaller steps, call the right functions, and iterate until a task is complete.Contrary to traditional apps with predefined workflows, AI agents adapt in real time. They can sequence multiple tool calls, fetch context from external data sources, and refine outputs dynamically.Why Pricing AI Agents is DifficultThe technical nature of how agents operate introduces variability and uncertainty that conventional SaaS models by design cannot accommodate:Volatile Cost of Goods SoldEvery agent invocation consumes resources; they can be tokens, compute power, retrievals, and sometimes external APIs that charge per call. The cost profile of a request can swing widely depending on prompt size and complexity, sequence depth, or cache reuse. If you don’t meter carefully, you risk profit margin or unpredictable unit economics.Non-Deterministic TasksAn evidently simple user prompt may result in dozens of tool calls, retries, or checks. For example, you can ask an AI agent to summarize a document. However, it might end up with multiple queries, embeddings generation, and multiple LLM passes. The workload variance makes it difficult to standardize what constitutes a “unit of work” in billing.Seat-Based Pricing No Longer FitsAI agents promise automation and efficiency, replacing seats rather than adding them. Charging per seat also shrinks the revenue base as the product becomes more valuable.Seat in traditional software is a proxy for value: a human using the tool to create value. In an agentic AI model, the AI itself creates value; a seat-based scheme can massively understate the AI’s output. Customers instead expect pricing to map to the savings or outputs delivered.Immaturity in ROI AttributionCharging per fraud prevented, ticket resolved, or sale closed—charging for outcomes like these are appealing but hard to operationalize. How can you prove that the AI agent directly caused the outcome? Most companies don’t yet have the telemetry, baselining, and customer trust for that. Until attribution improves, usage often becomes the only tenable stand-in for value.The Pricing Model Spectrum for AI AgentsHere are the dominant pricing archetypes we can observe in current market trends:Seat-BasedSeat pricing mirrors classic SaaS and is often the easiest to buy through existing procurement motions. It fits, for example, augmentation tools where humans remain primary operators and usage is broadly correlated with headcount.Risks  Seat compression when agents replace human efforts, shrinking the revenue base  Poor cost alignment if a few power users drive disproportionate compute usageAgent-BasedIt charges per named agent or agent type, essentially mapping budget to digital labor. This model works when AI agents encapsulate a role, like a support representative, and carry clear SLAs.Risks  Compute costs can grow while revenue stays flat  Lesser used agents may appear overpriced and reduce renewal ratesOutcome-BasedThis model charges customers based on what agents deliver:  Jobs completed: operational KPIs like tickets resolved, workflows finished successfully  Financial outcomes: cost savings, revenue generatedRisks  Attribution dispute over whether the agent directly brought about the outcome  External factors like market conditions may affect results outside vendor’s control  Overhead for defining outcomes and arbitrating disagreementsCredit-BasedAbstracts heterogenous costs (tokens, tool calls, RAG) into a credit system with a burn table. Buyers pre-purchase credits and spend them as the agent works. This model simplifies complex agent runs and gives a visible usage balance.Risks  Conversion rates can feel ambiguous without clear communication  Rollover credits can accumulate and impact revenue predictability  Unclear credit burn rules frustrate costumes; balance may deplete faster than expectedUsage-BasedCharges by measurable consumption:  Resource-centric: aptly ties to cost of goods sold; for example, per 1k tokens, per compute minute  Interaction-centric: easier for customers to audit and predict; for example, per request, per conversationRisks:  Bill surprise without thresholds or alerts, especially when workloads upturn  Potential disputes if customers can’t easily audit usage or understand the metric behindHybrid ModelsMost vendors combine a base amount with included usage and overage, sometimes through credits for simplicity. Hybrid pricing models protect MRR while letting revenue scale with adoption. They are often a practical solution toward outcomes as attribution matures.Risks  Poorly designed thresholds can frustrate customers who frequently overrun or underuse limits  Complexity in explaining plan details to buyersThe Case for (and Against) Usage-Based PricingUsage-based pricing, although pragmatic, doesn’t work in every situation for monetizing AI agents.When Usage-Based Pricing Works  Outcomes Cannot be Substantiated Yet  Most companies lack the telemetry and attribution tooling to charge directly for outcomes like “sales closed” or “fraud detected”. Usage provides a measurable, auditable proxy until outcome-based models mature. You can defend more easily in negotiations because logs demonstrate the exact consumption.  Costs are Material and Variable  Agent invocations always have associated costs from tokens, context length, tool chaining, and external API calls. These inputs can fluctuate a lot across requests. To protect gross margins while still scaling with adoption, vendors must align revenue with these cost drivers.  Customers Value Elasticity  Customers value flexibility: they want to start small, experiment, and then grow without having to commit to immutable seat counts. Usage-based pricing adapts naturally to different consumption levels. With clear usage caps and alerts, it can offer predictability while retaining the elasticity buyers covet.When To Avoid Usage-Based Pricing  Workloads are Too Unpredictable  It might be difficult to predict cost if agent behavior is highly fluctuating or opaque. It can damage confidence, even if the pricing model is fair.  Outcomes are Well-Defined  Mature domains like support resolutions or invoice processing have clear, explicit outcomes. So outcome-based pricing may align better with customer-perceived value; it also means less disputes over whether usage matches the impact.Designing the Usage Meter: Identifying and Measuring Billable UsageThe billable metric for usage-based model must be clear, predictable, and auditable; otherwise you risk losing customer trust and billing disputes. A defensible billing meter balances technical feasibility, cost alignment, and customer comprehension.Picking the Right Unit of UsageThe best billing metric is the one that customers can understand, forecast, and validate, for example:  Per request: Clean and auditable, but may obscure underlying cost variance between “light” and “complex” requests  Per 1k tokens: Aligns revenue with model costs, though tokens are less intuitive for non-technical customers  Per tool call: Great fit for multi-agent systems where each tool invocation drives incremental cost. However, you must carefully exclude or discount retries and validation runsHandling Edge CasesWithout guardrails, edge cases can skew invoices and cause disputes:  Retries and timeouts: A failed or repeated request should not double-charge the customer  Cache hits: Cached responses reduce actual compute, so customer shouldn’t be charged full price  Background agents: Long-running or autonomous background tasks must have explicit billing rules to avoid unbounded costs  Evaluation or safety runs: Extra runs might be carried out to verify an agent’s output; for example, checking accuracy or screening for harmful content. While important for reliability, customers rarely accept them as billable unitsA well-designed meter defines which events count towards billing and which are absorbed as part of platform overhead.Multiple Meters and NormalizationYou can have multiple billing meters in place. In Moesif, you can create as many billing meters as you need and activate them selectively. Multiple meters doesn’t mean double or multiple charging. Rather, it helps balance fairness and sustainability:  A workflow fee may already account for average token usage. You can establish another billing meter to track token data for visibility or overages.  You might have one unit of usage that anchors pricing, like workflows, while another sets boundaries. For example, workflows may include up to N tokens; beyond that, you apply token-based overages.  The ultimate goal is to protect margins from heavy users while keeping bills predictable and customer-friendlyYou can use Moesif’s Scripted Fields to transform messy or inconsistent data into clean billable metric. You can combine data to compute a metric; for example, combining input and output tokens:Then specifying it as the billable metric in a billing meter:Scripted Fields also allows you to normalize different data across services through arithmetic formulas and conditional expressions.Keeping Usage-Based Pricing Predictable and TrustworthyUsage-based pricing works when:  Data is clean enough to bill on  Customer can clearly see what they are paying forAnd to that end, a structured and disciplined approach to capturing data with built-in controls and customer-facing info go a long way.Capture the Right Data      Link each customer-visible charge to specific requests and related steps or sub-steps so you can reconstruct any invoice line without conjecture. In Moesif, you can access a customer’s billing usage statistics in their Profile View.      From here, you can dig deeper into usage and associated costs. For example, you can interact with a reported usage amount to open a time-series of their usage:        Then click the funnel icon to open the associated events responsible for the reported usage and therefore the specific charge:        Moesif automatically assigns a session token to each API event for correlation and reliability; and the OpenTelemetry support further helps keep end-to-end traces.    Make sure to log sufficient metadata or contextual information for each invocation: user ID, company ID, agent, tool, tenant ID, tokens input/output, model type, and so on.  Ensure consistent event definitions; change in field names or meanings without notice can shift invoices even though usage hasn’t.  Moesif supports custom actions so you can explicitly define custom start-and-stop points to identify successful workflow runs.  Live Event Log in Moesif can help with testing and validating whether or not you’re capturing the usage data you want to.Include Guardrails  Exclude retries, timeouts, discount cache hits; you don’t want to charge your customers for background and housekeeping tasks. Same goes for incomplete workflows, failed validations, and so on.  Add soft thresholds with alerts and hard stops to prevent runaway costs from misconfigured agents, for example, using Moesif’s real-time API monitoring and alerting.  Watch for spikes, deep sequences, or sudden cache miss rates at the tenant level; Moesif can help you set up real-time and calendar-based dynamic alerts to watch over those anomalies.  Alongside your gateway-specific governance features, make use of Moesif’s customer cohorts, quotas, and governance rules to ensure structured, safe, and transparent usage.Make Usage Visible to Customers  Show real-time consumption and approximate monthly cost.  Dispatch alerts with prudence about usage thresholds and plan renewals; this protects your customers from unexpected surprises.You can use Moesif developer portal and Embedded Templates to provide your customers self-service solutions.How to Pilot, Validate, and Iterate on PricingAgentic AI workloads are unpredictable, and therefore can shift customer expectations quickly. So it’s very important to validate your usage-based system before rolling out to production.Make Use of Historical DataMoesif’s Billing Report Metrics can provide insights into historical trends to help evaluate your agent pricing strategy. You can visualize usage across your existing billing meters and slice and dice that data by different criteria for insights like:  Does the model sustain margins without overcharging?  Do similar customers pay similar amounts?  Do bills spike unpredictably for certain cohorts?It also allows you to export your analysis data for external analysis, auditing, and scenario testing.Pilot with Real CustomersYou can run pilot programs; select a small cohort of customers, set a time, and define your success criteria:  Business metrics like revenue health, churn risk, customer adoption  Customer experience: transparency into how changes are calculatedPilots help you validate the metric you have chosen is both fair to customers and sustainable for the business.Moesif provides a robust set of customer and product-centric analytics. Combining dashboards and workspaces and App, you can isolate and organize analytics for such pilot programs.Iterate with ConfidenceAfter pilots, compare across churn, gross margin, and cohort health. You may need to redefine the billable metric or set new thresholds; do those incrementally with clear versioning and communicate with your customers.Moesif’s Billing Report Metrics can support your iteration and ongoing monitoring by aggregating financial flows across your active meters.For example, here, a Composition analysis looks at the MRR from Enterprise customers that have successfully completed a workflow at least 10 times in the past 7 days:Build a Continuous Review LoopYour pricing should keep up and evolve with how AI workloads and model costs do. So schedule regular reviews to re-run scenario tests, analyze profit margins, and check customer sentiment.ConclusionThe huge potential of AI agents can be attributed to how they represent a type of convergence of software, services, and automation. When implementing a monetization strategy around them, companies need to keep that mind. Usage-based pricing, when done right, can provide a reliable foundation for such a strategy. It can be flexible enough for the unpredictability of agentic workloads, while transparent and intuitive to customers about consumption and the associated cost.                Meter, Bill, and Report on AI Agent Usage              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Price Your AI Agents with Confidence.            Implement a fair, auditable usage-based model that aligns your costs with customer value.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/How-To-Best-Plan-Usage-Based-Pricing-For-AI-Agents/",
          "author": "Sakib",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-strategy-api-monetization-monetizing-content-through-api-for-llm-training": {
          "title": "Monetizing Content Through API for LLM Training",
          "content"	 : "To monetize digital content, we have used means like ad networks, affiliate links, and paywalls. However, with the fast and widespread adoption of AI, demand for high-quality data has increased. To make sure Large Language Models (LLMs) models deliver value and accurate results, a wide spectrum of content is often scraped and trained on without permission or compensation. This includes blogs, product and technical docs, forums, and research papers.Organizations now have the opportunity to treat their content not just as a marketing asset, but as a licensable product. The future of content monetization lies beyond ads and subscriptions, in providing structured, API-based access for machine learning. This unlocks a powerful revenue stream and adoption of new business models by allowing AI developers to pay for the high-quality, domain-specific data they need. Platforms like Moesif can provide the critical infrastructure to track, meter, and bill for that access, making sure creators receive compensation for the true value of their work. Oxford University Press (OUP), for example, has taken this approach and successfully leveraged Moesif to monetize their content.                Productize and Monetize Your Content Through API              14 day free trial. No credit card required.              Try for Free        Why Monetize Content in the LLM Era?The fundamental value of digital content has expanded beyond human readership. The rise of LLMs has transformed your existing content repositories into highly valuable training data. Companies developing AI models require vast, diverse, and high-quality datasets to improve model accuracy and relevance and get rid of hallucinations. And your unique content is a prime source for this material. This has substantiated a new, direct, and scalable revenue opportunity that didn’t exist just a few years ago.Traditional monetization strategies like display ads, affiliate marketing, or even standard paywall subscriptions don’t align with this new demand. An AI model doesn’t click ads or appreciate a premium user experience; it needs raw access to the underlying information. This has paved the way for API monetization to emerge as the crucial supplement to older methods. It allows you to package and sell your content as a data product specifically for machine consumption, shifting from simply monetizing human attention.We have observed major content platforms already proving this trend. For example, Stack Overflow and Reddit have established paid API tiers specifically for large-scale data access, allowing AI companies to legally and ethically train their models on high-quality conversational and technical data. Similarly, specialized data providers like LexisNexis have long monetized their curated legal and news archives through data licensing deals. These examples demonstrate a clear market validation: significant demand exists as well as willingness to pay for structured access to quality content for LLM training.For example, StackOverflow offers OverflowAPI to allow LLMs and AI product (like generative AI) developers to access StackOverflow’s vast dataset. Reddit also enforces commercial agreements for LLM training on their data, like Google’s partnership deal.The Value of Quality Content for Large Language Model TrainingThe core drivers of value in content are accuracy, coverage, and structure. LLM developers seek content that reflects real-world knowledge, adheres to domain-specific terminology, and captures context that models can generalize from.Domain AuthorityLLM training pipelines often filter data sources for credibility and relevance before ingestion. That means your content needs to be both present online and trusted. For example, a structured medical terminology database with treatment protocols and peer-reviewed references carry more weight in training than loosely written health articles with anecdotal advice.Format and StructureTraining systems can efficiently parse and tokenize content that consistently follows a clean format, whether in HTML, Markdown, or JSON. Moreover, metadata like timestamps, author identifiers, tags, and semantic markup increases the dataset’s utility since it enables filtering, deduplication, and targeted training. For example, a product documentation API that tags functions, parameters, and return values makes it easy to create fine-tuned programming assistants.Coverage and DiversityA dataset that contains multiple perspectives, languages, or formats allow a model to generalize better. They also support training models with broader comprehension and reasoning skills. LLM engineers often actively seek out diverse but coherent content collections to reduce bias and increase the model’s robustness.Consistency Over TimeModels benefit from content that’s regularly updated and historically versioned. This allows training teams to construct temporal datasets, so that models learn both current and historical context. If your content updates in predictable ways, you can expose it through a versioned API. This increases your content’s appeal to model vendors who want to retrain without rebuilding entire pipelines.Common Forms of API-Based Content MonetizationOnce you have decided to monetize your content through API, you must select the right pricing model. The best strategy depends on a number of things, for example:  The nature of your content  The target audience of LLM and AI-product developers and their requirements  Your business model and goals.Taking a hybrid approach to mix different content monetization models can also prove very practical depending on your scenario.Subscription or Tiered PricingThis is a very common and predictable model, offering access to your API for a recurring fee. It allows you to design your tiers to segment your customers—from small teams to large enterprises. Every customer has a clear path to upgrade if necessary.Here’s how the tiers might look:  Free/Developer tier: Offers a limited number of requests or tokens per month at no cost. This tier lowers the barriers to entry. Potential customers can experiment and validate your content’s utility to determine whether or not it suits their models.  Pro/Business tier: Offers significant higher usage quotas, access to more valuable or recent data, and standard support. It suits small to medium-sized teams actively training LLM models.  Enterprise tier: Features custom pricing, very high or unlimited usage quotas, premium support (SLAs), and potentially more flexible licensing terms for derivative works. This tier supports large enterprises or commercial AI operations.Moesif can help enforce rate limits, trigger alerts based on usage thresholds, and track how often enterprise customers hit their quotas. Having such control and data means your organization can perform renegotiations and upsell higher tiers.Usage-Based PricingThis model directly ties cost to consumption. Developers find this fair and reliable since they only pay for what they use. Projects that deal with unpredictable or fluctuating data needs will find this model very ideal.You can implement a usage-based model in different ways, for example:  Per API call: Charging for each call to the API a fixed amount. This works well when the responses have uniform or predictable size and value, and incurs consistent computational cost.  On content volume: Charging based on the volume of data transferred. It provides an equitable way to charge when your payloads vary greatly in size and value, like images or full document workloads.One possible caveat is that while small use cases appreciate paying for what’s used, enterprises may view pay-per-call as unpredictable. Moesif can provide a decisive advantage here through its powerful product and customer analytics tools. If you have definitive insights into consumption patterns and customer behavior, you can confidently strategize your billing meters and pricing.Outcome-Based PricingAn outcome-based pricing model aligns your pricing structure directly with the value or computational unit relevant to your customer. For example:  You can charge a flat rate for each individual document analyzed or retrieved, like a financial report or news article.  Charging for successful delivery and validation of a dataset  Charging for each tokenized word or sentence; it aligns with how LLM pipelines measure training data at scaleAnother example can be data transformation: if your API enriches or cleans raw data, you can price by successful transformation.An outcome-based scheme might prove harder to implement since outcomes often occur downstream of the request itself. However, Moesif’s high-cardinality and high-dimension analytics can easily help you capture those outcome events, which you can then make use of in the billing meter logic.Dynamic Pricing by Content TypeDynamic pricing acknowledges the heterogeneity of content:  A general news editorial may have a base price per request  A peer-reviewed research paper with structured metadata has more value and therefore is more expensiveTo make this model work, you need to appropriately tag the content so the billing infra can accurately identify what content the API delivered. With Moesif, you can attach any custom metadata to your API events; they become available in a dedicated Metadata field in the UI.Dynamic pricing can maximize revenue; it also prevents undervaluing niche, high-quality content that carry disproportionate importance for model training.Metering and Charging for API Access: How Moesif Can HelpLLMs consume millions of records or gigabytes of data. Without precise measurement, you will either undercharge and lose revenue, or overcharge and lose customers.Define Billable UnitsFirst decide what counts as a billable metric or event. For example:  A finance API might bill per row of historical stock market data.  A dictionaries API might charge for each requested dictionary entry.You can include custom fields and metadata to your API events that can help you track these chargeable units. In the following example, Live Event Log shows real-time API events for CSV exports in a content API. Notice that it also filters out unsuccessful export jobs.Inspecting API traffic helps you pinpoint the chargeable unit and verify whether you have sufficient instrumentation to capture that data.Convert Requests into Metered UsageAfter you have enriched your API events with contextual information to help track billable units, use Moesif Billing Meters to track, meter, and charge your customers for their usage. Billing Meters allow you to define the usage metric you want to bill on, with fine-grained filters to only consider events that matter.For example, consider an API that associates a document ID for each document requested. You can think about the pricing in two ways:  Per API call  Per content volumeTo charge customers 1 USD for each 1k successful API calls, you can create the following billing meter:For the latter strategy, you can change the billable metric from Event Count to events having distinct document IDs:This meter has several benefits over the other:  Counts only distinct document ID values, thereby ignoring duplicate fetches  Meters and charges appropriately when responses have multiple documentsBoth meters include pricing information:  The plan or tier  The price associated with the plan or tierThe Pro API price in this example configures the following about how to charge customers:  Charge 1 USD for each 1k units of billable metric  Use a Stripe Meter to measure usage by adding up each month’s usageThe billing meter shows each company’s usage in the past 7 days under their respective subscription plans. So a metric value of 18 means they have a bill of 18 USD so far.Enforce Quotas AutomaticallyEnterprises expect hard and definite guardrails around consumption, more so when LLMs train on their data. Moesif compliments your gateway-specific guardrails and enforcements by allowing you to administer quotas and governance rules for complex, longer-term, and business-specific requirements. For example, a basic tier might allow 50k requests per month, while enterprise customers get custom limits. Moesif can trigger alerts when a customer exceeds a monthly quota, block additional requests, and inform customers to upgrade or refill credits.Here, a quota rule blocks premium plan users once they cross 10k requests in a billing period; the response also provides useful context:Provide Transparent Usage ReportingYou can share Moesif’s analytics with customers so they can view their own API usage in real time, with breakdowns by criteria like endpoints and billing periods. Moesif makes analytics data and visualizations available in different ways:  Embedded metrics templates  Search API for API and customer analyticsThese features not only support different use cases but also promotes transparency for less billing disputes and easier vendor compliance checks.Integrate with Billing SystemsLastly, the metered usage must flow into invoices and revenue systems. To simplify that process, Moesif supports native integrations with popular billing providers like Stripe, Recurly, Zuora, and Chargebee. You can integrate custom billing solutions as well. Moesif keeps metering and billing decoupled from API; so you can easily experiment with monetization models and dispense with costly re-architecture.ConclusionContent has always been valuable, but in the LLM era, it has become a scarce and highly monetizable asset. Organizations that structure and productize their content using APIs are supporting digital experiences and driving the next wave of AI models. Content not monetized will still be consumed, just without any compensation. The urgency comes from the fact that model developers are aggressively sourcing data; early actors are setting market prices, standards, and licensing norms. So there are major opportunities to secure revenue and influence in a rapidly consolidating market.                The Infrastructure for API-Based Content Monetization              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Make Your Content a Source of Revenue in the AI Era.            Use Moesif to track, meter, and bill for access to your content for LLM training.            Try for Free            No credit card required            ",
          "url": " /api-strategy/api-monetization/Monetizing-Content-Through-API-For-LLM-Training/",
          "author": "Sakib",
          "categories": "API-Strategy, API-Monetization"
        }
      
    ,
  
    
        "monitoring-model-context-protocol-comparing-mcp-model-context-protocol-gateways": {
          "title": "Comparing MCP (Model Context Protocol) Gateways",
          "content"	 : "The rise of Model Context Protocol (MCP) has given AI agents and large language models (LLMs) a standardized way to talk to external tools, APIs, and data sources. In theory, it solves the messy integrations and custom connectors that have slowed down real-world agent adoption. A clean protocol should mean smooth interoperability.However, we’re observing certain patterns of fragmentation. Each MCP server runs in isolation. Agents have to handle multiple connections. And enterprises possess little control over discovery, governance, or observability. Security-wise, too, no central place exists to enforce policies or audit usage. It’s as if the ecosystem is facing risks of repeating the same clutter we saw before API gateways emerged.An MCP gateway, acting as a unified endpoint, can route, secure, and help monitor MCP traffic. It can potentially bring the same discipline to AI agents that API gateways have brought to microservices. Several open-source and enterprise-ready gateways are already competing to define the standard of what an MCP gateway should be.In this article, we will look at some of the MCP gateways available today and their features. We will also discuss the necessity of a dedicated analytics layer that works in tandem with the gateway to help enterprises realize their AI roadmap.                Monitor MCP Traffic and Agent Behavior with Moesif              14 day free trial. No credit card required.              Try for Free        What is a Model Context Protocol (MCP) Gateway?An MCP gateway is a piece of infrastructure that functions as a centralized control plane to securely, reliably, and idiomatically connect AI agents to the growing ecosystem of MCP servers. It provides a single endpoint that simplifies discovery, security, and traffic management for MCP workloads. Thanks to MCP gateway, an agent doesn’t need to open a connection to every tool it requires. Instead, the gateway aggregates secure and trusted servers under one addressable entry point, to which the agent connects to.We can look comparatively at traditional API gateways and MCP gateways in five dimensions:            Dimension      Traditional API gateway      MCP gateway                  Primary consumer      Human-driven apps and backend services      AI agents, AI assistants, large language models              Routing logic      Path, headers, and standard request metadata      Semantic intent, agent context, and task structure              Protocol      HTTP, REST; sometimes gRPC      Native support for MCP (JSON-RPC) for standardized agent-to-tool interactions              State management      Largely stateless, with each requests handled independently      Can preserve context across multi-step workflows; may also cache tool outputs for reusability              Orchestration role      Minimal; each request is isolated      Can orchestrate tool selection, chaining, or combining results across overlapping capabilities      Why Do You Need an MCP Gateway?An MCP gateway brings compelling benefits and solves problems that are frustrating standardized AI adoption through the Model Context Protocol.Centralized Access and Policy ManagementEnterprises are adopting more MCP servers to power their agent workflows. Without a gateway, each AI agent must manage multiple direct connections, which quickly becomes unsustainable. It results in fragmented security, duplicated integrations, and makes visibility into usage harder.A centralized gateway can solve these issues by acting as a single access point. You avoid the hassle of implementing authentication, authorization, and rate limiting in every tool. Instead, you enforce these policies at the gateway. It also becomes a lot easier to onboard new servers and meet compliance across sensitive environments like finance or healthcare.Foundation for Observability and MonitoringMCP gateways also standardize traffic paths between agents and tools, the indirect benefit of which is a consistent stream of telemetry. Every call, whether successful, failed, or retried, passes through a single checkpoint. As a result, you get reliable records of usage patterns, performance, and cost drivers. Gateways themselves don’t offer advanced analytics, but they can provide the raw data and make integration with platforms like Moesif simpler and more powerful; you can achieve deeper monitoring, reporting, and business intelligence.Scalability and ReliabilityUsage in agentic AI systems can spike unpredictably. To complete a task, an agent might  generate hundreds of tool calls. A gateway can handle these demands through different strategies like load balancing, cache, and request queuing. This prevents upstream MCP servers from becoming bottlenecks. It also allows enterprises to scale their agent usage without rewriting integrations or overprovisioning individual servers.Composability and InnovationPerhaps the most strategic role MCP gateways play is enabling composability. Since they abstract away direct connections, enterprises can mix and match different MCP servers into a single unified interface. They can be internal APIs, vendor tools, or open-source utilities; as long as they are compatible with the MCP gateway, the integration simply just works.This reduces friction between developers and allows AI agents to perform complex, multi-step workflows that don’t possess brittle point-to-point integrations. Naturally, it also pushes innovation forward and keeps operations governable.The Top MCP Gateways in 2025Although still early, we already have several enterprise-ready solutions available in the market, as well as experimental open-source projects. Let’s look at some of them and their features.WSO2WSO2 allows you to create, discover, and manage MCP servers with a centralized control plane through the open-source API Manager (APIM) and the SaaS API management platform Bijira. This means having a unified platform where you can streamline workflows across both traditional API and agent-based AI services. You can create an MCP server from its OpenAPI spec, from an existing API, and also set up a proxy for an existing MCP server.Conveniently, using the AI Gateway, you can create, deploy, and manage AI APIs which you can then convert to MCP servers. This way, WSO2 APIM and Bijira can build and maintain your organization’s AI capabilities end-to-end across varying business requirements and product models, all under one platform.Docker MCP GatewayDocker’s MCP gateway is positioned as an OSS, production-grade gateway for orchestrating and managing MCP servers from the Docker MCP catalog. It’s an obvious choice for organizations already invested in Docker and containerized apps and now wants to integrate MCP into their infrastructure. It centralizes routing, policy enforcement, and other managerial tasks. Deployment is also simple with familiar Docker Compose, both locally and in production.Solo.io Agent GatewaySolo.io’s Agent Gateway is another open-source project that supports MCP. It functions as a production-ready data plane for agentic AI, supporting both agent-to-agent and agent-to-tool communication. You can have multiple backend types to which Agent Gateway proxies traffic to, all under one endpoint; these types include MCP servers and other agents. Irrespective of the number or type of backends, Agent Gateway exposes only one endpoint for receiving requests. As a data plane, it helps enterprises consolidate AI tool access through a managed endpoint.Kong AI GatewayKong has extended its AI Gateway to support MCP servers, allowing organizations to govern their AI tool usage through the same platform they use for API traffic. You can expose any MCP server through AI Gateway and then use plugins to establish policy enforcement, observability, and access control. It’s an appealing option for enterprises aiming to standardize traffic management across both APIs and agent workflows under one platform.Tyk AI StudioTyk’s AI Studio lets you expose internal tools and APIs to AI agents. You can also convert your APIs to MCP servers with access control mechanisms in place to prevent misuse. The integrated AI Gateway provides a single entry point with built-in governance, monitoring, and security features for your AI services and deployed MCP servers. Like Kong, Tyk helps organizations manage authentication, traffic policies, and analytics consistently across both AI and API environments.IBM ContextForge MCP GatewayIBM’s ContextForge MCP Gateway is an open-source community project still in early beta, but not a supported IBM product. It implements a rich set of features you’d expect from an MCP gateway and also acts as an MCP registry. It can convert REST endpoints into MCP servers as well, bearing one of its goals of federating MCP and REST services. While not enterprise-backed, it adds diversity to the ecosystem and provides a way for experimentation, especially for multi-agent and multi-tool orchestration.Microsoft’s MCP Gateway SolutionsMicrosoft supports MCP gateway capabilities in two ways:  The open-source MCP gateway provides traffic routing and control plane for MCP servers in Kubernetes environments, including enterprise-ready integrations for security and observability.  Azure API management acts as an auth and security gateway for MCP servers, leveraging Azure APIM’s scalability as integrations grow.Lunar MCPXLunar.dev MCPX offers a comprehensive orchestration and security solution for MCP servers and tools. Its focus areas include centralized tool usage, granular and configurable access control, authorization, and Prometheus-compatible metrics. MCPX is one of the first purpose-built MCP gateways, designed to help enterprises with their growing catalogs of MCP servers and AI agent workflows. They have an open-source solution as well, and pricing plans for both free and commercial usage.The following table summarizes the different aspects of the gateways we’ve discussed so far.            Gateway      Offering type      Primary focus      Ideal use cases                  WSO2      Open-source and enterprise      Open-source and SaaS control plane for complete lifecycle management of MCP servers using one platform      Feature-rich open-source solution for prototyping and evaluating use cases, or SaaS solution for enterprise-grade MCP use cases              Docker MCP Gateway      Open-source and enterprise-ready      Unified MCP endpoint for MCP servers in Docker MCP Catalog, container-native integration      Enterprises already using Docker and have containerized workloads              Solo.io Agent Gateway      Open-source      Data plane for agent-to-agent and agent-to-tool communication in agentic AI systems      Enterprises consolidating AI usage through a managed endpoint              Tyk AI Studio      Commercial      Unifying and controlling AI usage, improving developer and customer experience      Exposing internal APIs and tools to AI agents in enterprise use cases              IBM ContextForge      Open-source      Unified access point for AI usage and federation of REST and AI services      Developers and researchers evaluating AI usage in hybrid backend architectures consisting of RESTful and MCP-based services              Microsoft      Open-source and commercial      Open-source gateway solution for MCP servers in Kubernetes environments Enterprise-grade auth gateway for MCP servers , supporting security and scale      Developers prototyping MCP, or enterprises using Azure API Management.              Lunar MCPX      Free and commercial      Orchestration, security, and management for enterprise MCP usage      Large organizations building complex agentic AI systems      The Importance of Analytics for MCP Gateways and Agentic AIMCP gateways solve the operational challenge of consolidating the management of MCP-compatible tools, their usage, and lifecycles. However, to build reliable and cost-effective AI systems, you need a dedicated platform like Moesif that allows you to observe past basic telemetry and logs.Understanding Agent Behavior at ScaleAI applications and agents are not predictable clients. They may call multiple tools in succession, retry failed calls, or generate traffic bursts unlike anything we see in human-driven apps. If there’s a surge in requests, it could be either productive automation or an agent stuck in a loop. Traditional gateway metrics won’t reveal the difference. But analytics capable of correlating agent identity, tools, and session context can.Linking MCP Server Usage to Costs and OutcomesEach MCP request carries costs, whether that’s compute cycles, API credits, or licensing fees. Gateways aren’t designed to explain who is driving those costs or whether those calls lead to successful outcomes that define the actual value enterprises get from your product. To make roadmap and pricing decisions, product and finance teams need metrics like cost per successful tasks and adoption by customer segment. Without this attribution layer, MCP adoption risks cost overruns with little accountability.Making Continuous OptimizationAnalytics also promotes iteration. It makes engineering leaders knowledgeable about:  Underperforming services  Workflows incurring failures the most  Adoption of new MCP servers or toolsAnalytics platforms provide customizable performance analytics that you can associate with various parameters, like specific servers, agents, or tools. These provide valuable insights to act on and improve agent workflows and prioritize engineering investments.Monitoring in Real TimeWithout real-time monitoring, it’s impossible to stay on top of the high risks that come with the dynamic and unpredictable nature of AI agents. Real-time analytics tools not only detect anomalies according to your specification, but also provide configurable means to dispatch alerts in real time to appropriate channels. This allows you to resolve issues before they escalate and improve user experiences.Moesif’s Role in MCP Gateway for Analytics and ObservabilityMoesif adds actionable analytics and observability as your MCP system scales through the gateway to more and more agent workflows. It provides the longitudinal analytics necessary for better and confident decision making without stressing or altering your gateway’s role; for example:  Churning agents  Tools driving the most outcomes  Behavioral changes  AnomaliesInsights like these allow engineering and product teams to run AI systems reliably, cost-effectively, and with confidence. And Moesif unifies all of this even if your setup involves multiple MCP servers or gateways, with native integrations available for platforms like WSO2, Kong, and Tyk.Deep Visibility into Agent and Tool BehaviorYou can use Moesif to enrich your MCP traffic with agent identity, session context, and tool-level metadata; for example, consider fields like these:  agent_id  mcp_server  retry_countMoesif can capture the entire request and response context, including headers. So you can add custom headers as well, for example, to tie usage back to specific customers and teams, or attach token consumption information.For example, here’s some MCP traffic data in Moesif’s real-time Live Event Log:Moesif also supports tracking custom actions outside typical HTTP activities. For example, in streaming or SSE (Server-Sent Events), you can emit a “start” and a “complete” event to get an end-to-end visibility into a workflow through Moesif. Moesif’s OpenTelemetry support also allows you to capture span actions.See the article Monitoring MCP Security and Agent Behavior with Moesif for a more detailed discussion about making agent and tool behavior observable.Business-Level Cost AttributionMoesif maps the requests flowing through your gateway to customers, teams, and agents. This opens up different ways to analyze usage and surface metrics like cost for each completed task or customer segment. It becomes easier to understand things like adoption patterns and true ROI of adding or deprecating tools. Using such insights, you can align engineering capacity with business values. Apart from tracking costs, Moesif can also help you monetize your MCP servers.KPIs Dashboards with Custom AnalyticsEngineering leaders can create real-time analytics dashboards, for example, to track percentile latencies like P90 and failure rates per tool and per MCP server. For example, here we look at average latency by JSON-RPC method using a time series:Having agent and server data as first-class analytics entities means you can easily correlate error spikes with specific tools or versions and highlight regressions by different criteria.Product and platform teams can monitor activation funnels, task success and error rate, and cost per successful task by agent, tool, and customer segment. For example, the following Segmentation analysis looks at API errors in an MCP server, broken down by company ID and JSON-RPC method:Real-Time Monitoring and AlertsMoesif’s robust monitoring features can detect anomalies in real-time and route alerts to the right teams. You can customize every step of what you want to monitor, alert criteria and type, and where you want Moesif to dispatch those alerts. For example, you can set up alerts for agents crossing quota limits, or a specific server experiencing degraded performance.For example, here we set up an alert to monitor latency: Moesif alerts you whenever the threshold P90 latency of 200 milliseconds is exceeded:ConclusionMCP gateways are quickly becoming a major component in how enterprises scale their AI infrastructure and succeed. As agentic AI systems get more adoption through the MCP standard, we also need systems that don’t collapse under erratic agent behavior. Enterprises also need to make sure their efforts drive measurable value rather than hidden costs.Without a gateway, every new tool integration becomes an operational burden, since there’s nothing to centralize traffic and governance. And without proper analytics, it’s impossible to grow since there’s no data to base decisions on. That’s where Moesif comes in, supporting business goals by layering on analytics and observability that can tackle the unpredictable nature of AI systems. This way, enterprises possess both the control to govern their AI systems and the intelligence to improve it over time.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Unlock the Intelligence Your MCP Gateway Needs.            Get actionable insights on agent behavior, costs, and value.            Try for Free            No credit card required            ",
          "url": " /monitoring/model-context-protocol/Comparing-MCP-Model-Context-Protocol-Gateways/",
          "author": "Sakib",
          "categories": "Monitoring, Model-Context-Protocol"
        }
      
    ,
  
    
        "technical-gloo-moesif-gloo-gateway-deep-api-analytics-and-observability-at-the-edge": {
          "title": "Moesif + Gloo Gateway: Deep API Analytics and Observability at the Edge",
          "content"	 : "Solo.io Gloo Gateway gives teams a reliable way to secure, route, and monitor API traffic in real time. With its high-performance Envoy core and Kubernetes-native design, it meets the demands of distributed applications and modern service architectures.However, performance metrics alone don’t reveal how developers engage with your APIs or why adoption stalls. Gateway-level logs may confirm an endpoint works, but they don’t explain underused features, where onboarding breaks down, or what patterns drive retention.That’s where Moesif compliments Gloo’s strength. By combining Gloo Gateway’s real-time traffic control with Moesif’s deep API analytics, your team can gain both operational visibility and product-level insight. This article demonstrates how this integration helps engineering and product leaders debug faster, improve developer experience, and drive smarter decisions through accessible data.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        What is Gloo Gateway?Gloo Gateway is a Kubernetes-native, “next-generation” API gateway engineered for performance, extensibility, and operational consistency across environments. It builds on Envoy Proxy, and has been designed to integrate with the Kubernetes Gateway API. By doing so, Gloo helps platform teams standardize how they expose, secure, and manage API traffic across services and clusters.Its declarative configuration model and support for enterprise features like rate limiting, JWT validation, and function-level routing make it ideal for managing modern API workloads at scale. Irrespective of your use case—serving public APIs, internal microservices, or hybrid apps—Gloo Gateway provides the control and reliability to meet production demands.While Gloo delivers extensive metrics for traffic health and behavior, Moesif, on top of that, can provide a complementary lens on user interaction and product-level trends. Gloo directs and secures your API traffic, while Moesif analyzes who is using your API and how. This allows you to better align infrastructure performance with broader product and developer experience goals.What is Moesif?We have built the Moesif platform to help teams understand how their APIs are used, by whom, and why—because system health checks aren’t enough; you need business transaction intelligence.Moesif graduates from simple pass/fail signals to providing the complete and high-fidelity record, with context, behind every transaction. It captures every API call, with metadata—including headers, payloads, status codes, and response times—and ties that data to users and companies in real time.Unlike many tools that stop at infrastructure metrics, Moesif delivers product-level observability. You can track developer behavior across onboarding, integration, and retention stages. Metrics like Time to First Hello World (TTFHW), active API users, or drop-off points in usage funnels help teams identify where consumers succeed—or get stuck.For engineering leaders, Moesif enables targeted debugging and precise root cause analysis with powerful event-based filtering, anomaly detection, and long-tail latency tracking. If you’re working in the product team, you have clear windows into which endpoints drive adoption and where to focus future improvements.With lightweight integration options and real-time dashboards, Moesif fits cleanly into cloud-native environments—providing the kind of usage intelligence you need to shape better API products.Using Moesif and Gloo Gateway Together for a Complete Observability StackMoesif’s integration with Gloo works through Envoy’s External Processing (ExtProc) interface. This allows Moesif to capture enriched API call data from Gloo in real time, without injecting latency or risking path stability. This section outlines how the integration unlocks visibility across three critical areas: real-time performance monitoring, developer experience, and usage-aware operations.Real-Time Monitoring at the Edge, Without Trade-OffsMoesif’s ExtProc plugin forwards request and response data to Moesif asynchronously as traffic flows through Gloo Gateway. This enables high-frequency logging without impacting throughput or introducing additional hop latency. More importantly, the integration runs out-of-band with configurable failover modes. Your production traffic stays free from any reliability risk.This architecture, on top of Gloo’s infra metrics, gives teams access to detailed telemetry across every route and method, including:  Latency distributions at high percentiles—like P95, P99—to catch tail-end degradations  Time-series breakdowns by endpoint, method, or client ID  Spike detection for both traffic surges and error responsesThis conjunction makes request context always available for faster root cause analysis—and continuous monitoring for anomalies, error responses, and unexpected traffic behavior.Linking API Calls to User JourneysMoesif makes it possible to track how real users interact with your APIs by automatically capturing user and company IDs from request headers. You don’t need any changes in your backend services for Moesif to enrich API events with identity-aware context right at the gateway. You get a clear view into how customers experience your APIs over time.You can trace signals back to specific users, bringing a product analytics lens to API traffic:  Monitor onboarding success with metrics like TTFHW.  Analyze when users drop off in common integration paths.  Identify underused endpoints or features across customer segments.  Visualize usage funnels and conversion stages  Detect recurring friction points like spikes in 401s during auth workflows.  Track retention and segment behavior by SDK, plan tier, or customer verticalPlatform owners can easily distinguish between technical failures and user-facing usability gaps.For more information on how customer identification works, see Identifying Users and Companies in Gloo Gateway.Analytics That Drive Operational and Product DecisionsMoesif turns traffic patterns into decision-ready data for both engineering and product leaders. You can leverage dashboards and reporting tools to define KPIs across availability, adoption, and engagement—all in a way that’s sharable and aligned across functions.For example:  Identifying the top API methods and endpoints by traffic and error rates.  Filtering by SDK to prioritize developer tooling investments.  Detecting sudden drops in activity that might signal integration issues.  Correlating response time regressions with user impact.Setting up Moesif with Gloo GatewayFollow the integration guide to get started. The documentation contains examples, configuration options, and usage instructions to further help you adjust your setup.Since Moesif integrates with Gloo through Envoy’s External Processing (ExtProc) filter, traffic data streams to an external gRPC service. Moesif provides a lightweight ExtProc-compatible service that you can deploy as a standalone Kubernetes pod within your environment. The service receives API call data from Envoy and asynchronously forwards it to Moesif.Live Monitoring and Operational MetricsMoesif extends Gloo’s built-in metrics with live, high-resolution telemetry on how your APIs behave in production. For engineering teams managing uptime and SLAs, this translates to faster detection, clearer visibility, and more effective troubleshooting.Here are some ways Moesif strengthens your operational observability at the gateway layer:Track P95/P99 Latency By EndpointYou can analyze latency percentiles in real time using Moesif. For example, if a login route maintains a 150ms median but spikes to 1.2s at P99 during peak hours, an average analysis will miss that.In the following 24-hour span Time Series, we perform an hourly analysis to observe P99 latency across API endpoints:Segment Error Responses Across Routes and ClientsBy filtering 4xx and 5xx API errors by customer ID, API key, or SDK, you can pinpoint problematic patterns. For example, the following analyzes 5xx server error counts within the past 12 weeks, breaking down the analysis by response status codes:It also folds the time series (notice the grey activated button next to the analysis time period) to help visualize periodic patterns, making it easier to spot regular anomalies, such as spikes in error rates. By isolating specific time frames, you can pinpoint the worst-case scenarios and investigate underlying problems like:  Performance bottlenecks when API usage reaches its peak.  Service disruptions caused by routine maintenance or scheduled tasks.  Recurring failures from external dependencies that occur at specific times.Let’s look at another example that visualizes all error types:Being able to break down errors and contextualize them allows you to create error heatmaps to identify problematic parts of your product offerings. For example, the following Segmentation analysis uses two categories—response status code and SDKs, to break down errors. The result clearly illustrates the erroneous SDKs causing the most friction.Product Analytics and Developer InsightsLinking API traffic with individual users and companies allows you to perform value-aligned product and customer analysis in Moesif. This gives product and platform teams the necessary behavioral signals to improve developer experience and API adoption.Here are some ways teams use Moesif to drive product decisions with usage-level visibility:Measure Onboarding SuccessTracking metrics like TTFHW can provide insights on the period of time developers need to make their first successful API call. If, for example, you observe that most drop offs occur before completing authentication, it might suggest  a documentation or SDK usability issue, rather than an infrastructure one.For example, here’s a funnel analysis in Moesif for a GenAI API, constituting of these steps:  Signing in successfully  First call to the embeddings endpoint  Consuming more than 100 input tokensMap Usage FunnelsUsing funnels, you can go further to evaluate your product’s sustained usage. For example, if you have an AI API, you might define a multi-stage funnel like this:  Generating model key  First prediction  Triggering batch inference  Initiating feedback loopIf Moesif illustrates that many users stop after single predictions but never reach batch or feedback stages, it may indicate a number of issues:  Unclear pricing  Limited onboarding docs  Undercommunicated API performance constraintsYou can segment funnel drop-off by customer tier or SDK version to target improvements that reduce churn and support higher-value usage patterns.Analyze Engagement, Adoption, and Popular FeaturesYou can segment usage by different criteria like endpoint, SDK, or customer tier to understand which features drive sustained engagement, and which ones go underused.For example, here a Segmentation analysis illustrates top customers for existing endpoints:To prioritize what to build next and where to invest team effort, Moesif can help with API adoption analysis, through its usage segmentation and cohorts. You can confidently answer queries like these:  Which customer tiers are adopting our new features the fastest?  Are enterprise accounts activating key features, or are they stalled in early integration steps?  How does adoption vary across SDKs, environments, or regions?Consider you’ve released a new version of your API. You want to compare its uptake against the prior one across your existing customers. Using a monthly time window, you can segment API call volume by customer domain and version identifier. You can perform a rolling aggregation (rolling average or sum) to visualize adoption trends over time—highlighting how fast customers are migrating, and whether specific accounts or cohorts are lagging behind.The following time series example shows such a use case:AI Explain: Fast Insights Into Complex API BehaviorMoesif has built AI-powered features into its analytics and observability suite to enhance your experience.AI Explain provides an interactive, natural language-based conversational interface to empower non-technical shareholders with analytics insights. It works across API analytics, customer analytics, and alerting.AI Explain surfaces key observations directly from your API traffic in one click, according to the analysis configuration you’ve defined in a workspace. For example, consider analyzing the growth of an embeddings API in Gloo Gateway:Select Ask AI to start asking questions about the analysis:Moesif’s investment in AI features is grounded on making API analytics more accessible, explainable, and actionable across teams. To avoid every stakeholder needing to configure and build dashboards from scratch, these features lower the barrier to insight. Teams have at their disposal fast, natural ways to interrogate traffic patterns, detect anomalies, and explore customer behavior.For engineering leaders, this means shorter incident investigation cycles, even in high-volume Gloo environments. Product and platform owners have quicker answers to strategic questions.ConclusionModern API programs’ success hinges on two things: control and clarity. And successful APIs, in addition to being stable, are also adopted, retained, and evolved thoughtfully. Gloo Gateway delivers the control and makes sure the delivery path remains strong. And Moesif makes sure the feedback loop exists and provides clarity all around.This article has tried to demonstrate how those layers complement each other for a shared visibility across engineering and product—empowering faster interaction, better onboarding, and more strategic planning. When you account for both performance and context, your teams will move faster with fewer blind spots.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Add Product Observability to Your Gloo Gateway.            Translate raw metrics into clear signals on adoption and retention.            Try for Free            No credit card required            ",
          "url": " /technical/gloo/Moesif-Gloo-Gateway-Deep-API-Analytics-and-Observability-at-the-Edge/",
          "author": "Sakib",
          "categories": "Technical, Gloo"
        }
      
    ,
  
    
        "technical-api-development-analyzing-opentelemetry-logs-in-moesif-for-operational-and-business-insights": {
          "title": "Analyzing OpenTelemetry Logs in Moesif for Operational and Business Insights",
          "content"	 : "As your application grows, the volume of telemetry data expands with it. Every additional service, customer, and feature generates more log entries. It becomes harder to quickly isolate the events that actually matter. Without the right tools, finding a single root cause can feel like searching for a needle in an ever-growing haystack.You may have already been using log search tools to find and act on errors. However, those tools rarely connect that data back to your API traffic patterns or metrics. OpenTelemetry logs are standardized, but developers still need a way to slice, filter, and analyze them at high cardinality and high dimension without losing performance.To overcome these challenges, we have added logs support in our OpenTelemetry integration. You can now send your OTel logs directly to Moesif. This allows you to correlate logs with traces, link them with API calls, and drill into the data with the same powerful analytics used for API metrics.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        From Noise to InsightYou likely send logs to monitoring tools like Datadog. However, OTel logs also contain hidden business insights; you can leverage those insights to track product experience and usage metrics. Moesif’s powerful analytics can analyze OTel logs, assuming they contain structured JSON:Logs tell you what happened, whereas API analytics tell you how often it happened and under what conditions. Moesif’s OTel logs support puts both in the same place for better operational analysis. Using Moesif’s API analysis types, you can now incorporate logs into your metrics filters and visualizations. You can instantly see trends, patterns, and hotspots, all within the context of your API activity.What is New  Natively ingest logs from any OpenTelemetry Collector or OpenTelemetry SDK-instrumented application.  Analyze structured JSON containing business-specific information to drive critical decisions. Moesif automatically parses log data and makes them available for analytics.  Cross-link logs with traces, API calls.  Reduce data silos by reviewing both API usage metrics and logs in the same analytics views.The following Segmentation chart uses log data to illustrate event count across deployment regions:OpenTelemetry logs are available in filters for API analysis types only. They arenot supported in customer analytics like funnels and retention.How it WorksSimply configure your OpenTelemetry Exporter to send logs to Moesif over OTLP/HTTP:exporters:  otlphttp/logs:    endpoint: https://api.moesif.net/v1/logs    headers:      X-Moesif-Application-Id: 'YOUR_MOESIF_APPLICATION_ID'Make sure to include your Moesif Application ID in the request headers as X-Moesif-Application-Id to authenticate requests.As your application runs, OpenTelemetry should start sending log data to Moesif. Open a Live Event Log workspace in Moesif and you should see logs appear:Analyze OTel Logs in MoesifOnce you have logs flowing into Moesif in real time, you have more control and finesse over your analytics, for example:  Search and filter in Live Event Log: Quickly isolate log entries or API activities by status code, trace and span data, or log-specific fields and attributes.  Build Time Series: Track error rates or other metrics over time.  Segment by different criteria: Compare error volume by service, region, or deployment version.  Visualize on geo heatmaps: See where errors originate geographically.Here’s an example of looking at logs by their severity levels in a Time Series.Why Use Moesif for OTel Logs?We’ve purpose-built Moesif for high-cardinality, high-dimension analytics. Now we’re extending that to telemetry data like traces and logs. So you can:  Filter and group logs across millions of unique values  Correlate log patterns with API performance trends  Use familiar Moesif dashboards, workspaces, and other utilities to investigate incidents faster.If you have set up customer identification, you can pivot to the customer profile screen from a Live Event Log workspace and get more info on exactly who’s impacted.You can also observe all events associated with a user or company in the current context:Correlate Trace and Log Data in MoesifMoesif’s Live Event Log allows you to view your API traffic interactively through different lenses so you can navigate between API calls, custom actions, and telemetry data.OpenTelemetry automatically injects trace and span IDs into logs. So a log message emitted during the execution of a specific span will include the trace ID and span ID of that active span.In Moesif, you can view logs associated with a specific span or trace in both Stream and Trace views:In Stream view:  Expand an API event element.  Select the trace ID or span ID value.  Select Open trace or Open span.In Trace view, select the span ID, or expand the API event element and then follow the same steps.Best PracticesStructured JSON in your logs will unlock powerful analytics and possibilities for you through Moesif. Emit JSON bodies that:  Describe the domain event (what happened)  Mirror a minimal set of dimensions into attributes, to help with filtering and defining metricsThe log attributes appear as metadata in Moesif. Favor attributes that make filtering and grouping useful, and therefore contextual analysis more constructive in Segmentation, Time Series, and Geo Heatmaps.Here are some tips to make the best out of OpenTelemetry logs in Moesif:Model Domain EventsIn the log body, include fields that mirror domain events and contextual details, like request method, route, and outcome (successes and errors). This gives you stable semantics across services and helps build consistent charts.Mirror Only Filterable Dimensions Into AttributesKeep attributes lean and stable. For example:  http.route  user.id  company.id  region  country  deployment_version  feature_flagUse lowercase keys and avoid spaces and mixed types.Attach Release and Experiment ContextAttach deployment version, feature flags, and A/B testing variant data so you can segment errors by release or experiment in Segmentation and Time Series. It often provides the fastest way to prove or disprove a regression.Treat High-Cardinality With IntentInclude IDs you actually filter on, like customer, trace, and span IDs. Keep unbounded text, like stack traces and full messages, in the log body, not attributes. This preserves query performance while keeping rich details available in an idiomatic way.Add trace ContextMoesif allows you to open a trace and observe every span and log event within that trace. Despite that, we recommend you include trace and span IDs in log attributes. It makes correlation and filtering more dynamic and easier. And if you export your data out of Moesif, having those IDs inline in attributes makes correlation easier.SamplingMoesif’s dynamic sampling doesn’t apply to the ingested OpenTelemetry data. So you must configure sampling manually in your OpenTelemetry setup. Ensure stable sampling to avoid situations like biased Segmentation when comparing regions or releases.Privacy and GovernanceAvoid PII in attributes. If you require PII in the body, use redaction or hashing. Never log secrets or tokens. Keep customer IDs synthetic and stable. This keeps your analytics useful without creating or exposing your systems to risks.Get Started TodaySee the OpenTelemetry integration docs to get started. You can also get up and running by walking through our Integrating with OpenTelemetry guide that features an example Node.js application.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Unlock Business Insights from Your OpenTelemetry Logs.            Correlate structured logs with user-level traces to see the full business context.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Analyzing-OpenTelemetry-Logs-in-Moesif-For-Operational-And-Business-Insights/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "monitoring-model-context-protocol-monitoring-mcp-security-and-agent-behavior-with-moesif": {
          "title": "Monitoring MCP Security and Agent Behavior with Moesif",
          "content"	 : "The Model Context Protocol (MCP) has pioneered a new interface layer between AI agents and tools. It has become easier to enable seamless access to external services, APIs, workflows, and data with natural language. MCP servers are now powering the decentralization of AI intelligence and orchestrating the interplay among modern AI systems. In doing so, they also introduce a more open, fluid, and automation-driven attack surface.However, traditional API security models weren’t built for this. Agent workloads are unpredictable. Payloads mutate constantly. And malicious use—like scraping, over-permissive tool chaining, or prompt-based exploits—can go past your detection until it’s too late.This article demonstrates how to secure your MCP server by making agent behavior observable. With Moesif, you can monitor JSON-RPC calls, detect scraping or high-risk tool use, set intelligent alerts, and trace anomalies back to specific users or models.                Monitor MCP Server Traffic and Agent Behavior with Moesif              14 day free trial. No credit card required.              Try for Free        Why Security in Model Context Protocol (MCP) is ComplexMCP servers, unlike traditional REST APIs, operate as runtime environments for AI agents. These servers accept structured JSON-RPC requests that agents generate from user prompts, ultimately resulting in dynamic tool executions. This effectuates security challenges that stem not from protocol flaws, but from the unpredictable and autonomous nature of the clients themselves.MCP clients are not humans—they are large language models (LLMs) or agents acting programmatically. Your single prompt can generate hundreds of requests, chaining multiple tools in the process. These requests might not follow rate limits, adhere to expected usage patterns, or even originate predictable flows. They can trigger storage access, execute shell-like operations, or stream unstructured data back to the user. That means you can’t derive intent from traffic shape—and traditional WAFs or API gateways often miss real threats.The payloads are just as unpredictable. Because MCP uses JSON-RPC, every request can look syntactically valid while semantically dangerous. For example, an agent calling a file retrieval tool may specify arbitrary file paths, query large datasets, or pull records only meant for restricted access. Since agents generate payloads, prompt injection becomes a real attack vector; a compromised instruction can push an agent to chain tools in unexpected or malicious ways.Complicating matters further, MCP tool interfaces are often exposed without granular security controls. Many teams launch a server with file access, API wrappers, or internal automation tools and expose them through MCP’s standardized method layer. Without fine-grained authorization or telemetry, it’s difficult to know who is doing what, and when. Its ramification also provokes cost-based abuse. An attacker may not steal data but instead overwhelm your infrastructure with calls to expensive tools—triggering third-party APIs or compute-heavy workloads that rack up unintended costs.Traditional API security has been built around predictable analytics, static schemas, and authenticated users. Its architecture proves ill-suited in environments where LLMs behave as clients, tools form dynamic chains, and traffic reflects emergent agent behavior.MCP security requires a different mindset. You have to assume unpredictability, focus on runtime visibility, and treat tool invocation patterns as part of the security surface. Without telemetry and behavior monitoring, you risk blind spots that static access controls can’t catch.Key Security Risks in MCP ServersScrapingA user prompt might result in dozens—or hundreds—of file retrievals, search operations, or API calls. Without visibility or usage caps, this behavior can result in silent scraping of large volumes of sensitive or proprietary data.Each request, being JSON-RPC, appears structurally valid. Therefore, it becomes difficult to distinguish automated overuse from legitimate access—especially when the agent is simply following the logic encoded in its prompt.Suspicious Tool Chaining BehaviorAI agents often invoke multiple tools in sequence to fulfill a prompt, but some usage patterns indicate elevated risk. When tools that typically operate independently are accessed in brisk succession—or repeatedly by the same user—it may signal prompt injection, misuse, or inadvertent escalation.These tools are hard to detect with static request validation alone. Each individual call may appear legitimate, but in aggregate, they may reflect abnormal behavior. The real threat lies not in the tool access itself, but in how tools form a chain to amplify access or cost.Excessive Data ExposureMCP servers often return large, loosely structured responses, for example, full documents, datasets, and HTML content. These payloads rarely go through inspection for volume, structure, or sensitivity. An unbound agent—or malicious user—can repeatedly extract large quantities of information simply by iterating over such high-yield responses.This form of passive exfiltration might be difficult to detect if your server doesn’t log response sizes or classify content types. And because MCP calls look semantically similar regardless of intent, detection requires tracking usage patterns, not just request signatures.Overuse of Costly Tool InvocationsSome of the most expensive misuse happens when AI agents repeatedly invoke tools that trigger compute-heavy tasks. Since agents are stateless from a billing perspective, they don’t distinguish between free and expensive tools. If your server exposes actions that incur third-party costs or backend strain, a single agent loop—or misaligned prompt—can exhaust resources quickly.Weak Observability into Agent BehaviorThe vast majority of MCP implementations lack sufficient telemetry to audit what users and agents are doing. Consider metadata like tool category, payload shape, token counts, and content size. Without logging such details, it becomes nearly impossible to detect suspicious behavior until damage has occurred.A single user could trigger tools in succession, perform access attempts, or consume high-cost resources, all without tripping alarms—simply because you didn’t have the necessary fields to correlate activity at runtime.Inadequacy of Traditional REST-Based ControlsMCP servers differ from REST conventions. They typically expose a single endpoint through method-based dispatch logic. This breaks common assumptions around URI-based access control, static schema validation, and endpoint-specific rate limits.Without parsing each request’s method and intent, your server’s controls can’t differentiate between high- and low-risk operations. This makes it easy for misuse to hide in plain sight—especially if all access appears to be hitting the same surface area.How Moesif Helps Provide Visibility in MCP TrafficTraditional API logs and transport-level monitoring fall short because they lack visibility into structure and intent of agent-driven calls. Moesif helps you solve this by transforming your raw MCP server traffic into structured, meaningful insight.Parsing Method-level Activity from JSON-RPC PayloadsMost MCP servers expose a single RPC endpoint, encoding actual tool usage in fields like method, params, or tool_name. Moesif captures each request out of the box and logs them with details like the following:  Method name  Response status code  Customer identifiers (like user ID and company ID)  Request and response headersYou can add custom metadata as well.The following shows a Live Event Log in Moesif with some MCP traffic details.This allows teams to distinguish between otherwise identical calls to the endpoint based on what the calls are actually doing—whether that’s accessing a document, querying a database, or running a transformation pipeline. It also means every tool invocation becomes traceable and attributable.Visibility into Patterns of MisuseMany of the most damaging behaviors—like scraping, agent looping, or excessive chaining—only become visible in aggregate and context. Moesif helps you detect anomalies by comparing behavior over time, across users, sessions, or methods, using time series. For example, you can use Segmentation to filter and group traffic by different criteria:  Total number of requests per method or tool category  Unusual spikes in response size, latency, or payload complexity  Usage by users or API keysSince traffic is available in real time, Moesif can also help investigate sessions escalating from low-risk to high-impact tool usage. You can observe the shape of the abuse, even if no single request looks malicious.Trace Incidents Across Sessions, Agents, and ModelsAttacks on MCP servers sometimes unfold slowly, across dozens of requests or over long-lived agent sessions. Moesif’s ability to contextualize and link events by user ID, session ID, or custom fields (like agent_version or model) helps you follow that progression. It becomes easier to investigate after the incident or detect an issue while it’s still unfolding.You’ll find it especially useful when diagnosing high-cost activity or confirming whether a behavior originated from a human, an automated agent, or an injected prompt.Reveal Hidden Cost and Abuse SignalsSecurity risks can also have economic repercussions. Recurrent calls to expensive tools, high token counts, and excessive outbound API usage can quietly consume resources. Moesif can help reveal:  Users driving disproportionately high infrastructure cost  Patterns of redundant tool invocation  Frequent retries or failures that may signal scraping attemptsFor example, consider an agent invoking the same service with identical input. With Moesif you can:  Filter those requests.  Observe the lack of variance in the params.  Group them under a single user or API key.  Spot and get alerts on the abnormal call frequency compared to the rest of your user base.Setting up Security Alerts in MoesifOnce you’ve instrumented your MCP server and gained visibility into agent behavior, the next step is to set up automated alerts that catch abnormal usage and notify you in real time.Moesif provides a flexible alerting and monitoring system where you can define behavioral thresholds, monitor usage in real time, and escalate issues through your notification channels of choice.Define Behavioral ThresholdsUnlike traditional API rate-limiting tools, Moesif alerts have been designed to track semantic patterns across requests. Rather than merely setting limits on request counts, you can enforce thresholds based on user attribution, patterns, and resulting system impact.Here are some examples that you can base your alerts on in Moesif:  Tool usage frequency—the following creates an alert to monitor privileged data access, defining a threshold of 5 requests per identified user in 15 minutes:  Response size anomalies—for example, responses larger than 1 MB.  Payload shape—like unusually nested or large input payloads, using key count and count operators.Create Alerts from Time SeriesIn Moesif Time Series, you can configure alerts directly as you set up the analysis. You can start inspecting a specific call pattern or behavior, then a rule based on that filter.For example:  Inspect spike in document-access requests.  Filter for a specific user, content type, and response size.  Select Alert to monitor for recurrences of that pattern.For example, the following Time Series defines a custom metric to analyze input token consumption, and categorizes the data by user IDs.Selecting Alert takes you to alert configuration with the existing criteria applied where you can start fine-tuning the alert.You can select View Events in the alert pane to see the events under the same event filters criteria and time range.Alerting Channels and IntegrationYou can configure alerts in Moesif to dispatch them to various channels:  Slack for real-time team visibility  PagerDuty for incident response  Email for security notifications or user audits  Webhook endpoints for custom automations or throttling logicFor email notifications, a triggered alert looks like this:Select View in Moesif to view the details in Moesif dashboard. There, you can select View Events to observe the corresponding events in a Live Event Log workspace. This allows you to quickly zero in on events that have triggered a rule.Alerts Without Latency or Performance ImpactMoesif alerts operate asynchronously and do not introduce latency into your MCP server flow. Since Moesif handles observability through SDKs or gateways with lightweight, non-blocking instrumentation, alerting doesn’t slow down your agent workflows or impact user experience. This makes Moesif suitable for not just production use, but also for high-throughput or real-time systems where you can’t afford to compromise performance for monitoring.Final ThoughtsMCP security is a systems problem—one rooted in behavior rather than access. The autonomy AI agents now possess has introduced risks that you can solve only by understanding how tools are used, how behavior changes over time, and how real users and agents shape your system. You must treat observability as a design requirement, to successfully build MCP servers that scale without silently failing. For software teams building AI agents and tool-based orchestrations, this is the baseline for doing it responsibly and sustainably.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Full Observability to Secure Your MCP Server.            Trace alerts back to the specific user, model, or call in seconds.            Try for Free            No credit card required            ",
          "url": " /monitoring/model-context-protocol/Monitoring-MCP-Security-And-Agent-Behavior-With-Moesif/",
          "author": "Sakib",
          "categories": "Monitoring, Model-Context-Protocol"
        }
      
    ,
  
    
        "api-strategy-model-context-protocol-monetizing-mcp-model-context-protocol-servers-with-moesif": {
          "title": "Monetizing MCP (Model Context Protocol) Servers with Moesif",
          "content"	 : "The Model Context Protocol (MCP) is quickly becoming a foundational layer for AI systems. It enables large language models and AI agents to interact with external tools and data sources over standardized JSON-RPC interfaces. By doing so, MCP transforms how intelligent applications consume APIs. Reading local files, controlling IoT devices, orchestrating backend workflows—MCP servers act as structured gateways between AI and your business logic.Undoubtedly, this opens up interesting and incredible possibilities, but also introduces new challenges.AI agents behave differently than human users. They can trigger hundreds of requests per second, chain multiple tool calls, and generate unpredictable spikes in traffic. Your traditional subscription or seat-based pricing will fail to reflect this kind of usage. Without observability and fine-grained control, your MCP server is vulnerable to overuse, misuse, and revenue leakage.                Monitor and Analyze MCP Servers with Moesif              14 day free trial. No credit card required.              Try for Free        Why You Should Monetize MCP Server UsageFirst, consider how MCP servers differ from traditional APIs. Each MCP server acts as a runtime adapter. According to the requests of the AI apps—for example an AI agent like Claude, MCP servers execute multi-step actions on behalf, often without human supervision. Depending on the data or service an MCP server exposes through the MCP standard, the server reads files, launches tools, calls third-party services, and more. A single AI prompt can initiate dozens of tool invocations in rapid succession, each with its own compute or data cost. If you don’t have a monetization layer, your infrastructure ends up shouldering the burden for unbounded and unaccountable usage.Let’s say your server handles lightweight tasks like returning structured data or querying a knowledge base. However, the volume and concurrency patterns of AI agents are entirely different from human usage. Claude or ChatGPT can loop through thousands of requests while resolving a single user instruction, intentionally or not. These calls often go beyond information retrieval and may trigger actions like:  Sending Slack messages  Updating CRMs  Calling  external APIsAll of them incur downstream costs.Consequently, traditional subscription or seat-based pricing models become insufficient. They assume predictable interaction patterns and human pacing, both of which MCP interactions don’t abide by. You need pricing that scales with resource consumption or value delivered—whether that means per tool call, per output, or per successful outcome. You need to meter usage at the context level.Finally, monetization creates a forcing function for governance. When usage has cost, it creates incentives to optimize. Developers pay more attention to tool call efficiency. Teams are more likely to request rate limits, set quotas, and review logs. Without monetization, overuse stays invisible, and often unintentional. Monetization aligns incentives, increases reliability, and ensures your MCP server doesn’t become a resource sink with no accountability or cost visibility.Identifying and Measuring Billable Usage in MCPBefore you can monetize an MCP server, you need to decide what exactly you’re charging for. This means identifying the unit of context or interaction that reflects the cost incurred or value delivered. Because MCP servers are not traditional REST APIs, but rather execution surfaces for agents, they require more deliberate thinking about what to meter.Method Calls as the Base UnitThe simplest approach is to charge per JSON-RPC method invocation. Each request maps to a specific tool or function your MCP server exposes. Billing per method is easy to track, especially when agents trigger methods programmatically. It provides a good starting point for services with uniform operational costs.However, methods vary in cost or intent. While simple, method-based metering can quickly become inadequate if some tools consume significantly more compute or data than others.Charging by Data Volume or Payload SizeWhen MCP methods return large datasets, embeddings, or document content, metering by bytes transferred or payload size provides more accuracy. For example:  Vector database lookups  File reads or document downloads  External data queries like weather feedsYou might charge per megabyte returned or per thousand tokens generated, similar to how OpenAI structures pricing. Moesif supports measuring payload size and filtering based on HTTP fields, making this very easy to implement.Metering by Action or OutcomeIn outcome-based pricing, you meter what gets done—the executed task, not the number of times an app hits an endpoint. For example:  An IoT server might charge per command that sets the device state.  A summarization service might charge per document the service successfully summarizes.  A database tool might charge for each valid query that returns results.This model works best when each successful action represents a business-aligned value. Moesif allows you to easily track and meter specific outcomes using API usage data and custom actions, letting you charge only for meaningful results.Session Memory and StateMCP servers often support persistent memory, especially when serving agents that rely on context across multiple calls. Maintaining session state incurs real costs:  Memory usage like stored tokens and embeddings  Read-write operations to external storage  Latency overhead for lookups and updatesYou can meter this in multiple ways:  Charge per session created  Price based on active session time  Bill based on memory depth—for example, 8k tokens retainedThis model aligns with how AI assistants and autonomous agents consume long-term context.Tool-Specific Meters for Multi-Tool MCP ServersMCP servers often expose multiple tools or methods, each with different cost profiles. A lightweight metadata fetch might return near-instantly with minimal computation. On the contrary, a workflow could trigger multiple downstream calls or heavy processing. Charging both the same creates pricing mismatches and misaligned incentives.To solve this, you can define billing meters per method or tool. Moesif supports filtering by method name, payload fields, or HTTP headers, allowing you to meter specific operations independently.This way, you can apply the right pricing model to each tool—some charged per call, others by data volume or result. It also gives you flexibility to mix models within a single server, while keeping metering aligned with actual cost and value delivered.The most important rule: your billing metric should reflect what your customer perceives as valuable. Good metering is as much a product design challenge as a billing one. Your goal is to align the cost structure of your MCP server with how it’s used, what it enables, and what it costs you to run.How Moesif Helps Monetize MCP Server UsageIn addition to a sound pricing strategy, you also need visibility, attribution, and control to turn MCP traffic into revenue. Moesif provides the infrastructure to meter, monitor, and monetize MCP server usage with minimal code changes. Let’s break down how Moesif supports each part of the monetization workflow.Real-Time Observability for MCP TrafficMoesif captures each JSON-RPC request hitting your MCP server, including method name, parameters, response status, and latency. This is critical in MCP environments, where AI apps can initiate rapid, multi-step invocations that behave more like background systems than human-facing APIs.The platform supports several integration methods. You can use server-side SDKs:  Python (like moesifasgi for ASGI-based frameworks like FastAPI)  Node.js (Express, Koa)  JavaAlternatively, if your MCP server is fronted by an API gateway like WSO2, Kong, or AWS API Gateway, Moesif’s plugins let you instrument traffic at the edge. These integrations work with asynchronous patterns, including Server-Sent Events (SSE) and Streamable HTTP.Billing Meters and Usage AttributionOnce you have traffic flowing into Moesif, you can define billing meters. These are rules that describe what to count towards billable metric and how to aggregate it for billing. Meters can track simple metrics like the number of times a method is called. Or they can track more complex ones like total payload size or even conditional outcomes—for example, count only the events with a scoring_accuracy field value over 90.Moesif’s filtering and scripting features allow you to implement outcome-based pricing easily. For example, if your MCP server summarizes documents, you could define a meter that counts usage only when the result is non-empty and the status code is 200 OK. This aligns monetization with the value you deliver.Each meter aggregates usage per user or company, depending on how you configure identity attribution. This can map to the end-user who initiates a prompt or an API key of a partner using your MCP server.Integration with Billing ProvidersMoesif integrates directly with billing providers like Stripe, Chargebee, and Zuora. You can also roll out your own billing system through webhooks. Moesif dispatches the usage data from billing meters at configurable intervals, where the provider uses it to calculate invoices based on your price points.Each meter maps to a product or usage unit—for example, charging $0.01 per unit where the unit is the API call count. Moesif also supports hybrid models, where you might offer a base subscription tier per month and then charge customers for overages once they cross that threshold.Such setups offload billing logic to platforms designed for invoicing and payments while keeping all usage intelligence centralized in Moesif.Enforcing Quotas and Preventing AbuseMoesif enables you to enforce usage limits and governance policies. You can configure quotas for each user or plan, trigger alerts when a value exceeds the threshold, and even block traffic through governance rules.In AI-agent contexts, retries, loops, or rapid chaining can result in unexpected load. Moesif can help protect resource-intensive tools from abuse and make sure that users stay within their allocated plans.Developer Portal and Accessible ReportingThrough Moesif’s open source developer portal or Embedded Templates, you can improve developer experience and expose usage data to customers in real time. Users can log in to view how many MCP calls they’ve made, how close they are to quota limits, and what they’ve been charged for.Such transparency and accessibility reduces support tickets, increases trust, and makes it easier for customers to self-manage plan upgrades.Minimal-Code Integration for MCP ServersOne of Moesif’s key strengths is that it works with your existing architecture. You don’t need to refactor your billing pipeline or MCP logic. A simple middleware drop-in suffices to start capturing traffic. For fully customizable setups—or if you’re running MCP servers in serverless environments—you can also directly use Moesif’s API to push events manually.Having this flexibility means you can experiment with billing models and usage meters without locking into or committing to a rigid backend. You can start simple, refine over time, and roll out changes gradually.Setting Up Moesif with Your MCP ServerLet’s walk through the general steps of integrating your MCP server with Moesif.Before you proceed, if you haven’t already, sign up for Moesif. During the onboarding process, you will get your Moesif Application ID. You can access it anytime by following these steps:  Log into Moesif Portal.  Select the account icon to bring up the settings menu.  Select Installation or API Keys.  Copy your Moesif Application ID from the Collector Application ID field.Installing MoesifUsing Moesif Middleware in a Server FrameworkIf you’ve implemented your MCP server as a web API in Python, Node.js, or Java, the easiest integration path is through one of Moesif’s server integrations for the respective framework, for example:  For Python-based (FastAPI or Starlette) MCP servers, install the moesifasgi middleware:  For Node.js-based servers, install the Node.js middleware:Integrating Moesif with API GatewaysFor API Gateways, you can install a Moesif plugin at the gateway level, without having to modify server code.Some of the gateways Moesif supports are:  WSO2 Choreo  WSO2 Kubernetes Gateway  Kong (Kong Konnect, Kong Gateway, and Kong Ingress Controller)  Amazon API GatewayUsing Moesif API DirectlyIf you have implemented your MCP server in a custom stack or run the server on serverless platforms, you can use Moesif’s Collector API to send events manually. This approach can give you more control and works well for background jobs, batch pipelines, or fine-tuned billing events that don’t map 1:1 with HTTP traffic.Setting up Identity AttributionFor monetization to work, you must attribute every MCP call to a user or company. Moesif provides customizable hooks in every SDK to let you associate requests with authenticated identities. For more information, see Identifying Customers.Testing and Verification  Trigger some agent requests through the MCP server and confirm visibility and attribution.  Visit the Live Event Log in Moesif to verify that events are flowing in.  Filter events based on different criteria like methods and tool-specific metadata.  Set up and test a billing meter to verify usage metering and tracking.Example MCP Server with MoesifLet’s set up Moesif with an example MCP server running on Python and Starlette.You can find the corresponding code for the example on GitHub.Before You Begin  Install Python and uv.  Make sure the MCP server can use Server-Sent Events (SSE) or Streamable HTTP1. Install MoesifInstall using the uv package manager:uv add moesifasgi2. Initialize the Middlewarefrom moesifasgi import MoesifMiddlewaremoesif_settings = {    'APPLICATION_ID': 'YOUR_MOESIF_APPLICATION_ID'}# Add Moesif to your starlette appstarlette_app.add_middleware(MoesifMiddleware, settings=moesif_settings)# Run the appuvicorn.run(starlette_app, host="0.0.0.0", port=3001, log_level="info")3. Run the MCP Serveruv run src/mcp_server_fetch4. (Optional) Run the MCP Client Toolnpx @modelcontextprotocol/inspectorYou should see traffic flowing in in Live Event Log:Define Customer IdentificationDefine the user and company identification functions and add them in the middleware options object. For example, the following code extracts user ID from the Authorization header:def identify_user(request, response):    # Your custom code that returns a user id string    return request.headers.get('Authorization')def identify_company(request, response):    # Your custom code that returns a user id string    return "67890"MOESIF_MIDDLEWARE = {    'APPLICATION_ID': 'YOUR_MOESIF_APPLICATION_ID',    'IDENTIFY_USER': identify_user,    'IDENTIFY_COMPANY': identify_company,}ConclusionMCP is shifting how AI consumes APIs—faster than most teams are ready for. With every tool call, inference, or data query, you’re delivering real value. And when that usage goes untracked, so does its business impact; you give away functionality in the form of unclaimed revenue. Moesif gives you the infrastructure to treat your server like a product: observable, billable, and sustainable.You don’t have to aim for a perfect pricing model out of the gate—start simple, measure what matters, and evolve based on usage patterns. With Moesif, you can confidently take small, testable steps towards effective MCP monetization.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Reliably Meter Usage from Your MCP Servers.            Get the accurate data you need to architect and scale your billing system.            Try for Free            No credit card required            ",
          "url": " /api-strategy/model-context-protocol/Monetizing-MCP-Model-Context-Protocol-Servers-With-Moesif/",
          "author": "Sakib",
          "categories": "API-Strategy, Model-Context-Protocol"
        }
      
    ,
  
    
        "technical-nginx-using-moesif-for-api-observability-and-analytics-in-nginx-one": {
          "title": "Using Moesif for API Observability and Analytics in NGINX One",
          "content"	 : "NGINX One provides a modern solution for enterprises to manage infrastructure at scale across globally distributed systems. The platform has built-in tools for essential performance and uptime metrics, giving DevOps teams visibility into the health of their NGINX instances.But for effective API observability and analytics, you have to go beyond infrastructure metrics. You can’t improve customer experience or troubleshoot failing endpoints without understanding what users are doing, where they’re encountering errors, and why performance varies across API consumers.By layering Moesif’s deep API traffic inspection and user-centric API analytics on top of NGINX One, you can gain end-to-end visibility into every API call. This visibility covers not just the perspective of systems, but the behavior of customers themselves.In this article, we’ll discuss how Moesif can help you establish deep API observability and analytics in NGINX One.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        Table of Contents  What is NGINX One?  What is Moesif?  Why Use Moesif with NGINX One?          Infra Metrics vs. API Observability      How the Moesif Plugin Works      Real-Time API Monitoring and Filtering      Traffic Segmentation      Faster Troubleshooting and Root Cause Analysis      Alerting, Anomaly Detection, and Automation      Built for Enterprise Observability        Integrating Moesif with NGINX One          Step 1: Install the Moesif Lua Plugin      Step 2: Configure NGINX      Step 3: Initialize Moesif      Step 4: Capture Requests and Responses      Step 5: Verify Your Setup      Try The Docker Demo        Key Metrics to Track for API Analytics and Observability          Latency Distributions (P95, P99)      Error Rate by Route, Segment, or Plan      User Activity and API Usage Patterns      API Call Volume and Traffic Trends        ConclusionWhat is NGINX One?F5 NGINX One, building on the core NGINX tech, is a fully managed application delivery solution for modern applications. It provides a centralized interface to observe, manage, secure, and scale both NGINX Open Source and NGINX Plus instances across diverse environments like Kubernetes, cloud, and on-premises setups.NGINX One enables fine-grained operational visibility using NGINX Agent that runs alongside your NGINX instances. The Agent collects system metrics about your data plane systems as well as traffic metrics like basic HTTP status code distributions. NGINX One also allows you to pre-configure and stage NGINX configurations, allowing DevOps teams to standardize deployments across environments while minimizing drift. The platform also supports secure integration of your NGINX instances with NGINX One Console through data plane keys, in addition to role-based access control features to govern permissions and access.If your organization is already invested in NGINX, NGINX One offers operational consistency and governance at scale. However, if you want to understand the behavior of API products or the users who consume them, you need a bespoke solution.What is Moesif?Moesif is a powerful API observability platform that captures, enriches, and analyzes API traffic to give engineering and product teams actionable insights into usage patterns, customer behavior, and performance. Unlike traditional logging or APM tools, Moesif operates at the API layer—extracting context-rich telemetry from every API call, including headers, payloads, query parameters, latency, authentication tokens, user IDs, and more.Moesif uses lightweight SDKs and plugins (like its Lua-based plugin for NGINX) to instrument API traffic directly at the edge, without impacting API performance. Moesif can enrich each event with metadata like geo-location, cloud provider, and API version before asynchronously streaming to its cloud backend for real-time analysis. This means you can slice and filter traffic by virtually any dimension—endpoint, HTTP method, customer ID, error type, API version, deployment region, or SDK used—and correlate issues directly to impacted users.A core tenet that distinguishes Moesif is its commitment to making observability customer-centric. Some of the features that make it happen are profile dashboards for individualized account metrics, cohort analytics, funnel tracking, and customizable API monitoring and alerts. These features empower teams to not only detect problems but understand who they affect, how often, and why. You may have use cases ranging from performance monitoring to debugging, user onboarding analysis, and security anomaly detection—Moesif grounds all of them in a single API event stream.Why Use Moesif with NGINX One?While NGINX One gives you operational control over your NGINX infrastructure, it doesn’t tell how your services are being used or by whom. Moesif’s specialized observability layer, designed for API-first products, brings real-time access to the kinds of data engineers, DevOps, and product teams need to make informed decisions.Infra Metrics vs. API ObservabilityNGINX One collects infrastructure-level metrics like the following:  Active and idle connections  Requests per second  Upstream health and load distribution  HTTP status code aggregates  SSL certificate status and expiration  Cache hit-and-miss ratiosInfrastructure teams use these data to monitor routing health, optimize load balancing, and track web server throughput. However, they don’t capture application-level behavior: you can’t see the payload of a failing API call, segment traffic by authenticated users, or analyze endpoint-specific latency distributions.Moesif, operating at the API layer, allows you to understand how clients use your APIs. If you can analyze each API request and response in full context, your teams can dig out answers to questions like these:  Which customers are seeing the most errors?  What’s the average latency for this specific endpoint?  Has onboarding conversion dropped this week?How the Moesif Plugin WorksThe Moesif Lua plugin runs in NGINX through the NGINX Plus Lua Module that integrates Lua coroutines into the NGINX event processing model. This gives Moesif access to NGINX’s HTTP request processing phases and therefore the ability to capture data like the following:  HTTP request and response headers  Full request and response bodies (configurable)  Route, method, and HTTP status code  Latency, user agent, geolocation, and IP address  Customer identification and attribution through request header and NGINX variablesMoesif asynchronously captures these events and batches them before dispatching to its analytics engine. As a result, you get non-blocking operations, preserving NGINX’s high-throughput model. The plugin works on both NGINX Open Source and NGINX Plus. For performance-sensitive environments, an optional C module is available for NGINX Plus for more advanced use cases.You can configure the plugin to mask sensitive fields, or enrich events with custom context—making it flexible for observability needs without barring compliance requirements.Real-Time API Monitoring and FilteringMoesif’s Live Event Log gives real-time view into API events:  Watch API requests as they happen across all NGINX instances.  Filter by criteria such as response code—for example 500 Internal Server Errors, user ID, route, or specific headers.  Drill into request-response bodies to troubleshoot errors or understand edge cases.This fundamentally differs from looking at logs through access_log and error_log directives, or waiting on delayed metric exports. With Moesif, you can build an observability discipline that ensures interactivity with your observability data in real time, to debug, validate deployments, or support customers.Traffic SegmentationMoesif’s API and customer analytics lets you group, filter, and chart API traffic across any dimension:  By customer: authenticated user or company ID  By endpoint: URI patterns, methods, query parameters  By client type: SDKs (Node.js, Python, Go), CLI tools, or custom agents  By geography: country, region, city  By time: minute, hour, day, or custom time bucketsThis enables API teams to compare performance across different segments. For example, you can see whether a specific enterprise customer is experiencing slower P99 latency or whether a new SDK release has led to a spike in malformed requests. NGINX One does not support this kind of slicing — it sees requests as raw traffic, not structured interactions.Faster Troubleshooting and Root Cause AnalysisWhen something breaks in production, speed matters. Moesif empowers teams to resolve incidents faster by:  Highlighting error rate spikes by route, customer, or API version  Correlating 4xx client and 5xx server error responses with specific payloads or query parameters  Tracing a user’s activity across multiple endpoints for post-mortem analysis  Filtering traffic by API key or IP to isolate suspicious or abusive usage  Enhancing observability by leveraging your app’s existing distributed tracing withEspecially, if you’re managing dozens of NGINX instances under NGINX One, rather than sifting through raw logs, your teams can narrow down incidents in Moesif within seconds.Alerting, Anomaly Detection, and AutomationMoesif supports both static and dynamic alerting:  Static alerts: Set thresholds for latency, error rates, or request volume  Dynamic alerts: Identify unknown unknowns using Moesif’s anomaly detection engine  Customer-scoped alerts: Notify teams when a specific user’s error rate increases  Behavioral triggers: Detect when a new developer makes their first API call or usage drops offYou can specify where to dispatch alert notifications to—Slack, PagerDuty, webhook, or email. This enables automation, support workflows, or incident response processes. Moesif’s real-time API intelligence sharply complements the infrastructure-focused alerts available in NGINX One.Built for Enterprise ObservabilityFor enterprise engineering teams, Moesif enables:  Compliance reporting: Track access and API usage across users and regions  SLA tracking: Monitor latency and error rates for each customer segment  Cross-functional dashboards: Share API and customer analytics insights with product, ops, or support teams  OpenTelemetry integration: Correlate API calls with distributed traces and logs across servicesIntegrating Moesif with NGINX OneBefore installing Moesif’s Lua-based NGINX plugin, make sure you install the NGINX Plus Lua Module. Then follow these steps:Step 1: Install the Moesif Lua PluginUse Luarocks to install the plugin:luarocks install --server=http://luarocks.org/manifests/moesif lua-resty-moesifStep 2: Configure NGINXIn your nginx.conf, configure the shared memory dictionary and Lua package installation paths:lua_shared_dict moesif_conf 5m;lua_package_path "/usr/local/share/lua/5.1/lua/resty/moesif/?.lua;;";moesif_conf contains static configuration options like your Moesif Application ID and request and response masks.Step 3: Initialize MoesifUsing the init_by_lua_block directive, initialize the Moesif client, replacing YOUR_MOESIF_APPLICATION_ID with your Moesif Application ID.:init_by_lua_block {    local config = ngx.shared.moesif_conf;    config:set("application_id", "YOUR_MOESIF_APPLICATION_ID")    local mo_client = require "moesifapi.lua.moesif_client"    mo_client.get_moesif_client(ngx)}This step loads the Moesif library and prepares it to process API events.After logging into Moesif Portal, you’ll find your Moesif Application ID during the onboarding steps. However, you can obtain it anytime by following these steps from the Moesif Portal:  Select the account icon to bring up the settings menu.  Select Installation or API Keys.  Copy your Moesif Application ID from the Collector Application ID fieldStep 4: Capture Requests and ResponsesUse the access_by_lua_file and log_by_lua_file directives to capture request and response data:server {  listen 8080;  # Customer identity variables that Moesif will read downstream  set $moesif_user_id nil;  set $moesif_company_id nil;  # Request/Response body variable that Moesif will use downstream  set $moesif_res_body nil;  set $moesif_req_body nil;    access_by_lua_file /usr/local/share/lua/5.1/lua/resty/moesif/read_req_body.lua;  body_filter_by_lua_file /usr/local/share/lua/5.1/lua/resty/moesif/read_res_body.lua;  log_by_lua_file /usr/local/share/lua/5.1/lua/resty/moesif/send_event.lua;  # Sample Hello World API  location /api {     add_header Content-Type "application/json";     return 200 '{rn  "message": "Hello World",rn  "completed": truern}';  }  location / {    proxy_pass http://ai_api.com/internal;  }}In the preceding example configuration:  access_by_lua_file allows Moesif to capture request data.  body_filter_by_lua_file allows Moesif to read the response data.   log_by_lua_file sends the complete event data to Moesif asynchronously after the response finishes.Notice the variables $moesif_user_id and $moesif_company_id. These allow you to tag traffic with customer-specific identifiers so you can perform analysis like Segmentation in Moesif.Step 5: Verify Your SetupOnce the integration completes, send a few API requests to your NGINX instance. Then, after logging in to your Moesif account and opening a Live Event Stream workspace, you should see new events appear in real time.If you face any issues, see the Server Troubleshooting Guide or contact our support team. See the Moesif Lua plugin documentation for a detailed list available config options and sample templates you can adapt to your environment.Try The Docker DemoThe NGINX One Docker Demo provides a working setup of Moesif and NGINX One. You can observe how the integration works and how Moesif captures analytics data and makes them available in real time. This allows you to evaluate before proceeding with an integration into a live environment.Key Metrics to Track for API Analytics and ObservabilityEffective API observability hinges on the ability to track metrics that reveal not just whether an API is functioning, but how it performs, who it impacts, and where you need to perform optimizations. Let’s go through some examples to understand how Moesif makes it easy to obtain these metrics:Latency Distributions (P95, P99)Latency metrics help distinguish between average performance and tail latency issues that degrade user experience. Using Moesif, you can break down response times into percentiles, allowing teams to see:  The median latency (P50)—a general health indicator  The long tail (P95/P99)—which highlights rare but critical slowdownsThe following analysis in Moesif computes a P99 latency analysis using a Time Series.Error Rate by Route, Segment, or PlanBasic counts of 4xx or 5xx errors don’t tell the whole story. Moesif allows you to:  Segment API errors by status code, route, API version, or SDK  Track error rates per customer or plan  Identify spike patterns and correlate them with recent deploymentsFor example, a sudden rise in 401s on a specific route may indicate an expired token issue or auth regression.Let’s look at two examples in Moesif:  Analyzing hourly error rate of 5xx server errors over the past 12 days using Time Series  Breaking down error types in the last 12 weeks using SegmentationUser Activity and API Usage PatternsWith Moesif, you’re tagging your customers with user and company IDs at the gateway level. As a result, blunt request totals into sharp engagement insight—active-user curves by day, week, and month. You can also investigate user journeys and session flows. For example:  Identifying how many unique users actively call the API each week.  Locating where new users succeed or struggle during their first API interactions.You can even group API calls by user agent to see which of your SDKs or CLI tools are driving most API traffic. As an example, here we analyze an embeddings API’s usage across different user agents and SDKs using a Time Series:API Call Volume and Traffic TrendsRequest volume reveals product adoption patterns and API usage trends. Moesif allows you to:  Track calls per endpoint, SDK, or region  Detect traffic anomalies, like sudden spikes from a bad deploy or usage drop from a broken client  Analyze time-series trends (per minute, hour, day) to predict scaling needs  Analyze usage change following a product launch, SDK release, or API version updateThe following two Segmentation charts illustrate traffic volume across API versions and different models in an AI API product using:  First we visualize the analysis in a bar chart  Then we display the data in tabular formConclusionModern APIs require visibility into how they’re used and who they serve. By combining NGINX One’s infrastructure management with Moesif’s deep API analytics, you can establish a full-fidelity observability system that accelerates debugging, improves customer experience, and reveals real usage patterns. As APIs scale and customer expectations rise, having this level of visibility can make a measurable difference—both in day-to-day operations and long-term planning.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design, Build, and Deliver Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /technical/nginx/Using-Moesif-for-API-Observability-and-Analytics-in-NGINX-One/",
          "author": "Sakib",
          "categories": "Technical, NGINX"
        }
      
    ,
  
    
        "podcasts-developers-podcast-apis-over-ipas-api-product-management-with-emmanuel-paraskakis-level-250": {
          "title": "APIs Over IPAs 19: API Product Management with Emmanuel Paraskakis, Level 250",
          "content"	 : "In this episode, Emmanuel Paraskakis, CEO Level 250 breaks down the core responsibilities of an API product manager. Speaking from his experience in product management for over fifteen years, Emmanuel distinguishes an API product manager’s focus from conventional product roles, underscoring their critical importance in building scalable digital platforms.An API product manager requires a unique blend of technical awareness and customer-centric strategy. Emmanuel illuminates the nuanced challenges API product managers face, including the imperative to understand and cater to two distinct user tiers: the developers who directly interact with the APIs and their end-users. He covers the crucial aspects of balancing internal and external stakeholder needs and pinpoints the common pitfalls that impedes aspiring API PM leaders. The discussion also uncovers essential metrics for gauging API product success and offers a forward-looking perspective on the transformative impact of generative AI on API development and consumption.You can watch the podcast on our YouTube channel.Table of Contents  Introduction and Background  Defining API Product Management  Essential Skills and Common Misconceptions  The Dual Customer Persona in API Products  Balancing Internal and External Stakeholder Needs  Common Pitfalls for Aspiring API PM Leaders  Resources for Staying Updated in the API Ecosystem  Key Metrics for API Product Success  Aligning API Metrics with Business Outcomes and Leadership  Exploring API Business Models and Monetization Strategies  GenAI: Impact and Essential Strategies for API Product Managers   Final Takeaways                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Introduction and BackgroundDerric: Hi, folks! Welcome to another episode of APIs Over IPAs. Joining us today is Emmanuel Paraskakis from Level 250. We’d love to hear a little bit about yourself first, Emmanuel.Emmanuel: Yeah. Hi, everyone. My name is Emmanuel, and I’m an API product manager. I have been one for more than 15 years, either building APIs for various companies or building API tools, API design tools, documentation tools, testing tools, and so on. And these days I consult around API product management, and I also teach API product managers on Maven.Defining API Product ManagementDerric: Awesome, super happy to have you here today! Just to kick things off, what is an API product manager, and how is it different than traditional product management?Emmanuel: Yeah, I guess API product management is not that different. I think the difference is in having an actual product manager for APIs. That didn’t used to be the case a while ago, because we thought of APIs as more technical types of artifacts that were byproducts of our engineering process. And as we found success with APIs as an industry, we discovered that we need to be more intentional about building our APIs, and sometimes we were able to productize APIs and monetize them and sell them with great success, as we’ve seen in many examples that are out there.Essential Skills and Common MisconceptionsDerric: And what are some misconceptions you see different companies have as they’re bringing on product managers? And are there any specific skills that are required that are different from typical product management?Emmanuel: Yeah I would say the API product manager has to be necessarily more technical than other product managers, because the subject matter is more technical, and their users are going to be technical people. They’re going to be developers, they’re going to be engineers, DevOps, and so on. So API product managers, and as the platform product managers, have to be able to speak their language, and they have to be able to address their needs. So there is some element of technical capability that’s there. And that’s why a lot of the really good API product managers are former engineers, though not all of them.The Dual Customer Persona in API ProductsDerric: And you speak sometimes on this idea of two product managers at the same time, or two customer personas at the same time. What does that mean?Emmanuel: Yeah, this became apparent to me a while ago, where traditional product management looks at the use cases of their direct users. You can go look at your product analytics and figure out what people are doing with your product, or you can go talk to them, talk to the users, talk to the customers, and find out exactly what they’re doing. But with APIs, as with most developer-facing products, we build our products for others to also build experiences with them. So as an API or platform product manager, I have to be aware of both levels. I have to be aware of how my direct users are going to employ my product, what problems they’re going to solve, but also what their users are going to solve and what use cases are interesting to them. And I think that opens up a whole world for API product managers where they essentially have to be twice the product manager, if you will, and anticipate a little bit the use cases of their users’ users, because if they don’t, they will only create a limited set of options of APIs.Balancing Internal and External Stakeholder NeedsDerric: That’s a really interesting point. And you know, sometimes we see APIs are used both externally but also used internally as well, even as products. And so, you know, as an API product manager, how do you balance the needs and requirements of these different stakeholders—external, internal—and making sure, you know, all of them are met?Emmanuel: Yeah, I would say, first off, try to treat internal platform and API products pretty much the same as external ones, because, especially in larger organizations, well, your users deserve good experiences internally as well, not just the external users. So if you want adoption, if you want success of your product, even treat internal products the same. Do customer discovery, measure the outcomes, make your products intentionally, do good design, and so on. So in that sense, there’s not that much difference. Of course, with internal audiences, we’re liable to know them a little bit better, be able to reach them a little bit more easily. So that’s a little bit of a difference there, but the techniques and the goals should not change.Common Pitfalls for Aspiring API PM LeadersDerric: Makes sense. And you know, as someone who is now looking to become a better API product manager, maybe they already have a role and they’re aspiring to be in a leadership position, what are some of the common pitfalls that you see for these folks who are trying to go for those positions?Emmanuel: Yeah. A lot of times, we talked about API product managers being former engineers, and a lot of former engineers, when they transition into the product role, they kind of tend to regress back to engineering, if I may put it that way, in the sense that they will try to specify solutions rather than speaking about the why and about the problem, and speaking about what their customers want. So that’s one really big pitfall. We kind of tend to go back and say, “Oh, well, you know, here’s the architecture that you should use,” because as a former engineer, I know exactly what I’m talking about, and that really shouldn’t be the case. The other case, kind of on the other side of the spectrum, is not being technical enough and not keeping up with the latest technology. And so that’s something that you should do. You should get educated about what’s happening with APIs, what’s happening with techniques around APIs, new processes, new tooling, and so on. So definitely keep that up.Resources for Staying Updated in the API EcosystemDerric: That’s a really interesting point, that as engineers or former engineers, we get tunnel vision and, you know, forget about the different use cases or even technologies out there. And what are some resources that you go to to keep, you know, up to date on the API ecosystem and, you know, latest developments in product management?Emmanuel: Yeah, I try to, first of all, follow—I have about a dozen people that I follow and read religiously. They have amazing newsletters, and most of them are people that you know and have brought on to your podcast, so they won’t be new to your listeners. You had Mike Amundsen last time. Mike’s a great resource. So there are folks that I read almost religiously, I should say. Also, pretty much the same cohort of folks have written books around APIs, and those are a great resource. And definitely keep going to events—sometimes conferences, sometimes even your local meetups. And I have to say that here in San Francisco, we’re very lucky to have amazing events that happen pretty much daily, and if you’re in a major city, try to go to those technical events. That’s probably the number one source of new know-how for me.Key Metrics for API Product SuccessDerric: Definitely, and glad to have Mike on. We just talked about customer observability, so he’s very timely as we’re, you know, talking about API product management. But what are some metrics and, you know, ways to track these metrics, you know, for a product manager?Emmanuel: Yeah, I mean, look, all the usual technical metrics apply because the technical metrics translate into experience for users. So if you’re thinking about uptime or if you’re thinking about latency, keep measuring those and keep tracking those, because that will translate—improving those metrics will translate to a better experience for your users. But I think there’s also a couple of other categories of metrics, and one very important one is how you instrument your, let’s call it, a funnel, a marketing funnel, but for developers, for developer experience. When people reach our developer portals, they usually don’t know what to expect. They are trying to find out what capabilities we have with our products, if those products are suitable for them, and if they can use them easily. So, don’t expect everybody that comes to your developer portal to immediately adopt your APIs. There’s going to be people that are not interested or that don’t understand your product. And then there’s going to be a percentage of people that sign up to explore your product. And out of those people, there’s going to be the people that are successful with your product. They make, you know, that one first API call that we talk about. And then they might make, you know, more API calls because they are now imagining what they can do with your products. They are now exploring use cases, and they might, you know, find success. And you could measure that by tracking the number of API calls they do. And out of those people, some will actually sign up as a paid or as a higher tier user. So all of those stages are stages that you should instrument and track and understand. And of course, to find success with your product, you have to improve those metrics, all those conversions from one gate to another. And that’s really, really important. And I do believe that sometimes, just because it’s a developer product or a technical product, we don’t do a good job of tracking those metrics.Derric: Definitely getting to that first API call and helping a developer, you know, to really get to production, or at least build something of interest, is, you know, of course, that’s what we’re all after. You know, outside of just getting to that first API call, what are some more, you know, both quantitative and also qualitative metrics, you know, that you think of when we talk about like developer experience and adoption?Emmanuel: Yeah, and especially for product managers, there are things that will be interesting, like the adoption of a particular feature. Let’s say you release a new API, and you want to see if people use it. You should be instrumenting and tracking that. And kind of the reverse of that is, hey, we just put out a new version of an API because we decided we want to do away with all the old stuff—that happens over time. When is it safe to take away the older APIs? Is anybody still using them? Is anybody transitioning to the new version? So those are things we need to keep track of as product managers. And then, of course, you have all of the, let’s call them, sales or marketing metrics that have to do with either adoption, or maybe I want to find out, you know, who my top users are, who are the top users that are exceeding, let’s say, their rate limits, which means that we might be able to offer them a better plan.Derric: And so I guess, as you’re digging into this data, how do you balance, you know, between, okay, for the developer needs and customer needs versus sales, right? Because we also don’t want to force them into, you know, upgrading too quickly or into a sales cycle. Is there anything that a product manager can do to balance different stakeholder needs?Emmanuel: Yeah, and you know, it depends on whether your organization is taking a PLG approach, or if you’re a small organization or a larger organization, we can dig into those. But typically, what happens is you may want to instrument for marketing and sales data more in your, let’s say, sandbox environment—the environment that people or developers play in before purchasing or before becoming paid users. So those are things that are very tactical, and your sales and marketing should be looking at—who, you know, who signed up but is not making API calls, or who signed up and is making a lot of API calls; they must be very interested, let’s go talk to them. And especially if you’re doing product-led growth, those are things that you should be monitoring throughout that cycle. Now, in your production environment for your customers, that’s data that’s more for either, let’s say, product managers, where they want to know how the product is being used. Are people being successful? Are they finding difficulty with the product? Or perhaps, if you’re a larger organization, you might have customer success, and they care about the renewal or the adoption of a particular customer, and there you should offer them more of the metrics in the production environment. Not that sales are not that interested in what people are doing in production, but typically, especially in larger organizations, that moves off towards the customer success function.Aligning API Metrics with Business Outcomes and LeadershipDerric: That’s an interesting point in that, you know, nowadays people are looking at APIs way before and doing their investigation before they even talk to sales, right? And so there’s a lot of steps that now need to be tracked. But at the same time, we also hear this idea of outcomes, and making sure that we’re aligned to outcomes from a business standpoint. Am I just trying to, you know, get the highest request per second, or is there something else that I should be measuring when I think about the success of an API or success?Emmanuel: I’m glad you bring up outcomes because that kind of ties very closely with the value of our product, and the value of our product is not just the technical aspect of it. Oh, I was able to make an API call. Oh, I was able to, you know, move this much data from one place to another. That’s not particularly interesting per se. But the value is what we get out of it. And if you think about products that are truly API as a product, let’s say, for example—I mean, the classical example is Stripe—the outcome there, and the value is very clear. If you’re successful, you’re selling more, then Stripe is also successful because they’re taking a cut of the revenue. So the outcome there is very, very clear. And I think that’s part of the reason why Stripe has been very successful with APIs. They’ve been able to clarify that, and they’ve been able to find that use case where the outcome is so aligned with that of their users that the value is super clear to everybody. And I think those two are related.Derric: And so if I’m just getting started, you know, trying to start tracking some of these outcomes and metrics, what should I, as a senior API PM, do to help, you know, convince my leadership team and stakeholders?Emmanuel: Well, I mean, the first thing is to think about exactly what you’re going to measure, because even with the best measuring tools, you’re not going to get everything out of the box. You have to actually instrument it where you’re going to be measuring, and also what outcomes you’re going to be measuring. If the outcome is going to be that a sale was made, it has to be clear somewhere in the code—there is something that tags that and then that’s measured in your analytics. So be clear about that. The second thing would be being able to persuade or convince people to adopt your tools and your APIs internally; and to basically invest in your projects as an API product manager would be to make sure that the proposals that you have, the products that you are going to launch, align with the company strategy. And very often that’s not the case, because we kind of have this myopia where we say, “Well, of course, it should be obvious that by doing, you know, API governance, or by having better API design, or by exposing everything that we have as APIs out there for people to do who knows what? Of course, that should be a good idea. Good things will come from that.” But it does cost money to do that, and businesses are constrained about the resources that they have. And I found that if you don’t align with—not just any priority or any business metric that exists, but really the top three priorities of an organization—not much is going to happen. Pretty much anything that’s not top three is just going to fall by the wayside, because we all have a lot to deal with. And usually those initiatives have to do with just a few, a handful of things. It could be, of course, more revenue, it could be more efficiency, it could be compliance with legislation, compliance with new things that are happening, or it could even be a better customer experience. So traditionally, businesses think about these four things, and if you’re not aligning with these four things, and if you’re not aligning with the business’s strategy, you’re not going to get that much done. So you have to find a way to align and improve on those.Exploring API Business Models and Monetization StrategiesDerric: That’s a really interesting point to take your own metrics and make sure it does roll up into one of those, I guess, four points, or what matters at the business overall. You know, as we’re looking at these APIs, and you know, new folks are trying to productize and expose these APIs, does that mean I should be generating revenue, or what are the different business models that come around these new API programs?Emmanuel: You know, that’s really interesting. Because even in the case where we don’t directly monetize APIs, like the case of Stripe, or let’s say if I’m Google Maps and I’m selling these billions of API calls at a particular price point—there it’s very easy to attribute that revenue to, you know, here: that many API calls means this much revenue. But in most cases, in a lot of cases—and you know, what could be more of an example than Salesforce—you are creating a better or a more sticky or a more persistent experience for users. You are delivering value that comes from automating things and integrating things that you couldn’t do otherwise, and that has a lot of value. You have to be able to express this value; and I do believe that Salesforce, for example, publishes a number—I’m not too sure exactly what it was—but it’s in the billions of value that they attribute just to their APIs. So don’t think about only directly monetizing APIs. It doesn’t make sense in all cases. If you have data that nobody else has, if you’re able to do something really, really well—let’s say you’re Twilio and you’re able to do phone calls and text messages really, really well—great, then you can, you know, monetize that directly. But if what you’re doing is going to give your customer that much more capability, if they can automate and integrate, then, you know, keep on doing that because that’s going to deliver value.Derric: That definitely makes sense on attributing value regardless of its dollars and cents or not. You know, if I’m trying to figure out, does it make sense to charge, you know, for this API, or some type of model there, what is the process that a product manager should go through as you’re, you know, trying to figure out that right strategy?Emmanuel: Yeah, I think it would be trying to categorize a little bit where your API falls. We talked about if I have data or resources that nobody else has, then I am able to put those behind monetization and say, “Here you go, pay me for the outcome that happens.” If I’m able to do things more easily or more efficiently. If I’m SendGrid, for example, and I can send an email really, really well and deliver it actually versus you doing it by yourself and actually not having the email delivered, I can charge for that increase in value. So I would say, try to categorize what the benefit is that you bring to the table. And if your data is generic, well, then you’re not bringing that much benefit. And sometimes we—it turns out we don’t charge for that data, like for example, there’s an API for the U.S. Government to distribute weather. Well, they don’t—obviously they have some data that nobody else has, but I don’t think they’re going to charge for that, because it’s not only a public good, but it gives the ability for others to build on top of that. So that’s another way where it might be a good idea to charge for the data, but you might explicitly decide not to—not to do that. But I would say, categorize what the value is that you bring, and see if you’re able to express it in a way that you can monetize.GenAI: Impact and Essential Strategies for API Product ManagersDerric: That’s a really interesting point around categorizing the value, and also, is it more of an ecosystem play? Is it direct for revenue play? And there’s a lot of different businesses that can be created through APIs. Revenue is not the only one. Just on the last topic here, you know, everyone keeps talking about Gen AI, and how it’s impacting everyone. So not surprised, I’m gonna bring it up as well. But how is Gen AI impacting API product management, either from tooling or leveraging Gen AI?Emmanuel: I think there are two different angles to this, and one angle is as product managers, we have to think about the consumption of our APIs by entities or agents that are not going to actually read that documentation, and that we can talk to, and they can ask us questions if they get confused. I think our ability to make APIs usable for agents is going to help us, especially if we’re doing an API as a product. If I’m a data provider, if I’m Google Maps, I want to make sure that all my APIs are suitable to be consumed by agents, because if they’re not, an agent is going to go elsewhere and get the data where it’s easiest, or it’s going to go scrape some website, or it’s just not going to find the data. So I think we’re going to have a proliferation of agents; it appears that way. And those agents are going to need API data, and it’s on us to make that data easier to consume for agents. So there’s a whole conversation happening there. How do we do that? What does it mean to prepare for agent consumption? That’s one thing—so the agent as a new persona, if you will, that’s both a buyer and a consumer of our data and our services. And then, of course, like any other job or any other category, product managers in general are either afraid of or benefiting from AI, depending on who you ask. And the API product managers more so. Why API product managers more so? Well, it turns out API product managers, like everyone in APIs, deals a lot with structured data. And one of these structured data artifacts is an API description such as OpenAPI. Well, it also turns out that LLMs and AI are very helpful at producing these structured artifacts, such as OpenAPI. And where it was very difficult for us to sit there and write an extensive, very descriptive document that does everything accurately, it’s much more easier today to produce it automatically or to produce it through an LLM—I shouldn’t say automatically. And then, of course, we can reuse it everywhere, and we can even reuse it to codes using AI from the OpenAPI or from the API description. We can use it to scaffold API management. We can even use it to create and write our stories and our PRDs and create diagrams for people that are going to implement our API. So there’s a lot we can do. And the benefit is that it takes away the toil and the drudgery of what used to be really boring, templated kind of work. So I think we’re seeing a lot of improvement there.Derric: That’s a really interesting point about thinking you’re almost like SEO optimization, but for APIs, you gotta optimize for the agent, as it were.Emmanuel: Call it agent experience—a new term, AX.Derric: We have developer experience, and now soon we’ll have agent experience, I guess, right? So what should then a product manager be doing today to be prepared for, I guess, this Gen AI world that we’re coming into?Emmanuel: Yeah, I mean, if you take it from the agent consumption perspective, I think first of all, a lot of the good recommendations and techniques that people advocate for still apply. So you should have a machine-readable version of your API, like an API description document like OpenAPI, that an agent can come and read. You should be able to onboard somebody—an agent or a human—extremely quickly, like instantly. So no waiting for the credentials, no waiting to be approved. I should be able to request credentials and receive them immediately so I can consume my information immediately. Same for making that first API call or the `n`th API call. I should be able to deal very well with spikes. So agent traffic, I think, is going to be very, very spiky. It’s not going to be predictable. You might have a lot of data that you have to distribute in a very short amount of time, and then no data at all, as the agents move on to something else. In fact, there are dedicated tools like, I think, AI gateways that are kind of more API gateways, but for being able to deal with the spiky type of traffic. And then, I would say, be consistent. Follow standards. Make it easy for the LLM or any kind of tool to understand your API. And that’s good advice, you know, even for human developers, and you should be doing that anyway.Final TakeawaysDerric: Definitely sounds like we already have to be tracking developer experience metrics and how long it gets to generate that key and make an API call. But now it’s beyond, you know, much more accelerated. So getting that type of metrics and tracking and improving is going to be even much more important. Well, thanks a lot, Emmanuel. I really appreciate having you here on the podcast. Any takeaways or items that you want to share with our audience today?Emmanuel: Yeah, I think definitely, if you don’t have product managers in your API organization, definitely look at that. It’s going to enable you to do a much better job at APIs, and a product manager is going to be able to bring that customer point of view inside the organization. Now, if you don’t have a product manager, chances are somebody’s already doing that, and they might not just have that title. Maybe a lead developer is doing that. So I would say, formalize that and make sure that you do have an API product manager that’s trained to do that, and maybe offload some of those customer conversations to a person whose role is dedicated to that. The problem with not having that role is that you might have some myopia, and you know—have only kind of built products looking internally. And there, there’s a risk that your products might not find success in the market, even though you did all the work and spent all the resources. So I would say, look at some API product management as a thing even for internal APIs, not just external APIs.Derric: Important piece. Make sure if you’re gonna treat it as a product, make sure you truly treat it as a product, not halfway there. Otherwise, you just cause a lot of commotion between the different engineersEmmanuel: 100%, yeah.Derric: Well, thank you very much, and have a good day.Emmanuel: Yeah.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /podcasts/developers/Podcast-APIs-Over-IPAs-API-Product-Management-With-Emmanuel-Paraskakis-Level-250/",
          "author": "Sakib",
          "categories": "Podcasts, Developers"
        }
      
    ,
  
    
        "api-strategy-api-engineering-how-engineering-teams-should-monitor-customer-health-and-api-usage": {
          "title": "How Engineering Teams Should Monitor Customer Health and API Usage",
          "content"	 : "Most engineering teams have infrastructure monitoring nailed down—they are tracking uptime, latency, and error rates, and have set up alerting in places. But API issues don’t always start there.Infrastructure metrics don’t tell you how your API users experience your API. A critical integration may have been repeatedly facing failures due to invalid authentication tokens. A new version you have deployed might have introduced a subtle schema change that breaks older clients. These issues don’t surface in your logs until someone complains or churns.That is where you need customer-level API observability. With Moesif, engineering teams can see not just whether the API is responding, but how it is being used—and by whom. You can track which clients are sending malformed requests, identify usage regressions before they escalate, and build alerting systems based on real user behavior. In this article, we will walk through how to monitor API usage with engineering precision—from debugging in production to catching issues your logs miss.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        Table of Contents  Rethinking API Monitoring for Engineering Teams  What Healthy API Usage Looks Like          Using Moesif to Observe Healthy and Unhealthy Patterns                  Example: Spotting Onboarding Drop-off with Behavioral Funnels          Example: Identifying Misbehaving Clients with Error Heatmaps          Example: Diagnosing Excessive Prompt Token Usage                      Account Health from an Engineering Lens  Debugging and Alerting in Production          Debugging with Real-Time Visibility      Configuring Actionable Alerting      Notification Channels that Fit Your Workflow      Leveraging AI to Explain What Goes Wrong        Wrapping upRethinking API Monitoring for Engineering TeamsMost engineering teams associate monitoring with system-level metrics: response latency, CPU load, memory usage, and service uptime. However, they only reflect the infrastructure’s performance, not the behavior of API clients consuming your web services. API monitoring, done right, offers insights into how your APIs are used—not just whether they are up.This distinction matters because APIs don’t only function as transport mechanisms. Web APIs and web servers build the contracts between your system and the outside world: mobile apps, third-party integrations, frontend clients, or even internal teams. Each API call represents a decision an external system makes. Infra metrics doesn’t tell you when a partner misuses your authentication tokens or when a public API client unknowingly sends malformed payloads after a version change.Traditional monitoring tools also fall short in account-level visibility. Engineers often want to answer questions like these:      Which API clients are causing 401 Unauthorized errors due to expired tokens?        Are newly onboarded users actually calling the API, or stalling after signup?        Has a recent API change increased the error rate or reduced request volume?  These aren’t infrastructure concerns—they’re product-integrated behaviors. The following table illustrates some examples.            What you monitor      Infra monitoring (Datadog, Prometheus)      Moesif API observability                  API server uptime      Check if the service is running      No visibility into how it is being used              Auth errors (401, 403)      Often aggregated, lacks context      Can tie to specific API clients or tokens              Deprecated endpoint usage      Not tracked      Detect clients using outdated APIs              Retry storms or rate limits      May go unnoticed unless impacting infra      View by client, IP, or endpoint              Drop-off after onboarding      Invisible      Funnel analysis tied to API behavior              High-value account usage      No account mapping      Group API calls by user or account ID              Breaking change detection      Requires manual correlation      Detect schema or behavior regressions              Webhook or API consumer issues      No client-side feedback loop      Monitor and alert on failed deliveries      Moesif enables engineers to trace these behaviors to specific API keys, endpoints, and accounts. You don’t replace tools like Prometheus or Datadog, but complement them with observability at the edge where systems interact.Another common gap is that traditional monitoring tools don’t offer session context. Engineers trying to debug failed API requests often jump between logs, request traces and logs, and support tickets. Moesif simplifies this by allowing you to group API usage by client, user, or account. By tying requests to clients, this context uncovers patterns such as a malfunctioning integration triggering a 5XX retry storm—issues that infrastructure dashboards tend to smooth over.Engineering teams need API monitoring that moves beyond HTTP 200s and server health. The focus shifts to understanding client behavior, identifying integration failures early, and correlating API usage with product outcomes.What Healthy API Usage Looks LikeUptime is not a proxy for API health. An API can return HTTP 200s all day while silently failing its users. For engineering teams, understanding API health means going beyond status codes and measuring how APIs are used, by whom, and with what effect.Healthy API usage shows up in patterns of consistent, intentional interaction. These patterns often include predictable request volume, low client-specific error rates, and interaction across multiple API endpoints that align with expected workflows. For example, consider an API that supports embedding generation followed by semantic search:      POST /v1/embeddings to vectorize input text        POST /v1/search to query a vector index  You expect clients to call both endpoints in tandem, often within the same session. If users consistently hit only the embeddings API endpoint without ever querying, that may signal misuse—like caching abuse, onboarding friction, or an incomplete integration.You also want to track endpoint diversity. A client hitting only one endpoint over time might be under-integrated, misconfigured, or using the API in ways you or the customer doesn’t intend to. Healthy usage tends to spread across several endpoints—authentication, metadata fetches, user actions—revealing alignment with your intended API surface.Errors constitute another important dimension. A low global error rate doesn’t mean everything is fine. Engineers need to know which clients, endpoints, or API keys are generating errors, and in what context. One account might cause 90% of your 403 responses due to expired authentication tokens, or flood your system with 422 errors from malformed payloads. Without client-level granularity, these problems go undiagnosed.Latency patterns follow the same rule. Average latency across all API calls doesn’t tell you which customer is experiencing delays. Healthy usage means consistent and optimal performance across accounts. If one client sees persistent slowdowns on GET /invoices, they may be sending expensive filter queries—something infrastructure monitoring alone doesn’t reveal.Finally, healthy API behavior looks intentional.       Are clients authenticating before making calls?         Are they hitting documented endpoints in a logical sequence?         Are calls spaced in a way that reflects real user behavior—not automated scripts or misbehaving integrations?   You can differentiate real usage from noise by observing intent.Using Moesif to Observe Healthy and Unhealthy PatternsWith Moesif’s ability to group requests by different parameters like API key, user ID, or company, engineers can zoom into usage patterns that matter, and act before issues escalate. Here are some examples:Example: Spotting Onboarding Drop-off with Behavioral FunnelsSuppose your intended onboarding flow involves these steps:      POST /signup        GET /profile        POST /data/upload  With Moesif’s funnel analytics, you can visualize how many users move through each step. A steep drop-off after step one may reveal frontend bugs, missing environment variables, or incomplete docs. Healthy usage flows through the funnel. Unhealthy usage drops out early, and often silently. Using this feature, you can, for example, catch product-led integration failures before support tickets arriveThe following demonstrates a Moesif funnel that models a typical integration flow in a GenAI API stack:      Successful sign-in event        First call to the v1/embeddings/generate endpoint        Input token usage exceeding 100 tokens  Example: Identifying Misbehaving Clients with Error HeatmapsHealthy APIs distribute errors evenly—or better, rarely. With Moesif, you can segment traffic by API key or account to identify clients responsible for a disproportionate number of errors.For example, an engineering team might use Moesif to identify that a single API client causes a disproportionate number of 422 Unprocessable Entity errors. This can happen if the client sends malformed JSON payloads due to outdated request logic. The backend might remain stable, but the integration clearly breaks—and infra monitoring doesn’t catch it. Moesif helps you triage integrations causing the most friction or wasted compute.Here’s another illustration using Moesif where we break down API errors by status codes and SDKs in a Segmentation chart, noticing that most of the errors stem from one particular SDK:Example: Diagnosing Excessive Prompt Token UsageSuppose you have built a LLM(large language model)-powered chat-based API. You notice that one account is generating significantly higher hosting costs than others. However, your infra metrics show stable response times and no errors.With Moesif, you can break down usage by API key or account and discover that this account consistently sends massive input payloads—like 20,000+ tokens per call—well beyond typical usage patterns. They’re essentially treating your endpoint as a bulk batch processor, rather than a conversational API.Having this insight, you can enforce usage guidelines, apply rate-limiting, or update your API documentation to reflect best practices.This way, you can, for example, spot outlier behavior that inflates cost or degrades performance, even when infra health appears stable.Account Health from an Engineering LensEngineering teams often think of “health” in terms of system uptime, error budgets, or incident response. However, for API-first products, account health becomes just as much a technical concern as it is a business metric. A customer account can be healthy from a CRM perspective while actively failing at the integration layer—and if engineers can’t see that, no amount of uptime will prevent churn.From an engineering standpoint, account health means the following about the systems interacting with your API:      Behave predictably        Perform within acceptable error and latency thresholds        Follow your expected integration patterns  Moesif lets you define health by whether an account makes requests as well as how those requests behave over time.One signal of poor health is sustained high error rates from a single account, even if the errors don’t affect infrastructure. A partner integration that consistently sends invalid payloads or calls deprecated endpoints may look like usage from a distance—but in reality, they’re burning cycles and failing silently. Engineering teams must monitor error composition and frequency segmented by account, not just endpoint or region.Another signal is sudden drops in API volume from historically active accounts. This may indicate an internal service failure, a misconfigured deployment, or an upstream breaking change in the customer’s stack. These issues often don’t trigger alerts unless you’re explicitly monitoring volume trends per account. Moesif enables this by letting you visualize usage deltas over time and compare against baselines.Authentication patterns also serve as indicators of health. If an account frequently receives 401s or token expiration errors, they may be using the API without proper session management. That often leads to unnecessary retries, excessive load, or degraded client experience.Finally, repeated access to deprecated endpoints is often a lagging indicator of poor API hygiene. If a customer hasn’t migrated off older versions after a deprecation warning, engineering should flag the integration as at-risk. You can set up alerts in Moesif on versioned endpoint usage so you can enforce contract changes without breaking unaware clients.Debugging and Alerting in ProductionProduction debugging rarely starts with perfect context. Logs have noises, dashboards show deltas—not causes—and distributed tracing often misses the human or client-side dimension. Moesif gives engineering teams a behavioral lens into production issues by connecting individual API requests to specific users, clients, or accounts. This allows you to trace problems back to who caused them and how.Debugging with Real-Time VisibilityMoesif’s Live Event Log provides a stream of all API requests and responses. You can enrich them with user IDs, API keys, payload metadata, and more. You can drill into these sessions to perform tasks like the following:      Inspect malformed requests without relying on raw logs.        Compare expected versus actual payload structure.        Spot trends like token expiration loops, missing headers, or invalid auth flows.  You can filter requests by user ID, endpoint, status code, or custom metadata, making it easy to isolate incidents that tie to specific customers or versions. You will find it especially useful during on-call when infra metrics show elevated 4XXs or degraded latencies, but don’t pinpoint the source.Moesif also captures entire request and response bodies, critical when debugging issues like these:      Invalid JSON schema in POST payloads        Unexpected values downstream systems return        Empty or malformed error messages  To protect sensitive data, Moesif supports field-level redaction through functions, so you can preserve visibility while meeting compliance requirements. For example, see the maskContent configuration option in Moesif Node.js middleware documentation.Configuring Actionable AlertingUnlike infrastructure alerting system, typically firing on server resource metrics or uptime checks, Moesif lets you create alert rules based on API behavior and client-side signals. For example:      Trigger an alert when a specific account hits 100+ 401 Unauthorized errors in an hour.        Notify engineering when the error rate on a critical API endpoint exceeds 5%.        Detect behavioral anomalies like users calling your API from 50+ unique IPs in 15 minutes—often a sign of credential abuse or misconfigured systems  You can define alert conditions in Moesif using combinations of Moesif’s powerful filters, group, and metrics parameters. For example:      HTTP status code filters        Request volume thresholds        Specific endpoint paths or HTTP methods        Custom fields like user ID, company ID, or version  Notification Channels that Fit Your WorkflowMoesif integrates with common notification systems and routes them to where you can act on them:      Slack—for example, to engineering or on-call channels        PagerDuty—for example, for incident escalation        Custom webhooks for internal alert routers or ticketing tools  You can create and manage notification channels directly from the Moesif dashboard and associate them with individual alert rules. This allows you to route critical errors to ops, retry failures to backend devs, and auth anomalies to security—all with different sensitivity thresholds.Leveraging AI to Explain What Goes WrongMoesif’s AI Explain can assist engineers during incident response by analyzing filtered request logs and summarizing trends in natural language. For example:      “Most 422 Unprocessable Content errors in this view are caused by missing the user_id field.”        “Error rate increased after version v2.3 was rolled out to clients.”  This feature works across API analytics, user behavior, and alert-based data slices, giving engineering teams a faster way to triage production issues—without having to reverse-engineer dashboards under pressure.For example, consider a breakdown of HTTP errors across endpoints using a Segmentation analysis:Let’s ask AI Explain the following question to see if some endpoints in the product have been encountering more errors:  Do you observe any correlation between the endpoints and error counts, as in specific endpoints are more error-prone than others?Here’s how it mind respond:This way, AI Explain helps you troubleshoot potential issues and initiate investigations more quickly.Wrapping upAPI monitoring encompasses both uptime or error rates and understanding how your systems behave in the hands of real users. Engineering teams need more than dashboards that track availability. They need visibility into integration health, client behavior, and the subtle regressions that precede incidents.Moesif makes that visibility more accessible. It lets you debug with context, monitor accounts beyond raw traffic, and configure alerting system that reflect actual usage patterns in addition to server metrics.You don’t need to replace your observability stack. Rather, it’s about covering as much ground as possible. If you’ve ever been stuck debugging in the dark, or found out about a failure from a customer first, you already know what that visibility is worth.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design, Build, and Deliver Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /api-strategy/api-engineering/How-Engineering-Teams-Should-Monitor-Customer-Health-and-API-Usage/",
          "author": "Sakib",
          "categories": "API-Strategy, API-Engineering"
        }
      
    ,
  
    
        "technical-nginx-moesif-for-api-observability-and-analytics-in-nginx-openresty": {
          "title": "Moesif for API Observability and Analytics in NGINX OpenResty",
          "content"	 : "NGINX with OpenResty offers unmatched performance for serving APIs (application programming interfaces) at scale, with the added benefits of the open-source ecosystem. It’s fast, flexible, and production-proven—an ideal choice for scalable web platforms and high-throughput APIs. But even the most reliable platform can leave teams blind to what matters: real-time API usage, user behavior, and production errors.Access logs and basic monitoring tools can’t tell you why a customer churned, explain complex usage patterns and queries,  why requests fail, or how fast customers reach value. That’s where Moesif comes in. Moesif brings real-time API observability, error tracking, and deep behavioral analytics to your OpenResty stack by extending it through a Lua-based configurable plugin—without rewriting your app or compromising performance. It logs API traffic with full context: user identity, response times, payloads, and more. You get access to dashboards that highlight API bottlenecks, drop-offs, and usage spikes—backed by data you can actually act on.In this blog post, we’ll demonstrate how you can achieve deep API observability and analytics for NGINX using OpenResty and Moesif.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        Table of Contents  What is NGINX OpenResty?  The Role of Moesif: Visibility into Every API Event  Benefits of Using Moesif with NGINX OpenResty          How Moesif Captures NGINX API Logs      Troubleshooting API Issues in Real Time        Set Up Moesif with OpenResty          Step 1: Install the Moesif Lua Plugin      Step 2: Configure NGINX      Step 3: Initialize Moesif      Step 4: Capture Requests and Responses      Step 5: Verify Your Setup      Try The OpenResty Docker Demo        Key Metrics to Track for API Observability and Analytics          Total Response Time (Latency)      Status Code Trends and Error Analysis      Active Users and API Usage Patterns      Request Volume and Traffic Distribution        ConclusionWhat is NGINX OpenResty?OpenResty extends the core of NGINX by bundling it with LuaJIT and a rich ecosystem of Lua libraries. This fusion turns a fast reverse proxy into a programmable web platform—ideal for teams building scalable APIs and dynamic web gateways. Engineers use OpenResty to handle routing logic, caching, access control, and custom traffic shaping directly within the NGINX server.One key advantage lies in OpenResty’s event-driven architecture. It allows Lua scripts to run efficiently at various phases of the NGINX lifecycle—without sacrificing throughput. This makes it an effective foundation for real-time API observability.Because OpenResty allows inline scripting in phases, developers can log structured request and response data at the gateway layer. That’s where Moesif fits in. Its Lua-based plugin leverages these hooks to capture critical API data like user IDs, status codes, payload details, latency—and forward them for analysis. This enables deep visibility with minimal overhead and without disrupting NGINX’s performance profile.OpenResty’s modularity, combined with Moesif’s analytics layer, creates a lightweight but powerful path to understanding API behavior across complex systems.The Role of Moesif: Visibility into Every API EventMost OpenResty setups log requests for basic troubleshooting—status codes, timestamps, maybe upstream response times. But understanding real-world API usage requires more than log lines, for example:      Which users triggered which endpoints        How long responses took        What payloads passed through        Whether usage patterns reflect product success or operational issues  Moesif captures this level of detail natively through its Lua-based plugin. During each request cycle, Moesif reads request and response bodies, applies your plugin configuration, and ships structured event data to Moesif’s analytics platform. The data includes headers, routes, latency, and user context. This occurs asynchronously, preserving OpenResty’s performance model.What sets Moesif apart in this context lies in how it stores and exposes the data. Instead of funneling logs into unstructured files or third-party collectors, Moesif automatically associates events with contextual data like user ID, company ID, or endpoint. It gives you a rich suite of filtering tools and breakdowns by error type, request duration, and more—all from the browser. You can break down performance metrics by customer, track API usage across segments, and identify anomalies with precision. Moesif also integrates with OpenTelemetry to leverage your API’s distributed tracing and logging and makes those data directly available in the platform.For engineering teams maintaining OpenResty services, this visibility translates to faster debugging, better alerting, and actionable feedback loops. With Moesif in place, every API request becomes a trackable unit of insight.Benefits of Using Moesif with NGINX OpenRestyCombining Moesif with OpenResty introduces API analytics and visibility at the gateway level without disrupting the server’s performance model. Together, they enable high-fidelity API observability with minimal configuration and virtually no runtime penalty.How Moesif Captures NGINX API LogsOpenResty supports Lua scripting across HTTP phases like access_by_lua and log_by_lua, allowing Moesif to capture and process event data with the request lifecycle. This integration results in a clean pipeline for structured analytics without modifying application logic. The API logging and governance enforcement is designed for non-blocking execution so your performance is not impacted.Collected data includes the following:      Timestamps for requests        Route, status code, latency and headers        Request and response bodies for powerful payload analytics        IP geolocation and user agent  Troubleshooting API Issues in Real TimeOpenResty gives teams flexibility to build and deploy custom API gateways—but with flexibility comes complexity. When you lack proper observability, issues like slow endpoints, inconsistent errors, or customer-specific timeouts often go unnoticed until they impact SLAs.Moesif helps you fill this gap. Once events reach Moesif’s platform, teams can:      Filter traffic by API key, company, or IP address        Visualize request volume and latency over time, segmented by endpoint or plan        Set alerts on status code thresholds or latency spikes for specific routes        Create funnel reports to measure onboarding or time-to-value  This reduces time to resolution, particularly in distributed systems where gateway logs rarely tell the full story. With Moesif dashboards in place, teams no longer need to grep logs or trace requests across multiple layers. They can view error spikes by endpoint, segment 5xx errors by company, and track latency changes in real time—directly from Moesif’s UI.Set Up Moesif with OpenRestyStep 1: Install the Moesif Lua PluginFirst, ensure OpenResty runs with the lua-nginx-module included, which ships by default. Then, install the Moesif plugin through Luarocks:luarocks install --server=http://luarocks.org/manifests/moesif lua-resty-moesifStep 2: Configure NGINXIn your nginx.conf, configure the shared memory dictionary and Lua package installation paths:lua_shared_dict moesif_conf 5m;lua_package_path "/usr/local/openresty/luajit/share/lua/5.1/?.lua;;";The shared dictionary holds static configuration options like your Moesif Application ID and request and response masks.Step 3: Initialize MoesifIn your main http block, initialize the Moesif client:init_by_lua_block {    local config = ngx.shared.moesif_conf;    config:set("application_id", "YOUR_MOESIF_APPLICATION_ID")    local mo_client = require "moesifapi.lua.moesif_client"    mo_client.get_moesif_client(ngx)}This step loads the Moesif library and prepares it to process API events.Step 4: Capture Requests and ResponsesUse access_by_lua_file and log_by_lua_file directives to read request and response bodies:server {  listen 8080;  # Customer identity variables that Moesif will read downstream  set $moesif_user_id nil;  set $moesif_company_id nil;  # Request/Response body variable that Moesif will use downstream  set $moesif_res_body nil;  set $moesif_req_body nil;  access_by_lua_file /usr/local/openresty/luajit/share/lua/5.1/resty/moesif/read_req_body.lua;  body_filter_by_lua_file /usr/local/openresty/luajit/share/lua/5.1/resty/moesif/read_res_body.lua;  log_by_lua_file /usr/local/openresty/luajit/share/lua/5.1/resty/moesif/log_event.lua;  location / {    proxy_pass http://ai_api.com/internal;  }}In the preceding example configuration:      access_by_lua_file allows Moesif to capture request data.        body_filter_by_lua_file allows Moesif to read the response data.         log_by_lua_file sends the complete event data to Moesif asynchronously after the response finishes.  By setting $moesif_user_id (and optionally $moesif_company_id), you tag traffic with customer-specific identifiers, enabling analysis types like Segmentation inside Moesif dashboards.Step 5: Verify Your SetupOnce the integration completes, send a few API requests through your NGINX OpenResty instance. Then, log in to your Moesif account and open a Live Event Stream workspace.You should see new events appear in real time.If you face any issues, see the Server Troubleshooting Guide or contact our support team. For detailed configuration options and example templates to customize your configuration, see the Moesif Lua plugin documentation.Try The OpenResty Docker DemoTheOpenResty Docker Demo provides a working setup of Moesif and NGINX OpenResty. It lets you see how requests flow through the system before integrating into a live environment.For a hands-on tutorial using this demo for API observability, see API Observability and Monetization with NGINX OpenResty and Moesif Developer Portal. Key Metrics to Track for API Observability and AnalyticsWhen APIs power critical systems, surface-level monitoring no longer suffices. Teams need real-time, structured insights into traffic, performance, and user behavior—straight from the gateway. By integrating Moesif with OpenResty, developers and engineering leaders can track meaningful API metrics without introducing new overhead or disrupting their architecture.Let’s look at some of these metrics through practical examples:Total Response Time (Latency)Moesif measures the full time from the initial request arrival to the final response sent by your server. Teams can analyze this data through built-in visualizations and analysis types like the following:      Maximum latency        Minimum observed response times        90th percentile (P90) latency to detect slow-tail performance issues        Custom latency types to highlight requests exceeding target thresholds—for example, greater than 500ms.  These valuable insights help pinpoint high-latency endpoints and track how performance evolves across customer segments or deployments. For example:      Which endpoints consistently show slow response times?        Do enterprise customers experience better or worse latency compared to free-tier users?  In the following illustration, a Time Series analysis in Moesif shows P99 latency across API endpoints in the past 24 hours:Status Code Trends and Error AnalysisMoesif automatically records every API response’s status code, making it easy to monitor client-side 4xx and server-side 5xx error patterns over time. Teams can break down errors by endpoint, customer, or request method to understand operational health at a glance. This allows you to find answers to questions like:      Where do authentication or validation failures occur most often?        Has a recent deployment increased server error rates on critical routes?  For example, the following time series analysis breaks down 5xx server errors on an hourly interval for the last 12 days. It also categorizes the analysis by response status codes to highlight the exact error types.To showcase Moesif’s advanced analytics capabilities, we’ve applied time series folding to better visualize the periodic behavior of our metric. Folding proves especially useful for uncovering recurring patterns, like fluctuations in error rates or time-based anomalies. It highlights worst-case scenarios by compressing cycles into a single view. In this example, folding helps identify whether specific hours consistently experience error spikes. This type of analysis can reveal underlying issues like:      Peak traffic periods overwhelming the API        Scheduled maintenance or batch processes disrupting normal operations        External service failures tied to specific times  The following demonstrates another example where it gives a breakdown of all API errors in the last 12 weeks across APIs:Visualizing daily error trends like this in Moesif highlights patterns that raw logs miss. You can spot rising error rates early, correlate spikes with deployments or traffic changes, and prioritize fixes before they impact users.Active Users and API Usage PatternsBy tagging requests with user IDs and company IDs at the gateway, you can move beyond generic request counts to measure real customer engagement. Moesif tracks active users daily, weekly, and monthly while allowing drilldowns into user journeys and session flows.      You can identify how many unique users actively call the API each week.        You can pinpoint where new users succeed—or struggle—during their first API interactions.  For example, the following analyzes usage across different user agents and SDKs in an embeddings API:Request Volume and Traffic DistributionMoesif enables full visibility into request volume trends over time. You can visualize traffic growth, monitor load distribution across endpoints, and detect usage spikes that might require scaling decisions. For example:  Which API routes account for the majority of traffic?  Has usage changed after a product launch, SDK release, or API version update?The following Segmentation analysis breaks down request volume across API versions and different models for an AI API product. The first chart visualizes the analysis in a bar chart and the second one shows the data in tabular form.ConclusionNGINX OpenResty offers the speed and control developers need—but alone, it leaves too many blind spots in production. Moesif fills that gap with the necessary visibility to operate APIs with confidence by turning raw API activities into structured, actionable insights, with a few lines of configuration.This level of visibility doesn’t just help with better debugging. It shortens incident resolution, improves system reliability, and informs smarter platform decisions—especially at scale. For teams maintaining high-throughput APIs, Moesif extends NGINX even farther and makes observability at the edge more accessible and easier.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design, Build, and Deliver Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /technical/nginx/Moesif-for-API-Observability-and-Analytics-in-NGINX-OpenResty/",
          "author": "Sakib",
          "categories": "Technical, NGINX"
        }
      
    ,
  
    
        "api-monetization-api-strategy-usage-based-vs-outcome-based-pricing-for-apis": {
          "title": "Usage-Based vs. Outcome-Based Pricing for APIs",
          "content"	 : "Usage-based pricing has long been the default for APIs—straightforward to implement and easy for customers to understand. You charge based on consumption: API calls, compute time, or data volume. It is predictable, measurable, and scales well with usage.But as APIs become more intelligent—especially in AI-driven platforms—raw consumption no longer remains a reliable proxy for customer value. A user can rack up thousands of API calls and still achieve nothing meaningful. Meanwhile, a single well-placed call might generate a qualified lead or close a sale. Usage-based models fail to capture that difference.This gap is fueling a shift toward outcome-based pricing—where customers pay for results, not requests. In this article, we will explore how this shift is redefining API monetization, what product teams need to consider, and how to instrument outcomes effectively using platforms like Moesif.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        Table of Contents  What is Usage-Based Pricing?  What is Outcome-Based Pricing?  The Shift Toward Outcome-Based Pricing Model          Infrastructure Costs Have Become Outcome-Sensitive      Finance Teams Demand Value Justification      Instrumentation Tools Enable Outcome Tracking      Contextual APIs Reinforce the Value Shift        How Outcome-Based Pricing is Changing API Monetization          From Metering Activity to Mapping Value      Business Logic Now Drives Billing Logic      Monetization Moves Closer to Product Analytics      Impact on Sales, Customer Success, and Support      A More Accountable, Value-Aligned Revenue Model        How to Meter Outcomes Inside Your API Product          1. Define Outcome Events That Reflect Value      2. Instrument Outcomes      3. Create Billing Meters for Outcome-Based Models      4. Monitor and Improve Outcomes      5. Connect Instrumentation to Revenue Operations        ConclusionWhat is Usage-Based Pricing?In a usage-based pricing model, customers pay in direct proportion to the resources or services they consume. In the context of APIs, this usually translates to billing by units such as the number of API requests, data volume, compute time, or generated tokens. It is a simple model to implement because every interaction leaves a measurable trace. Platforms like Moesif make it easy to collect, aggregate, and bill on usage metrics with minimal friction.The usage-based approach gained popularity during the rise of cloud computing and API-first platforms because it aligned well with infrastructure cost curves. If a service incurred costs per request or CPU cycle, it made sense to bill customers accordingly. It was also flexible—users paid only for what they used, which made it appealing to startups and developers experimenting with low-scale use cases.However, usage-based pricing models assume that resource consumption is a proxy for value delivered. That assumption holds in some infrastructure scenarios, such as object storage or serverless computing. But it starts to break down when you apply it to APIs that deliver business logic, intelligence, or end-user experiences. In those cases, the same usage pattern can result in wildly different levels of value across customers—yet the pricing remains fixed to units of activity instead of outcome.It is also worth noting that usage-based billing has deep integrations into many SaaS and cloud pricing structures, from Twilio (per message) to OpenAI (per token) to AWS Lambda (per millisecond). While this consistency makes it easy to adopt and standardize across services, it also locks platforms into a mindset where activity equals value—a mindset increasingly at odds with modern product strategy.What is Outcome-Based Pricing?Outcome-based pricing model charges customers based on the results they achieve instead of the infrastructure they consume. Unlike usage-based models that count API calls or compute time, outcome-based models focus on metrics like verified leads, successful transactions, resolved support cases, or other domain-specific signals that reflect real business value.This approach shifts metering from backend activity to customer success. Instead of tallying HTTP requests, product teams define and instrument events that signify meaningful outcomes like a completed shipment. These metrics live closer to the customer’s goals and require careful design across application logic, analytics, and identity systems to ensure traceability.The model appeals to both vendors and buyers. Product teams align revenue with actual impact, rather than chasing higher request volume. Buyers gain confidence that they only pay when the product works as intended. This alignment reduces billing friction, especially in complex sales environments where finance teams demand justification tied to performance rather than traffic.AI and ML platforms, in particular, highlight the limitations of usage-based pricing. Two identical inference calls may yield vastly different outcomes—one helpful, one irrelevant. Treating them equally in pricing overlooks the variability in value delivery. By adopting outcome-based pricing models, vendors can define success—for example, relevancy thresholds, user engagement with outputs, and price accordingly. This allows the vendors to reward accuracy and effectiveness instead of volume alone.This pricing strategy requires more than a billing tweak—it demands deep observability across user flows and a reliable method to attribute outcomes to specific product interactions. Without that instrumentation, outcome-based pricing lacks the data to function. But for teams who invest in this infrastructure, the payoff includes clearer ROI, better customer alignment, and pricing models built around value instead of volume.The Shift Toward Outcome-Based Pricing ModelMarket dynamics, rising operational costs, and advances in observability have exposed the limitations of usage-based models—especially for AI, data, and workflow-centric APIs. Let’s break down the core forces accelerating the shift toward outcome-based pricing.Infrastructure Costs Have Become Outcome-SensitiveThe rise of AI APIs has transformed cost dynamics. A single API call might trigger inference on a billion-parameter model, consume GPU clusters, or hit third-party inference providers like OpenAI—all in seconds. These costs vary widely and accumulate fast. Usage-based models, which bill per request or per token, often ignore the value produced by that request. One user might trigger thousands of costly operations that produce zero business impact. Product teams now face pressure to design pricing that scales with actual outcomes besides usage volume.Finance Teams Demand Value JustificationCFOs and RevOps leaders no longer accept pricing strategies based solely on traffic or activity. They expect clean, traceable connections between what the product costs and what the customer gains. Outcome-based pricing model supports this expectation by tying revenue to measurable performance metrics like cost-per-lead, transaction completion rates, or user activation. These models offer better alignment between product performance and financial accountability—something increasingly necessary in long sales cycles or enterprise contracts.Instrumentation Tools Enable Outcome TrackingUntil recently, outcome-oriented models felt aspirational. Teams lacked the observability to measure what really mattered to customers. But the tooling landscape has matured. Platforms like Moesif allow teams to automatically collect user behavior events, define custom success metrics, and analyze conversion paths in real time. These capabilities enable developers and product managers to instrument outcome events—like successful transactions, lead captures, or model-generated completions—and tie them back to individual accounts or usage patterns. Without this visibility, pricing for customer outcomes would remain impractical.Contextual APIs Reinforce the Value ShiftWith the emergence of protocols like MCP (Model Context Protocol), API interactions now include more structured and intention-aware context. Instead of sending raw prompts or stateless calls, clients now inject metadata—user goals, session history, environmental parameters—that shape the model’s response. This structure makes outcomes more predictable and measurable, enabling pricing strategies that charge based on what the model achieves, not how many tokens it consumes. Context-rich APIs support outcome-aware pricing by design.These forces converge to push API businesses away from measuring activity and toward measuring accomplishment. Outcome-based pricing lets product teams monetize based on what customers actually achieve—better aligning revenue with customer satisfaction, retention, and long-term value. It also helps product teams escape the volume trap: more usage doesn’t always mean more value. Measuring and monetizing outcomes creates a path to healthier, more sustainable growth.How Outcome-Based Pricing is Changing API MonetizationAdopting outcome-based pricing changes what you charge for. It also redefines how teams think about value, accountability, and product instrumentation. In this section, we examine how monetization strategy evolves when pricing aligns with results instead of requests.From Metering Activity to Mapping ValueTraditional API monetization strategies rely on request logs and resource counters. With usage-based pricing models, billing systems track volume. Here are some usage-based pricing examples:       How many requests a user makes        How many seconds of compute they consume        How many tokens they generate  This works well for infrastructure providers but limits flexibility for APIs delivering domain-specific intelligence or workflow automation. On the contrary, outcome-based pricing models force teams to identify and measure value-producing events. This change reorients the entire monetization layer—from what you log, to how you interpret those logs and link to billing logic.Business Logic Now Drives Billing LogicIn outcome-based models, product teams must define pricing rules around real-world success events, for example:      A package delivered        A fraud attempt caught        A support ticket resolved  These events often live outside the scope of the API gateway. Billing systems no longer plug directly into the infrastructure layer—they depend on custom instrumentation within the application code. This increases the complexity of billing design but allows teams to charge in ways that reflect how their product creates value. Engineering teams must collaborate closely with product, finance, and customer success to define what counts as an outcome and how to track it reliably.Monetization Moves Closer to Product AnalyticsAs outcome-based models mature, the monetization stack starts resembling the product analytics stack. API providers now require event-driven logging, identity resolution, and conversion tracking—not just request counts. Platforms like Moesif bridge this gap by enabling teams to define behavioral events, track user journeys, and connect those outcomes to billing triggers.For example:      A logistics API might charge based on the number of deliveries completed within SLA.        A voice transcription API could price per call recording that achieves >95% transcription accuracy.        A fraud detection API may bill per distinct fraudulent transaction blocked before execution.        An AI summarization API might charge only when users rate the generated summary above a relevance threshold—or when customers click to expand or use that summary downstream.        A fintech compliance API might charge per unique transaction flagged and confirmed as suspicious rather than per rule evaluation.  These pricing models reflect how customers experience value rather than how many times they hit an endpoint. That shift requires teams to treat outcome instrumentation as a core layer of their product and revenue infrastructure.Impact on Sales, Customer Success, and SupportOutcome-based pricing reshapes how go-to-market teams communicate value and measure success.Sales teams must position pricing around business outcomes rather than usage tiers. Instead of selling “X API calls per month,” they anchor conversations in metrics like cost-per-verification or successful fraud interventions in sales processes.Customer success teams focus on enabling outcomes besides onboarding. They guide customers toward measurable milestones that affect billing—like increasing conversion rates or reducing time-to-resolution.Support teams address more than technical issues. They help surface value delivery gaps, such as low-quality AI responses or silent workflow failures that prevent outcomes—even when usage appears normal.Each team contributes to ensuring that customers use the product and succeed with it. Outcome-based pricing makes that alignment measurable, cross-functional, and central to revenue growth.A More Accountable, Value-Aligned Revenue ModelAPI providers that adopt outcome-based pricing improve their ability to retain and grow accounts. Customers gain visibility into what they pay for and why. Vendors can justify pricing with clear, product-driven metrics—contributing directly to stronger net revenue retention over time. As pricing and revenue models shift toward value alignment, teams gain leverage to optimize for outcomes, avoid usage data noise, and build long-term monetization strategies that scale with customer impact.How to Meter Outcomes Inside Your API ProductDesigning outcome-based pricing starts with identifying what success looks like for your customers. Then you have to build the instrumentation to track those signals reliably. Unlike usage-based models that rely on infrastructure metrics like API call counts, outcome-based pricing requires observability at the business logic level.1. Define Outcome Events That Reflect ValueStart by identifying key product actions that map directly to tangible value creation. For example, in a fintech API, this might mean:      A verified transaction        A loan approval        A successful fraud alert trigger  For an AI API, relevant outcomes could include the following:       Documents queried and  rated above a quality threshold        Generated content was factually correct        Completed workflows with positive outcome  These outcome events form the core billing units in an outcome-based pricing model. They should reflect customer value clearly enough that sales, success, and finance teams can all agree: this event matters.2. Instrument OutcomesOnce you define your value events, use Moesif’s API monetization platform to track outcomes. For APIs, Moesif has integrations with most API gateway and management vendors making setup super quick.Since Moesif captures raw API calls with granular context, including payload and metadata, you can easily define a billable metric that reflects the outcome you want to charge for.You cannot determine all outcomes from API traffic. For tracking custom outcomes, you can leverage Moesif’s custom actions—for example:       Data Processing Job Finished        Generated Qualified Lead  As part of the action, you should track the result such as score or quantity as part of metadata. This enables you to  filter, group, and visualize them across customers, making them ideal for outcome-level analytics.3. Create Billing Meters for Outcome-Based ModelsMoesif’s monetization features allow you to set up billing meters in a few clicks to bill on outcomes and results. Moesif has out-of-the box integrations with popular billing providers like Stripe, Chargebee, and Zuora.You can meter specific events or track filtered subsets. Once the outcome data flows into your billing provider, it powers pricing plans based on actual results—aligning charges with business impact instead of infrastructure usage.For example, the following billing meter in Moesif charges as the percentage of order volume in a logistics API:The following illustrates a billing meter for a telecommunications company. It defines the billable metric as the number of people receiving text messages by counting the number of request body fields.4. Monitor and Improve OutcomesTracking outcomes requires full lifecycle visibility. To do this, you can build a funnel report to understand your customers’ journey as they use your product to reach desired outcomes, and pinpoint where they drop off. For example, the following funnel analysis in Moesif analyzes customer journey for an embeddings API by defining these steps:      First sign-in        First request to generate embeddings        100 input token consumption  Moesif also provides tools like retention charts to analyze customer retention and time-series breakdowns to monitor how users move from raw API calls to meaningful results. For example, a product team can measure time-to-first verified transaction or drop-off rate from API call to completed signup. These insights help identify where value creation breaks down—whether due to integration failures, misaligned onboarding, or gaps in model output quality.By analyzing these metrics over time, teams can fine-tune both the customer journey and the pricing logic attached to it. You can surface accounts that generate high usage but low outcomes, then engage through success outreach or pricing realignment.5. Connect Instrumentation to Revenue OperationsThe last step involves integrating Moesif with your billing and RevOps stack. Billing meters can sync with Stripe to automate invoicing, while tracked outcome events populate dashboards for customer success teams. Moesif also lets you map outcomes to companies, users, and even sales stages through CRM or analytics integrations like Salesforce or Segment.This full-stack visibility turns outcome metering into an operational system in addition to a metrics layer. With proper tracking, teams can support outcome-based pricing at scale: generating accurate invoices, surfacing product gaps, and aligning business outcomes with engineering execution.ConclusionOutcome-based pricing reframes how API businesses think about value. Instead of measuring activity, it encourages teams to track the results their product actually delivers—results that drive revenue, retention, and real-world impact. But achieving successful implementation and adoption of this model doesn’t require a complete overhaul on day one.Start small. Identify one outcome that signals success. Instrument it. Moesif gives you the tools to capture those events, connect them to customer behavior, and pass the data to your billing system.Whether you’re iterating on an existing model or launching a new one, Moesif helps you operationalize outcome-based pricing with confidence.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design, Build, and Deliver Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Usage-Based-vs-Outcome-Based-Pricing-For-APIs/",
          "author": "Sakib",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-analytics-api-strategy-api-management-how-to-monitor-api-usage-across-multiple-api-gateways": {
          "title": "API Management: How to Monitor API Usage Across Multiple API Gateways",
          "content"	 : "Enterprise organizations rarely operate with a single API gateway. As business units adopt technologies independently, it is common to find, for example, Kong in one domain, AWS API Gateway in another, and additional platforms elsewhere. This flexibility drives velocity—but it also fragments visibility. Without a unified view of API activity, enterprise teams face inconsistencies in reporting, gaps in customer insights, and difficulty enforcing governance policies.The consequences compound quickly. When siloed systems trap API usage data, product managers lack the context to make informed roadmap decisions. Security and compliance teams struggle to audit behavior across services. And revenue opportunities tied to API monetization go unnoticed or unmeasured.Moesif solves this by offering a unified analytics and governance layer that works across multiple gateways, without requiring changes to your infrastructure. In this article, we will demonstrate how to monitor API usage across two gateways—Kong and AWS API Gateway—by tracking a GenAI platform with endpoints split between them. You will understand how Moesif centralizes metrics, enables consistent billing, and helps unlock insights that align product strategy with operational performance.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        Table of Contents  Understanding the Problem: API Sprawl and Siloed Data  Why a Unified Pane of Glass is Non-Negotiable  Moesif for Multi-Gateway API Monitoring  Example: Tracking Usage from Kong and AWS API Gateway          Architecture Overview      Why This Setup Matters for Product Teams      What You Can Track      How Moesif Ties It Together        Example: Monetization API Usage Across Two API Gateways          Tracking Token Usage      Create the Billing Meter        Decoupling API Infrastructure from API Analytics   ConclusionUnderstanding the Problem: API Sprawl and Siloed DataAs organizations scale, APIs no longer remain centrally managed assets—they’re distributed across teams, regions, and technology stacks. For example, a product-led business unit might deploy Kong for its flexibility and Lua-based plugins. Meanwhile, a cloud-native team might default to AWS API Gateway to stay aligned with their broader serverless architecture. Each decision is rational in isolation. But collectively, they lead to API sprawl—a growing number of APIs you have deployed across gateways, clouds, and teams with little coordination.This fragmentation creates significant blind spots for stakeholders who need a deeper understanding into how customers are using the APIs. Siloed data—where each gateway stores analytics, logs, and performance data in its own format—means you possess no single source of truth:      Product managers can’t track adoption across services.        Platform teams can’t compare performance baselines.        Security and compliance teams lack consistent audit trails.  Even simple questions like “which customers are generating the most API calls?” become time-consuming to answer.It gets worse when you have configured each gateway differently. One team might log enriched metadata like customer ID and rate limit tier, while another logs only basic path and status code. This lack of consistency breaks downstream analytics, making it difficult to create unified metrics or enforce cross-team governance. The more APIs you ship without coordination, the harder it becomes to make data-driven decisions that span your organization.The root issue is both technical and structural. As each product unit makes independent decisions about API design, gateway configuration, and analytics tooling, no standardization exists in how usage data is collected, tagged, or reported. Without a governance layer that spans all APIs, you are left with an operational black box. You can’t manage what you can’t measure—and right now, you can’t measure across silos.Why a Unified Pane of Glass is Non-NegotiableFragmented API environments demand centralized observability. When you have deployed APIs across different gateways, clouds, and teams, there must exist a single interface—a unified pane of glass—to monitor, analyze, and act on usage across all services. Without it, you have to stitch together partial metrics from siloed systems, often manually. This slows down decision-making, introduces data inconsistencies, and prevents organizations from scaling API programs efficiently.A unified API analytics layer solves three critical problems:      Visibility        Consistency        Coordination  First, it allows teams to monitor API usage regardless of the underlying infrastructure. Whether you serve an endpoint from Kong, AWS API Gateway, or a third-party edge service, all requests and responses are ingested into a single system with normalized fields. That includes metadata like customer ID and version, which becomes essential when segmenting customers by usage tier, product line, or behavior, enforcing rate limits, or tracking monetization.Second, it ensures consistent metrics across services. Response time, error rates, traffic volume, and retention can’t mean different things across teams. A unified layer aligns definitions and data models across APIs, making dashboards, alerts, and billing logic accurate and trustworthy. Compliance reporting, SLA enforcement, and executive-level performance reviews rely on shared definitions and consistent measurements across all APIs.Finally, it enables cross-functional alignment. Product teams can prioritize features based on real usage data. Network operations centers (NOCs) can detect anomalies across gateways in real time, regardless of deployment architecture. API providers gain clear insights into what works and what doesn’t, and API consumers benefit from reliable performance backed by data. A single analytics layer becomes the bridge that connects developers, operations, product, and security—without forcing a migration or consolidating gateways.The goal isn’t to replace your existing API infrastructure. Rather, it decouples observability and governance from the specifics of your runtime environment. That flexibility is what makes the unified pane of glass not just a convenience—but a requirement for scaling modern API ecosystems.Moesif for Multi-Gateway API MonitoringWhen organizations manage APIs across multiple gateways—AWS API Gateway, Kong, Azure APIM, NGINX, and more—the complexity of tracking API usage grows exponentially. Each gateway surfaces different metrics, exposes different logging mechanisms, and structures its data differently. Moesif addresses this by acting as a centralized API analytics and observability layer, purpose-built for cross-gateway environments, where traditional logging and monitoring tools fall short.At its core, Moesif aggregates API traffic across gateways and standardizes the data into a unified format. This enables product teams to compare usage, error rates, and performance across services, even when those services live in completely different runtime environments. This aggregation happens in real time without requiring API developers to rewrite their APIs or migrate infrastructure. Moesif uses native gateway plugins and integrations to ingest data with minimal configuration and no downtime.One of the factors that distinguishes Moesif from gateway-native monitoring comes from its user-centric model. Instead of treating API calls as infrastructure-level events, Moesif attributes them to specific users, customers, or organizations—linking behavioral analytics with operational data. This lets teams perform cohort analysis, measure retention, and identify which customers are most affected by slow endpoints or frequent errors. It moves monitoring out of the realm of DevOps and into the toolkit of product management and business strategy.Additionally, Moesif supports high-cardinality, high-dimensional analysis, making it suitable for environments with complex usage patterns. You can break down traffic by customer plan, region, API version, or custom metadata fields like llm_model or subscription_type. If you manage APIs as commercial products, where billing meters, quota enforcement, and usage-based pricing depend on accurate, granular metrics, you will find this flexibility particularly valuable.For teams that need to move fast, Moesif’s out-of-the-box integrations with platforms like AWS and Kong make setup straightforward. You can stream AWS API Gateway logs through Kinesis Firehose, while the Kong plugin captures and enriches traffic at the gateway level before forwarding it to Moesif. These integrations avoid the need for sidecars, custom logging agents, or backend rewrites, allowing organizations to decouple their API management from API analytics without friction.Example: Tracking Usage from Kong and AWS API GatewayLet’s walk through how an organization can monitor and analyze API usage across two different product groups, each with different API gateways —Kong Konnect and AWS API Gateway—using Moesif. Because Moesif is decoupled from the API runtime, Moesif ties them together into a single, actionable analytics layer.Architecture Overview      Kong Konnect serves an embeddings API at v1/embeddings/generate.        AWS API Gateway serves a text generation API at v1/text/generate.  Why This Setup Matters for Product TeamsMost analytics platforms tied to a single gateway can’t answer questions like the following:      How much are our customers using our GenAI platform across products?        Which API is nearing quota first—and why?        Which customer segment is driving error spikes?  Moesif makes this possible by aggregating and normalizing traffic across gateways. Regardless of whether a call goes through AWS or Kong, usage data flows into Moesif thanks to a shared application ID, where Moesif tags, filters, and transforms the usage data into metrics.What You Can TrackOnce you complete the integration, you can access valuable information about your product’s usage and the customers. For example:      API calls by endpoint (you can split by API Gateway and Kong if you want)        Customer-specific usage, based on user and company identification methods. In Kong Konnect, you can pass them using headers or authorization tokens. In API Gateway, Moesif by default leverages context variables and supports IAM authorization. For more information, see Overview of User Tracking and Enabling Company Tracking .         Quota setup and tracking (how close each user or company is to their usage limit)        Error breakdowns (4xx or 5xx HTTP errors by product or endpoint)        Latency trends (you can segment by gateway, plan, or geography)  You can enrich and visualize all this data in workspaces and organize them in dashboards to suit product, growth, and ops teams—not just backend engineers.Moesif automatically adds a kong metadata field for Kong Konnect and API and stage details for API Gateway. This allows you to filter by Kong service name, request ID or API key for API Gateway, and so on. Here, for example, we use those metadata fields to filter out API calls in a Live Event Log workspace to observe recent traffic into the two GenAI APIs:You can view the metadata fields by selecting Metadata filters in the Filters pane or by expanding an API event. The following shows the kong metadata fields for a request to the embeddings API:For the text generation API in API Gateway, here’s how the metadata might look in Moesif:For more information about enriching events with metadata in API Gateway, see Event Metadata. How Moesif Ties It TogetherWhile the Kong Konnect and AWS setups differ under the hood, both stream event data into Moesif. By using the same Application ID for both setups, you make analytics data of both business units (or as many business units you have) under a single app in Moesif.You can then create custom dashboards to compare APIs, flag performance regressions, and measure the impact of product changes—all without changing the infrastructure.Example: Monetization API Usage Across Two API GatewaysIf you have used the same Application ID for the two REST APIs, you can aggregate usage from both into a single billing meter. This unification makes it possible to apply one pricing model across the product line—without rewriting infrastructure or standardizing log formats across gateways.Tracking Token UsageFor GenAI platforms, an accurate metric to bill on is token usage. Moesif allows you to easily extract the token value from response headers or metadata fields and define it as a billable metric in the billing meter. For example, the embeddings API response may look like this:{  "generated_text": {    "object": "chat.completion",    "created": 1744296855,    "usage": {      "total_tokens": 39,      "completion_tokens": 17,      "prompt_tokens": 22    },    "id": "chatcmpl-pWOFA9cwZdRxPe2zGhoTR9BNxcD"  }} And the text generation API response may look like the following:{  "embeddings": {    "usage": {      "total_tokens": 67,      "completion_tokens": 19,      "prompt_tokens": 48    },    "choices": [      {        "index": 0,        "finish_reason": "stop",        "logprobs": null      }    ],    "object": "chat.completion",    "id": "chatcmpl-QDtgBuwgrpJ2tvHy6A6NkPjxQ2Z",    "created": 1744712264  }}Create the Billing Meter      Go to your Moesif Web Portal.         Select Billing Meters in the navigation menu and then select Add Billing Meter.        Enter your billing meter name.        Select your billing provider, product, and prices.        In the Filters pane, define your filters to only count events for the two GenAI APIs.        In the Metrics pane, define the metric you want to bill on.        Select Create to finish creating the meter.  For example, here is a billing meter that meters output token usage:Decoupling API Infrastructure from API Analytics API gateways excel at routing traffic, enforcing security policies, and managing load—but they are not designed to act as governance or analytics platforms. When observability, billing logic, and policy enforcement depend directly on your gateway configuration, you introduce a tight coupling that limits organizational agility. As APIs proliferate across clouds, gateways, and product teams, this coupling becomes a liability—not just a scaling challenge.Consider this: a product unit might migrate from Kong to AWS API Gateway for performance or compliance reasons. Many organizations implement governance logic—like rate limit tiers, usage enforcement, or custom billing rules—through ad hoc scripts or internal tooling layered on top of each gateway’s APIs. When infrastructure shifts, that logic doesn’t port cleanly. As a result, you have to rebuild usage tracking pipelines, alert criteria, and billing code from scratch—introducing friction and inconsistency that product teams can’t afford.Moesif avoids this by treating observability and governance as a layer above your infrastructure. Whether you serve APIs through Kong, AWS, Azure, or NGINX, Moesif centralizes traffic analysis and governance logic in one place. You don’t need to duplicate rules or data pipelines per gateway. This architectural decoupling gives product managers and platform teams the freedom to evolve infrastructure without impacting how you track, analyze, or monetize usage.It also improves consistency and compliance. You should uniformly apply governance policies like SLAs, audit trails, and API usage limits—regardless of where you host the API. Moesif supports this by using normalized metadata and customer-level enrichment that abstract away gateway-specific quirks. For example, you can enforce a quota across AWS and Kong-served APIs based on customer ID, without worrying about how each gateway logs or forwards data.Decoupling gives organizations strategic leverage as well. It enables experimentation with new gateway technologies, shifts between cloud regions, or even multi-cloud strategies—without compromising compliance, observability, or revenue tracking. This is what mature API governance looks like: infrastructure-agnostic, data-consistent, and built to adapt.ConclusionManaging APIs across multiple gateways doesn’t have to mean sacrificing visibility, billing accuracy, or governance consistency. Moesif allows you to avoid the hassle of standardizing your infrastructure. Your APIs can live behind Kong, AWS API Gateway, or both. Moesif brings them together under a single pane of glass, ready for observability, monetization, and customer insight.So where should you begin?Sign up for Moesif today and start by integrating one gateway. Configure accordingly and enrich it with meaningful metadata, and watch how usage patterns emerge. Then layer in your second gateway. Within hours, you will have unified dashboards, billing meters, and alerts that span your entire API footprint—without touching  your backend.The sooner you decouple infrastructure from analytics, the sooner your API strategy stops lagging behind your architecture and starts driving business forward.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design, Build, and Deliver Better APIs!            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          "url": " /api-analytics/api-strategy/API-Management-How-to-Monitor-API-Usage-Across-Multiple-API-Gateways/",
          "author": "Sakib",
          "categories": "API-Analytics, API-Strategy"
        }
      
    ,
  
    
        "monitoring-model-context-protocol-how-to-setup-observability-for-your-mcp-server-with-moesif": {
          "title": "How to Setup Observability for your MCP Server with Moesif",
          "content"	 : "The Model Context Protocol (MCP) has taken the internet by storm by rapidly becoming the standard for Large Language Models (LLMs) to communicate with external data sources or tools. MCP provides a structured way to fetch data and trigger workflows through APIs and functions. However, with great power comes great responsibility.  MCP exposes a tremendous amount of data and capabilities via high-volume interactions driven by AI agents causing unique challenges for monitoring and maintaining the reliability of your MCP server. This is where observability comes into play. Moesif API observability is uniquely designed to provide deep visibility into your MCP traffic and resolve experience issues before they become a greater concern or security threat. You can also gain business level analytics to understand how prompts and agents are interacting with your MCP server.Why is Observability Important for MCP?Observability for MCP provides performance and usage statistics on how LLMs are interacting with your tools and data. This can be both users entering prompts into a conversational interface, but also AI agents interacting with your tools programmatically. Unlike a traditional user interface where the “customer journeys” are predefined, MCP servers provide raw access to your tools which can be called and fetched in unexpected ways. In addition, the machine driven nature of AI agents can cause far larger variances in usage pattern and customer experience metrics. Thus, it’s critical to measure “AI Experience” ensuring your tools are reliable and useful.As a new technology still early in development, MCP server implementations are being developed and shipped at record pace. This increases the probability of defects and issues dramatically in production. Observability provides you peace of mind to ship fast knowing you have good metrics in place to know if a change is impacting customers. This can help you proactively identify and diagnose issues before they impact users.Furthermore, the open nature exposes your application to a new frontier of potential security vulnerabilities. Bad actors could potentially scrape massive amounts of proprietary or sensitive data. Unintentional or intentional abuse can impact reliability and performance of other legitimate users of your MCP server. Similarly, malicious actors can cause economic harm through excessive or abusive queries. Strong observability enables you to identify bad actors and ensure the right guardrails are in place.Why is Observability Hard for MCP?Achieving effective observability for MCP servers presents several hurdles. Unlike traditional applications with relatively static API endpoints, MCP interactions are characterized by highly dynamic prompts and queries generated by LLMs. This makes it difficult to rely on predefined metrics, alert rules, or dashboards.  Traditional observability tooling doesn’t capture enough context to be useful when troubleshooting problems around your MCP server and usage.In addition, the sheer volume of requests can also be significantly higher, especially when AI agents are continuously interacting with the server, dwarfing the traffic seen by interactive websites. Real User Monitoring (RUM) metrics designed for frontend applications can’t be leveraged for machine generated data. This requires an observability solution that’s uniquely designed for tracking deep context, but also high volume of data.Lastly, MCP in production relies on server-side events and the new HTTP Streamable HTTP support. Traditional observability tooling was not designed for these newer asynchronous and real-time protocols.How Moesif solves observability for MCP?Moesif is an API observability and analytics solution uniquely tailored for MCP servers. A key advantage of Moesif is its ability to provide deep visibility into JSON-RPC payloads, the standard communication format for MCP. This means you can not only see the raw requests but also understand the specific data being fetched or the actions being triggered by the LLM. This level of detail is crucial for understanding the context of each interaction and diagnosing issues effectively.Beyond debugging and performance monitoring, Moesif also offers the capability to understand and monetize usage of your MCP server. Your organization is likely sitting on a treasure trove of data which can suddenly be monetized via MCP. By tracking requests and usage patterns, you can implement metering and billing based on consumption and outcomes.Integrating Moesif with your MCP server is straightforward. There is out-of-the-box support for most frameworks used to build MCP servers including FastAPI, Node.js, and Spring Boot.  In addition, plugins available for most API gateways like WSO2, Kong, Amazon API Gateway, and others.Example MCP Server with Moesif API ObservabilityAs part of this tutorial, we’ll walk through setting up Moesif API Observability for an example MCP server running on Python and Starlette.  A working example is available on GitHub here. This is a modified version of Anthropic’s reference MCP servers.Prerequisites:  Python and uv are installed.  Your MCP server is set up to use Server-Sent Events (SSE) or the new Streamable HTTP.1. Install MoesifFirst, install the moesifasgi package which is compatible with FastAPI, Starlette, and other ASGI-based frameworks:uv add moesifasgi2. Enable the middlewareIf you don’t already have it, you can get your Moesif Application Id by signing up for a free account.from moesifasgi import MoesifMiddlewaremoesif_settings = {    'APPLICATION_ID': 'YOUR_MOESIF_APPLICATION_ID'}# Add Moesif to your starlette appstarlette_app.add_middleware(MoesifMiddleware, settings=moesif_settings)# Run the appuvicorn.run(starlette_app, host="0.0.0.0", port=3001, log_level="info")Remember to replace “YOUR_MOESIF_APPLICATION_ID” with your actual Moesif application ID obtained from your Moesif dashboard.3. Run the MCP serverYou can run the MCP server with the following command:uv run src/mcp_server_fetch  It is also crucial to implement robust security measures for production to prevent unauthorized access and vulnerabilities. This includes best practices for input validation, access control, and error handling to safeguard against malicious attacks.4. Run the MCP Client Tool (Optional)To trigger functions without going through the Anthropic Claude app, you can run the local client.npx @modelcontextprotocol/inspectorViewing MCP TrafficOnce Moesif is integrated, log in to your Moesif dashboard and navigate to the live event log.You should see your MCP traffic flowing in. From there you can inspect the detailed JSON-RPC request.  Moesif provides a rich interface to explore your API calls, filter by various criteria (e.g., method, status code, user ID), and inspect the detailed JSON-RPC request and response payloads. You can create custom dashboards, set up alerts for specific events or thresholds, and gain invaluable insights into the behavior and performance of your MCP server.Optimizing performance is essential for managing and troubleshooting MCP traffic effectively. You can create custom dashboards, set up alerts for specific events or thresholds, and gain invaluable insights into the behavior and performance of your MCP server.Identifying UsersSo far we setup monitoring of your MCP server’s traffic. To better understand usage patterns across different users and AI agents, it’s recommended to also identify the user of the request. This can be done using the middleware options. You can extract the user id from an Authorization token or other context variable.def identify_user(request, response):    # Your custom code that returns a user id string    '12345'MOESIF_MIDDLEWARE = {    'APPLICATION_ID': 'YOUR_MOESIF_APPLICATION_ID',    'IDENTIFY_USER': identify_user,}ConclusionAs the Model Context Protocol continues to evolve and drive new AI use cases, the need for robust observability becomes paramount. Moesif offers a purpose-built solution to tackle the unique challenges of monitoring MCP servers, providing deep visibility into dynamic payloads, high-volume traffic, and emerging communication patterns. By integrating Moesif, developers and operators can proactively ensure the reliability, security, and optimal performance of their MCP deployments, ultimately fostering better “AI Experiences” and unlocking the full potential of this transformative technology.",
          "url": " /monitoring/model-context-protocol/How-to-Setup-Observability-For-Your-MCP-Server-with-Moesif/",
          "author": "Derric",
          "categories": "Monitoring, Model-Context-Protocol"
        }
      
    ,
  
    
        "podcasts-developers-podcast-apis-over-ipas-platform-engineering-and-reducing-operational-overhead-with-nuwan-dias": {
          "title": "APIs Over IPAs 18: Platform Engineering and Reducing Operational Overhead with Nuwan Dias, WSO2",
          "content"	 : "In this episode, Nuwan Dias, Vice President and Deputy CTO at WSO2, joins to explore what effective API lifecycle management really looks like. Drawing on his deep experience building API platforms, Nuwan outlines the key pillars of successful API programs—from strategy and governance to security, testing, and deployment. He shares how organizations can operationalize API-first thinking at scale, and the role of dedicated API teams in enabling this shift.The conversation unpacks the importance of treating APIs as products, establishing centralized governance, and driving consistency across internal and external developer experiences. Nuwan also weighs in on the relationship between platform engineering and API management, offering a pragmatic view on where they intersect and diverge. Whether you’re defining your API strategy or scaling existing programs, this episode delivers practical guidance for aligning teams, tools, and processes around a unified API vision.Moesif · 18: Platform Engineering and Reducing Operational Overhead with Nuwan Dias, WSO2Listen to the episode on SoundCloud, Apple Podcasts, YouTube Music, or wherever you listen to podcasts. You can also watch the video on our YouTube Channel.Table of Contents  Foundations of API Lifecycle Management  Platform Engineering and Developer Enablement  Challenges of Platform Engineering at Scale  Aligning Platform and Product Teams  AI's Role in Platform Engineering and API Development                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Foundations of API Lifecycle ManagementDerric:Hi folks, welcome to another episode of IPAs and APIs with Moesif. Joining us today is Nuwan Dias, Deputy CTO over at DEVSA2. Very timely, we’re talking about today how to think about platform engineering and reduce your operational overhead. Nuwan, really happy to have you here today.Nuwan:Thank you for having me, Derric, and it’s a pleasure to be here as well.Derric:Awesome. Just kicking things off, we’d love to hear a little bit more around what is API lifecycle management and, you know, the high-level process to ship an API.Nuwan:Oh, well, so I think, so like lifecycle management for an API is, you know, just like lifecycle management for a product. It’s basically a process of product management. You know, it starts with identifying the requirements of your customers. It starts with, you know, understanding what they really want and what kind of experiences they want. It starts with capturing those and documenting them properly. And then, of course, it has a process of developing the first version of it. And then beyond that, you, okay, of course, go through your engineering process, put it into, you know, production, give it to the hands of your customers. And then you have to think of things like discoverability, you know, ease of consumption, you know, the documentation, all that stuff, and then you put it into production. And, of course, then you keep monitoring it, right? So you keep looking at how are people using this, what are the issues they are facing. At the same time, you keep capturing the new requirements, and then you go through an iteration process, right? You capture your feedback, your issues, you plan out the next version, you know, think of a migration. So I think, I mean, to sum it up, API lifecycle is nothing but just another act of, you know, product management, just for your APIs.Derric:Yeah, definitely taking that feedback, you know, making sure we’re listening to customers and looking at our data to improve those products and ship new versions of APIs. But, you know, as someone was thinking about scaling out their API program, what makes API lifecycle so important these days?Nuwan:Well, so it’s APIs, you know, aren’t just code, right? So they are a product by itself, right? So just like any good product, your API needs to be treated like a well-designed, reliable, and, you know, something that’s able to evolve, right? So think of an organization creating one API, or their first API, right? So, you know, you create your first API, put it up into production, okay, all’s good, nothing’s wrong. You know, if you create your second one, okay, everything’s good. Now, imagine you have 25, 30, 50 APIs in production, right? So if you don’t have proper lifecycle management, now this is going to be a mess the moment you process, let’s say, like 15, 20 APIs, right? So if you don’t have proper lifecycle management, your APIs will have no standard, you know, no consistency. So their consumption experiences will differ from API to API, and that becomes a mess for people, you know, trying to consume the APIs of your organization. And, you know, you’ll not have a standard way of capturing how your API is performing, you know, it will all be done in different, different ways. So I think this is why lifecycle management is critically important, right? Because if you don’t treat your APIs as a product, if you don’t, you know, standardize things, if you don’t monitor things consistently, the moment you go beyond, let’s say, 10, 15 APIs, then, you know, things start falling apart, it becomes a mess. So that’s why I think it’s very, very important to think of it from day one itself.Derric:Definitely, having that consistency across the different products, so super easy for new developers and customers to get up and running with APIs.Platform Engineering and Developer EnablementDerric:We’ve also heard this term platform engineering. What is the goal of platform engineering, and how is this helping with development, delivery of all these API products?Nuwan:Yeah, so if you, you know, broadly look at platform engineering, platform engineering was bought in, it’s basically the evolution of, you know, DevOps, as I would, you know, like to put it. So the whole idea behind platform engineering is to make things faster, you know, make things more reliable, make things more consistent, and so on, right? So that’s the whole process of platform engineering. Now, when it comes to the context of APIs, right, so, you know, think of it, think of delivering APIs. APIs without a platform engineering process, right? So you’d have to write your code for your API, you’d have to host it, and now to expose this, you’d have to think of an API gateway, right? You’d have to install it, and then you’ll need to figure out, okay, how do I get my APIs contract onto my gateway? You’d have to think of making this discoverable, and so on. So it’s a, you know, it’s a whole lot of tools and processes to expose your API without a process of platform engineering. Now, platform engineering is all about making that entire process, you know, automated and consistent. So that means, as a developer, your focus is basically on your API, on the logic and the code of your API. The platform engineering process, or the platform, which is the outcome of the platform engineering process, should basically do all those things for you, right? So you should no longer be thinking about API gateways, you should no longer be thinking about how do I get my contract onto the gateways, how do I scale my gateways, you know, what do I have to do for the discovery part of the API? So all of that will be taken care of by the platform in engineering process and the platform eventually, right? So that’s why, you know, platform engineering exists, and that’s the whole goal of it, right? So this way you deliver things faster, and of course, it becomes consistent so that everyone building APIs goes through the same pipeline.Derric:I like that. So trying to take away a lot of the infrastructure and just make it easier for teams to be shipping their APIs. But, you know, if we start moving everything into platform engineering, how do you balance autonomy so that developers can still innovate and ship, you know, different APIs and test new things while trying to reduce and ensure that standardization is in place?Nuwan:Yeah, I mean, yeah, that’s a tricky one, because on one hand, you want, you know, you want standardization and everything to be consistent, everything to be reviewed and authorized and so on. And then on the other hand, you want to give full autonomy to your developers so that they can basically keep doing, you know, what they do best. So it’s a challenge, but I think that the trick here is in moving to a more of an enablement mindset or rather shifting to an enablement mindset from an enforcement mindset. So instead of, you know, instead of giving them rules to follow, documents to read, best practices, guidelines to read and learn and do stuff, you know, the platform should enable them to do it by default, right? So this means templates of APIs so that you can get, you know, easily started and so on, right? So basically what I’m trying to say is you build the platform in such a way where the easiest thing to do or the easiest way to build your API is also the best way to do it, right? So your platform makes sure that the easiest way to build an API is also the best way to do it, right? So these are also called golden paths, right, in our platform. So I think that’s the trick to it, you know, instead of putting boulders and, you know, trying to block developers, you enable them to do things the right way through the platform.Derric:I like that, enabling developers versus trying to add too many blocks and everything else. It’s always, you know, just like in SAS, where we get the shadow IT model and people start doing their own things and, or they can’t do anything, right?Nuwan:Exactly.Challenges of Platform Engineering at ScaleDerric:But, you know, there’s some challenges to platform engineering as well. So, you know, what do you see as a platform team, some of the challenges in maintaining all this infrastructure and, you know, some of the operational overhead that we see with a platform engineering model?Nuwan:Yeah, so I think one of the key challenges is building this, you know, platform as a product, getting into the platform as a product mindset is one of the key challenges. Because what I’ve seen at least is, I mean, many organizations, so there’s the development team, and then there is the platform engineering team. So, so the platform engineering team builds a platform to make their life easy. So the platform engineering team’s life’s easy. So what that means is developers, when they want something, they create a ticket, the platform engineering team picks it up, and then they use the platform to execute it, right? So they build this tool for their ease, and they don’t give a lot of empathy to the actual developers who want things done fast, right? So this is one of the key challenges, I think. So I think to address that, you should get into this platform as a product mindset. They should understand that this is a product that they are building, and their customers are the developers. So you need to have empathy on your customers, listen to their pain points, you know, identifying where they are getting blocked or slowed down, and really, you know, think of your developers, right? So that, you know, they can do stuff better.And also, I think over-engineering is also one of the challenges, because this whole platform has really a lot of things to do, you know, all the way from CI to CD. To security, to security, to security, observability. So it’s a challenge to figure out one process that works for everything sometimes, and therefore, this ends up in a situation where you over-engineer stuff, and that sometimes leads to shadow ops, right? So when developers find it hard to troubleshoot things, so someone puts up a platform where, when there’s an issue, developers have to go to one tool to look at the deployment logs, they have to go to another tool to look at the application logs, they have to go to a different tool to look at the access logs, and so on, right? So it becomes very painful, and because this has been over-engineered, and so what happens there is developers basically try to bring in their own tools behind, you know, behind the hood, right? Behind the scenes. So that’s another challenge.And resource management is also, I think, another challenge, you know, especially when you’re running stuff on the cloud. It tends to get pretty expensive, pretty fast, so that is another challenge, I guess. And also, you know, adoption, right? Adoption is also a challenge, so you build stuff, but of course, now you need to get your users to use it, right? So that’s, again, going back to that platform as a product mindset where, you know, you need to be evangelizing and so on to get people to use it. So, yeah, I guess these are some of the challenges that I’ve seen.Derric:Definitely treating your platform as a product and listening to the customers, which is the development teams, you know, what do they care about? And at the same time, you know, really like this notion of make sure you don’t have too many different tools to choose from, right? Because that’s just creating operational overhead.Nuwan:Yeah, too many tools, yeah.Derric:What are some tools that you can create to help create that more productive developer experience for these various teams?Nuwan:Yeah. Yeah, so, I mean, to be honest, there are too many tools in the market.Derric:Indeed.Nuwan:So, there are, like, take a single aspect. So, if you take CI, there are so many choices to pick from. If you take CD, there are so many choices to pick from. So, I think the problem is not that we are lacking any tools. We have all the tools we need. The challenge is creating an integrated experience with all of that, right? So, like I said, the platform engineering teams, most often, they build the platform for themselves to make their life easy, actually, right? Not the developer’s life easy. And therefore, they just pick a bunch of tools and give it to the developers and say, okay, fine, here’s your platform. Everything’s in place. All the features are done. You have CI, you have CD, you have, you know, API gateway, you have observability and logs and tracing and metrics. You have all of it, right?But the reality is, you know, it’s just very hard to navigate through all of this. So, I think the trick is not that there are not enough tools. I think there are too many tools to pick from. The key challenge is in how do you create, like, a single interface, a unified experience, and create the platform in such a way that developers love to use it, right? So, I think that’s the challenge that many organizations are facing.Derric:So, thinking more about the experience than just shipping a bunch of tools over the fence, right?Nuwan:Yeah.Derric:How do you take your approach to the shift left around API monitoring, testing, trying to empower developers without, I guess, just giving them a bunch of tools at the same time? So, I guess it sounds like, is that somehow incorporating the experience and what are the best approaches there?Nuwan:So, again, I think it’s a matter of, you know, productizing those tools. The, the, the, the, so, you know, there are individual tools in the market that address different, different problems. So, instead of saying, you know, go to this tool to, to do your job, what you have to do is, again, this is, again, going back to the platform as a product mindset. So, instead of throwing in a tool, I think the platform engineering team has to use that tool and build an experience on top of it, right? So, use it in the, in the backend.So, this is something that we are doing in WSO2, right? So, we do, we do, we also use a lot of tools, but our developers, they don’t get direct exposure to those tools. Those tools are in the background. So, what we do is we, we do a lot of integration work across these different tools and we give a, a, a unified interface, like one interface for developers to work with. So, they don’t have to move away from that UI for doing their stuff, but when they are navigating through that UI, they are crossing a lot of tools in the process, right? So, they’re actually getting stuff from different, different tools, but they don’t leave that single interface. They’re always in that one interface, right? So, so, I think that, that’s the key, you know, you can, it’s okay to use the tools in the background, but you have to do a lot of work in the front end to unify it and create one, you know, one experience across everything.Derric:So, it sounds like making the platform invisible almost to the typical development workflow of these, these APIs.Nuwan:Exactly.Derric:Yeah, I think you, you, you hit the nail on the head. It’s basically about making the platform invisible.Aligning Platform and Product TeamsDerric:Now, speaking on API productization, we’ve heard that term around as well. For these different product teams, how do you actually enable consumption of these APIs and, and what is the different process there for, you’re shipping an API product, speaking more for the product team itself?Nuwan:Yeah. So, I think, again, this, this goes back to, at the root of it, I think this goes back to treating your API as a product. So, if you were selling a product, right, what would you do? You would, you would first have a lot of empathy towards its consumers, you know, you would listen to them. So, so, you know, your tool is going to be useful and used by the consumers. So, a lot of customer empathy has to go in. And then, you know, you also bring in your, your marketing team to make sure the world knows about it. So, that’s how we, you would treat a, a product.So, you have to apply the same thing to your API as well. You need to do a lot of evangelizing, whether it could be inside the organization, outside the organization, on your API. And also, you have to make it easy to use, right? So, that’s what we would do when we were, if we were building a product, for example, we’d make it as easy to use as possible, right? We’d go to great lengths to simplify the user experience. So, the same thing has to apply to your APIs, right? You need to make it easy to find, which means you need like a nice developer portal kind of a thing. You need to have good documentation, SDKs, basically make it as easy to use as you can, right?So, so that’s that. And then, you need to have some feedback, some mechanism for, for gathering feedback, so that you can, you know, improve your, the experience that you give to your consumers, listen to their requirements, and come up with new features and new versions and so on. So, so I think, yeah, so, so these are, these are some of the things that I can think of, but, but at the root of it, I think the way we think of an API and the way we think of a product that we put out on the internet to use as a SaaS or download and use, they have to be the same. So, the process you follow for your product should be the same process you follow for your APIs as well. And depending on your consumer, you have to do whatever is right to, you know, to make it super easy to use and valuable.Derric:So, it really sounds like there’s multiple abstraction layers with these APIs. You have the platform team, which is trying to treat the platform as a product and listen to their customers. But at the same time, you got these product teams that are trying to treat their APIs as products, listen to their customers. I guess we’re really decoupling the product team from the platform team in a way. So, well, I guess, what does that mean? What does the future look like around, you know, these product and platform teams?Nuwan:Oh, so, so I think they should be very close together, right? So, the product and the platform teams, because, you know, like I said, the goal of platform teams should be to work as closely with their customers as possible. And their customers are the product teams which are building the products. So, yeah, I guess they have to be very close together.So, one pattern we follow in WSO2 is that, you know, we have dedicated or allocated engineers from the platform team that are closely associated to the product teams. So, we have, like, several product teams building different, different kinds of products. And we have, like, a common platform team, but in this common platform team, we have representatives representing different product groups so that, you know, we make these teams as close as possible, right? So, so I think it’s going to be a very close relationship, or it has to be a very close relationship between the platform team and the, and the team’s building products.Derric:That’s interesting. So, embedding someone inside the product team to help ensure that feedback loop is as quick and short as possible.Nuwan:Yeah. Yeah, exactly.Derric:So then talk about API metrics. What are some competent operational business metrics and who owns them? Is it a platform team? Is it a product team? A little of both?Nuwan:Yeah. Yeah, so that’s an interesting question because I tweeted out an article just today, earlier today, which said, this was on Newstack, this said, you know, observability is owned by the developers, not, not by the platform, you know, not, not by the platform engineers. So, I think there are two kinds of things here. So, there is the, the logs and the application. Stuff that it is, you know, that it is, you know, emitting out. And then there is, you know, tracing and various kinds of metrics that are important for SLAs and so on. So, I think developers should have the freedom to put in like the logs and whatever things they, they think are important. So, I think it’s important because when it comes to troubleshooting an issue, right, it’s going to go into the hands of, if it’s an issue on the application side, it’s going to go into the responsibility bucket of the developers. So, they should have the freedom to put in whatever they think is useful and important.But at the same time, there are things like, you know, SLA violations and, you know, these kinds of metrics. So, they, I think, fall under the bucket of the platform engineering team so that they get alerted when things are, you know, going out of SLA and so on. So, I think it’s a bit of both, but in the market today, there’s this kind of this belief that all metrics and logs and everything should be owned by the platform engineers. I don’t think that’s necessarily true. I think developers have an equal, say, should have an equal, say, in this as well.Derric:Definitely having that, I guess, dual ownership and trying to figure out what is the right things to be tracking for each team is important here. But we’d like to also talk about trying to treat APIs products and aligning them to business outcomes. So, I guess, trying to give some of that visibility to developers themselves to understand those outcomes and define those outcomes is important. How do you incorporate that in the API lifecycle?Nuwan:Yeah. So, I think it’s a, this should be, this should, is kind of, should come from the platform as well. So, when you put out an API into some kind of API management solution or if you’re running an API on a platform, the platform should, you know, cater to two different, at a very high level, two different kinds of metrics. Which is, you know, the things that are useful for your operations. So, these are like the latencies, error rates, you know, uptime and these kinds of things that are in discrete, right. So, whatever that is important for troubleshooting issues or running the APIs, you know, in production.And then, from the platform of API management solution should, either by itself or by using a third party, should give important business metrics, right. So, it should just be plug and play, right. I don’t expect developers to, you know, understand all these metrics that are important for the business. They should definitely have a say in it, but they shouldn’t be responsible for implementing it, right. So, their platforms or the API management solution should take on that responsibility. So, when you just plug it in, it should somehow give out the business set metrics as well, such as, who are your consumers? Who are the unique number of consumers you are having? You know, what is your adoption rate, like your growth rate, right? And what is your churn rate, like how many people are coming in, how many people are staying over, how many people are, you know, letting go after the first day of a stover and so on.Like, and also things like, you know, how fast do you get to the first request and so on. So, there’s these operational metrics and the business metrics. While the developers should have a say, I think the responsibility of implementing it and giving it should, you know, happen from the API management solution or from the platform, either by using features they build themselves or by integrating with some third-party solution.Derric:So, again, making that data layer, that observability layer invisible for the respective product teams, they just need to think about the business metrics and that’s it, right? So, not really think about the underlying infrastructure.Nuwan:Yeah, yeah.AI’s Role in Platform Engineering and API DevelopmentDerric:So, I guess, you know, can’t do a podcast without discussing AI and, you know, that’s one of the big talks these days. What do you see some of the risks around AI, especially some of these newer AI applications around security, governance, and some of the other things as we discuss platform engineering?Nuwan:Yeah, so, I mean, using AI gives a lot of power to applications, obviously, right? But, you know, as they say, you know, with a lot of power comes a lot of responsibility. So, I mean, AI is all great, but you have to use it with a little bit of caution. You first have to understand how AI works really thoroughly, you know, before really getting into it. And, you know, there are some risks, but of course, none of these risks should fear you from using AI, right? So, every organization should be bold enough to, you know, take the steps towards it, but with caution.And that is why understanding how it works, what kind of data it is processing, and the nature of, you know, its outcomes needs to be clearly understood. So, for example, there’s always this risk of data leakage, right? You know, so you have to be cautious about what data do you feed it in, right? And then processing of personally identifiable information, PII, right? So, you have to be cautious about, okay, what kind of, if there is PII going in, how do I mask it out, right? And, you know, things like that.And also, there could be other risks, such as, you know, that thinking that AI is always right is also a, like, you know, common mistake. So, the one, the nature of AI today is that it is, you know, non-deterministic. So, which means that the outcomes may not be exactly right all the time. So, that means you need to put in the right guardrails so that, you know, you don’t screw things up when it misbehaves, right? So, understanding that it is non-deterministic and working around it is very important.And, you know, when everything’s up and running, again, running without proper understanding of, like, observability aspects, right, such as, you know, how many tokens you are using, you know, and so on. And, you know, these kinds of things are also risky because, you know, it may drive up your costs. Unintentionally, there are that side of the risks as well.So, I think that the key is there could be different, different kinds of risks. And what is a risk and what is not a risk, I think, depends on the nature of the organizations and the type of data it is processing. So, but I think the key is, again, not to fear, you know, AI, just because there are risks. Everyone should take the step towards it. But the key is to understand exactly how it works and, you know, proceed with caution.Derric:I really like that. Trying to approach it with caution, but at the same time, you don’t want to fear it, right? And everyone’s going to start incorporating AI and really make platform engineering even more important, right? Have the right guardrails in place. We’ve heard these terms like economic denial of service where it’s not really hacking, but I guess you could accidentally run up a big cloud bill these days, right?Nuwan:Yes, definitely, yeah.Derric:Any other risks that you see, you know, that could impact platform engineering that the AI is introducing?Nuwan:Well, so some of the risks could be something like, you know, like all automation without understanding the real need, right? So, I mean, platform engineering is all good with all kinds of automation and so on. But at the end of the day, it’s very important. You always question yourself about, do you really need this? You know, going back to the first principles about asking the question of why you are doing this and so on.So, if you use AI, you know, AI just knows how to read some stuff and, you know, apply it, but maybe the real need for it could be different, right? Or there may not be a real need at all, right? So, there could be the risk of overengineering because of the use of AI. So, that could be one risk you have to be a little bit cautious about.And also, poor quality suggestions. So, there are also cases in AI where I think you sometimes need, like, to implement, like, human-in-the-loop systems, right? So, AI is all good for giving suggestions and so on. But sometimes you need to implement human-in-the-loop, right? So, AI could come up with poor quality solutions. So, you could, you know, maybe in certain situations, you need, like, a human-in-the-loop kind of workarounds to get through it.And also, you have to be a little careful about, like, the security and the data exposure that it might risk in, right? Basically, whether the use of AI results in your data or your customer’s data landing up in places that you really don’t want to, right? So, if you incorporate AI without understanding it properly, there is that risk.And, yeah, I think these are some of the risks that I can think of when it comes to applying AI in the context of platform engineering.Derric:Yeah, I wonder if shipping some APIs super fast just through leveraging AI yourself, you’re going to induce a lot of cybersecurity risks and everything else. So, it’s going to be interesting to see, you know, how platform teams try and mitigate that risk and make sure, you know, you’re not shipping, shipping basically unauthenticated API accidentally for whatever reason.Nuwan:Yeah, yeah, yeah. So, yeah, that can kind of be mitigated if you have the proper guard risk. So, one of the requests that one of our customers came up with recently is to say, look, we know in your platform we can turn on and off security. But we want to make sure that nobody in our organizations is ever allowed to turn off security on any of the APIs, right? Can you help us implement that, right? So, we came up with a policy which said, okay, no, nothing doing, nobody can turn off security, even though that’s a feature, right? So, I mean, these kinds of guardrails can be implemented, yeah. So, yeah, yeah, you’re right, yeah, you’re right. Those risks exist and we have to understand them and put in the right set of guardrails to counter them.Derric:Makes sense. And then, I guess, flipping to the product teams, you know, how can they also leverage AI as they’re really trying to treat APIs as products and ship the best possible APIs for their customers? What are some trends you see there?Nuwan:Yeah, I mean, so, on product, like, all applications today are, like, you know, kind of, like, AI enabled. So, there is, you know, some kind of chat interface and so on. So, we have this, you know, concept in WS02 called AI for code and code for AI, which is basically a matter of how AI can help you write better code. So, that’s basically one angle to it, right? So, this is all about generating better code, making it more efficient, making it more faster, and so on. So, there is that side of the equation where how AI can help you build better applications.And then, on the other side, there’s this process of how can AI really help your product itself, right? So, I think every product team needs to think of that. How can they utilize AI to make their product better, right? So, to give you some context, now, we are working on, we have a platform in, like, a software engineering platform that we offer at WS02. And we’ve also gone through this cycle, and we figured out that, okay, through this, we can implement capabilities like, you know, implementing guardrails, or basically the fact that I talked about earlier.How to switch from enforcement to enablement. So, we figured out that, you know, using AI, it’s much easier to shift from enforcement to enablement, right? So, what this means, to be very precise, is to say, so, you have best practices documents in organizations, right? And we have, for a long time, been expecting engineers to read them, understand them, and do things according to that, right? But now, with AI, you don’t have to get the developers to read it. You can feed this document into the system and tell developers, okay, you just write your code, right? You focus on your application. And the engine will make sure it reads the documentation, identifies, you know, where things are not really right, and say, look, dude, everything’s fine. But, you know, these are some places you need to take a second look at, right?So, that basically, and it can also generate templates for you, right, for doing your job right. So, this is something that applies to our product. So, similarly, I think AI can help different, different products in different, different ways. So, it’s the job of these product owners to think of how they can help in these angles and come up with it. It’s more of a creative process, I would say.To sum it up, there’s the aspect of how AI can help you write better code and build better products. And at the same time, there’s the aspect of how AI can help you improve your product. And that is unique to, I think, each product and its capabilities.Derric:I like that. AI for code and code for AI. And kind of, hopefully, developers, they don’t ever read docs. So, what can you do to make it easier for them and still, you know, have that governance in place?Nuwan:Yeah.Derric:But, thank you very much, Nuwan, and glad to have you here today. And looking forward to joining the next episode of Moesif’s Webcast.Nuwan:Thank you for having me, Derric. It was a pleasure.                Deep API Observability with Moesif              14 day free trial. No credit card required.              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        "podcasts-developers-podcast-apis-over-ipas-aligning-api-design-to-business-outcomes-with-james-higginbotham": {
          "title": "APIs Over IPAs 17: Aligning API Design to Business Outcomes with James Higginbotham, LaunchAny",
          "content"	 : "In this episode, Derric Gilling chats with James Higginbotham, founder of LaunchAny, about designing successful API strategies that drive business value. James dives into his “Align-Define-Design-Refine” (ADDR) methodology, a framework that guides organizations from initial business alignment through iterative API design and deployment. Drawing from over a decade of experience helping companies mature their API programs, he shares practical tips on blending business goals with technical execution, empowering cross-functional teams, and maintaining consistency across the API lifecycle.They also discuss the evolving role of API product managers, how to embed governance without stifling innovation, and why strong design-first practices are essential for scalable platforms. Whether you’re just starting your API journey or looking to level up your current approach, this episode is packed with actionable advice for building APIs that are not just technically sound but strategically impactful.Moesif · 17: Aligning API Design to Business Outcomes with James Higginbotham, LaunchAnyListen to the episode on SoundCloud, Apple Podcasts, YouTube Music, or wherever you listen to podcasts. You can also watch the video on our YouTube Channel.Table of Contents  Introduction and Background  The ADDR Framework  Adoption and Implementation  Outcomes and Metrics  Design and Refinement  Federated API Coaching                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Introduction and BackgroundDerric:Welcome to Moesif Podcast. Here’s another episode. Joining us today is James Higginbotham, founder of LaunchAny, speaking around API strategy from the initial design process to getting APIs out to production. Happy to have you here, James. Love to hear a little bit about yourself first.James:Yeah, absolutely. Thanks for having me here. So as you mentioned, I focus a lot on API strategy. The last decade or so, I’ve been working with organizations around the world to help them establish, grow, and mature their API program and to help them understand how to design APIs effectively so that it delivers value to the business. And having a development background, I kind of, you know, walk that line or straddle that line between business and technology. And so I kind of blend those things together where APIs kind of collide with all of that and intersect with all that. So it’s been really fun to do a lot of training, a lot of consulting, and it’s been an absolute blast. So I’m excited to chat with you today.The ADDR FrameworkDerric:Awesome. And just digging into things, you know, I know you like to speak on the align, define, design, refine process, the ADDR process, and this has a lot of obligation, you know, in the API space. Of course, at the end of the day, we want to make sure we’re building, you know, things that are creating value. Tell us more, what does that mean? You know, what is the ADDR process and how does a business get started with it?James:Yeah. So the origins of it’s kind of interesting about when I started consulting and focusing on APIs about 10 years ago, Keith Casey and I sat down and started writing a book. We were self-publishing it through LeanPub. And then subsequently, I ended up refining this idea of ADDR and publishing it for Addison Wesley back at the end of 2021 And it is really rooted in some of my experience of trying to balance requirements, gathering and understanding what we need with the ability to shift as we learn more to bring that agile software methodology in. So not big A Agile, but little a agile of how do we learn and grow and improve? And since our APIs tend to be forever, once we put them out there and people start using them, it’s very hard to change them. We can add to them, but it’s very hard to change them or remove something that someone’s using. So what I needed was I needed a way that I could repeatedly teach people and people could repeatedly apply in their day to day to go from requirements to an API design. And that’s what ADDR is about.So it’s broken into four phases, aligning what your business needs are, what your end user needs are, to what your API is going to do. So making sure you understand everything. And that first phase I actually didn’t have in the first iteration of this process. And over time, what I realized was people were coming to me, showing me an API design and saying, what do you think? And I go, great. What’s it supposed to do? What outcomes is it delivering or what problems is it solving? And the developers like scratching their heads and going, well, I don’t know. I was kind of told to build this API and it’s supposed to look a little something like this. And they go, okay, well then we need to have some other parties in here that know what that is.So what I did is I backed off and said, okay, we need to go back to without swinging the pendulum too far to the old days of waterfall, where we write these 600 page software requirements, specification documents. And believe me, I’ve done that and don’t want to do that ever again, but we also don’t want to just put together an API, not really have an idea of what kind of problem we’re trying to solve, just toss some data over the network and hope that it all works out. And so somewhere in between is what that ADDR process is.So we align on what we need, and then we define what the API operations need to be in an agnostic way. So I’m not looking at HTTP specifically, just kind of, you know, what are the operations? I need to get something, I need to create something, I might need to calculate something, I might need to orchestrate a workflow, submit, approve, decline, those kinds of things. And so we define what we need to support what we’ve identified in the align phase, then we design it. So we pick REST, GraphQL, GRPC, whatever it is, and apply that technique. And then refine, which is, now let’s get feedback and incorporate that. When the cost of change is lower, when we start getting feedback from our early consumers, and they say, you know, that’s not really what I had in mind, what about this?And you start working through that, and you say, okay, well, how does that change? Does that change anything from my align phase? Does that change my requirements? Or is this just refining the design and some of the nuances of how the developer’s going to use that API? So that’s the idea behind ADDR. And so I’ve been teaching on it for about a decade, I wrote about it in 2021. And it’s kind of a foundation for everything else that I do.Adoption and ImplementationDerric:I really like that thinking about the initial business case and requirements before, you know, jumping into design, because, you know, otherwise, sure, it could be a good API design. But as you mentioned, you know, what is it actually trying to achieve? You know, for a new organization that is typically API first, how do they typically layer in this align process? And who takes ownership of that? What does that typically look like for an organization trying to implement this ADDR process?James:Yeah, when I come in, most of the organizations I work with are enterprise IT. I do work with a few software as a service companies as well, but most of them are enterprise IT. And they may have a few hundred developers. I actually have one client that has 10,000 developers, about 2500 agile scrum teams. And so how that looks in each organization is a little different. Typically, either enterprise architecture or someone responsible for the API program is going to decide we’re going to use ADDR because we like the idea of blending business and technology together and not just running straight in looking at our solution, looking at our data model, and slapping an API on top.And so it might be a product owner, a director that might be responsible for an API program. If there is an API COE or C4E, like a center of excellence or center for enablement, might be someone from that group. And they’ll use that ADDR process and roll that out. And if I’m engaged, then I’ll do some live training sessions and also license my training content, my pre-recorded training content, so that they can get their staff up and running and familiar with it.But it really goes back to who’s running your API governance, because this is a fundamental element of your API lifecycle. And so when you look at the governance process, not just how am I naming my JSON fields and, you know, what HTTP methods am I using for what kinds of operations and all those kinds of decisions we might be making, there’s more, from a more broad perspective, there’s the idea of when are we going to use ADDR, who’s going to be responsible for it. And so from a governance perspective, they’re the ones that kind of decide this is what we’re going to do.And then within each team, typically, a product manager will run with the align phase. They’ll take work that they’ve already done. And they’ll turn it into things like job stories, activity steps, workflow diagrams. And that will then help feed to your architect, dev lead that’s there in that team to help them start transforming that through the rest of ADDR into an API design. So it’s going to be a cooperative effort. It’s very collaborative. And it might be something that one team uses within the organization, or it could be something as broadly deployed as a result of the API governance efforts that they have.Outcomes and MetricsDerric:When does one move from one stage to the next? How do I know when I’m done with, say, the align stage and where I have to start defining out these goals and then start designing the APIs themselves? Is there any guidance or recommendations you have for any organizations there?James:Yeah, there is. I mean, when we’re looking at the align phase, what we want to make sure we understand is that we can identify the problem or situation that we’re trying to solve. And we want to know what the outcome is. If we know those two pieces, then everything in the middle is what we need to work out. And that will tell you when you’re done with the align phase.And like I said, you know, a lot of product managers, product owners, they have this in their head, they may have it in a Confluence page or some sort of wiki or something somewhere or a diagram or something, but they may not have really transformed it into anything that is necessarily actionable by a dev team to turn into an API. So the align phase, the goal of that is to take the work that’s already been invested and turn that into a format that allows us to drive the API design.And if that’s net new work, because we’re doing ADDR from the very beginning and we just have a problem statement or, you know, we’re sitting down with business and trying to understand what they need. Great. If it’s something a little bit broad and, you know, and, and all encompassing that we might need to break that down into smaller chunks and focus on one particular aspect of it. It could be that all of the work has been done. A product manager has been working on this for some time, or this is a known issue that people have been working on kind of in the background cycles for a while. And now it’s coming to the forefront. Now this is really important to the business. Now’s the time to go execute and we’ll do that.And then throughout the rest of the phases, it’s very similar. So in the define phase, you’re going to take those activity steps and you’re going to turn them into an API profile. This sort of just says, here’s what we needed to do. And then you’re going to start making decisions about, well, based on the consumers of who’s going to be using this API, what API design style or styles do I need to use or do I want to use? Is it going to be REST, GraphQL, GRPC, whatever it might be. And we’re going to pick that and start mapping it over. And then we’re going to start socializing it in the refine phase. And we’re going to start incorporating that feedback.And of course, you can go between the phases. You can hop through. So you might get into, and I’ve done this several times before, you get into the define phase and somebody brings up something and says, well, what about that exception case where this happens? And you go, oh, well, that’s interesting. That didn’t come up during the align phase. So let’s capture that. Let’s investigate that a little bit and see what does that really mean? Did we uncover a new job story, new job to be done? Did we uncover just a new step in the process that we missed or an error case that we missed, but it’s not that big of a deal?Or is this really opening up things and we go, oh, wow. Okay. Wait a second. What you’re saying is we just did a happy path exercise for a little bit here, maybe an hour or two. And now you’re drawing up and saying, no, no, wait, there are these different phases that this goes through, or there’s these different needs, or there’s different personas. There’s another persona here we haven’t even talked about. And so you can hop back to those other phases. And that’s what they’re meant for, is to put these artifacts into a format that allows you to drive your API design and be effective with it, but also to iterate through it and to be able to jump back when you need to and say, wait, let’s call timeout and let’s go back and revisit something real quick.So each phase has a certain set of artifacts that it recommends, but each phase along the way also has some exit criteria that says, you know, once we know what the problem and the outcome are, and we’ve figured out what those steps are to get from point A to point B, then we can move forward. And the fascinating thing with that is if we can align on that and build, say, like a job story—when some situation occurs, I want to have some job to be done or, you know, perform some task or something so I can—and I have some sort of outcome.If I have that framework that I’m working with, I can now define acceptance tests. I can write automated acceptance tests. I know exactly as a product manager when my API has delivered on that outcome. And I know what it’s going to take to get there. And that helps me to understand if there’s a problem to be solved, my API is here to solve that problem. It’s doing the job to be done. And it may be with collaborative interaction with a human or could be in an automated way, you know, all the way through. And then we deliver on an outcome.It gives us a box or a frame in which we can start thinking about everything else. And it really drives a lot of the rest of the lifecycle and then can drive those discussions and keeps discussions from going off into all kinds of different directions. There’s a time and place for that. But when it’s time to start designing the API, that’s the last thing you want to do is start deviating way far out and start talking about things that are six months down the road. You know, it’s too early to figure that stuff out yet.Derric:Definitely. And you speak on, you know, outcomes as we’re thinking about, you know, aligning and defining requirements. You know, what are some, you know, good outcomes and, and have you also seen, you know, bad outcomes? And how can a product manager really plan for defining those right outcomes as you’re jumping through these different stages?James:Yeah, absolutely. And this is actually one of the biggest difficulties that I see, whether they’re a product manager, whether they’re, say, an architect that has a domain expertise and they’re doing the work, whoever it might be, whatever role that might be. It’s pretty challenging sometimes to think about it because sometimes we’re so close to it that we have what’s called the curse of knowledge. You know, we’re so close to it that we can’t really, you know, kind of step back and see things independently. And so we just plow through.Some of the best outcomes that I’ve seen are things that are transformative to a person. An example might be, recently I was in the health insurance space. We were working with FHIR APIs. We were extending those, but we wanted to go at it from a job story perspective using ADDR, mapping out what the API needed to do, and then figuring out which of those operations were FHIR-related. And we could use the FHIR standards and specification to define how it was going to behave, and then which ones were outside of FHIR. They were, you know, very specific to this healthcare provider. And so in that case, they might be something that they would use FHIR as an inspiration for.So we started with ADDR. Then we figured out what we needed in the API. Then we figured out how to map it to the standards. And when we did that, one of the things we were doing was a referral. And you think about a physician referral from one physician to another. I go to my general physician, you know, they say, oh, I see something on your skin. I’m going to refer you to a dermatologist. You go, okay, great. And you work with that dermatologist for a while.Now, what happens if you move, if you relocate? At least within the U.S., whenever you’re relocating, you need to have a referral to get that specialist in that new region you’re in. And that referral process isn’t quite as smooth as it is from like a person within the same network, within the same state, going from a general practitioner, my doctor that I’m doing a yearly physical with, into a specialist like a dermatologist. Not quite as easy to do that when you’re going across state lines, when there’s not some sort of very specific thing happening.The other thing to keep in mind is with FHIR standards, there’s multiple ways to do things like referrals. They even say it in the guide, in the standards. Hey, there’s about three different ways to do this. So you’re going to have to make sure whenever you build software to check all three of those ways, because the referral could come in three different ways. And you go, okay, so the user experience—or in this case, the patient experience—is not that ideal. So they wanted to optimize those kinds of things.So what we did is we sat down and wrote job stories. So the job stories were more centered on that patient. You know, when a patient is moving to another state or to another area that’s a specialist or healthcare provider that meets my needs and that has a similar or better practices than the one that currently have. That is a much different problem than just saying, here’s a referral to some random specialist in a place I’ve never gone to before.To solve that problem, we have to aggregate a lot of stuff in. We can’t just find the list of doctors and pick one. We might want to look at reviews. We might want to pull up other things. So the flow for that is much different than what a FHIR-based standards-based healthcare interoperability standard is ever going to give you. They’re worried about getting data in, getting data out, and data portability. That’s really what they’re worried about. They’re not worried about those kinds of workflows.Whereas some of the poor job stories I’ve seen is, you know, the opposite of that would be something like, well, when I need a referral, I want to be able to search a list so that I can select the referral that I want. Well, that’s not really producing an outcome. That’s not giving you confidence of where you’re going. So it requires stepping back and thinking a bit more about empathy with the people that are involved and writing that job story in a way that gives them a completion milestone.Now, it might be a very large workflow, and so this job story is one slice of it, but have we reached some sort of milestone point? Or if we think of it like Mario or something, have we reached a checkpoint? You know, do we reach a checkpoint in our game where we don’t have to go back to the beginning? Have we reached a point in which we’ve solved some of the problem?And searching doesn’t solve a problem. Selecting something from a search result doesn’t solve the problem. There’s more to it than that. So we have to really step back and think about it differently. And I’ve found that a lot of people when they’re designing APIs, they think about screens in their web app and their mobile app. And they think about clicking through. And they think when they click a button or select something, they’re done. And so that’s a job story. And that’s not.We might have supporting job stories that break that big idea down into smaller milestones. But the idea is, what’s that unifying job story? And how does that unifying job story change someone’s life, solve their problem, you know, automate something away from them or whatever it is, and look at it that way? That’s how we blend business and tech. Not, you know, using a couple of vocabulary words from the business that they threw out and a bunch of screens. And I fight with that a lot, you know, when we’re starting to work with it, because those skills have kind of atrophied over the years. We used to be really good at it in our industry. And we’re not as good at it anymore. And we have to kind of bring those skills back up. How do we ask the deep questions? How do we understand and gain empathy? And then how do we find out that we’ve actually solved the problem, instead of just pushed someone a step through a process?Derric:Definitely. And having that customer empathy and thinking about how they’re planning on using, you know, whatever workflow or problem they want to solve with the API—I think that’s always an important piece. We actually sometimes speak on things like managing metrics, you know, looking at a number of requests or requests for a customer. And it sounds like sometimes that could be a really poor metric because, you know, it doesn’t make sense to even have that many requests. And sometimes, you know, APIs can be better designed to reduce number of requests. So to think about things like observability and analytics, you know, how does one, you know, tie that back to the initial outcomes and, you know, have that in place? And what are the right best practices there?James:Well, I think there are some operational metrics that we have to look at, which is, you know, what you were talking about—requests and error rates and those kinds of things. And those are important for managing our operational side of things. But from a value proposition perspective, you know, they look different to different places.So for our healthcare, did we find the right person? We verified who they are, and I sent the referral to them. And maybe even there’s a survey at the end and that’s what closes the loop. Did we successfully find them someone that satisfied their need and that they’re happy with? And if not, then did they go, you know, request another referral and try to find someone else? And were they able to successfully do that? And that might be something that runs for a very long period of time. It could take days, weeks, months before that loop closes. And that’s an important metric.You know, are we giving them enough information? What are we lacking? Did we not have any reviews for the specialist that they selected, and so they just took a chance because it was near their new home that they’re moving to? Those types of things are really insightful and will drive what we do with our platforms, with our APIs, with our internal operations and how we manifest our business capabilities. What are we bringing to the marketplace? What value are we bringing?If you can help someone through some sort of problem like that, that’s very personal, very serious, very necessary and very scary in some situations—I’m leaving the town that I used to live in to go to another town in another state, and because of job reasons or family reasons or whatever—and now I’m suddenly thrust into trying to change all my health care providers, my specialists as well as my general practitioners. That’s a lot. It’s heavy enough to try to move and relocate and reestablish yourself. And then to not know if you have the health support that you need can be difficult.So that was one of the things that we looked at there from the metrics perspective—was how satisfied was the person with the referral once they got there, which is a longer term metric.But to give you a different example, let’s go to banking. Let’s switch from healthcare to banking and think about commercial insurance—sorry, commercial loans. There aren’t that many businesses on a daily basis making an API call saying, I’d like to have a quarter of a million dollar business loan, please. But they happen.And so the number of requests per second aren’t that important, aren’t very important to us from the perspective of if we’re a commercial business loan organization and we have an API that backs that, and we have these API requests coming in, we might get maybe a dozen requests and we filter those down. Underwriters look at them and then approve three. But those three requests—or those twelve requests—are really important because it gets us down to three approvals from our underwriters, which gives us perhaps a few million dollars across three different people’s lines of credit. That’s going to produce some amount of income over a period of time from interest that we’re loaning.So the frequency, the quantity isn’t always necessarily the case. We want to know operationally, did that API succeed? And we want to know that we’re resilient enough that when those twelve come in, we can receive all twelve and process them successfully, get them queued up and get them processed. But we might not have billions of requests per day or per month like you might see in some other businesses. But every one of those requests comes with it the potential for tens to hundreds of thousands of dollars of revenue for the business.So what it does is it means that we have to look at both the operational side when we look at metrics, but we also have to look at the business side. And you can’t look at the business side if all you’re doing when you’re designing your APIs is looking at your data models and saying, yep, I’ve got an application form for a commercial loan in this database and here’s how it’s modeled. So I’m just going to turn that into JSON and that’s what they need to submit. Okay, that might be a good starting point, but what are your metrics? What are you trying to do? What are your goals? Who’s using this? Who’s allowed to call into this?Do you have—this is where it gets into the business ecosystems or the digital ecosystems you’re in. You may be in a country where there’s, you know, open banking standards and you need to participate in those open banking standards. But what you do is above and beyond what those open banking standards handle. So you have to think about, well, how am I going to balance an ecosystem that demands I have interoperability and data portability and data privacy with these additional differentiating capabilities that my business is delivering? And how do I blend those things together?Well, if I just look at one API platform that has one ecosystem, it’s never going to click for anybody. But if I think of one API platform that’s catering to different ecosystems, then it’s different. And that—that’s the difference. So our metrics, our observability can be both business side and operational. And I think sometimes we get caught up in the operational. We just want to know is the API working because we deployed it and just make sure there aren’t any issues and, you know, our SLAs are being met and things like that. And that is all good stuff.But if the API doesn’t deliver on outcomes and we’re not measuring that, then we’re missing out. And so sometimes it means looking at what that lifecycle is from the business perspective and capturing metrics along the way or tracking the lifecycle of that resource—that API resource—as it goes through the different phases of its lifecycle until it’s approved or declined or, you know, whatever that workflow is.Derric:Yeah, definitely. And I guess separating out the operational metrics and the business metrics, that’s always an important piece. Us as engineers, it’s easy to, you know, get too caught up in, hey, there’s no errors here, or we have some nice performance metrics. But, you know, customer doesn’t care about performance. They care about the outcomes.As you’re thinking about, you know, these different business metrics, you know, when are they defined? Are they defined at the very beginning, you know, during the align phase? Are they defined later on? And, you know, how do we make sure that, you know, we have the right outcomes and metrics that we’re defining that are truly measuring the value that the customers are expecting out of the platform?James:Yeah, usually the business metrics I try to encourage in the align phase. If we can’t define the metrics by which we’re defining success or value has been delivered, then we don’t understand the align phase well enough. We need to spend some more time. And that’s really what it comes down to.Now, it doesn’t mean that that’s your only time to identify them. And what we might do is we might identify three to five, you know, key metrics. And these are the key metrics that tell us we’ve delivered on the workflow—we have success or we failed in this area, you know, we didn’t deliver what we’d hoped to deliver, but the workflow completed. You know, those types of things—wherever the decisions are being made and so on.And then over time, we might decide that we have more. We might find a few more. Or we might take one metric and say, you know, it’d really be helpful if we could break that down a little bit further. So we take the key metric that says yes or no—decision was made, decision was not made, or decision for or decision against, whatever that metric might be—and then say, okay, let’s break that down and say, how far did we get? What were the metrics along the way? How long did it take us to reach that decision?You know, how long did it take if we said we need some more documentation to process this insurance claim or, you know, or to approve this business loan or whatever it is? How long does it take for us to communicate back and forth? And can we now optimize that flow and ask for it ahead of time? Because 90% of the time, we’re going to need it anyway, but we just weren’t asking for it.Those types of insights come as you mature the product. And it just takes a little while to be able to figure that out. But at least starting with a few in the align phase is really important, because it also tells you, you know, where are you striving to deliver? You’re not just pushing data over a wire. You’re changing someone’s life, hopefully in a better way, in some way. Even if it’s just capturing the canonical to-do list or whatever it is, you’re doing something to help someone.So do we understand enough about the problem? Do we understand enough about the solution? Do we know what those metrics are? And then where are those key metrics going to be gathered based on how the API’s operations are going to be orchestrated through the workflow to some sort of end result?Design and RefinementDerric:Makes sense. And just, you know, jumping ahead—now that we’re aligned on what the right outcomes are and set some goals and define those goals—you know, what’s next? How do we actually start thinking about designing the APIs in the right way, given those initial requirements?James:Yeah. So the define phase, it comes after align. And what that does is helps us take the steps, the tasks, the work that we have to do that we found in the align phase and turn those into some sort of definition of an API. And, you know, a lot of times we focus on CRUD APIs in the REST world. And that’s kind of unfortunate, but I like to use the example of a content management system and the one that you have multiple parties involved.So you have an editor that has to approve things. Well, you have a workflow now where you create a draft, you work on it, you submit it, the editor reviews it, they approve it or request revision. And so you have this workflow that goes beyond CRUD. And so when you identify those types of things in the define phase, it helps you to understand what are the resources you’re probably going to need and what are the operations you’re going to be performing.And I like to encourage people to diagram during the define phase. And that’s usually using things like sequence diagrams—or maybe a use case or a sequence in text or something that says, first, I’m going to call this operation and pass in this value, and then I’m going to take the result of that and I’m going to call this next operation, and so on. And it lets me see not just each operation in isolation, but how they collaborate together. And that gives me an API profile in the define phase that helps me understand what my API needs to do. It gives me a surface area to focus on.And if you’re in a large organization, you may have different services laying around that do some of these things. You don’t necessarily need to build net new everything that you’ve identified, but you’ve identified what you’ve needed. And that gives you kind of a list. It’s like a shopping list—here’s what I need, here’s what I’m going to need. Now, what do I already have? So I’m going to go search my API catalog, my service catalog. What do I have? What can I leverage? What do I need to build net new?And then I can go into the design phase. And the design phase is where I take that profile that defines how everything’s going to work and helps me understand what my resources are and how everything’s going to behave and how it’s going to flow through the different API operations, how they’re going to be called in sequence and so on, and start turning that into an actual API design. So I’ll take my REST API style guide or my GraphQL style guide I might have or whatever it is that I’m using and start applying those things into the design. So I take that profile and start transforming it into a design, and then I capture an OpenAPI spec and so on and go from there.Derric:And is there something we can do during the design phase to make sure it’s still meeting the initial outcomes that we’re defining initially? Or what are the kind of the gates that we consider this as design completes?James:Yeah, so when we go from the define to the design, it’s a pretty straightforward mapping. So we’ll take that profile and we’ll map it into something like a REST API or whatever. The refine phase is really where we spend a lot of our time saying, does this really map back? So we’re always going back to our align phase. Every artifact we produce in the ADDR process is used and reused.So when we get to the point where we have, let’s say, a high-level API design that maybe we’re not quite ready to put it in an OpenAPI spec, but we know—get to this path, post to this path, and so on—and kind of figure those things out. I can now start diagramming that. I can start writing a README doc to—almost like a getting started guide—and say, here’s how it’s going to work. I could transform that into some Postman collections and maybe have an OpenAPI spec generated for me or something else—whatever. I generate the Postman collections, generate OpenAPI spec, and explore it against a mock API.So I might have one of those mock API tools that are out there generate a mock for me and I can just bang against it inside of a series of Postman collections or whatever it is—curl or whatever—and start playing around with that API and put it in front of other people. So it’s no different than if somebody were to take a Figma sketch and put it in front of someone and make it interactive, and you try it out and you like it, and they go, okay, now let’s turn it into HTML. You put it in HTML. Okay, how does that work? And we kind of work our way through it and we increase our fidelity as we go and get closer and closer to the real thing as we go.It’s the same kind of concept. So that refine phase is—yeah, the goal is to get that feedback and to produce just enough artifacts to be able to get that feedback. And I’ll tell you, when you’re designing an API and you have to write a README doc of how you’re going to use it, before you even put it in front of a consumer, you’re probably going to find some things that you missed or some things you want to improve or, oh, that’s just not—you know, it didn’t look right or whatever the case is.So that’s what that is. And that helps you kind of get to a closer, ready-for-production API design. And this can go—once you get familiar with ADDR, it can go pretty quick. Particularly if it’s a moderate or small scoped item, you can sit down in a couple hours and figure something out. Sometimes I’ll have a couple of days where we’ll sit down in a conference room and workshop with people and get the right subject matter experts in the room.But after all of that, now you’ve freed up everybody. Front-end developers can start working against the spec. Back-end developers are starting to code against the spec. While some of that’s happening and the contract’s being defined for the API, you might have a couple of developers doing some technical risk mitigation by looking at, hey, can I query this data? Is the data of good enough quality to be able to do what we’re asking you to do? They’re investing in that while you’re designing the API. And then everybody comes back together. You have that one asset and you’re ready to start moving forward.And you can still continue to refine that as you go based on other learnings and things, but it gives you a really solid contract that everybody can confidently move forward with. And it gives you the business metrics you need. So now you have your product team, your tech team, your ops team can start seeing, well, how many API calls are you going to need to be able to get something done? Well, we’ve mapped that out because we had to do that in the refine phase.So a lot of those different elements all come together with this. And it makes for a more collaborative and smoother process because we’re not tossing things over a brick wall and hoping the next people understand what’s going to be required because they weren’t in those meetings that you were in, but you didn’t write it down or you didn’t convey it in a great way. So collaboratively working is really important. It helps to refine that design as you go because you’re never going to get it right the first time, but to be able to refine and improve it is really critical.Derric:That’s a really interesting point of thinking about drafting a README and almost forcing yourself to what it would be like for the customer to use the API and help drive some empathy even before coding it, right? So it sounds like that could be a key piece to the design and refine phase. You know, what happens after refine? So it sounds like it jumps right into development. And when do we go back into the ADDR process?James:Yeah. So it’ll—at that point, then you have a really good solid, collaboratively designed API design that you’re ready to start doing delivery on. And then ADDR will usually start over again whenever you have a new piece of scope going on.So now on larger scoped efforts, ADDR—you’ll spend a lot of time in the align phase, and then you might take a slice of that and take it through the rest of ADDR. So you might say, okay, I’ve got a pretty large scope effort here. Let me take this first piece—you know, maybe I found six job stories. Let me go ahead and let’s map out all the job stories and all the activity steps, make sure we get all of our domain vocabulary and all that sussed out and we understand what’s going on as a whole. Now let’s take the first job story and let’s deliver that one. And then we’ll come back.So in some cases, you’re just going to revisit the artifacts you’ve done, validate, you know, nothing’s changed. Have we learned anything since we released that last—those last set of API operations and maybe tied them into a frontend and received some feedback from someone—end users or other stakeholders? And then we’ll just take what we’ve already done and start executing that, you know, refining it as we go.In other cases, maybe the ADDR process is really a one cohesive API and we go through the ADDR process and we work through that and we deliver that API. Now we’re kind of more in the delivery management side of things. We’re dealing more with ops. We’re still looking at that. Now your product manager is coming around and saying, okay, let’s take some of what we’re learning now. We tried to roll this out and it was a little too difficult. It was missing this capability. Or, you know, we’ve got this opportunity—now someone approached us and they’re wanting to do business. We’ve got a business development opportunity. Let’s go explore that. And we’d use the align phase at least to do that.So I’ve seen some organizations—they’ll spend time in align phase and then they’ll stop for a while and they won’t immediately go into the rest of ADDR. They’ll use the align phase to help capture everything that’s important and then they’ll validate. And then if they get, you know, funding or if other business decision makers say this looks like a viable path, we’ll keep going. If not, we’ve done the align phase and then we’ve explored enough to say—or maybe align and define—and we know roughly how big is this bread box of an API that I have to build and how many people am I going to need and what’s the cost going to be to build this thing. And then they might decide, let’s put a pause on it or let’s expedite this. And so it’ll just loop around like that.And if you could imagine an organization like I’d mentioned before that has 10,000 developers—they have 2,500 teams—they’re all building APIs all the time. So this particular group, this particular company, they have at least 6,000 internal APIs. So they’ve been producing APIs for quite a while. So it’s not like they’re building them every day, but in a given calendar quarter, they’re probably building one or two. And for whatever reason they need, or they’re adding on to an existing one or something.So you’re doing this ADDR process in tandem with all these different teams. So you can imagine—I mean, it’s just, you know, plates are spinning all over the place. And if you had a centralized group that was required to do the design of every API, their plates would be falling and breaking. They wouldn’t be able to keep them spinning long enough to keep everything going and to be able to talk about all the business metrics and understand each domain nuance and everything else.And so that’s where I get into federated API coach programs, where you train coaches to support the teams out on the edge doing development for those larger scale situations.Federated API CoachingDerric:What is this federated coach process? I’m curious now.James:So I think what I’ve come to realize is as much as we assume that anybody that can write code can design an API, that isn’t always the case for a variety of reasons. They may not be familiar with API design, or maybe they are, but they don’t know the domain very well. Maybe they’re contractors brought in to supplement a team or something, and they just don’t know the domain very well, or whatever it is.So what we realize is you cannot build a large enough center of excellence or center for enablement—an API core team, an API guild, an API office—there’s different terms for it in different companies. But you cannot build an API practice with enough people centrally located full-time to be able to support the number of developers that are in your organization, even if you only have a couple hundred.Because they’re always going to be asking questions. They’re always going to be requiring you to spend a few hours to understand how does their domain area work. You know, I don’t understand e-commerce. Maybe I don’t know anything about API payment gateways and payment processors and all that. Or maybe I don’t understand how to send a text message, and I don’t understand SMS networks and so on and so forth. Or I don’t understand claims and insurance. Or I don’t understand how to underwrite an insurance policy. Or I don’t know how to underwrite a loan for, you know—or whatever it is.So if you had a centralized group, they’ve got to understand all those domains at any time, all the time. And it’s just nearly impossible.So a federated coach program recognizes the fact that the COE or C4E is probably made up of one, two, five—a small number of people that are passionate about APIs and bring their technical expertise, their product expertise, their management expertise, their network connections within the organization to other executives and so on that helps to tie all of the initiatives together that are going on, all the big things that are happening.And then the coaches are people that are passionate about APIs, but they live in different parts of the organization. They’re not in the COE. So they have more like a dashed line or a dotted line to them.So what the COE does is we—and what we do is we train up these coaches. We make sure that they understand how HTTP works, how to design APIs. We make sure that they can design with ADDR. And then we work on making sure that they can facilitate design sessions. That’s an entirely different skill. It’s an entirely different skill to be given an API design and then try to figure out how do I change it? How do I need to improve it? What patterns have I seen before that might apply here?That’s much different than sitting down designing an API—one singular API. It’s a scale that’s much different and the skill set is much different.So what we do is we make sure that everybody has that base foundation. And then we go through training and we talk about how do you facilitate design discussions, how do you facilitate the align phase, the design phase, and so on all the way through ADDR. We talk about how do you do design reviews and do it in a way that’s positive and constructive. How do you make sure you’re clear about your design reviews so that someone doesn’t go off and spend three months redesigning something because you said, “Hey, wouldn’t it be great if…”—and they thought that was a demand of that person to say, no, this API is not going to go out the door until you’ve done this thing. No, that’s not what I meant. It would just be great if—So the difference between required changes to your design to comply with your style guide or to make it a little bit more flexible or better fit or reusable or whatever it is, and recommendations that if you have time or if your framework, programming language allows you to give you an affordance to do something, you can do it.So these coaches—they exist out on the edge of the organization. They’re in the domain areas. They’re in the different lines of business or different domain areas. And they’re out there and they’re blending. They’re acting like representatives of the COE or C4E. They’re representatives of the API platform or program. They’re out there supporting other teams.So a team can go to them instead of everybody coming to that group of three that’s running everything for the organization.So to give you an idea of the—yeah, it’s fascinating. And to give you an idea, the company I mentioned has 10,000 developers. We have four people in our central COE. And then we have over a hundred coaches. So those coaches are out there doing the work. They’re the ones that understand the domain nuances, the context, the vision, the different systems that it has. It supports them.So that when an API is being designed, they’re taking all those things into account. But they also know kind of where is our line of business going? Or where is our domain area going? What are our goals? What are our goals for the next quarter, for the next year? And they’re applying all those things. And they’re giving API design advice out there contextually, which is way more valuable than someone who’s never been in that area before, doesn’t understand it at all, looks at the API and says, yeah, that looks great. When in actuality, it really won’t work within the context of that domain because of various reasons—cardinality of calls, or complexity, or standards, interoperability, or all kinds of different things that central group wouldn’t know about.So it’s not a completely distributed way to do API governance. It’s a federated way. You have your core group. They’re the ones defining the guidance. And then the coaches are out there helping to execute, giving design support and being out there and being an extension of that COE.And it’s really, really powerful. And it’s something that I’ve been helping a lot of organizations do. It takes a lot of training. So I spend a lot of time training up coaches in organizations. And it also requires you to adjust your governance model just a little bit in the way that you do business day to day with your APIs.Derric:Makes sense. I really like that approach of trying to take the domain knowledge and marrying that with, I guess, the API design knowledge as well, because, you know, they can be very separate, you know, different folks and everything. Really exciting to talk about the ADDR process here with you, James. Any parting thoughts that you want to share with the audience?James:Yeah. I would just encourage anyone—anyone listening to this—you’re involved in APIs in some way. You may have a software as a service. You may be launching a startup and you’ve got a startup idea. Or you might be, you know, further on in your business life sector. You might be part of enterprise IT. You’re just trying to figure out how to, you know, observe your APIs and find metrics or report on those—whatever it is.I would encourage you to start stepping back and thinking about the business aspects of things. We spend a lot of time in tech. And I love, you know, talking about JSON payloads and RFCs and all that—just like any other API nerd out there that just absolutely loves doing that. But when it comes down to it, we’ve got to solve problems.So stepping back, thinking about that is going to be really key. And if I can, I’ll just drop a quick note that I’ve started to externalize some of this training—a lot of the training about ADDR. So I do a lot for corporate engagements, and I’m happy to talk about that if there’s some sort of engagement that would make sense to help, you know, train up a large group of people.But if you’re just an individual and you’re just wanting to grow your career too, I’ve launched apicoach.io, where I have training there that’s available on API fundamentals. If you’re a product manager and you just want to learn more, or if you’re a practitioner and you want to dig in and understand how to apply ADDR—I have that as well. So apicoach.io, you can go there and check that out as well. And it’ll cover a lot of the concepts we talked about today in the midst of the training and show you how to do it and what it looks like and let you get hands on and try things out.Derric:Awesome. Happy to have you here, James. And again, thinking about the outcomes of what a customer cares about and not just—it’s great to debate GraphQL and REST and everything else—but, you know, those are just technologies to achieve an actual outcome.James:Absolutely. Absolutely. There’s a time and place for it all. It’s good discussion, but in the end, it needs to be solving problems. Just like you said.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /podcasts/developers/Podcast-APIs-Over-IPAs-Aligning-API-Design-to-Business-Outcomes-with-James-Higginbotham/",
          "author": "Dylan",
          "categories": "Podcasts, Developers"
        }
      
    ,
  
    
        "api-engineering-api-observability-unlocking-api-observability-and-monetization-with-gravitee-and-moesif": {
          "title": "Gravitee + Moesif: Unlocking API Observability And Monetization",
          "content"	 : "APIs (application programming interfaces) have transformed how we build modern software systems. As our digital experiences become more modular and interconnected, APIs power mobile apps, partner integrations, and internal services. Most companies now expose APIs not just for technical access, but as part of a larger API business model.But simply deploying APIs through a gateway like Gravitee doesn’t suffice. Without real-time tracking, user-level insights, and usage-based analytics, providers stay blind to how their APIs perform or how end users consume them. That’s a problem—not just for reliability, but for revenue. Monetizing APIs without observability leads to poor pricing strategy, unpredictable costs, and missed growth opportunities.Moesif changes that. When you pair it with Gravitee, Moesif unlocks true API observability and analytics and enables sustainable API monetization. Together, they give providers visibility into the entire system—who’s calling what, how often, and what drives value. Moesif gives you the right data in meaningful ways, rather than more data, that ties to real-world business decisions.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        Table of Contents  What is Gravitee?  What is Moesif?  Why API Analytics Is the Foundation for Monetization          Observability is more than monitoring      Monetization relies on usage patterns and behavior      Revenue insights require user-level attribution      API Observability aligns engineering and business        Benefits of Using Gravitee and Moesif Together          Bridge the Gap Between Access Control and Business Intelligence      Faster Time to Monetization with Low Overhead      Cross-Functional Visibility Without Tooling Fragmentation      A Future-Proof Stack for API-Driven Businesses        How to Set Up Gravitee With Moesif  Moesif Dashboards Overview  Key Insights: What You Can Achieve with Gravitee and Moesif          Operational Metrics with Business Context      Platform Intelligence for Scaling API Operations      Usage Signals to Support Monetization and Pricing      Understanding Usage, Revenue, and Cost With Billing Report Metrics        ConclusionWhat is Gravitee?Gravitee is an open-source API management platform built for control, flexibility, and speed. It enables teams to design, secure, deploy, and monitor API services across a range of protocols and event-driven architectures—HTTP, WebSocket, Kafka, MQTT, and more. What makes Gravitee stand out is its policy engine, which lets providers enforce access controls, rate limits, and transformation rules with minimal overhead.For teams managing internal, partner, or public APIs, Gravitee is often the first line of defense. It handles API access, versioning, throttling, and service-level governance—all critical for uptime and reliability. But while Gravitee shines in enforcement and control, it doesn’t natively offer deep insight into actual usage, customer behavior, or business impact. That’s where Moesif steps in.What is Moesif?Moesif helps product and engineering leaders drive more value from their APIs with powerful API analytics and monetization capabilities. This enables teams to understand API consumption, resolve customer issues, and generate more revenue from API programs.Unlike traditional logging or tracing tools, Moesif focuses on API-level behavior—tracking not just whether APIs work, but how developers and applications interact with them. It supports real-time tracking, user segmentation, and links usage patterns to customer outcomes like upgrades, churn, or conversion. Moesif also integrates with billing systems, CRMs, and marketing tools to support end-to-end visibility across customer journeys.Moesif collects rich behavioral and performance data with high-cardinality across every API call. It then turns that data into valuable insights for product, engineering, and growth teams like the following:      Where are developers dropping off or getting blocked?        Which features drive engagement or plan upgrades?        What usage patterns are generating unpredictable costs?  Whether you monetize with a freemium model, tiered pricing, or pay-as-you-go, Moesif helps you connect API usage to pricing and revenue strategies. This results in a more scalable API program—one that optimizes for developer experience, operational efficiency, and predictable revenue.Why API Analytics Is the Foundation for MonetizationYou can’t monetize what you can’t measure. Every API monetization strategy, from tiered pricing to usage-based billing, depends on knowing exactly how customers are using your APIs. That includes not just how often endpoints are called, but who is calling them, when, and in what context.Observability is more than monitoringTraditional API monitoring tools focus on uptime and error rates. Those are necessary for operational health, but insufficient for revenue visibility. API observability goes deeper: it captures structured, queryable data across three core dimensions—metrics, logs, and distributed tracing. Together, these dimensions allow you to correlate usage with outcomes like conversion, plan upgrades, and churn.Moesif excels here by making high-cardinality analytics accessible. You’re not just slicing by status codes—you’re asking:       How many enterprise customers hit this endpoint more than 100 times in the last 24 hours?         Which developer accounts show signs of becoming high-value users based on usage spikes?  Monetization relies on usage patterns and behaviorMost API monetization models—freemium, pay-as-you-go, tiered pricing—build on the assumption that you can measure actual usage accurately. But in practice, what qualifies as usage varies wildly. It might mean:      Number of API calls        Data transfer volume        Feature-specific access like premium endpoints        Aggregate user behavior over time  For example, AI-powered apps might measure usage in tokens processed, model inference time, or number of generated responses. A single request to a text generation API can consume way more resources than ten CRUD operations in a basic REST API. Without visibility into these metrics, it’s impossible to price accurately, or fairly.As another example, an API provider might offer a simple flat subscription fee but overlook that one customer’s traffic regularly exceeds 10x the average. That creates unpredictable costs and erodes margins. With observability, you can detect these anomalies early and enforce higher usage limits or price adjustments based on usage tiers.Revenue insights require user-level attributionA critical blind spot in most basic API analytics stacks is user attribution. It doesn’t suffice to see spikes in traffic—you must know which user, team, or company is generating those spikes. Moesif’s SDKs and plugins support full attribution through user and company identification and custom metadata—tying API access to real user identities.This unlocks a long list of API provider benefits:      Identify high-value customers based on usage trends.        Correlate feature adoption with customer purchases.        Detect abuse or suspicious behavior tied to individual accounts.        Inform a pricing strategy that aligns with perceived value.  User-level visibility is especially important when you’re monetizing external developers, where the usage patterns vary more and are unpredictable compared to internal teams.API Observability aligns engineering and businessFinally, API observability bridges engineering telemetry and business intelligence. Engineering teams care about performance, latency, and uptime. Product and growth teams care about conversions, retention, and revenue streams. Observability tools like Moesif bring these worlds together—by surfacing technical events in a way that maps directly to customer behavior and economic impact.This alignment makes observability not just a DevOps concern, but a core enabler of API business models. Without it, teams are forced to make monetization decisions based on guesses. With it, they operate from a foundation of real, continuous feedback.Benefits of Using Gravitee and Moesif TogetherEngineering leaders aren’t just looking to ship APIs—they actively make sure those APIs are reliable, secure, and monetizable at scale. By pairing Gravitee and Moesif, teams manage access and policy at the gateway, while extracting usage insights that drive business decisions.Bridge the Gap Between Access Control and Business IntelligenceGravitee is responsible for enforcing who can access what. Moesif captures how that access translates into behavior, value, and cost. Together, they form a complete feedback loop: operational enforcement on one side, strategic observability on the other. This allows platform teams to enforce rules and understand the downstream impact of those rules on customer experience and revenue.Faster Time to Monetization with Low OverheadInstead of building a custom logging pipeline to track API usage, pricing tiers, or overages, the Moesif-Gravitee integration enables teams to ship revenue-ready APIs with minimal engineering effort. Moesif captures usage data automatically, enriched with customer context, and makes them available for billing workflows, plan enforcement, and revenue reporting. You don’t have to write custom instrumentation or backend logic.Cross-Functional Visibility Without Tooling FragmentationEngineering, product, and growth teams all interact with APIs—but usually through disconnected tools. Moesif consolidates this view by tying API behavior to actual users and accounts. Then with Gravitee’s policy enforcement and rate limits, this gives everyone—from ops to product managers—a shared understanding of who uses the APIs, how, and to what end.A Future-Proof Stack for API-Driven BusinessesAs organizations shift toward usage-based monetization and external developer ecosystems, API infrastructure must support both control and insight. Gravitee and Moesif do this natively, without requiring invasive code changes or complex event systems. You get performance and adaptability—across pricing models, customer types, and growth stages.How to Set Up Gravitee With MoesifSee the Moesif integration docs for Gravitee for step-by-step instructions on how to set up these platforms. If you face any issues, have a look at the Server Troubleshooting Guide or contact our support team. Moesif Dashboards OverviewMoesif dashboards are fully customizable to match how your teams work and what they care about. You can build separate dashboards for engineering, product, marketing, or any function that depends on API insights. Each dashboard organizes key metrics into workspaces, containing charts and analyses tailored to your goals.For example, a product team might have workspaces like Top APIs over 7 Days, 30-Day API Usage by Company, or Feature Adoption by Plan—all within a single Product dashboard, like in the preceding image. You can also nest dashboards to organize views by customer segments, environments, or API versions.This flexibility allows teams to share context, stay aligned, and dig into the metrics that matter—without jumping between tools or writing custom queries.Key Insights: What You Can Achieve with Gravitee and MoesifLet’s go through some practical examples to demonstrate how these platforms expose insights that help engineering leaders scale operations, support pricing models, and prioritize platform investment.Operational Metrics with Business ContextGateway-level metrics, like traffic volume, latency, and error rates, are important. But without user attribution, they don’t tell you who’s being affected or what it’s costing you. With Moesif, teams can perform analysis like the following:      Track latency and error rates segmented by specific customers or plans.        Identify endpoints or consumers generating excessive call volume.        Detect regressions in API performance post-deploy across usage tiers.        Set alerts based on usage thresholds for premium accounts or SLA-bound customers.  This gives engineering teams a clear link between technical health and customer experience, especially when performance issues affect high-value accounts.Consider two examples here:      Reducing MTTR (mean time to resolution): Engineering teams can correlate high error rates with specific customer accounts and endpoints—enabling targeted fixes rather than sifting through aggregated logs. For example, here’s a 28-day time series analysis broken down by endpoint and company domain that can quickly highlight which enterprise customers are repeatedly hitting 5xx errors and on which routes. Visualized as a stacked bar chart, this helps teams isolate noisy endpoints, detect regressions, and escalate customer-specific issues with clarity.            Prioritizing engineering resources: Instead of guessing which endpoints to optimize, teams can look at call volume by endpoint and customer tier. If enterprise accounts consistently use a latency-heavy endpoint, that becomes an optimization priority tied directly to revenue.  Platform Intelligence for Scaling API OperationsAPI growth often comes with hidden operational costs like underused endpoints, excessive traffic from non-paying users, or uneven version adoption. Moesif enables platform teams to:      Detect uneven traffic across plans or endpoints and rebalance resources        Track adoption rates for new API versions or premium features        Monitor customer-specific traffic to guide deprecation or sunset planning        Understand how different regions or apps use the system over time  These insights make it easier to enforce contractual SLAs, adjust rate-limiting policies, and align infrastructure investment with actual usage.For example, here we use Moesif Time Series analysis to track a new API version’s adoption (v4) compared to older, but still supported versions (v3). The analysis looks at API usage for the past 30 days. It breaks the data down by company domains and API versions, using a custom rolling function Rolling Window to quantify the API call volume metric.Usage Signals to Support Monetization and PricingWhether you’re charging per API call, bundling by tier, or running a freemium model, the biggest risk is pricing blind. Moesif provides the behavioral telemetry you need to support sustainable monetization:      Identify customers consistently hitting rate limits who may require upsell outreach        Correlate usage trends with revenue milestones or account expansion        Segment power users from low-cost, low-usage customers to refine pricing structure        Flag usage behaviors that generate unpredictable costs or violate fair-use terms  These metrics help engineering teams support usage-based pricing models and provide the data that sales, finance, or growth teams need to act with confidence.To demonstrate, here we use Moesif to perform a Segmentation analysis for an AI API. It identifies the top customers based on their average input token consumption in the past 7 days. It defines two metrics: the number of API calls and the associated costs.Understanding Usage, Revenue, and Cost With Billing Report MetricsWhen APIs are core to your business model, you need more than traffic stats—you need to know how usage drives revenue, and how much it costs to serve that usage. Moesif’s Billing Report Metrics, powered by a robust metering system, makes this easy by turning API activity into structured reports based on monetizable events that you have defined in Billing Meters—like API calls, messages sent, or custom-defined units like input tokens for AI apps.With Billing Report Metrics, you can:      Apply custom filters to refine analyses        Group by customer ID, company domain, plan, or other metadata        Evaluate your monetization model        Understand usage and revenue across billing providers like Stripe or even custom billing solutions  For example, here we use Billing Report Metrics to understand daily revenue for two AI products in the past 7 days.See our guide How to Understand Revenue and Cost of API Usage in Moesif that walks you through leveraging reporting-only billing meters and Billing Report Metrics for digging into usage-based revenue.ConclusionAPIs do more than power backend systems—they function as products that drive revenue, influence adoption, and shape the customer experience. Gravitee’s API management solution gives you control over access, traffic, and enforcement. However, without visibility into usage and behavior, you miss critical signals about product performance and growth.When teams lack observability, they overlook where users drop off, which customers push usage limits without converting, and which endpoints degrade silently. These blind spots delay growth, hide costs, and leave revenue untapped.Moesif fills that gap. It connects API behavior to outcomes that matter—adoption, monetization, and retention. With actionable insights, your teams can resolve issues faster, optimize pricing models, and make smarter decisions backed by real usage data.No need to rebuild your stack. Add Moesif to your existing Gravitee deployment and start uncovering the opportunities inside your API traffic.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design, Build, and Deliver Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /api-engineering/api-observability/Unlocking-API-Observability-And-Monetization-With-Gravitee-And-Moesif/",
          "author": "Sakib",
          "categories": "API-Engineering, API-Observability"
        }
      
    ,
  
    
        "podcasts-developers-podcast-apis-over-ipas-api-first-strategy-for-banking-platforms-with-dave-biesack": {
          "title": "APIs Over IPAs 16: API-First Strategy for Banking Platforms with Dave Biesack, Apiture",
          "content"	 : "In this episode, Derric Gilling sits down with David Biesack, Chief API Officer at Apiture, to discuss building an API-first, customer-centric digital banking platform from the ground up. David shares the origin story of Apiture, a joint venture born from a vision of open banking, and how his team embedded API design and governance into every layer of the product. From establishing API University to codifying internal tooling and style guides, David explains how they’ve scaled API excellence across product and engineering teams.They explore the critical role of collaboration between product managers, architects, and API designers, emphasizing use-case-driven development, robust internal tooling, and strict security validation. David also talks about empowering partners through a tailored developer portal, balancing compliance with usability, and the cautious optimism around using generative AI in API workflows. Whether you’re managing API platforms or designing developer experiences, this episode offers a wealth of insights into building secure, scalable APIs that actually solve real-world problems.Moesif · 16. API-First Strategy for Banking Platforms with Dave Biesack - ApitureListen to the episode on SoundCloud, Apple Podcasts, YouTube Music, or wherever you listen to podcasts. You can also watch the video on our YouTube Channel.Table of Contents  Introduction  Launching the API Program at Apiture  Customer-Centric API Design  Measuring API Success  Regulatory Compliance in API Design  Building and Enhancing the Developer Portal  Proactive Security and Partner Guidance  Handling API Issues and Communication  Incorporating Generative AI into API Design  Closing Thoughts and Community Insights                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        IntroductionDerric:Hi folks, welcome to another episode of Moesif podcast discussing APIs and how to best productize them in the enterprise setting. Joining us today is Dave Biesack, Chief API Officer at Apiture. I’d love to have you here today, David.David:Thanks, Derric. I’m really happy to be here.Launching the API Program at ApitureDerric:Awesome. We’d love to just kick things off and hear about your API journey at Apiture, especially, you know, I know you’ve been discussing being API first and being very customer-centric. How did you get going with launching these APIs?David:Yeah, well, I mean, I was really fortunate in that I came into an organization that was, as a startup, basically, it was a joint venture between Live Oak Bank and First Data. And the idea was to create an API-based digital banking platform for credit unions and small community banks across the United States. And the name reflects that. Our company name Apiture starts with API. And that’s intentional. It’s kind of a play on words when you think about cameras and aperture. You know, “aperture” is wide open. And that’s what we’re trying to do is create open banking. So, again, I was very fortunate in that I came into an organization that was already thinking API first. They wanted to build an API program. And with my prior experience at SAS in building an API program there and running the API Center of Excellence for about five years, you know, I had a good background in kind of what’s necessary to put together an API program. What are the key elements that are necessary to keep track of over time? And so, that’s when I started. So, I came in when the company was founded. I was actually the first outside hire to join the company at its founding. Well, one of the very first. And came in really kind of running to say, okay, let’s create an API program. So, I did a lot of things. Primarily, it was kind of educating folks. So, I put together what at the time was called API University. And it was basically just an online peer learning program for people to learn about APIs and what it means to be API first. I wrote one of the first blog entries at Apiture about, you know, what does it mean to be API first? And just kind of going on from there. I created a set of API design principles that kind of guide the way that we design APIs, trying to put the customer and the use case first. And that’s really what I’ve been doing ever since is really trying to focus on collaboration with the other stakeholders at the company. Primarily, the people in our product team who are the ones that envision what we want the product to become. What are the new features we need to add to stay competitive or to meet customer needs and things like that and they come along and say, we need to add this particular product. You know, we want to support credit unions better. So, let’s add the ability to create member-to-member transfers, for example. We identify what the need is. What are the end-user use cases and stories that they need to be able to use with the software. And that drives our API design. And then that comes to my team. We’ve got three API designers on our team. I’m one of them. And that’s primarily the most work that I do is API design. But then we take these requirements from the product team, and then using our style guide and previous experience with our APIs, we design new APIs that satisfy and that fit into the suite of other APIs that we already have. And then we work with people, architects, et cetera, to help with the implementation of that. And we go from there. But a big part of it is maintaining that API-first nature with the rest of the organization. So, people are thinking—and we often do—you know, we need APIs to solve this problem. Or how can we employ APIs to solve this problem? So, it’s been very successful.Customer-Centric API DesignDerric:I really like that. Not just taking the API-first approach, but being very customer-specific and use-case-specific before even jumping into, you know, implementing some APIs. And otherwise, you know, we’re never going to be solving any actual, you know, business problem. But you’ve been at Apiture since the very beginning. We’d love to hear, you know, how has the different use case expanded? And how do you make sure that you’re still focused on those use cases, even though there’s more and more added? You know, we know, especially in, you know, finance and banking, maybe a lot of different complex use cases and transactions and endpoints. How do you make sure that the APIs stay simple and easy to understand?David:Right. So, there’s a lot of different answers to that question. The first of which is I have to really give a tip of the hat to our product team, because they’re the ones that handle that first—the answer to your first part of that question. How do you keep track of everything? Okay. So, they’re the ones that know what the features are, what the product does, and where the gaps are, what the customers need, what new features we need to add. And they’re the ones that tell us, you know, we need to add this new feature. So, we have, for example, a couple years ago, we had a pretty major initiative to redefine the way that business banking is done in our platform. So, it was taking our user interface and redoing that to understand what problems customers had, what speed bumps did they have, or what other types of issues do they have that made the current or the older system harder for them to use. So, how do we improve the system to make it easier for them to do things, especially when you get to things like doing wire transfers? Customers are very nervous about wire transfers because once you hit send on a wire transfer, you know, it’s gone. You know, you can’t recall it. You can’t say, oh, wait, I changed my mind. So, we want to make sure the customers are really familiar and comfortable with those types of features so that they’re sure that when they do send a wire, that they’ve got everything right. So, it involves a lot of things like validating the data and things like that. And there are business processes that we support. So, there are approvals within a business context, a business banking context. You may have other people within your business that have to approve a wire before it actually gets sent to the bank. So, it gets staged in an area and people can review those and approve them. So, that’s really what our product team does is they understand the business use cases. And they’re really, really excellent at that. And then our job is to translate those business use cases into technology, into API designs. And then we also have really good architects on the team who understand how the system is built, what capabilities are already there. And as I said, this was a joint venture. So, we inherited a very robust and mature online digital banking platform that we were adding APIs to as we’re evolving. So, we’re adding new features; they have to be implemented within that backend system. So, we rely on the architects to tell us what’s possible, what’s not, what kind of constraints do we have to make the API work within in order to satisfy those needs. So, that’s a hard part, but we also rely very heavily on those architects to tell us how we can do things. And that impacts a lot of—sometimes it impacts the API design—but in a good and reasonable way because it reflects reality.And then the rest of our job, as I said, is that translation mechanism of taking those requirements and then mapping them. So, we take into account our style guide, and we have some tools to automatically generate APIs. So, we can put down a very high-level abstract description of what an API needs to do and say, for example, we’ve got a new resource. So, say it’s going to be a wire transfer or an ACH batch or something like that, or an ACH template to be able to create future batches from a template. And we determine, you know, what are those resources and what are those operations that need to be captured? And we have some tools that can automatically generate—you know, if we want to follow standard patterns like the simple CURDL type operation—create, read, update, delete, and list. You know, we have stuff that can automatically generate, you know, from a very concise representation of what a resource is, it can generate a bunch of stub OpenAPI boilerplate code. And then we can fill in the blanks to fill in the details of what’s in most schemas and things like that. So, we have a lot of tooling that can accelerate our work. But that tooling kind of is custom-built at Apiture. So, we built it all in-house because it understands our style guide and our idioms and our API design patterns of use and things like that. And so, you know, it understands how to do things like pagination in a consistent way. It understands how to reuse the standard application/problem+json error response or problem response, you know, if there are problems with the request. So, it understands, you know, if there are any kind of syntax errors—you know, you specify, you pass a string where a number is needed or you pass an object where an array is needed. So, you know, we have all kinds of standard practices for handling things like that. And we fully believe, and when I mentioned earlier, you know, we have these API design principles and our first principle is security. We’re doing online banking, digital banking. So, security is of utmost importance. Securing the customer’s data, securing their access to that data, et cetera. So, we always consider API security as one of our very first constraints in our design process. So, validating input is really, really crucial. So, it’s a kind of a zero-trust model. We always validate all input that comes in. We never trust that the data is correct, right? We always validate it against the JSON schema, and we really employ JSON schema as part of OpenAPI very heavily. And that’s a big, important aspect of what we do. So, these tools kind of account for that. And we use Spectral linting and use the OWASP API Security Top Ten rule set as part of Spectral to help us ensure that we haven’t forgotten anything by accident.So, our design process and our tools get us most of the way, but it does take, you know, human intelligence to actually look at something and say, is this right? Does it actually map to the requirements that we have? Does it actually solve the problem at hand? We can’t do that through automated tools and things like that yet. Perhaps AI is going to get there, but I think it needs a little bit more than what we have now with generative AI. I think it’s going to be closer to AGI, you know, general AI. But anyway, that’s in the future. So, right now we sort of rely on human intelligence to do that verification—is this design really going to work? And then we iterate too. It’s an agile process. So, we can give the API design and then the developers will start implementing it. And if they run into problems—oh, there’s an inconsistency here, or this particular piece doesn’t really work—you know, we will tweak the API design early in the process to account for that. But everybody is kind of on board with this supporting, you know, getting the APIs as a foundational piece of what we do and building from there. It’s pretty fun.Measuring API SuccessDerric:I like that—taking this API standards approach, making sure that pagination is done, you know, the same way across the entire platform, not just for, you know, internal use and for usability, but that’s also a trust and security thing as well, right? Making sure that you are conforming to certain standards and, you know, thinking about things like API governance. I’m curious, you know, as you’re thinking about launching new APIs, how do you measure success, right? You know, if you want to drive API excellence at Apiture and baking security and governance into that process, what does that look like when you’re shipping a new API, and is there some type of checklist that you walk through?David:Most of it is driven by, you know, can the front-end team build the application that they want, and how difficult is that, or how easy is it for it? And does it cover all the use cases? So again, you know, we’re building on knowledge of what we’ve done in the past. So we have these kinds of patterns that we can reuse and call upon when we do this type of work. So the measure of success there is most of the APIs that we have are really kind of internal APIs. We have our own proprietary—it’s kind of our own white-label web application, and we also have a generic mobile application that we then customize for each individual financial institution customer or client of ours. So each bank gets their own app that goes into the app stores for their banking customers to download when they want to use online banking with their mobile devices and stuff. So we build all that. So the primary use case for most of our APIs is our own internal systems. We do have these public APIs. We document them on our developer portal. So, and then we just look for feedback from those customers who are using those APIs. Can they solve the business problems that they’re really interested in? And we’ve had good success with that.Derric:Definitely going back to what is the original problem they’re trying to solve. At the end of the day, this is what we’re all trying to use APIs for, right?David:Right, right. Yeah. So we start with, you know, what are you trying to do? You know, what problem are you trying to solve? And then ultimately, does the API let you solve that particular problem? And that’s the feedback that we’re most interested in.Regulatory Compliance in API DesignDerric:Makes sense. And, you know, when we talk about things like open banking and, you know, access to customer data, how do you incorporate that into the API design process, ensuring that new regulation and compliance is actually met as well, and then balancing those customer needs between enabling these new use cases while at the same time helping customers stay compliant with the new regulation coming out?David:Right, right. So again, I’m not a banking expert. I’m not a regulation expert, but I do rely on our product team to be the domain experts in that realm. So they tell us what has to be done and what’s necessary. So, you know, when you get into banking, critical things are okay—you have to have the right disclosures so that when a new customer comes on board there, they can see those disclosures and we can capture their acceptance of those disclosures. So those parts of the compliance aspect or the regulatory aspect of online banking, open banking, really come in as product features as to what has to be done. And we codify those in API designs. So we have like a disclosures API that can list the disclosures related to a specific banking account product. So if you’re going to open up a new checking account, right, here are the disclosures that we expect you to view and accept. And then we capture that information as part of our digital account opening solution in order to comply with those types of regulatory compliance things.Building and Enhancing the Developer PortalDerric:Makes sense. And you mentioned, you know, building out a developer portal—how do you help empower your partners to make sure they’re able to find the right APIs that they’re looking for, for these new use cases? Especially, you know, there could be a lot of different ones, right? A large bank versus a smaller credit union—I’m sure they have very different needs. And how do you, you know, balance both needs at the same time?David:Yeah, right. So we have a two-pronged approach for our developer portal. Part of our developer portal is generated from source. So we have a source project in a Git repository that captures the developer portal. And when we have a new API, we just add—we have a JSON file that lists all the APIs that are published on our developer portal. So we just add a new API to that JSON list, regenerate the portal, and publish that out. And so that is fairly automated in that regard. Not fully automated because you have to go in there and manually add it to a list, but that’s a decision that you have to do. Someone has to make a decision that, okay, this API is done, this API is in production, now we’re going to document it on the developer portal and make it available. So that’s okay—it’s done that way.We have another part of our developer portal, which is really kind of managed sort of as a WordPress site. So, you know, our technical writers can go in there and add documentation that explains how to use certain APIs or certain scenarios and things like that. So we have, for example, one of our APIs, which is kind of critical for fraud detection and fraud prevention, is an audit or banking events API. So as things occur within the digital banking system—as a customer schedules a new transfer, or as a customer tries to even log in and fails a login attempt—we generate events to capture all this information, and it goes into this event history API.And so our partners can then—once we provision their access to that particular API, if it’s a secure client that we trust—they can look at all these banking events, all this banking data, in order to do fraud detection, for example. And so we have a partner, DefenseStorm, that we partner with very closely. And so when we have an FI client, a financial institution client working with us, they can basically license access to the DefenseStorm product. And then that integration is very tight with us. So they can look at this data and use our APIs to get information.So that’s kind of how we try to view this, is we make those APIs available through the developer portal. Partners can come in and register, and they get access to—they can register a client application and then get credentials to authenticate against our APIs and things like that. So all that works really, really nicely and is pretty good and mature enough. What we’re going to do next is integrate things like Moesif into that developer portal so that, you know, if I’ve registered my client, I should be able to come back in and just see, well, show me what my API traffic is looking like for my client. We don’t have that yet, but that’s on our roadmap for 2025—to have that type of integration. So they can get the Moesif data and get some analysis of, you know—oh, you know, my client is misbehaving. I’m seeing a lot of 400 errors on this particular API call. Maybe I’ve got a defect in my client, you know. And we have a sandbox area where they can develop their client application against a kind of a sample or test financial institution and build and test their applications prior to them going production.We segregate API access between dev and test and QA and prod. So you can’t use credentials for your test application in production, and that’s all managed through our developer portal. We kind of keep those things separate, so you can’t just start developing something and then use it for a production instance. And we encourage people to maintain their credentials securely and things like that.So that’s our primary kind of success metric—is how many people are using the developer portal, how many applications are being registered, and then eventually using Moesif to look at what their API traffic is, and seeing that they’re building it and they’re being successful with that.Proactive Security and Partner GuidanceDerric:Glad to hear, and super excited to see that launched later this year. You know, speaking more around API excellence and these different very specific use cases that can be quite critical to make sure you get it right—you know, you talk about using the right credentials in a production setting versus a sandbox environment, and what goes into setting that environment up to be secure and follow the right best practices. But as a follow-up to that, how do you best support your partners to make sure they are following these best practices, things that may not even be inside your control, right? Is there any type of influence that you do there, or is this something that’s baked into the API design itself?David:So, we try to engender these partnerships as much as we can. So we work with partners and communicate. Like, even this week, we have one partner who came in and requested a client application, and they specified, okay, here’s the description of what the application wants to do. And it was basically going to be similar to what I mentioned earlier about kind of pulling the events within the banking system in order to do fraud detection and then feed it into a third-party fraud detection system. And they requested a client application and credentials, and when they do that, they list which APIs are you going to be hitting, and which environment are you going to be talking to—which financial institution—and is this going to be for a QA environment, or is it going to be a production environment, et cetera.And they listed what they needed, but that they included—and when you do this, you select the APIs that you want to use. And then from those APIs, we extract out all the OAuth scopes for all the operations within those APIs and give them a list and say, “Which scopes is your client going to use?” And they had just checked all the scopes that were available, unfortunately, right? So it included read scopes, but also included write scopes, you know?So we see that when it comes in at the request. And even before we go any further with that, you know, then we go back to the customer and say, “Okay, did you really intend to do this? The description of your client looks like you’re trying to read data, but you’re asking for write scopes. But I don’t think you really intended that.” And it’s, “Oh yeah, you’re right.” And so they go back in, and our developer portal—we have a very easy checkbox—you go in and you select all these APIs, and you can have one checkbox that says “Exclude write scopes,” which basically means you’re a read-only application, right? And so it’s very easy for them to fix that. And so they do that.So we catch those types of things again with manual inspection before we go any further with a request. Once everything looks good, then we go back to the financial institution and ask—we have a list of approved authorities at each institution—to say, “Here’s a client that is trying to build an application, and they want to be able to access data on your financial institution.” And oftentimes, it’s a partner of that financial institution or someone in their IT shop, you know? So it’s pretty obvious they’re developing something internal in-house.But anyway, we do that check anyway. We always ask them, “Do you want to grant them access to this?” And when they do, then we come back and we provision that access through our developer portal. And then they get notifications to say, “Okay, your access is granted, and you go over here to get your credentials and stuff.” So that’s a very robust process.I created our first dev portal, I think it was 2019, and we’ve been evolving it ever since. And we have lots of different types of API access or access to our platform. So the majority of it is API access—you’re just going to use our APIs—but we also have a feature called embedded banking, which lets you build your own custom application for your customers in whatever domain or market that they’re running in. So it might be a vet clinic or something like that. But they want to embed banking components inside their application to view your account and see, you know, “Do you have enough funds in there for your capital campaign you’re just about to start?” et cetera. And so they can embed these digital banking components inside of their applications. That’s an embedded banking solution.So we have a way to register for that in our dev portal. And as I mentioned before, with these banking events, we also have a webhook facility so that you can register. And then when these events occur, you can listen and subscribe to getting certain events of different event categories. So they come directly to you through a webhook that you register with us. So we have all these different types of things that we register in our dev portal.And that’s what’s nice about kind of building it ourselves—is we have all these different needs because of what we do that you can’t get from an off-the-shelf developer portal that just says, “Okay, well, here’s your API doc. Go in here and get an API key,” or something like that. So it’s more sophisticated than that. But it all hinges on understanding the use cases and what our product provides and what our customers want to be able to do, what problems they’re trying to solve. And then we kind of marry the two.And then we have a great support team here as well. Actually, they’re kind of an award-winning support team, which is really kind of exciting. And so we’re kind of growing them into this right now. They typically support kind of the end customers and then our clients who are running their own bank—they’re running our software to manage their bank. And so we have an admin platform for the bank and things like that. And so, you know, most of the support is focused around that and then training for how to do that, how to use the product, et cetera. But we’re getting into more and more support for these third-party applications coming in and connecting to our platform.Handling API Issues and CommunicationDerric:It definitely sounds like having a very tailored onboarding process and taking that proactive approach to security—that’s really how you help enable your customers and partners to be successful, much more than just let them deal with it and see what happens, right? And that’s, you know, taking that very customer-centric thinking approach. When things do go wrong, you know, how do you help your customers? You know, they had a risk or they messed up somewhere, and it could be a security risk. What are some best practices that you’ve seen in terms of keeping folks informed and trying to make sure they’re on their path to succeed with the platform?David:Yeah. So, we haven’t had too many of those types of issues around the API-focused areas. So most of those types of things—really any type of those problems—go directly to our customer support team. And they’re the ones that manage that, and they perform—they do the communications. So we have customer success managers with each of our clients and things, so they’re the ones that do that communication to get things cleared up. We’ve been fortunate. We haven’t had any kind of major issues with APIs and things like that.With API evolution, we’ve had a couple of cases where, due to things like security or changing, evolving practices and things like that, we’ve had to make a couple of API changes that would affect customers—sort of like breaking changes, you know. They do need to update their system to account for this. But they weren’t really breaking changes, but we were just changing and say, “Okay, the implementation is changing a little bit. So we used to give you this information, but now that value is no longer there; this is replaced with this other value.” So we always try to communicate as much as we can for things like that, to share what’s happening and then have release cycles planned ahead, so we can communicate release notes, you know, what’s changing in the system, to give everybody a sufficient heads-up so they can plan accordingly.Derric:Definitely over-communicate versus under-communicate. No one wants to wake up to a four-in-the-morning PagerDuty alert, right?David:No, of course not.Incorporating Generative AI into API DesignDerric:Well, my last question was just around how Gen AI is impacting your API design, API-first process. We keep hearing quite a bit about it. But how do you think about, you know, Gen AI tooling and, you know, incorporating different Gen AI tools into your API process?David:Yeah, we’re still kind of nascent in that space yet. What I’m hoping is that—we’re looking at various options there—I want to be able to get to the point where I can feed a generative AI system our existing API designs and our style guide and our spectral rulesets and things like that, such that then we can say, “Okay,” and maybe some of the other stuff like our tools that generate APIs from these concise descriptions and stuff. I think there’s a lot of potential there. We haven’t incorporated it yet—we’re still kind of looking at where that’s going to go. But definitely, you know, the company overall is very interested in seeing how AI can help the people who are on staff be more productive and get more done and do so with assurance.So I think the important part there is to treat an AI entity as a junior programmer for a while, at least until they can prove themselves, but employ the rest of your best development practices to that work. So, you know, if there’s any kind of code that you’re getting from an AI—from a generative AI type system—make sure it’s going through all of your regular code audit processes. You know, it’s going to be writing code, it goes through a peer review through pull requests and things like that through your Git repo, et cetera. But treat it—don’t treat it as trusted until you’re really proving that it’s doing what it’s supposed to do. But do code reviews on that code and things like that, and make sure you have good regression tests. And in our case, you know, we want to have good security tests. And that’s where I mentioned things like the spectral OWASP top 10. So that would be one of the things you’d want to feed in as your prompts—to follow this set of rules.And I don’t know yet how you create correct prompts to say, you know, make sure that when you define an API, all of your requests have a security requirement on them, right? We have no unauthenticated APIs. Every API has to be secured. But in our case, you know, we’ve got lots of different types of APIs and lots of different types of API consumers. There are APIs that are meant for a front-end banking customer, right? I want to list my accounts—so that requires one type of authentication, an end-user authentication. But then we have systems that talk to each other, so we use client credentials. All of our APIs—we want to have OAuth authentication on them, OpenID Connect, and things like that.So we have to be able to have enough information to guide an AI to say, “Okay, this particular API is targeting a secure back-office application as the client, so its authentication model is going to be different than an API that’s being used by the end customer, which is using authorization code grant flow.” So, you know, we have to have that kind of thing built into our system before we can effectively use it. And the same is going to happen with generating your schemas for your API, for your request and response bodies. You know, we have a lot of patterns that we use. And I’ve documented a lot of these. I’ve got a Substack called “API Design Matters,” and I’ve kind of talked quite a bit about common API design patterns and strategies and quite a bit of stuff on using JSON schemas and stuff. So we kind of want an AI that can understand how we compose schemas to have maximum reuse, so we don’t have a lot of copy and paste.And we’re seeing right now—there’s kind of a lot of analysis when people look at what’s happening more recently, now that these AI systems are out there, whether it’s with Visual Studio Code and things like GitHub Copilot, et cetera—people are starting to see a lot more copy-and-paste-type stuff in these Git repos. And that’s just in the open-source stuff that they can do analysis on. And that’s what happens when you have these AI systems. It tends to be kind of copy-and-paste-type stuff. And, you know, so I’m still a little bit hesitant about how well they’re going to work. But I think by integrating them in but still having oversight over what they’re generating to make sure that it’s still secure and it solves the right business problem and it fits the style guide and things like that.Because really, APIs—there’s a lot of metrics for how an API is considered good, right? The first of which is: Does it meet the business needs? Does it solve the right problem? Right? And you can use the API to actually get what you want out of it and solve a problem. That’s the first thing. But then there’s a lot of heuristic-type things, right? How easy is it to use? How well is it documented? How complete is that documentation? And we found, with kind of case studies—I’m a really huge advocate of having good documentation. I don’t want rote documentation that, okay, well, property X means this is the X value for this object—it doesn’t tell me anything, right? You have to tell me what it actually means. So I’m really kind of adamant about having good documentation that explains what the API is, how it’s used, so it’s very complete.And that really helps with AI—if you have something that’s doing code generation against the API. And we’ve had quite a bit of success with that in the research efforts that we’ve done, where we feed our API definitions to the AI, and then they can generate code to use that API. And it’s actually pretty good because we’ve got good documentation that captures that. So I’m really interested to see what we can do to add semantics to the API so that they understand really what it means.So some of that is kind of , but I’d like to be a little bit more explicit, because I want to have that information there. It’s pretty easy to know—you’ve got a POST operation that creates a resource. And then there’s a GET operation that kind of returns that same resource. And then there’s a DELETE operation to delete it, and there might be a PUT or a PATCH to update it or edit it. And that’s kind of well-understood. But then there’s a lot of other things on the edge. So there may be other operations that do actions on behalf of you. So there may be—like with a debit card or credit card resource—you know, you can certainly see here’s a GET to get that particular resource. But then there’s another POST operation that’s kind of adjacent to it that says, “Okay, I want to lock that card because I’ve misplaced it. I don’t want someone using it accidentally. So I’m going to lock it so that no one can use it until I come back in, and I’ve found it, and I can unlock it.” So that’s kind of an adjacent thing. That’s a POST, and it’s not very clear from the standard patterns—like a CRUD pattern—how that fits in. So, you know, you have to make sure that an AI can kind of understand that.So, if you can identify the semantics—and some of that is in the description of what the operation does, right? Because the operation name is “lock debit card.” So, it’s kind of obvious, but the description says, “This locks the debit card so that someone can’t use it until it becomes unlocked,” you know? And so that helps someone who then wants to be able to employ an agent to say, “Hey, give me a list of my credit cards.” You say, “Oh, you’ve got two credit cards with this bank,” and say, “Okay, I want to lock the first one,” right?So, AI should be able to figure out, well, that’s going to map to this “lock debit card” or “lock credit card” operation. And if then it says, “Okay, well, I’m going to try to lock this. Is that what you want to do? Please confirm.” You say, “Yes,” and the agent goes off and does that. So that’s kind of where I think things are going to evolve in the near term, is to allow these agents to be able to use APIs.But I think what we need is something akin to a robots.txt file that tells a web robot—a bot that’s out there—can you navigate this site? Can you spider it and find out all the resources on this particular website? And you have to look at the robots.txt file. And so Google can’t index it, for example, or Google can. And the same thing—I think we need the same type of thing for APIs. We may have some APIs that are low-risk and say, “Okay, it’s okay to allow an agent to be able to invoke or use this API on behalf of a customer.” But this API is really sensitive, or risky, or potentially dangerous, and we say, “Well, we’re not going to allow an agent to access that.”So I think that’s kind of the next phase that we need to look at with talking about agents and AI and APIs.Derric:I like that—treating AI as a junior developer, making sure it’s embedded in your change management process. But on top of that, some of these APIs can be really risky. That wire transfer thing that you mentioned—what if someone didn’t even realize, you know, it was copy-pasted into some generated code, and suddenly it was supposed to read the wire transfer, but it actually sent it, right? Like, there’s a lot of risk when we start thinking about these types of banking platforms.David:Yeah, we definitely need feedback loops and things like that to ensure that the AI is doing what you expect it to do. And people have to be careful. You can’t be complacent and just say, “Oh, that looks good. The code compiles—push it.” That’s not sufficient. So we have to be very careful.Closing Thoughts and Community InsightsDerric:Definitely. And having this API governance and having the right process there, regression testing—hopefully, it catches these things. But it’s really around the process of how do we ship APIs to meet the requirements but at the same time fitting all the security rules and everything else that you have. But really appreciate having you here, David—talking about APIs, having an API-first mindset, especially in the finance and open banking and banking space. Any parting thoughts that you want to share before we end the podcast?David:So, anytime I talk to someone about APIs and kind of the API space, I always want to reiterate how grateful I am for the community that’s around us. So these are the people that you meet when you go to conferences or you go online—whether it’s just LinkedIn or some other place—or you’re looking at Moesif. The community here—and we’re all very, very supportive. So if anybody’s getting into this space, reach out. People are so willing to help because everybody’s very passionate about APIs. And they know that they’re critical for business success. And they know that it’s critical for people to build these API skills. And so everybody is very willing to help everybody else grow.So I’m very appreciative of all the people who have helped me over the many years that I’ve been in this space and have been very welcoming in letting me come to conferences and talk or come on podcasts and things like that. So just kind of turn that around. So don’t be afraid to reach out. And then when you learn something, share it, you know—share it back. So I just want to make sure that I get that out there.Derric:Yeah, that’s fine. I mean, the API community—it’s a small, close-knit community. But we’re all very passionate about APIs, and it’s growing. More and more companies are thinking about treating APIs as products, and more folks are branching into API product management. And these newer types of roles are being created. But at the same time, it is a small community, and I’m thankful as well to learn about APIs and branch into this space as well.Well, thank you very much, David. And have a good rest of the week.David:All right. Well, thanks, Derric. It’s been fun.                Deep API Observability with Moesif              14 day free trial. No credit card required.              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          "url": " /podcasts/developers/Podcast-APIs-Over-IPAs-API-First-Strategy-for-Banking-Platforms-with-Dave-Biesack/",
          "author": "Dylan",
          "categories": "Podcasts, Developers"
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        "technical-api-development-vercel-nextjs-moesif-ai-apps": {
          "title": "AI Application with Vercel and NextJs with Moesif for Analytics",
          "content"	 : "Vercel and Next.js Are Popular for AI ApplicationsExcellent Developer Experience (DX) & Rapid PrototypingNext.js (a React framework) and Vercel (the platform created by the Next.js team) are renowned for providing a smooth and efficient development workflow.This allows developers to quickly build, test, and iterate on interfaces for AI features, which is crucial in the rapidly evolving AI space. Features like Fast Refresh, easy setup, and integrated tooling speed things up considerably.Performance Optimized for Interactive ExperiencesAI applications often involve fetching data from models, which can sometimes have latency.Next.js offers various rendering strategies:  SSR (Server-Side Rendering)  SSG (Static Site Generation)  ISR (Incremental Static Regeneration)  Client-Side RenderingIt also leverages React Server Components, allowing developers to optimize loading times and create snappy user interfaces, even when interacting with potentially slow AI backends.For more details on rendering strategies, check out the Next.js documentation.Streaming UIThis is a key factor. Frameworks like the Vercel AI SDK, built on top of Next.js and React Server Components, make it easier to stream responses directly from AI models (like large language models) to the user interface.This dramatically improves the perceived performance for things like chatbots or content generation, as the user sees results appearing token by token rather than waiting for the full response.Vercel AI SDKVercel has actively invested in this space by releasing the Vercel AI SDK. This open-source library provides hooks and utilities specifically designed to simplify integrating AI models (from providers like OpenAI, Hugging Face, Anthropic, Cohere, etc.) into Next.js/React applications.Key Features of the Vercel AI SDK  Handles common patterns like streaming text responses.  Manages chat history.  Provides UI components.These features significantly lower the barrier to entry for building AI frontends.Edge Functions & Global InfrastructureVercel’s platform heavily utilizes edge computing. Running functions (like API calls to AI models or light preprocessing) closer to the user via Edge Functions can reduce latency compared to traditional serverless functions located in a single region.While complex AI model inference might still happen in centralized data centers, the edge is ideal for orchestrating calls, caching, and handling the UI logic quickly.To learn more about edge computing, check out this article.Serverless ArchitectureVercel’s serverless functions (and Edge Functions) are well-suited for the often bursty or unpredictable traffic patterns of new AI applications.  No need to manage servers.  Scaling is handled automatically.This is great for API routes that might call AI services.Strong Ecosystem and CommunityNext.js has a large, active community and a rich ecosystem of libraries and integrations.This means developers can often find existing solutions or get help when building complex AI-powered features.For example, you can explore the Next.js GitHub repository for community contributions and discussions.How Analytics become more important than ever for AI AppsModern AI-driven applications, particularly those leveraging Large Language Models (LLMs) or other generative AI services, rely heavily on API calls—both to external model providers (like OpenAI, Anthropic, Google Gemini, etc.) and potentially to internal microservices managing AI logic. Moesif provides crucial visibility into this complex web of interactions. By capturing and analyzing every API request and response, Moesif allows developers and product teams to understand exactly how their AI features are being used. This includes monitoring the frequency of calls to different models or endpoints, tracking latency and error rates for AI responses, and understanding the overall load these features place on the system. This detailed monitoring is essential for ensuring the reliability and performance of AI functionalities, which often have variable response times and can be resource-intensive.Beyond basic performance monitoring, Moesif offers deeper insights specifically valuable for AI applications. It allows teams to inspect the actual payloads of API calls, meaning they can analyze the prompts being sent to AI models and the responses being received, without necessarily storing sensitive data long-term. This is invaluable for debugging issues, understanding user behavior (what kinds of prompts lead to successful outcomes?), and identifying patterns in AI model usage. Furthermore, Moesif enables cost tracking by associating API usage with specific users or features, helping teams manage the often significant expenses associated with third-party AI model APIs (e.g., tracking token usage). By correlating API usage data with user information, businesses can gain insights into which customer segments are most engaged with AI features, paving the way for better product development, personalization, and cost optimization strategies.For more information on API analytics, visit Moesif’s website.Integrating Moesif with Next.jsThis guide provides step-by-step directions on how to implement the moesif-nodejs SDK within a Next.js application, based on patterns from the official Moesif Next.js example repository and general SDK documentation.We’ll cover two primary integration methods:  Integrating with API Routes (Server-Side Runtime): Recommended for capturing detailed request/response bodies and using the full feature set of moesif-nodejs. Runs in a Node.js environment on the server. Best suited for the Pages Router or if full server-side features are needed in App Router (though requires more careful setup).  Integrating with Next.js Middleware (Edge Runtime): Useful for capturing events processed at the Edge. Ideal for the App Router and Pages Router when Edge processing is desired. Note that Edge runtime has limitations, requiring manual event construction.Prerequisites:  A Next.js project set up.  Node.js and npm (or yarn) installed.  A Moesif account and your Moesif Application ID. You can get one at Moesif.Step 1: Install Moesif SDKNavigate to your Next.js project directory in your terminal and install the moesif-nodejs package:npm install moesif-nodejs# oryarn add moesif-nodejsStep 2: Configure Moesif Application IDStore your Moesif Application ID securely using environment variables. Create or open the .env.local file in the root of your project and add your ID:# .env.localMOESIF_APPLICATION_ID=YOUR_APPLICATION_ID_HEREReplace YOUR_APPLICATION_ID_HERE with your actual Moesif Application ID.Important: Remember to add .env.local to your .gitignore file to avoid committing sensitive credentials.# .gitignore.env.localMethod 1: Integrating with API Routes (Server-Side Runtime)This method uses a Higher-Order Function (HOF) or wrapper to apply the Moesif middleware to your individual API route handlers, running in the Node.js runtime.Step 3a. Create a Moesif Middleware WrapperCreate a utility file (e.g., lib/moesifNode.js or utils/moesif.js) to initialize Moesif and create the wrapper function:// lib/moesifNode.jsimport moesif from 'moesif-nodejs';const moesifOptions = {  applicationId: process.env.MOESIF_APPLICATION_ID,  // --- Optional configurations ---  // Capture outgoing API Calls made by your server  // captureOutgoingRequests: true, // Requires separate Moesif dependency  // Function to identify users (highly recommended)  // identifyUser: function (req, res) {  //   // Example: Extract user ID from request object (e.g., after authentication middleware)  //   return req.user ? req.user.id : undefined;  // },  // Function to identify companies/accounts  // identifyCompany: function (req, res) {  //    return req.user ? req.user.companyId : undefined;  // }  // Log request/response bodies (be mindful of sensitive data and performance)  logBody: true,  // Function to skip specific events from being logged  // skip: function (req, res) {  //   return req.url.startsWith('/_next/') || req.url.startsWith('/healthcheck');  // }  // Function to mask sensitive data in headers or body  // maskContent: function (eventData) {  //    if (eventData.request && eventData.request.headers) {  //        eventData.request.headers['authorization'] = '**** masked ****';  //    }  //    // Add more masking logic as needed for body fields etc.  //    return eventData;  // }  // --- Add other moesif-nodejs options as needed ---  // See: [https://www.moesif.com/docs/server-integration/nodejs/options/](https://www.moesif.com/docs/server-integration/nodejs/options/)};// Initialize Moesif middleware, ensuring Application ID existsconst moesifMiddleware = process.env.MOESIF_APPLICATION_ID  ? moesif(moesifOptions)  : (req, res, next) => { // Fallback if no ID is configured      console.warn('Moesif Application ID not configured. Skipping Moesif middleware.');      if (typeof next === 'function') next();    };// Export the initialized middleware function directlyexport default moesifMiddleware;// Optional: Helper HOF to simplify wrapping Next.js Pages Router API handlersexport function withMoesifPagesApi(handler) {  return (req, res) => {    // Ensure Application ID is present before applying middleware    if (!process.env.MOESIF_APPLICATION_ID) {        console.warn('Moesif Application ID not configured. Skipping Moesif wrapper.');        return handler(req, res);    }    // Apply the Moesif middleware logic before the handler    moesifMiddleware(req, res, () => {        // Call the original handler after Moesif logic runs        // Ensure handler result is returned (esp. for async handlers)        return handler(req, res);    });  };}Step 3b. Apply the Wrapper to your API RoutesImport the wrapper into your API route files and wrap your handler function.      For Pages Router (pages/api/your-route.js):  Use the withMoesifPagesApi helper for cleaner code.      // pages/api/hello.js  import { withMoesifPagesApi } from '../../lib/moesifNode'; // Adjust path if needed  function handler(req, res) {    // Your API logic here    console.log('API handler executed');    res.status(200).json({ message: 'Hello from Next.js Pages API!' });  }  // Wrap the default exported handler  export default withMoesifPagesApi(handler);            For App Router (app/api/your-route/route.js):  Directly using the moesif-nodejs middleware function (which expects Node.js req/res objects) with App Router’s Request/Response objects requires careful adaptation or shimming. Using Next.js Middleware (Method 2) is generally the recommended and simpler approach for App Router.    If you must use it server-side within an App Router route handler, you would need to manually invoke the middleware logic, potentially creating shimmed req/res objects or using lower-level Moesif functions. This is complex and error-prone.    Recommendation: For App Router, strongly prefer Method 2 (Next.js Middleware).  Method 2: Integrating with Next.js Middleware (Edge Runtime)This method uses the built-in Next.js Middleware (middleware.ts) which often runs on the Edge runtime. This requires manually constructing and sending event data using MoesifController because the standard Express-style middleware function isn’t directly compatible with the Edge environment and NextRequest/NextResponse objects.Step 4a. Create the Middleware FileCreate a file named middleware.ts (or .js) at the root of your project (or inside the src directory if you use one).Step 4b. Implement Moesif Logging LogicPaste and adapt the following code into your middleware.ts. This mirrors the approach shown in the moesif-next-js-example repository.// middleware.tsimport { NextRequest, NextResponse } from 'next/server';import { MoesifController } from 'moesif-nodejs'; // Import the controller// Initialize Moesif Controller directly for Edge compatibility// Ensure options here match general intent (logBody, identifyUser etc.)// but apply logic manually below.const moesifOptions = {  applicationId: process.env.MOESIF_APPLICATION_ID,  logBody: true, // Example: Set config option, but body logging is handled manually below  // Add other relevant options used in manual data construction};// Ensure Moesif is initialized only if the Application ID is presentconst moesifController = process.env.MOESIF_APPLICATION_ID  ? new MoesifController(moesifOptions)  : null;// Helper function to safely get identifyUser resultfunction getUserId(req: NextRequest): string | undefined {  // Add your logic to extract user ID from the request  // Example: using headers, cookies, or decoded JWT from another middleware  // if (req.auth) return req.auth.userId; // Example: Clerk auth object  // const userCookie = req.cookies.get('user-id');  // if (userCookie) return userCookie.value;  return undefined;}export async function middleware(req: NextRequest) {  const startTime = Date.now();  // IMPORTANT: Must call NextResponse.next() or rewrite/redirect to get response object  const response = NextResponse.next();  // If Moesif isn't configured, just return the response immediately  if (!moesifController) {    return response;  }  // --- Capture Request Data ---  const reqHeaders: { [key: string]: string } = {};  req.headers.forEach((value, key) => {    reqHeaders[key] = value;  });  // Carefully read request body if enabled and necessary  let reqBody: any = undefined;  if (moesifOptions.logBody && req.body && req.method !== 'GET' && req.method !== 'HEAD') {    try {      // Clone the request to read the body non-destructively      const reqClone = req.clone();      const contentType = reqHeaders['content-type'];      if (contentType?.includes('application/json')) {        reqBody = await reqClone.json();      } else if (contentType?.includes('application/x-www-form-urlencoded') || contentType?.includes('text/plain')) {        reqBody = await reqClone.text(); // Log form data or text as string      }      // Add handling for other content types (e.g., multipart/form-data) if needed      // Be mindful of Edge function size and memory limits for large bodies    } catch (error) {      if (error instanceof Error) {        console.error("Moesif Middleware: Error reading request body:", error.message);      } else {         console.error("Moesif Middleware: Unknown error reading request body:", error);      }      // Decide how to handle body reading errors (e.g., log error message)      reqBody = { moesif_middleware_error: 'Failed to read request body' };    }  }  // --- Capture Response Data ---  // Response data (status, headers) captured here is from NextResponse.next()  // Modifications made by the actual page/API handler are NOT captured here.  // For accurate final response logging, Method 1 is more reliable.  const resHeaders: { [key: string]: string } = {};  response.headers.forEach((value, key) => {    resHeaders[key] = value;  });  // --- Construct Moesif Event Data ---  const eventData = {    request: {      time: new Date(startTime).toISOString(),      uri: req.url,      verb: req.method,      headers: reqHeaders,      ip_address: req.ip,      api_version: undefined, // Extract from URL/headers if applicable      body: reqBody,    },    response: {      time: new Date().toISOString(), // Time approximates end of middleware processing      status: response.status, // Status from NextResponse.next()      headers: resHeaders,      // Headers from NextResponse.next()      body: undefined,        // Response body cannot be captured here reliably    },    userId: getUserId(req),   // Call your user identification logic    // companyId: getCompanyId(req), // Call your company identification logic    // sessionToken: req.cookies.get('session')?.value,    // metadata: { edge_processed: true },  };  // --- Send Event to Moesif (Non-blocking) ---  // Use trackEvent() and run asynchronously without awaiting  Promise.resolve(moesifController.trackEvent(eventData)).catch(err => {      console.error("Moesif Middleware: Error sending event:", err);  });  // Return the response to allow request processing to continue  return response;}// --- Configure Middleware Path Matching ---// Apply the middleware only to the paths you want to monitorexport const config = {  matcher: [    /*     * Match all request paths except for the ones starting with:     * - _next/static (static files)     * - _next/image (image optimization files)     * - favicon.ico (favicon file)     * Feel free to adjust this to be more specific, e.g., only '/api/:path*'     */    '/((?!_next/static|_next/image|favicon.ico).*)',  ],};Key considerations for Edge Middleware:  We use MoesifController directly, not the Express-style middleware function.  Event data (request details, response status/headers available at this stage) is manually constructed.  Reading the request body requires req.clone() and careful error handling. Be aware of potential size limits on the Edge.  The final response details (body, status/headers modified by the page/API handler) are not reliably captured in this middleware method after NextResponse.next() is called. Use Method 1 for full response capture.  The Moesif event is sent asynchronously (Promise.resolve().catch()) to avoid delaying the response to the client.  Customize the matcher in config to precisely control which paths trigger the middleware.Step 5: Verification  Start your Next.js development server:    npm run dev# oryarn dev        Trigger the API routes or pages that are configured with Moesif (based on the method and matcher you implemented). Use tools like curl, Postman, or simply access them via your browser.  Log in to your Moesif account dashboard.  Navigate to the Live Event Log. You should see incoming events from your Next.js application appearing within a minute or two. Verify that the request details (URL, method, headers, body if enabled) and user identification (if configured) look correct.Step 6: Optional Configuration & FeaturesMoesif offers many configuration options to tailor its behavior. Explore these in the moesifOptions object (lib/moesifNode.js for Method 1, or adapt for manual construction in middleware.ts for Method 2):  User/Company Identification: Implement identifyUser and identifyCompany functions (highly recommended for associating traffic with users/accounts).  Body Logging: Toggle logBody: true/false.  Content Masking: Implement maskContent to hide sensitive data in logged headers or bodies.  Skipping Events: Implement skip to prevent certain requests (e.g., health checks, static assets) from being logged.  Metadata: Add custom metadata to events using the metadata field in Method 2 or the getMetadata option in Method 1.  Session Tokens: Associate events with user sessions using sessionToken (Method 2) or getSessionToken (Method 1).  Outgoing Request Capture: Monitor API calls made by your Next.js server (requires moesif-nodejs-capture-outgoing).Refer to the official Moesif Node.js SDK Options documentation for comprehensive details: https://www.moesif.com/docs/server-integration/nodejs/options/Choose the integration method that best suits your Next.js architecture (Pages Router vs. App Router) and your specific logging requirements (Edge vs. Server-side, full response capture needs).A detailed example repo for NextJs with Moesif can be found on github.",
          "url": " /technical/api-development/Vercel-NextJs-Moesif-AI-Apps/",
          "author": "Xing",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-wso2-kubernetes-gateway-moesif-api-analytics-drive-api-performance-and-adoption": {
          "title": "WSO2 Kubernetes Gateway + Moesif API Analytics: Drive API Performance and Adoption",
          "content"	 : "WSO2 Kubernetes Gateway provides a robust, Kubernetes-native platform for managing APIs. It’s purpose-built for cloud-native teams requiring fine-grained control over APIs in modern, distributed environments. With support for microservices architecture, secure ingress, and service discovery, Kubernetes Gateway solves the infrastructure side of the API equation.But API success doesn’t end at infrastructure. Engineering and product teams also need to understand how APIs are used, by whom, and what drives value. True success hinges on delivering value from APIs and ensuring best possible customer experience. Without deep API analytics and behavioral insights, it becomes difficult to optimize performance, troubleshoot issues, and drive API adoption.That’s where Moesif becomes essential. While Kubernetes Gateway powers secure, scalable delivery, Moesif powers insight and visibility with powerful monitoring, reporting, and analytics—empowering teams to monitor usage, resolve issues faster, and grow their API programs with data. Moesif enables access to crucial API analytics metrics by retrieving and utilizing data from API dashboards and reports.In this article, we’ll demonstrate how integrating WSO2 Kubernetes Gateway with Moesif unlocks better performance, adoption, and long-term value from your APIs.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        Table of Contents  Introduction to API Analytics          What is API Analytics?      Benefits of API Analytics        What is WSO2 Kubernetes Gateway?  What is Moesif API Analytics?  Benefits of Using WSO2 Kubernetes Gateway and Moesif Together          Extending Kubernetes Gateway’s Central Intelligence Hub with Actionable Insights      Enhanced Observability      Better Troubleshooting with More Context and Detail      Data-Driven Product Decisions        Setting up Moesif with Kubernetes Gateway  Key Insights: Engineering and Product Metrics          Engineering-Focused Metrics                  Analyzing Latency          Monitoring API Error Rates                    Product-Related Metrics                  Analyzing API Adoption          Analyzing User Engagement          Understanding API Usage                    Analyzing HTTP Payload        AI Explain: AI-Powered Insights  ConclusionIntroduction to API AnalyticsAPI analytics is the process of collecting, analyzing, and interpreting data related to API usage, performance, and behavior. It provides valuable insights into how APIs are being used, which endpoints are most popular, and how to improve API performance and scalability. By leveraging API analytics, businesses can gain a deeper understanding of their API ecosystem, identify trends, and make data-driven decisions to enhance their API offerings.What is API Analytics?API analytics is a critical component of API management, enabling businesses to make data-driven decisions about their APIs. It involves collecting data on API usage, performance, and behavior, and using that data to identify trends, patterns, and areas for improvement. By understanding how APIs are being used, businesses can optimize their API strategies, improve user experiences, and drive growth. API analytics helps in monitoring key metrics such as response times, error rates, and usage patterns, providing a comprehensive view of API performance.Benefits of API AnalyticsAPI analytics offers numerous benefits, including:      Improved API Performance and Scalability: By analyzing usage metrics and identifying performance bottlenecks, businesses can optimize their APIs for better performance and scalability.        Enhanced Customer Experience: Understanding how users interact with APIs allows businesses to improve the overall user experience, leading to higher satisfaction and retention rates.        Increased Revenue and Business Growth: Data-driven insights enable businesses to identify new opportunities, optimize pricing strategies, and drive revenue growth.        Better Decision-Making and Strategic Planning: With comprehensive data on API usage and performance, businesses can make informed decisions and develop effective strategies for their API programs.        Improved Security and Compliance: API analytics helps in monitoring and identifying security vulnerabilities, ensuring compliance with industry standards and regulations.        Enhanced Agility and Responsiveness to Changing Market Conditions: By continuously monitoring API performance and usage, businesses can quickly adapt to changing market conditions and user needs.  What is WSO2 Kubernetes Gateway?WSO2 Kubernetes Gateway is an open-source, cloud-native API management platform designed specifically for Kubernetes environments. It allows organizations to manage the full API lifecycle—design, deployment, policy, enforcement, and security—while staying aligned with Kubernetes-native principles like declarative configuration and automated scaling. If you’re building microservices and you want agility and resilience, WSO2 Kubernetes Gateway fits naturally.Kubernetes Gateway follows a modular architecture that separates the control plane from the data plane. The control plane defines API configurations and policies, while the data plane handles runtime traffic and enforcement. This separation enhances both performance and security, especially at scale.Kubernetes Gateway supports multiple API types out of the box, including REST, GraphQL, and even AI workloads, giving you flexibility across consumer needs. It includes built-in support for deploying APIs directly inside Kubernetes clusters to take full advantage of the platform’s scalability and fault tolerance.While Kubernetes Gateway provides basic monitoring capabilities, pairing it with a dedicated API analytics platform like Moesif allows you to understand both operational and business API metrics, empowering your teams to deliver more value from your APIs for customers.What is Moesif API Analytics?We’ve designed Moesif as a comprehensive API analytics and monitoring platform that provides deep insights into how customers are using your APIs and how the APIs are performing, all in real time. Moesif goes beyond basic monitoring to offer advanced analytics, as we’ll soon demonstrate in the article. Through Moesif, both engineering and product teams can understand user behavior, troubleshoot issues, and make data-driven decisions confidently. Moesif is built to handle the complexities of modern API programs, including ones with microservices architectures and deployed in Kubernetes environments.When you want to maximize the benefits of API analytics, it’s important to remember its three primary aspects—collecting, analyzing, and visualizing data related to API usage and performance. Moesif handles that entire process automatically in real time with minimal intervention. Once you set Moesif up, the analytics data collection happens automatically, and becomes available for you in the platform for analysis. Users can download raw data for offline analysis or integrate it into their own visualization tools.Moesif gives you valuable information like the following:      How users interact with your APIs        The most popular endpoints, or in business terms, features        Areas where performance bottlenecks exist        How to improve the overall API experience.  Moesif excels in providing both product-centric and engineering-centric metrics. For product managers, Moesif offers insights into API usage patterns, user segmentation, adoption rates, and retention. This helps them understand the most valuable features, identify opportunities for new features, and track the success of their API products. For engineers, Moesif provides detailed information on API performance, including response time, error rates for API methods, and latencies. Developers can quickly identify and resolve bottlenecks, troubleshoot errors, and make sure the API platform continues to run smoothly.Benefits of Using WSO2 Kubernetes Gateway and Moesif TogetherBy integrating Moesif with Kubernetes Gateway, you get a unified ecosystem that can perform both API management and API analytics. The power lies in how they complement each other , creating a complete solution to help you succeed in building, deploying, and managing your cloud products.Extending Kubernetes Gateway’s Central Intelligence Hub with Actionable InsightsWSO2 Kubernetes Gateway’s control plane provides the foundation for managing the API lifecycle. However, the control plane’s built-in monitoring often lacks the depth you need for comprehensive analysis of API calls. This is where Moesif steps in. By seamlessly integrating with Kubernetes Gateway’s data plane, Moesif captures detailed, real-time information about every API call, including user actions like signups. We’re not only talking about surface-level data; Moesif can inspect request and response payloads and the entire context around them, providing granular visibility that Kubernetes Gateway alone cannot offer. This bridges the gap between managing APIs and truly understanding their usage.Enhanced ObservabilityKubernetes Gateway gives you the what of API activity—which APIs are being called, at what frequency, and with what basic success and failure rates. Moesif adds to it the why and how, ensuring APIs are functional and accessible. With Moesif, you can find definitive answers to questions like:      Why is a particular API experiencing high latency?        How are users interacting with different API endpoints?  Moesif’s advanced analytics, including customer segmentation, cohort analysis, and funnel analysis, provide answers to these critical questions. This combined observability empowers teams to move beyond reactive troubleshooting to proactive optimization.Better Troubleshooting with More Context and DetailWhen an API error occurs, WSO2 Kubernetes Gateway’s logs can indicate that a problem exists. How do you go from there? Moesif gives you the context you need for rapid and effective resolution of problems. As an engineer, you can immediately see the specific user, the exact request, the full response, and even the activities leading up to the error. This frees you from the time-consuming process of manually correlating data from multiple sources. The combination of Kubernetes Gateway’s infrastructure-level view and Moesif’s detailed API-level view fosters far better and refined troubleshooting processes.Data-Driven Product DecisionsFor product leaders, the integration offers a holistic view of the API program. WSO2 Kubernetes Gateway gives you peace of mind that the APIs are running smoothly, while Moesif reveals how those APIs are being consumed and contributing to business goals. Are users adopting new features? Which API endpoints are most valuable? Where are users encountering friction? Moesif’s insights, in conjunction with Kubernetes Gateway’s operational data, provide the complete picture necessary to make informed decisions about API roadmaps, feature development, and overall strategy.Setting up Moesif with Kubernetes GatewayYou can set up WSO2 Kubernetes Gateway with Moesif in four simple steps following the instructions in Moesif WSO2 Kubernetes Gateway Plugin installation docs. Before you follow these steps, make sure you meet the following prerequisites:      An active Moesif account (sign up if you haven’t already)        Moesif Application ID  If you sign up, Moesif shows you your Application ID during the onboarding process. You can always get your Application ID anytime by following these steps:      Log into Moesif Portal.        Select the account icon to bring up the settings menu.        Select Installation or API Keys.        Copy your Moesif Application ID from the Collector Application ID field.   Key Insights: Engineering and Product MetricsLet’s go through some practical examples to illustrate how Moesif can help engineers and product leaders with valuable insights into their APIs.Engineering-Focused MetricsFor engineering teams, Moesif provides the toolkit to monitor API performance, troubleshoot issues, and maintain reliability. Let’s explore some examples.Analyzing LatencyMoesif can analyze latency and error rates of your API in different ways. For example, you can break down performance for individual API endpoints and thus identify bottlenecks that might be plaguing your customers.You have several prebuilt latency analysis types to get started, including a P90 latency analysis type, along with a maximum and a minimum. You can of course define your own custom analysis by configuring the metrics. For example, here, we use Moesif Time Series  to define a custom P99 metric and break the analysis down across API endpoints.Monitoring API Error RatesAPI errors directly affect customer experience. Especially, in a microservices architecture, you have various components that communicate with one another. Errors can originate from anywhere and it propagates through the system. Moesif’s Live Event Log captures every activity in real time so you have the best visibility into what’s happening.Moesif also captures all the contextual information like headers and payloads, giving you a broader context for each API call and custom actions. Consider you have an AI product that uses a third-party AI API. These APIs often have custom error messages. Without Moesif, you’ll miss out on that context.Here’s an example that gives a breakdown of 5xx server errors in a time series analysis. It plots the metric on a per-hour basis and chooses the past 12 days as the period of time to analyze for. Using Group By, the example also categorizes the analysis by response status codes to easily understand the exact error types.Here’s another cool thing this example does—folding the time series: notice the active-state button beside the analysis period Last 12 Days. The folding or wave-slicing technique helps visualize the periodicity of a time series graph, for example, if you want to understand recurring error patterns or periodic anomalies. In this case, we look for a particular hour where errors rise to especially high levels. This can help you investigate issues like the following:      Peak load times overwhelming the API        Scheduled maintenance or batch jobs that cause temporary disruptions        Time-dependent errors in external services that the API relies on  Let’s see another example:This example breaks down all errors by their respective HTTP status codes. Moesif’s powerful grouping and segmentation utilities allow you to break down metrics by different criteria and look at them from different perspectives. In this example, we can easily observe and get an idea about the distribution of errors in the API in real time. Let’s say you observe an increasing number of `401 Unauthorized` errors. That can indicate a security issue. If you see a spike in `502 Bad Gateway` errors, it can point to issues in your WSO2 Kubernetes Gateway instance.Product-Related MetricsTo build a successful API product, you must thoroughly understand your customers and how they consume your API’s services. You need to look at different metrics in various ways to decisively gauge the business impact of your APIs so you can drive growth through precise API product strategy. Let’s go through some examples and demonstrate how Moesif can help.Analyzing API AdoptionBy understanding API adoption trends, you can easily answer product-critical questions like these:      Are our newly released APIs gaining traction?        Which user segments are adopting them the fastest?  With clear adoption metrics, you can make data-backed decisions to improve marketing efforts and craft better API roadmaps.Take this example for instance: You’ve recently released a major version of your API, v4. You want to compare its uptake among existing customers relative to the latest release of the prior version. The following time series analysis captures API traffic over the last 30 days, categorizing requests by company domains and API versions. To accurately measure API call volume, the analysis applies a Rolling Window function for a more refined trend analysis.Analyzing User EngagementSuccessful API products prioritize understanding how developers move from onboarding to meaningful engagement. Moesif’s funnel analysis in the customer analytics suite makes it easy to measure this journey—from initial sign-up to first successful call and beyond.In the following example, we visualize a three-step funnel in a GenAI API product:      Sign-in        First embedding request        100+ tokens consumption  Each step represents a deeper level of engagement, helping teams track both time-to-first-hello-world and time-to-value. With Moesif, you can see exactly how many users complete each step, how long it takes them, and where they drop off. This data helps you:      Identify onboarding friction and optimize documentation or SDKs.        Benchmark the average time-to-first meaningful usage.        Spot behavioral patterns that correlate with retention or churn.  Understanding API UsageIf you want to efficiently plan resources and scale, you must understand how customers engage with your APIs. Moesif gives your teams the necessary means to track API traffic volume, pinpoint high-usage endpoints, and analyze long-term trends. With these insights to guide you, you can optimize infrastructure costs and allocate efficiently while avoiding unnecessary over-provisioning. On a strategic level, product teams can double down on this data to strategize API expansion efforts.And if you’re working with AI-driven applications in Kubernetes Gateway, Moesif has your back with real-time tracking of AI consumption. You can monitor token usage, analyze API demand, and troubleshoot AI traffic with ease. Since Moesif captures request-response data with deep context, you can filter and break down API usage by factors like rate limits, token consumption, and cost efficiency.As an example, here we perform a Segmentation analysis to rank the top customers by API call volume. We also observe the average input token usage and related costs for each customer over the past 7 days.Analyzing HTTP PayloadOne of Moesif’s most powerful debugging features comes from its detailed request and response payload analysis. When error messages lack sufficient detail, engineers can examine the full API data exchange to dig out the root cause of issues.If your API handles deeply structured payloads, Moesif makes it easy to navigate, filter, and focus on the most relevant piece of data so you can have a precise and efficient troubleshooting process. For example:{  "request_id": "req-xyz-987",  "timestamp": "2024-11-09T14:45:00Z",  "response": {    "status_code": 429,    "headers": {      "Retry-After": "60",      "X-Model-Version": "gpt-4-turbo"    },    "body": {      "error": {        "code": "rate_limit_exceeded",        "message": "Token limit exceeded.",        "retry_after_seconds": 60,        "usage": {          "current_tokens_used": 498500,          "allowed_limit": 500000        }      },      "ai_output": {        "summary": "The document discusses a fox jumping over a dog.",        "tokens_used": 750,        "confidence_score": 0.92      }    }  }}To give you an idea what you can achieve with Moesif’s HTTP body analytics, here are some more examples and sample use cases:      Consider building an AI-powered medical image analysis API, integrated into WSO2 Kubernetes Gateway. The system has to process MRI scans, X-rays, and CT images to detect anomalies like tumors and  fractures. For accurate diagnoses, the AI model requires high-resolution image data, patient history, and additional metadata like scan modality and radiologist notes. As a result, payloads can get large and complex, often including base64-encoded images, structured metadata, and deep learning model parameters. With Moesif’s Live Event Log, developers can monitor these payloads in real time. They can troubleshoot image upload failures, optimize API request sizes, and guarantee compliance with medical data regulations.        With Moesif’s granular capture of request-response data, you can analyze their full context and make sure your AI app performs optimally, even when handling large inputs.        By using key count operators, you can identify payloads with a high number of keys. This allows you to detect large or overly complex API requests that might slow down performance.        Moesif allows you to define custom metrics tailored to your API traffic for insights into user behavior and business performance. For example, in an e-commerce API, tracking the average cart value (ACV) can help pricing strategies and discount optimizations. It’s not your run-of-the-mill metric, but understanding how cart values fluctuate over time gives product teams actionable insights. With Moesif, you can create a scripted field to extract the cart total directly from request or response payloads, for example, Request.Body.cart.total_amount or Response.Body.order_summary.total_price.  AI Explain: AI-Powered InsightsMoesif has introduced AI-driven features into its analytics suite for smarter intelligence, giving you deeper insights from your analysis minimal effort:      Smart Search gives you quick navigation across dashboards, workspaces, and alert rules, so you can enjoy a more streamlined workflow.        AI Explain, an AI-powered conversational assistant, provides instant insights across API analytics, customer analytics, and alerting, making investigating issues faster and more intuitive.  With these AI-enhanced tools, Moesif simplifies API analytics for both technical and business teams. For engineers, AI Explain serves as an intelligent assistant, summarizing API usage trends and helping detect anomalies. As a result, you have minimal troubleshooting time during incidents. Instead of manually parsing complex data, teams receive concise explanations of what’s happening. Product teams can leverage AI-driven insights to track user adoption trends, identify unusual usage patterns, and measure feature impact.For instance, in a WSO2 Kubernetes Gateway-powered embeddings API, AI Explain can analyze adoption trends over time from a time series analysis, providing clear insights into growth, retention, and usage patterns:Then, you interact with Ask AI to quickly analyze and interpret the data:ConclusionManaging and delivering APIs at scale takes more than just a capable gateway. WSO2 Kubernetes Gateway lays the foundation with Kubernetes-native control and flexibility, but true API success requires visibility into how these APIs drive adoption, retention, and revenue. That’s where you need Moesif. By pairing Kubernetes Gateway with Moesif, you have the insights to resolve issues faster, optimize, and grow strategically—turning every API call into a growth opportunity.To see for yourself how, sign up today for a free trial, no credit cards required.                Deep API Analytics with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design, Build, and Deliver Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/WSO2-Kubernetes-Gateway-Moesif-API-Analytics-Drive-API-Performance-And-Adoption/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-wso2-choreo-moesif-api-analytics-optimize-api-performance-and-drive-adoption": {
          "title": "WSO2 Choreo + Moesif API Analytics: Optimize API Performance And Drive Adoption",
          "content"	 : "As developers, we know how essential APIs have become in building modern software systems, from internal services to public-facing products at scale. We spend countless hours crafting elegant code, meticulously designing interfaces, and ensuring everything should work flawlessly. And, we must ship these services fast to stay competitive.But what happens after deployment? In reality, even the most beautifully crafted API can fall flat without deep insight into its real-world usage. Without the right analytics, we’re essentially flying blind, relying on intuition rather than data. This holds especially true when many use COTS (commercial off-the-shelf) for platforms.The good news is, you can combine a comprehensive all-in-one platform like WSO2 Choreo with a powerful analytics tool like Moesif to gain the visibility you need. Choreo streamlines the development and deployment process, while Moesif API analytics helps you with granular, actionable insights to truly understand API usage and performance.This article will dive into the practical steps of integrating these tools and practical examples on how to use this integration to build, monitor, and optimize your APIs with confidence.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free          What is WSO2 Choreo?  What is Moesif?  Benefits of Using WSO2 Choreo and Moesif Together          Enhanced Visibility and a Single Source of Truth      Accelerated Time to Market and Reduced Development Cycles      Resolving Issues Proactively and Improved API Performance for Developers      Data-Driven API Product Strategy      Optimizing AI API Costs         How to Set Up WSO2 Choreo With Moesif  Key Insights: Engineering and Product Metrics          Dashboard Overview      Engineering Metrics                  API Performance and Latency Analysis          Error Analysis                    Product Metrics                  User Engagement          API Adoption          API Usage Analysis                    HTTP Payload Analysis        AI Explain: From Data to Understanding  ConclusionWhat is WSO2 Choreo?WSO2 Choreo is an AI-native internal developer platform as a service designed to streamline the creation, deployment, and management of cloud-native applications. It serves as a comprehensive toolkit for the entire API lifecycle, enabling developers to build, expose, and manage APIs more efficiently and at scale.WSO2 Choreo also offers a unified platform for managing infrastructure, pipelines, and deployments. This allows platform engineers to provide an internal developer platform that enables developers to focus on building digital experiences without the complexities of underlying infrastructure.Choreo simplifies API development and delivery by integrating service deployment, traffic control, and authentication into a single platform. From a technical perspective, Choreo includes a robust API gateway—a key component for managing API traffic, enforcing security policies, and enabling integration with external tools like Moesif. This integration provides a deeper understanding of API usage and performance, enhancing the overall observability of applications. In addition to API management, Choreo supports the development of microservices and automations, offering features such as deep observability, AI-assisted development, and a marketplace for sharing and reusing components. These capabilities collectively empower organizations to innovate rapidly and efficiently in their digital transformation journeys.What is Moesif?The Moesif platform gives you deep insights into how your customers are using your APIs and how those APIs are performing in real time. Moesif takes you beyond basic monitoring to reveal the intricate details of API traffic, user behavior, and potential bottlenecks. Moesif automatically collects data related to API usage, performance, and user interactions and allows you to analyze and interpret them in meaningful, accessible ways. This goes beyond simple metrics like request counts and error rates. You can dive into the who, what, when, where, and why of API consumption:      Who are your most active users?         What are your most popular API endpoints?         When are peak usage times?         Where are users experiencing friction?         Why are certain API calls failing?   We’ve built Moesif to answer these questions and more.Moesif’s key strength lies in its ability to track not just API events like API calls, but also the contextual data around them such as the user and their journey. With  powerful body analytics, you can understand complex usage patterns and identify patterns that cause a bad API experience. This can prove extremely invaluable for troubleshooting and debugging. In such cases, you must understand the context of an API event in its entirety to resolve issues effectively so they don’t escalate.Beyond troubleshooting, you can leverage Moesif to track API adoption and understand how  API usage drives  business metrics like revenue or customer satisfaction. can therefore make business-critical decisions with confidence Using such a holistic view, product managers and business leaders can help their organizations understand the true value of their APIs and make strategic decisions about future development efforts. Moesif empowers both technical and business teams to collaborate around a single source of truth for API data.Benefits of Using WSO2 Choreo and Moesif TogetherWSO2 Choreo’s strengths in API development and management, combined with Moesif’s deep API analytics capabilities orchestrate a greater collective impact on your product’s growth. You can build and deploy quickly, and you have the insights to ensure optimal performance and continuous improvement.Enhanced Visibility and a Single Source of TruthChoreo provides a centralized platform for managing the entire API lifecycle, while Moesif adds a layer of granular, actionable analytics. This combination eliminates data silos and creates a single source of truth for all API-related information. Engineering teams can quickly identify performance bottlenecks, product teams can track API adoption, and business leaders can understand the overall impact of their API program. Thanks to this unified view, you can collaborate better and make decisions faster and accurately. Moesif reinforces your goals by equipping you with everything you need for API analytics, observability, and productization.Accelerated Time to Market and Reduced Development CyclesProduct teams can use Moesif’s data to prioritize new features and iterate faster based on actual user behavior. As a result, you significantly reduce time-to-market and allow teams to ship faster and more confidently.Resolving Issues Proactively and Improved API Performance for DevelopersBy integrating Moesif, you get more detailed monitoring and alerting capabilities for your product, covering both operational aspects like uptime and performance, and deeper API Analytics. Instead of reacting to problems after they impact users, teams can identify and address anomalies before they escalate. This helps you provide a better customer experience and satisfaction.Data-Driven API Product StrategyWhen it comes to data-driven decision-making, you need actionable insights into customer behavior and API usage patterns. Moesif makes them readily available. Product managers can quickly investigate and observe business-critical information like the following:       The most popular APIs        Most used features and ones that are underused         How different user segments are interacting with APIs  Product managers can inform API design, prioritize new features, and refine the overall product strategy with data-backed insights. And with a well-defined product strategy that’s customer and market-driven, teams can build APIs that truly meet the needs of their users, leading to increased API adoption and greater business value.Optimizing AI API Costs By combining Choreo’s platform capabilities with Moesif’s detailed API analytics, organizations can optimize resource allocation and control operational costs—a critical factor for APIs with high computational overhead like AI APIs. As they often possess expensive compute resources, understanding usage patterns directly correlates with cost control.For example, consider an organization running an AI-powered text-generation API. They might notice spikes in API calls during business hours but significantly lower traffic at night. With Moesif, they can identify low-usage periods and dynamically scale down compute resources to reduce costs. Similarly, Moesif’s retention analysis can reveal whether free-tier users are overusing certain AI endpoints—for example, high-resolution image processing.This data-driven approach leads to better resource utilization, reduced costs, and improved overall operational efficiency.How to Set Up WSO2 Choreo With MoesifMoesif has native integration with the Choreo platform and installs in three simple steps. For detailed instructions on how to integrate Moesif with Choreo, see Integrate Choreo with Moesif. If you face any issues setting it up, see the Server Troubleshooting Guide or contact our support team. Key Insights: Engineering and Product MetricsOkay, so we’ve got the integration set up. Now comes the fun part—actually seeing what goes on with our APIs. In this section, we’ll go through some practical examples and demonstrate how Moesif makes various engineering and product metrics accessible to you easily and meaningfully.Dashboard OverviewWe’ve designed Moesif’s dashboards to be useful and fully customizable so you can control how you arrange, share, and consume API analytics data. You can customize them to focus on the metrics that matter to your team and your specific project, for example, separate dashboards for product, marketing, and engineering teams. Here’s a Product dashboard containing several product-related metrics:Each dashboard contains workspaces, each containing an analysis or visualization chart. In the preceding screenshot, the Product dashboard contains workspaces like Top APIs over 7 days, 30-Day API Usage by Company, and so on. You can also create sub-dashboards within a dashboard.Engineering MetricsFor engineering teams, Moesif provides a wide range of analytics tools to analyze and monitor APIs to make sure they remain performant, identify bottlenecks, and troubleshoot issues promptly. Let’s go through some example metrics. API Performance and Latency AnalysisMoesif can track key performance indicators (KPIs) like latency, error rates, and throughput. You can perform simple average latency analysis, and also percentile analysis like P90 or P99. The corresponding visualizations allow you to easily interpret the data in real time to spot anomalies and trends.Percentile analysis more often than not can give you a more accurate idea of your APIs’ performance characteristics, as they don’t suffer from outliers unlike averages. You can do this easily in Moesif.  For example, the following illustrates a 24-hour Time Series analysis with an hourly interval performing a P99 analysis across API endpointsError AnalysisWhen it comes to API errors, we typically mean 4xx client errors and 5xx server errors. Moesif goes beyond simple error counts or error code tracking. It captures all the contextual information like headers and payloads of API methods. This example visualizes hourly 5xx server errors over a 12-day period, using a time series analysis. To pin down the exact issue, we classify the errors further by response status codes.As another demonstration of Moesif’s powerful API analytics utilities, we’ve folded this time series to visualize the periodic nature of our metric in a better way. If you want to understand recurring patterns like error rate patterns and periodic anomalies, folding is the way to go. This allows you to focus on worst-case scenarios as well in our operational metrics. In this scenario, we check for a particular hour where errors surge to unusually high levels. It can help dig out issues like these:      Peak load times overwhelming the API        Scheduled maintenance or batch jobs that cause temporary disruptions        Time-dependent errors in external services that the API relies on  Here’s another example that breaks down all API error responses in a time series:That’s one of the neat aspects of Moesif—being able to categorize and break down metrics by different criteria, using various segmentation and grouping utilities. It allows you to observe the distribution of a metric, like in this case, API errors. For example,      An increasing number of 401 Unauthorized errors can indicate a security issue.         An increase in 502 Bad Gateway errors can signal backend service failures or bottlenecks in database queries.  Product MetricsFor product teams, Moesif provides insights into user behavior, API adoption, and the overall business impact of your APIs. Let’s demonstrate with some examples.User EngagementMoesif has built a separate analytics suite for customer analysis that, in conjunction with API analytics, gives you the ability to make accurate product decisions. For example, here, using a Segmentation analysis, we look at the top users for each endpoint in an API and visualize the data in a stacked bar chart:API AdoptionAnalyzing API adoption gives you answers to questions like the following:      Are our new APIs gaining traction?         Are certain user segments adopting them faster than others?   When you have these answers and the actionable data to back them up, you can confidently inform marketing efforts and future development.For example, let’s say you’ve launched a new version of your API (v4), and you want to track its adoption among your existing customer base compared to the immediate prior version (v3). In the following time series, we look at API usage for the past 30 days. We break the data down by company domains and API versions. We choose a custom rolling function Rollling Window to quantify the API call volume metric.API Usage AnalysisUnderstanding how customers are using your APIs is an important part of capacity planning and resource allocation. Using Moesif, you can dig into metrics such as API call volume, the most popular endpoints, and usage patterns or areas over time. This information can help engineers identify areas of optimization or where resources might be over-provisioned. From a big picture perspective, it also gives your product team an idea about focus points for growth.For example, when building a cloud-native AI product in Choreo, Moesif helps you  monitor AI consumption, analyze user behavior, and debug API traffic in real time. By capturing HTTP request-response data with high granularity, Moesif allows you to filter and analyze product usage based on token utilization, rate limits, and other cost-related factors.The Segmentation analysis here provides an overview of top customers ranked by average input token usage, API request count, and associated costs over the last seven days.HTTP Payload AnalysisOne of the most powerful techniques Moesif provides you for troubleshooting is analyzing the full request and response payloads of your API calls. Thanks to this, you can comfortably debug complex issues where the error messages alone don’t suffice. You can examine the exact data being exchanged between your API and its consumers, giving you greater context around errors.With Moesif, you can inspect complex, deeply nested payloads and quickly zero in on the information that matters most.Here are some more illustrative use cases using HTTP body analytics:      Consider building an AI-powered coding assistant and deploying this API on Choreo. It receives snippets of code from users, and returns suggestions, completions, or even bug fixes. The context—the surrounding code, the project type, even the user’s coding style—is crucial for accurate results. This context can lead to very large request payloads. You can use Moesif to analyze this in a Live Event Log, for example:        Moesif lets you see inside these requests, understand the context, and help you make sure your AI app is performing as expected, even with large inputs.        Key count operators help you analyze the number of keys in an object, making it easier to detect large and complex payloads for performance debugging and optimization.        You can specify custom metrics based on the data flowing through your services to gain valuable insights into user behavior and API usage. For example, for a ride-sharing service API, let’s say you want to measure the distribution of ride distances. This isn’t a standard metric like latency or error rate, but it helps understand user behavior and optimizing pricing. With Moesif, you can create a scripted field that extracts the ride distance from the request or response payload, for example, Request.Body.trip.distance_miles and Response.Body.trip_summary.distance_miles  AI Explain: From Data to UnderstandingMoesif has introduced AI-powered features to augment your expertise in leveraging API analytics data:      Using Smart Search to navigate through customer and API analytics dashboards, workspaces, alert rules, and more.        Using AI Explain, our AI-powered conversational interface, in API analytics, customer analytics, and alerting, to quickly uncover valuable insights and investigate issues.  Through these AI-enhanced features, we aim to make API analytics more accessible and understandable. For engineers, AI Explain acts like an intelligent assistant that can summarize complex API usage patterns and highlight anomalies, drastically reducing troubleshooting time during incidents. Instead of manually interpreting data, you get a concise explanation of what’s happening. Product teams benefit from AI-powered insights into user behavior, like identifying cohorts with unusual usage patterns or understanding the impact of new feature releases. Moesif’s AI cuts through the noise to deliver the key insights that both technical and business teams need to make informed decisions, faster. It’s about working smarter, not harder.For example, consider an embeddings API in WSO2 Choreo. Here’s how you can analyze its growth and adoption using a time series analysis:Next, you start chatting with Ask AI to quickly get insights into the data:ConclusionUsing Choreo and Moesif together gives you streamlined API development and delivery with actionable API analytics in every step of your API lifecycle. To see for yourself how Moesif can help you unlock the full potential of your API products, sign up today for a free trial, no credit cards required.                Deep API Analytics with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design, Build, and Deliver Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/WSO2-Choreo-Moesif-API-Analytics-Optimize-API-Performance-And-Drive-Adoption/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "podcasts-developers-podcast-apis-over-ipas-customer-observability-mike-amundsen": {
          "title": "APIs Over IPAs 15: Customer Observability with Mike Amundsen, Amundsen.com",
          "content"	 : "In this episode, Derric Gilling, CEO of Moesif, sits down with Mike Amundsen, a leader in the API ecosystem, to explore the evolving role of observability in API design and customer experience.They break down the differences between machine vs. customer observability, best practices for tracking key metrics, and the importance of designing observability into APIs from the start. Mike also shares insights on API product management, the lifecycle of APIs, and how organizations can adapt to AI-driven changes in observability. Whether you’re a developer, product manager, or business leader, this episode is packed with valuable takeaways on optimizing API-driven businesses.Moesif · 15. Customer Observability with Mike Amundsen - Amundsen.comListen to the episode on SoundCloud, Apple Podcasts, YouTube Music, or wherever you listen to podcasts. You can also watch the video on our YouTube Channel.Table of Contents  00:00 Introduction - What is Observability?  01:40 Machine vs. Customer Observability  03:09 Designing for Observability  05:10 Best Practices for API Design and Observability  07:40 Who Owns Customer Observability?  09:14 The Role of an API Product Manager  11:10 API Lifecycle and Deprecation  14:12 Measuring API Value and Sunset Planning  17:39 Common Pitfalls in Observability  20:15 The Impact of AI on Observability  23:42 Future of Observability and APIs  26:10 Final Takeaways and Advice                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        IntroductionDerric Gilling: Alright, welcome to the Moesif podcast learning around APIs and customer observability. Joining us today is Mike Amundsen, leader and expert within the API ecosystem, especially around machine observability but also talking about product observability. And so Mike would love to hear just a little bit about yourself and you know what comes to mind when you, you know, hear the term observability, or especially customer observability.Mike Amundsen: Yeah. Well, first of all, it’s great to join you, Derek. I always enjoy talking with you. We don’t get to do it often enough so I’m happy to be here and share. You and I have talked about this before. I really think of observability as that way to figure out what’s really going on, how it can affect a customer, how it can affect a product, and how that can affect the experience. I tend to think of observability a lot from the design perspective. How can we design early on in the process, designing for the ability to monitor, observe and react in ways that positively affect the product? So. So I tend to think of things like machines, services and products, different versions of observability but also just the design side as well.Machine vs. Customer ObservabilityDerric: Awesome, and you mentioned customer observability and some of this machine-level stuff. How do you differentiate between these different types of metrics, you know? What would be some key examples in terms of the different key metrics to track?Mike: Yeah, so starting from the machine side of things. I think people are pretty familiar with metrics like latency, memory usage, disk space, response time or error rates. These are just the nuts and bolts of getting a system up and running, but they don’t really say much about what’s going on with your product or services between machines. So thinking about service abilities, like how many completions of a cart checkout, how often carts are abandoned, and how many times there are errors connecting to another API. That’s the service layer. And then you think about the product itself, how long it takes a customer to complete a task, do they abandon? Do they not understand or make common mistakes over and over again? Observing the way people behave, the way your service reacts to them is also really critical in helping you build a good product. I think.Designing for ObservabilityDerric: Makes sense. And does this change the approach to observability? You know, it’s one thing to track uptime. But you know tracking a cart checkout, there are lots of ways to run into issues with the cart checkout experience. So how do you wrap your head around what to be tracking and making sure we’re tracking the right things.Mike: Right. I think that’s an excellent question, like you had pointed out. People sort of understand how to do the basics of machines but how do you start tracking other bits? I think that’s where the design part comes in early. When you’re designing an API itself, I’m going to want to pay attention to this transaction—this notion of completing a payment or selecting shipping, collecting the information I need in order to serve this customer better. Or the amount of time it takes a customer to fill out a complex onboarding experience. So, the way I design very often has that monitoring built in. So there’s a constant thread and activity, you know, see how you design by identifying the customer experience itself, not just the user but also the service they’re doing, or authentication or authorization. Just having a tag on this service experience and then collecting that experience gives us an opportunity to say, you know, did you notice it takes a lot of time? Everybody seems to slow down when we get to this point. Maybe we need to re-engineer or redesign the product or service to improve it. So I think putting on that design hat is really important.Derric: I really like that, taking it from the initial design versus treating observability as an afterthought. Because otherwise you start running into trying to pull on and band-aid a product, maybe not even tracking the right things. You’re just kind of throwing observability into the kitchen sink in a way.Mike: Yeah.Best Practices for API Design and ObservabilityDerric: How do you incorporate that in terms of a design process? You know, what are the best practices especially for design and architecture teams who are thinking about shipping new APIs.Mike: Yeah, I try to encourage teams to think the way I was saying about there being an experience—onboarding is an experience, checkout is an experience. Selecting a product is experienced, approving a contract is an experience. Anything that touches that should be monitored as part of the product. So anything touching a particular flow or experience is in the chain something I want to monitor. So, for example, I want to monitor the amount of time it takes. But also monitor things like did they abandon the process? Did they stop? Did they have to save for later and come back for more? Did they get hung up on some feature that seems complicated, like creating a list or something. So really identifying those things is important. I’ll say another thing about observability and monitoring. You know, I mentioned how long it takes or did they abandon? These are what I call proxies. They may not be very good proxies. So one of the other things is really important is to be prepared to say maybe measuring time isn’t the right measure. That’s not the pain point for the customer. Maybe the way we’re asking for information is the pain point. So sometimes at the product or customer level, you need to be pretty creative. You need to get customers in a room and see how they behave and do that becomes much better proxies or representations for what you want to monitor. And can affect the way you create your product.Who Owns Customer Observability?Derric: That’s a really good point. And you know we speak on things like time to first API call or the time to first “Hello World”. And you know, these types of metrics are very cross-functional and can impact documentation to the actual API experience. And you know, what does that mean from an ownership standpoint? Who owns customer observability and ability, and how do you ensure you have the right process to be tracking against those metrics that you’re defining?Mike: Yeah, that’s a good one too. I encourage the notion of product ownership. Whoever your designated product owner or team needs to look out for these experiences I just mentioned. They often need to be the customer’s voice in the room, thinking about not just user interfaces but APIs. Often, the API Dev Rel person is the customer in the room and they need to be pretty vocal and attentive. If we’ve got a target audience of enterprise developers, I need to spend time in the enterprise space and own that experience. That person needs to translate whatever those enterprise people say into things that make sense for our product team, developer team and design team. That goes for all kinds of audiences. I tend to think of this as a way for people to inform products on how they can better support customers.The Role of an API Product ManagerDerric: Awesome, and just changing gears a little. You know we’ve heard this term API product manager and more recently AI product managers are now becoming a thing. And what does that mean? Should you have a product manager for your APIs? You know, if so, what is their day-to-day responsibilities like? What are their goals and responsibilities?Mike: Yeah, that’s a good one. My short answer is yes, you definitely should have a product owner, even for internal APIs. So if my job is to create a set of APIs that make other developers in our organization more productive, effective and reduce mistakes, get things done with higher quality in a shorter amount of time. That’s my product, you know. What can I do? Often I think of product owners as the ones that carry water for everyone else. What can I do to make your job better? How can I improve this experience, reduce time and quality without added effort? And that doesn’t matter whether it’s APIs you’re creating or third-party ones like I mentioned, addressing management. Let’s get a third-party to help us do address management and make sure we’re doing things correctly or taxes. But now I’m responsible for making sure those APIs work well in our ecosystem.API Lifecycle and DeprecationDerric: I like that treating your APIs as products even in internal organizations where you have other teams and business units. That might be the customer in this case, and you need to treat them as a customer just like an external customer. But me, as the API product manager, you know what should I be tracking in terms of business outcomes? As my API matures and eventually potentially an end-of-life scenario, you know we could talk about different experience metrics and adoption metrics. Is it always the same or evolving? And how do I think about what I should be looking at from an observability standpoint?Mike: Well, you know, I think of this again with a product owner hat on. There’s a lifecycle in every product right there, it’s a new product, an experiment—we’re not sure if it solves the problem. We think it will get uptake or maybe it does and we’re realizing its potential, earnings potential or whatever the payback is. Reduced amount of time for internal things and so on. Eventually, that API will start paying off, it’s doing what it should do. That’s probably a good time to leave it alone as a product owner. It’s not really a good idea for me to invest more time and money into changing it. It’ll probably take it a bit further from what its original purpose was. If we need some other behaviors, maybe we need to create a new API. Leave this one alone and let it churn. A great design can get a great audience. Some products have been on the shelf for 60, 70 or 80 years and they’re still serving their customers fine. They don’t need a new box, label or ingredients—they just stay the way they are and so you can. My goal as a product owner is to get that maintenance life, keep it healthy and clean. Maybe fix some bugs but not introduce new material. Eventually that product’s gonna get passed over, people don’t use it as much anymore, they don’t need it that much. Sometimes you build an API to solve a problem and within a year or two, the problem is solved. So it’s time to deprecate that whole thing.Measuring API Value and Sunset PlanningDerric: I really like that approach to being pragmatic before we don’t need to replace and recode every single service or API out there. There’s no value in doing that. That’s just creating work for the sake of it. Speaking more around delivering value through APIs, you know how do you measure that? And how do you ensure the APIs you shipped two or three years ago are still creating value? Then, you know, how do you determine when it makes sense to deprecate at a certain point. You know, you do have to deprecate.Mike: Yeah, that’s good. The value proposition is kind of baked in at the beginning, right? You need to reduce onboarding time, or increase customer engagement by 10% more than last year. We have some kind of engagement metrics, then we need to see if that metric really got realized. You know, did you change the amount of onboarding time? Are people using it more now? So I have to come up with observability elements that make that observable. Now, eventually if I’m paying attention, I might find the number of people using this API is dropping off. Sometimes that’s because we’re getting some errors or a problem was introduced sometimes it’s just the audience is getting smaller. So once I know what those value propositions are, and I know what you need to keep a steady stream. If it’s starting to wane, it may be time to deprecate, and that means it’s a part of the deprecation process. I need to figure out if there’s going to be a replacement, do we have one that’s better, more appealing or solves the problem in a better way? Or is it just not needed anymore. Then I’ve got to give people a chance to get used to the notion that this is not going to be here anymore, and depending on the audience. If you’ve got a large audience of thousands or tens of thousands of sites, not just developers but organizations. That’s going to take some time. They can’t just stop on a dime, and you need to offer them alternatives. You need to say we have a new API that’s much faster and better or you need to say there are other options out there. You need to give them a way to replace what’s left, they’re not updating anymore, doing security fixes. And finally, you’re just not taking any traffic anymore. That’s the lifecycle. It’s a circle of life and every organization needs to face that.Derric: You have to face it, as long as you’re not pissing off people by shutting something off tomorrow, then usually it’s okay. But as long as there’s a plan of action that you know someone can take, and hopefully a good enough time period to do that within. Then that usually helps a lot.Common Pitfalls in ObservabilityDerric: Speaking more around, what can go wrong, you know. What do you see in terms of determining the right metrics? You know, maybe tracking too much or not enough.Mike: Right, that’s a good point. So there’s the classic case where you want to make sure you’re not overloading so much transactional information and internal metrics that you affect the performance of your product itself. You want to make sure you don’t change your design to be more amenable to observability than it is for customers. Right? Observability and monitoring are behind the scenes, Job. That’s the hard part. It should be super easy for developers. And you’ve got to select proxies well, a lot of times. You figure out where the value is and you can think of a cloud service like people uploading a lot of photos and then having social interaction. The value is that they get a lot of social interaction, so you start monitoring that and designing the product to pay off on the social interaction. Well, it may turn out that storage is actually a better proxy for success than interaction. So if you pick the wrong proxy, you might create a billing model or marketing campaign that really doesn’t add value to your customer. So you have to be prepared to change proxies in some ways, or rethink it. You need to constantly talk to customers.The Impact of AI on ObservabilityDerric: That’s a good point about understanding and being open to changing proxy metrics. And we’re seeing this, especially within the Gen AI space where APIs are relatively cheap and have some storage costs or CRUD apps behind them, but now with more data and Gen AI APIs there’s real cost to consider. So getting that type of ability to understand are you upside down? Are you underwater in terms of generating revenue for the business is becoming more important.Mike: Yeah. And you bring up a good point. We’re still very early in the curve or hype phase on these Gen AI situations. But there’s a big push on AI-driven agents, they’re becoming a whole new customer for your API. They have their own pains and troubles like most of these AI agents really have a limited amount of memory. You can’t just keep shoving lots of data at them to get them to figure out how to use your API. You’re going to have to start designing this API specifically for the Gen AI agent community. That’s like designing for mobile or another different community. This is going to be important and it’s a great example where, when you get bots that are involved with your API, they could really throw off your metrics and value add. You have to pay attention now. I’m telling customers to be very observant, monitor exactly who’s making these calls and watch for AI bots because that could be a new market or threat.Derric: Indeed, and sometimes watching for that unintentional abuse. You know it’s not necessarily a bad actor but just accidentally hand your API the wrong way, but your bill still goes up. And making sure you’re able to detect that and prevent some massive overage or spend is now becoming more important.Mike: Or to work the other way, it’s not just you as the host having a massive spend but all of a sudden your customers who are using your API to generate revenue or manage costs, are suddenly upside down. You don’t want them to end up with a huge bill that they don’t understand because someone else is using their API in unintended ways, and often those are great gifts but you can only do that if you’re really observing your API.Derric: Sounds like great power comes with great responsibility. APIs already give you a lot of ways to integrate, but with AI agents it’s even an explosion of different use cases and ways to get value out of the platform. So I like your point around you have to be even more diligent around observability for these types of new products.Future of Observability and APIsDerric: My last set of questions is really around, you know how is observability and the API space changing especially with the proliferation of Gen AI?Mike: Yeah. The line is, there is no AI without APIs, right? A truism that we all need to remember. Years ago, we used to focus so much on machine observability because we were writing all our own code, consuming and hosting it ourselves. But now we’ve got so many cloud services that are run by AI, what’s really happened in this observability space is it’s just gotten more complex. Not just complicated, but there are more and more different kinds of players interacting with each other. So the job of being a product manager, whether internal or external, really ends up observing and paying attention to finding value in your own APIs, other people’s APIs, cloud platforms, client applications. That’s a huge job and organizations that pay attention to that span and have someone or team look at it in their organization will get ahead in this innovation round involving mostly AI.Derric: That makes sense. This is more and more use cases, more data to look at, you have to interpret it. You have to understand is this the right decision to grow my team or business, and where I need to invest.Final Takeaways and AdviceDerric: Any last takeaways in terms of customer observability and what’s happening in the API space?Mike: I guess more than anything else, pay attention. Make sure you’re paying attention because things are changing so quickly. Unintended use is happening more and more, often a gift so if you pay attention, you can be on the leading edge of the curve. You won’t get so surprised and sometimes you might end up actually well ahead of your competitors because you saw it before others did. Observability is a fantastic tool for improving your business, so pay attention all the time.Derric: That’s a really good point. Which is one thing to have observability in place but you have to look at it, set up the right alerts and dashboards. Make sure there’s a process at the organization to make sure you’re getting the most value out of observability.Mike: That’s a great idea. Is there a process to vet this material? Not just figure out what’s happening but what does it mean and are we willing to experiment with that process? That’s really good.Derric: Well, thank you so much for having us on the podcast and looking forward to sharing this with our audience.Mike: I’m looking forward to it too, and I’d be happy to come back anytime. Thanks a lot.Derric: Appreciate it.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /podcasts/developers/Podcast-APIs-Over-IPAs-Customer-Observability-Mike-Amundsen/",
          "author": "Dylan",
          "categories": "Podcasts, Developers"
        }
      
    ,
  
    
        "technical-api-development-achieving-api-observability-in-kong-konnect-with-moesif-api-analytics-plugin": {
          "title": "Achieving API observability in Kong Konnect with Moesif API analytics plugin",
          "content"	 : "Kong Gateway offers a fast, scalable, robust, and modular API gateway for your application programming interfaces (APIs), irrespective of your cloud architecture. Kong Konnect adds more benefits on top of Kong Gateway by providing a centralized platform to manage your gateways. However, even with such a powerful platform, you’ll face challenges in achieving that benchmark of API observability and monitoring to meet modern industry standards and therefore your growth projections.As engineers, you often encounter visibility gaps that impedes effective troubleshooting and performance optimization. Even if you’re utilizing the world’s fastest API gateway, without granular insights and the ability to act on them, you can’t truly understand how your APIs are performing in production in real time.Fortunately, you can remediate these issues completely by integrating Moesif’s API analytics plugin with Kong Konnect. By leveraging Moesif with Kong Konnect, you can turn your API into a fully observable system. In this article, we demonstrate how Moesif can improve your API’s observability and compliment Kong Konnect to drive proactive decision-making and growth.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free          An Overview of Kong Konnect API Management  Moesif API Analytics Plugin for Kong  How Moesif Analytics Plugin Works with Kong Konnect          Set up Kong Gateway Docker Image with Moesif Plugin      Add Moesif Plugin to a Data Plane Node      Add and Enable Kong Moesif Plugin to Control Plane        Benefits of Using Kong and Moesif          Seamless Integration      Enhanced API Monitoring and Observability      Simplified API Troubleshooting      API Performance Optimization      Data-Driven Decisions        Key Insights and Engineering Metrics          Core Engineering and Key Performance Metrics                  Latency Analysis          Error Rate and Status Code Analysis                    AI Consumption Analysis for AI Apps      Advanced Metrics and Analysis                  HTTP Payload Analysis          Distributed Tracing with OpenTelemetry                          How Moesif Leverages OpenTelemetry with Kong Konnect                                            Leveraging AI Explain for API Usage Explanation  ConclusionAn Overview of Kong Konnect API Management[Kong Konnect provides a modern cloud-native API management platform for building, managing, and deploying APIs. It works with on-premises, hybrid, or fully-cloud systems. Konnect encompasses the features of Kong Gateway by making it more accessible and streamlined to get started with from one central platform.The most compelling aspect of Kong Gateway comes from its modularity as an API gateway, through its extensive support and ecosystem of plugins. It essentially removes any limitations in building custom integrations and extending current functionalities. You can choose from a range of native plugins and also incorporate custom plugins through Lua. Kong Konnect’s Dev Portal further allows engineers to customize, experiment, and fine-tune their API solutions.Moesif API Analytics Plugin for KongThe Moesif API analytics plugin for Kong Konnect brings deep API observability for Kong Gateway APIs. In a nutshell, it allows engineers to monitor traffic, analyze performance, and track user behavior in real time. Once you set it up, it doesn’t need any manual setup for instrumentation and collection, as they happen automatically and scale with your app. The plugin captures request and response data in great depth, including the following:      Latency        Errors        Payload content        Authentication details  Unlike basic API logging tools, Moesif offers advanced filtering, anomaly detection, and product-level insights. For example, as we’ll soon demonstrate, Moesif allows you to execute granular analytics on HTTP bodies. Having all of these features at hand empowers teams to correlate API activity with specific customers or applications, performance metrics, and more. Engineers can visualize trends, set alerts for unusual behavior, and generate real-time reports to improve reliability and user experience.The plugin integrates seamlessly with Kong Konnect through the high-performance Lua language. It works asynchronously to avoid adding latency to your app while making sure to capture comprehensive data. The plugin supports protocols such as HTTP, HTTPS, and gRPC, giving you adaptability for various API architectures.How Moesif Analytics Plugin Works with Kong KonnectThe Moesif Analytics Plugin integrates with Kong Konnect as a custom plugin. Therefore, it doesn’t work for Dedicated Cloud Gateways in Kong. Here are the general steps involved to set up the plugin with Kong Konnect:Set up Kong Gateway Docker Image with Moesif Plugin      Clone the Kong Konnect Docker demo repository that contains the Kong Moesif Plugin.      git clone --recurse-submodules https://github.com/Moesif/moesif-kong-konnect-docker-demo.git            Go inside the moesif-kong-konnect-docker-demo repository and build the image:     docker build -t custom-kong-gateway:3.9.0.0 .      Add Moesif Plugin to a Data Plane Node      In your Konnect UI, open the Gateway Manager and then open a control plane.        Select Data Plane Nodes and then select Create a New Data Plane Node.        Select a self-managed Gateway version and choose Linux (Docker) as the platform.        Select Generate certificate.        Copy the generated docker run command and add the following snippet to it:     -e "KONG_PLUGINS=bundled,moesif"  -e "KONG_LUA_PACKAGE_PATH=/tmp/custom_plugins/?.lua;;"         Also make sure you modify the image name with the custom-kong-gateway:3.9.0.0 you built earlier.        Run the command to start a data plane node with Moesif plugin loaded in.  Add and Enable Kong Moesif Plugin to Control Plane      In your Konnect UI, open the Gateway Manager and then open your control plane.        Select Plugins from the navigation menu and then select New Plugin.        Select the Custom Plugins tab and then select Create in Custom Plugin.        Upload the schema.lua file of the Kong Moesif plugin.        Check that you can view your upload correctly in Preview and then click Save.        Navigate back to the Plugins screen, select Custom Plugins, and enable the Moesif Plugin.        Enter your Moesif Application ID in the Application Id field.        Click Save to finalize the configuration.  To get your Moesif Application ID, follow these steps:      Log into Moesif Portal.        Select the account icon to bring up the settings menu.        Select Installation or API Keys.        Copy your Moesif Application ID from the Collector Application ID field.  After you successfully set up the plugin, Moesif starts capturing requests to your Kong API. You should see them appear in Moesif’s Live Event Log workspace.With the plugin set up, Moesif captures all the details of your API traffic, including full HTTP request-response details, custom metadata, latency, and more. Moesif can also track your Kong routes and services. They become available in event filters in the Metadata.Kong field:Moreover, if you’re using Kong’s OpenTelemetry Plugin, you can configure Moesif to have the trace and log data available in Moesif. This allows you to leverage distributed tracing to further contextualize and utilize Kong Konnect and Moesif’s capabilities without configuring a separate observability backend.Benefits of Using Kong and MoesifBy integrating Kong Konnect with Moesif, you get a powerful combination for managing and observing your APIs. This synergy takes you beyond basic monitoring, with deep insights into API performance, usage patterns, and overall operational status. Moesif and Kong provides you with the key components you need for effective API observability, including metrics monitoring, log analysis, and tracing. Kong Konnect handles the delivery of your APIs, while Moesif provides the intelligence to refine and optimize your API strategy to drive more value from your APIs.Seamless IntegrationWe’ve designed the Moesif plugin for Kong Konnect for effortless integration with Kong Konnect. This means you can quickly add, set up, and deploy the plugin for your API straight from Kong Konnect’s Gateway Manager. You don’t need to perform any complex configurations or have to incur significant operational overhead. Using the high-performance Lua language ecosystem, Moesif’s lightweight plugin ensures minimal impact on API performance while paving the way to valuable insights without sacrificing speed. Kong Gateway being a Lua application itself, Moesif’s plugin integrates natively without introducing any performance or incompatibility hiccups. The plugin forwards crucial performance and observability data and metadata from your API to Moesif’s analytics platform. Once you set it up, you don’t need to manually instrument for data collection and dispatch, as they happen automatically in real time.Enhanced API Monitoring and ObservabilityCombining Kong Konnect and Moesif gives you a holistic view of your entire API ecosystem. With Kong Konnect, you get your peace of mind for traffic management, API security, and API lifecycle management. Then Moesif adds a layer of deep API observability. This combination allows you to move beyond simple uptime checks and dig into granular details about how your APIs serve your applications. You gain visibility into not just if your APIs are working as you expect, but how they are performing through insights into your API’s performance.Simplified API TroubleshootingOne of the most significant benefits comes from the drastically simplified troubleshooting process. When issues arise, you have access to Moesif’s detailed metrics and payload inspection and analytics capabilities. Instead of sifting through logs across different software systems, you have a centralized platform to analyze API requests, identify errors, and understand the context surrounding them. Moesif works and scales in real time with its observability and monitoring features. So you react to problems as they happen and nurture a proactive approach.API Performance OptimizationAPI observability doesn’t involve just finding problems, it also promotes optimizing performance. Moesif, coupled with Kong Konnect, provides the key metrics your teams need to identify performance bottlenecks. For example, Moesif can discover that a specific database query called by a specific endpoint is causing high latency.By analyzing latency, error rate, and request throughput, you can pinpoint areas for improvement. This might involve optimizing your backend services, adjusting Kong Konnect’s configuration—for example, caching and rate limiting, or even redesigning parts of your API. The performance trends Moesif visualizes and allows you to organize and customize them, help you make informed decisions about resource allocation and scaling.Data-Driven DecisionsThe comprehensive performance data and analytics capabilities this integration provides empower you to make data-driven decisions about your API strategy:      Understand how customers are using your APIs.        Identify the most popular endpoints.        Identify potential security vulnerabilities that might exist.   These insights can inform decisions about API design, feature development, and even pricing strategies for your API products. The ability to develop operational insight transforms your API management from reactive to proactive. Moesif lets you track key performance metrics for business and engineering, significantly reducing your mean time to resolution (MTTR) across your API lifecycles.Key Insights and Engineering MetricsLet’s go through some practical examples of the key insights and monitoring metrics Moesif unlocks for your Kong APIs.Core Engineering and Key Performance MetricsMoesif equips you with a range of crucial engineering metrics that provide a comprehensive view of your API’s health and performance. These metrics help you understand how your APIs are behaving in real-time so you can identify potential issues before they impact users. Let’s explore some of the most important ones.Latency AnalysisMoesif has built-in performance analysis types like maximum latency, minimum latency, and P90 latency. You can also define your own custom latency analysis. For example, here we use Moesif’s Time Series analysis to perform a P99 latency analysis across API endpoints in the past 24 hours, in an hourly interval:A simple average latency analysis, while useful, can become skewed by outliers. Therefore, performing percentile analysis like P90 or P99 gives a more robust measure than the average, as extreme values affect it less. Being able to drill down into performance this way can identify and therefore equip you to address the slowest requests. Slow requests often have the biggest impact on user experience and directly impact your product’s health. High P99 latency can indicate problems with backend services, network bottlenecks, or resource constraints.Error Rate and Status Code AnalysisAnalyzing error rates gives you a precise understanding of the percentage of API requests that result in an error, typically 4xx or 5xx HTTP status codes. If you observe a high error rate, it clearly indicates issues such as bugs in your code, issues with downstream dependencies, or insufficient resources. Moesif allows you to track the overall error rates in a number of ways and drill down into specific error types.For example, here we break down the number of 5xx server errors on an hourly basis for the past 12 days in a time series analysis. We also categorize the analysis by response status codes so we know the exact error types.If you notice, we’ve folded the time series to better visualize the periodicity of our metric here. This feature can be a very powerful tool to understand recurring patterns like error rate patterns and periodic anomalies. You can focus on worst-case scenarios as well. In this particular example, we can see if a particular hour exists where errors spike to especially high levels. It can help identify issues like the following:      Peak load times overwhelming the API        Scheduled maintenance or batch jobs that cause temporary disruptions        Time-dependent errors in external services that the API relies on  You can also approach this in a different way and break down all API errors in a time series:It allows you to quickly observe the distribution of API errors. You can therefore understand the nature of your API interactions and identify potential problems. For example, if you observe a sudden increase in 401 Unauthorized errors, it might indicate a security issue. A spike in 502 Bad Gateway errors on the other hand can point to problems with a backend service.AI Consumption Analysis for AI AppsIf you’re building an AI product using Kong Konnect, Moesif can help you track AI consumption, analyze usage, and debug AI traffic. Since Moesif captures HTTP request-response data with deep granularity, it’s very easy to track, filter on, and break down your product’s usage data by token usage, rate limits, and so on.For example, here we do a Segmentation analysis to look at the top customers based on average input token consumption, the number of API calls, and the associated costs for the past 7 days:Advanced Metrics and AnalysisBeyond the core metrics, Moesif supports advanced analytics like payload analysis, customer analytics, alerting, and governance rules. They bring more valuable insights and observability for your Kong Konnect APIs.HTTP Payload AnalysisOne of the most powerful features for troubleshooting is Moesif’s ability to inspect the full request and response payloads of your API calls. Analyzing HTTP bodies gives you invaluable support for debugging complex issues where the error messages alone aren’t sufficient. You can examine the exact data being sent and received, identify malformed requests, and understand the context of errors. This eliminates guesswork and significantly reduces the time required to resolve problems.For example, you can have payloads like the following with deeply nested structures. With Moesif, you can focus anywhere and figure out what you want.{  "order_id": "ORD-2024-12345",  "timestamp": "2024-11-08T15:45:30Z",  "customer": {    "customer_id": "CUST-7890",    "email": "customer@example.com",    "shipping_address": {      "street": "123 Main St",      "city": "Anytown",      "state": "CA",      "zip": "90210",      "country": "USA",      "delivery_instructions": {        "leave_at_door": true,        "signature_required": false,        "notes": "Ring doorbell twice."      }    },    "billing_address": {      "same_as_shipping": true,      "street": "123 Main St",      "city": "Anytown",      "state": "CA",      "zip": "90210",      "country": "USA"    }  },  "items": [    {      "product_id": "PROD-111",      "product_name": "Wireless Mouse",      "quantity": 2,      "unit_price": 24.99,      "discount": {        "type": "percentage",        "value": 10,        "reason": "Black Friday Sale"      },      "attributes": [        {          "name": "color",          "value": "black"        },        {          "name": "connectivity",          "value": "bluetooth"        }      ]    },    {      "product_id": "PROD-222",      "product_name": "Keyboard",      "quantity": 1,      "unit_price": 79.99,      "discount": {        "type": "fixed",        "value": 5,        "reason": "Coupon Code"      },      "attributes": [        {          "name": "color",          "value": "white"        },        {          "name": "layout",          "value": "QWERTY",          "details": {            "backlit": true,            "mechanical": true,             "switch_type": "Cherry MX Red"          }        }      ]    }  ],  "payment": {    "method": "credit_card",    "card_type": "Visa",    "last_four": "1234",    "transaction_id": "TRANS-XYZ",    "status": "success",     "gateway_response": {        "code": "200",        "message": "Approved",        "auth_code": "AUTH123"    }  },  "shipping": {    "carrier": "UPS",    "tracking_number": "1Z12345E0312345678",    "estimated_delivery": "2024-11-12",    "shipping_cost": 9.99  },    "coupons": [      "WELCOME10",      "FREESHIP"    ]}Here are some more illustrative use cases:      Filter and analyze requests with large context size for your AI application in a Live Event Log, for example:            Use the key count operators that work on the number of keys in the object, to identify large payloads for performance troubleshooting and analysis:        Specify custom metrics to gain valuable insights into user behavior and API usage. Since Moesif makes the HTTP body data available in segmentation, filter, and group settings, engineers can easily see distributions and patterns in the data. It can uncover performance issues related to specific data values or user segments.         Debug and troubleshoot modern APIs possessing complex data models by navigating intricate JSON structures.        Get precise error context quickly by finding the exact API call that caused the problem and observe the full request-response data.  Distributed Tracing with OpenTelemetryMoesif’s Kong integration supports extracting OpenTelemetry traces and logs as well if you already have an OTel integration installed.You can also use Kong Konnect’s OpenTelemetry plugin and send your trace and log data to Moesif’s OpenTelemetry endpoint.How Moesif Leverages OpenTelemetry with Kong KonnectIf you have already instrumented your services with OpenTelemetry or have an OpenTelemetry Collector capable of exporting through OTLP over HTTP, Moesif can seamlessly integrate with this setup. Kong’s OpenTelemetry plugin, after configuring, can forward trace and log data to an OpenTelemetry Collector. Moesif, in turn, can receive this trace and log data from the collector, providing a unified view of your API traffic and its journey through your distributed systems. Note that you must have existing OTel instrumentation; Moesif integrates with OpenTelemetry, it doesn’t replace the need for OpenTelemetry instrumentation within your services.The trace and log data Moesif collects becomes available in Live Event Log workspaces. To view the trace data, select the Trace View tab in a Live Event Log workspace. You can expand an event for more details about it. In this detailed view, you can interact with individual event data fields and field values. For example, you can select a trace ID value and then select Open Trace, giving you more details about the trace and its constituent spans:Furthermore, trace data elements like trace IDs, span IDs, and span statuses become available as event filters in Moesif’s API and customer analytics suites.Leveraging AI Explain for API Usage ExplanationDespite all these analytics, metrics, and visualizations at your disposal, understanding complex API usage patterns can still pose challenges. That’s why Moesif has introduced AI-powered features to augment your API observability and monitoring needs:      Use Smart Search to navigate through analytics dashboards, workspaces, and more.        Use AI Explain in API analytics, customer analytics, and alerting to quickly uncover valuable insights.  With these features, Moesif aims to make API observability and monitoring easier, more accessible, and faster. For engineers, they provide an additional layer of intelligence for troubleshooting and insights. Instead of manually sifting through your analysis, you can use AI Explain to generate concise summaries of API usage trends. For example, it might highlight the most significant changes in API traffic over a given period, identify the most frequently accessed endpoints, or summarize the most common error types.For example, consider you have an embeddings API in Kong Konnect. You want to understand its growth and adoption by companies. Here’s how such an analysis might look in Moesif using a time series analysis:You can select Ask AI and get instant insights about the analysis:ConclusionModern apps have been growing in their complexities, and so do the demands from the consumers. Needless to say, therefore, utilizing observability and monitoring tools to gain a holistic understanding of your product’s components can no longer stay a secondary choice in your API strategy. With Moesif and Kong Konnect, you cover a lot of ground in that aspect while avoiding the overhead and risks of stiching together a bunch of observability tools. You can get started quickly, more easily, get to value fast, and continue to scale your product with confidence.To see for yourself how Moesif can help you with observability-driven development, sign up for a free trial no sign up for a free trial.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design and Build Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Achieving-API-Observability-In-Kong-Konnect-With-Moesif-API-Analytics-Plugin/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "monitoring-monetizing-proprietary-data-through-apis": {
          "title": "Monetizing Proprietary Data Through APIs: How to Unlock New Revenue in the AI World",
          "content"	 : "A report by Bloomberg Intelligence projects the AI industry will reach $1.3 trillion by 2032, with proprietary data fueling much of this growth. As businesses increasingly adopt generative AI (genAI) to enhance efficiency, data is rapidly becoming one of the most valuable assets in the digital economy.Foundational AI models require vast amounts of data for training, and many AI products are now leveraging proprietary datasets alongside these models to power innovative applications and AI agents. These tools have the potential to transform business processes across engineering, sales, support, and beyond.Chances are, your organization already holds a wealth of proprietary data. Whether it’s internal data supporting a traditional SaaS application or user-generated content, this data isn’t just a strategic asset for internal use — it can also be monetized by selling it to enterprises that need high-quality datasets for training their models or driving their applications.Monetizing data can bring numerous benefits to an organization. By leveraging their existing data, companies can unlock new revenue streams and gain a competitive edge in the market. But how do you unlock this value while navigating the challenges of monetizing your proprietary data?                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding Data MonetizationAs a data provider, balancing customer needs with revenue growth is no small feat, and without a well-thought-out data monetization strategy, you risk losing potential revenue, stifling your growth, and limiting your ability to scale. Imagine a worst-case scenario: A customer signs up, downloads all the data they need in a single day, and never returns. Effective data monetization strategy can mitigate these risks by aligning pricing models with customer usage patterns and ensuring sustainable revenue growth.Unlike API businesses, where API consumption is typically predictable “up and to the right,” data consumption often follows a sporadic pattern. Customers typically consume data only when needed. For example, if you’re providing data to assist marketing teams, they might only need the data ahead of large marketing launches. Similarly, if you are providing financial data around real estate transactions, you may find customers only care about the data during end-of-year planning or ahead of the spring buying season.Simply charging a flat monthly or yearly fee might not align with the value customers receive, especially when their consumption is irregular. This raises an important question: How can you ensure predictable revenue and cash flow for your business while reducing obstacles for customers whose usage fluctuates and is unpredictable? A well-thought-out API business model is crucial for balancing customer needs with revenue growth.What is Data Monetization?Data monetization is the process of generating revenue from data, transforming it from a mere asset into a significant income source. This can be achieved through various methods, including data-as-a-service (DaaS), data licensing, and data analytics. With the exponential growth of data generated daily, companies are increasingly seeking ways to unlock its value and turn it into a revenue stream.One of the most effective channels for data monetization is through application programming interfaces (APIs). APIs allow companies to offer their data to third parties in a structured and accessible manner. Additionally, data marketplaces and data analytics platforms provide avenues for companies to sell or license their data, further expanding their monetization opportunities.Benefits of Data MonetizationData monetization offers several compelling benefits to companies:      New Revenue Streams: By monetizing data, companies can create new revenue streams, diversifying their income and reducing reliance on traditional sources.        Improved Decision-Making: Access to valuable insights and analytics derived from monetized data can enhance decision-making processes, driving business growth.        Competitive Advantage: Companies that effectively monetize their data can gain a competitive edge over rivals, increasing their market share and driving business success.        Increased Efficiency: Monetizing data can streamline operations and reduce costs, leading to higher efficiency and improved profitability.  Different Common API Monetization ModelsOne effective approach to monetizing data is usage-based billing (also referred to as consumption-based billing). This model allows customers to pay only for what they use, offering flexibility and avoiding the commitment of a subscription. Moreover, it enables your revenue to scale naturally as customers’ data needs grow.Cloud providers and API platforms have widely adopted usage-based billing. It’s in practice in both modern SaaS companies like NexHealth and traditional enterprises like Siemens. A typical implementation involves tracking API usage over a billing period (such as a month) and invoicing customers at the end of that period. This model works well if the cost of providing the API is low and the risk of abuse is minimal.However, data providers often face higher costs of goods sold (COGS) or risks of misuse. For example, a customer might download all the data they need and then cancel their subscription or simply fail to pay their invoice. To mitigate these risks, many providers are adopting a prepaid pay-as-you-go (PAYG) model.Modern AI companies like You.com and OpenAI, along with telcos such as Sinch and Twilio, are leveraging PAYG to help align usage-based revenue to their usage-based cost. With prepaid PAYG, customers purchase credits upfront, which are then consumed based on a pre-negotiated rate — similar to buying a prepaid phone card. This model reduces the risk of abuse and provides immediate cash flow for your business, making it a win-win for providers and customers.Another popular strategy is the freemium model, which is effective in lowering entry barriers for developers by providing basic access for free while charging for advanced features. A basic API plays a crucial role in this model by offering developers free access to fundamental functionalities, encouraging experimentation and adoption. This approach is widely used among popular platforms like Spotify and GitHub, driving rapid user base growth but requiring careful management of conversion rates from free to paid tiers.Overview of API Monetization ModelsAPI monetization models are strategies used by API providers to generate revenue from their APIs. Here are some common API monetization models:      Pay-per-Use: This model charges developers for each API call they make, aligning costs with usage.        Subscription-Based: Developers pay a recurring fee for access to the API, providing predictable revenue for the API provider.        Freemium: A basic version of the API is offered for free, with charges applied for premium features or higher usage limits.        Revenue Sharing: This model involves sharing revenue with developers who use the API to generate income, fostering a collaborative ecosystem.  These models allow API providers to choose the best fit for their business needs and customer usage patterns.Developing an API Monetization StrategyDeveloping an effective API monetization strategy requires careful consideration of several factors. Here are the steps to follow:      Identify the Target Market: Understand the needs and requirements of the developers who will be using the API. This involves market research and customer segmentation.        Define the Value Proposition: Clearly articulate the benefits and features of the API and how they will be delivered to the target market. This helps in positioning the API effectively.        Determine the Pricing Strategy: Decide on the pricing model, pricing tiers, and revenue sharing model. This involves balancing affordability for customers with profitability for the provider.        Develop a Revenue Sharing Model: Establish the revenue sharing percentage and payment terms. This fosters a collaborative relationship with developers and incentivizes usage.        Implement the API Monetization Strategy: Set up the API infrastructure, develop comprehensive API documentation, and launch the API to the target market. Continuous monitoring and optimization are crucial for long-term success.  By following these steps, API providers can develop a robust API monetization strategy that drives revenue and business growth.How to Meter API Usage and Data ConsumptionEven with a PAYG model, determining how to charge customers requires careful consideration. Metering by API calls alone is often ineffective, as customers prioritize efficient batch queries to maximize throughput. A single API call could result in the export of massive datasets. Accurate metering can also highlight the api provider benefits, as it aligns provider earnings with the value delivered to customers, encouraging collaboration and maximizing both usage and revenue.For example, if you are offering a financial data enrichment API, your customers may want to enrich thousands or millions of records in a large batch. In this case, the ideal flow would be that the customer submits a batch job for all the entities needing enrichment. Since this is a batch job, some items may not be found or fail. Customers shouldn’t be charged for missing or low-quality data.To address this, it’s important to align your billable consumption metrics with customer value. For example, you could charge per successful data element or row accessed, excluding rows that are incomplete or of poor quality. Tracking and metering such granular data usage can be complex and typically requires additional monitoring tools to analyze API consumption effectively.Managing Asynchronous JobsFor APIs that involve backend jobs (like exporting large datasets), asynchronous processing adds another layer of complexity to monetization. You must decide when to deduct credits from a customer’s balance and how to handle failure scenarios. Artificial intelligence can enhance the efficiency and accuracy of managing asynchronous jobs, optimizing the overall API performance.A common approach is to “lock” the credits until the job completes, ensuring customers cannot trigger excessive jobs that would cause their balance to drop into negative territory.Job Handling Scenarios            Job Status      Action              Job completes successfully      No change to customer’s balance              Job completes partially      Missing items are refunded back to the customer’s balance. Artificial intelligence can be used to predict and manage job outcomes, ensuring better handling of partial and failed jobs.              Job fails entirely      100% of the credits are refunded back to the customer’s balance      Example: Partial Completion  Customer makes an API call to fetch 1,000 items.  Customer balance reduced by $1000.  Backend job triggered to export 1,000 items.  Job completes, but 100 items are missing.  Customer credited back $100 for the missing items.By leveraging artificial intelligence, businesses can predict and manage partial completions more effectively, ensuring accurate credit adjustments.Example: Failure Case  Customer makes an API call to fetch 1,000 items.  Customer balance reduced by $1000.  Backend job triggered to export 1,000 items.      Job fails.    Customer notified and credited back the entire $1,000 due to job failure. Utilizing artificial intelligence can help in predicting and managing such job failures, ensuring accurate and timely credit refunds.Measuring Data Quality and API ExperienceProviding high-quality data through a seamless API experience is essential for retaining customers. Leveraging data analytics and artificial intelligence can help you measure and improve this quality, ensuring a seamless API experience.Unlike traditional APIs, where success can be measured by [HTTP response codes](https://nordicapis.com/what-do-the-http-status-codes-mean/) (like 200 vs. 400), evaluating data quality is far more nuanced. A common approach is to apply a different number of credits depending on the quality.            Price      Description              $0.05 per exact match      Data item had an exact match to the query              $0.02 per fuzzy match      Data item was a “best guess” but may not be correct              No cost when not found      Could not find the item      A recommended approach is to assign a Response Quality Score using the formula:Response Quality Score = sum( accuracy of row * relevancy of row ) / total rowsThis scoring system helps developers understand where their API descriptions may fall short and how they can improve alignment.Security and Regulatory ConsiderationsSelling proprietary data via APIs comes with regulatory and security responsibilities, especially when offering data as a service (DaaS) through the cloud. Before diving in, ensure you have the legal right to sell the data. Some datasets may be governed by copyright laws or regulations like HIPAA, PCI, or GDPR. For example, GDPR’s “right to be forgotten” requires mechanisms to delete specific data upon request. Artificial intelligence can help in ensuring compliance with security and regulatory standards, protecting sensitive information.Additionally, it’s critical to implement robust security measures to protect sensitive information and build customer trust. This includes encrypting data with either server-side encryption or client-side encryption, securing API endpoints, and maintaining compliance with relevant data privacy standards.The Future of Data Monetization in the Artificial Intelligence WorldIn an era where generative AI drives innovation across industries, proprietary data has become an increasingly valuable resource. In fact, data marketplaces are becoming crucial platforms for buying and selling data. By monetizing your data, you can transform it from a cost center into a profit center, unlocking new revenue streams while fueling advancements in AI. API services play a significant role in the future of data monetization, transforming from mere technological assets into valuable products integrated into various applications. Additionally, cloud service providers leverage APIs for automatic scaling of computational resources, allowing for flexible revenue models and precise billing based on actual customer usage. APIs are also crucial for developers building mobile apps, emphasizing their importance in driving revenue through mobile applications.With the right monetization model and a focus on delivering high-quality, valuable data, you can position your organization at the forefront of this rapidly evolving landscape. The concept of revenue share is essential in future data monetization models, as it creates mutually beneficial relationships by dividing revenue from transactions conducted through the API between the API provider and the developer.                Deep AI API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design and Build Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /monitoring/Monetizing-Proprietary-Data-Through-APIs/",
          "author": "Derric",
          "categories": "monitoring"
        }
      
    ,
  
    
        "monitoring-top-7-ways-to-monetize-jobs-api-data-in-2025": {
          "title": "Top 7 Ways to Monetize Jobs API Data in 2025",
          "content"	 : "Currently, the job market is fully driven by data, so companies can either miss out on the opportunities this offers or leverage them as additional income generation source. Jobs API provide a goldmine of opportunities for businesses looking to generate revenue.The Jobs API is a powerful search tool for HR technology companies who wish to access an up-to-date database or have the capability to obtain pertinent job postings as needed. To ensure that job posting data is accurate and that new information is available daily, the database is updated on a regular basis.Moesif, a leading platform for API analytics and monetization, helps businesses optimize their API usage, track key metrics, and implement effective monetization strategies. With tools designed for API observability and analytics, Moesif enables companies to better understand API consumption patterns, improve performance, and ensure seamless integrations which are critical factors for maximizing the revenue potential of Jobs APIs.Companies can use corporate data, job postings, and recruiting patterns to unleash a range of revenue strategies. Here is a list of the top seven ways to profit from Jobs API data in 2025.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        1. Subscription-Based AccessSubscription-based access is among the most straightforward methods of making money off of Jobs API data. Depending on their requirements, companies, recruiters, and job boards can purchase tiered pricing models that provide them with different levels of access.Standard job listings may be included in basic subscriptions, but more extensive data, including wage insights, candidate demographics, and hiring patterns, is available in premium plans. Businesses can offer tiered pricing structures according to a number of criteria:  Volume of API requests: Different pricing tiers based on the number of requests a user can make within a given period.  Richness of data: Basic subscriptions may include standard job listings, while premium plans offer deeper insights such as candidate demographics, salary trends, and hiring activity.  Premium features: Access to real-time updates, historical job trends, AI-driven analytics, and forecasting tools can be provided in higher-tier plans.  Custom enterprise solutions: Large organizations may require bespoke data packages tailored to their hiring needs, allowing businesses to charge higher fees.Moesif can help businesses optimize these subscription models by providing API analytics that track usage patterns, detect overages, and monitor customer behavior. With Moesif’s real-time insights, companies can ensure fair usage, prevent abuse, and offer usage-based pricing that scales with demand. This not only enhances customer satisfaction but also maximizes revenue by aligning pricing with actual API consumption.2. Lead Generation for RecruitersSince hiring companies and recruitment agencies are always looking for competent applicants, lead generation is a useful service. Businesses can give recruiters customized lists of qualified applicants who fit job opportunities based on hiring trends, skill demand, and market trends by evaluating Jobs API data. By automatically recommending the most suitable applicants, AI-powered matching systems can expedite this procedure.Recruiters can also learn about new employment trends, which helps them contact prospects in high-demand industries in a proactive manner. Through recurring data access and lead generation services, this strategy not only increases recruiter efficiency but also generates a steady flow of income.3. Job Market Analytics and InsightsData from the Jobs API can be aggregated and analyzed to provide insightful information on labor needs, salary benchmarks, and hiring patterns in the industry. For example, Indeed shared a comprehensive analysis of job postings and job market predictions for 2025, mentioning a trend in years’ experience requirements is similar, as the share of job postings with a specific experience requirement, which has fallen in recent years. In October 2022 it was 40% and in October 2024—32.6%.Companies can compile this data into interactive dashboards or thorough reports that are specific to HR departments, recruitment agencies, and businesses trying to maximize their hiring practices. Companies can make money off of these insights by:  Providing salary intelligence: Helping HR teams set competitive salaries based on real-time market data.  Industry-specific analytics: Tailoring insights to specific sectors, such as tech, healthcare, or finance, to attract targeted clients.  Custom dashboard solutions: Creating interactive dashboards that businesses can subscribe to for real-time job market analysis.4. Affiliate Marketing and Job Post PromotionPartnering with job boards and employers, businesses can earn revenue through affiliate marketing by:  Redirecting job searchers: Receiving a commission for each applicant who is sent to an employer’s website or job board.  Putting high-paying job advertisements in search results or focused marketing is known as “promoting premium job listings.”  Sponsored placements: Paying companies to appear higher in job search results.  Revenue models depending on performance: earning based on applications for jobs, booked interviews, or employment brought about via referral links.5. Programmatic Job AdvertisingBusinesses can leverage Jobs API data to create programmatic job advertising platforms by utilizing AI-driven algorithms. In order to guarantee that recruiters and employers access the most qualified applicants, this entails automating the placement of job advertisements across many web platforms. Advertisers may optimize their spending and increase their visibility among job searchers by competing for ad spots using real-time bidding systems.Employing machine learning, companies may improve their targeting tactics and make sure that job advertisements are seen by the appropriate people based on browsing history, job preferences, and behavioral data. Employers only pay for real engagement with their job postings, thus implementing pay-per-click or pay-per-application models guarantees consistent revenue.6. Resume and Candidate Matching ServicesBusinesses can improve the recruiting process by developing AI-powered services for resume screening and candidate matching using data from the Jobs API. Employers can find individuals who best fit job requirements and weed out unqualified applicants with the aid of automated resume processing technologies.Recruitment can be made more efficient by using candidate ranking algorithms to evaluate suitability based on experience, abilities, and work history. Businesses can create AI-powered solutions with access to comprehensive job listings and specifications.Opportunities for monetization could also include:  Automated interview scheduling: Providing API-powered solutions to streamline the hiring process.  On-demand candidate insights: Selling deep analytics on candidate trends, availability, and skill gaps.Offering these features as standalone products or integrating them with existing HR platforms ensures broad market appeal and consistent revenue generation.7. Integration with HR and ATS SystemsAccording to Fortune Business Insights, the global HR tech market will expand up to $39.90 billion by 2029, meaning the software used by HR is getting more popular. There is a big chance for API integration because applicant tracking systems (ATS) and HR platforms are frequently utilized to manage job applications.When companies incorporate employment data directly into these systems, they can charge license or usage fees, which enables HR teams to easily access real-time job posts and market analytics. Offering smooth API interfaces allows companies to make money in the following ways:  Licensing fees: Charging HR software vendors for embedding job data within their platforms.  Usage-based pricing: Billing companies based on the number of job postings or applicants processed.  White-label solutions: Providing customizable job API solutions that companies can brand and integrate within their hiring workflows.  Automation and AI-powered features: Enhancing HR platforms with automated job posting, candidate tracking, and market insights.ConclusionJobs APIs are a valuable resource for companies trying to make money because of the growing need for real-time job data and analytics. Businesses that successfully use Jobs API data can take advantage of enormous opportunities presented by the changing landscape of workforce analytics and digital recruitment.Through advertising, analytics, lead generation, or subscription models, companies can generate a variety of revenue streams that meet market demands. Businesses will have a competitive advantage if they incorporate Jobs API data into their operations as technology develops and hiring procedures become more data-driven. Businesses may guarantee long-term profitability in the competitive labor market of 2025 and beyond by consistently improving their monetization methods and embracing AI-driven innovations.                Deep AI API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design and Build Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /monitoring/Top-7-Ways-to-Monetize-Jobs-API-Data-in-2025/",
          "author": "TomWilson",
          "categories": "monitoring"
        }
      
    ,
  
    
        "api-product-management-api-analytics-why-a-unified-view-of-api-usage-is-critical-for-managing-multiple-api-gateways": {
          "title": "Why a Unified View of API Usage is Critical for Managing Multiple API Gateways",
          "content"	 : "APIs have become the backbone of our digital world, with surveys showing that over 70% of developers plan to increase API usage year-over-year. They power everything from mobile apps and SaaS integrations to IoT devices and partner platforms, enabling businesses to deliver seamless services and experiences to customers. As organizations grow, however, so does the complexity of their API ecosystem. Many teams end up deploying multiple API gateways, often to manage different product lines, microservices, or regional deployments.While multiple gateways can offer flexibility and specialized features, they also create new challenges. Each gateway might have its own monitoring dashboards, security configurations, documentation portals, and analytics tools. Product managers, developers, and operational teams quickly find themselves juggling scattered bits of data as they attempt to track API usage, performance metrics, and monetization results across these disparate solutions.That’s why forward-thinking organizations are turning to unified API management for multiple API gateways. By consolidating data from all their gateways into a single source of truth, they gain the ability to track both operational KPIs (like uptime and error rates) and business KPIs (like usage growth and revenue impact) from one shared dashboard. This not only streamlines troubleshooting and optimization but also enables product managers to “package” their APIs effectively whether for new subscription tiers, developer-friendly bundles, or usage-based pricing models.Let’s explore the reasons organizations end up with multiple API gateways, the pitfalls and pain points they encounter, and the specific benefits that a unified approach can deliver. Along the way, you’ll see how incorporating centralized analytics and leveraging platforms like Moesif, can help unravel the complexity, drive better decisions, and ultimately boost your bottom line.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        Unified API Management for Multiple API Gateways: Why They Exist in an OrganizationFor many organizations, having multiple API gateways isn’t necessarily an intentional design choice. Instead, it often evolves organically over time as the business scales or adapts to new requirements. Here are a few common scenarios:      Organic Growth Across Teams: Teams select API gateways based on their specific needs, leading to multiple “best-of-breed” gateways operating in parallel.        Mergers and Acquisitions: Companies inherit different API infrastructures and maintain multiple gateways to ensure compatibility and avoid integration disruptions.        Varied Deployment Environments: Different regions require specialized gateways for compliance, data sovereignty, or latency optimization (e.g., GDPR in the EU, domestic payment systems in the U.S.).        Specialized Use Cases: Internal microservices may use lightweight gateways, while customer-facing APIs require advanced features like authentication flows and developer portals.  An API management solution can help manage the complexities of having multiple API gateways by providing a flexible protocol that allows businesses to switch between different gateways without needing to rework their developer portal. This enhances integration and functionality across the organization.The Problem and Why It MattersAlthough multiple gateways can serve unique demands, it quickly becomes tricky to manage. Teams end up with fragmented data on API usage, performance, and developer adoption. Each gateway might provide its own analytics dashboard, making cross-team collaboration and strategic decision-making difficult.Without a unified view across these gateways, product managers and leadership struggle to see which APIs are truly driving revenue, where traffic is spiking, or how user onboarding differs from one product line to another. In fact, recent research shows that 38% of revenue-generating digital assets in enterprises are API-driven, making visibility across multiple gateways critical. Let’s dive deeper into these challenges and how a consolidated approach can save organizations from operational headaches while unlocking valuable new business insights. A developer portal can help manage these issues by providing a seamless interface with various API gateways, enabling fast key provisioning and integration, and offering a unified view that simplifies the evaluation process for API management solutions.Challenges of Managing Multiple API Gateways and the Power of Unified API ManagementAs organizations scale, different teams, business units, or regions may adopt various API gateways based on their unique requirements—whether for performance, security, compliance, or regional deployment. While this flexibility allows for tailored solutions, it also introduces operational inefficiencies, governance inconsistencies, and fragmented data visibility. Without a unified approach, companies struggle to optimize API performance, enforce security policies, and drive monetization strategies effectively. API providers can help manage these challenges by offering centralized solutions that streamline operations, enhance security, and create new revenue streams through various pricing models like pay-per-use and partnerships.The Hidden Costs of API FragmentationData Silos and Limited Insights      Inconsistent API Metrics: Each gateway provides separate analytics, making it difficult to gain a comprehensive view of API usage, performance, and adoption trends.        Difficult Monetization Tracking: API monetization models vary across gateways, leading to inconsistent revenue tracking and missed optimization opportunities.  Understanding API users can help overcome data silos and gain better insights by enriching user profiles and analyzing API usage alongside revenue and customer success.Limited Business Visibility and Growth Potential      Unclear API Adoption Metrics: Understanding customer behavior and high-value integrations is challenging when data remains siloed. Tracking API usage growth can help improve business visibility and growth potential by identifying trends and ensuring consistent usage over time.        Hindered API Packaging Strategies: Product managers lack the insights needed to refine pricing tiers, subscription models, and usage-based billing structures.  The Solution: A Unified View for API ManagementA unified API management strategy eliminates these challenges by consolidating API analytics, governance, and monetization tools into a single platform. Moesif provides deep visibility into API performance, security, and usage patterns across multiple gateways, empowering teams to make data-driven decisions and maximize API business potential. API management solutions can provide a unified view for API management, offering innovative features and developer portals that are essential for organizations, especially in multi-gateway environments.Holistic API Observability and Governance      Real-Time API Monitoring: Moesif can aggregate usage and performance metrics from multiple API gateways, enabling rapid issue detection and resolution. Monitoring CPU and memory usage can help in holistic API observability and governance by providing insights into application responsiveness and server health, which are crucial for resource planning and diagnosing performance issues.        Standardized Security Policies: Enforce rate limiting and compliance requirements consistently across all API deployments.  Smarter Monetization and Product Strategy      Cross-Gateway API Insights: Product managers can analyze API adoption, revenue impact, and retention rates in a single unified view within Moesif.        Optimized Pricing and Billing: Gain the intelligence needed to refine monetization models, implement tiered pricing, and maximize API revenue. Understanding the API product lifecycle can further enhance these strategies by providing insights into key performance indicators relevant to business impact and operational success.  Aligning Business Goals with API Strategy      Unified KPI Tracking: Moesif enables organizations to track API performance, customer engagement, and bxusiness impact through a single analytics platform with custom API dashboards.        Scalable API Growth: Centralized visibility into API usage trends supports infrastructure planning and expansion strategies.  With Moesif, companies can transform chaotic, multi-gateway environments into streamlined, data-driven ecosystems that enhance developer experience, improve security, and drive new revenue opportunities.Unified Analytics Help Product Managers Package and Monetize APIsWhen an organization has visibility into who is using their APIs, how they’re using them, and why they’re valuable, it becomes far easier to design effective pricing and packaging strategies. This is where unified analytics across multiple gateways truly shines. Let’s explore how product managers can leverage this holistic data.Segmenting and Identifying High-Value API Usage      Usage Segmentation: Aggregating data across gateways helps segment usage by customer tiers, feature adoption, or endpoint activity. If enterprise customers heavily rely on a particular API, it may justify premium pricing or SLAs.        Customer Profiling: Understanding which customers generate the highest traffic or revenue allows for better API packaging and monetization strategies.  Optimizing Pricing, Packaging, and Monetization      Subscription vs. Pay-Per-Use: A clear view of API consumption enables Product Managers to choose the right pricing model—subscriptions for predictable costs or pay-per-use for flexibility.        Feature-Based Monetization: Advanced features, such as analytics or premium data, can be placed in higher-tier plans based on real usage data.        Multi-API Bundles & Upsell Paths: Bundling related APIs and tracking usage thresholds can drive upsells, ensuring that developers move into higher tiers when they exceed their limits.  Data-Driven Adjustments and Transparency      Real-Time Optimization: A unified view allows for quick adjustments to pricing and packaging based on under or overutilized features.        Promotional Campaign Tracking: Integrated analytics provide insights into how trials or promotions impact usage, guiding future marketing efforts.        Clear Metering & Billing Transparency: Giving customers visibility into their API usage reduces billing disputes and builds trust, while automated alerts prevent unexpected overages.  A singular view of usage is a powerful enabler for API productization and monetization. It provides the granular insights product managers need to craft compelling offers, set competitive pricing, and nurture stronger relationships with customers.Key API Metrics: Why a Unified View MattersBringing together multiple API gateways under one management framework grants you the power to observe performance, reliability, and revenue-generation in a consistent, holistic way. These key metrics form the bedrock of continuous improvement, helping you spot bottlenecks, optimize resource allocation, and validate strategic decisions. Increased API usage is a key metric for understanding API performance and customer engagement.Product and Business KPIs      Revenue or Billing Metrics: For monetized APIs, track revenue per call, average revenue per user (ARPU), and churn rates. These metrics can confirm whether your pricing models and product tiers are viable.        Conversion Rates: If you offer freemium or trial tiers, measure how many developers or businesses upgrade to paid plans. This indicates how compelling your paid features are and where you might refine your funnel.        Feature Adoption: Monitor usage of key endpoints or functionalities. Understanding which features gain the most traction guides future roadmap decisions and helps you prioritize improvements that yield the highest ROI.  Adoption and Usage Metrics      API Call Distribution: With multiple gateways, it’s vital to see how traffic is distributed across various services and endpoints. This reveals which APIs are under heavy load or show strong growth potential.        New vs. Returning Developers: A unified view of developer onboarding metrics—like time-to-first-successful-call or repeat usage—indicates how well your APIs retain interest and whether your documentation or portals are effective.        Geographical/Regional Usage: Tracking where calls originate can guide infrastructure decisions (e.g., data center locations) and help you tailor security policies for each region.  Operational Metrics      Latency (Average and Peak): Monitoring response times is crucial for ensuring a seamless end-user experience. High latency can signal underlying issues in either the gateway configurations or your backend services. In fact, Cloudflare’s API Performance Report shows that 57% of internet traffic is now composed of API requests, making speed and efficiency a top priority.        Error Rates: Tracking the frequency and type of errors (4XX, 5XX) helps surface potential configuration problems, code bugs, or external dependencies failing.        Throughput and Request Volume: By understanding how many requests each gateway handles—and during which time frames—you can proactively manage capacity, load balance effectively, and predict scaling needs.  In a multi-gateway environment, relying on fragmented statistics can lead to misinformed decisions. A unified dashboard ensures every team, from product management to engineering, has the data they need to troubleshoot issues, iterate on features, and prove ROI.Business and Growth: Why You Need a Unified DashboardWhile operational stability and a strong developer experience are vital, most organizations ultimately measure API success by its impact on the bottom line and long-term market positioning. A unified API strategy can serve as a foundation for sustainable growth, allowing product teams to focus on strategic initiatives rather than firefighting.Customer Acquisition and Retention      Targeted Upsell/Cross-Sell: By analyzing usage across gateways, you can identify which customers or partners might benefit from additional API features or higher-tier plans. Automated alerts or in-app notifications can then prompt timely upsells, increasing average revenue per user.        Reduced Churn through Insights: A single pane of glass for customer activity enables quick identification of at-risk accounts—e.g., a sudden drop in API calls could signal dissatisfaction or technical hurdles. Product managers can intervene proactively, offering support or new features to keep users engaged.  Strategic Growth and Market Expansion      Data-Backed Roadmap Decisions: Comprehensive usage data drives more accurate prioritization of new features, expansions, or acquisitions. Leadership can see precisely which APIs or endpoints are fueling growth—and invest accordingly.        Confident Expansion into New Markets: If the data shows strong traction in a specific region or industry, leadership can allocate resources strategically, designing localized solutions or forging new partnerships.        Preparing for Emerging Business Models: Whether it’s launching new subscription tiers or integrating with ecosystem partners, a unified API platform makes it simpler to experiment with—and measure—new revenue models.        Staying Agile in Fast-Changing Markets: Unified metrics and streamlined workflows help your teams pivot quickly in response to market feedback or disruptive trends, ensuring long-term competitiveness.  Cross-Functional Visibility and Scalable Collaboration      Single Source of Truth for Stakeholders: When executives, product managers, and DevOps share the same metrics, conversations shift from “do we have the data?” to “how do we use it?” This alignment empowers better coordination on product strategy, sales, and support.        Bridging Silos in Large Organizations: In multi-division or globally distributed companies, a unified approach ensures that successes and challenges are visible across the organization, reducing duplication of effort and fostering synergy.        Scalable Growth and Future-Proofing: A well-structured API management strategy ensures that teams can scale efficiently, breaking down data silos and creating a foundation for long-term business growth.  Centralized API management isn’t just about operational efficiency; it’s also a strategic growth lever. When teams have clear, unified data on API usage and performance, they can make stronger decisions about product direction, pricing strategies, and market expansion. In the next section, we’ll discuss how these elements come together in real-world scenarios, using unified analytics to maximize both technical efficiency and business ROI.ConclusionFrom the moment an organization deploys its first API gateway to the point where dozens of microservices and product lines are live, the complexity of managing and measuring API usage can grow exponentially. Multiple gateways, each with its own analytics and operational quirks, can easily lead to data silos and fragmented insights. Yet by consolidating these gateways under a single lens, companies unlock new levels of transparency and control.While each organization’s path to unified API management is unique, the overarching takeaway remains constant: centralizing your API data is a multiplier on success. It nurtures a holistic understanding of user behavior, helps maintain strong SLAs, and aligns teams around shared, data-driven goals.If you’re exploring solutions in this space, Moesif is capable of providing deep insights into both the operational and business aspects of your APIs. The key is to find tools and processes that facilitate, rather than hinder, your multi-gateway strategy. Sign up today for a free trial, no credit card required.                Deep AI API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design and Build Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /api-product-management/api-analytics/Why-a-Unified-View-of-API-Usage-is-Critical-for-Managing-Multiple-API-Gateways/",
          "author": "Dylan",
          "categories": "api-product-management, api-analytics"
        }
      
    ,
  
    
        "monitoring-how-to-debug-ai-traffic-using-moesif": {
          "title": "How to Debug AI Traffic Using Moesif",
          "content"	 : "We strive for predictable, controllable systems. We demand precise understanding of resource utilization, performance characteristics, and failure modes for our AI apps. An ideal AI-powered application behaves like any other well-instrumented component of the architecture—transparent, debuggable, and optimizable.But integrating with AI APIs, particularly from third-party providers, often brings with it an unwelcome element of opacity. For example, without clear visibility, token counts can become a cost concern. You might observe latency spikes without obvious causes. API-specific errors emerge, leaving you to decipher cryptic messages. This black box behavior, contradicting your engineering principles, creates friction, impedes product growth, and increases operational risk. Forcing to guess instead of knowing doesn’t bode well for driving innovation forward, which adoption of artificial intelligence should help us achieve.With Moesif, you can transform the unpredictable “black box” into a manageable, engineering-grade system. In this article, using practical examples for a GenAI API, we’ll demonstrate how Moesif delivers the observability you need for your AI product. We’ll also cover the basics of AI traffic debugging, discussing the key concepts behind the process.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        Table of Contents  Understanding AI Traffic          Key Concepts and Terminology                  Tokens          Prompt          Model Parameters                      Challenges in Debugging AI Traffic  Key Metrics To Track          Token Usage      Latency or Response Time      Error Rates and Breakdowns      Request and Response Details      Model Identifier      User and Session Identifiers      Custom Metadata      Usage and Billing      Considerations For Image, Video, and Audio Generation        How to use Moesif to Debug AI Traffic          Understand Token Usage      Analyze Performance      Analyze Errors      Enhance AI Observability With OpenTelemetry      Track Usage And Cost        Conclusion  Next StepsUnderstanding AI TrafficTo put simply, AI traffic refers to the interactions between your application and an AI model’s API, as well as the exchanged data. AI traffic is similar to web traffic, for example HTTP requests and responses, but one that represents interactions with an AI model:  You send HTTP requests to an AI API defining the prompts, parameters (for example, maximum number of tokens), and contextual data.  The API returns the generated responses—text, images, and so on.You can consider each API call and its payload constituting a unit of AI traffic.An HTTP request may look like this:{  prompt:"Explain quantum computing in simple terms"  max_tokens:100  context_size:50  model:"gpt-3.5-turbo"}And the response might be similar to the following:{  "generated_text": {    "object": "chat.completion",    "usage": {      "completion_tokens": 9,      "total_tokens": 47,      "prompt_tokens": 38    },    "choices": [      {        "index": 0,        "message": {          "role": "assistant",          "content": "Quantum computing is like having a super-fast computer that can think about many things at once. Instead of binary bits, it employs 'qubits' that can hold both 0 and 1 at the same time. This allows it to solve complex problems much faster than regular computers."        },        "finish_reason": "length",        "logprobs": null      }    ],    "id": "chatcmpl-xdK15hflCCsjhXJ79toh8zPKSUR",    "created": 1740480690  }}In a traditional production application, the API responds to incoming requests by making database queries. You have a well-defined contract and predictable behavior. AI traffic, on the other hand, involves interactions with a probabilistic system: an AI model. This means the responses aren’t always deterministic. You have to therefore approach differently to effectively analyze and debug AI products.Key Concepts and TerminologyLet’s briefly go over the key concepts that relate to debugging AI traffic and how they matter.TokensTokens act as the fundamental units that AI models use to process text. Instead of processing entire words or sentences, models break down the textual input into tokens. Depending on the model and its tokenization process, tokens can be words, parts of words, or even punctuation marks. For example, an AI model might break down the sentence “Hello, Moesif!” into these four tokens:  Hello  ,  Moesif  !Token usage directly correlates with the cost of using most AI APIs dealing with text. Many APIs build their pricing based on the number of tokens they process—both for input and output.When debugging AI traffic, monitoring token counts can help you identify inefficient prompts, optimize your application’s resource consumption, and manage expenses without unexpected surprises. For example, if you observe high token usage, it may indicate overly verbose prompts or incorrect model settings.PromptPrompts are the inputs you provide to an AI model. If you can design and refine the inputs (prompts) you send to an AI model, you can produce reliable, relevant, and high-quality outputs while maintaining minimum token usage.Understanding how to write effective prompts can help you construct a guideline for the end users that they can follow to get the best out of your app’s services. It may also help you understand how customers use your application and how models perform, along with the rest of your infrastructure, by correlating prompt details with token usage and other data.Model ParametersThese parameters control the behavior of the AI model during response generation. For example:  Temperature  Controls the randomness of the output. Higher values produce more creative but potentially less coherent results. Lower values make the output more deterministic and focused.  max_tokens  Sets a hard limit on the number of tokens the model can generate in its response. Setting a maximum limit can prevent runaway generation and control costs.Incorrect parameter settings can lead to unexpected outputs, excessive token consumption, or truncated responses. By monitoring these parameters alongside the model’s output, you fine-tune the model’s behavior for specific use cases.Challenges in Debugging AI TrafficTo effectively debug AI traffic, you can’t apply the same perspective and strategies like in traditional web applications, the latter of which involves well-established tools and techniques. We have decades of experience with predictable request-response cycles, deterministic behavior, and code you can readily inspect.On the contrary, integrating AI services introduces a fundamentally different set of challenges. These challenges stem from the probabilistic nature of AI models, the “black box” effect as we’ve stated already, and the unique cost and performance considerations of AI APIs.Traditional web requests are largely deterministic. The same input to the same endpoint, under the same conditions, generally produces the same output. This predictability, right from the get-go, simplifies debugging. AI model responses, however, are probabilistic. Even with the same prompt, you might get slightly different outputs each time, for example, with higher temperature settings. This inherent randomness makes it harder to reproduce issues and pinpoint the root cause.You have full access to your application’s source code. You can, for example, use debuggers to step through the code, inspect variables, and understand the exact execution flow. The other part of the picture, however, involves interacting with AI models through an API. You can’t see the internal workings of the model, even if you’ve trained a custom model yourself for your use case. This opacity makes it difficult to understand why a model produces a particular output or why an error occurred. You must capture as much details as possible on these interactions, and possess the advanced analytics tools to analyze them, to countermeasure the opacity and achieve effective debugging.Then consider the pricing and cost model. Web API costs often relate to the number of requests or bandwidth you use. You can track and predict them in a  relatively straightforward manner. In contrast, many AI APIs have a cost model based on tokens. Inefficient prompts or model settings can incur unexpectedly high token usage and costs. So you must accurately track and meter token usage (prompt, completion, total, and so on).When analyzing performance and latency issues in AI apps, it’s important to keep in mind that the issues can originate from multiple sources: your network connection, the AI provider’s infrastructure, or the model itself. To isolate the bottleneck, you must carefully analyze timing data along and leverage profiling tools for traces and logs.Lastly, consider the different errors. In traditional web traffic, error codes and messages are well-defined and documented. However, AI APIs can return a variety of errors, including ones that are specific to the model or provider. You must consolidate and contextualize these errors to better understand them for debugging purposes.Key Metrics To TrackNow let’s discuss the key metrics you need to track to effectively debug AI traffic:Token UsageAs already mentioned, token usage directly impacts the cost. Therefore, monitoring their usage comes as a no-brainer.Usually, the AI API reports the number of tokens associated with each API interaction in the responses. For example, in the example response we’d demonstrated earlier, the usage field contains the detailed token usage counts:  prompt_tokens represents the number of tokens in the input.  completion_tokens represents the number of tokens in the generated response.  total_tokens gives the sum of prompt_tokens and completion_tokens.Latency or Response TimeThis measures the total time it takes for an AI API request to complete, from sending the request to receiving the full response. Ideally, you should break it down into the following components if possible:  Network latency  Time spent in transit over the network.  AI processing time  Time spent within the AI provider’s infrastructure.You can easily achieve this by instrumenting your app with something like OpenTelemetry. And with Moesif’s OpenTelemetry integration, you will have the trace and log data available with the rest of the analytics data in Moesif.Error Rates and BreakdownsYou must also track the frequency of errors, types of errors, and their occurrence across different endpoints. High error rates can point towards a number of issues:  The requests, for example, invalid input and exceeding rate limits  Authentication issues  Problems with the AI service itselfAnalyzing errors from different perspectives helps pinpoint the root cause.Request and Response DetailsThis refers to the complete content of the data you send to (HTTP request) and receive from (HTTP response) the AI model:  The request method  The HTTP headers  The payload or bodyInspecting these details and analyzing them can give you significant insights for debugging. For example, you can verify that the request body doesn’t have incorrect formatting and understand the generated output. More often than not, the payloads have important data like the following:  AI model information  Token usage  Rate limit informationModel IdentifierThis identifies the specific AI model that processes the request, for example, gpt-3.5-turbo-0613, gpt-4-0314, and claude-2.Different models have different performance characteristics, cost structures, and potential failure modes. Therefore, having the model identifier allows you to compare performance across models, identify model-specific issues, and make sure you’re using the intended model.User and Session IdentifiersIf your application serves multiple users or has distinct sessions, you might want to track a unique identifier like user ID, session ID, and company ID for each AI API request. It allows you to associate API calls with specific customers.Custom MetadataCustom metadata allows you to attach any additional application-specific information to your AI API requests like the following:  Feature flags  Experiment IDs  Task types  API versionCustom metadata provides valuable context for analyzing your AI traffic. For example, you can track which feature flag a particular request enabled, or which A/B testing group a user belongs to.Usage and BillingLastly, we recommend you track usage and the associated revenue or cost. This allows you to confidently allocate resources and grow your product.Considerations For Image, Video, and Audio GenerationWhen you want to debug AI products that generate images and videos, you have to look at a set of slightly different metrics. Instead of token usage, you have to focus on the content itself—quality, resolution, duration, and so on.For both images and videos, these are the defining metric types for the generated output:  Dimension (width and height)  Resolution (pixels per inch or dots per inch)  DurationFor audio, you might also consider the sample rate or bitrate, along with duration.How to use Moesif to Debug AI TrafficMoesif provides a robust set of tools to effectively track, collect, and observe AI traffic metrics so you can debug them with ease and accuracy. The following sections demonstrate some example scenarios of debugging AI traffic for a GenAI-based product. The product uses a GenAI API  to generate text, image, and video. It also allows training AI models, as well as generating embeddings.Understand Token UsageYou can use Moesif to understand and look at token usage in many ways. For example, here we look at input token consumption for different companies for the past 30 days against a maximum quota of 1.5k tokens:              Analysis settings for token usage analysis.                  Plot of token usage analysis for different companies.    You can analyze similarly with output tokens as well, for example, for evaluating performance.Analyze PerformanceMany AI APIs require you to specify the AI model in your request. It can also reside in request or response headers, or in custom metadata of your application. Since Moesif captures request and response details, and allows you to add custom metadata, you have AI model data available to you through different means. This allows you to perform comparative analysis of AI models for different metrics.For example, you can use Moesif Time Series analysis to compare performance of AI models. The following chart illustrates a P90 latency analysis for image generation across different models:              P90 performance analysis settings for different AI models.                  Plot of P90 performance analysis of different AI models.    As you can observe, the Dall-E 2 model performs better and more consistently than other models for image generation.Moesif offers flexibility, customizability, and a collection of built-in functions to define your custom metric for performance. For example, here we analyze throughput of API calls for the past 7 days across different AI models and API endpoints, visualizing it in a stacked bar chart:              Analysis settings to understand max throughput of different AI models across endpoints.                  Plot breaking down max throughput across different AI models and API endpoints.    Analyze ErrorsMoesif’s segmentation and group features can break down different metrics across entities. For example, you can plot a bar chart for all 4xx client errors and 5xx server errors across endpoints.              Analysis settings to understand 4xx and 5xx errors in the API.                  Plot breaking down 4xx and 5xx errors across different endpoints.    Being able to visualize errors like this allows you to debug more efficiently, identify volatile resources, and address pain points of your customers.Enhance AI Observability With OpenTelemetryAs we’ve discussed already, the opacity of AI apps impedes the ideal visibility for effective debugging. To alleviate that, one of the strategies we recommend is instrumenting your apps with observability frameworks like OpenTelemetry. If you’ve already done that, after you integrate Moesif, you’ll have the trace and log data available to you in the Moesif platform. This vastly improves your observability, troubleshooting, and analysis. You have a unified platform giving you greater context around all the analytics data, tools, , traces, and logs.See the following resources to learn how OpenTelemetry and Moesif can give you better observability for your AI apps:  Achieve API Traceability with Moesif and OpenTelemetry  Integrate OpenTelemetry with Moesif in your Node.js appTrack Usage And CostWhen it comes to cost and revenue, analyzing token usage paints only half of the picture. Thanks to Moesif’s Billing Meters, you can accurately define how you want that usage to tie to the monetary side of things.Moesif supports popular billing providers like Stripe out-of-the-box. You can have your custom billing solution where Moesif accurately tracks, meters, and reports usage. Then you can leverage Billing Report Metrics to get detailed usage reports for usage-based cost or revenue.For example, here we define a billing meter, charging customers based on their input prompts:              A billing meter monetizing input token usage    Moesif, in real time, tracks the usage and associated cost or revenue for this billing meter.              Usage and cost/revenue plot for the AI API’s billing meter.    ConclusionUnfortunately, simply knowing which metrics to track for debugging AI traffic doesn’t suffice. You need a powerful, unified platform to collect, analyze, and visualize this data in a way that’s actionable.With Moesif, you can quickly achieve comprehensive observability for your AI apps with minimal setup. Moesif aims to provide a customer and product-centric analytics platform for modern products, solving the unique challenges that come with them. Moesif offers much more than we’ve been able to demonstrate within the scope of this article.If you want to see for yourself how Moesif can transform the opaque world of AI interactions into a transparent and manageable system, sign up today for a free trial, no credit cards required.Next Steps  Get started with Moesif  Integrate Moesif with your platform of choice.  Use Moesif to understand revenue and cost of API usage.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design and Build Better AI Products!            Use Moesif to build better AI apps with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /monitoring/How-To-Debug-AI-Traffic-Using-Moesif/",
          "author": "Sakib",
          "categories": "monitoring"
        }
      
    ,
  
    
        "technical-api-development-real-time-api-observability-using-moesif-and-heroku-together": {
          "title": "Real-time API observability: Using Moesif and Heroku Together",
          "content"	 : "Heroku provides a robust platform to deploy and scale your applications. However, inherent complexities within your app’s performance and user interaction remain obscured. Your app may be encountering errors and performance bottlenecks. Moreover, a lack of API analytics and API observability impedes your ability to truly grow your product.With Moesif, you can easily gain that visibility into your APIs on Heroku. It integrates with your Heroku app as an add-on in two simple steps. In this article, we walk you through deploying a Django application on Heroku and then integrating Moesif with it. Then we’ll see some of the features Moesif offers for deep API observability and analytics.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        Table of Contents  Why Moesif and Heroku work better together  Prerequisites  Set up your Django application  Create and deploy the Heroku app  Set up Moesif with Heroku  Access Moesif from Heroku  Usage examples          Debug API failures      Deep API analytics for API endpoints      Detect and block API abuse in real time      Tracking Business Value of APIs      Improve API monetization        Wrapping up  Next StepsWhy Moesif and Heroku work better together  Install and set up Moesif directly from Heroku Elements Marketplace.  Access, manage, and use Moesif through Heroku CLI or Heroku Dashboard.  Pay for Moesif through Heroku without having to manage a separate subscription.  Automatically monitor API traffic and gain deep insights into your app’s adoption, customer behavior, and profitability.You don’t have to manually instrument your app or define collection procedures for analytics data. Moesif automatically captures and collects API requests and responses, including headers, HTTP bodies, query parameters, and associated customer data.PrerequisitesBefore proceeding, make sure you have active accounts in Heroku and Moesif. Then install the Heroku CLI so you can deploy, manage, and run your application from the command line. After you’ve installed the Heroku CLI, log into your Heroku account:heroku loginSet up your Django application      Clone our Django example application from GitHub and enter the root directory.     git clone https://github.com/Moesif/moesifdjangoexample cd moesifdjangoexample            The requirements-django4.txt file in the example app contains more recent versions of the app’s dependencies. Therefore, we recommend you use that for your Heroku app’s requirements.txt file. Then, add gunicorn as a dependency in the requirements.txt file. Gunicorn is a Python WSGI HTTP server that Heroku uses to serve your application. In the next step, we define the exact gunicorn command that starts the app. After adding Gunicorn, your requirements.txt file should look like this:     Django==4.2.0 moesifdjango==2.3.12 djangorestframework==3.15.0 gunicorn            Add a Procfile in the root directory of the application that specifies the following command:     web: gunicorn mysite.wsgi            In mysite/settings.py, define the Django secret key as an environment variable to fetch from a Heroku config var:     SECRET_KEY = os.environ.get("DJANGO_SECRET_KEY")        In the next section, you’ll create a Heroku app and then add the DJANGO_SECRET_KEY config var to the app.        In mysite/settings.py, configure ALLOWED_HOSTS:     IS_HEROKU_APP = "DYNO" in os.environ and "CI" not in os.environ     if IS_HEROKU_APP:    ALLOWED_HOSTS = ["*"]    SECURE_SSL_REDIRECT = True else:    ALLOWED_HOSTS = ["127.0.0.1:8006", "127.0.0.1:8000", "127.0.0.1", "localhost"]            We recommend using Python version 3.12.3 with the example application. Therefore, specify the version in the .python-version file in the root directory of the app:     3.12.3            The example Django app doesn’t contain any static files. So disable running Django’s collectstatic command when Heroku builds the app:     heroku config:set DISABLE_COLLECTSTATIC=1      Create and deploy the Heroku appMake sure you’re in the root directory of the application. Then follow these steps:      Create your Heroku app with the following command:     heroku create            Deploy your application code to Heroku:     git push heroku master        After the build and deployment finishes successfully, the Heroku CLI shows you the URL where it hosts your app at the end of the logs. For example:     ... -----> Restoring cache -----> Using cached install of Python 3.12.3 -----> Installing pip 24.3.1, setuptools 70.3.0 and wheel 0.45.1 -----> Installing SQLite3 -----> Installing dependencies using 'pip install -r requirements.txt' -----> Skipping Django collectstatic since the env var DISABLE_COLLECTSTATIC is set. -----> Discovering process types       Procfile declares types -> web -----> Compressing...       Done: 28.8M -----> Launching...       Released v12       https://YOUR_APP_NAME-12345678xy12.herokuapp.com/ deployed to Heroku      Set up Moesif with Heroku      Install Moesif add-on:     heroku addons:create moesif        During the installation process, Heroku informs you about creating a MOESIF_APPLICATION_ID config var. You also can get this application ID in the next step.        Install Moesif server integration for your Heroku app:    a. Go to your Heroku dashboard and select your app.    b. In the Installed add-ons section, select the Moesif add-on.            Selecting the Moesif add-on in Heroku Dashboard.     This takes you through the Moesif onboarding steps.            Moesif Web Portal onboarding screen.     During these steps, Moesif shows you your Application ID that Heroku puts in its MOESIF_APPLICATION_ID config var when installing the Moesif add-on.  After you finish the onboarding steps, Moesif integration with your Heroku app should complete, giving green checkmarks for all four steps like in the preceding image.To check if everything is working correctly, send an HTTP request to your application endpoint. For example, sending a POST request to https://YOUR_APP_NAME-12345678xy12.herokuapp.com/api/users/66640/ returns the following response:{  "user_id": "66640",  "update_users": "success"}Moesif should also capture the event and log its details. In your Moesif Web Portal, you can see the event’s details in the Live Event Log.For the available API endpoints in the Django example application, see the urls.py files.Access Moesif from HerokuYou can always access your Moesif Web Portal from Heroku CLI with the following command:heroku addons:moesif openAlternatively, go to your Heroku Dashboard, select your app, and then select Moesif add-on from the Installed add-ons section.Both of these methods allow you to log into Moesif and access your Heroku app’s analytics.Usage examplesWIth Moesif integrated with your Heroku app, you have a dedicated platform that unifies observability, analytics, and monetization in one place. Here are some usage examples to demonstrate how Moesif can improve your Heroku application’s monitoring and insights:Debug API failuresYour application may rely on external APIs or services. If you spot API calls failing intermittently, the root cause can likely lie elsewhere.Since Moesif captures HTTP requests and responses with greater context, including payloads, it makes debugging such issues much easier. It improves correlating with Heroku logs and metrics for API failures, errors, and contextual information like HTTP headers. Furthermore, if you’ve instrumented your app with OpenTelemetry, you can use Moesif’s OpenTelemetry support to get trace and log data straight into your Moesif analytics dashboards.Deep API analytics for API endpointsConsider the example Django application. It essentially serves a REST API using the Django REST framework. With Moesif integration, you can track the API’s performance, error rates, and user behavior.You can leverage a rich set of filters, segmentation, and analysis types to analyze traffic for each endpoint, identify bottlenecks, and understand usage trends. This provides insights into API clients making the most requests and how they interact with different endpoints.For example, you can use Live Event Log to dig into recent 4xx and 5xx errors:              Moesif Live Event Log showing 4xx and 5xx errors for recent API calls.    You can also get a breakdown of 4xx client errors by endpoints using a Segmentation chart:              Understanding 4xx client errors for API endpoints in Moesif.    Detect and block API abuse in real timeYou may notice your API experiencing excessive traffic from certain IP addresses or API tokens. This can potentially lead to rate-limiting or security risks. In situations like these, here’s how Moesif can help:  Detect anomalous traffic patterns, like a single user making thousands of requests in a short time, and block them from further access with Quotas and Governance:            Detecting and blocking API access using Moesif’s governance rules.      Establish automated and real-time alerts that you can customize according to your needs..  Perform heatmap analysis and get geo-location insights to identify traffic from suspicious regions.Tracking Business Value of APIsIf you have a SaaS API that customers use to integrate your services into their workflows, you can analyze consumption of your APIs using Moesif:  Understand how customers interact with your API over time.  Segment users based on different criteria, for example, active versus inactive users, demographics, and so on.  Track adoption of APIs such as through conversion funnels.Understanding your customers allows you to identify issues like onboarding friction. You can make decisive decisions to work on improving customer experience and send targeted onboarding emails.Improve API monetizationIf you’re monetizing your API, through, for example, a usage-based pricing model, you can leverage Moesif’s robust usage metering and tracking features.Moesif integrates with popular billing providers like Stripe. You can also integrate a custom billing solution with Moesif where it accurately tracks, meters, and reports usage. Through Billing Report Metrics, you can get detailed usage reports for metered billing.For example, you may face a customer dispute that claims an incorrect charge amount. With Moesif, you can use Billing Report Metrics and a per-user API request log during the billing period, to investigate and resolve the issue.Wrapping upThis article uses a Python Django app for demonstration, but both Heroku and Moesif support a variety of application frameworks and tools. Heroku’s scalable, fully-managed services combined with Moesif’s API intelligence can help you scale your app while maintaining deep observability and security.To try out Moesif for yourself and see how it can help you build better products, sign up today for a free trial, no credit cards required.Next Steps  Integrate Moesif with your platform of choice.  Learn about dashboards and workspaces.  Achieve API traceability with Moesif’s OpenTelemetry integration.  Use Moesif to understand revenue and cost of API usage.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design and Build Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Real-Time-API-Observability-Using-Moesif-And-Heroku-Together/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "monitoring-best-practices-for-monetizing-ai-successfully": {
          "title": "Best Practices for Monetizing AI Successfully",
          "content"	 : "Artificial intelligence has become a driving force behind modern innovation, helping businesses across all industries optimize processes and generate income. But how do you monetize AI usage effectively? Whether you’re integrating AI features into an existing plan or launching entirely new AI products, choosing the right approach can unlock steady revenue growth and strengthen competitive advantage.In this article, we’ll explore several proven monetization strategies for artificial intelligence, from direct monetization to indirect monetization, and shed light on developing an AI pricing strategy that suits your target audience. We’ll also highlight best practices for defining value metrics, managing cost structures, and building long-term customer satisfaction. By the end, you’ll see how even a significant investment in AI can be recouped through the right pricing model and thoughtful execution.Understanding AI Monetization“Monetizing AI” involves generating revenue from artificial intelligence tools, such as AI-powered tools, AI chatbots, or AI-enhanced analytics. To effectively monetize AI usage, businesses typically offer AI capabilities—such as NLP, computer vision, or large language models—as paid features or standalone solutions that deliver actual value to customers.Businesses can adopt a variety of monetization strategies, including usage-based pricing, bundled pricing, or a value-led pricing strategy. Regardless of the approach, the key is to determine how much value your AI functionalities bring to users and to align pricing with that actual usage. Doing so ensures customers feel they’re paying the right price point for the benefits they receive.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        What is AI Monetization?AI monetization refers to the process of generating revenue from artificial intelligence (AI) capabilities, features, and products. This involves developing and implementing effective pricing strategies, packaging, and licensing models to capture the value created by AI. For businesses, AI monetization is critical to recoup their investments in AI research, development, and deployment. By strategically leveraging AI capabilities, companies can unlock new revenue streams, enhance customer experiences, and maintain a competitive edge in the market.Why It MattersMonetizing AI is more than just an opportunity—it’s quickly becoming a necessity for businesses looking to stay competitive. AI is reshaping industries by improving efficiency, personalizing customer experiences, and driving automation. However, understanding how to extract real monetary value from AI investments is a challenge that many businesses face. By leveraging the right strategies, companies can ensure that their AI innovations not only enhance operations but also contribute directly to revenue growth.      AI can reduce manual labor and handle complex tasks that would otherwise require human intelligence. By automating repetitive processes, AI allows employees to focus on higher-value work, leading to increased productivity and cost savings. Additionally, AI-driven automation can scale operations more efficiently, handling large volumes of data-driven tasks without the need for proportional increases in workforce size.        AI uncovers insights in usage data for better decision-making and customer behavior analysis. By leveraging machine learning algorithms, businesses can detect patterns, predict future trends, and personalize customer interactions. AI-driven analytics can provide real-time feedback, allowing organizations to refine marketing strategies, optimize pricing models, and enhance customer segmentation for maximum impact.        Many organizations incorporate generative AI (e.g., AI-generated art) to differentiate their offerings. This includes leveraging AI to create unique marketing assets, generate personalized content at scale, and develop innovative digital products that stand out in competitive markets. Additionally, businesses are using AI to streamline creative workflows, reducing costs and enhancing efficiency in content production.  Ultimately, artificial intelligence can be a significant investment. However, with proper planning and a well-defined AI monetization strategy, it becomes easier to monetize AI usage and see a return on that investment.Key Trends in AI MonetizationThe AI monetization landscape is rapidly evolving, with several key trends emerging. Businesses are continuously exploring innovative ways to capitalize on AI’s capabilities, from advanced pricing models to AI-driven market analysis. As AI adoption increases across industries, companies must stay ahead of these trends to maintain a competitive edge and unlock new revenue streams.      Hybrid Pricing Models: Companies are increasingly adopting hybrid pricing models that combine subscription-based, usage-based, and tiered pricing. This approach allows businesses to capture the value of AI features and products more effectively by aligning pricing with customer usage and needs.        Value-Based Pricing: AI vendors are shifting towards value-based pricing, where the price of AI features and products is tied to the actual value they deliver to customers. This ensures that customers perceive they are getting their money’s worth, fostering long-term satisfaction and loyalty.        AI-Powered Pricing Optimization: Companies are leveraging AI-powered pricing optimization tools to analyze customer behavior, market trends, and competitor pricing. These tools help businesses fine-tune their AI pricing strategies, ensuring they remain competitive while maximizing revenue.        Increased Focus on Customer Lifetime Value: AI vendors are prioritizing customer lifetime value (CLV) as a key metric to measure the success of their AI monetization strategies. By focusing on CLV, companies can develop strategies that enhance customer retention and drive long-term profitability.  By taking a structured approach to AI monetization, businesses can harness the full potential of their AI investments. Whether through direct revenue models, data-driven insights, or enhanced customer experiences, AI presents numerous opportunities to generate income and improve operational efficiency. The key lies in selecting the right strategy, continuously optimizing offerings, and adapting to market trends to stay ahead of the competition.Identifying AI CapabilitiesIdentifying AI capabilities is crucial for developing effective AI monetization strategies. Understanding the full range of AI functionalities allows businesses to leverage these technologies in innovative ways, enhancing both operational efficiency and revenue potential. By identifying AI capabilities, companies can better align their offerings with customer needs, optimize pricing structures, and create unique value propositions that set them apart in competitive markets. AI capabilities can be categorized into several types, including:      Machine Learning: Machine learning capabilities enable AI systems to learn from data and improve their performance over time. This can be applied in various domains, from predictive maintenance to personalized recommendations.        Natural Language Processing: Natural language processing (NLP) capabilities enable AI systems to understand and generate human language. This is essential for applications like chatbots, virtual assistants, and sentiment analysis.        Computer Vision: Computer vision capabilities enable AI systems to interpret and understand visual data from images and videos. This technology is used in areas such as facial recognition, autonomous vehicles, and medical imaging.        Predictive Analytics: Predictive analytics capabilities enable AI systems to analyze data and make predictions about future outcomes. This is valuable for applications like demand forecasting, risk assessment, and customer behavior analysis.        Large Language Models (LLMs): LLMs enable AI systems to process and generate human-like text, facilitating advanced applications like conversational AI, content creation, and automated code generation. These models are widely used in virtual assistants, customer service automation, and knowledge management solutions.  By staying informed about emerging AI trends and continuously refining strategies, businesses can maximize the value of their AI investments. The evolving landscape of AI presents endless opportunities, and companies that adopt a proactive approach will be best positioned for long-term success.AI Features and ProductsAI features and products can be monetized in various ways, including leveraging AI-driven automation, enhancing existing services with intelligent capabilities, and integrating AI-powered insights into business operations. Companies can implement diverse strategies to create new revenue streams, expand market reach, and differentiate their offerings from competitors.      Direct Monetization: Charging customers directly for AI features and products through one-time purchases, subscriptions, or pay-per-use models. Businesses can implement this by offering premium AI functionalities, API access, or AI-driven enhancements as paid upgrades. Additionally, companies can monetize AI through licensing agreements, enterprise contracts, and tiered service offerings that provide advanced capabilities for higher fees. The flexibility of direct monetization allows organizations to tailor pricing structures to customer needs while ensuring a steady revenue stream from AI innovations.        Indirect Monetization: Generating revenue from AI features and products through indirect channels, such as advertising, data analytics, or strategic partnerships. Businesses can leverage AI to enhance targeted marketing campaigns, optimize customer insights for improved engagement, and create AI-driven solutions that drive customer retention. Additionally, companies can monetize AI by offering data-driven insights to third parties, providing enhanced analytics for advertisers, or integrating AI-powered recommendations into existing services to increase value without direct fees.        Freemium Models: Offering basic AI features and products for free while charging customers for premium features and products. This approach allows businesses to attract a large user base and demonstrate the value of their AI-driven services. Free-tier users can experience core functionalities, increasing the likelihood of upgrading to paid tiers for advanced capabilities, enhanced performance, or additional integrations. Many successful AI companies use freemium models to create a strong customer pipeline while monetizing premium-tier users effectively.        Subscription-Based Models: Charging customers a recurring fee for access to AI features and products, providing a steady revenue stream. Subscription models allow businesses to build predictable revenue while fostering long-term customer relationships. Companies can structure subscriptions with multiple tiers, offering varied levels of AI functionalities, customer support, or additional integrations. Many AI-driven platforms use this approach to provide ongoing updates and improvements, ensuring continued customer value and retention.  By implementing the right monetization strategy, businesses can unlock the full potential of AI-driven features and products. Whether through direct sales, indirect revenue streams, or subscription-based models, companies can leverage AI to enhance their offerings and drive long-term growth. The key is to align monetization strategies with customer needs, ensuring both value delivery and sustainable revenue generation.Pricing Models for AISelecting the right AI pricing strategy is crucial to profitability. A well-structured pricing model ensures that businesses can maximize revenue while maintaining customer satisfaction. Companies must consider factors such as market demand, competitive positioning, and scalability when determining the most effective approach. Common models include:      Usage-Based Pricing: Charging per transaction, per query, or per processing cycle.        Tiered Pricing: Offering multiple subscription tiers, each unlocking different AI features.        Value-Based Pricing: Pricing AI based on perceived value and user impact.  Choosing the right pricing model for AI solutions can significantly impact revenue and customer adoption. By aligning pricing with usage patterns, feature accessibility, or perceived value, businesses can optimize profitability while ensuring customer satisfaction. A flexible approach allows companies to adapt to market trends and evolving user needs, creating a sustainable and competitive pricing strategy.Operationalize Your AI Monetization StrategyTo successfully monetize AI, businesses need the right infrastructure, compliance measures, and performance monitoring. Best practices include:      Building a solid AI infrastructure: Partnering with AI providers or developing in-house expertise.        Using AI pricing optimization tools: Leveraging AI-powered insights to refine pricing strategies.        Focusing on customer feedback: Implementing beta programs to assess user value.        Ensuring security and compliance: Adhering to data protection regulations like GDPR and CCPA.  By focusing on these key operational elements, businesses can effectively implement their AI monetization strategy, ensuring that their AI capabilities are not only innovative but also profitable. A well-executed strategy involves a combination of robust infrastructure, strategic pricing, and a commitment to customer satisfaction.ConclusionSuccessfully monetizing AI requires a blend of pricing strategy, value metrics, and ongoing customer engagement. Whether through direct monetization or indirect monetization, aligning AI functionalities with real-world user needs is key to driving revenue and maintaining a competitive edge.If you want to experience how an AI-powered API platform can transform your API product management, sign up today for a free trial, no credit card required.                Deep AI API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design and Build Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /monitoring/Best-Practices-for-Monetizing-AI-Successfully/",
          "author": "Dylan",
          "categories": "monitoring"
        }
      
    ,
  
    
        "technical-api-development-achieving-api-traceability-with-opentelemetry-and-moesif": {
          "title": "Achieving API Traceability with OpenTelemetry and Moesif",
          "content"	 : "APIs power complex and modern applications and in doing so they’ve also become a challenge to observe, monitor, and analyze. Even the apps we rely on daily consists of numerous services, each glued together by dedicated APIs that interact with one another in intricate ways. In today’s market, you have to make sure that API complexity doesn’t hurt the visibility you need to possess into your product.Using an industry-standard observability framework like OpenTelemetry, you can guarantee that APIs generate the raw data necessary to effectively monitor and debug your application in real time. With Moesif’s OpenTelemetry integration, you have a consolidated platform where you can leverage Moesif’s robust API analytics and observability features contextually with OpenTelemetry traces.Let’s explore how Moesif can help you foster a clearer picture of what happens in your system and drive growth with precise business decisions.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        Table of Contents  OpenTelemetry Primer          OpenTelemetry Basic Concepts                  Logs          Traces          Metrics                      How Moesif OpenTelemetry Integration Works  Install Moesif OpenTelemetry Integration               Step 1: Configure the OpenTelemetry Exporter                  Example YAML Configuration          Example Configuration Using Environment Variables                    Step 2: Set Up Authentication      Step 3: Run Your Application        Observe Trace Data in Moesif          View Trace Data      Dig Deeper Into Trace Data      Use Trace Data Elements For Granular Analyais      Use Artificial Intelligence To Debug Issues With Traces        Conclusion  Next StepsOpenTelemetry PrimerOpenTelemetry provides a standardized way to add instrumentation to your application so it can generate data about its execution as the app runs. This data includes metrics, logs, and traces. You can then collect and analyze the data to understand your application’s performance, identify bottlenecks, and debug distributed systems. OpenTelemetry is vendor-agnostic, meaning it doesn’t matter what backend you use to collect and analyze OpenTelemetry data.OpenTelemetry Basic ConceptsThe telemetry data OpenTelemetry creates and manages consist of three core types:  Logs  Metrics  TracesLogsLogs provide textual records of specific events, errors, or states in an application. They represent discrete events during an app’s runtime, for example, an error message, a user login, or the application starting up. Each log entry includes a timestamp indicating when the event occurred. Logs can follow a structured format, like JSON, or be unstructured like free-form text. Logs also often includes contextual information like severity level, source, and metadata.TracesOpenTelemetry traces help understand the full path of a request through your application. In doing so, you also get a clear picture how the different services interact and function to satisfy the request.Traces consist of spans. Each span represents individual operations.For example, consider an ecommerce order. The entire process of fulfilling a user’s order, from the user placing the order through the website to delivery confirmation, represernts a trace. Then the following individual steps in this process each constitute a span associated with the trace:  API Call  Creating the order after receiving the order details  Database query  Checking the inventory to verify product availability  API call  Using the payment gateway to processes the payment  Message queue  Sending to delivery system or warehouse to notify shipping to customer  API call  Updating the order status to confirms order completionMetricsOpenTelmetry metrics capture numerical measurements of an applications performance and behavior over time. Here are some examples of metrics:  Error rates  Resource usage like CPU and memory consumption  Number of active usersMetrics differ from logs and traces in that they focus on quantifiable measurements rather than individual events or request flows. For example, consider request your API receives to access a particular resource:  A metric tells you that the request latency is 200ms.  A trace illustrates the end-to-end journey of the request and the specific steps the request involved for fulfilling it.  A log records the details of an error that has occurred during the request.How Moesif OpenTelemetry Integration WorksThe integration works by configuring your OpenTelemetry Exporter to send trace data to Moesif using the OTLP/HTTP protocol.Moesif supports OpenTelemetry traces and logs. It doesn’t support metrics. Within traces, Moesif supports HTTP Spans, capturing HTTP request span data that follows the OpenTelemetry Semantic Conventions for HTTP Spans. For logs, Moesif receives a structured log event for each log record that you can correlate with traces.Moesif treats each OpenTelemetry HTTP request/response span as an API event, capturing key information like request and response details, user identification, and metadata.Moesif maps OpenTelemetry spans to its API event model. It treats each span as a single API event with the addition of Trace ID, Span ID, Parent Span, Span Links, and metadata.For more information about how Moesif maps different span elements and log  record fields, see the following:  Spans  LogsBesides request and response spans, Moesif also supports the following:  Span Events  Custom Span Attributes  Request and response bodies  User, Company, and Subscription trackingInstall Moesif OpenTelemetry IntegrationTo install and start using Moesif OpenTelemetry integration, make sure to meet the following requirements first:  You have an application have instrumented with OpenTelemetry SDKs. Alternatively, you have configured your application to send telemetry data to an OpenTelemetry Collector capable of exporting through OTLP over HTTP.  A Moesif account.Step 1: Configure the OpenTelemetry ExporterSet up your OpenTelemetry SDK to export trace and log data to Moesif through OTLP/HTTP protocol.Example YAML Configuration   exporters:     otlphttp:       endpoint: https://api.moesif.net/v1/traces       headers:         X-Moesif-Application-Id: 'YOUR_MOESIF_APPLICATION_ID'     otlphttp/logs:       endpoint: https://api.moesif.net/v1/logs       headers:         X-Moesif-Application-Id: 'YOUR_APPLICATION_ID'Example Configuration Using Environment Variables   # Set the OTLP endpoints   export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=https://api.moesif.net/v1/traces   export OTEL_EXPORTER_OTLP_LOGS_ENDPOINT=https://api.moesif.net/v1/logs   # Include your Moesif Application ID in the headers   export OTEL_EXPORTER_OTLP_HEADERS=X-Moesif-Application-Id=YOUR_MOESIF_APPLICATION_IDReplace _YOUR_MOESIF_APPLICATION_ID_ with your Moesif Application ID. To get your Application ID, follow these steps:  Log into Moesif Portal.  Select the account icon to bring up the settings menu.  Select Installation or API Keys.  Copy your Moesif Application ID from the Collector Application ID field.Step 2: Set Up AuthenticationMake sure to include your Moesif Application ID in the X-Moesif-Application-Id request header to authenticate requests.Step 3: Run Your ApplicationStart your application, and OpenTelemetry begins sending trace data to Moesif.Observe Trace Data in MoesifThe trace data Moesif collects for your application becomes available in Live Event Log workspaces. Live Event Log shows a real-time continuous record of all the API events like API calls and actions that take place within your application or services. With OpenTelemetry trace data, you can better contextualize and understand your application’s behavior, performance, and states. Let’s look at some of the features Moesif offers to leverage your application’s telemetry data.View Trace DataSelect the Trace View tab to view the trace data of your application’s events.              Accessing Trace view in a Live Event Log workspace.    Trace View breaks down event data into spans. By default, it shows you the following details:  Span IDs  The duration of each span  Contextual data:          HTTP request and response data      Span Event as Span Action      Custom actions as Action      Dig Deeper Into Trace DataInteracting with an element opens up a detailed view about the event:              Accessing a detailed view for an event in Trace view.    You can interact with individual event data fields and field values in the detailed view:              Context menu for a data field.    In the dropdown, you can view more detailed information about the data element and perform further actions on that specific data. For example, depending on the data type, you can filter and sort, plot the data as a metric in a different chart, and so on.In the preceding image, if you select the trace ID value instead of the Trace Idfield name, you can select Open Trace to view details about the trace the span is a part of. Moesif then displays the individual spans that constitute the trace.              Context menu for a trace ID value.    For example, let’s consider you have a machine learning API that allow querying existing models, training, and evaluation. You can choose to open the trace for a failed GET request to the /api/query endpoint:              Viewing data of the constituent spans of a particular trace.    From here, we observe the exact cause of failure: a POST request to the /api/transform/output endpoint has failed with an HTTP 400 Bad Request status code.Use Trace Data Elements For Granular AnalyaisAdditionally, you can use different trace data as event filters across different API and customer analysis types in Moesif.              Trace data like span ID, parent span ID, span status, and trace ID available as event filters.    Use Artificial Intelligence To Debug Issues With TracesIn your Live Event Log workspace, select a number of events in Stream view and then select Ask AI. This brings up an AI-powered conversational interface where you can to ask questions, dig out critical information quickly, and troubleshoot issues more effectively.              Using Ask AI to debug issues with trace data.    ConclusionMoesif’s OpenTelemetry integration greatly improves your API’s observability and the existing benefits of Moesif’s robust API analytics and monitoring tools. To try out Moesif for yourself and see how it can help you monitor, analyze, and grow your APIs more effectively, sign up today for a free trial, no credit cards required.Next Steps  Explore the OpenTelemetry documentation  See Moesif OpenTelemetry server integration docs.  Instrument your Node.js app with OpenTelemetry and integrate Moesif.  Explore other server integrations from Moesif.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design and Build Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Achieving-API-Traceability-With-OpenTelemetry-And-Moesif/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "monitoring-expert-advice-on-integrating-apis-with-legacy-systems-in-2025": {
          "title": "Expert Advice on Integrating APIs with Legacy Systems in 2025",
          "content"	 : "Application programming interfaces (API) play a critical role in the digital ecosystem. This online middleman allows software systems to communicate with each other. But what is it exactly, and how does it work?Case in point: When you use an app to check the weather today, the API pulls data from the weather service and shows you the information on your device. Or when you pay for an e-commerce product using your PayPal account, the API connects the click-and-order store with the PayPal service provider to ensure a successful transaction.That’s why companies and organizations invest in API products/services for seamless communication and transaction. However, incorporating API into your legacy systems can be challenging due to compatibility issues and more. How do you address these problems?Keep reading for some expert advice on integrating APIs with your legacy systems in 2025. Let’s dive right in!                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        Challenges of Integrating APIs with Legacy SystemsTo begin, API has been growing and expanding. Now powered by robotic process automation (RPA) and artificial intelligence (AI), it has become more efficient and effective in sharing information across various digital platforms.On the flip side, legacy systems remain as is—using old technology, whether software or hardware. Still, some companies have been using these systems since they have long worked for them.The problem starts when you plan to integrate modern APIs with conventional legacy systems. For informed decisions, here are common challenges encountered with API-legacy system integration:  Compatibility issues: As cited, APIs are modern with new standards, while legacy systems are traditional using old protocols. So, expect some API features and functions to be incompatible with your outdated systems. For instance, you might struggle to incorporate APIs with your landline phones for your contact center—leveraging voice over Internet Protocol (VoIP) is key!  Lack of documentation: Some companies heavily rely on legacy systems as they’ve been working for them. However, these systems often lack proper documentation since their early deployment. As such, it can be challenging to understand how they work and ultimately connect with modern APIs. See how often organizations update their APIs on average below:Image source - salt.security  Limited flexibility and scalability: Most legacy systems are old, outdated, and not designed to work with external systems. Hence, it will be hard for you to integrate APIs with these systems due to a lack of customization features,  making it also difficult to scale your business in the long run.  Privacy and security risks: Most legacy systems are designed not to support modern security protocols, such as OAuth or TLS. This makes them vulnerable to security breaches and privacy issues. Even API integration with advanced platforms still faces potential risks, like authentication problems (39%), privacy incidents (38%), and network vulnerabilities (37%):Image source - salt.security  High integration costs: Integrating APIs with legacy systems can be costly due to complex installation and upgrade requirements. Often, you’d have to modify or upgrade your systems to ensure they work with APIs requiring specialized expertise. Meanwhile, modern systems have customization features, making API integration more seamless and less expensive.Learn strategic tips from business experts on integrating API with legacy systems below.Strategic Approaches to API IntegrationThere’s no denying the continued rise of the API management industry. In fact, its global market is projected to grow from $5.42 billion in 2024 to $34.17 billion by 2032 at a 25.9% compound annual growth rate (CAGR). Rapid digital transformation and early technological adoption drive this market growth.Image source - fortunebusinessinsights.comThat’s why even big companies with established legacy systems invest in API development. They want to integrate API features and functions into their systems for seamless communication and collaboration. However, they confront underlying issues like those mentioned above when doing so.Some business experts have shared how to integrate API with legacy systems—Here’s how:1. Examine the systems and plan the integration thoroughlyAs cited, modern APIs and legacy systems are worlds apart. APIs are constantly evolving while keeping up with industry trends. Meanwhile, legacy systems rely heavily on traditional databases with zero-to-minimal customization features. To meet halfway, you should examine your systems thoroughly and plan how to integrate APIs with them.Reyansh Mestry, Head of Marketing at TopSource Worldwide, recommends system upgrades and modifications to make the integration plausible.Their company has its fair share of undergoing full audit and integration designs when working with clients needing payroll services. “A thorough system audit is essential before integrating APIs with legacy systems. It helps identify limitations and plan the necessary upgrades or adjustments to ensure smooth and efficient integration.”2. Bridge the gap with middleware or API gatewaysAPI and legacy systems require a bridge to facilitate complex integration. First, you can capitalize on middleware—from SOAP to REST—to bridge the gap between old and modern systems. Second, you can establish centralized control by using API gateways (like Apigee API gateway) to handle traffic management, authentication, and scaling.Take it from Jeffrey Zhou, CEO and Founder of Fig Loans. With the rise of mobile/online banking, he recognizes the value of API investments in the banking, finance service, and insurance (BFSI) industry. He has witnessed how banks have begun integrating APIs with their legacy systems.Zhou suggests, “To bridge the gap between APIs and legacy systems, consider using middleware or API gateways. Middleware can connect older systems to modern technology, while gateways help manage traffic, security, and scaling effectively.”3. Modernize without replacing through API wrappersIntegrating APIs with legacy systems can be challenging due to compatibility issues, compelling businesses to replace their systems altogether. However, there’s a trick of the trade—build API wrappers to modernize old systems without replacing them entirely. The idea is to add a modern layer on top of old tech without messing with its core.Learn from Gary Hemming, Owner and Finance Director at ABC Finance. As a financial institution, they aim to keep up with industry trends but find technological upgrades challenging. However, they see how API wrappers are valuable for integration with legacy systems.Hemming explains, “To modernize legacy systems without a full replacement, consider using API wrappers. They add a modern interface to outdated technology, allowing seamless integration while preserving the system’s core.”4. Leverage digital tools and technologies for integrationThe best way to combat outdated technology is to use modern technology. But as mentioned, you don’t need to replace your legacy systems—only upgrade them. To make API-legacy system integration possible, utilize AI-powered platforms like MuleSoft, Zapier, or Apache Camel to streamline integration.Stanislav Khilobochenko, VP of Customer Services at MacKeeper, recommends harnessing the power of digital tools and technologies. He believes that they enable seamless integration between APIs and legacy systems. Employ automated testing platforms to test compatibility and performance issues and API monitoring tools to see areas for improvement.Khilobochenko shares, “Using digital tools like AI-powered platforms can make API integration with legacy systems much smoother. These tools streamline the process, enable compatibility testing, and help monitor performance to identify areas for improvement.”5. Standardize API practices and optimize continuouslyFinally, performance optimization should be part of the overall integration equation. The end goal is to standardize API workflows and optimize them for significant improvements. For one, use modern standards, such as  REST, GraphQL, or OpenAPI specifications. Then, guidelines should be set in place, such as naming, error handling, and data formats.Further, Chris Aubeeluck, Head of Sales and Marketing at Osbornes Law, emphasizes the need for performance tracking and optimization. He suggests setting API metrics and using application performance monitoring to track performance. However, he believes optimizing yourAPI-legacy system integration should be based on modern standards.Aubeeluck shares, “To get the most out of your API-legacy-system integration, standardize workflows and use modern specifications. Continuously track performance and optimize based on clear metrics to ensure everything stays efficient and effective.”Best Practices for API-Legacy-System IntegrationAt this point, you learned strategic approaches to integrating API with legacy systems. Now, it’s time to implement some best practices for such an integration to ensure stability. Below are some:  Conduct a full audit regularly. A thorough examination should start before the actual integration to avoid issues in the long term. However, make it a habit to check your systems regularly as part of your upkeep. That way, you can fix minor issues before they escalate to major ones.  Comply with API standards. To begin,API integration with legacy systems defies modern standards. But strive to follow standard protocols as much as possible to keep up with the latest innovations. This way, your business won’t be left behind.  Design for scalability and flexibility. Integrating APIs with legacy systems might be difficult. That’s where the challenge begins—resort to modifications and upgrades to make your systems flexible and scalable. Gartner predicts that by 2026, over 30% of the growing demand for APIs will come from AI and tools powered by large language models.  Perform rigorous testing. Testing is a crucial part of the overall equation. First, conduct a pilot run before the integration and adjust until all systems work smoothly. Also, test your system integrations regularly, whether using functional, performance, or usability testing. Lastly, fix your systems as needed.  Document the integration. Since legacy systems lack proper documentation, the integration with legacy systems now requires API documentation. Document everything, from the technical structure to the API and legacy system details to data mapping and security measures. Doing so will make your system maintenance, repair, and future integration much easier, simpler, and faster.  Focus on security and privacy.Both should be your top priority during the API-legacy system integration, as you don’t want to put your business at risk. Consider the rise of cyberattacks, such as phishing, malware, and Denial of Service (DoS) attacks, and invest in security. The API security market could achieve a 17.5% CAGR—so capitalize on it!Image source - marketsandmarkets.com  Track and measure performance. Start by setting API metrics, whether core usage (API call volume, usage growth, and latency) or user-specific (feature usage frequency, endpoints access, and usage patterns. Then, conduct regular API monitoring and measure your performance against the set metrics. Finally, fix and optimize your systems to ensure they’re working properly.Final WordsAPI is a critical component across different industries in today’s digital landscape. It enables systems to communicate with each other, allowing stakeholders to work together. However, integrating API with legacy systems is one major problem due to compatibility issues, limited documentation, flexibility concerns, security risks, and high costs.That said, consider some business experts’ strategic approaches to API integration with legacy systems. Likewise, follow the best practices shared to ensure a seamless and successful integration. With all these practical tips and steps, you can maximize the API potential to establish a collaborative digital platform for your business!Planning to incorporate API products into your legacy systems? Consider integrating API Products automatically with Moesif for a smooth transition. Sign up today for a free trial, no credit card required.                Deep AI API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design and Build Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /monitoring/Expert-Advice-on-Integrating-APIs-with-Legacy-Systems-in-2025/",
          "author": "DylanMyers",
          "categories": "monitoring"
        }
      
    ,
  
    
        "podcasts-developers-podcast-from-vision-to-venture-getting-more-value-out-of-apis-kin-lane": {
          "title": "From Vision to Venture Ep. 04: Getting More Value out of APIs with Kin Lane, API Evangelist",
          "content"	 : "In this engaging conversation, Derric Gilling, CEO of Moesif, and Kin Lane, API Evangelist, explore the challenges and opportunities of API monetization and productization in today’s enterprise world. Kin shares insights on rising API costs, the shift toward efficiency, and the balance between openness and control—especially in an AI-driven landscape.With a no-nonsense take on API fundamentals, he emphasizes that while technology evolves, strong governance, analytics, and business alignment remain key. Whether you’re scaling an API program or refining your strategy, this discussion is packed with practical insights you won’t want to miss.Moesif · From Vision to Venture 04: Kin Lane - API EvangelistListen to the episode on SoundCloud, Apple Podcasts, YouTube Music, or wherever you listen to podcasts. You can also watch the video on our YouTube Channel.Table of Contents  00:00 Introduction  00:34 Productizing and Monetizing APIs in Enterprise Environments  04:50 How To Drive Efficiency  07:42 Value Exchange Vs. Monetization  11:45 Key Challenges Faced When Deploying A New Public API  16:17 AIs Impact With Monetization And Closed API Programs  19:20 Current Day API Product Managers  20:52 What Is Next With APIs  24:55 Closing Thoughts                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        IntroductionDerric Gilling - Welcome Kin, it’s really great to have you here, from the API Evangelist. Today, we’re gonna be speaking on how to best productize and monetize APIs, especially in a large enterprise settings.Productizing and Monetizing APIs in Enterprise EnvironmentsDerric - Just jumping into things, what does it mean to productize and monetize APIs? And why is it growing traction within enterprise environments?Kin Lane - Well, I mean, folks, you gotta justify your spend. I mean, first and foremost, APIs cost money. I think the last research bit I did, you know, it costs 50 grand to stand up an API and sustain and maintain it, you’re spending money. And then secondarily, we all need to make money. So you’ve gotta be able to, all right, what resources are we making available here? And how are we gonna not give away the farm, especially in an AI world and just give away all of our digital resources? I think that’s one of the biggest misconceptions people have about public or, you know, externally available HTTP APIs, they’re gonna give away something. And so that monetization is really just about getting a handle on your digital resources and understand, you know, how they’re being applied.Derric - Makes sense. And definitely, you know, as we’re looking at the cost of deploying those APIs and standing those up, has anything changed, you know, from APIs been around since, you know, back in the enterprise service bus and the like, but why is it such a topic now?Kin - Yeah, you know, interestingly, you know, my Amazon bill has gone up. It has not gone down since 2010. I’ve been running APIs ever since, you know, the shift, I mean, and I’m clearly not an enterprise operations, but just, you know, relating it here is, you know, with, you know, regular instances and VMs to a more containerized and or serverless approach, I kind of leaned heavily into the serverless, but I’m coming back to a kind of common, a more basic virtualization ‘cause my resource management and what I’m having to do hasn’t changed a lot to be honest, and my bill has gotten worse. So, you know, I’m really trying to figure out how to get back to basics. And I know a lot of enterprises that I work with, they’re, you know, they’re questioning some of the more spendier parts of their rollouts that they’ve done around other types of APIs, GraphQL, Kafka, other things that may have not have achieved exactly what they needed, and then the sprawl, the API sprawl and figure out, you know, how are we gonna, you know, pay for all this and how we’re gonna make money here. So not as a lot has changed. It’s gotten more expensive and more complicated and more sprawling, but I wouldn’t say the technical bits have really changed all that much.Derric - So it sounds like driving a lot of efficiency, trying to get more out of those investments into these API programs, right?Kin - Yeah, yeah, I mean, think of FinOps, you know, and how that’s kind of spun up as far as financial, you know, understanding your financial operations. So think of that in terms of APIs from just, you know, what’s our cloud bill, what’s our spend, what does it cost to run these things? But you can’t just pinch pennies and you can’t just, you know, impose austerity on API operations. You’ve got to understand and make sense of that and actually try to figure out, well, what do we have here that’s worth keeping? What digital resources, what capabilities do we have here that are needed and that are useful? And maybe we’re not squeezing as much revenue out of them as we could if we just kind of carved them up in new and interesting ways, or we paid attention to different groups of users in different ways. We looked at things regionally. I just think there’s a lot of ways we can, A, I mean, my business is helping you survey and assess that sprawling API landscape that you have, but you’re really in the business of, all right, you know, once we, as we’re surveying this, what’s here, what’s valuable, what’s useful? How are people using it currently? And then how do we incrementally maybe start optimizing and generating new revenue from those digital resources?How To Drive EfficiencyDerric - So it sounds like a lot of analytics and trying to understand the sprawl and consumption of these different APIs, but from there, then how do someone actually execute on that strategy? What are the key things that you typically see to try and drive efficiency through these different API programs?Kin - I mean, I’ve talked to quite a few folks. I talked to people in the public sector, as well as the private sector, just taking existing APIs, whether they’re REST, whether they’re SOAP, whatever they can, trying to expose them as just simple REST, really focusing there, ‘cause that’s the cheapest, that’s the easiest, that’s what teams know, but they’re just taking individual resources and assessing, well, who’s using them, getting an awareness of who’s using them, what are they doing with it, and making sure that they have the analytics in place to kind of understand that and observe and see that, and then start really kind of inventorying those digital resources. So each path, you have images or videos or accounts or whatever those slash digital resources you have, and then understanding how people are using them. Hopefully they have existing customers. If they don’t, deprecate them, they should go away, they’re there. Who’s using them? And then start thinking about, well, how do these work as bundles with other resources? In government, I’m seeing a lot of groups who are like, how do we join forces with other departments, other agencies, ‘cause we know our resources, we know their resources, different applications spread across those, how do we optimize and build better business models and value exchange at those levels? So really it’s pretty fundamental. It’s just basic API paths and operations, tagging and organizing those, pulling the data on who’s using them, pulling reports on that to try to understand what’s happening, and then getting all the business and engineering stakeholders together to talk about, all right, what’s next, what’s sensible, where can we start maybe making, changing up our plans a little bit, changing up our rate limits, using fewer resources. I mean, that’s the interesting thing. I know you guys are analytics and very monetization product focus, but this is an intersection of technical and business. So like rate limits is a common conversation in the technical side, but rate limits feed into your business plan and your usage and what this costs and what people are paying for. And so you should really understand that technical details, but then put them on the table with the business details and then have a conversation with the human beings who reflect both sides of that coin as well.Value Exchange Vs. MonetizationDerric - I really like this term value exchange around these APIs, especially as we try and drive efficiency through these businesses. Is that the same as monetization or when does it make sense to monetize and if it’s not monetization, what is value exchange?Kin - I mean, that’s all part of that understanding your consumer. And I would say even having a conversation is it’s pretty identical right up to the point where maybe the credit card doesn’t get charged. You know, you’re still metering, measuring, understanding your consumers. You’re still invoicing them and saying, “Hey, here’s how much of this resource you use,” department C or, you know, West Coast group. And here’s those resources that we maintain, the ones that cost us 50 grand per resource to stand up and you’re using them, you know? And let’s have a conversation about that on a regular basis because you’re a producer or I’m a producer, you’re a consumer and we’re exchanging value. There’s gotta be reciprocity here. So you either gotta be paying and, you know, or it’s gotta be justified that, “Hey, we’re incurring these costs and you’re using them.” And then there’s gotta be some other value exchange or something, you know? So it’s just really, a lot of times, I’ve come across government agencies. One was in the Department of Commerce and they said, “Well, we’ve got,” you know, they were talking at a panel. We’re doing these kind of round table sessions as part of the General Services Administration and a whole bunch of different agencies came in. So Commerce is up there on the panel, I’m on the panel, we’re talking and they said, “Well, we’ve got like this API, it’s got like five users, but it’s quite a bit of traffic on it. It’s, you know, it’s a useful resource, but you know, it’s just never really exploded into the thing we thought it was gonna be. And so we’re probably gonna figure out how to wind that down, you know, and move on to something else.” And then a hand went up in the audience and person goes, “Well, you know, I’m with NOAA, the National Oceanic Administration, and we’re one of those users and you’re totally critical to like six different groups I know who are doing research. And if you shut it down, it’s gonna be a real big problem for a bunch of projects we have going on. So can we maybe talk about that?” And so what I see is a lot of things like that happening over and over and over where groups just don’t talk, these are digital, we don’t have a handle on who’s using it, and we don’t have a plan, a business plan in place. We don’t have any monetization strategy. It’s just as simple as that.Derric - That’s a really interesting point where you have an API, it’s used by only very handful or small group, a number of people, but maybe there is a lot of value being delivered to those APIs or, you know, one of those users happens to be, you know, one of the largest business units or customers.Kin - Yeah, it’s important. And I think we get, there’s a kind of a sickness that creeps in, you know, around scale sometimes that everything’s gotta be big and massive. And unfortunately, a lot of the promise around public APIs was that you kind of build it, they will come mentality where, you know, you put out a pretty basic resource that costs you $50,000 to stand up, but it’s useful to 15 people who, you know, will pay anywhere from 1,000 to 2,000 a month for those resources or something, you know, and then maybe you can crack that open, find some new interesting ones. You know, there’s a lot of opportunities there without going big, you know, not everything’s gotta be big and there’s a lot of value in the cracks.Key Challenges Faced When Deploying A New Public APIDerric - Yeah, we’re starting to see that as well, you know, starting smaller with a smaller group of design partners to help really iterate quickly on that API strategy versus a big marketing launch and the days of 2021 and spending lots of money on that type of stuff, you know, these days it is much more important to drive efficiency even through these new initiatives. But, you know, as someone who’s, you know, sending up a new API and trying to externalize that, you know, what are some of the key challenges that you see and, you know, what are folks doing today to address that versus even a couple of years ago?Kin - Well, I mean, I don’t wanna be a downer on it, but I’m seeing a lot of people kind of closing up and tightening up their portals. And because there’s a lot of, there was already a lot of fear. I remember Pinterest, when Pinterest first was kind of talking to people on the forum about launching an API. They’re like, oh, we wanna do this thoughtfully. We don’t wanna like make the same mistakes as Twitter. So this is like, what, 2017 or 18, I forget when they launched. So there’s always been a lot of anxiety around public APIs and how do you launch it? How do you support it? How do you do it right? But look at like Twitter and Reddit and anybody else who’s got a valuable resource right now, what’s happened to it because of kind of the appetite around AI consumption. It’s kind of scary to have your valuable resource out there, even if you do have a portal and a login and keys and analytics in place, but you gotta have a pretty tight business plan and plan on the front end of that. And I’m not just thinking, saying like well thought out initially, like you need to be well down this road and have some experience and be playing around with what works and doesn’t work and building up the muscles on your team to understand that design of your API, that design of your business plan, know your audience, know your customer and be able to really iterate before you’re gonna have, I think, a solid defensive strategy in an AI era that’s so volatile and so crazy. So honestly, I think more people are worried about having public APIs, but that doesn’t mean they’re going away. They’re just kind of going shadowy and darker and people are needing tools like Moesif to like make sense of this and understand it. So it’s gonna kind of go back to that, I think that partner dimension where it’s like other public APIs, but we only tell you about it if you talk to us and we know you and we trust you, which just, you still need to keep your public portal, your public strategy. You need to have solid security on the front end of that. But like I said, your security, your rate limits, the technical details give way to the business, part, the metrics, the dashboards, the plans, the revenue, the costs, the billing, the invoicing, all of that stuff in there. It’s just as important as security and DDoS attackers. Like you need a business defense, you know, a business line, a plan. Otherwise you just, it’s gonna be too volatile, too crazy out there to be out there with anything valuable, any resources.Derric - Does this mean maybe things like developer first and all the PLG stuff we’ve heard for the last few years, is that going wayside to more of these, I would say closed, build a relationship with someone? Kind of what does that mean from a go-to-market standpoint? You know, how do you think about go-to-market of an API program or external API?Kin - I don’t think that’s gonna go away. I mean, these things don’t just disappear, but it’s gonna get much harder and it’s gonna get much more, you know, the wins against it and the things against you and the things you’re gonna have to think about are gonna be a lot more difficult to do. And so it’s gonna be partner, know your customer, know, you know, KYD, know your developer, have a firm kind of grasp on that. And a lot of groups don’t really, a lot of large enterprises don’t understand that with a public API portal, like how to do that performance properly, how to, what to put out there, what not to, how to have an access control layer, how to have that set up. So it’s scary. And, but it’s, you still need that transparency and that kind of public access. You know, honestly, it should just be part of your regular public website, in my opinion, and be out there and you should have, it’s just like anything else on your website, what do you put publicly? What do you put behind a paywall? And, you know, how do you monitor its usage and understand its usage and evolve it so that you can, you know, be doing this well. It’s really not any different than your web properties and your mobile properties. It’s just whatever your applications are.AIs Impact With Monetization And Closed API ProgramsDerric - That totally makes sense there. And, you know, speaking on getting analytics and understanding consumption, you know, we’ve been hearing, you know, quite a few folks think around how AI is impacting that cost. You know, it could be sometimes quite high, you know, you’re building on new API until there’s real calls associated with it that we haven’t really seen before just to run the API. How is that impacting, you know, these things like monetization and potentially some of these somewhat closed or open API programs? And, you know, where do you see that going, right? Is, especially as now a policy being considered.Kin - Well, AI just got a lot cheaper. I don’t know if you heard.Derric - Very true, very true.Kin - Seriously though, you know, that’s part of that cost management. I mean, you’ve got to understand what it takes to operate these things. You know, what does each API cost to stand up, maintain? What are the direct and indirect costs? You got to have a handle on that. And that’s what the cloud was kind of supposed to promise. I think us to kind of slice and dice this a little bit better. I don’t think it’s fully lived up to that, but you got to have that cost side and have real numbers and a real spreadsheet, you know, real formula for like, what’s expensive here, what’s worthwhile doing, and then translate that into the consumption side, you know, and with the right metrics and analytics, service composition, organizing your API resources, understanding your consumers, and you got to do the math on all of that. You got to have a spreadsheet or, you know, a good solution like Moesif, to help you kind of see, you know, build a dashboard and understand it. ‘Cause it’s going to get tight here. I think a lot of people spend a lot of money, not just, I don’t want to just point at AI. I think there’s been a lot of spend and a lot of technological areas that people are going to pull, are pulling back on. I’m already seeing companies start having to do more with less. I think there was a lot of promises around labor and employees and being able, you know, the, what was the Gartner thing with Twitter is O’Gartner was saying that, you know, people either need their existing staff to do 10X or they need to cut their staff 10%, you know? And so how does, how do you translate that into a dashboard and a set of API resources? All right, what are our API resources and how does our existing staff take that and squeeze 10% or, you know, 10X more work out of those resources we have by cleaning house, organizing better understanding customers, getting some freeloaders off some APIs, maybe that were launched at a certain time for a certain thing that doesn’t exist anymore. So just get more efficient about, you know, assessing the API landscape and enterprise and public and then, you know, figuring out, well, how are we going to make money on this? How are we going to pay for it and do it sensibly?Current Day API Product ManagersDerric - Makes sense. Does that mean that the role of API product manager or product owner is changing? You know, especially as, you know, these teams are trying to get more efficient and lean. What does API product manager do today that, you know, they’re not doing five years ago with these APIs?Kin - Honestly, I don’t think it’s going to change a whole lot. I mean, I think the, you know, for me, AI is just another application of our digital resources. Web had certain constraints, desktop has certain constraints. If you’re doing IoT, if you’re doing mobile, if you rode the last bot wave was like 2016, if you got on any of the I-PASS kind of integration platform is there’s been a lot of different waves. There was, you know, there was an AI wave around 2012, 13, Wolfram Alpha and IBM Watson. And, you know, it wasn’t as big as this moment we’re in right now. This is, you know, massive, but, you know, there’s all these waves and you’ve got to assess like, what are the applications of my digital resources? What new digital resources are we going to invest in? Which ones need to go away? And you run it like any other supply chain and factory and warehouse and distribution, you know, channels is figure out what’s making you money and what’s not. And I think hopefully the smoke and mirrors from AI kind of fall down, will go down a little bit and people can kind of get a little bit more honest about the math of what all this costs and start figuring out where the actual value is.What Is Next With APIsDerric - Makes sense there and we’re definitely seeing a lot of, you know, innovation, especially with the AI stuff, but, you know, it’s hard to also, you know, parse through what is next. You know, we’ve been hearing about AI gateways and trying to understand AI as a product or AI products, just like we saw with the API wave. What do you see coming in next with, you know, APIs and how AI is impacting that?Kin - So here’s my stance as API evangelist. Nothing, I don’t care. Like AI, anything, I’m sticking to the fundamentals and this starting next week, like Tuesday, one o’clock, I’m gonna have a basic fundamentals of schema workshop. They’re paid and here’s basics of schema. Two o’clock is basics of APIs, open API, whether the different types of APIs, a lot of what we just talked about. Three o’clock is operations. What should you have? You should have a portal. You should have documentation. You should have an open API for these. So that’s Tuesday. Three fundamentals that I feel like are really missing people just don’t know their schema. They don’t know a open API and they don’t know their operations and have a consistent operational definition. Now, Wednesday or Thursday, excuse me, is gonna be governance of that, that we just talked about. So spectral rules and how do you lint that and then change, two o’clock is gonna be changes where you just deal with changes, change management, versioning, how do you source control? How do you have a source of truth? And then three o’clock is gonna be evangelism. How do you talk to people about this? How do you engage stakeholders? How do you have a conversation? And so three, six fundamentals that are ramping across every enterprise, nothing’s changed is HTTP. I’m not even talking GraphQL. I’m not gonna talk Kafka. I’m not gonna talk any of that. Your applications, how you apply your digital resources are up to you, web, mobile, desktop, AI. I’ll talk to you about those nuances and how that impacts, but really outside of that. And then once this has stood up, Wednesdays are gonna be where I start diving into other fundamentals. So monetization is gonna be one of the one o’clock where it’s just basic service composition, basic analytics, basic metrics. And then I’m wavering between HTTP, pagination, rate limiting, and a couple other fundamentals. And I’ll probably rotate between those on Wednesdays. It’ll be the kind of looser day. I’m not gonna talk to you about anything that’s outlandish, crazy ‘cause those are the same topics I’ve been talking about since 2010. And that basic monetization piece is like, very little has changed, very little has gotten better. We’ve gotten better tools and better services and better approaches, but really the need and the challenges have remained consistent. And I don’t see them going away at all. So I’m gonna just stay true to the course, stick to the fundamentals, and then I’ll let y’all, everybody else worry about the application of those resources. I’ll just help you kinda think about those essentials.Derric - Totally agree. And definitely staying close to fundamentals and ensuring meeting customer needs is still more important than chasing the next shiny object. And we’ve seen so many waves where APIs impact everything from crypto and other waves as well. So at the end of the day, it’s about good API design and API governance, right? So.Kin - Yeah, yeah. Just the basics. And I’m gonna do that for a few years. And I mean, I think that’s why what you guys offer is so important because the value exchange piece, yes, the monetization piece, the analysis, the analytics of that API landscape is critical, is essential. It’s not rocket science, but you gotta do it. And you gotta do it consistently over time before you’re gonna get better at it.Closing ThoughtsDerric - Appreciate having you here, Kin, for our podcast. Any parting thoughts you wanna leave with the audience?Kin - I mean, just, you know, figure out, you know, what do you do as an enterprise? Really kind of hang on to that in this moment. I think things feel like they’re moving faster than they ever have before. They’re volatile, they’re crazy. That’s not true. That’s just fabricated, manufactured. Stick to what you do best. Understand that, understand your customers and figure out the best ways you can kind of iterate and work on that. The rest, you know, will work itself out. It’ll kind of calm down a little bit here until whatever’s next, you know? And if you just have your kind of API strategy together, you’re gonna be able to weather this stuff and you’re gonna be able to make money and you’re gonna be able to adjust over time.Derric - Awesome. Well, really appreciate being here, Kin, and until next time.Kin - All right, thanks.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            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          "url": " /podcasts/developers/Podcast-From-Vision-To-Venture-Getting-More-Value-out-of-APIs-Kin-Lane/",
          "author": "Dylan",
          "categories": "Podcasts, Developers"
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        "technical-api-development-how-api-product-managers-can-leverage-ai-md": {
          "title": "How API Product Managers Can Leverage AI to drive better decisions",
          "content"	 : "The responsibilities of an API product manager varies depending on the organization and industry they work for, among various other factors. However, the common set of tasks they carry out include managing the diverse user needs, ensuring reliability, and aligning API strategies with organizational goals. Performing these duties requires a delicate balance. In addition, API product managers face increasing challenges as APIs evolve into strategic business drivers. Without substantial insights, decisions often rely on intuition or incomplete data, which in turn hurts your product’s growth and user satisfaction.Think about the vast amounts of data APIs generate, ranging from usage logs to error metrics. However, just traditional analytics tools alone can’t grapple with the sheer volume of data, their intricacies and complexities. You might be missing critical insights that you are not tapping into. This data paralysis limits a product manager’s ability to identify growth opportunities, optimize performance, and enhance user experiences.With AI, you can transform this data into strategic advantage. Through analysis automation, pattern detection, and user behavior prediction, AI equips product managers with the confidence to make smarter and faster decisions. It turns overwhelming data into actionable insights that empower leaders to craft exceptional API experiences that drive adoption and revenue.In this article, we discuss how API product managers can strategically leverage AI to address key business drivers.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Table of Contents  Why AI Matters for API Strategy Leaders          The Strategic Role of APIs      The Problem with Traditional Approaches      AI-Enhanced Tools Empower API Strategy Leaders        AI for Smarter Product Management Decisions          Bridging API Product Strategy with Business Goals      Turning Data into Actionable Product Insights      API Design and Specification Optimization      Improving Developer Experience Through AI-Driven Insights      Optimizing API Monetization and Growth Strategies        Data-Driven Personalization for Customers  Things to Consider When Adopting AI  ConclusionWhy AI Matters for API Strategy LeadersLet’s start by briefly discussing the challenges in maintaining modern API ecosystems. Then we’ll tie that in with APIs no longer being only “technical interfaces” and why adopting AI into the process can lead to competitive edge and better strategic execution.The Strategic Role of APIsWe used to look at APIs (application programming interfaces) as only “technical interfaces”. We used to think that they merely glue together different components for better product viability and power integration patterns for larger, complex apps and services. However, we’ve been observing a shift towards “customer-centric” approaches in the industry, with methodologies like API-first and product-led growth (PLG) making strides. Connecting APIs to larger business ecosystems and capabilities now makes more sense than ever.This has resulted in APIs driving business growth through integrations, automation, and new revenue models. A Postman report found that 89% of companies consider APIs essential to their digital strategies. However, as APIs scale, strategy leaders have been facing increasing complexity in maintaining their responsibilities. Without a clear understanding of user behavior and performance bottlenecks, strategic decisions, like aligning API initiatives with overarching business goals, become guesswork.APIs are also becoming direct revenue sources. According to the 2024 State of API report, 62% of respondents report working with revenue generating APIs, and that number continues to rise. Yet, many API leaders struggle to determine what features drive engagement, which endpoints generate revenue, and where friction exists in the developer experience. This comes as no surprise due to the new shift and the huge influx of data and usage patterns. As an API product manager, if you fail to extract these insights, you risk inefficient resource allocation and slower growth.The Problem with Traditional ApproachesConventional tools provide metrics but lack the depth today’s standards require to anticipate challenges and seize opportunities. For example, static dashboards often focus on isolated KPIs like latency or request volume without revealing how these metrics impact broader business objectives.For example, let’s say you observe an API getting increased traffic, but is it due to higher developer engagement or inefficient integrations causing redundant calls? API leaders cannot answer these questions with basic monitoring tools alone. Gartner predicts that 75% of APIs in enterprise environments will fail to meet their business expectations by 2025 due to lack of proper management and planning. AI-enhanced tools have the potential to instead bolster them through making insights into the technical factors impacting user retention, monetization, and business scalability more accessible.AI-Enhanced Tools Empower API Strategy LeadersStandard analytics tools, due to their limited scope, require you to manually correlate between API performance and business impact. AI-enhanced tools bridge the gap between raw API telemetry and business strategy. Instead of relying on manual reviews or static reporting, AI-enhanced platforms offer dynamic query capabilities that make real-time insights more accessible. Furthermore, platforms offering AI-powered semantic search features can take it one step further and allow you to quickly find the data you need with just a few keystrokes describing your intent.Imagine an API leader trying to understand the reason behind enterprise customers churning. Instead of cross-referencing multiple dashboards and manually identifying trends, they can ask an AI-driven analytics tool, “Which API endpoints are causing the most errors for high-value customers?” and get an immediate response. When you combine artificial intelligence with a platform like Moesif’s comprehensive API productization platform, you can align API investments with user needs and company goals faster than before.AI for Smarter Product Management DecisionsProduct managers often find themselves drowning in API data without a clear way to turn it into strategic action. You may have the right questions in mind, but you struggle to find the contextual thread of analysis that can lead you to a concrete answer to start planning out your next strategy. AI can transform those raw metrics into targeted insights quickly so that every decision supports long-term business growth.Bridging API Product Strategy with Business GoalsAPI product managers are responsible for more than just technical execution; they must align API investments with company-wide objectives, including revenue growth, user adoption, and ecosystem expansion. AI can help product managers connect the dots between API usage trends, customer behavior, and business priorities. They get the clarity needed to communicate API impact to all stakeholders. By building data-driven roadmaps this way, you can resonate with both consumers and other stakeholders, ultimately driving measurable business outcomes.Turning Data into Actionable Product InsightsProduct managers must constantly evaluate how APIs contribute to business growth. AI helps API product managers understand not just how customers are using APIs, but also why certain patterns emerge. This means they can quickly uncover insights like these:  Which API endpoints contribute the most to customer retention?  What feature requests correlate with the highest-value users?  How do changes in API consumption reflect shifting customer needs?By complimenting analytics with AI, product managers can validate assumptions, prioritize features, and optimize product roadmaps with greater confidence. Instead of relying on intuition or retrospective analysis, they get real-time, data-backed insights that help them bring to fruition a robust API lifecycle, from its design, implementation, and production, to monetization.API Design and Specification OptimizationEffective API design goes beyond usability—you need to thoughtfully structure endpoints, implement clear specifications, and build adaptive documentation that evolves with user needs. With AI, you have deeper context into inconsistencies in implementation, inefficient data models, and unclear specifications. By analyzing API traffic patterns and user interactions, you can detect misaligned API specifications, redundant endpoints, and inefficient request-response models. Then you can go ahead with very specific requirements to refine API structures for optimal performance.You can leverage AI to validate API specifications by revealing where developers struggle with unclear request parameters, authentication flows, or response structures. If a specific API method results in frequent misconfigurations, it may indicate a need for better-defined data models or a restructured endpoint. If an endpoint consistently results in failed requests, you can figure out whether developers are missing key information in the documentation or if the API’s structure itself leads to confusion. As a product manager, you can use this information to prioritize documentation updates, refine API reference materials, and introduce contextual guides that reduce friction.Beyond refining API specs, AI-enhanced analytics facilitate smarter API iteration, versioning decisions, and deprecation strategies by assessing widely adopted endpoints and ones causing inefficiencies. By continuously monitoring endpoint-level interactions, product teams can make data-driven updates to API specifications, optimize request flows, and make sure new versions maintain backward compatibility while reducing technical debt. If an endpoint shows declining usage or frequent misconfigurations, teams can assess whether they should restructure, deprecate, or better document that endpoint. This essentially makes you more proactive about standardizing API governance to reduce breaking changes and ensuring long-term maintainability.Improving Developer Experience Through AI-Driven InsightsAPI adoption depends on how developers can integrate, use, and scale with an API. AI-enhanced tools help API product managers identify friction points that thwart developer success. For example, AI can analyze support tickets, API error logs, and onboarding behavior to reveal:  Common integration challenges across different customer segments.  Endpoints with the highest failure rates among new developers.  Documentation gaps that cause frequent misunderstandings.By proactively addressing these issues, API product managers can continuously and iteratively improve onboarding, reduce developer frustration, and increase long-term API adoption. AI-enhanced analytics helps you pinpoint where developers struggle the most, allowing teams to implement targeted improvements to drive engagement and customer trust.Optimizing API Monetization and Growth StrategiesFor companies monetizing APIs, pricing models and consumption patterns directly impact revenue. AI-enhanced analytics can highlight trends that dig out information like the following:  Which API usage tiers generate the highest customer lifetime value.  When customers are likely to hit rate limits and consider upgrading.  Which features drive API consumption among enterprise clients.As a result, API product managers can move away from trial-and-error pricing experiments. Instead, they have the means to fine-tune pricing models and maximize revenue growth with precision. These insights help businesses stay agile and adapt to evolving customer needs without making costly miscalculations that often come with guessworks. All of this promotes healthy competition where you can stay competitive and build better products that benefit all.Data-Driven Personalization for CustomersAI brings a new level of contextual intelligence to API product managers. For example, Moesif’s powerful and feature-rich API and customer behavior analytics suite now supports AI Explain. AI Explain gives you a conversational interface where you can quickly dig deeper into your product and its users’ behavior at a granular level.Not all API consumers have the same needs—enterprise customers, independent developers, and third-party partners each interact with APIs differently. For example, let’s say you’re segmenting users based on behavioral patterns, feature adoption rates, or engagement levels. With AI at your side, you can now quickly initiate further research with questions like “Which user segments have the highest drop-off rate after the first API call” or “Do you see an increasing number of customers experiencing negative growth?” The AI can even suggest next steps like observing trends for a longer time period to figure out whether the negative growth is part of a longer trend or just a short-term fluctuation.This means you get to directly interact with advanced customer analytics like funnel, retention, and composition without extensive data expertise or domain-specific knowledge about analytics. AI increases both the utility and accessibility of data-driven analytics to API product managers as well as other stakeholders.Being able to quickly find answers like this launches data-driven and responsive API strategies and helps them stay that way. You can become confident that your API strategies maximize retention and satisfaction by delivering the right experience to the right user segment.Things to Consider When Adopting AIWhen adopting any new technology, it’s important to evaluate the factors that affect the overall benefits you receive from that adoption.You’re getting the powerful insights you want and you’re getting them quickly—the most appealing aspect of leveraging AI. However, they should complement human decision-making rather than replace it. As API Product managers, you must make sure to evaluate that AI-generated recommendations within the broader business context. AI can surface trends, anomalies, and predictions, constituting half of the picture. The other half comes from human oversight to interpret nuanced customer behaviors, ethical concerns, and strategic priorities.Then comes aligning AI insights with business goals. Not all AI-generated insights are immediately actionable or aligned with business priorities. Before implementing AI recommendations, API teams must evaluate their impact on major objectives like customer retention, revenue growth, and developer experience. Establishing clear success metrics ensures that AI investments drive meaningful improvements rather than generating noise.A major challenge with AI-enhanced analytics is making sure you understand and act on the insights AI surfaces. Product managers should focus on AI tools that provide explainable, transparent reasoning behind their recommendations rather than black-box predictions. For example, Moesif has built its AI Explain feature on top of its intuitive analytics platform. You already have incredible granularity into API and customer statistics through various types of analysis. Then AI Explain helps gain further clarification about trends in API performance, user retention, and funnel progression in a way that teams can easily act on.Lastly, integrating AI into API analytics should not require a complete overhaul of existing workflows. For example, Moesif has integrated AI capabilities into its existing analytics and observability platform, naturally promoting AI adoption into API strategy for companies. It’s important to make sure you can leverage conversational analytics, real-time insights, and contextual recommendations without requiring complex setup to ensure that teams can start benefiting from AI without disruption.ConclusionThe role of AI in API product management has become a necessity for teams looking to stay competitive. As the API landscape continues to evolve, businesses that integrate AI-driven insights into their strategy will possess a better position to drive adoption, reduce friction, and build APIs that meet both technical and business needs.If you want to experience how an AI-powered API platform can transform your API product management, sign up today for a free trial, no credit cards required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design and Build Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/How-API-Product-Managers-Can-Leverage-AI.md/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "monitoring-comparing-ai-api-gateways": {
          "title": "Comparing AI API Gateways",
          "content"	 : "AI Gateways are rapidly becoming essential tools for businesses leveraging artificial intelligence and large language models (LLMs). Traditional API Gateways have evolved to meet the demands of modern AI workloads, offering features that cater to the unique requirements of AI/ML applications. These include advanced routing for models, latency management, and comprehensive analytics to optimize performance. AI API Gateways integrate and manage AI frameworks to enhance application performance and governance, ensuring ease of use and code simplicity for developers migrating existing AI applications.The leading players in the AI API Gateway space each bring unique capabilities to address the demands of AI/ML applications. Moesif’s analytics platform complements these gateways by offering businesses a way to track costs, monitor tenant-specific metrics, and optimize LLM app performance. This combination provides the insights needed to scale AI-powered solutions effectively, without explicitly depending on a single gateway.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        What is an AI API Gateway?AI API Gateways extend the capabilities of traditional API Gateways to address the unique demands of AI workloads. While standard gateways focus on routing and securing API traffic, AI Gateways can offer advanced features like model-specific routing, data governance, latency optimization, and real-time observability. These tools enhance the performance of AI infrastructure by facilitating automated monitoring of machine learning (ML) pipelines, data, and models, ensuring smooth operation and reliability. They ensure seamless integration with AI/ML services while maintaining robust security and compliance.Key Features of AI API Gateways      Model Routing: AI workloads often involve multiple models that specialize in different tasks, such as text generation, image recognition, or natural language processing. AI API Gateways intelligently route incoming requests to the most appropriate model based on the type of workload, ensuring efficient processing and optimal utilization of computational resources. This routing is often dynamic, taking into account factors such as model performance, availability, and workload priority.        Latency Management: Delivering low-latency responses is crucial for real-time AI applications, such as chatbots and recommendation engines. AI Gateways leverage sophisticated techniques like traffic shaping, caching, and load balancing to reduce response times. By ensuring minimal delays, they enhance user experience and support the performance requirements of latency-sensitive applications.        Data Governance: Handling sensitive data, such as user information or proprietary datasets, requires stringent governance policies. AI API Gateways provide tools to manage data access securely, enforce compliance with regulations like GDPR or HIPAA, and maintain audit trails. These features are critical for businesses operating in highly regulated industries, ensuring that their AI applications meet legal and ethical standards.        Observability: Effective observability is key to maintaining and improving the performance of AI services. AI observability provides comprehensive insights into machine learning model behavior, data quality, and performance throughout their lifecycle. AI Gateways offer observability solutions that facilitate root cause analysis, enabling businesses to proactively monitor and address issues within ML models. By leveraging these insights, businesses can optimize performance, ensure model reliability, and enhance the overall effectiveness of their AI applications.  The Role of API Gateways in API ManagementAPI gateways play a pivotal role in API management by serving as the single entry point for clients to access backend services. They provide a crucial layer of abstraction between the client and the backend services, enhancing security, scalability, and performance. By handling tasks such as authentication, rate limiting, caching, and routing, API gateways simplify the management and monitoring of API traffic. This centralized platform allows for the enforcement of security policies, implementation of traffic management rules, and seamless integration with backend services. In essence, API gateways streamline the complexities of API management, ensuring efficient and secure interactions between clients and backend services.Benefits of Using an API GatewayUtilizing an API gateway offers numerous benefits, including enhanced security, scalability, and performance. API gateways act as a protective shield for backend services, providing robust security and authentication mechanisms to guard against external threats. They also improve scalability by efficiently managing high volumes of API traffic through load balancing and caching capabilities. Additionally, API gateways offer real-time analytics and performance metrics, enabling organizations to monitor and optimize API performance effectively. By leveraging these features, businesses can ensure their backend services remain secure, scalable, and performant, ultimately delivering a superior user experience.API Gateway ArchitectureAn API gateway typically comprises several key components, including a reverse proxy, a routing engine, and a security module. The reverse proxy serves as the entry point for API requests, directing them to the appropriate backend service. The routing engine ensures that requests are efficiently routed based on predefined rules, optimizing the flow of API traffic. The security module provides essential features such as authentication, rate limiting, and encryption to safeguard backend services. API gateways can be deployed on-premises, in the cloud, or in a hybrid environment, offering flexibility to meet diverse API management needs. This architecture ensures that API gateways can effectively manage and secure API interactions across various deployment scenarios.Key Players in the AI API Gateway SpaceWSO2 AI GatewayWSO2 helps you build, scale, and manage AI APIs using AI Gateway in both the open-source API Manager (APIM) and the SaaS API management platform Bijira. It puts a strong emphasis on governance and guardrails for safe, compliant, and transparent AI use, while keeping business and customer objectives aligned.Key features include:  Secure creation of AI APIs with leading vendors like OpenAI, Azure OpenAI, Anthropic Claude, Amazon Bedrock, and Mistral, including custom AI vendors  Dynamic routing of AI API requests across multiple models within a vendor  Built-in and external guardrails, including Azure Content Safety and Amazon Bedrock Guardrails  Built-in observability and governance to manage traffic, track usage, and optimize performanceWith these features, WSO2 AI Gateway enables organizations of all sizes to drive superior outcomes with their AI-powered applications.KongKong’s AI Gateway focuses on extensibility and integration, making it ideal for businesses deploying AI/ML applications. Built on Kong’s core platform, the AI Gateway is designed to securely manage and route traffic between applications and AI services. It includes features such as centralized governance, advanced observability, and native integrations with AI/ML tools. Its robust plugin ecosystem and API lifecycle management tools simplify the integration of AI models into existing workflows, enabling businesses to scale AI adoption seamlessly.Solo.ioSolo.io emphasizes flexibility and performance, offering real-time model orchestration designed for modern AI/ML environments. The Gloo AI Gateway leverages Solo.io’s Envoy-based architecture to deliver advanced traffic control, fine-grained security, and seamless integration with AI/ML frameworks. This architecture supports large-scale AI workloads with features like intelligent request routing, policy enforcement, and low-latency data processing, ensuring that critical applications perform reliably under demanding conditions.CloudflareCloudflare combines its extensive network capabilities with AI Gateway features to optimize performance, scalability, and security for AI applications. The Cloudflare AI Gateway offers seamless integration with AI models and tools while providing powerful features like API security, rate limiting, and advanced traffic management. Its ability to handle large-scale AI interactions ensures that businesses can scale their operations efficiently, while features like DDoS protection and data privacy controls make it a reliable choice for enterprises prioritizing security.AWSAWS integrates its AI Gateway services seamlessly into its cloud ecosystem, offering tools for cost control, scalability, and advanced security. AWS offers a fully managed service for building and scaling generative AI applications with AWS Bedrock, allowing businesses to access foundation models via simple API calls without managing infrastructure. This approach allows for streamlined integration, cost-effective scaling, and flexible deployment options. Businesses can leverage AWS’s vast suite of cloud services to efficiently manage their AI workloads while benefiting from advanced monitoring and model customization capabilities.AzureMicrosoft Azure connects its AI Gateway capabilities to its AI and ML ecosystem, providing enterprise-grade observability, governance, and seamless model integration. With the GenAI Gateway capabilities in Azure API Management, businesses can now simplify interactions with generative AI services while ensuring scalability and security. This includes features such as automated API generation for AI models, secure key management, and enhanced monitoring tools. Azure’s strong focus on compliance and data privacy, coupled with its deep integration into the Microsoft ecosystem, makes it a preferred choice for large organizations and enterprises managing sophisticated AI workflows.Comparison Table            Gateway      Strengths      Ideal For                  WSO2 AI Gateway      Intelligent routing, MCP support, centralized management, governance, and observability with full lifecycle API management      Projects and businesses of any scale looking to adopt and scale GenAI services              Kong      Centralized management through cloud control plane, advanced observability, plugin ecosystem      Extensible AI/ML integrations              Solo.io      Intelligent routing, fine-grained security, low-latency processing      Large-scale, latency-sensitive workloads              Cloudflare      Scalability, API security, advanced traffic management, DDoS protection      Enterprises with high traffic demands              AWS      Simplified API integration with foundation models, flexible scaling, cost-effective      Cloud-native AI and generative AI deployments              Azure      Automated API generation, secure key management, compliance focus      Enterprises scaling generative AI apps      Security and ComplianceAPI gateways are equipped with a range of security features designed to protect backend services. These include authentication mechanisms to verify user identities, rate limiting to control the flow of API traffic, and encryption to secure data in transit. Additionally, API gateways help organizations comply with industry standards and regulations, such as PCI-DSS and HIPAA, by providing tools to enforce security policies and maintain audit trails. They also offer real-time monitoring and analytics to detect and respond to security threats promptly. By leveraging these capabilities, businesses can ensure their backend services are secure and compliant with regulatory requirements.Scalability and PerformanceAPI gateways are engineered to handle high volumes of API traffic, providing the scalability and performance needed for modern applications. They offer load balancing and caching capabilities to improve response times and reduce the load on backend services. Real-time analytics and performance metrics enable organizations to monitor and optimize API performance continuously. Additionally, API gateways support multiple protocols, including HTTP, HTTPS, and WebSocket, making them a versatile solution for API management. By ensuring efficient traffic management and performance optimization, API gateways help businesses deliver reliable and responsive services to their users.The Importance of Analytics for AI/LLM AppsDeploying AI and LLM applications demands a focus on monitoring and optimizing performance to maintain efficiency and scalability. Detailed insights into AI infrastructure operations are critical for maintaining efficiency and scalability. These workloads often incur significant computational costs and resource consumption, making detailed insights into operations critical. Granular analytics help organizations manage costs, streamline resource allocation, and maintain operational excellence, all while ensuring AI solutions meet the growing demands of modern applications. Some key challenges addressed by analytics include:  Cost Attribution: Managing the costs of AI workloads can be challenging, especially when multiple models and tenants are involved. Advanced analytics enable businesses to allocate costs accurately by associating them with specific models or customers. This level of granularity helps organizations identify the most resource-intensive models, evaluate their return on investment, and make informed decisions about resource allocation and cost optimization.  Performance Optimization: AI applications, especially those with real-time requirements, demand peak performance to meet user expectations. Analytics platforms provide deep insights into performance metrics such as latency, throughput, and error rates. By identifying inefficiencies and bottlenecks, businesses can fine-tune their infrastructure, improve response times, and ensure a seamless user experience even during peak usage.  Real-Time Monitoring: The dynamic nature of AI workloads necessitates constant monitoring to ensure stability and reliability. Real-time analytics tools detect anomalies such as unexpected spikes in latency or errors, enabling teams to address potential issues before they escalate. This proactive approach minimizes downtime, enhances system reliability, and ensures consistent performance for end-users.Moesif tackles these challenges with advanced analytics tailored specifically to the complexities of AI workloads. By offering granular insights into key areas such as cost attribution, performance metrics, and real-time monitoring, Moesif empowers organizations to make data-driven decisions. For instance, businesses can track high-usage clients, optimize latency-sensitive operations, and proactively address system inefficiencies. These capabilities not only ensure effective management of AI investments but also foster scalability and operational excellence in dynamic environments.Moesif’s Role in AI Gateway Analytics and API TrafficMoesif’s analytics platform complements AI API Gateways by offering key capabilities in AI observability and integrating seamlessly with various gateway solutions like WSO2, Kong, Solo.io, Cloudflare, AWS, and Azure. By bringing all your analytics into one centralized platform, Moesif enables organizations to manage their API traffic and performance metrics across multiple gateways. This unified approach reduces complexity, enhances visibility, and ensures consistent data-driven decision-making across diverse infrastructures.  Cost Attribution: Moesif enables organizations to track the costs associated with individual models or tenants in great detail. By offering granular insights into usage patterns, businesses can identify the specific AI models or users driving the highest costs. This level of visibility allows companies to implement targeted cost-control measures, optimize billing structures, and ensure financial sustainability for their AI operations. Additionally, these insights help align costs with value, improving overall operational efficiency.  Performance Metrics: Moesif empowers businesses to monitor critical performance indicators, including response times, throughput, and error rates. These metrics provide a comprehensive view of how AI applications are functioning in real-time. By identifying performance bottlenecks and inefficiencies, Moesif helps organizations fine-tune their infrastructure to ensure consistent and reliable service delivery. This focus on performance optimization supports a seamless user experience and enhances customer satisfaction.  Real-Time Alerts: Moesif’s real-time alerting capabilities allow businesses to proactively detect anomalies and inefficiencies in their AI/LLM workloads. Whether it’s a sudden spike in latency, an unexpected error rate, or unusual usage patterns, Moesif provides timely notifications that enable rapid response. By addressing issues before they escalate, organizations can minimize downtime, maintain system stability, and ensure uninterrupted service for end-users.By integrating Moesif with gateways like WSO2, Kong, Solo.io, Cloudflare, AWS, and Azure, businesses can gain unparalleled visibility into their AI applications. Moesif’s detailed analytics enable organizations to track API usage trends, monitor performance bottlenecks, and attribute costs to specific models or tenants. This integration fosters smarter decision-making by providing actionable insights that drive operational efficiency, reduce costs, and enhance user experiences. Through this synergy, businesses can optimize their AI deployments while ensuring scalability and sustainability.Use Case: Analytics for LLM Apps and Model PerformanceConsider a business deploying LLM-based machine learning applications. These applications often handle high volumes of API traffic, requiring real-time monitoring and precise cost management to remain efficient and scalable. With Moesif, they can achieve this by leveraging our comprehensive analytics platform, which provides insights into key performance indicators, usage patterns, and operational inefficiencies. Importantly, Moesif supports setups that utilize multiple API gateways, allowing businesses to centralize their analytics across WSO2, Kong, Solo.io, Cloudflare, and other gateway solutions. By incorporating Moesif, businesses gain the ability to streamline workflows, reduce costs, and deliver superior performance to end-users.  Monitor Costs: Moesif provides detailed insights into cost attribution by breaking down expenses for specific models or customers. For instance, businesses using a Gen AI API can leverage Moesif to analyze input token usage for each customer and could create a dashboard like “Top Customers By Avg Input Token Count per Call”. This visibility allows companies to track high-usage clients and assess their contribution to overall operational costs. By identifying such patterns, businesses can implement usage-based pricing strategies or optimize resources allocated to high-demand clients, ensuring accurate billing and cost efficiency.  Optimize Resources: Moesif’s analytics enable organizations to identify resource-intensive patterns and take proactive steps to optimize them. For example, a “30-Day Input Tokens by Company” chart could highlight usage trends across customers, with certain accounts nearing quota limits. This information helps teams predict potential resource bottlenecks and allocate server capacity or computational power more effectively. By acting on these insights, businesses can improve service reliability and reduce latency for their top-performing APIs.  Improve Performance: Moesif helps businesses enhance their API performance by monitoring latency issues and error rates in real time. For instance, creating a “4xx Errors by Endpoint” or “Newest 4xx Errors” chart can provide granular error tracking, enabling teams to quickly address issues. Coupled with performance metrics from something like “Top APIs Used” and “Daily User Retention” graphs, Moesif equips teams with the tools to prioritize fixes and ensure a smooth experience for users.Moesif’s advanced analytics make scaling AI apps more predictable and cost-efficient by providing deep insights into user behavior, performance metrics, and cost attribution. For example, businesses deploying LLM applications can leverage Moesif to monitor API usage trends, identify high-cost models or customers, and optimize server capacity. This empowers organizations to fine-tune their operations, reduce unnecessary expenses, and enhance overall system performance, enabling them to grow their AI capabilities with confidence.ConclusionAI Gateways are revolutionizing the way organizations manage and scale AI/LLM applications. While WSO2, Kong, Solo.io, Cloudflare, AWS, and Azure provide robust solutions for routing and managing AI traffic, Moesif adds unparalleled value through its advanced analytics platform. By leveraging Moesif, businesses gain critical insights into cost management, performance optimization, and user behavior, making it an essential tool for scaling AI-powered solutions.Sign up for Moesif’s analytics platform today and unlock the full potential of your AI applications—no credit card required.                Deep AI API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design and Build Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /monitoring/Comparing-AI-API-Gateways/",
          "author": "Dylan",
          "categories": "monitoring"
        }
      
    ,
  
    
        "technical-api-development-top-api-metrics-for-product-led-growth": {
          "title": "Top API Metrics to Track for Product-Led Growth",
          "content"	 : "Product-led growth hinges on delivering exceptional user experiences. If you build a great product with valuable features, users are bound to come and stick to your services. But companies often neglect the role of APIs. Modern software relies heavily on APIs for integrations, automation, and data exchange. Not to mention, for API or API-first products, APIs drive the most crucial pieces of success goals. A poorly performing API creates friction, frustrates users, and impedes growth.For successful product-led growth, companies must prioritize both excellent user experiences and robust API performance. And to achieve that, you have to understand the API metrics that matter. In this article, we explore these essential metrics and discuss how you can optimize your API and fuel product-led growth.Table of Contents  The Role of APIs in Product-Led Growth  Key API Metrics for Product-Led Growth          User Engagement Metrics                  Adoption Rate          Active Users (DAU/MAU)          Retention Rate                    API Performance Metrics                  Latency          Error Rate          Uptime                    Business Impact Metrics                  Revenue Attribution          Expansion Revenue          Cost Efficiency                    Developer Experience Metrics                  Time to First API Call (TTFC)          Support Ticket Volume          SDK/Documentation Quality Score                      Tools to Monitor API Metrics for Product-led Growth          Moesif      Postman      Datadog        How to Align Metrics with Business Goals          Start with Clear Business Objectives      Map Metrics to User Journeys      Set Benchmarks and KPI      Leverage Tools for Real-Time Insights      Incorporate Feedback Loops      Communicate Metrics Across Teams      Adapt to Market Trends        Conclusion                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        The Role of APIs in Product-Led GrowthProduct-led growth relies on the product itself to drive user acquisition, engagement, and retention. Instead of heavy sales efforts, the product’s value propels adoption. Every team member and their respective team efforts center around the product itself. And APIs lay the foundation of these product-led growth strategies.APIs directly impact user experiences by facilitating smooth interactions between different applications and services. For example, consider ordering food online. A restaurant’s API connects their ordering system with delivery services like DoorDash for real-time updates and a seamless experience for the customer. APIs in SaaS platforms like Stripe allow businesses to integrate payment capabilities without building infrastructure from scratch. Businesses can reduce time-to-market and increase customer satisfaction, both being significant contributors to product-led growth.APIs help promote self-service adoption by enabling scalable, user-driven functionality. Developers can integrate APIs directly into their applications without extensive support. This in turn also drives customer acquisition and organic adoption. For example, using Twilio’s well-documented APIs, users can independently build messaging capabilities in their apps.APIs also help you make data-driven decisions by collecting and exposing usage insights. API analytics track performance, adoption, and engagement metrics in real time. For example, Moesif’s API monitoring tools give you an end-to-end comprehensive view to ensure that APIs meet user needs while helping you identify opportunities for optimization. This data helps teams align API performance with business objectives as the product and market evolves.Another core driver of product-led growth is API monetization. APIs open new revenue streams by enabling companies to offer functionality as a service. For example, Shopify’s API ecosystem allows third-party developers to create and sell integrations, expanding the platform’s reach while generating revenue. APIs shift value creation from the company to its ecosystem for a more amplified growth potential. It almost feels like the product and its customers help each other grow.If you can execute your API strategy well, it aligns directly with customer needs. APIs make products extensible, customizable, and interoperable, creating value across diverse use cases. Businesses can confidently take more calculated risks because they possess the means to do so and penetrate more into the market with their products or ideas. By meeting customer demands through APIs, businesses build loyalty, attract new users, and exceed their expectations. And these ultimately contribute to strong customer retention and growth.Key API Metrics for Product-Led GrowthTracking the right API metrics aligns your product’s technical performance with user engagement and business outcomes. Let’s discuss the essential metrics across adoption, performance, business impact, and developer experience.User Engagement MetricsAdoption RateAdoption rate measures how quickly users start using your API after onboarding. Track the number of new users making their first successful API call within a defined period. A high adoption rate means you have a solid onboarding process. If you craft intuitive documentation and accessible SDKs, you can build onboarding processes that users find delightful and get fast value out of. For example, Twilio Code Exchange has ready-to-use code samples that demonstrate hands-on what users can build and accomplish.To improve adoption, identify drop-off points in the onboarding journey and address them through developer feedback or funnel analysis.Funnel analysis chart in Moesif that illustrates sign-in to first and then 10 sign-ups conversion funnelActive Users (DAU/MAU)Active users metrics, like daily active users (DAU) and monthly active users (MAU), highlight ongoing engagement with your API. DAU measures daily interactions, while MAU tracks monthly trends, helping you detect growth or churn patterns and leading, lagging indicators.A time series chart in Moesif showing daily active users for an API productFor example, if MAU rises but DAU stagnates, your API might lack stickiness or daily relevance. On the other hand, if you observe DAUs going up, but your MAUs going down, it means a large amount of churn—you have an increasing number of new users using the app everyday, but they aren’t coming back.Analyze these trends and correlate them with product updates, marketing efforts, or seasonal shifts to make informed improvements.Retention RateRetention rate measures the percentage of users who continue using your API after their initial adoption. High retention indicates ongoing value and a positive developer experience. Retention also correlates with API reliability and performance. Being able to drill down into retention data helps you evaluate the long-term appeal of your products.A retention chart in Moesif showing user retention by different subscription plansYou can segment retention data by user cohorts to identify variations, for example, free versus premium users. Monitoring retention trends like this helps you identify what segments find the most value and when drop-offs occur.API Performance MetricsLatencyLatency defines how responsive your API feels to users. Measure average response times and percentile metrics like P90 and P99 to understand performance across different use cases. For example, a payment API with a P99 latency above 500ms risks losing transactions. High latency often correlates with decreased user satisfaction, especially for real-time applications. By comparing latency across endpoints or regions, you can identify areas where performance optimizations can enhance user interactions.A time series chart in Moesif showing p99 latency by different endpointsError RateThe error rate tracks the percentage of failed requests and reveals areas users face struggles. You can break errors into client-side (4xx) and server-side (5xx) to identify the root source of the problem—user actions or system problems. For example, recurring 403 Forbidden errors may point to access control misconfigurations, while frequent 500 Internal Server Errors often signal backend capacity issues.A time series pie chart in Moesif showing 4xx HTTP errors by different endpoints.UptimeUptime measures the availability of your API and reflects how consistently users can access your service, usually expressing it as a percentage. For mission-critical APIs, even a few minutes of downtime can damage user trust and cause financial losses. By monitoring your products and services’ uptime, you can understand their overall health, recurring downtime during peak usage, scheduled maintenance impacts, and so on. It helps you make sure your service meets reliability expectations.Business Impact MetricsRevenue AttributionThis metric ties API usage directly to revenue and thus illustrates how the API drives financial outcomes. For example, SaaS platforms like Stripe calculate revenue based on API call volumes tied to payment transactions.Revenue attribution provides clarity on which user segments or API features deliver the highest business value. If you track which endpoints or integrations contribute the most revenue, you can prioritize features and refine your existing pricing models.Expansion RevenueExpansion revenue measures growth opportunities from existing users by analyzing increases in API usage over time. This metric identifies high-growth customers who may be ready for upsell opportunities. By tracking expansion revenue by usage tier or feature adoption, you can find out the exact parts of your API ecosystem that drive additional revenue.Cost EfficiencyCost efficiency measures the expense associated with each API call, including infrastructure, development, and support costs. High costs per call often indicate inefficiencies, such as redundant API calls or inefficient database queries. By tracking this metric, you can plan out and execute optimization strategies for resource utilization and profitability. You can uncover areas where scaling issues may arise, or operational improvements can increase margins.Developer Experience MetricsTime to First API Call (TTFC)TTFC or TTFHW (Time to First Hello World) measures how quickly developers can make their first successful API call. A shorter TTFC indicates smooth onboarding experiences, while a longer TTFC may point to barriers in integration. By tracking TTFC over time, you can thoroughly understand whether changes in onboarding resources, such as documentation or SDKs, impact developer engagement.Support Ticket VolumeSupport ticket volume tracks how often users face issues with your API and require assistance. High ticket volume often points to unclear documentation, usability problems, or bugs. Regularly analyze ticket trends to identify recurring problems and take actionable steps so they don’t become a major hurdle. For example, if many tickets relate to authentication, consider improving your guides or error messaging.SDK/Documentation Quality ScoreThis metric measures how effectively your documentation and SDKs meet developer needs based on user feedback or engagement. For example, a low documentation quality score might correlate with higher support ticket volume.Monitoring this metric helps you assess the impact of updates and ensures your resources stay aligned with user expectations.Both versions emphasize Moesif’s strengths and position it as the ideal tool for tracking API metrics. However, there are notable differences in focus, depth, and framing. Here’s a thorough evaluation and suggestions for a refined version that combines the strengths of both while improving readability, clarity, and alignment with product-led growth principles:Tools to Monitor API Metrics for Product-led GrowthTo effectively track API metrics that compliments your product-led growth strategies, you need tools that can deliver actionable insights into user behavior and product performance. Let’s briefly explore some leading options.MoesifMoesif provides advanced API analytics that go beyond basic monitoring. It captures detailed user interaction data, including endpoint usage, common errors, and trends over time. These insights help uncover friction points in the user journey and optimize the API experience. For example, by analyzing error patterns, you can identify recurring issues affecting specific user groups and address them proactively.One of Moesif’s key strengths comes from its powerful segmentation and grouping utilities. Businesses can segment users by behavior, demographics, subscription tier, or geographic location. This segmentation provides a more granular understanding of how different user groups interact with your API. For example, identifying power users allows you to tailor features and drive expansion revenue. You can create custom cohorts and set up automated alerts that can notify you about critical events in real time. These features empower you with data-driven decisions that align with product-led growth objectives.Moesif also tracks critical metrics like Time to First Call (TTFC), adoption rate, and retention rate through its comprehensive user and company analytics suite. You can get an end-to-end view into product use, adoption, and user behavior.Furthermore, Moesif provides intuitive visualization dashboards that you can customize, extend, and further build up on, including raw analytics data to build custom workflows yourself. Moesif has also built AI-powered conversational interfaces for metrics. This allows you to immediately get actionable insights into metrics data, irrespective of your skill level in navigating or comprehending analytics data. This highly resonates with one of the core philosophies of product-led growth—your teams participate in decision making and growth efforts together.Moesif has a rich ecosystem of growing integrations with various platforms and tools, including CRMs like HubSpot and Salesforce. You can easily add Moesif to your existing technology stack with minimal friction.Moesif also has robust monetization features that natively works with top billing providers like Stripe and Recurly. You can even implement your own custom billing through webhooks where Moesif accurately tracks and meters usage. The monetization features build on top of the existing analytics tools to drive growth in a coherent and structured manner.PostmanPostman is a popular platform for creating, testing, and documenting APIs. Postman’s monitoring features help teams validate API performance and uptime to make sure production APIs function as expected. Postman offers excellent utility for development teams during the API build and test phases.While Postman provides basic monitoring, it is not designed for tracking long-term user behavior or business impact metrics. Its focus on testing and development limits its ability to provide actionable insights for product-led growth.DatadogDatadog is a versatile monitoring tool designed for tracking application performance, infrastructure health, and APIs. You get visibility into API uptime, latency, and error rates, helping teams maintain reliable services. Datadog’s intuitive dashboards allow teams to monitor performance metrics across distributed systems, making it a strong choice for organizations managing complex environments.However, Datadog lacks advanced user behavior analytics and segmentation capabilities. While it identifies technical performance issues, it does not provide the granular insights into user adoption, retention, or monetization that product-led growth strategies demand. For businesses prioritizing API-driven growth, Datadog serves as a complementary tool but does not replace Moesif’s targeted analytics.How to Align Metrics with Business GoalsAligning API metrics with business goals provides measurable outcomes that directly contribute to successful product-led growth for organizations. To properly align metrics with bigger-picture objectives, you need to take a structured approach to identifying priorities, setting benchmarks, and making data-driven decisions. Here are some general guidelines that can help you.Start with Clear Business ObjectivesBegin by defining your organization’s primary goals, whether it’s driving revenue, improving customer retention, or scaling operations. For example, if you prioritize revenue growth, focus on metrics like revenue attribution and expansion revenue. These metrics reveal the endpoints or user groups that generate the highest financial value and thereby help you prioritize investments.Map Metrics to User JourneysConnect metrics like adoption rate, retention rate, and TTFC to specific stages of the user journey. For example, TTFC reflects the effectiveness of your onboarding, while retention rate shows long-term satisfaction. Mapping these metrics helps teams identify gaps in the user experience and align improvements with business objectives.Set Benchmarks and KPIEstablish realistic benchmarks for each metric to measure progress. For example, you can aim for a retention rate of 85% or reduce TTFC by 20% within a quarter. Having such KPIs provide clear targets for teams to work toward and ensure alignment across departments.Leverage Tools for Real-Time InsightsUse platforms like Moesif to track metrics and gain actionable insights in real time. Moesif’s segmentation and grouping capabilities allow you to analyze user behavior by region, subscription tier, engagement level or source, and other criteria. These insights help you plan and execute targeted strategies, such as improving adoption rates in underperforming regions or tailoring pricing plans for high-value customers.Incorporate Feedback LoopsRegularly review metrics with cross-functional teams. For example, if error rates increase, involve engineering teams to diagnose and resolve issues. Efficient feedback loops ensure that metrics remain relevant and actionable.Communicate Metrics Across TeamsEnsure that all teams—from product managers to executives—understand the metrics relevant to their roles. Use dashboards and reports to provide a unified view of progress toward business goals. Through a transparent and collaborative mindset, you can keep everyone focused on shared objectives.Adapt to Market TrendsMonitor how your metrics compare to industry standards and competitors. For example, a low adoption rate may signal that your onboarding process lags behind market leaders. Use these insights to refine your strategy and maintain a competitive edge.ConclusionProduct-led growth has shifted how businesses think about their products and scale. The product itself leads the multifaceted efforts of a company towards success where all stakeholders take part, fostering a collaborative mindset that focuses on delightful user experiences. But not all product-led companies succeed. For API-first or API products, it’s very important that you leverage the relevant metrics to guide your decision making and strategies.If you want to see for yourself how Moesif can help you with product-led growth, sign up today for a free trial, no credit cards required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design and Build Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Top-API-Metrics-For-Product-Led-Growth/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-saas-pricing-models": {
          "title": "SaaS Pricing Models: 7 Strategies and Real Examples (2026 Guide)",
          "content"	 : "Updated: May 2026Choosing a SaaS pricing model is one of the highest-leverage product decisions a software company makes, and one of the easiest to get wrong. Price too low and you leave revenue on the table. Price too high and you bleed deals at the demo. Pick the wrong structure and you spend the next two years explaining quotas to customers who only wanted to pay for what they used.This guide walks through the seven pricing models that dominate modern SaaS, with real examples from Slack, HubSpot, Twilio, Notion, and others. It also distinguishes pricing models (how you charge) from pricing strategies (why you set the number you do), goes deep on usage-based pricing (currently the fastest-growing model in SaaS), and ends with a decision framework you can actually use this quarter.We have skin in the game on this topic. We moved Moesif from fixed tiers to usage-based pricing in 2023, and we work with API and AI companies running every model below. The mistakes and trade-offs in this post come from operating these models, not just describing them.What Is a SaaS Pricing Model?A SaaS pricing model is the structural framework a software company uses to charge customers for ongoing access to its product. It defines what customers pay for (users, usage, features, or flat access), how often they’re billed, and how the price scales as the customer gets more value from the product.Pricing models matter because they affect three numbers at once: customer acquisition cost (CAC), customer lifetime value (CLTV), and net revenue retention (NRR). A flat-rate model produces predictable revenue but caps your upside on heavy users. A per-seat model scales with team size but stalls when adoption is sticky and accounts stop hiring. Usage-based pricing tracks value almost perfectly but introduces forecasting pain on both sides. The right model is the one that matches your value metric, your buyer’s purchasing process, and your finance team’s tolerance for variability.A successful pricing model also has to clear five operational hurdles:  Costs are understood. Infrastructure, support, and marginal costs per customer are mapped before the price is set.  Value is specific. What the customer pays for is tied to one or two concrete outcomes, not a fuzzy “everything in the product.”  Market position is intentional. The price signals premium, parity, or undercut, and the product backs it up.  Structure fits the buyer. A flat monthly fee suits self-serve buyers; complex tiers suit enterprise procurement.  The model can flex. Pricing reviewed every 12 to 18 months catches misalignment before it becomes churn.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        SaaS Pricing Model vs. SaaS Pricing StrategyThese terms get used interchangeably, and they shouldn’t. The distinction is worth getting right because it changes what you optimize.A pricing model is the structure: per seat, per call, flat fee, tiered. It answers how the customer pays.A pricing strategy is the reasoning behind the number: cost-plus, competitor-based, value-based. It answers why you set the price at $79 instead of $49 or $299.You always pick both. A per-seat model (structure) with value-based pricing (strategy) might land at $99/user/month because the buyer’s team saves 10 hours per user per week. The same per-seat model with cost-plus pricing might land at $19/user/month because your unit cost is $14 and you want a margin. Same model, different strategy, very different business.The three pricing strategies most SaaS companies pick from:  Cost-plus pricing takes your cost to serve a customer and adds a markup. It’s transparent and easy to justify internally, but it ignores what the customer is actually willing to pay.  Competitor-based pricing anchors to the going rate in your category, usually with a small discount or premium. Useful for entering a crowded market, dangerous as a long-term plan because it ties your revenue to other companies’ decisions.  Value-based pricing prices to the customer’s perceived ROI. It captures the most margin but requires real research into how customers measure value. For deeper coverage, see our guide to API monetization models.Get the model right and customers understand what they’re paying for. Get the strategy right and they understand why the bill is the size it is.The 7 Main SaaS Pricing Models (with Examples)These seven models cover the great majority of SaaS pricing pages you’ll encounter. Most companies pick one as a backbone and layer one or two others on top.1. Flat-Rate PricingFlat-rate pricing charges a single, fixed price for access to the product, regardless of seats or usage. Customers usually pick between monthly and annual billing, with annual carrying a 15-20% discount.When to use it: Simple, single-persona products where usage doesn’t vary much between customers. Consumer subscriptions. Tools where seat counts are unpredictable but small.Real example: Basecamp Pro Unlimited is $299/month billed annually ($399/month billed monthly) for unlimited users and projects.Pros: Simplest possible pricing page; predictable revenue; trivial to forecast.Cons: Leaves money on the table for heavy users; one price rarely fits both a 5-person team and a 500-person team.2. Tiered PricingTiered pricing offers two to four packages at distinct price points, each bundling a set of features and quotas. The buyer self-selects into the tier that fits.When to use it: Products serving multiple personas with different needs (individual users, small teams, and enterprise buyers, for example) where it makes sense to gate advanced features behind higher tiers.Real example: HubSpot’s Marketing Hub runs Starter, Professional, and Enterprise tiers, with each tier unlocking additional automation, reporting, and integration features.Pros: Captures more revenue from larger buyers; clean upgrade path; works well for sales-assisted motions.Cons: Tier design is hard, and bad tiering pushes buyers to the wrong plan or causes “feature jumping” instead of upgrades.3. Per-User (Per-Seat) PricingPer-user pricing charges a fixed amount per seat per month. Total bill scales linearly with team size.When to use it: Collaboration tools where each new user gets real value (project management, design, knowledge bases). Less suited for products with mostly read-only users.Real example: Slack’s paid Pro plan charges per active user per month. Microsoft 365 and Google Workspace use the same structure.Pros: Easy to budget; revenue grows with the customer’s team; clear value-to-price relationship for collaboration use cases.Cons: Discourages broad rollouts (“just give Sarah a seat”) and penalizes you when adoption stalls because nobody added users this quarter.4. Per-Active-User PricingA refinement of per-user where you only bill for users who actually engaged with the product in a given period. Removes the “we’re paying for 200 seats but only 60 people log in” problem.When to use it: Enterprise rollouts where IT provisions many accounts but only a subset of users are active. Tools where adoption ramps over months, not days.Real example: Slack’s Fair Billing Policy credits customers for users who are deactivated or inactive. The calculation is based on active-user time, not seat count alone. The practical effect: IT can provision broadly without paying for users who never log in.Pros: Reduces buyer friction enormously, especially in large enterprises; aligns incentives so the vendor cares about activation.Cons: Revenue depends on engagement, which is harder to forecast; requires a credible “active user” definition the customer agrees with.5. Usage-Based Pricing (Pay-As-You-Go)Usage-based pricing (also called consumption pricing or pay-as-you-go) charges customers based on what they actually consume. The unit of consumption is the value metric: API calls, events processed, tokens generated, data ingested, transactions cleared.When to use it: APIs, infrastructure, AI products, anything where one customer can use 10x more than another and “fairness” matters. Particularly common in developer tools.Real example: Twilio charges per SMS sent and per minute of voice. AWS bills per second of compute and per GB of storage. OpenAI prices per million tokens.Pros: Tracks value almost perfectly; removes purchase friction; rewards product growth inside accounts; correlates strongly with above-average net dollar retention according to OpenView’s research.Cons: Customers can’t easily budget; bill shock is a real churn risk; demands accurate metering infrastructure and usually a customer-facing usage dashboard.We expand on this model in the closer-look section below because it deserves more than four bullets.6. FreemiumFreemium offers a permanently free tier with limited features, quotas, or both, plus paid tiers that unlock the rest.When to use it: Product-led growth (PLG) motions where users discover the product themselves and convert when their needs outgrow the free tier. Works best when there’s a clear, visible “wall” users hit as they get value.Real example: Notion’s free tier supports unlimited blocks for individual use; teams pay when they need collaboration and admin features. Figma is free for individual designers and bills per editor on team plans.Pros: Lowest possible adoption friction; the free tier becomes its own marketing channel; product usage data informs which features convert.Cons: Free users cost real money to support and host; the free-to-paid conversion rate is rarely above single digits; the wrong free-tier limits can either give too much away or push users to abandon before they see value.7. Feature-Based PricingFeature-based pricing bundles features into packages and charges by package rather than by users or usage. Distinct from tiered pricing in that the gating is purely about feature access, not quota.When to use it: Enterprise products with a wide feature surface where different buyer segments value different capabilities (security, compliance, automation, advanced reporting).Real example: Salesforce’s editions (Starter, Pro, Enterprise, Unlimited) gate features like advanced workflow automation, sandbox environments, and 24/7 support.Pros: Lets you charge for features that genuinely matter to high-end buyers without penalizing smaller customers; works well in sales-led motions.Cons: Buyers find it hard to compare plans; the temptation to gate everything leads to bloated pricing pages and lost deals.Hybrid Pricing ModelsMost mature SaaS companies don’t pick one model; they combine. Two patterns show up over and over:  Subscription plus usage-based add-ons. A base subscription covers core features and a usage allowance; overages are billed by consumption. This is how Twilio Flex, HubSpot’s add-on products, and most modern API products work.  Tiered plus per-seat. Different tiers at different per-seat rates, with feature gating between tiers. HubSpot’s full pricing structure does this, as does most of the work-management category.Hybrid pricing handles the messy reality that buyers want predictability and fairness. The catch is that every additional pricing dimension makes the pricing page harder to read. Two dimensions is usually fine; three starts requiring a calculator.SaaS Pricing Models ComparedSide by side, the differences become much clearer. None of the top-ranking guides on this keyword includes a comparison table, which is part of why most readers leave more confused than they arrived.            Pricing Model      Best For      Revenue Predictability      Adoption Friction      Real Example                  Flat-Rate      Simple, single-persona products      High      Low      Basecamp              Tiered      Mixed personas, sales-assisted      High      Low      HubSpot              Per-User      Collaboration tools      High      Medium      Slack (Pro plan)              Per-Active-User      Enterprise rollouts      Medium      Very Low      Slack Fair Billing              Usage-Based      APIs, infrastructure, AI      Lower      Very Low      Twilio, AWS, OpenAI              Freemium      Viral / PLG products      Lower      Lowest      Notion, Figma              Feature-Based      Wide-feature enterprise tools      High      Medium      Salesforce      A few patterns are worth pulling out of the table:  Predictability and friction trade off. Flat-rate is the most predictable revenue stream and feature-based is close behind, but both create more buying friction than usage-based or freemium.  Usage-based wins the friction comparison. Customers can start with $0 commitment and grow into a real bill. That’s a big part of why so many self-serve API and AI products choose it.  Hybrid pricing isn’t in the table on purpose. Hybrid is a layering pattern rather than a standalone model. Pick a primary model from the seven above and layer where the math supports it.How to Choose the Right SaaS Pricing ModelPicking a model comes down to a small set of structural questions about your product, your buyer, and your value metric. The framework below works for most teams.1. Does usage vary widely between customers?If your heaviest customer uses 100x more than your lightest, usage-based or hybrid is almost always right. A flat fee will either underprice the whales or overprice the minnows.2. Is value tied to the number of people using the product?If yes, per-user or per-active-user fits. Collaboration tools, design suites, and CRMs default here for a reason.3. Is the product simple with a single buyer persona?Flat-rate is enough. Don’t introduce tiers just because everyone else has them, because complexity for its own sake costs deals.4. Are you selling to enterprises that want feature gating?Tiered or feature-based pricing maps cleanly to enterprise procurement, where security, SSO, audit logs, and SLAs are the negotiation points.5. Is the goal viral adoption?Freemium, ideally with a clear wall (collaboration, scale, or admin features) that signals when it’s time to pay.A common mistake is picking a model based on what competitors do, then learning six months later that your customers actually wanted something else. Run pricing interviews before launching, watch real usage patterns after, and revisit the model annually.For a deeper framework specific to APIs and developer tools, see our companion post on pricing for API products.Usage-Based Pricing: A Closer LookUsage-based pricing has grown rapidly in API, infrastructure, and AI products, and many newer companies in those categories launch with it from day one. It’s also the model we get the most operator questions about. According to OpenView Partners’ 2023 SaaS Benchmarks, 61% of SaaS companies now offer some form of usage-based pricing, up from 23% in 2020. The same research found that public companies with usage-based pricing post higher net dollar retention than subscription-only peers, because usage growth inside accounts compounds the way subscription expansion typically doesn’t.That growth comes with real operational requirements. Three deserve specific attention.Picking a Value MetricThe value metric is the unit you charge by. Pick wrong and the rest of your pricing falls apart. Good value metrics share three properties:  They correlate tightly with the customer’s perceived value. Twilio charges per message because the value of a sent message is intuitive; charging per CPU second on the same product would baffle buyers.  They scale with usage. A metric that’s the same for a small and large customer (“number of integrations enabled”) isn’t a usage metric.  They’re easy to count. API calls, events, tokens, GB ingested. If you can’t count it accurately in real time, you can’t bill on it.Common value metrics by product category:            Product Type      Typical Value Metric                  Communication APIs      Messages sent, minutes of call              Payment APIs      Transactions processed, dollars cleared              Data infrastructure      GB ingested, GB stored, queries run              AI / LLM products      Input tokens, output tokens, generations              Analytics platforms      Events tracked, users tracked              Workflow automation      Tasks executed, runs completed      A value metric isn’t permanent. Snowflake famously moved from per-query to per-second of compute as usage patterns shifted. Expect to revisit yours.Metering RequirementsUsage-based pricing only works if your metering is accurate, real-time, and auditable. Three things have to be true:  Every billable event lands in your metering system within seconds. A delayed event is a lost or disputed dollar.  Usage data is reconciled to revenue. Finance needs a clean audit trail from “event happened” to “line item on invoice.”  Customers can see their own usage. A customer-facing usage dashboard is one of the highest-impact churn-prevention investments in usage-based pricing, because when buyers can predict their bill they don’t panic.Most teams underestimate the engineering cost of building this in-house. When we moved from fixed tiers to usage-based pricing in 2023, the metering, commitment-level math, and customer-facing dashboards took longer to design than the pricing itself. We built it once for our own product and now provide it to other companies running the same playbook. Our metering layer pushes usage events into Stripe, Recurly, or Chargebee, so teams keep their existing billing system while gaining accurate, customer-visible usage tracking. For more on running this in production, see our usage-based billing best practices and the pay-as-you-go playbook.Common PitfallsThree pitfalls show up consistently in usage-based pricing rollouts:  Bill shock. A spike in customer usage produces a surprise invoice. The fix is alerts, soft limits, and committed-use discounts so buyers can lock in budgets.  No spend ceiling for budget-bound buyers. Procurement teams often can’t approve “variable” line items. Offer commitment tiers (e.g., “5M events/month for $X”) that give buyers a fixed number to sign off on, with overages billed separately.  Engineering can’t keep up with the metering load. Building event ingestion that handles 10K events/sec without losing data is a real engineering project. If you’re a small team, buy this layer rather than build it.Usage-based pricing isn’t always the right answer. If your product has steady, predictable per-customer usage and a simple buyer journey, flat-rate or per-seat will outperform it. The honest test is whether the value your customers get genuinely scales with what you can meter.Real SaaS Pricing Examples to Learn FromLooking at how successful companies actually implement these models is faster than any framework.SlackSlack combines per-active-user pricing with tiered features. The Pro and Business+ tiers add SSO, compliance reports, and unlimited message history at higher per-seat prices. The Fair Billing Policy, which credits customers for inactive seats, is the unlock that made Slack viable in large enterprises, because IT could provision broadly without paying for users who never logged in. Lesson: Per-seat pricing and broad enterprise rollouts can co-exist, but only if your billing accounts for actual usage.HubSpotHubSpot’s Marketing Hub, Sales Hub, and Service Hub each have Starter, Pro, and Enterprise tiers. The packaging maps to buyer maturity: a 5-person startup picks Starter; a 50-person revenue team picks Pro; enterprises picking Enterprise pay for advanced reporting and ABM features. Bundled packages across hubs encourage customers to expand within the platform. Lesson: Tier structure should reflect buyer journey, not feature count.TwilioTwilio is one of the most-cited usage-based examples in SaaS. Customers pay per SMS, per voice minute, per WhatsApp message, with volume discounts that kick in automatically. The pricing page is essentially a menu: no quotas, no tiers, no negotiation for self-serve customers. Lesson: When the value metric is intuitive and meterable, usage-based pricing removes nearly all buying friction.NotionNotion’s free tier gives individual users essentially everything they need. The paid tiers kick in for collaboration: unlimited file uploads, version history, admin tools, and team workspaces. The product itself drives upgrade conversations because the wall hits exactly when a team forms. Lesson: A great freemium tier is one where users genuinely succeed for free and pay when their needs change, not one engineered to frustrate.Google WorkspaceGoogle Workspace uses per-user pricing layered with feature-based tiers (Business Starter, Standard, Plus, Enterprise). Storage limits, security features, and support level differ by tier. The pricing is transparent enough that small businesses can self-serve and complex enough to support large enterprise procurement. Lesson: Per-user plus feature tiers is a stable pattern for productivity tools serving everyone from solopreneurs to Fortune 500s.ZendeskZendesk uses per-agent pricing with tiered features and usage-based add-ons. The modular structure lets companies start with a single product (Support, Sales, or Chat) and add modules as needs grow. Lesson: Modular packaging works when your product line is wide and customer needs vary; it falls apart when bundles are arbitrary.SaaS Pricing Best PracticesSix practices consistently separate companies that price well from companies that don’t.  Keep the pricing page simple. Three to four plans, one or two pricing axes, no calculators required for the most common purchase. If you need a sales rep to explain it, you’ve already lost the self-serve buyer.  Tie price to a single value metric. Whether it’s seats, events, transactions, or features, the buyer should be able to point at the line item and say “I understand why this number went up.”  A/B test before changing. Don’t ship pricing changes blind. Run them with a subset of new signups and measure conversion and ACV before rolling out.  Grandfather existing customers. When you change pricing, give existing customers the option to stay on their current plan. Surprise increases reliably spike churn.  Communicate price changes openly. Email the customer base, explain the reasoning, give 30-60 days of notice. For deeper guidance, see our SaaS billing best practices.  Revisit pricing every 12-18 months. Markets shift, products mature, and a model that worked at $1M ARR almost never works at $20M.The single biggest mistake we see is treating pricing as a finance decision instead of a product decision. Finance can model the revenue implications; only product can tell you whether the model still matches how customers get value.Pricing Is a Product Decision, Not a Finance OneThe “best” SaaS pricing model is whichever one most tightly aligns price with the value your customers actually care about. For a project management tool, that’s probably seats. For an API or AI product, it’s almost always usage. For a wide-feature enterprise platform, it’s feature packages.Whichever model you pick, the operational layer underneath it matters as much as the model itself. Accurate metering, real-time usage tracking, customer-facing dashboards, and a billing-provider integration that doesn’t break under load are the things that decide whether your pricing model survives contact with customers.If you’re building a usage-based or hybrid pricing model for an API, AI, or developer product, we handle the metering and customer-facing usage analytics layer so you can use Stripe, Recurly, or Chargebee as your billing engine without building the event pipeline yourself. Start a free 14-day trial, no credit card required.FAQPage Schema:Frequently Asked QuestionsWhat are SaaS pricing models?SaaS pricing models are the structural frameworks software companies use to charge for recurring access to their products. The seven most common are flat-rate, tiered, per-user, per-active-user, usage-based, freemium, and feature-based. Most mature SaaS companies layer two or more into a hybrid model.What is the 3-3-2-2-2 rule of SaaS?The T2D3 model (Triple-Triple-Double-Double-Double) is a revenue growth benchmark for venture-backed SaaS companies. It targets tripling ARR for two consecutive years, then doubling ARR for three more, taking a company from roughly $2M to $100M+ ARR over about five years. It’s a growth target, not a pricing rule, but it influences pricing because the underlying unit economics have to support that trajectory.What are the 4 types of pricing?In SaaS, the four primary pricing strategies are cost-plus pricing, competitor-based pricing, value-based pricing, and dynamic pricing. These are distinct from pricing models (flat-rate, tiered, usage-based, etc.). Strategy determines the price; the model determines the structure.How do you develop a SaaS pricing model?Start with the customer’s value metric (what they’re really paying for), pick a model that aligns the price with that metric, validate with 10-15 customer interviews before launch, and instrument usage analytics so you can watch how customers actually consume the product post-launch. Revisit pricing every 12-18 months.What is the best pricing model for an early-stage SaaS?For most early-stage products, flat-rate or simple tiered pricing wins, because the goal is to minimize friction and learn quickly. Usage-based pricing is the right answer when your product has obvious usage variability (APIs, AI tokens, data volume) and you’ve already built or can buy the metering infrastructure. Freemium works only if you have the runway to support free users for 12+ months while conversion rates stabilize.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /technical/api-development/SaaS-Pricing-Models/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-api-metrics-you-should-add-in-crm": {
          "title": "API Metrics You Should Add to Your CRM",
          "content"	 : "Looking at the 2024 State of the API Report, it becomes clear that a strong API strategy guarantees better revenue and growth. No matter the exact nature of the framework you’ve adopted to drive success, a crucial chunk of it must consist of understanding the customers. However, the traditional CRM (customer relationship management) systems we see often lack the depth and precision to truly understand customer behavior and product usage.One solution comes from injecting API metrics into your CRM and then really doubling down on leveraging those metrics. Moesif gives you access to granular visibility into different customer metrics, while possessing a rich suite of third-party integrations, including CRMs like HubSpot and Salesforce.In this blog post, we’ll discuss the key customer metrics we recommend you consider for sample use cases. We’ll also go over Moesif’s integrations with platforms like HubSpot and Salesforce. At the end, you’ll have a solid understanding of how to make the best use of your CRM’s capabilities when you compliment them with fine-grained and actionable customer data.Table of Contents  Table of Contents  Why Integrate API Metrics with Your CRM?  Key Metrics to Track          Core API Usage Metrics                  API Call Volume          API Usage Growth          API Errors          API Latency                    User-Specific API Metrics                  Feature Usage Frequency          API Endpoints Accessed          Time Spent Using Specific API Features          API Usage Patterns                    Business-Specific API Metrics                  API Revenue          API Usage by Plan or Tier          Custom Metrics                          Feature Adoption Rate for New Users              API Usage for Key Workflows              API Usage Correlated with Customer Success              Measuring the Impact of API-Driven Initiatives                                            Actionable Strategies for Using API Metrics in Your CRM  Moesif’s HubSpot and Salesforce Extensions          Moesif’s HubSpot Extension      Moesif’s Salesforce Extension        Conclusion  Next Steps                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        Why Integrate API Metrics with Your CRM?Your CRM, no doubt, provides valuable information about customer interactions like sales calls, emails, and support tickets. However, it often misses a critical piece of the puzzle: how customers actually use your product. To fill this gap, you need to incorporate API metrics with the rest of your customer success strategy.These metrics provide granular data on product usage, feature adoption, and user behavior. Such a deep understanding of the user base allows you to go beyond basic demographics and engagement metrics. For example:  You can identify power users who rely heavily on specific API endpoints.  Predict potential churn based on declining API activity or drop in valuable API calls.  Personalize onboarding flows based on the features customers access through your API.Key Metrics to TrackKnowing what metrics to track to act on them through your CRM can help you refine strategies and nurture stronger customer relationships. The ones that truly matter depend on your use case, business objectives, and various other aspects. In general, you must prioritize those that offer actionable insights into product usage, user engagement, and potential roadblocks.Let’s try to understand some of these metrics in three categories.Core API Usage MetricsThese provide a high-level overview of your API’s overall performance and adoption. For example:API Call VolumeTrack the total number of API calls made within a specific timeframe. This metric serves as a fundamental indicator of your API’s overall usage and popularity. Spikes or dips in call volume can reveal important trends, for example, increased adoption of a new feature or potential service disruptions.API Usage GrowthMonitor the rate at which API call volume increases or decreases over time. If you notice a consistent growth, it suggests an increasing platform adoption and therefore a successful API strategy. On the other hand, if you observe a declining usage, through Moesif’s Time Series analysis, you may need to improve or make changes in your API offerings.API ErrorsKeep a close eye on the frequency and types of API errors your users encounter. High error rates indicate a number of potential issues:  API’s functionality  API documentation or integration  OnboardingSuch issues lead to frustration and potentially churn. If you analyze error trends, you can proactively identify and address problems to reward your customers with smooth and reliable user experience.Moesif provides detailed error breakdowns for API calls. You can also set up customized alerts that integrate with your CRM so when error rates spike, you can take swift action to address problems and ensure a smooth and reliable user experience.API LatencyMonitor the time it takes for your API to process and respond to requests. High latency can significantly impact user experience, especially for time-sensitive applications. Moesif has built-ins for calculating average, maximum, P90 latency metrics, and further customization. Coupled with your CRM, you can easily identify performance bottlenecks and optimize your API’s infrastructure for faster response times.User-Specific API MetricsUser-specific API metrics give you granular insights into individual customer behavior and preferences. These metrics can become your ultimate utility when setting up marketing and product roadmaps, including targeted campaigns and so on. Moesif has a robust set of user and company analytics tools that can cover a large ground when it comes to understanding your user base.Some user-specific metrics to keep in mind are the following:Feature Usage FrequencyTrack how often individual users access specific features through your API. It can easily reveal the most popular features and ones that different user segments find valuable. You can precisely identify power users who rely heavily on specific functionalities. This can help you tailor your communication and support accordingly.API Endpoints AccessedMonitor the specific API endpoints each user interacts with. This reveals their workflows, preferred functionalities, and how they integrate your API into their applications. With this information, you can set up personalized onboarding, targeted support, and recommend customized features to users.Time Spent Using Specific API FeaturesMeasure the time users spend interacting with specific API features. It helps you gauge user engagement and identify areas for improvement. Features with high usage time indicate high value, while low usage might suggest usability issues or a lack of perceived value.API Usage PatternsAnalyze patterns in user API activity, such as peak hours, days of the week, and typical usage durations. It can yield a number of benefits, for example:  Reveal user habits  Help optimize resource allocation  Predict potential surges in API traffic  Proactively address scalability challengesBusiness-Specific API MetricsLastly, you want to connect API usage directly to your business goals and revenue streams to maximize your product’s sustainable evolution and survivability. Here are some metrics to observe:API RevenueIf your API has a direct revenue model, track the income generated through API usage. This allows you to measure the financial performance of your API program and identify opportunities for monetization and growth.Moesif can track API usage by plan and map it accordingly on your CRM side, allowing you to analyze revenue generation and identify upselling or cross-selling opportunities. For example, consider a customer consistently exceeding their API usage limits. Moesif’s robust alerting system can alert your sales team to reach out with an upgrade offer. Or better yet, you can automate this with Moesif’s Behavioral Emails or your CRM’s equivalent feature.API Usage by Plan or TierAnalyze API usage across different subscription plans or user tiers. This helps you understand how different customer segments utilize your platform so that you can tailor your offerings to meet their specific needs and usage patterns.Moesif’s segmentation features, combined with your CRM data, allow you to create targeted campaigns and offers for each user segment. For example, users on your free plan may consistently access a specific set of API endpoints. You can use this information to design a premium plan that meets their needs and encourages them to upgrade.Custom MetricsDefine and track custom metrics that align with your unique business objectives. These can include the following:  Specific feature adoption rates  API usage related to key workflows  Metrics that measure the success of specific API-driven initiativesFeature Adoption Rate for New UsersTrack how quickly new users adopt a specific feature through your API. This metric helps measure the effectiveness of your onboarding process and identify areas for improvement. Moesif can track how long it takes new users to make their first API call to a specific endpoint. You can easily identify users struggling to adopt key features and thus provide timely support.API Usage for Key WorkflowsIdentify and track API usage related to critical workflows in your application. For example, if you have an e-commerce platform, you can track the API calls related to order fulfillment and identify potential bottlenecks or issues therein. Moesif lets you track custom events based on specific user interactions. By having that data in your CRM, you can monitor such key events.API Usage Correlated with Customer SuccessTrack API usage patterns that correlate with customer success or churn. For example, you might find that customers who integrate your API with their internal systems have a higher retention rate. Therefore, you can identify and prioritize high-value customers and proactively address potential churn risks.Measuring the Impact of API-Driven InitiativesIf you launch a new API-driven feature or campaign, you can track its impact by defining custom metrics in Moesif. For example, you can track the number of API calls to the new feature’s endpoint or the increase in API usage from a specific customer segment. This data, integrated with your CRM, can help you measure the effectiveness of your initiatives. Furthermore, you gain confidence in making data-driven decisions and strategizing your marketing and customer success efforts.Actionable Strategies for Using API Metrics in Your CRMNow that we’ve discussed the key metrics to track in your CRM, here’s how you can apply those API data to achieve measurable results.  Enhance customer segmentation  Use API metrics to segment customers based on behavior and product use rather than demographics alone. Identify power users of advanced features or segment customers by the API endpoints they access most. Granular segmentation allows tailored communication and personalized recommendations, improving customer satisfaction and engagement.  Personalize customer outreach  API data makes customer interactions more relevant. If a user reduces API usage, reach out to understand their struggles and offer help. For new users, customize onboarding based on their accessed endpoints. Personalized outreach builds stronger relationships and increases retention.  Predict and prevent churn  A drop in API usage often signals churn risk. Therefore, monitor for declining activity and pair this data with other CRM signals, like fewer support tickets or website visits. You can use Moesif to track these patterns and act early with targeted offers or support, improving retention rates.  Monitor customer lifecycle stages  API metrics can help you pinpoint where customers are in their lifecycle. Track usage patterns to identify customers going through onboarding steps, actively scaling, or nearing a plateau. Tailor communication and support to match their stage. These efforts reward customers with a seamless experience aligned with their needs.  Identify upsell opportunities  Spot customers approaching usage limits or adopting advanced features through API data. These patterns indicate that they can commit to higher-tier plans or add-ons. Proactively suggest upgrades that match their needs and thereby increasing revenue and customer satisfaction.  Track and improve API performance  API latency, error rates, and downtime directly impact customer experience. Regularly monitor these metrics to ensure optimal performance. Use Moesif’s analytics to identify bottlenecks and areas for improvement to enhance customer trust and retention.  Analyze regional or industry trends  Segment API metrics by region or industry to uncover trends that inform your product strategy. For example, if users in a specific region utilize an endpoint more frequently, create targeted features or marketing campaigns for that audience.  Measure the success of integrations  Track API usage for customers integrating your API with third-party systems. High engagement indicates effective adoption, while low usage may indicate the need for additional support or better integration documentation.  Enable proactive support  Combine API metrics with customer support data to identify customers who may face issues before they reach out. For example, frequent API errors or timeouts can indicate technical challenges. Proactively assist these customers to reduce frustration and strengthen loyalty.  Optimize product development  API metrics highlight what features customers value the most. Focus development on high-use endpoints and address errors or issues users frequently encounter. Moesif’s detailed analytics make it easier to align your roadmap with customer needs for better adoption rates and satisfaction.  Optimize developer experience  Use API metrics to understand how developers interact with your API. High error rates or low adoption of specific endpoints can indicate confusing documentation or functionality gaps. Address these pain points to enhance developer experience and increase API adoption.  Create custom benchmarks for customers  Aggregate API metrics to create benchmarks that help customers understand their performance relative to peers. For example, show how their usage compares to industry standards or similar businesses.Moesif’s HubSpot and Salesforce ExtensionsMoesif has dedicated integrations with both HubSpot and Salesforce. Let’s briefly discuss how these integrations work.Moesif’s HubSpot ExtensionWith Moesif’s HubSpot extension, your API usage metrics in Moesif automatically maps and syncs to HubSpot records. The sync happens automatically in real time, so you don’t have to worry about valuable insights going stale and irrelevant.Any user or company profile fields in Moesif can map to equivalent properties in HubSpot contacts and companies. Moreover, you can specify how Moesif’s analytics data map to record properties in HubSpot. Since you have more contextualized and granular data about your product use, you can set up more robust marketing emails and workflows in HubSpot.Here are some examples of workflows you can set up using this integration:  Email new users when they successfully integrate, or nudge them if they haven’t sent any API calls.  Warn customers if they have neared or exceeded their plan’s quota.  Notify all customers who have been using a down or broken API.These illustrate some trivial use cases of course. But with the features this extension provides, you can go far beyond these trivial workflows and handle more complex and interesting scenarios effortlessly.Moesif’s Salesforce ExtensionMoesif’s Salesforce extension allows you to access your customers’ API usage metrics in Salesforce. Like the HubSpot extension, the data syncs automatically in real time.Any user or company profile fields in Moesif can map to equivalent properties in Salesforce contacts and companies. You can also specify how Moesif’s analytics data map to record properties in HubSpot. The ability to visualize and track API metrics in Salesforce allows you to get more utility out of Salesforce’s CRM. For example:  Monitor and ensure pilots have a successful implementation and see usage growth.  Reach out to customers close to or exceeding their plan quota.  Set up workflows to nudge customers who haven’t integrated yet.ConclusionAPI metrics give you actionable insights that can revolutionize how your business engages with customers. And with Moesif in your stack of tools, you can transform your CRM from a static system into a dynamic platform for customer understanding and business growth.To see how Moesif can help you and what it has to offer, sign up today for a free trial, no credit cards required.Next Steps  Get started with Moesif  Integrate Moesif with your platform of choice  Learn about Moesif API analytics suite  Learn about Moesif user and company analytics suite  Learn about dashboards and workspaces                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design and Build Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/API-Metrics-You-Should-Add-In-CRM/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "api-monetization-api-strategy-announcing-general-availability-of-the-moesif-developer-portal": {
          "title": "Announcing General Availability of the Moesif Developer Portal",
          "content"	 : "We’re thrilled to announce the General Availability of the Moesif Developer Portal , a major leap forward enabling you to deploy a seamless experience for developers to subscribe and pay for your APIs. With a complete refactor and upgrade, we’ve brought substantial improvements that make productizing and monetizing APIs even easier. The developer portal provides a secure path for customers to get started with your APIs and has integrations for popular identity providers Okta and Auth0.What’s New in Moesif Developer PortalModern Responsive Design, Right Out of the BoxThe Moesif Developer Portal has been re-imagined with an all-new responsive design, tailored to ensure a great user experience on any device. Whether it’s a laptop, tablet, or smartphone, your portal now looks sleek and professional everywhere. As an open-source project, the CSS is fully customizable to match your brand.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        New Plugins for Easy API Gateway IntegrationSupport for provisioning API keys and integrations with API gateways just got easier. Our new plugin architecture makes it a breeze to integrate the developer portal with your preferred API gateway vendor with out-of the box plugins for popular API gateways like Kong Gateway, Tyk, and AWS API Gateway . Not using an API gateway? You can also leverage our newly released plugins for various auth providers such as JWT or Auth0 Machine2Machine. You can also develop a custom plugin for more complex  provisioning flows for complex requirements.With these plugins, onboarding developers and managing API keys becomes seamless, enhancing the developer experience significantly.Powerful Pricing Table FeaturesWe’ve completely upgraded the way you can display pricing with a new dynamic pricing table. The table now supports a range of pricing models, from flat rates to tiered, graduated, and per-unit pricing—all out of the box. The developer portal also supports subscribing to multiple products and plans as well for complex pricing models. . The developer portal supports credit card check out via Stripe. Plans and products can be customized to your requirements such as invoicing, free trials, and more.Smoother Subscription Upgrade ExperienceWe’ve improved the subscription flow, making it simpler for users to upgrade their existing plans and manage their subscriptions. This enhancement ensures that developers can easily access more features and capabilities as their needs grow—removing barriers and reducing friction in your monetization strategy.Embedded Reports to Delight CustomersAnother major upgrade is our embedded reports which provide usage metrics and API logs to your customers in a self-service way. Now, these reports match the overall theme of your developer portal, offering a consistent, professional look and feel across all pages..Easy Deployment with DockerDeploying the Moesif Developer Portal is now easier than ever. It is available as a Docker container. An example docker compose file is available to deploy.Security and Production ReadinessThe developer portal is production ready ensuring all APIs are protected with proper auth. You should deploy the developer portal behind a load balancer for SSL. The developer portal is a modern React and Node.js based application. As an open-source project, you have full control over the codeReady to Explore?These are just a few of the many improvements we’ve made to make API management smoother, more responsive, and adaptable to your unique needs. We’re also continuing to build out our current documentation and will be introducing some new guides very soon. You can explore our detailed documentation here. The new Moesif Developer Portal is ready for production use and is designed to help you deliver a premium developer experience that scales with your business.If you’re looking to offer a more delightful developer experience or want to monetize your APIs effectively, the new Moesif Developer Portal has everything you need—all with a few clicks. Checkout the Moesif Developer Portal on GitHub. Sign up for a 14-day free trial, no credit card required, and experience the difference Moesif can make for your business.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design and Build Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Announcing-General-Availability-of-the-Moesif-Developer-Portal/",
          "author": "Dylan",
          "categories": "api-monetization, api-strategy"
        }
      
    ,
  
    
        "monitoring-introducing-ai-explain": {
          "title": "Introducing AI Explain",
          "content"	 : "We are thrilled to introduce our newest feature to Moesif - AI Explain - a powerful conversational tool that lets you interact with your API analytics data like never before. Instead of spending time digging through numerous charts or building complex queries, you can simply ask questions and get meaningful answers in just a few seconds. AI Explain is designed to make analytics accessible to everyone, whether you’re new to working with data or simply want a more efficient way to gain insights. By using AI Explain, you can quickly understand your data, identify trends, and make informed decisions without the need for advanced technical skills.Currently, AI Explain is available when interacting with API Events. There are typically found within the Live Event Log but can also be utilized when viewing API events within the context of User or Company profiles. It works with up to 30 events at a time, providing a manageable dataset to start with, and we’re constantly expanding its capabilities to improve your experience even more. To access AI Explain, you’ll need a paid Moesif plan. If you’re interested in learning more about our plans, visit the Moesif Pricing page for more details.How AI Explain WorksAI Explain is powered by Azure OpenAI, one of the most advanced language models available today. With AI Explain, you can ask questions in plain, everyday language, and the tool will generate insightful responses based on your API event data. This makes interacting with your data more intuitive and natural, reducing the complexity often involved in understanding analytics.Data Used to Power Your AnswersTo provide you with the best possible answers, AI Explain uses several types of information from your events. When you use AI Explain, Moesif sends the following data to Azure OpenAI:  Event properties such as event IDs, timestamps, and event names  Request and response data, including body content, HTTP status codes, headers, and IP addresses of API callsAI Explain also keeps track of your conversational history during a session, allowing it to provide more context-aware responses. This means that as you ask follow-up questions, the system can provide richer and more connected insights, making your experience even more efficient and interactive.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        Getting Started with AI ExplainReady to get started? Here’s how you can make the most out of Moesif AI Explain feature. First, navigate to the Live Event Log:      Use the Last 30 Events: By selecting Ask AI, you can use the last 30 events recorded in your Live Event Log as context for your question. This option is great if you want a quick summary or an overview of the most recent activities in your API. It’s perfect for spotting trends, understanding user activity, or identifying issues in real-time.        Select Specific Events: If you want to focus on particular events, you can do that too. Just select the specific events from the Live Event Log that you’re interested in and then click on Ask AI. This way, AI Explain will include only those specific events to generate a response. This targeted approach is ideal when you have a specific issue or scenario that you want to investigate more deeply.  Once you’ve selected your events, simply type in your question. AI Explain will then analyze the selected data and provide insightful answers. This can be particularly useful when you’re trying to identify patterns, diagnose technical issues, or gain a better understanding of how users are interacting with your API. Here are some example questions you can try:  “Can you summarize these events?”  “What stands out here?”  “What should I investigate further?”No need to dive deep into complex metrics—just ask, and get answers quickly. AI Explain is built to remove the friction from data exploration, helping you make better decisions faster.Why You Should Use AI ExplainAI Explain isn’t just a tool; it’s your partner in making sense of data. Here’s why you should consider using AI Explain:      Simple and Accessible: With AI Explain, you don’t need to be an analytics expert to understand what’s happening with your API. The tool turns complex data into clear, easy-to-understand insights that anyone on your team can use, regardless of their technical background. Whether you’re a product manager, developer, or customer support representative, AI Explain makes understanding your data straightforward.        Time-Saving: One of the most challenging aspects of data analytics is the time it takes to set up dashboards, craft queries, and interpret results. AI Explain streamlines this entire process. Instead of creating complex queries or manually filtering through data, you can just type your question and receive direct, actionable insights. This efficiency helps you stay focused on your main tasks without getting lost in data analysis.        Real-Time Insights: With AI Explain, you can get immediate insights into your API data. Whether you want to understand what’s happening in real-time or look back at recent events to see what’s been going on, AI Explain empowers you with instant information. This means you can make timely decisions, address issues as they arise, and proactively improve your services.        Collaborative Benefits: AI Explain makes it easy to share insights across your team. By having a conversational interface, everyone from technical developers to non-technical stakeholders can interact with and understand the data. This facilitates better collaboration and ensures everyone is on the same page when making decisions.  Your Data and PrivacyWe know that your data is sensitive and privacy is a top priority. When using AI Explain, Moesif sends your event data and conversational history to Azure OpenAI for processing. However, it is important to note that Azure OpenAI does not store, train on, or share your prompts, responses, or Moesif data with others. Your data is used solely for generating the responses you need, and it remains secure and confidential at all times.Our commitment is to maintain transparency and integrity regarding how your data is handled. We take privacy seriously and ensure that your information is used responsibly, strictly for enhancing your experience with AI Explain.SummaryMoesif AI Explain is here to help you make smarter, data-driven decisions effortlessly. Whether you’re summarizing recent events, investigating specific scenarios, or looking for real-time insights, AI Explain is your go-to partner for interacting with your API analytics data. We’re working on adding even more features to AI Explain and expanding AI-powered functionalities across the platform, making your analytics journey even more powerful and seamless.Ready to transform the way you interact with your data? Sign up for Moesif today and start exploring how AI Explain can make analytics simple, efficient, and actionable for your team.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design and Build Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /monitoring/Introducing-AI-Explain/",
          "author": "Dylan",
          "categories": "monitoring"
        }
      
    ,
  
    
        "monitoring-monitoring-cost-and-consumption-of-ai-apis-and-apps": {
          "title": "Monitoring Cost and Consumption of AI APIs and Apps",
          "content"	 : "The rise of AI has transformed how businesses operate, creating a surge in demand for AI-driven APIs, particularly those that leverage Large Language Models (LLMs). These APIs are at the heart of many modern applications, driving automation, customer interaction, and sophisticated data analysis. However, with this increased use comes a need for organizations to effectively monitor and manage the costs and consumption of these APIs. Understanding how different customers and applications interact with your APIs is crucial to maintaining profitability and ensuring efficient resource use.In this blog post, we’ll explore how Moesif can help organizations achieve full observability into their AI APIs. We’ll discuss common challenges like cost tracking, consumption monitoring, and how Moesif’s capabilities can simplify cost attribution, helping you stay in control of your AI-related expenditures. We’ll also look into best practices for managing costs and ways to improve profitability using data-driven insights.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        The Challenge of Understanding Costs in AI APIsAI-powered APIs, especially those relying on LLMs such as OpenAI’s models, introduce unique challenges when it comes to understanding costs. Unlike traditional APIs, the cost of LLM-based APIs can vary significantly depending on the nature of each request. Factors like the complexity of prompts, the volume of data processed, and compute intensity can all impact costs. This variability makes it challenging for organizations to maintain predictability and control over their operational expenses.For AI businesses to maintain a sustainable financial model, it’s essential to calculate the Cost of Goods Sold (COGS) accurately. This requires comprehensive tracking of direct expenses, such as provider fees for AI models, as well as indirect costs, like infrastructure and server maintenance. Without accurate cost tracking, businesses risk running into budget overruns and profitability issues. Moesif offers a detailed view into these cost components by monitoring API interactions in real time, providing valuable insights that help ensure you are fully aware of what is driving your COGS.With Moesif’s capabilities, organizations can set up custom dashboards that provide detailed breakdowns of costs by different dimensions, such as request type, endpoint, or customer segment. This level of detail empowers finance and engineering teams to work together to optimize both cost efficiency and performance. By identifying where costs are highest and why, organizations can make informed decisions to improve their overall API strategy.Identifying High-Cost CustomersOne significant challenge that many companies face is identifying which customers are responsible for the bulk of their API costs. Different users interact with AI APIs in varying ways—some use straightforward, low-cost requests, while others may make highly complex or frequent requests that drive up costs considerably. This disparity in usage often means that a small percentage of customers contribute disproportionately to the overall API expenses.Moesif provides granular visibility into customer-specific API usage, enabling organizations to pinpoint which users are contributing most to operational expenses. With these insights, companies can make informed decisions about implementing tiered pricing models, optimizing customer usage, or even adjusting service levels to better align with their costs. By using Moesif, businesses can create user segmentation based on usage intensity and cost impact, allowing for more personalized communication and pricing adjustments.For example, a SaaS company offering an AI-based API could use Moesif to identify customers that frequently make high-cost API calls. By understanding these usage patterns, the company could introduce premium pricing plans tailored to customers who derive significant value from more intensive API use. Alternatively, they could work with these customers to optimize their API requests, potentially reducing their own costs while improving efficiency for the customer.Monitoring Consumption for LLM APIsLarge Language Models are powerful tools, but their cost structure can be challenging. The way customers use LLMs—such as making complex queries or frequent calls—can directly affect the overall expenses. In particular, queries that require significant computational power, such as those with extensive context or specialized responses, can increase the cost per request. Moesif enables real-time monitoring of LLM consumption, helping companies understand usage patterns that lead to higher costs.By analyzing customer interaction data, businesses can identify usage trends that may be leading to inefficiencies. For example, if a small subset of customers is responsible for an outsized portion of LLM costs, organizations can engage with those customers to optimize prompt usage or even shift them to a more cost-effective pricing tier. This level of insight allows companies to fine-tune their API strategy to manage costs without compromising customer satisfaction.Moesif also allows for proactive alerts and notifications. If a customer’s usage suddenly spikes, leading to higher-than-expected costs, teams can be alerted in real-time. This enables companies to take immediate action—such as reaching out to customers to understand the changes in their usage patterns, offering guidance on more efficient usage, or implementing rate limiting to prevent runaway costs.Breaking Down Costs by TenantFor companies that operate multi-tenant SaaS products, understanding the cost of supporting each tenant is essential. Moesif offers the ability to attribute costs accurately on a per-tenant basis, helping businesses understand how much each client is contributing to the overall expenditure. Tenant-level cost attribution provides crucial visibility that helps in financial planning and customer profitability analysis.This tenant-level visibility is especially valuable for SaaS providers who need to assess the financial impact of different tenants. By accurately attributing costs to each tenant, companies can make better decisions about pricing, resource allocation, and even customer support prioritization. For instance, if a particular tenant is driving significantly higher costs compared to others, the business can investigate why this is happening and whether it makes sense to adjust the pricing structure or impose usage limits.Additionally, having detailed insights into tenant-level costs allows businesses to better understand the value they provide to their customers. By correlating revenue generated from each tenant with their respective costs, companies can determine which customers are most profitable and which may need more attention to ensure they are a sustainable part of the business. This enables data-driven discussions with customers about the value they are receiving and potential ways to optimize their usage.How to Use Moesif to Monitor and Manage CostsTo achieve effective cost monitoring and control with Moesif, follow these steps:      Set Up Real-Time Monitoring: Moesif allows you to track every API request in real time. Begin by integrating Moesif into your API infrastructure. Once integrated, Moesif captures critical data such as request paths, response times, and payloads, which helps you understand your API’s overall usage.        Create Custom Dashboards: Use Moesif’s custom dashboards to visualize cost-related metrics. You can build dashboards that show detailed breakdowns by customer, endpoint, or type of request. This helps in identifying which parts of your API are contributing most to the costs and which users are driving up usage.        Use Cost Analysis Metrics: Moesif provides the ability to assign costs to different API transactions. Set up metrics to track usage by customer, including the number of calls, data transferred, and the complexity of prompts sent to LLMs. This data helps in identifying the top customers contributing to costs.        Set Alerts for Usage Spikes: Establish alert triggers to notify you when a customer’s usage exceeds predefined thresholds. This helps in mitigating unexpected cost spikes before they impact your budget. Alerts can be set for different dimensions, such as high-frequency API calls or unusually large payloads.        Segment Customers by Usage: Use Moesif’s segmentation tools to categorize customers based on their usage patterns. Identify heavy users who make complex or high-frequency requests and create segments for them. This will help tailor pricing models that better reflect the costs they incur.        Analyze Customer Behavior: Use the behavioral analysis tools in Moesif to understand how different customer segments interact with your API. Understanding the journey customers take and which endpoints they hit the most often allows you to optimize both user experience and cost efficiency.        Leverage Cost Attribution Features: For multi-tenant SaaS platforms, Moesif’s cost attribution features allow you to break down costs per tenant. This provides clarity on which tenants are using the most resources, enabling precise cost allocation and ensuring pricing structures are fair and reflective of usage.        Optimize Usage Patterns: Once you have visibility into the cost drivers, work with customers to optimize their usage. This could involve helping them craft more efficient prompts, advising them on reducing request frequency, or recommending features that provide the most value at a lower cost.  Driving Profitability with MoesifMoesif doesn’t just help you monitor costs—it also helps optimize profitability. By combining usage data with cost insights, companies can better understand the relationship between customer behaviors and profitability. Moesif enables organizations to identify opportunities for upselling high-value features to customers who are already consuming significant resources or even to introduce throttling mechanisms for customers whose usage exceeds acceptable cost limits.These actionable insights empower teams to proactively manage both customer experience and operational costs, ensuring that AI APIs remain both effective and profitable. Moesif’s real-time monitoring, alert capabilities, and advanced analytics equip companies to make data-driven decisions that enhance efficiency and boost the bottom line. For example, identifying the most resource-intensive endpoints allows engineering teams to optimize those endpoints, potentially reducing the cost per request and improving the overall performance of the API.Moesif’s analytics enable teams to understand long-term trends in API usage and costs, which is vital for strategic planning. By visualizing how costs evolve over time and how different customers contribute to those trends, companies can adjust their growth strategies and anticipate future needs. Whether it’s refining pricing models, reallocating infrastructure resources, or changing product offerings, Moesif’s data-driven approach ensures that decisions are backed by comprehensive insights.Best Practices for Cost ManagementTo effectively manage costs associated with AI APIs, companies should adopt several best practices:  Segment Customers by Usage: Use data to identify different segments of customers based on how they interact with your APIs. This can help tailor pricing and optimize resource use.  Optimize Prompts and Requests: Work with customers to streamline their prompts and requests to minimize computational overhead while maintaining effectiveness.  Set Up Alerts for Unusual Activity: Leverage Moesif’s alert system to quickly respond to unexpected usage spikes that could lead to significant cost increases.  Regularly Review Cost and Usage Data: Periodically assess your API usage and cost data to identify trends and make adjustments as needed. Moesif’s dashboards make this easy to visualize and analyze.  Implement Tiered Pricing Models: Consider implementing pricing models that align more closely with the value delivered to customers and the costs incurred by their usage.ConclusionAI has opened up remarkable opportunities for innovation, but it has also brought new challenges in managing the costs associated with API consumption. Moesif helps organizations overcome these challenges by offering comprehensive observability into the usage and cost of AI-driven APIs. From understanding LLM usage patterns to accurately attributing costs across tenants, Moesif provides the tools needed to turn complex cost structures into clear, actionable insights.By adopting best practices for cost management and leveraging Moesif’s advanced analytics and monitoring tools, businesses can ensure they stay ahead of the cost curve while continuing to deliver exceptional value through their AI APIs. If you’re ready to take control of your API costs and get a clearer picture of your AI-powered applications, start your 14-day free trial with Moesif today—no credit card required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design and Build Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /monitoring/Monitoring-Cost-and-Consumption-of-AI-APIs-and-Apps/",
          "author": "Dylan",
          "categories": "monitoring"
        }
      
    ,
  
    
        "technical-api-development-http-body-analytics-with-moesif": {
          "title": "Unlock Deep API Insights with Moesif's HTTP Body Analytics",
          "content"	 : "Understanding how your APIs handle and process data drives better decision-making. Different kinds of data flow through APIs and the services that consume them. But the actual information lives inside the HTTP request and response bodies. If you can effectively leverage that data with the contextual API metrics like status codes and routes, you can better understand how your customers consume your services. However, we see a scarcity of tools that can efficiently analyze the data in the contents of HTTP request and response bodies.Moesif’s HTTP body analytics offer a new level of detail for API monitoring and analytics. Instead of only analyzing headers or status codes, you can inspect the actual data your API exchanges. This includes diving into nested fields consisting of arrays and objects. With Moesif, you gain a comprehensive understanding of your API’s real-world usage.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        Table of Contents  Table of Contents  What is HTTP Body Analytics?  Key features of Moesif’s body analytics  Use cases and benefits  Conclusion  Next StepsWhat is HTTP Body Analytics?HTTP body analytics dive deep into the content of API requests and responses. Tools capable of body analytics can examine the data associated with the request or response. Therefore, you get meaningful and definitive insights about what data flows through your API and how.Many monitoring tools focus on contextual information and only capture surface level metrics such as HTTP headers, verbs, response codes, and different API routes. These metrics essentially provide you the basic information around your APIs but not the actual business data that flows through. These tools track errors, latency, and response codes but miss critical information contained in the body data. Analyzing the body reveals trends that can affect user experience, security, and data quality. For example, payload size or the structure of data fields can indicate performance issues or integration challenges.A tool with solid body analytics capabilities can handle both simple and complex data formats. They can analyze arrays, which provides visibility into how your API handles lists or batch operations. For objects, they can count keys to help detect unexpected changes in data structure. Especially, when you want to performance-tune your product and maintain data consistency, these metrics come in very handy.You can also perform advanced data inspections like searching for strings or patterns within body fields. This can help identify sensitive information and validate data. You can also check for critical fields in payloads so they conform to expected standards.Key features of Moesif’s body analyticsIf we take a simplified view of analyzing HTTP bodies, we can boil it down to these areas:  Using filters to define the criteria for the data you want to look at.  Grouping and segmenting data based on specific parameters.  Defining the specific metric you want to calculate.Moesif covers all of these areas with the level of granularity that allows you to analyze bodies from simple to complex data structures. Moesif can capture and perform analytics on deeply nested body fields consisting of arrays and objects. In your Moesif Portal, you can access these nested body fields in the same hierarchy as they appear in request or response context.For example, consider a request body similar to the following:{  "_id": "672c998e4305045c83fc4819",  "index": 0,  "guid": "b39b2a23-0487-4e12-ac82-e83ea3dc4ecd",  "isActive": false,  "balance": "$2,070.99",  "picture": "http://placehold.it/32x32",  "age": 38,  "eyeColor": "brown",  "name": "Emma Gentry",  "gender": "female",  "company": "PLASMOX",  "email": "emmagentry@plasmox.com",  "phone": "+1 (877) 581-3808",  "address": "210 Dekalb Avenue, Jacumba, Oklahoma, 8790",  "about": "Nulla laboris cillum elit dolore ex deserunt voluptate amet sint adipisicing ut voluptate. Duis consectetur nulla proident in dolore. Eiusmod duis mollit minim ut sint enim proident id laboris anim enim culpa est ut. Laboris cupidatat culpa est pariatur aliqua sit labore minim ea fugiat esse adipisicing. Labore et exercitation laboris do labore ut.rn",  "registered": "2014-08-28T09:08:00 -06:00",  "latitude": -54.574483,  "longitude": -141.422491,  "tags": [    "irure",    "consequat",    "dolor",    "id",    "sint"  ],  "friends": [    {      "id": 0,      "name": "Rosa Tanner",      "age": 40,      "hobbies": [        "aute",        "non",        "magna",        "Lorem",        "non",        "tempor",        "consectetur",        "dolor",        "occaecat",        "laboris"      ]    },    {      "id": 1,      "name": "Bettye Duncan",      "hobbies": [        "labore",        "veniam",        "voluptate",        "ex",        "adipisicing",        "ea",        "commodo",        "enim",        "magna",        "in"      ]    }  ],  "greeting": "Hello, Emma Gentry! You have 6 unread messages.",  "favoriteFruit": "strawberry"}The request body consists of an array of objects. It also contains nested arrays and objects.You can define filters, groupings, segmentations, and metrics based on these nested body fields in Moesif. It doesn’t matter how deeply nested structures make up the request or response body.For array fields, you can use the count operators to filter based on the number of elements in the array. In the sample JSON request body, we see we have a list of friends of Emma. We can define a filter that shows us API calls where the number of friends is greater than 15:For object fields, you can use the key count operators to filter based on the number of keys in the object.Body data extends to groups and metrics as well. For example, here we create a segmentation chart with the following criteria:  We want to include entries where friends have more than 15 entries.  We want to break down the chart by the person’s age.  We want to see the average number of hobbies in friends.With these criteria set, Moesif generates the following chart:In addition to specifying a custom metric, Moesif also includes these default body metric options:  Request body count  The number entries present in the request body  Response body count  The number of entries present in the response bodyYou can also perform search operations on string data types. For more information about different filter options and operators available in API analytics, see Filters Reference.Use cases and benefitsBeing able to analyze body fields like arrays can help you set up powerful charts with Moesif that monitor data size, track usage trends, and identify performance issues related to bulk operations. It can also help detect anomalies like unexpected spikes in batch sizes.On the other hand, tracking object keys can help identify changes in data structure, which can impact integrations or application behavior. When new keys appear or existing ones disappear, you can use Moesif’s analytics filters to flag these changes in real time. This can help you maintain data integrity and consistency.Using the search features, you can find keywords or sensitive data patterns that might otherwise go unnoticed. This makes it easier to verify data accuracy and security. Teams can spot issues quickly by setting up targeted searches, whether they need to find specific terms or verify that fields meet security requirements.Since Moesif makes the body analytics available to other parts of the platform, you can go further.For example, with Moesif’s advanced segmentation analysis, you can analyze body data across different user segments. You can combine body analytics with Moesif’s real-time monitoring and alerting features to stay updated on critical data changes. You set up customized alerts for significant shifts in body data, such as spikes in array elements or new object keys. You can configure alerts to monitor for specific conditions so you get immediate visibility when unexpected changes occur.These key features make Moesif a powerful solution for teams needing in-depth body analytics for proactive management and optimization of API performance. They provide you the flexibility to analyze, search, and monitor complex data with ease, irrespective of your payload complexity and size.ConclusionBody analytics have been an untapped area when it comes to API and user analytics. They can provide invaluable insights into your product through data trends, user behavior, and potential issues. With Moesif, you now have the means to go beyond traditional monitoring and make body analytics a part of your robust API management.Next Steps  Get started with Moesif  Integrate Moesif with your platform of choice  Learn about Moesif API analytics suite  Learn about dashboards and workspaces                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design and Build Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/HTTP-Body-Analytics-With-Moesif/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "monitoring-how-moesif-api-observability-compares-to-treblle": {
          "title": "How Moesif API Observability Compares to Treblle",
          "content"	 : "API observability plays a crucial role in making sure that your API delivers its fullest potential and consistency. Therefore, it’s important to choose the right tool for your observability requirements.In this article, we take a look at two API observability tools—Moesif and Treblle. We briefly discuss the key components of API observability. Then we dive into how Moesif and Treblle compare with each other. By the end of this article, you can make an informed decision about which tool to pick for your use case.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        About MoesifMoesif is a comprehensive API platform with powerful tools for user-centric API observability, providing in-depth insights into API usage and customer interactions. Some highlights of Moesif include the following:  Advanced user behavior analytics.  High-cardinality data analysis.  Real-time event monitoring and alert systems.  API monetization with quotas and governance enforcements.About TreblleTreblle provides an end-to-end APIOps platform for REST APIs supporting API observability with real-time analytics and metrics data. Other features include API governance and automated API documentation. Some highlights of Treblle’s features include the following:  Analyzing over 40 API-specific data points.  Error tracking and alert notifications.  User agent and location based usage and consumer analysis.What is API observabilityAPI observability provides deep and valuable insights into APIs. In complex and distributed systems, observability helps you understand APIs as they interact with different services. Observability empowers teams with the ability to proactively maintain and optimize their products based on real-time data.An API observability platform provides a centralized hub for analytics and visualization around the four key components of API observability:  Metrics  Events  Logs  TracesMetricsAn API metric provides quantitative data in specific time intervals in a period of time. For example, response times and errors and request counts. Metrics help you assess the health and performance of your API, leading to optimizations and performance gains. For example, analyzing throughput and latency can measure your API’s efficiency in processing requests.EventsIn the context of APIs, an event represents a meaningful business decision taken by part of the system to provide value. More often than not, it also accompanies a state change in the system. For example, API calls, users signing up, or subscribing, and so on. An API observability tool tracks these events in real time.LogsLogs keep thorough records of events within an API. For example, timestamps, durations, user agent data. Most API frameworks and observability tools support keeping API logs.Logs help debug and understand more about the data flow within an API. Logs also provide context around requests, responses, and errors, helping developers troubleshoot issues such as latency or error spikes.TracesTraces give detailed visibility into a request’s entire journey through the API and the services the API connects to. When debugging, developers can correlate traces with events and logs. Thus in a distributed system, traces help pinpoint the exact underlying component or stage causing issues.API Observability with Moesif and TreblleNow that we have discussed the basic concepts in question, let’s start comparing Moesif and Treblle in terms of their offerings for API observability:API Metrics and AnalyticsMoesifMoesif’s API analytics suite allows you to grow your business through a deep understanding of product usage and customer behavior in real time. Moesif’s multifaceted analysis types can serve different business requirements and scenarios:  Real-time event tracking.  Time-series and heatmap analysis.  Segmentation and aggregation by criteria such as demographics and HTTP data.  Funnel analysis like Time to First Hello World (TTFHW).  Lookups, retention, and customer composition analysis for users and companies.TreblleTreblle prioritizes pre-defined analysis widgets that you can compose your dashboard with. These widgets contain a variety of metrics like the following:  Monthly and weekly breakdowns of your APIs usage.  Top geographic locations of your users.  User device statistics.  Daily request frequency and request maps.API Logs and MonitoringMoesifMoesif boasts a robust set of monitoring features that monitor your API’s journey and the user experiences end-to-end. Moesif can automatically send important alerts, such as API performance issues, security threats, and more. You have complete control over notifications, including what to get notifications for and how.Some of these API monitoring features include the following:  Creating real-time alert rules on any analytics chart.  Setting up notifications through PagerDuty, Slack, email, or custom webhooks.  Multi-dimensional alerts for customer-based metric tracking, quiet periods, and more.  Advanced anomaly detection.Moesif collects logs for both API calls and custom actions. Custom actions represent custom events like users signing up or payment processing. You can leverage event streaming services like AWS Firehose to track and log custom actions.Moesif’s Live Event Log captures API request and response details including timestamps, IP addresses, user agent data, and so on. In Moesif, you can search and inspect full event logs from last year, not only previews of new eventsTreblleTreblle measures and collects data about your API’s health, performance, and security in the dashboard, but doesn’t support real-time notifications and alerts out-of-the-box. But you can use separate Treblle applications to get real-time alerts about your API.Treblle also doesn’t support making custom alert rules that you want to get notified about.Custom Dashboards and Embedded MetricsMoesifMoesif offers customizable and self-serving metric reports that you can organize into workspaces. You can also share metric reports and charts in a variety of ways with different audiences. Moesif also allows you to securely embed metrics into user-facing applications and internal workflows. These features let you collaborate across every layer of your business to make important decisions that directly bring value.TreblleTreblle offers pre-defined analysis widgets that you can customize your dashboard with. Custom widgets or metric reports aren’t supported.It supports custom integrations by creating your own SDK, but you can’t embed the charts Treblle generates into external mediums like apps and web pages.Treblle also allows you to share individual requests or a set of requests publicly. Sharing metrics and charts aren’t supported.API Protocol SupportMoesifMoesif supports the following API protocols:  REST  GraphQL  JSON-RPCTreblleTreblle only supports REST APIs.Compliance and SecurityMoesifMoesif maintains SOC 2 Type 2 compliance and possesses certification with EU-US Data Privacy Framework. For more information, see Security Program at Moesif.TreblleTreblle is SOC 2 Type 1 and ISO 27001 certified. For more information, see Security at Treblle.ConclusionChoosing the right API observability tool marks a critical decision for any organization that relies on APIs for its success. Both Moesif and Treblle offer valuable features, but Moesif’s comprehensive approach to API analytics, monitoring, and security gives it a distinct edge.With Moesif, you gain a deeper understanding of your API performance, user behavior, and potential bottlenecks. Its advanced features, such as high-cardinality analysis, real-time alerts, and robust security compliance, allow you to proactively address issues, optimize your APIs, and drive business growth.If you’re looking for a powerful, user-centric API observability platform that provides unparalleled insights and control, Moesif comes out as the clear winner.If you’re ready to experience the difference, sign up today for a free trial, no credit cards required, and discover how Moesif can revolutionize your API strategy.Next Steps  Moesif Case Studies  Moesif Documentation  Moesif Pricing                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design and Build Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /monitoring/How-Moesif-API-Observability-Compares-To-Treblle/",
          "author": "Sakib",
          "categories": "monitoring"
        }
      
    ,
  
    
        "podcasts-developers-podcast-from-vision-to-venture-gregory-koberger-readme": {
          "title": "From Vision to Venture Ep. 03: Gregory Koberger, Founder ReadMe.",
          "content"	 : "Our guest on this episode is Gregory Koberger, founder of ReadMe, an API documentation and developer tools platform. In today’s episode, Greg shares the highs and lows of building ReadMe, from designing developer-centric tools and launching new product lines to handling market shifts and avoiding layoffs during tough times. Greg dives into his approach to scaling sustainably, managing burnout, and the invaluable role of a strong team. Join us for an inspiring conversation on overcoming founder challenges with resilience and purpose.Matt Tanner, former Head of Developer Relations at Moesif, is your host today.Moesif · From Vision to Venture 03: Gregory Koberger, Founder ReadMe.Listen to the episode on SoundCloud, Apple Podcasts, YouTube Music, or wherever you listen to podcasts. You can also watch the video on our YouTube Channel.Table of Contents  00:00 Introduction  02:10 From Engineer to Entrepreneur  03:37 The Art of Developer-Centric Design  04:45 Building a Developer Hub: The ReadMe Approach  09:47 Surviving Market Challenges as a Founder  12:30 No Layoffs: A People-First Leadership Choice  14:49 The Importance of a Co-Founder  17:52 Recommendations for Aspiring Founders                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        IntroductionMatt Tanner - Hello everyone and welcome back to the Vision to Venture podcast. Today we have Greg Koberger joining us from ReadMe. Very excited to have you, Greg. Thanks for joining us.Gregory Koberger - Excited to be here.Matt - Awesome. So yeah, just to give you a bit of a rundown, the first part that we’re gonna tackle is a little bit about you, a little bit about ReadMe. Then we’ll move into some of those typical startup challenge questions and would love to hear some of your input. And then lastly, for aspiring founders or already founders, we’d love to hear some of your tips and tricks to make sure that things are running smoothly, whether that be for developing ideas, scaling out a product or even raising funds. All right, let’s get started then. So first thing we’re gonna tackle is who you are and a little bit about your background.From Engineer to EntrepreneurGregory - Perfect. So like you said, my name’s Greg Koberger. I’m the founder of ReadMe. My background is engineering. I’ve been writing code for decades now at this point, I guess, and had a few jobs at startups and bigger companies as an engineer. At some point, switched a little more to design and kind of really enjoyed both design and engineering. And that’s kind of what got me into dev tools. I like designing for developers a lot. That’s always one of the two things that really excites me. The other is just designing bigger experiences, which is why we do a conference for a lot of reasons and all that, but I like doing stuff like that. So the two things I’m designing for are experiences, but the other one is developers. And I think developers, I mean, you probably get the same thing. And most of them, I think developers are interesting because there’s this concept around usability that everyone gets for apps, but developers never really got the love that they kind of needed and deserved for years, and that’s changed the past like five, six, seven years at this point. But yeah, it’s a weird thing ‘cause like most apps, the goal is to make it as simple as possible, whereas for dev tools, it’s to make it as powerful as possible while also being simple. And that’s kind of a really interesting thing. So that’s me and my background.The Art of Developer-Centric DesignMatt - Awesome. Yeah, so at Readme, based on that, I mean, we can kind of probably see the prelude to why you’ve dug in at Readme. So maybe give a little bit of background on what Readme does and the main challenge or challenges that you’re tackling over there.Gregory - Yeah, definitely. So Readme does API documentation for developers. So we sell to companies. So if you have a company, we would be your developer.whatever.com. We do documentation. We do debugging tools for your users. They can look through their logs, API logs, stuff like that. They can play around with it. We generate code snippets and all that. So our goal is to kind of just be a developer hub for anyone who has an API or anything like that.Building a Developer Hub: The ReadMe ApproachMatt - Awesome, yeah. And I can imagine that that’s getting a lot of traction right now because as you mentioned, developer tools, developer websites, all these types of things are getting a lot more attention now, especially with the advancements that we’ve seen in a lot of different areas where kind of everyone has their hands on APIs these days. So yeah, so moving on past the company and a little bit more towards some of the challenges that you face as a founder, whether that’s at Readme or a previous company, let’s hear a bit about a challenge that you’ve encountered. And I don’t mean to sound too much like an interview, but a challenge that you encountered and kind of some ways that you were able to get around that problem. So how did you solve it?Gregory - Yeah, definitely. I thought a lot about what to say for this ‘cause I knew this question was coming. And the thing that kept coming back was just this past year. There’s a lot of things over the past seven or eight years I’ve been doing this that I could have talked about, but luckily it’s been pretty easy. It’s been incredibly hard, but there’s not been any gigantic things that have shown up and been truly horrible except for this past year. This past year was tough for everyone, not just at Readme. The marketing distance changed. We got really lucky that we didn’t raise money the past year or sorry, the past like the 2000, 2001 when things were like really, really great. So we didn’t have like a gigantic valuation to worry about or anything like that. But that being said, it was kind of like going, like last summer we’re coming off years of COVID and I think people were like tired and a little depressed. And then there was market issues and I don’t tend to spend much time thinking about the market in general because there’s really no point. But I don’t know what it was like for you guys at Moesif, but it was people were scared, like churn was going up ‘cause companies were showing up at business. And again, it wasn’t like crazy or anything, but it was like, oh, this could go really badly over the next year. And it was the first time, ‘cause I said before, there’s been problems, a lot of problems over the past like eight years or so, but the problems were very like fixable or whatever, whereas this felt like it could be like an existential crisis that we had very little control over. And like there’s a lot of like talk about like, well, other companies were definitely doing layoffs and I had a hard rule where I was like, we’re not even talking about this. And that kind of takes a big thing off the table when you say no layoffs. And the reason I didn’t wanna do layoffs was because I just want Riemann to be a place that people enjoy working at and I think layoffs are just really, really tough. They’re really good at solving the money issue. Like you do the math and you’re like, okay, well, if we lay off five people, then all of a sudden like this happens or whatever and it’s a good thing. But like, for me, that’s such like a callous way to look at things ‘cause what I care about is not money or building a billion dollar company or anything like that, but like building a good place to work and it just undermines it to do that. So I took layoffs off the table like easily and pretty quickly, but that still meant that, okay, we have to fix these problems. And it was hard. So what I did, and I’m gonna talk from my perspective, there’s a lot of people in my company and everyone did a lot. So this is just my version of the story, not the whole story. But for me, I was like, okay, like we have to do things differently. And I canceled all my meetings and like started writing code again. A bunch of people around me started like getting and building new features and stuff like that ‘cause we’re like, okay, we have to fix the churn issue. We’re not gonna get these companies to stay in business. That’s outside of the realm. So we just started launching new products. So we launched three new products that had price tags on them. Before that we had one line of products like that we charged for and now we have four. So the original one plus three new ones. And we just, it was kind of tough ‘cause it felt like after COVID, like things were finally kind of getting better and like people were like a little happier and stuff like that, then all of a sudden we get hit with this like, oh, like, it’s kind of like, now we gotta work, now we gotta build. And yeah, like we didn’t have to do layoffs. Like everyone on the team worked really hard and did great work. No one was pressured in a bad way. Like no one was working nights and weekends. Like luckily we were able to keep it where it was like, you know, it was still, my big belief is like, unless there’s the sites down or something like that, like no one should work nights or weekends. But people still like worked really hard and like, you know, things kind of like flipped internally and yeah, it wasn’t fun. It was probably the worst year, well, definitely the worst year of my life as a CEO. But we got through it without any big negatives. You know, it was, I think it could have gone either way, obviously, and I saw a lot of my friends’ companies kind of go different directions and all that. And it wasn’t fun, but I am proud of everything we built, everything we did, and that we, you know, never considered layoffs or anything like that. And we got to a cashflow positive, which again, we were pretty close, so it wasn’t a big deal. I’ve always been kind of thoughtful about that, but yeah, it’s nice. It was nice to kind of like control our own destiny and not have to do anything bad. We kind of like fought this downside with like, by doing good things, by shipping more and exciting stuff, as opposed to like, you know, desperately like finding ways to even out the balance sheet.Surviving Market Challenges as a FounderMatt - Right, so I mean, really, instead of trimming the fat in terms of getting rid of resources to free up cash, you trim the fat probably around the productivity type of stuff where you’re like, okay, let’s double down on making our product better to hopefully increase the cashflow versus increasing the cashflow kind of from a less optimal way of, okay, we don’t, we can’t bring in new cashflow, so let’s kind of drop our expenses down. Instead, you were like, hey, let’s double down, let’s make something that we can sell even more of so that we can bump it up that way. Is that kind of the approach that you were thinking of when you made that decision?Gregory - Yeah, I think it’s really hard to build the good things when you have like a excess of resources. By resources, I mean people, I mean like money. Like, even if we weren’t raising money, there’s always that thing in the back of your head which is like, oh yeah, I could walk out tomorrow to like, you know, Sand Hill Road, I guess that’s an outdated reference, but like to, you know, San Francisco and just like, you know, raise a big round probably at a, you know, gigantic valuation. And it’s, when you have that kind of like backstop that you know, like, okay, if things go badly, like we always have that and raising money, by the way, is incredibly hard even when the market’s good. But like just kind of knowing that like that was possible, it makes it really hard to think about things as in like, ‘cause you’re always like, you have like abundant resources, so you’re like, well, let’s put a little more time, a little more effort into this, like let’s, you know, do it right or do like, you know, you don’t have like pressure and then the past year we had a lot of pressure and you know, sometimes that goes really badly and sometimes, and for the record, as much as I’m happy now, I don’t think, you know, I don’t think it was a fun year, I don’t think, if I could go back in time, I would prefer, it didn’t happen, but you know, it’s all the year later from then, this is probably the best case scenario. But then like, it wasn’t like, it was a big toll though, like I was so depressed and miserable and overwhelmed after the year, so I, you know, I was so out of it. So I took a six week soft right at like, a few months, or about two months ago, because like about a year after this happened, because I was just a year of just like working nonstop and I was exhausted, so I was lucky that I could do that, ‘cause I have a great team that made it so it didn’t even seem like I was gone, but yeah, I definitely needed some time off after all that.No Layoffs: A People-First Leadership ChoiceMatt - That’s actually, that’s a good point. So I mean, one of the things that a lot of founders and even, you know, people who are early into a company, stepping away for a little bit in order to really amplify what your productivity is later is tough to do. So kind of what were some of those steps like, did you just decide like, hey, I’m gonna take a bit of time off, kind of cut everything out, do what you need it to do, or was it a little bit more methodical like, hey, three weeks from now, I’m gonna take off six weeks or whatever? Yeah, kind of, how did you get to that point? ‘Cause that’s a really interesting one, is I see a lot of founders who burn out or just are just generally unhappy because they don’t get to take that time off.Gregory - Yeah, and I think I’m incredibly lucky that I was able to. I think, you know, I’ve been doing this for eight years and this is my first actual break. I might take time off a lot, but like, it’s hard to, it’s, there’s, the stress for my job isn’t the day-to-day work, it’s the like, the worrying about things or thinking about things or like, you know, and that seeps into weekends, even if I, you know, even in hours that I’m not working, like I’m still, I think about it. So this was the first time I could like, actually like, just not think about anything. ‘Cause it was six weeks and I was like, I’m just completely gone for six weeks. I wasn’t actually, I got bored a little bit and came back and did stuff here and there. But it was, I got really lucky because, so the reason was I knew I was gonna do it for a few months before, just because I was just so burnt out. But we needed a few hires to kind of, you know, fill in some gaps. So we hired some people and they’ve been great and that’s why I was able to do it, luckily. So it wasn’t, we didn’t hire them so I could take break, but like, we hired them ‘cause we were gonna hire them anyway and like, once all the puzzle pieces were in place, then I could sneak away and no one would really notice. So it was super nice, but it’s not something you can do, especially early on. I remember early, early on at ReadMe, I got so burnt out. This was like, maybe like three or four years in, maybe less than that. And like, I just wanted to quit and I couldn’t figure out who to quit to ‘cause I didn’t have a boss or anything. So I was like, I don’t know how to quit. And so, obviously I didn’t and I’m glad I didn’t. But yeah, but I felt, so I couldn’t take a break then because we were small and like, if I left for a week, like, so it was, it’s tough. Like, it took eight years before I could take time off and like, I can’t do this every year. So it’ll probably be a few more years so I can do it again. So it’s hard.The Importance of a Co-FounderMatt - So with that lesson, you know, we’ll move to this last piece here, which is those kind of tips that we can give to other founders or aspiring founders. And one that I would love to hear on is, if you were to do it again, okay? So thinking about kind of being able to separate the work life and the personal life, which is really hard to do as a founder, but would you, instead of taking, let’s say eight years to get to the point where you’re able to do that, do you think that there’s a couple of things that you could put in place if you were to start another company in another universe, that you would put it in place kind of right away to make it so that being able to distance yourself from the business to kind of get your equilibrium back, would you, what would those be?Gregory - Yeah, I don’t think there is an answer. I know a lot of founders and I think there’s always a grass is always greener type thing where it’s like, I should have raised more money. I should have raised less money. I should have hired this role sooner. I should have hired this role later. Like I should have not done enterprise this role. I should have done enterprise. Like I think there’s so many different like things and I’m not saying that I did things perfectly at all, but I can’t really think of anything huge that I would be like, oh, this is what I do differently. There is one big one. I don’t want to undercut what I just said, but I think the big one that I didn’t have was a really good co-founder from the beginning and one who understood businesses really well. I understand product well. I understand like a lot of things. One thing that doesn’t make sense to me just intrinsically is how to run a company. How do I think about money? How do I think about cash flow, stuff like that? And I’m not saying I would have wanted it sooner. I guess I would because like especially early on, like none of it really mattered because you’re just trying to make something work. So the spreadsheets are less predictable and all that, but I have a CEO Pat now and he just understands how it all works and like I can learn stuff and I can understand it to a certain amount, but like it’s never come naturally to me. So I do wish I had a co-founder from the beginning. Just someone who’s like in it with you. There’s a lot of people who have worked at ReadMe for many, many, many years that are absolutely phenomenal and like I love, but it’s different when you like, ‘cause the buck still stops with me and it’s still like, it’s a little bit different not to have a co-founder no matter how loyal and great employees are. So I would go back and if I go back I would probably, I wouldn’t do it again without a like, not just a co-founder ‘cause you just, you don’t want just someone there. You want someone who like really fills in the gaps, but I didn’t know what my gaps were at the time. Because at the time I didn’t have gaps early in the company that are the same gaps I have now if that makes sense. Like early on it was like desperately trying to just get a product that someone kind of wanted. And now like the gaps are very different. So and you just need, yeah, so it’s tough, but I think that’s the one that I would do differently.Recommendations for Aspiring FoundersMatt - Okay, okay. Any other kind of tips maybe? I mean, when you were developing the idea for Readme, kind of how did that come about? Because I think a lot of folks, especially aspiring founders go, I really wanna found a company. I just don’t know what I wanna build.Gregory - Yeah.Matt - Do you have a little bit of maybe some guidance there on how they can do that?Gregory - Yeah, so when I started Readme, I knew I wanted to start a company, which is the worst reason to start a company. And I was like, I just thought that like, this idea was not the best idea. And frankly, it’s not the best idea. Like it’s, there’s much better ideas out there. And I like tried different things, but like I would procrastinate by working on Readme because it was just what excited me. And I couldn’t get it out of my head. And I just like, that’s all I wanted to work on. And I kept like being like, ah, it’s just not a good, I don’t know, like I don’t know if it’s a good business. Like there weren’t many dev tools that were big back then. It was just GitHub and that’s it. And there was a few others, but like it felt like a really tough path. And I think like just that that product, that founder product fit is really important where it’s like something that you like, ‘cause this becomes your absolute, like every minute of every day. And like if you end up hating it or not loving it, like it’s brutal, it’s brutal when you love it. And it’s real brutal when you don’t. And the second thing is like, I think you have to be, it’s hard to be honest with yourself because everyone’s gonna be nice to you and be like, oh yeah, it’s a great idea. And for me, I got a lot of those, oh, it’s a great idea. Not for Readme, but like I was trying to find like the very, like I have the same idea, but it was like, where do I draw the line? Like what do I do? What don’t I do? What’s the, like how do you like? I knew there’s something there, but I wasn’t quite there yet. And then like I kept like putting off like launching ‘cause I kept like working with people and it wasn’t quite there and I could just see that people weren’t that excited about it. And then like I finally hit something and people were like, like you just tell that it flipped. They’re like, oh, like can I sign up for it now? And I’m like, no, it’s not ready yet. They’re like, well, when’s it ready? And like it just changed so quickly the way people were responding to me. And I was like, okay, like I’m so glad that I like kind of like took a beat and like try to understand like what people wanted and waited because it’s, you can just tell when people like are just being nice or trying it out or whatever. Or maybe you can’t tell, I don’t know. But like you definitely tell when they’re like, they love it and like that’s kind of the important part you have to wait for.Matt - Awesome, that’s great. I think that’s a great answer to a question that so many people kind of ask themselves of what should I build? How should I build it? What do I look for in something that I’m passionate about? So thank you so much for answering that.Gregory - Definitely.Matt - With that being said, I’d love to wrap things up but I always ask one last question. I think this is kind of like across the board every podcast asks this. But if you had to recommend a book, a podcast maybe even some type of publication that founders should be reading or maybe you should take a look at, what would you recommend?Gregory - Can I do a weird one? Or I’m gonna do a weird one I guess.Matt - Absolutely, absolutely do a weird one.Gregory - Okay, I want to take it some weird direction. So there’s a show called Mythic Quest. I’m not sure if you’ve heard of it. It’s by some of the people who do Always Sunny. It’s on Apple TV. It’s a sitcom TV show, workplace sitcom. It’s okay, it’s not a great show. It’s fine, like it’s fine. But the fifth I think episode of the first season is like this like micro story. It’s called A Quiet Dark Place or Quiet Dark Death, something like that. And it’s this episode with like completely different characters, completely different actors, like completely different storyline. It’s just like a completely different vibe. And it’s, this is, I’m recommending this for founders. It’s so good. He’s, the characters in there are starting a game. And so not a company, but like they are starting a company, but it’s a game, not a startup. And it’s, to me it just like kind of like mapped a lot of my experiences and stuff. And I think it’s such a fascinating, like everyone I’ve shown it to like has like a different takeaway and like felt something different from it. Some people just liked it. But some were like, oh my God, like that’s, you know, what I’ve been struggling with and stuff like this. And it’s a really great episode. You don’t have to watch the whole show to understand it. You just need to watch that one episode. There’s a few little things that maybe won’t make sense, but like it’s a standalone thing completely. So that’s my weird answer.Matt - Awesome, no, thank you so much. That’s great. With that, I want to thank you again for joining. It’s been absolutely great chatting with you. And yeah, hopefully we’ll chat again sometime soon.Gregory - Perfect, always happy to do that. Thanks so much for having me on.Matt - Awesome, thanks so much, Greg.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /podcasts/developers/Podcast-From-Vision-To-Venture-Gregory-Koberger-ReadMe/",
          "author": "Dylan",
          "categories": "Podcasts, Developers"
        }
      
    ,
  
    
        "technical-api-development-metered-billing-software": {
          "title": "Find the Best Metered Billing Software for Your Business",
          "content"	 : "Did you know that 72% of SaaS companies plan to adopt usage-based pricing in the near future? It shows a growing demand for flexible, consumption-based billing models. If you want your business to stay ahead of the curve, you need the right tools to capitalize on this trend.Metered billing aligns revenue with customer consumption. It offers numerous benefits like improved customer satisfaction, increased trust, and reduced churn. Customers appreciate the fairness and transparency of paying only for what they use.This post explores metered billing software, a solution that helps implement usage-based pricing effectively. Discover how metered billing automates billing, boosts revenue visibility, and enhances customer satisfaction. We cover they key features to look for, evaluation criteria, and implementation steps to help you choose the best solution for your business.At the end of this blog post, you’ll have a clear idea about optimizing your billing process to drive growth and monetization in the evolving subscription economy.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Table of Contents  Table of Contents  What is Metered Billing Software?          Definition and purpose of metered billing software      Benefits of using metered billing software for businesses        Understanding Metered Pricing Structures          Tiered Pricing: A Flexible Billing Solution        Key Features of Metered Billing Software          Billing Cycle Management: Automating and Optimizing Your Billing Process      Usage Rating and Mediation: Ensuring Accurate Billing for Complex Models        Evaluating Your Business Needs          Identifying Core Requirements for Metered Billing        Researching and Comparing Metered Billing Software Options          Comparing Features and Pricing        Implementing and Integrating Metered Billing Software          Steps to a Smooth Implementation Process        Top Considerations for a Metered Billing Solution          Scalability and Flexibility      Security and Compliance      Customer Support and Training        ConclusionWhat is Metered Billing Software?Metered billing software lets businesses charge customers based on actual product or service usage. Instead of a fixed monthly rate, customers pay for what they consume. This system works well for businesses with complex pricing structures or fluctuating usage patterns.Definition and purpose of metered billing softwareMetered billing software automates the entire billing process, from tracking customer usage to creating invoices. It eliminates manual calculation errors and saves time for your team. For example, SaaS companies can track API calls, storage use, or support tickets in real time. Accurate billing then happens automatically so that customers only pay for what they use.Metered billing software aims to operationalize usage-based pricing models. Businesses can implement pricing strategies based on consumption, whether for cloud storage, software features, or digital services. It ensures consistent billing accuracy while supporting a scalable and flexible pricing system.Benefits of using metered billing software for businessesMetered billing software provides comprehensive insights into customer usage and revenue. Businesses can identify power users, track product demand, and adjust pricing strategies to increase profitability. For example, a cloud provider can notice higher data usage among enterprise customers and adjust pricing tiers to match demand.This software also allows businesses to create flexible pricing models, such as tiered pricing or freemium plans. You can offer volume discounts or feature-based pricing to meet various customer needs. For example, a company offering communication APIs might charge more for high-volume customers while providing a free tier for low-usage clients.Aligning billing with customer consumption boosts satisfaction and reduces churn. Customers appreciate paying only for the services they use. For example, an AI service offering machine learning model training can charge based on the number of training hours or the volume of data processed. It reduces the chances of customers feeling they are overpaying for unused features.Ultimately, this approach fosters loyalty. Your relatively new customers soon become your long-time customers and therefore stable revenue sources for your business.Understanding Metered Pricing StructuresMetered pricing structures provide the framework for charging customers based on their usage. These structures offer a high degree of flexibility and granularity. They allow you to tailor pricing to different customer segments and usage patterns. In this section, we look at tiered pricing, a popular metered pricing structure with a compelling combination of flexibility and predictability.Tiered Pricing: A Flexible Billing SolutionTiered pricing charges customers based on their usage within defined tiers. Each tier represents a range of consumption with a corresponding price. For example, a cloud service provider may charge one rate for users consuming up to 1 TB of storage, and a higher rate for customers exceeding that limit. This model adapts to various customer needs and thus offers a clear and scalable pricing structure.With tiered pricing, businesses also has the power to influence customer behaviors. A cloud provider might offer discounts in higher tiers to encourage more storage consumption. Alternatively, businesses can promote responsible consumption by applying surcharges when customers exceed certain thresholds. These strategies help businesses optimize revenue and control resource usage.Both customers and businesses can benefit from the predictability tiered pricing brings to the table. By clearly defining the usage limits and prices for each tier, businesses establish a transparent view of their services. Customers know in advance how much they bill they need to pay, helping them budget more effectively. A clear structure like this reduces billing disputes and improves customer satisfaction.In AI services, you can see an example of tiered pricing in platforms offering natural language processing APIs. A business might charge a set rate for customers using up to 10,000 API calls per month, with increasing rates for higher usage tiers. This allows businesses with heavier usage to scale effectively while keeping entry-level costs low for smaller customers.Key Features of Metered Billing SoftwareTo select the right metered billing solution, you must understand the key features they come with and how they align with your business needs. In this section, we discuss two critical features: billing cycle management and usage rating and mediation.Billing Cycle Management: Automating and Optimizing Your Billing ProcessBilling cycle management automates the billing process with accuracy and timeliness. Metered billing software allows businesses to define billing cycles, such as monthly, quarterly, or annually, tailored to their customers’ preferences and business model. This flexibility simplifies the management of recurring payments by a large margin.For example, a SaaS company can set up automatic invoice generation. The software calculates charges based on customer usage, generates invoices, and delivers them at scheduled intervals. This automation reduces manual work, prevents errors, and ensures timely payments.Metered billing software also offers real-time visibility into billing operations. Businesses can track upcoming billing cycles, monitor pending invoices, and forecast revenue. You get the valuable insights that can improve financial planning and help identify issues before they affect revenue collection.Usage Rating and Mediation: Ensuring Accurate Billing for Complex ModelsUsage rating and mediation guarantee accurate billing in metered models. The software tracks customer usage, applies pricing rules, and calculates charges based on consumption. It supports advanced pricing models, including tiered pricing and volume discounts, so that businesses can implement flexible billing strategies.Consider a company providing AI-driven natural language processing (NLP) APIs for enterprises. These APIs allow users to process text for tasks like sentiment analysis, entity extraction, and document summarization. The company charges based on a combination of factors: the volume of text processed, the complexity of the task (for example, translation versus basic text analysis), and the model’s processing time. For example, sentiment analysis of a large document corpus with advanced models costs more due to higher computational demands.The system tracks API calls, measures processing times, and applies pricing rules based on task complexity. It might charge a basic rate for simple tasks like keyword extraction but a premium rate for advanced NLP tasks, such as multi-lingual document translation.The mediation engine resolves discrepancies in usage data before applying pricing rules. In the preceding example, it verifies that the data coming from various API endpoints remains consistent. The engine also resolves any discrepancies it finds. This process guarantees accurate billing for each customer based on their specific usage, irrespective of the complexity of the billing scenario.Evaluating Your Business NeedsBefore selecting metered billing software, assess your business needs thoroughly. A clear understanding of your requirements helps make sure the solution aligns with your billing processes, pricing models, and growth strategy.Identifying Core Requirements for Metered BillingStart by evaluating your current billing processes. Do you use manual systems, spreadsheets, or outdated tools? Identify the challenges there, such as error-prone calculations, delayed invoices, or scalability issues. Knowing where your current system falls short helps you prioritize essential features in a new solution.Consider your future billing needs. Do you expect to add new products or services with different pricing models? Will your customer base grow significantly? A scalable billing system should handle future expansion, support various pricing structures, and adapt to evolving business requirements. For example, a company introducing AI services might need to handle billing for both volume-based API calls and flat-rate subscriptions.Examine the pricing models you need. Do you plan to implement tiered pricing, volume discounts, or pay-as-you-go options? Ensure the software supports these models and allows flexible modifications. For example, a cloud-based SaaS company offering project management tools might implement tiered pricing based on the number of users or storage space. Smaller teams pay a lower rate, while larger enterprises receive volume discounts or premium features at higher tiers.Billing cycles and invoicing frequencies matter too. Do you need to bill monthly, quarterly, or annually? Can you offer customized cycles for enterprise clients or high-volume users? Ensure the software allows you to configure different cycles for specific customer segments and automates invoicing to minimize manual intervention.Then consider customization and integration. Do you need control over pricing rules, invoice formats, and integration with your CRM or accounting systems? The billing solution should offer flexibility and seamless integration to fit within your existing workflows. For example, consider a subscription-based e-commerce platform. For consistent invoicing and revenue tracking, the platform might need to integrate its billing system with a payment gateway and ERP (enterprise resource planning) system .Researching and Comparing Metered Billing Software OptionsOnce you have a clear understanding of your business needs, research and compare different metered billing software options. The market has a variety of solutions for you.  Each has its own strengths, weaknesses, and pricing structures. This section guides you through the comparison process so you can make an informed decision.Comparing Features and PricingStart by creating a shortlist of potential vendors based on your initial research and recommendations. Explore their websites, read customer reviews, and schedule demos to get a firsthand look at their software. Pay close attention to the features they offer, such as automated invoicing, usage tracking, and customizable pricing models. How do these features align with your specific requirements? For example, if your business charges based on API usage, prioritize software that offers exhaustive API metering and reporting.Compare the pricing models of different vendors. Some vendors offer subscription-based pricing with tiered plans based on usage volume or features. Others might charge a percentage of revenue or a combination of fixed and usage-based fees. For instance, a software company that scales rapidly might prefer a fixed monthly fee. On the other hand, a startup might benefit from a usage-based pricing model that adjusts with growth. Choose a pricing structure that fits your budget while considering how costs may evolve as your business scales.Evaluate the integrations each vendor offers. The software must seamlessly integrate with your existing business systems, such as CRM, ERP, and payment gateways, for a smooth billing process. For example, a SaaS business using Salesforce for customer management should choose a billing platform that integrates directly with Salesforce to prevent manual data entry and maintain accurate records. It also helps avoid data silos.Assess vendor support and training. Look for vendors that offer comprehensive support. It must cover areas like detailed documentation, training resources, and customer service. A responsive support team or a dedicated account manager can help resolve technical issues faster. Consider vendors that provide hands-on onboarding, especially if you expect a complex implementation process.Finally, evaluate scalability and flexibility. The software must handle future growth, support new pricing models, and manage increased transaction volumes. For example, if you plan to introduce new services or expand internationally, select a platform that supports multi-currency billing, regional tax compliance, and flexible invoicing options. A scalable platform guarantees that your billing system grows with your business.Implementing and Integrating Metered Billing SoftwareSo you’ve selected the perfect metered billing software. You’ve achieved a significant milestone towards optimized billing. However, to unlock the full benefits of the solution, you must successfully implement and integrate it. In this section, we outline a structured approach to a smooth and efficient rollout.Steps to a Smooth Implementation ProcessBegin by developing a comprehensive implementation plan. Outline your objectives, timelines, and key performance indicators (KPIs). Identify the stakeholders responsible for each part of the process for clear roles and accountability. A strong plan minimizes disruptions and keeps everyone coordinated.Configure the software to fit your business requirements. Set up pricing models, billing cycles, and customized invoice formats. Integrate the software with existing systems, such as your CRM, payment gateways, and accounting platforms. For example, if you use QuickBooks for accounting, make sure data flows smoothly between the billing system and your financial records. Sync data to and from business operations components so that you establish a cohesive flow of information among them.Migrate your existing data carefully. Transfer customer data, billing history, and usage records into the new system. If you prioritize accurate and complete data migration, you achieve continuity and avoid billing errors from the first invoice onward. For example, moving incorrect usage data can lead to overbilling or underbilling customers, causing trust issues.Test the integrated system thoroughly before launch. Validate data accuracy, confirm correct billing calculations, and verify proper invoice generation. Run multiple usage scenarios and edge cases—for example, handling customers with unique pricing models, to detect and fix issues early.Then deploy the software and gradually roll it out to your customer base . Consider starting with a pilot group of customers to gather feedback and fine-tune the system before a full launch. Monitor the system closely during this initial rollout phase to address any unexpected issues proactively. If you take a phased approach like this, you reduce risks and give your team time to adjust.Finally, train your team on the new billing system. Make sure they understand how to use the key features, generate reports, and troubleshoot common issues. Well-trained staff can manage the system efficiently and handle customer questions with confidence. As a result, both internal operations and customer satisfaction improve.Top Considerations for a Metered Billing SolutionWhen evaluating metered billing solutions, consider these critical factors to so the platform aligns with your business goals and scales with your growth.Scalability and FlexibilitySelect a solution that scales with your business. Your customer base will expand and product offerings will evolve over time. Consequently, the billing system must handle larger data volumes, complex pricing models, and new usage metrics without slowing down. A cloud-based solution often provides better scalability than on-premise software.Thoroughly consider the flexibility the solution offers. Can it support different pricing models like tiered pricing, volume discounts, and usage-based billing? Does it allow you to easily adjust billing cycles, invoice formats, and reporting dashboards? A flexible system adapts as your business grows, giving you room to experiment with different pricing strategies.Security and ComplianceSecurity and compliance should top your list of considerations. Your billing system manages sensitive customer and financial data. Therefore, you must enforce and maintain strong security measures. Make sure the platform complies with regulations like PCI DSS for payment processing and GDPR for data privacy.Look for features like data encryption, access controls, and regular security audits. These protections safeguard your data from unauthorized access and secure the integrity of billing operations. Always verify the vendor’s security practices and their track record for maintaining compliance.Customer Support and TrainingConsider the level of customer support the vendor offers. Responsive, knowledgeable support minimizes downtime when issues arise. Check for multiple support channels, such as phone, email, and live chat, and assess their response times.Look for vendors that offer comprehensive documentation, training resources, and onboarding assistance. These resources help your team maximize the software’s functionality and solve problems independently. Ongoing training and updates on new features guarantee that your team stays ahead of the curve as the system evolves.ConclusionMetered billing software is a powerful technology for businesses that want to charge customers based on usage.In the current market and monetization landscape, the transparency and flexibility of usage-based pricing models have become very appealing. Customers demand personalized experiences and cost-effective solutions. This particular billing approach therefore has become a way for modern Saas businesses to attract and retain new customers.Moesif simplifies API and AI product monetization with its robust metered billing capabilities. You can easily track usage, define pricing details, and automate billing processes. Moesif integrates seamlessly with various billing providers, including Stripe, to implement usage-based pricing for your APIs and digital services. Couple that with Moesif’s powerful set of analytics and monitoring tools , and you have yourself a platform that compliments you throughout your product  journey.To try out Moesif, sign up today for a free trial, no credit card required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            You’ve built the API. Now let's generate revenue!            Monetize your APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Metered-Billing-Software/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-fall-and-rise-of-embedded-plugins-part-4": {
          "title": "The Fall and Rise of Embedded Plugins: The Trend Away from IFrames",
          "content"	 : "IFrames were used, and are still used, in many embed frameworks. After much experimentation, including many unsuccessful platforms, iframes have proven themselves to be a simple and powerful way to let partners customize your experience.However, their limitations are also known:  Performance risks:  Platforms that allowed more than 1 iframed integration on the same page encountered poor load time and high memory usage. This came up on past OpenSocial-based platforms.  Security risks: You’re at the mercy of what your partner is running on your page.  Interface limitations: These integrations only work in rectangular interfaces with fixed dimensions  Compatibility: With the rise in mobile interfaces, iframed integrations to web interfaces don’t provide the greatest experience.Consequently, platform teams have tested and continue to test alternatives. Certain platforms now exclude iframes entirely, while others combine iframes with non-iframe alternatives. Here are a few common, and trending approaches beyond iframes.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Cards: For When You Really Don’t Want IFramesCards are the most trending replacement for iframes when one simply wants an interface. The idea is to provide a limited set of UI objects, defined by the platform host.  This ensures control and safety for the platform host:  No rogue Javascript: Partners are limited in what they can do in this experience. They can’t write code that slows the site or imposes security risks.  Consistent UI: The objects defined will be in a UI consistent to the platform’s core brand ensuring that integrations are consistent  Device compatibility: Card integrations are seen frequently in platforms that work on the web and on mobile.Cards are especially appealing to Slack, GMail, and Intercom.  When talking with team members on several such platforms, the reasons were consistent: security and performance.  Sometimes teams were prevented from allowing iframes by security teams.  Others decided that the mobile experience was vital, and that cards would be a better method to scale such platforms.  Although different teams had different priorities evaluated in their decision, security and performance were consistently the top two reasons to go this route. [TODO: I’m not citing specific interviews here. I hope that’s alright. These came up in conversation but not formally arranged interviews for Nordic APIs]  Slack’s card framework, allowing developers to add buttons and other basic inputs.Cards thus far are frequently deemed too limiting by those building apps on such platforms.  Many companies I’ve mentored and advised intended to build integrations on card-based platforms, initially bothered by the limitations but excited at the prospect of integrating into popular mobile apps that enabled such frameworks.  Ultimately, many developers gave up.However, that doesn’t mean cards should be disregarded as an option. Card platforms are still being tested and updated, mostly with more features and flexibility added to their platforms (platform hosts simply observe what partners are trying to do and add more cards or card options to make those possible). I’ve also seen APIs added to let cards be added and edited programmatically via API.  Code sample to programmatically create cards for Gmail add-onsBasically, apps can programmatically generate an interface, as opposed to static interfaces defined in a manifest. Developers just can’t place their own client-side scripts in the interface.Furthermore, when combined with other methods, cards may fit well as one component to an embed platform.Controlled HTMLAs a more liberal framework, one that gives more incentive than restriction, platforms may provide a front-end framework for writing applications.  Essentially, they provided a [modified] version of HTML/CSS/Javascript, allowing developers to freely code on the web, but benefit from additional tools of the platform.Such tools can allow a developer to more easily interact with APIs for deeper integrations, and to with a UI consistent to the rest of the platform.  Developers usually aren’t required to use such tools, but by using them, they can build better integrations faster.Salesforce pursued this path with Salesforce Lightning. Salesforce essentially provides XML markup that works within HTML and Javascript.This is one of several features Salesforce provides to developers to build apps consistent to the Salesforce experience. Visualforce is worth reviewing as well (markup where one builds entirely within Visualforce, as opposed to using Lightning withing HTML/Javascript).  There are entire books on that.Shopify’s Polaris, mentioned previously, also applies this strategy, working with the more modern React framework.  It’s a set of React components, letting developers apply Shopify styling easily into their web apps.Such platforms aren’t too common at the moment, however. They are difficult to implement and maintain, and are more frequently found on established platforms with a sizeable investment.  Furthermore, partners who want to mostly re-use what they’ve built for their core product may be less inclined to still use these tools for deep integrations.This goes back to understanding your developers.  Such a feature likely doesn’t make sense for your first version, as it’s best to start lighter, then add such functionality after observing integration efforts to identify a fit for your developers.  Do you have a community of small developers that builds customer apps for your platform? Or larger partners that want to highlight their own brand?  Depending on the use case, this may or may not be the right path for your platform.New WindowDepending on where these integrations are embedded in your application, cards and iframes may not be relevant. In fact, sometimes it makes sense to just launch a new window.Box and Google Drive both pursue this path in their file actions.  A user can right-click on a file stored in these services, and launch another web app to work with that file. There’s no need for this to appear in the Box or Google Drive interfaces.On these platforms, APIs/Oauth still apply, and iframes may still be used for technical reasons, such as tracking sessions.  But at its core, the experience is just directing the user to another site - trivial on a technical level, but the user experience just makes sense.Mix and MatchEach embed framework has its pros and cons.  Sometimes, the limitation pertains to particular devices on which your service resides, such as desktop vs mobile.  In such circumstances, there’s no need to limit yourself - instead, combine options.Although currently perceived as limiting for web platforms, cards are advantageous for platforms where the mobile experience is a key component.  When encountering the dilemma of UX vs freedom for development for iframe vs cards, there’s a potential and straightforward solution: provide both. Allow developers to specify an iframe integration for the web experience, and build a card-based integration for the mobile experience.  This can literally fit into the same application.I hope to see combinations like this more often. Currently Intercom encourages developers to primarily use their cards framework, but includes an iframe option when necessary.Worth a Review: AMPRather than create one’s own markup language, or accommodate full HTML/Javascript, platforms may try to adopt AMP.  We’ll dive in deeper on AMP in a later post, but AMP is basically a limited HTML, CSS and Javascript, to build dynamic pages that have less security and performance risks than free for all HTML+Javascript.AMP adoption was recently announced by Gmail as acceptable email content, although it isn’t part of Gmail or G Suite’s add-on platform.Keep a lookout for AMP. Although not currently a common means of integration on platforms today, it’s being considered by many platform providers.Trending and Winning: Remote RenderingFor many years, there was seemingly a tradeoff in having a flexible integration experience, vs having security, performance, and reliability.  With iFrames, partners could build what they needed to without limit. Compelling use cases were always possible, but also much risk.  With cards, we could have security and performance resolved (along with other benefits - UI consistency, better mobile friendliness, etc), but consistently third party developers found themselves limited in what they could do, and many valuable integrations wouldn’t be feasible.In recent years, as these embed frameworks gained traction again, more efforts came to a robust solution - remote rendering.  Essentially, it works as follows:  Apps can be built in a flexible environment, sandboxed and kept from the actual customer interface. Developers have flexibility to build what they need here, but this code isn’t directly embedded in a client-side interface  The host platform renders the app to embed it in a customer-facing interface.This provides flexibility to developers to create their application with HTML, CSS, and Javacript (although within formats required by the platform provider), while ensuring the code displayed to be secure, efficient, and reliable.This book is intended as a shorter read, but I highly recommend diving further into Shopify’s story, applying remote rendering to their platform with their Remote-UI framework.This rendering process may also theme apps better to look like the rest of the host’s interface. For instance, with Shopify, developers can build out their functionality and UI components, but choose to have Shopify theme their apps to the Shopify experience, and even have Shopify update that theme.  Again, more detail can be better found by studying Shopify’s framework.Looking ForwardWith the resurgence of embed frameworks, there is still plenty of experimentation happening.  Every company I interviewed asked me to follow up with them over the coming months with more updates.  Today, iframes and cards are still prevalent, with known pros and cons. Rendering has done very well, but solutions continue to be iterated upon. So, keep experimenting, and keep following what others try and share.In time, I anticipate embed frameworks transitioning to more consistent patterns, and ideally standards. We’ll get to that in a later article in the series.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue from your AI APIs?            Monetize your AI APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Fall-and-Rise-of-Embedded-Plugins-Part-4/",
          "author": "Jeremy",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-moesif-middy-serverless": {
          "title": "Using Moesif with Middy and Serverless for AWS Apps",
          "content"	 : "See the GitHub repositoryfor the source code of this article’s example project.Serverless is a popular framework to buildserverless appsusing AWS Lambda on the Node.js runtime. Serverless automatically orchestrates necessary resources on AWSand can scaffold a basic project for you that you can build up on. You can solelyfocus on your application’s core logic, development, and your Lambda functions.But your Lambda functions can get complex and hard to work on as your project grows.You may need to separate concerns in an idiomatic and consistent way. Otherwise,your Lambda functions can lose their clarity and conciseness. It becomes harderto maintain the different pieces of logic. That’s whereMiddy comes in with its middleware engine.And lastly, you want a robust and industry-tested tool to analyze and monitor your AWSapps. Moesif gives you a comprehensive Cloud platform for notonly API observability and monitoring, but also monetization.In this blog post, you’ll build a simple Node.js app using Moesif, Middy, and Serverless. Injust a few lines of code, you’ll have the basics in your belt for buildingserverless apps that Moesif can analyze, monitor, and lets you monetize on.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Table of Contents  Objectives  Prerequisites  Sign up for Moesif  Get your Moesif Application ID  Create an AWS Account  Install Serverless  Bootstrap the Project using Serverless          Connect a Provider      Create an App and Finish the Bootstrapping Process        Build the Application          Install the Dependencies                  Moesif AWS Lambda middleware for Node.js          Middy and Middy Middlewares          Dotenv                    Write The Lambda Function      Configure the serverless.yml File      Specify Environment Variables      Deploy        Wrapping Up  Next StepsObjectivesAt the end of this tutorial blog post, you’ll accomplish the following objectives:  Use Moesif AWS Lambda Middleware for Node.js to capture API calls to AWS Lambdaapplications.  Use Middy to compose and stich together multiple middlewares.  Use Serverless to set up, configure, and deploy your application to AWS.PrerequisitesBefore you proceed, make sure have Node.js 20 or greater installed on your system.Sign up for MoesifYou must have an active Moesif account to connect your Lambda application with Moesif. So if you haven’t already, sign up for Moesif. Otherwise, log into your Moesif Portal.Get your Moesif Application IDYou can get your Application ID during the onboarding process when you sign up for Moesif. Otherwise, you can get your Application IDany time by following these steps after you log into Moesif Portal:  Select the account icon to bring up the settings menu.  Select Installation or API Keys.  Copy your Moesif Application ID from the Collector Application ID field.Create an AWS AccountServerless framework deploys your application to AWS. So you need an AWS account as well. Later, you will set up Serverless with your AWS credentials so Serverless knows where to deploy and orchestrate the necessary resources. So go to the AWS sign up page or log into your account.Install ServerlessFollow these instructions to install Serverless on your machine.This makes a global serverless command available that you can use to deploy and manage your application in AWS, right from your command line! Later, you’ll use this command to initialize, set up, and deploy your Lambda function to AWS.Bootstrap the Project using ServerlessNow let’s start bootstrapping the project with Serverless.Execute the serverless command in a directory of your choice:serverlessThis command walks you through some steps to complete the bootstrapping process:  Select AWS / Node.js / HTTP API as the Template. This Template defines theService Serverless creates.  Then specify a name for the Service. Serverless creates a folder with the name you specify here andputs all your project files inside that folder.  Serverless now prompts you to log into Serverless or register. Go to Serverless Dashboardto log in or register.Connect a ProviderServerless needs to connect to aserverless infrastructure providerwith appropriate credentials to deployyour project. We recommend performing this step from the Serverless Dashboard for your organization. So followthe instructions in What is Serverless Dashboardto connect AWS with Serverless and proceed to the next step.Create an App and Finish the Bootstrapping ProcessAfter you’ve performed the preceeding steps, serverless command now asks you tochoose from an existing App to associate with the Service you created earlier.For this example, create a new  App and then specify a name for the App.The bootstrapping process finishes. You can see a new directory containingyour Lambda handler function and the Serverless configurationfile serverless.yml. The directory structure looks similar to the following:moesif-middy-serverless-demo/├── handler.js├── README.md└── serverless.ymlHere, we’ve named the Service moesif-middy-serverless-demo. The handler.jsand serverless.yml files contain the Lambda function and Serverless configurationrespectively. We’ll modify both of them in the next sections to fit our needs.Build the ApplicationWe have scaffolded a Serverless Service for a Node.js application that usesAmazon API Gateway HTTP API. We also have a Provider ready where Serverlesscan deploy our app to and manage it. To make the applicationready to deploy, the rest of the steps to make consist of the following:  Installing the dependencies.  Writing the Lambda function.  Configuring the deployment.Install the DependenciesLet’s briefly go through the main dependencies we’re going to use in this example. Beforeproceeding, let’s initialize the service directory with an NPM project:npm initIf you want to install all the dependencies at once, see the example’spackage.json file on GitHub.Moesif AWS Lambda middleware for Node.jsThis middleware will capture API calls to your API and send them to Moesif for analytics and monitoring.You can install it with the following command:npm install --save moesif-aws-lambdaMiddy and Middy MiddlewaresInstall Middy with the following command:npm install --save @middy/coreThen install the following middlewares:npm install --save @middy/http-error-handler @middy/http-json-body-parser @middy/http-security-headers @middy/validatorMiddy defines a specific set of rules to write AWS middlewares. Then it becomes very easy to plug in those middlewares to your application using Middy.You’re using Moesif for API analytics and monitoring. When you use several middlewares to achieve or perform specific tasks, you may want Moesif to capture those activities as well. For that reason, when we write the Lambda function, we plug in Moesif’s middleware in a way that allows Moesif to get full visibility into your API. It doesn’t matter how many other middlewares you use.DotenvLastly, install the dotenv NPM package to manage environment variables:npm install --save dotenvWrite The Lambda FunctionFor this example, we use this simple Lambda handler function:import moesif from "moesif-aws-lambda";import middy from "@middy/core";import httpSecurityHeaders from "@middy/http-security-headers";import validator from "@middy/validator";import { transpileSchema } from "@middy/validator/transpile";import jsonBodyParser from "@middy/http-json-body-parser";import "dotenv/config";import httpErrorHandler from "@middy/http-error-handler";const moesifOptions = {  application_id: process.env.MOESIF_APPLICATION_ID,  logBody: true,};const greetingsHandler = async (event, context) => {  const { greetingsMessage, greeterName } = event.body;  const response = {    statusCode: 200,    body: JSON.stringify({      message: "Greetings details received successfully!",      greetingsDetails: {        greetingsMessage: greetingsMessage,        greeter: greeterName,      },    }),    headers: {      "Content-Type": "application/json",    },  };  return response;};const schema = {  type: "object",  properties: {    body: {      type: "object",      properties: {        greetingsMessage: {          type: "string",        },        greeterName: {          type: "string",        },      },      required: ["greetingsMessage", "greeterName"],    },  },};// Wrap the final handler from Middy with `moesif`.export const lambdaHandler = moesif(  moesifOptions,  middy()    .use(httpSecurityHeaders())    .use(jsonBodyParser())    .use(validator({ eventSchema: transpileSchema(schema) }))    .use(httpErrorHandler())    .handler(greetingsHandler));Put this Lambda function code in the handler.js file inside your Service directory.Notice the following details about this Lambda function:  We set some configuration optionsfor the middleware. We also use the dotenv package to retrieve the environment variable holding the Moesif Application ID.  Then we define our handler function greetingsHandler.          We extract the greetingsMessage and greeterName values fromthe body.      We define the structure of the request payload in schema. The validator middleware validates the request playloadagainst this schema. For more information about the schema syntax and the validation,see the validator middleware documentation      Since we know what a valid request body contains, we extract the values we expect from the event object.Notice how the Lambda function remains clean and readable because we push concerns like validation and parsing intoseparate middlewares.      We specify the Lambda response.      Lastly, we stitch together all the middlewares. Here, make sure that you wrap the final Middyfied handlerwith Moesif middleware. You must also parse the body first and then call the validation middleware.See the order in which Middy executes middlewares for more information.      Let’s briefly look at the middlewares the Lambda function uses and what they do:  http-security-headers  Adds best practice security headers to the Lambda response.  http-json-body-parser  Parses HTTP requests with a valid JSON payload and converts them into an object. As mentioned before, you must call thismiddleware before you call the validation middleware validator.  validator  Validates the incoming request.event events of the Lambda handler.  http-error-handler  Handles any errors that may have gone unhandled and composes an appropriate HTTP response from them.Configure the serverless.yml FileFirst, let’s look at theserverless.yml file Serverless generated in the service directory. It lookssimilar to the following:# "org" ensures this Service is used with the correct Serverless Framework Access Key.org: moesif# "app" enables Serverless Framework Dashboard features and sharing them with other Services.app: moesif-middy-serverless-node# "service" is the name of this project. This will also be added to your AWS resource names.service: moesif-middy-serverless-demoprovider:  name: aws  runtime: nodejs20.xfunctions:  hello:    handler: handler.hello    events:      - httpApi:          path: /          method: getYou can see thefull serverless.yml reference documentationto understand what each of the properties mean. But for our example project,let’s focus on thefunctions propertythat specifies the configuration of your Lambda functions. In this case, it definesthese specifics:  The Lambda function name hello.  The file and module for the hello function.  The Lambda events that trigger this function. Here, it specifiesa API Gateway v2 HTTP API that triggers this Lambda wheneverthe API receives a GET HTTP request to the root path.So let’s modify the functions property like the following according to our usecase:functions:  greetingsHandler:    handler: handler.lambdaHandler    events:      - httpApi:          path: /          method: postSpecify Environment VariablesSpecify your Moesif Application ID in a .env file like this inside the Service directory:MOESIF_APPLICATION_ID=YOUR_MOESIF_APPLICATION_IDDeployFinally, deploy your Lambda with the following command:serverless deployAfter the deployment finishes successfully, your command line shows an output similarto the following:Deploying "moesif-middy-serverless-demo" to stage "dev" (us-east-1)✔ Service deployed to stack moesif-middy-serverless-demo-dev (104s)endpoint: POST - https://******.execute-api.us-east-1.amazonaws.com/functions:  greetingsHandler: moesif-middy-serverless-demo-dev-greetingsHandler (3.8 MB)Now you can start sending HTTP POST requests to the endpoint with thefollowing body structure:{  "greetingsMessage": "Good evening!",  "greeterName": "Alex"}Your API should return back a successful response in the following format:{  "message": "Greetings details received successfully!",  "greetingsDetails": {    "greetingsMessage": "Good evening!",    "greeter": "Alex"  }}And in a Live Event login your Moesif Portal,you should see your API calls appear.Remember that we have a validation middleware in place.So if your request body doesn’t follow the schema, the server sends backa 400 Bad Request HTTP status code with the message Event object failed validation.Wrapping UpThis article demonstrates how you can easily integrate Moesif into your AWS appsbuilt with Middy and Serverless. Think about how Middy and Serverless Frameworkmakes building AWS Lambda applications more manageable and less comlex.If you pair that with Moesif, you have access to powerful analytics and monitoring tools that canhelp you understand your users better and make critical decisions to grow yourproduct. Moesif’s robust monetization features seamlessly integrate with your favorite billing providers like Stripe.Illustrating how Moesif can help you unlock the full power of serverless cloudapplications is beyond the scope of this article. But you cansign up today for a free trial without anycredit cards totry Moesif out for yourself hassle-free.Next Steps  Explore integration options from Moesif:          Server integration options documentation      Client integration options documentation        Troubleshoot common server integration issues  Explore API analytics documentation  Build an AWS Lambda REST API with Express.js  Build an AWS Lambda GraphQL API with Apollo GraphQL  Build and monetize an AI API using Moesif, AWS, and Stripe                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            You’ve built the API. Now let's generate revenue!            Monetize your AWS Gateway powered APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Moesif-Middy-Serverless/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-fall-and-rise-of-embedded-plugins-part-3": {
          "title": "The Fall and Rise of Embedded Plugins: APIs For Embed Frameworks",
          "content"	 : "Although important to see alternatives to iframes, iframes are still a valuable and commonly applied method for successful embed frameworks. And even if considering alternatives, iframes help to understand possibilities, along with design and technical details to consider for a successful platform.There’s more to embed Frameworks than letting partners appear in your interface. The better frameworks enable deep integrations, not only by positioning partners in well-thought-out places of the interface, but by providing APIs specifically meant for these embedded integrations.Beyond enabling embedded integrations and providing API access, better frameworks have APIs, event triggers, and other features to let partner integrations really interact within an environment.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Getting Started: Letting Apps Know Where They FitGoing back to the “olden days” of iGoogle, Facebook Canvas, and OpenSocial, providers of embed frameworks knew, at minimum, to ensure that partners could easily identify where their integrations would be accessible in their interfaces, so that they can display the right experience at the right time.For Facebook and LinkedIn (OpenSocial), an app could be displayed on a user’s profile in a small area, and also, can have it’s own “app page” accessible from a sidebar, with more space to display a different experience.Developers of such apps are used to specifying in a manifest file (although not necessarily) the different interfaces in which their apps can be displayed, and specify the URL to load or code to run. This is still commonly the case, just with manifests that align to newer API standards (basically, JSON files instead of ye olde XML)."location": {  "support": {    "ticket_sidebar": "assets/iframe.html"  },  "chat": {    "chat_sidebar": "assets/chat_iframe.html"  }}Essentially, a platform provider has to let developers know where they may be positioned in the UI, and specify what content to load accordingly.Getting More Creative: APIs Interacting with the InterfaceIframes provide some security, in that they can’t interact much with their parent page. That is, without the parent page’s permission. When we want partners to do more than display something in our pages while confined to a box, we provide extra APIs to GET information about the page on which the partner’s app is embedded, and even interact with the page by using functions that trigger something in your UI.For example, Gmail Add-ons let applications view the sender’s information and text of a message:// Setting the access token with a gmail.addons.current.message. readonly// scope also allows read access to the other messages in the thread.var thread = message.get Thread () ;var threadMessages = thread.getMessages () ;// Using this link can avoid the need to copy message or thread contentvar threadLink = thread.getPermalink();Making Development Easier with SDKsPlatforms can make embedded integrations easy by providing extra tools on top of these APIs.  As a start, Javascript libraries can let partners call the embed-focused APIs with less code. This is one of the most common features of embed frameworks, often regarded as the next thing to build after allowing basic iframes.Zendesk, Trello, Shopify, and Twitch are just a few examples of these client-side libraries.  They’re very common, almost expected now, when providing an iframe-featured platform.Those libraries also help to tell a story, of building simple applications that run entirely client-side. APIs to interact with the interface, or with the core platform’s backend, could still be made from the frontend, supported by a Javascript API library. Partners can then create simple apps with HTML, CSS, and frontend Javascript.In fact, such applications can be easily and safely hosted on the platform itself, rather than by the third-party developer.  While many platforms today host partner server-code, hosting client-side code is an easy start.Server-Side RequirementsSome platforms allow not only allow small apps to be built easily by operating entirely client-side, but sometimes that just isn’t possible.  In enterprise software, partners are certainly hosting and running backend operations. Even in K-12 education technology, I managed a platform that required many API calls to happen server-to-server. Partners provided math and language games online, for students to try, and for grades to be submitted to a classroom grade book. Calls to submit grades had to happen server-side, or else a tech-savvy student could figure out the API call and cheat in English class.Twitch focuses on this scenario, with additional tools on top of both client and server-side libraries.More Security Considerations: API ScopesTo make those deeper integrations possible, new API functions are needed, designed specifically to work for these embedded integrations. And with new APIs, comes more responsibility. Scopes for these API endpoints tend to work very differently from other APIs.For a mobile application connecting with Google Drive, it may be necessary to have full access to a user’s Google Drive account, in order to create folders, upload files, share folders, etc. However, if embedded in Google Drive to interact with just one file, is that necessary? For Google Drive, it’s best to limit integrations to just work with the file a user selected.Google Drive has been updating its APIs so that file-only integrations don’t request scopes beyond their needs. Now, new tokens can be created every time a user launches a file into a third-party service, giving the third-party access only to the file the user just launched.How Not to Handle API AuthenticationIframes, APIs, and libraries seem simple enough, but remember, usability matters. All too often platforms miss a spot. Here’s a common one, from which companies frequently need to later update: the authentication experience.How does one enable a partner to have access to a user’s account? The latest Oauth standard is the most likely solution, but all too often it isn’t implemented as it should. Platforms frequently direct users out of their interface, forcing them to go to a partner, login, go through an Oauth again, just to have access to an integration.The experience for your users doesn’t have to be this way! Thinking through the flow, a user more likely will find these apps in a directory of integrations. When they do, they’ll be (or can be) logged into an account - ready to approve an app. How about allowing them to simply “Add” the app, and go through a proper Oauth prompt in your interface, here?Surely, many partners will also require access to their API. This could be handled either by the application at first launch, or ideally, through a redirection on this same experience. A one-time experience during a clear “installation” flow and the user is ready to go.Compliance ConsiderationsSome enterprise companies hesitate to launch iframes integrations, noting that they are trying to achieve HIPAA and FINRA compliance, which requires partner services to be integrated as well. I have been told by some platform hosts that iframes are out of the question for this reason.Salesforce encounters this, but takes a simple approach of disabling all integrations for customers requiring HIPAA. Clearly there are ways of resolving compliance issues while still allowing third party embedded integrations.And there are even more liberal options than blocking partners entirely for certain customers. Alternatively, a platform provider can mark which integrated services have certain certifications.  When a customer requires HIPAA certification, all non-certified Hipaa integrations can be made inaccessible to that account.If in an industry where compliance is important, this is an area to understand in early planning stages of a platform. The good news is that the issue is very much feasible to manage.Looking Beyond IFramesAfter considering use cases and applying good design principles, iframes can allow for a very compelling platform. Once combined with the right APIs and rules for implementation, such a platform can fit well with customers, and scale to hundreds of integrations.Despite numerous struggles and scars from the past, iframes are still valuable when applied properly. Nonetheless, they aren’t always the best solution. After understanding iframes and design principles for a good embed framework, it’s worthwhile to understand what other options are out there as platforms experiment with other experiences and technologies.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue from your AI APIs?            Monetize your AI APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Fall-and-Rise-of-Embedded-Plugins-Part-3/",
          "author": "Jeremy",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-fall-and-rise-of-embedded-plugins-part-2": {
          "title": "The Fall and Rise of Embedded Plugins: IFrames - the Default Choice for Embedded Integration Design",
          "content"	 : "With the rise in popularity of embedded integrations, and continued trial and error in this space, it’s best to review the core concepts of how embed frameworks operate.  There are many ways platforms open their UI to third-parties, but one is considered original - the oldest, and likely still the most common method on the web - the old iframe model.In the olden days, the common way to enable third-party content in one’s interface was by iframing. We commonly saw this in the nostalgic iGoogle and Facebook Canvas.  Today other options are trending, but iframes are still common.With what we have learned, in performance, security, and most importantly in my opinion, user experience, iframes are still relevant, when provided in a properly designed manner.An Overview of IFrame IntegrationsFor iframe platforms, the integration can be fairly straightforward - the host service decides where partners integrations can be displayed, and has an iframe in that location, where a third party URL is provided.And old and simple example would be iGoogle:This was an alternative homepage which allowed a handful of third-parties to be displayed in a dashboard interface.As a developer, the most basic integration is to specify that URL.  Of course, deeper integrations are expected. And when they do, we need to ensure that the platform can scale. There are both design and technical aspects to plan ahead.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Design Principles: Provide an Experience That Fits the Dimensions of the PageSome platforms enable near-full-screen integrations via iFrame.  Even then, most have certain interfaces where the partner can only integrate within a defined space.This is especially noticeable in Google’s G Suite where their integrated “add-ons” are restricted to a narrow portion of the UI, in the right sidebar.Trello, similarly with its “Power-Ups” integrations, positions partners within Trello cards.Clearly, size matters when allowing partners to integrate in your UI.Have a Design That Works Well with the InterfaceEarly versions of this model integrated for the sake of integrating. Many of us saw this prior and during the “2009 API Craze” when APIs “got cool” (not that any of us are hipsters, of course…).The most extreme cases were “Webtops” - webpages that looked like desktop Operating Systems.  They were free for all, and did not tell a [compelling story] or give users a reason to really use this.Other platforms have a purpose: clear use cases where it makes sense for partners to complement the product.  And, where a pattern can be found in which many partners can similarly work, an embed framework fits.Trello is a great example of this.  The team understands where third-party tools may fit in their interface - on cards, and on dashboards.  They thought through the customer stories and flows, and made sure to enable those aspects of their interface [todo: clarify].Box and Google Drive are great examples as well, with user scenarios translating to a different experience that fits their need.  A core aspect of both these services is working with files.  Although Google can edit documents, spreadsheets, presentations, and other files (Google Draw, Sites, etc),  they let others plug right into the file management interface.Establish Guidelines for PartnersFreedom and responsibility. These platforms find it more important to have design rules, or preferably, design guidelines.Facebook’s canvas drove a trend in these integrations, but most related platforms were unsuccessful.  While other articles delve into Facebook Canvas, and what worked and hadn’t worked, most agree that Facebook was too loose in what it allowed on the platform, allowing many integrated services to negatively affect the experience. For those of us old enough to remember getting inundated with Zombie Bite apps, we saw the challenge of an embed platform that’s too open.Now, all embed frameworks have restrictions on what they will allow.  Companies continue to test their rules, and depending on the industry and platform, some will have to be more restrictive than others.  However, among the more successful frameworks, one finds the following pattern:Less Rules, More GuidelinesMost platforms I encounter have design and technical guidelines, some more loose, some more strict.  When it comes to design, the more consistent a partner’s design style is with the platform, the better the customer experience, and the more likely the integration would be featured by the platform provider.Platform providers usually understand that many integrations have legitimate reasons for a less consistent UI in certain circumstances. Furthermore, some partners may be run by small teams, who can’t make a perfect app, but can solve an important problem for a few customers - and shouldn’t be held back if they can’t meet a perfect integration requirement.  Other apps may appeal to a wider audience, and if meeting design guidelines, may be better promoted to customers.Consequently, while certain platforms are strict in design requirements, others apply guidelines, recommending but not requiring that partners abide 100%.Less Restrictions, More IncentivesTo encourage custom branding, several platforms make development of a consistent UI easier, encouraging use of branding guidelines with positive motivation rather than forcing it.  Such platforms may provide embeddable components to perform common operations in an iframe, so that partner don’t have to write as much code themselves.Shopify went this route with their Polaris Components - a library of UI components for developers to implement the UI of their applications easily, and with branding more consistent with Shopify’s interface.Making development easier, by using tools that meet design guidelines, is win (user), win (developer), win (platform host).Some go to a greater level, providing their own markup language and scripts.  We delve into this more deeply in the next article.Security Considerations for IFramesWe are now allowing apps to work with our data, and with our interface, so there are security matters to resolve before opening a platform to partners. Security seems like an obvious topic to handle early in planning, but I have been unpleasantly surprised enough times by platforms that didn’t fully consider security implications.Many books are available on relevant security topics here, but here is a top 3 summary of considerations for iframe models:  Cross Origin Requests: IFrames pages from third-parties are by default blocked from making calls to third party servers.  The most common solutions is enabling CORS, while some apply somewhat hacky solutions with API proxies (call a same-origin server and make server-to-server API calls) or JSONP.  Iframe page access: Newer access and permission settings are permissible with HTML5 since to place a greater level of control over iframes. The “sandbox” attribute can restrict iframe content from running scripts, launching popups, and performing other operations not deemed secure or UI friendly.  This also gives a better sense of comfort to enterprise services, and others who just care more about safely managing integrations.  SSL: As long as your service is SSL enabled, non-SSL content cannot be displayed in an iframe on most popular browsers. There was once an event a few platform friends called the “SSL-pocalypse,” when SSL wasn’t expected on all sites, and security rules were more relaxed.  It was possible for a non-SSL page to be embedded in an SSL page in some browsers, but today it’s no longer the case - a logical rule for security.Looking BeyondiFrames alone can take UI integrations a long way. In its early days they struggled, due both to technology limitations and a lack of design thought. When applying proper Product Management and Design principles, the most basic technology to integrate can work. However, after use cases are considered, the technology needs to fit, and there are many technical intricacies to consider for iframes.In the process, many platform teams applied alternative means to enable integrations in their interfaces. But it helps to understand iframes first, along with supplementary and complementary options.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue from your AI APIs?            Monetize your AI APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Fall-and-Rise-of-Embedded-Plugins-Part-2/",
          "author": "Jeremy",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-fall-and-rise-of-embedded-plugins-part-1": {
          "title": "The Fall and Rise of Embedded Plugins: How Custom UI Integrations Are Making a Comeback",
          "content"	 : "As APIs become an expected feature of software businesses, the most popular web services extend their developer platforms beyond external API, allowing partners to integrate right into their interfaces.  With Salesforce’s Lightning platform, Google Docs’ connecting tools like Balsamiq and ShiftEdit, and Trello’s PowerUps, custom experiences powered by third parties is proving itself to be a great way to improve one’s product and grow your business.The story is compelling, and the opportunities seem incredible.  But in practice, many of these platforms were more “cool” than valuable, or usable.  There’s a large graveyard of failed embedded integrations over the years.However, after many years of disappointment, we’re finally seeing value propositions and user experiences that make sense.  And as embedded experiences become more popular, they may become expected functionality. So, let’s evaluate what has worked and hasn’t in the world of embedded integrations.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Rise, Fall, and ResurgenceYou may have seen other popular but forgotten platforms with embedded experiences in the not-to-distant past, such as Google’s iGoogle, Facebook Canvas, and OpenSocial.  These gained attention around 2005-2008, with mixed results, and were sunsetted over time.Arguably, those early platforms were good tests of the viability of extending web apps, and could be improved with further iteration.  We have learned a lot about User Experience over the past several users, and as our web services received upgrades, how did embedded frameworks do?Unfortunately, for many years, we hadn’t seen as much new innovation on this front as we had hoped.  Although APIs rose in demand, the focus appeared to be more toward external interface integrations and data integrations.  Furthermore, the rise of the mobile application ecosystem created additional challenges to operate embedded experiences that we’ll dive into in a later article.Many interesting experiences could still be observed through those years, such as Gmail’s multiple attempts to incorporate third-party tools.  Projects included Contextual Gadgets, allowing partners to display content depending on the text of an email one receives. Unfortunately, that project never made it out of Google Apps for Business (now G Suite) before getting sunsetted.Although experimentation and learning continued, for many years these embedded experiences didn’t show results. However, in recent years embedded frameworks are increasing again in popularity, as usability is better understood, and more solid, consistent APIs enable higher quality integrations.Lessons from the past embeddable platforms show that embedded integrations can help a product differentiate and demonstrate otherwise unattainable value.  However, to avoid the “platform graveyard,” it’s necessary to understand the needs of your users, and from there, determine how partners can best fit in.Experimentation by other businesses with partners and customers allows us to see what’s possible, beyond the basic iframe route.  Different stories and different technical means of integrations, each of which serves particular use cases with pros and cons, can be benchmarked in order to plan an embed framework for your platform that really works for your customers and partners alike.Applying Usability to Embedded FrameworksUsability learnings for both consumer and enterprise products, combined with improved availability and quality of APIs, can give embedded apps another shot.  It’s now easier than ever to connect products, but we have to apply the same standards in usability that we now see across software, and across APIs.Trello and Shopify have demonstrated the benefits of such extensions over the last several years, by first understanding their customers’ needs, establishing use cases, and then enabling partners to be positioned where needed to meet those use cases.  The technology is similar to what we’ve seen in the old days of basic iframes, but the experience is better.Trello’s Power-Ups allow partners to connect to various parts of the Trello interface and experience, to perform actions on a Trello board, within a Trello card, and more.Other businesses are following the trend toward open interfaces.  In 2017 Gmail announced its ”add-on” service, a new means of opening Gmail’s interface to partners.  They seemingly observed what others had accomplished through unofficial hacks of Gmail’s interface through browser extensions, and after seeing what works well with users, gradually enabling some of that functionality through their interface.This project is ongoing, with Gmail announcing more recently (October 2018) that partners can integrate into Gmail’s compose message experience.The Gmail team is aware that they have a long road ahead, and still need to fill in gaps to enable the integrations users want.  They also encountered a dilemma that is more and more common for embedded frameworks: how to work well with mobile.Facing the World of MobileDeveloper Advocates for Gmail, Slack, and other popular services have been inundated at times with requests to enable embedded integrations.  They didn’t pull the trigger due to a lack of awareness, or exclusively due to technical/security concerns (although those were factors), but out of a design concern - with the rise in use of mobile apps, how can they make the story of integrations still work?For some time, as usage in mobile increased dramatically, companies debated how it could be possible to make an integrated experience work with a complete desktop/mobile story, and often deprioritized embedded integrations over other mobile experiences.  However, new platforms have shown ways to work interchangeably.  A Gmail Add-on can work in Gmail’s mobile app, giving a compelling reason to integrate officially rather than through Chrome extensions.Similarly, Slack Actions work on Slack’s web service, desktop client, and mobile apps.How is this possible?  From a technology standpoint, they are doing something different (hint: not iframes).  And the decision to do so has, thus far, had pros and cons.These cross-device frameworks are still in their early days of market testing, and will experience hiccups over the near-term.  At this time, we can still learn from the experiments, for those just starting a platform, to build more smoothly.Developer ExperienceAlthough user experience is vital to the success of embedded integrations, one cannot forego the developer experience.  Just as standards for the developer experience with external APIs, such as the 3 column API documentation, helped to increase success rates of API initiatives, learnings from various embedded projects show patterns in developer experiences for embedded integrations.For instance, Gmail Add-ons and Trello Power-Ups require developers to define an “application manifest” - json files that they need to custom define, and upload to said platforms to be made available to users. Other platforms such as Box instead allow developers to manage everything from a web interface in a developer portal.The developer experience has become a science in itself.  Embedded experiences are starting to move toward best practices that we’ve seen in other aspects of the API developer experience, but have farther to go.StandardsJust as RESTful APIs defined under the OpenAPI specification have made integrations easier across services, as patterns become standards for embedded experiences, we are likely to see more platforms, and more integrations.  Partners will be able to build interoperable integrations that span across a range of products (as OpenSocial intended years ago, I know, but let’s give standards another chance).In APIs, in UX design, and in developer experience, guidelines at least can be seen, to achieve the success.  For instance, the manifest definitions for an embed framework, while not consistent across platforms, are similar.  Ideally, in time they will align to a standard.What’s nextIn this series, we’ll explore the various user experiences, developer experiences, and technologies of embedded frameworks, and corresponding APIs and developer portals.  We’ll see what works, what hasn’t worked, and continued experiments to identify a path to successful embedded integrations.  We’ll also highlight patterns and the opportunities for common standards in this space.We look forward to seeing technologies integrate more deeply and seamlessly with the latest iterations of embedded experiences.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue from your AI APIs?            Monetize your AI APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Fall-and-Rise-of-Embedded-Plugins-Part-1/",
          "author": "Jeremy",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-dev-portal": {
          "title": "Top 5 Best Practices for Building a Dev Portal",
          "content"	 : "A developer portal works as a centralized hub for accessing, managing, and integrating APIs. It supports developers with the tools, documentation, and support they need to succeed. In this article, you’ll learn the top five best practices for creating a dev portal that developers love.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Table of Contents  Key Takeaways  Understanding the Importance of a Developer Portal  Key Features of an Effective Developer Portal          Comprehensive Documentation      Interactive API Explorer      Code Samples and SDKs        Customizing Your Developer Portal          Visual Customization      Functional Customization      User Access Management        Best Practices for Developer Portal Content          Layered Content Architecture      Onboarding Processes      Regular Updates and Maintenance        Enhancing Community Engagement          Community Support Forums      Developer Events and Webinars      Feedback Mechanisms        Case Studies of Successful Developer Portals          Stripe’s API Reference      Visa’s Findability Features      Fiserv’s Coding Tutorials        Future Trends in Developer Portals  SummaryKey Takeaways      Developer portals consolidate documentation, tools, and support in one place. They simplify the integration process and improve developer experience by a huge margin.        Key features of effective developer portals include comprehensive documentation, interactive API explorers, and code samples or SDKs.        Future trends in developer portals involve the integration of generative AI and Large Language Models (LLMs). These will bring more productivity and task automation features, alongside continuous emphasis on content security and analytics.  Understanding the Importance of a Developer PortalA developer portal acts as the central hub for accessing and managing APIs. It provides all the necessary tools and resources that developers need for successful integration. A dev portal consolidates documentation, tools, and support in one place. Therefore, the integration process that comes with building an application or feature, becomes a lot easier. It also allows developers to easily experiment and implement APIs. The advantages of using a developer portal include:      Consolidated documentation, tools, and support.        Streamlined integration process.        Ability to test APIs in sandbox environments without affecting live systems.        Enhanced developer experience.  A well-designed developer portal can drastically improve the user experience, leading to higher adoption rates and successful integration stories. These portals have a vast amount or support and community resources. These resources are indispensable for modern API ecosystems in troubleshooting and knowledge sharing while crafting meaningful software.In essence, we may mistakenly take dev portals for granted in thinking that they only provide access to APIs. But their scope and potential results in a seamless and engaging experience that encourages developers to innovate and build effectively.Key Features of an Effective Developer PortalIf you want to build developer portal solutions that truly serve their purposes, you must incorporate key features that cater to the needs of developers. Fundamental aspects that make a developer portal effective and user-friendly include the following:      Comprehensive documentation        Interactive API explorers        Code samples and SDKs  Each of these features plays a unique role in enhancing the overall developer experience.Comprehensive DocumentationComprehensive documentation, including reference documentation, acts as a detailed guide for developers to understand and use APIs effectively. This documentation follow a logical structure so that users can easily navigate and find the information they need quickly. Documentation should also include examples to explain the structure of API calls and responses.To further improve usability and accessibility, implement advanced search and filtering options. It allows developers to narrow down their searches based on specific criteria.Interactive API ExplorerAn interactive API explorer is a vital component of an effective developer portal. It enables developers to:      Test and interact with APIs in real time using their API key or managing multiple API keys.        Make API calls.        Play with query parameters.        See immediate responses without writing any code.  This feature essentially provides a self-service experience for developers. It gives more room for experimentation and raises engagement.A product can strongly showcase its features and capabilities to potential users through elements like “Try it out” and sandbox environments. Both of these features include API explorer as well as other interactive elements that give hands-on experience with the product.Code Samples and SDKsDeveloper portals also provide code samples and SDKs in various programming languages. These developer resources eliminate the need for developers to rewrite code in different languages. Integration becomes more seamless. It also greatly raises adoption rates as more developers can try out and adopt the product, no matter their backgrounds and use cases.Customizing Your Developer PortalFor a personalized experience that aligns with your brand and meets specific user needs, you have to customize your developer portal. Personalizing the appearance, functionality, and user access management can greatly impact how useful your users will find the dev portal.This section explores various customization options available for developer portals.Visual CustomizationVisual customization ensures that the developer portal reflects the company’s branding and provides an intuitive navigation experience. A consistent design promotes usability and supports the user journey, reinforcing the overall brand identity. A good visual design and implementation doesn’t have harm user experience while trying to fit visual appeal and brand authority.Tools like Apigee allow for customization of themes, colors, and other visual aspects to align the portal with the company’s branding.Functional CustomizationFunctional customization tailors the portal features and layouts to specific business needs. This makes sure that every consumer finds it relevant and useful for their use cases. With tools like Apigee, you can modify pre-provisioned pages, add new content areas, and customize page layouts to better serve your users.If you want to create a portal that achieves complete functionality while aligning with your business objectives, pay attention to functional customization.User Access ManagementUser access management is very important as it controls visibility, permissions, and authentication within the developer portal. Some key aspects of user access management include:      Providing self-service registration options.        Managing user accounts through roles and access controls.        Ensuring secure and organized access to portal content.  By implementing these practices, you can effectively control user access and enhance the security of your developer portal.Apigee’s role-based authorization model is an excellent example of how to manage developer access and permissions effectively for API products.Best Practices for Developer Portal ContentIf you adhere to best practices that ensure clarity, usability, and engagement, you can create high-quality content for your developer portal. This section covers some of these best practices and key strategies.Layered Content ArchitectureA layered content architecture organizes information logically. It can cater to various user profiles from non-technical to highly technical. This structure helps users understand the value of services by providing business details at the top layer and technical details at the bottom layer.If you want to support API adoption for all user levels, provide clear and detailed tutorials for different user skill levels.Onboarding ProcessesA frictionless onboarding process can keep prospective users engaged. Consider including “Quickstart” guides and “Get started” tutorials that focus on simple use cases. These extremely helps new users perform their first API call quickly and understand the solution from start to finish. This approach minimizes friction and encourages users to explore further.Regular Updates and MaintenanceRegular updates and maintenance signal reliability and trustworthiness to users. Keeping the developer portal up-to-date requires collaboration across the organization, ensuring that all teams are responsible for updating their domain content.An API uptime status page can also help maintain transparency and trust, encouraging continuous feedback from developers.Enhancing Community EngagementWithout fostering community engagement, you cannot ensure the long-term success of a developer portal. Let’s discuss the strategies to build a strong community support system.Community Support ForumsCommunity support forums provide a platform for peer-to-peer interactions and troubleshooting. These forums can double as valuable documentation resources by capturing support interactions that address common problems and concepts. In addition to forums, users can also explore other resources for further assistance.Programs like RingCentral Developers’ ‘Game Changers’ reward top contributors and thus foster community engagement.Developer Events and WebinarsDeveloper events and webinars are excellent ways to showcase new features and use cases to the developer community. These events bring forth direct interaction, helping build a more engaged and knowledgeable community.Feedback MechanismsBy incorporating feedback mechanisms, you can gather user insights and continuously improve your developer portal. For example, you can implement options for direct messaging and forms. This way, you can collect real-time feedback. This allows you to solve user problems faster and more efficiently. Moreover, you can collect them for product roadmap development and future ideasCase Studies of Successful Developer PortalsStripe, Visa, Fiserv—all of them have built successful developer portals. If we examine them closely, we can gain valuable insights into the best practices they’ve adopted. These case studies showcase effective strategies in API reference, findability features, and programming tutorials.Stripe’s API ReferenceStripe’s API reference is renowned for its clear examples and well-paced introductions that guide users through the integration process. Stripe has organized the documentation around REST and using resource-oriented URLs. It includes examples using curl and official client libraries for different programming languages.Visa’s Findability FeaturesVisa’s developer portal has excellent findability features. A robust search and filtering system greatly boosts content discoverability, making it easier for developers to locate the APIs they need. As a unified platform, it offers a superior product catalog and clear navigation paths to help users find the right product on the landing page.Fiserv’s Coding TutorialsFiserv’s coding tutorials has the following key properties that shine:      Focus on real-world use cases,        Provide code samples in multiple programming languages,        Accessible to developers with different technical backgrounds.        Integrated directly into the main documentation, providing step-by-step examples of how to implement Fiserv’s APIs.  Future Trends in Developer PortalsFuture trends in developer portals will shift towards more usage of generative AI and Large Language Models (LLMs) as a means to enhance productivity and automate tasks. We’ve already seen some of these AI-powered tools come into play. For example:      GitHub Copilot: a tool for code completion and debugging        Google’s CodeGemma: a tool for code completion, code generation, and debugging        Domain-specific language models: being developed to perform specialized tasks more accurately  The advancements in AI and LLMs, like many other fields in the developer ecosystem, are also shaping the future of developer portals. They bring new possibilities for developers, earning recognition at the prestigious DevPortal Awards.SummaryAn effective developer portal can go a long way in solidifying the success of your product. It essentially works as a validation and usability test of your customer’s ideas that they want to build using your product. With dev portals, you make sure that customer experience stays top-notch and your product evolves by accommodating new ideas on top of your product.Moesif’s open-source developer portal, because of the different elements at play, takes a different approach to offer the most flexibility for customized solutions. For more information, check out the announcement blog post!Moesif has a lot more to offer when it comes to building a successful customer-centric product. We have powerful analytics and monitoring tools that can help you understand your users better and make critical decisions with confidence to stay competitive. Moesif also provides robust monetization features that seamlessly integrate with your favorite billing providers like Stripe. Pay-as-you go, real-time credit consumption—whatever your billing requirements, Moesif’s API observability platform can help you build better AI and API products.To try out Moesif, sign up today for a free trial, no credit cards required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            You’ve built the API. Now let's generate revenue!            Monetize your APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Dev-Portal/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-mastering-prepaid-usage-billing-in-real-time": {
          "title": "What Is Prepaid Billing? A Guide for API and SaaS Companies",
          "content"	 : "Prepaid billing is back in the spotlight because of how AI APIs charge for tokens. OpenAI sells prepaid credits. AWS sells prepaid Savings Plans. Twilio runs on a prepaid balance. Stripe introduced a Credit Grants API so SaaS companies can issue prepaid balances of their own. If you sell an API or a usage-heavy SaaS product, prepaid is a pricing lever your customers actively ask for.This guide is the strategic overview: what prepaid billing is, how it differs from postpaid and pure usage-based pricing, the models that work for APIs, and the architecture you need to run it in real time. If you want the Stripe-specific walkthrough, see our companion piece on the step-by-step Stripe prepaid setup guide.What is prepaid billing?Prepaid billing is a payment model where a customer pays for a service before they use it. The provider holds the payment as a credit balance, draws down the balance as the customer consumes the service, and prompts a top-up when the balance runs low. It is common in telecom, utilities, cloud platforms, AI APIs, and developer tools.The key contrast is with postpaid billing, where the provider tracks usage across a billing period and sends an invoice at the end. Prepaid moves the money first and the consumption second. Postpaid does the opposite. Both can be tied to fixed plans, metered usage, or a combination of the two.For B2B SaaS and API companies, prepaid solves three problems at once: it removes the credit risk of new customers, it gives buyers a hard ceiling on spend, and it lets a vendor charge for high-variance workloads (LLM tokens, video minutes, SMS sends) without being stuck chasing invoices.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Prepaid vs postpaid vs usage-based billingThe three models are often conflated, but they answer different questions. Prepaid answers “when does the customer pay?” Usage-based answers “how is the price calculated?” Postpaid is what most people mean when they say “invoice me later.”            Dimension      Prepaid      Postpaid      Pure usage-based (PAYG)                  When customer pays      Up front      End of billing period      End of billing period              Billing trigger      Top-up event      Period close      Period close              Revenue recognition      Deferred until consumed      At invoice      At invoice              Risk of non-payment      Near zero      Real (collections, write-offs)      Real              Customer budget control      Hard cap      Soft cap (alerts only)      Soft cap              Best fit      New customers, AI tokens, SMB, no-credit markets      Enterprise contracts, predictable workloads      Mid-market with established credit      Prepaid and usage-based are not mutually exclusive. OpenAI’s API ties them together: customers buy prepaid credits, then per-token rates burn those credits in real time. AWS does the same with prepaid Savings Plans on top of metered EC2 and Lambda consumption. The combination is often called prepaid usage billing, and it is the model most modern API products land on once they outgrow flat-rate plans.For a deeper look at the consumption side of the equation, see our overview of usage-based billing for APIs.How prepaid billing works in real timeA prepaid billing system has three jobs: hold a balance, decrement it accurately as usage happens, and stop the customer (or warn them) before the balance hits zero. The “in real time” part is what separates a working system from one that issues surprise overage bills.At a minimum, the architecture has four moving pieces:  Wallet or credit balance. A persistent record of how much prepaid credit each customer or account holds. Stored in the billing provider (Stripe customer balance, Chargebee credit notes) or in a wallet service of your own.  Usage meter. A real-time pipeline that captures every billable event, an API call, a generated token, a delivered SMS, and attaches it to a customer ID.  Rating engine. The component that converts raw events into currency. A /v1/chat/completions call might cost 0.000015 USD per input token; the rating engine applies the price and returns a debit amount.  Quota enforcement. The gate that blocks (or throttles) requests once the balance is exhausted, instead of letting customers run up unpaid usage.In telecom this stack is called an Online Charging System (OCS). For APIs, the same pattern shows up as an API gateway feeding a metering service, which writes against a wallet held in a billing provider. Stripe documents this pattern in its Credit Grants and Meters APIs. Twilio publishes it in its account balance docs. The names differ; the loop is the same.The reason real-time matters is settlement risk. If your meter lags by an hour, a runaway script can consume thousands of dollars of credit you cannot reclaim. Real-time metering with sub-second visibility, what API usage metering is built for, closes that gap.Common prepaid billing models for APIs and SaaSMost prepaid implementations in API and SaaS land in one of four patterns. The right choice depends on how predictable usage is and how much friction your buyers will tolerate.Prepaid creditsThe customer buys a dollar (or token) balance and consumes it across any product. OpenAI runs this model: customers add credit to their account, then any API call, chat completions, embeddings, audio, burns from the same pool. Anthropic, Cohere, and most AI gateways follow the same pattern. It works well when pricing is per-call or per-token and customers want to test before committing.Prepaid quota or allowance plansThe customer prepays for a fixed quantity, 1 million API calls, 10,000 SMS sends, 500 GB of bandwidth, usually as part of a monthly plan. Once the quota is exhausted, requests are either blocked or shifted to overage pricing. This is the SaaS equivalent of a prepaid mobile plan: predictable for the buyer, predictable revenue for the seller.Hybrid prepaid + usage overageThe customer prepays a base allowance, then pays metered overage for anything beyond it. Twilio uses a variant of this on top of its account balance. It captures both the commitment revenue of a subscription and the upside of heavy users. For most API companies trying to expand existing customers, hybrid is the model worth modeling first. We cover the broader strategy in our piece on pricing models for AI APIs.Prepaid auto-rechargeA variant on the above: when the balance drops below a threshold, the system automatically charges the saved payment method to top it up. OpenAI offers this so customers don’t get throttled mid-workflow when credits run out. Auto-recharge keeps the prepaid budget control while removing the friction of manual top-ups.When to choose prepaid billing (and when not to)Prepaid is the right call when one or more of these are true:  Usage is high-variance or unpredictable. AI inference, video processing, and SMS campaigns can spike 10x in a day. Prepaid caps the customer’s exposure and yours.  You sell to self-serve or SMB customers. Prepaid removes credit checks, collections, and the operational overhead of invoicing small accounts.  You operate in markets where postpaid credit is hard. Many developers outside the US and EU cannot easily get a corporate card with sufficient limit. Prepaid via wire, ACH, or local rails sidesteps that.  Your product is fraud-prone. Prepaid eliminates the “use the API for free, dispute the invoice later” pattern that plagues SMS, calling, and image generation APIs.  You sell AI tokens or per-event units. The OpenAI/Anthropic/Cohere standard is now prepaid credits. Selling differently makes you the odd one out.Prepaid is the wrong call when:  You sell large enterprise contracts. Procurement teams expect NET-30 or NET-60 invoicing, not credit top-ups.  Usage is highly predictable. A flat subscription is simpler and more revenue-stable.  Your unit economics depend on annual commitments. Prepaid tends to encourage shorter cycles unless you bundle it with a multi-month commitment discount.For a broader perspective on packaging decisions, see our guide to API monetization strategy.How to implement prepaid billing for your APIBuilding prepaid billing is less about the billing UI and more about the metering loop behind it. Here is the order that usually works.Real-time usage meteringStart with the meter, because nothing else works without it. Every billable event needs a customer ID, a timestamp, a unit count, and enough metadata to debug pricing disputes later. Capture this at the API gateway or directly in your application code with an SDK. We handle this layer for APIs; Stripe’s Meters API handles the lighter case. See our metered billing introduction for the data model.Credit balance and wallet managementYou need a single source of truth for the balance. The two common options: use your billing provider’s native balance (Stripe customer_balance, Chargebee promotional credits, Zuora prepaid funds) or run a wallet service in your own database that syncs to the provider. The first is simpler. The second gives you sub-second decrement latency and the ability to enforce quotas before the request runs.Billing-provider integrationStripe, Zuora, and Chargebee all support prepaid in some form, but the integration surface differs. Stripe’s Credit Grants API treats prepaid as one-time grants tied to a customer; Zuora has dedicated prepayment accounts; Chargebee uses promotional credits. Pick the one your finance team already runs on rather than introducing a second platform. If you are on Stripe, our step-by-step Stripe prepaid setup guide walks through the API calls. The same logic applies to Stripe’s broader usage-based billing setup with Moesif.Customer notifications and top-up flowsThe most common customer complaint about prepaid is “I didn’t know I was about to run out.” Build threshold alerts at 50%, 20%, and 5% of remaining balance. Send them by email, in-product banner, and webhook to whatever the customer is paying you for (so a Slack-bot vendor can warn their own customers). Offer auto-recharge as the default opt-in.Quota enforcement and governanceOnce a customer’s balance hits zero, you have a choice: hard-block requests, throttle to a slow lane, or allow a small negative balance (“grace overage”). Hard-block is safest for fraud-prone APIs. Throttle is friendlier for paying customers. Whatever you pick, enforce it at the gateway, not after billing, or you will end up writing off uncollectable usage.Reporting and analytics for prepaid plansA prepaid plan generates three numbers that matter more than the rest: burn rate, top-up cadence, and expiry.  Burn rate tells you how fast a customer is consuming credit. A burn rate that suddenly doubles is either a power user expanding (good) or a runaway integration (bad, page the customer).  Top-up cadence tells you whether customers are sticking. Customers who top up monthly on a consistent amount are effectively a subscription. Customers whose top-ups slow down are churning quietly.  Expiry matters if your credits have an expiry date. Track unexpired liability so finance can recognize revenue correctly, and track credits that almost expired to identify customers who over-bought.For B2B API products, layer in per-endpoint reporting: which routes burn the most credit, which customers concentrate on which routes, and which routes have the worst margin. The same metering pipeline that drives billing also drives this analysis. It is the same data set we describe in our writeup on building an internal chargeback model.The “prepaid invoice” your customer receives should make all of this legible: opening balance, top-ups, line-itemized consumption, closing balance. Vague invoices generate support tickets.Security, compliance, and fraud considerationsPrepaid changes the fraud surface compared to postpaid. The fraudster’s goal shifts from “use the service and dispute the invoice” to “compromise a customer’s account and drain their balance.” A few non-obvious defenses:  Velocity limits on top-ups. Anomalous top-up patterns (ten 500 USD charges in an hour) usually mean card testing, not a legitimate customer.  Step-up auth on balance withdrawal. If you let customers withdraw unused balance, treat it like a bank transfer: SCA, email confirmation, hold period.  Refund policy in writing. Prepaid credits regularly draw chargebacks if buyers expect a refund and you do not have a stated policy. Publish one and reference it on the top-up page.  Data protection. Wallet balances and consumption logs are personal data under GDPR and CCPA when tied to an identified user. Retention windows, deletion requests, and cross-border transfer rules all apply. Build them in from day one.  PCI scope. If you store any payment method to enable auto-recharge, do it through your billing provider’s tokenization. Never persist a PAN in your own database.How Moesif powers real-time prepaid billing for APIsWe sit at the metering and governance layer, not the wallet layer. We meter usage, evaluate governance rules, track usage credits where configured, and send usage records to billing providers (Stripe, Recurly, Chargebee, Zuora) or custom systems via webhook. The wallet/invoice ledger remains the customer’s chosen billing provider.The pieces that matter for prepaid specifically:  Usage Credit Tracking. Track credit balances per company or user, decrement them against any metered event, and surface the balance through the embedded developer portal. See the Usage Credit Tracking docs for details.  Quotas and Governance. Enforce hard limits or throttle at the gateway when a customer’s balance is exhausted, so requests stop before they create uncollectable usage.  Custom Actions. Trigger top-up reminders, account alerts, or auto-recharge calls when balance thresholds are crossed.  Pre-built integrations. Stripe, Recurly, Chargebee, Zuora, and webhook outputs for in-house billing or AWS Marketplace.  Embedded API Metrics and developer portal. Show your customers their consumption and remaining balance inside your product, without building the UI from scratch.We power the metering and governance layer. Your billing provider holds the wallet and issues the invoice. The two together, connected through pre-built integrations, give you a working prepaid system. There is a 14-day free trial with no credit card required.Frequently asked questionsWhat is prepaid billing?Prepaid billing is a payment model where a customer pays for a service before using it. The provider holds the payment as a credit balance, decrements it as the customer consumes the service, and prompts a top-up when the balance runs low.What is a prepaid billing system?A prepaid billing system is the software stack that runs prepaid: a wallet to hold the balance, a real-time meter to capture usage, a rating engine to convert events into currency, and a quota enforcer to stop or throttle the customer when the balance hits zero.What’s the difference between prepaid billing and usage-based billing?Prepaid describes when the customer pays (up front). Usage-based describes how the price is calculated (per unit of consumption). They are often combined: the customer prepays a credit balance, and per-unit consumption burns it down in real time.Can I combine prepaid credits with usage overage?Yes. This hybrid model, prepaid base allowance plus metered overage, is the most common pattern for B2B APIs. The customer commits to a baseline spend, you capture upside from heavy users, and neither side is surprised at invoice time.How do I set up prepaid billing for my API?Build the metering pipeline first (every billable event tagged with a customer ID), then choose where the wallet lives (your billing provider or your own database), then connect the two so usage decrements the balance and top-ups credit it. The step-by-step Stripe prepaid setup guide covers the Stripe-specific implementation.What are the disadvantages of prepaid billing?The main downsides are higher friction for enterprise buyers (procurement prefers invoices), the need to handle customer-facing balance UI, and the operational overhead of refunds, expiry, and disputed top-ups. For self-serve and AI API businesses, the trade-offs usually still favor prepaid.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /technical/api-development/Mastering-Prepaid-Usage-Billing-in-Real-Time/",
          "author": "Tara",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-top-product-observability-tools-to-boost-business-efficiency": {
          "title": "Top Product Observability Tools to Boost Business Efficiency",
          "content"	 : "As software systems grow more complex, ensuring reliable performance and delivering a seamless user experience becomes increasingly challenging. Product teams need the ability to track and analyze system behavior in real-time, allowing them to address issues before they affect users. Product observability offers a proactive approach, enabling teams to gather data, monitor system health, and make informed decisions based on a complete view of both infrastructure and user interactions.By leveraging the right observability tools, teams can improve system performance, reduce downtime, and better understand how new features impact users. In this blog, we’ll take a closer look at the top product observability platforms available today and how they can help enhance business efficiency and product quality.What is Product Observability?Product observability is an advanced approach designed to help product teams understand the full impact of their work. The concept of observability has its roots in control theory, which allows engineers to determine a system’s internal states based solely on its external outputs. It involves proactively monitoring, measuring, and analyzing various product metrics to ensure continuous improvement. By leveraging data and insights, teams can identify patterns, learn from past actions, and build better products in future iterations.Product observability tools are central to this process, providing features such as logging, tracing, and metrics that are customized for product use cases. These tools enable persistent monitoring of key business and performance metrics, helping teams track the overall product direction, catch potential issues early, and improve system performance. A comprehensive observability platform not only collects data but also aggregates and visualizes telemetry data from distributed and complex systems.Observability platforms give product managers and engineers the ability to see how users interact with their software, offering deep insights that contribute to optimizing both the product and its underlying infrastructure. This unified view ensures that teams stay ahead by making informed decisions based on real-time visibility into their systems.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Benefits of Product ObservabilityProduct observability delivers a range of critical benefits that empower product managers and engineers to make more informed decisions. By providing visibility into how users interact with a product and correlating this with key business metrics, observability tools help teams understand user behavior, preferences, and potential pain points. This understanding leads to more effective product iterations, improved user experiences, and ultimately, greater business success.A key advantage of observability platforms is their ability to reduce risks associated with product changes. By monitoring system behavior in real time, teams can quickly spot how new features or updates impact both user experience and system performance. This allows for faster identification and rectification of issues, minimizing downtime and improving overall system stability.Observability tools also assist in root cause analysis by aggregating and visualizing data from complex, distributed systems. With this visibility, teams can dig deeper into performance metrics and identify the sources of problems, whether they are related to memory usage, network performance, or infrastructure components. This capability significantly enhances the team’s ability to troubleshoot issues effectively and optimize both system and application performance.The continuous feedback loop provided by product observability enables teams to respond proactively to potential issues, ensuring that the product evolves in alignment with both user needs and business objectives.Choosing the Right Observability ToolsSelecting the right observability tool is a crucial decision for product teams. With the increasing number of platforms available, it can be difficult to determine which one best meets a team’s specific needs. Decision-makers must consider not only the current requirements of their product but also the flexibility of the platform to scale with changing business needs.When evaluating observability platforms, several key factors should be taken into account:      Unified Observability Platform: Look for a tool that provides real-time insights across both applications and infrastructure. A unified platform enables teams to monitor system performance, user behavior, and infrastructure components from a single pane of glass, reducing complexity and improving efficiency.        Scalability and Flexibility: As a business grows, so too does the complexity of its software systems. The right observability platform should be able to scale in line with growing data volumes and expanding infrastructure, ensuring it can continue to provide valuable insights without requiring multiple tools.        Advanced Troubleshooting and Investigation: The ability to quickly identify and resolve issues is essential for maintaining high system performance. Observability tools should offer structured views for investigation, such as tracing and log aggregation, which help teams pinpoint the root cause of problems faster and with greater accuracy.  Ultimately, the right observability tool should empower product managers and engineers to not only monitor system health but also proactively address issues before they impact users. The ideal platform will provide real-time visibility into all aspects of your system, helping to ensure business continuity and high customer satisfaction.Integration with Existing SystemsIntegrating observability tools with existing systems is crucial for a seamless and efficient monitoring experience. Most observability platforms offer APIs, SDKs, and plugins that enable integration with various tools and technologies. For instance, many observability tools can integrate with popular cloud services such as AWS, Azure, and Google Cloud, allowing users to monitor their cloud infrastructure and applications from a single platform.Additionally, observability platforms can integrate with multiple tools, such as logging tools like ELK Stack, monitoring tools like Prometheus, and tracing tools like Jaeger. This integration enables users to collect and analyze telemetry data from various sources, providing a unified view of their system performance and behavior.When integrating observability tools with existing systems, it’s essential to consider factors such as data consistency, scalability, and security. Users should ensure that the observability platform can handle large volumes of telemetry data and provide real-time insights into system performance. This approach not only enhances the monitoring capabilities but also ensures that the observability platform can grow alongside the business, adapting to increasing data volumes and complexity.Top Observability Tools for 2024As observability becomes an essential aspect of product management and engineering, a variety of tools have emerged to help teams monitor and optimize their systems. Below are eight leading observability platforms for 2024, presented in alphabetical order:      AppDynamics: AppDynamics provides deep insights into application performance and user behavior, helping teams ensure smooth user experiences and quickly resolve issues. It offers application performance management (APM) features that allow teams to monitor system health in real time.        Datadog: Known for its comprehensive approach to infrastructure and application monitoring, Datadog offers extensive support for distributed systems. It provides real-time insights into metrics, logs, and traces, all in one unified platform, making it ideal for monitoring microservices architectures.        Dynatrace: Dynatrace leverages artificial intelligence to provide automatic root cause analysis and proactive monitoring of complex systems. Its scalability and ability to monitor cloud infrastructure make it a top choice for businesses with growing and evolving systems.        Grafana: A popular tool for visualizing system metrics, Grafana excels at helping teams create real-time dashboards. It integrates well with a wide range of data sources, offering flexibility for teams looking to monitor infrastructure and performance metrics.        Honeycomb.io: Honeycomb.io focuses on providing real-time observability and fast querying of complex distributed systems. It enables product teams to dig deeper into telemetry data and troubleshoot issues quickly, improving the speed of delivery and system reliability.        Moesif: Moesif is a leading observability platform specifically designed for APIs and product analytics. It provides granular insights into how users interact with APIs, which helps teams optimize the developer and user experience while boosting product performance.        New Relic: New Relic provides full-stack observability with advanced analytics, helping teams monitor everything from infrastructure to user experience. It offers a range of features, including APM, infrastructure monitoring, and synthetic testing, ensuring a holistic view of system performance.        Splunk: Splunk excels at handling large volumes of machine data, making it a top choice for organizations with complex systems. Its ability to analyze log data in real time helps teams gain insights into system behavior, user interactions, and security events.  Each of these tools offers a variety of features, from performance metrics and infrastructure observability to distributed system monitoring. Selecting the right platform depends on the specific needs of your team and the complexity of your software systems.Observability in PracticeIn practice, observability plays a crucial role in ensuring the reliability and efficiency of a software system, particularly for Site Reliability Engineers (SREs) and DevOps teams. These teams rely on observability tools to maintain the seamless operation of software systems, detect anomalies, and respond to issues before they escalate into major problems.The core value of observability lies in its ability to provide deep, actionable insights into the performance and behavior of a system. By continuously monitoring system metrics, logs, and traces, teams can proactively identify potential problems, troubleshoot issues, and optimize system performance. Observability is essential in environments where distributed systems and microservices are increasingly common, as these architectures introduce additional complexity that requires a sophisticated monitoring approach.A typical process for leveraging observability involves what is often referred to as a “debug journey.” This journey includes:      Identifying an Issue: Teams are alerted to potential problems via observability data, which can surface early indicators such as increased latency, unusual error rates, or abnormal system behavior.        Analyzing Data: With logs, traces, and performance metrics readily available, teams can analyze the root cause of the issue. This process involves correlating data points across the system to understand how different components are interacting and where the problem originates.        Resolving Issues: Once the cause is identified, teams can implement fixes or optimizations to restore system performance. Observability tools help track the impact of these changes in real time, ensuring that the issue is fully resolved.  By following this debug journey, product managers and engineers can ensure that their systems remain resilient, responsive, and performant, delivering a better experience for users.Cost-Benefit AnalysisImplementing an observability platform can have significant benefits for organizations, including improved system performance, reduced downtime, and enhanced customer experience. However, it’s essential to conduct a cost-benefit analysis to determine the return on investment (ROI) of an observability platform.The costs of implementing an observability platform include the cost of the platform itself, as well as the cost of integrating it with existing systems and training personnel to use it. Additionally, organizations may need to invest in additional infrastructure, such as storage and computing resources, to support the observability platform.On the other hand, the benefits of an observability platform include improved system performance, reduced downtime, and enhanced customer experience. These benefits can lead to increased revenue, reduced costs, and improved competitiveness.To conduct a cost-benefit analysis, organizations should consider the following factors:      The cost of the observability platform and its implementation        The cost of integrating the platform with existing systems        The cost of training personnel to use the platform        The benefits of improved system performance, reduced downtime, and enhanced customer experience        The potential ROI of the observability platform  By carefully weighing these factors, organizations can make informed decisions about investing in observability tools, ensuring that the benefits outweigh the costs and contribute to long-term business success.Getting Started with ObservabilityFor product teams looking to implement observability, the journey begins with establishing the right foundation. Modern observability tools empower software engineers, developers, and product managers with a data-driven approach across the entire software lifecycle. To ensure a successful observability implementation, teams should follow these key steps:      Identify Key Business and Performance Metrics: The first step in any observability strategy is to determine the critical business and system performance metrics to monitor. These might include uptime, response times, memory usage, or specific application-level metrics that align with your business goals. Understanding what’s most important for your product will guide the setup of meaningful monitoring practices.        Choose the Right Observability Platform: It’s essential to select a platform that offers a unified view of both application and infrastructure performance. A platform that supports real-time monitoring of logs, metrics, and traces is ideal for gaining a comprehensive understanding of system behavior and user interactions.        Implement Logging, Tracing, and Metrics: Logging provides valuable insights into system events, while tracing allows teams to track requests as they flow through distributed systems. Metrics give a high-level view of performance, helping teams identify trends and anomalies. Together, these three pillars of observability give teams a detailed view of how systems perform under different conditions.  By following these steps, teams can build a solid observability foundation that not only enhances system performance but also helps improve the product’s overall quality and reliability.Common Challenges and SolutionsImplementing product observability comes with its own set of challenges, particularly when it comes to managing the complexity of modern software systems. Below are some common challenges teams may face, along with practical solutions:  Managing Multiple Tools: One of the most frequent challenges is dealing with numerous tools that each provide a different slice of observability. Using disparate tools for metrics, logs, and traces can create silos of information, making it difficult to get a holistic view of system behavior.Solution: Opt for a unified observability platform that integrates metrics, logs, and tracing into a single pane of glass. A unified platform ensures that teams can access all the necessary data in one place, reducing complexity and improving efficiency.  Integrating with Existing Infrastructure: Many organizations struggle to integrate new observability tools with their current infrastructure. Legacy systems or complex distributed architectures can make it difficult to implement modern observability practices without disruptions.Solution: Choose an observability tool that is flexible and can integrate seamlessly with your existing systems, whether they are on-premise or cloud-based. Many modern observability platforms offer native integrations with cloud platforms and common infrastructure components, making the transition smoother.  Scaling Observability: As the business grows, the scale and complexity of the system increase, making it harder to maintain effective observability. Monitoring thousands of services and components can strain resources and create information overload.Solution: Leverage cloud services for scalability and flexibility in your observability tools. Cloud-based observability platforms can scale automatically with your infrastructure, ensuring you can continue to monitor systems effectively as they grow.By addressing these challenges with the right tools and strategies, teams can ensure their observability efforts are not only effective but also sustainable as the organization and its systems evolve.Measuring Success with Performance Metrics and ObservabilityTo measure the effectiveness of observability, product teams need to track specific performance and operational metrics that reflect the health and efficiency of their systems. Observability tools provide various dashboards and reports that help teams gauge their success in maintaining system reliability and performance. Some key metrics to consider include:      Mean Time to Detect (MTTD): This metric measures how quickly a team can detect an issue or anomaly in the system. The faster the detection, the quicker teams can begin the resolution process, minimizing the impact on users.        Mean Time to Resolve (MTTR): MTTR tracks the time it takes to fully resolve an issue after it’s been detected. Lower MTTR values indicate that teams are not only identifying problems quickly but also implementing effective solutions in a timely manner.        System Uptime and Downtime: Monitoring uptime is crucial for ensuring a reliable user experience. Observability platforms help track downtime events and their causes, giving teams the insights they need to prevent future occurrences.        User Behavior and Business Impact: Observability tools can also measure how changes in system performance affect user behavior. For example, teams can track whether slower response times result in user drop-offs, allowing them to prioritize performance improvements that have the greatest business impact.  By consistently tracking these metrics, product teams can evaluate the success of their observability efforts. Observability platforms also allow for the customization of dashboards to track the metrics most relevant to your specific product or infrastructure.Future Trends in ObservabilityThe observability landscape is rapidly evolving, with new trends and technologies emerging every year. Some of the future trends in observability include:      Increased Adoption of Cloud-Native Observability Platforms: As more organizations move to the cloud, there is a growing need for observability tools that are designed specifically for cloud-native environments. These platforms offer better integration with cloud services and can scale more effectively with dynamic cloud infrastructure.        Growing Importance of Artificial Intelligence (AI) and Machine Learning (ML): AI and ML are becoming increasingly important in observability, helping to automate the detection and resolution of issues. These technologies can analyze vast amounts of telemetry data to identify patterns and anomalies, providing deeper insights into system performance and behavior.        Increased Focus on Security and Compliance: With the rise of cyber threats and regulatory requirements, there is a growing emphasis on security and compliance in observability. Observability platforms are evolving to include features that help organizations monitor and ensure the security of their systems and data.        Growing Demand for Unified Observability Platforms: Organizations are seeking unified observability platforms that can monitor multiple systems and applications from a single interface. This approach simplifies monitoring and provides a comprehensive view of system performance and behavior.        Increased Adoption of Open-Source Observability Tools: Open-source observability tools are gaining popularity due to their flexibility, cost-effectiveness, and community support. These tools offer a wide range of features and can be customized to meet specific needs, making them an attractive option for many organizations.  As observability continues to evolve, organizations should stay up-to-date with the latest trends and technologies to ensure they can effectively monitor and optimize their complex systems. By adopting a unified observability platform and leveraging AI and ML, organizations can gain real-time insights into system performance and behavior, enabling them to improve customer experience, reduce downtime, and increase revenue.ConclusionProduct observability is no longer just a nice-to-have; it’s a critical component of modern software development and operations. By implementing a comprehensive observability strategy, product teams can proactively monitor system performance, improve user experiences, and reduce the risks associated with new feature releases or system changes. Choosing the right observability platform is key to gaining deep insights into both system behavior and user interactions, which ultimately drives business efficiency.Moesif offers a robust set of features specifically designed to help teams optimize their APIs and product performance, such as:      API Analytics and Monitoring: Gain a deep understanding of how users interact with your APIs, allowing you to identify key trends, improve developer experiences, and ensure the reliability of your APIs.        Real-Time Insights: Moesif provides real-time visibility into your system’s health, with advanced monitoring capabilities that allow you to proactively address performance issues before they impact users.        User Behavior Tracking: Moesif allows you to track user behavior across your product, enabling you to correlate user interactions with API performance, which helps product teams prioritize the most impactful changes.  Ready to take your product observability to the next level? Sign up for Moesif today and start your 14-day free trial—no credit card required. With Moesif, you’ll have access to powerful tools that will help you improve system performance, enhance user experiences, and optimize your API usage.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue from your APIs?            Monetize your APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Top-Product-Observability-Tools-to-Boost-Business-Efficiency/",
          "author": "Dylan",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-15-best-api-monitoring-tools-for-api-observability": {
          "title": "15 Best API Monitoring Tools for API Observability",
          "content"	 : "Application programming interfaces (APIs) power countless applications and services in today’s world. However, the complexity and scale of modern API ecosystems often create a blind spot for developers and operations teams. API monitoring can solve a lot of these challenges.In this article, we discuss some core concepts and the benefits of API monitoring, along with key API metrics you should track. We also discuss how to select the right monitoring tool and a curated list of the top 15 API monitoring tools available today.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Table of Contents  Key Takeaways  Understanding API Monitoring          What is API Monitoring?      Importance of API Monitoring      Key API Metrics        Benefits of API Monitoring          Improved API Performance      Enhanced Security      Increased Reliability        Criteria for Selecting an API Monitoring Tool          Ease of Use      Comprehensive Monitoring Capabilities      Integration with Existing Systems      Scalability        Real-time Monitoring and API Observability          Importance of Real-time Data Collection      Analyzing Log Data for Insights        API Analytics and Performance Optimization  Top 15 API Monitoring Tools          Moesif      Datadog      New Relic      Postman      Better Stack      SigNoz      Prometheus      Graphite      Sauce Labs      Sematext      Assertible      RapidAPI      AppDynamics      SmartBear (AlertSite)      Dotcom-Monitor        How to Implement API Monitoring          Setting Up Monitoring Tools      Analyzing Data      Continuous Improvement        Best Practices for Effective API Monitoring          Regular Testing and Validation      Proactive Alert Systems with Real-time Monitoring Tools      Documentation and Reporting        SummaryKey Takeaways  API monitoring helps you maintain the performance, security, and reliability of applications. It tracks key metrics like response times, latency, and error rates to detect and resolve issues promptly.  An effective API monitoring tool offers capabilities like ease of use, real-time monitoring, detailed performance metrics, robust alerting, and integration with existing systems.  To pick the best tool, consider factors such as scalability, comprehensive monitoring features, integration with current platforms, and user-friendliness.Understanding API MonitoringAPI monitoring helps you continuously observe key metrics like response times, latency, error rates, and overall API health. Telemetry data plays a crucial role in API monitoring by gathering and visualizing data related to API performance, helping to identify potential issues such as errors and latency. It allows you to maintain robust APIs that function smoothly without any hiccups. As a result, applications and websites that depend on your APIs remain performant, secure, and healthy.A comprehensive API monitoring solution covers a lot of ground for organizations. It can maintain app and site performance, security, uptime, and user monitoring, while also facilitating recovery from outages. However, the challenges of API monitoring often revolve around scale, experience, and the need for comprehensive application monitoring.What is API Monitoring?API monitoring is the process of observing and evaluating the performance, functionality, and reliability of APIs to ensure they meet expected standards. It involves tracking the availability and speed of APIs, along with the correctness of data flowing to and from them.API monitoring is a continuous process, specially for those in production. It helps maintain their performance and reliability, by detecting issues like slowdowns, errors, and functionality problems before they affect end users. You can achieve this consistency and continuity through automation, which a lot of API monitoring tools offer out-of-the-box. Thanks to smart automation, you get real-time insights into API performance.Importance of API MonitoringAPI monitoring guarantees proper communication among software applications and different services. It acts as a preventive measure against issues that could disrupt services. Monitoring helps you identify bugs and changes in third-party API endpoints, thereby averting timeouts or latency issues. Additionally, robust alerting allows for timely responses to potential disruptions. You get minimum downtime and consequently, avoid disrupting and potentially improving user satisfaction.API monitoring also plays a critical role in optimizing performance and preventing security breaches. It makes sure that your API delivers accurate data to users, which directly improves user engagement. In this day and age, failing to monitor APIs effectively means violating basic security protocols and hence compromising millions worth of assets.Key API Metrics      Response time: Tracks the total time from the moment a request reaches the server until the server sends back a response. This metric includes both network delay and server processing time. Monitoring response time allows developers to spot performance bottlenecks and optimize processing efficiency. Key performance indicators (KPIs) such as response time, error rate, and throughput are crucial for identifying issues and anomalies within the system. When response time increases, it often signals server issues or network congestion.        Latency: Captures the time delay between when a request leaves the client and reaches the server, focusing on network-related delays. High latency often points to poor network conditions or long geographical distances between the client and server. Reducing latency improves data transmission speed and improves user experience.        Error rate: Measures the percentage of API requests that return errors, like 4xx (client errors) or 5xx (server errors). A higher error rate usually indicates bugs in the API, misconfigurations, or client-side issues. Tracking error rates helps teams quickly pinpoint and resolve underlying problems, ensuring reliability.        Errors per minute: Tracks how frequently errors occur in API requests over time, providing insight into the API’s stability. Sudden spikes in this metric usually suggest critical issues or outages. Regularly monitoring it ensures that teams respond quickly to maintain a smooth user experience.        CPU and Memory Usage: Displays the resources the server allocates for running the API. High usage often indicates inefficient code or excessive load, which can slow down response times. In serverless environments, monitoring CPU and memory can highlight cold start delays or potential resource limits.        Availability or uptime: Tracks the percentage of time the API remains accessible and operational, typically expressed as a percentage over a specific period. A higher uptime indicates a reliable and resilient API.  Benefits of API MonitoringImproved API PerformanceRoutinely monitoring APIs helps ensure they perform optimally by identifying slowdowns and performance bottlenecks. Some monitoring tools can monitor transactions end-to-end. You get real-time metrics that can pinpoint exact issues you’re troubleshooting for.Another valuable practice is to leverage tools that offer intelligent monitoring at scale. For instance, Moesif goes beyond traditional metrics by enabling proactive API monitoring. With advanced anomaly detection, Moesif helps teams surface potential issues before they escalate—reducing downtime and improving user trust. Its real-time alerts, combined with deep analytics, allow you to resolve API slowdowns and errors swiftly.Moreover, you can simulate real-world scenarios and continuously check API responses in those scenarios. This helps you identify possible points of failure and performance issues before your product hits production.Enhanced SecurityAPI monitoring can reveal anomalous behavior that may indicate potential breaches. You can use thresholds and anomaly detection in alerting systems to identify issues that routine checks might fail to spot.Artificial intelligence has impacted almost every aspect of today’s digital landscape, including API monitoring tools. These modern tools use machine learning algorithms to detect significant deviations from expected patterns.With more digitalization, cyber threats have been rising alarmingly. If you want to minimize security threats and safeguard sensitive data, you have to invest in monitoring your APIs. Look for real-time security features in monitoring tools such as authentication, authorization, and encryption.Increased ReliabilityRegular API monitoring contributes to higher uptime. It guarantees that applications stay available and reliable for users. Monitoring your API’s availability ensures that it remains operational and accessible to users. Specifically, include functional uptime validation in your monitoring pipelines so that various API services remain operational, not just available.In a nutshell, API monitoring brings increased reliability and availability of web services through the following:  Alerting administrators about dependencies.  Allowing IT teams to act to keep applications online, ensuring API uptime.  Helping avoid extended periods of downtime or degraded performance by addressing issues before they impact customers.  Proactively detecting and responding to potential outages for continuous service.  Collecting metrics on API usage and anomalies, so that you can fine tune your rate-limiting and management strategies for API calls.Criteria for Selecting an API Monitoring ToolChoosing the right API monitoring solution can significantly impact the success and efficiency of your digital operations. As a matter of strategic investment, you have to consider several key aspects, including operational requirements and feature sets, before closing in on a tool. Let’s discuss the key features API monitoring should provide you:Ease of UseA user-friendly API monitoring tool greatly compliments your existing resources. It has great accessibility features so your team members, irrespective of skill levels, can easily embrace it. An ideal tool in this regard should have the following key traits:  Easy to set up and configure without extensive coding or technical skills.  User-friendly, reducing the learning curve and increasing team productivity.  Allows quick onboarding and efficient utilization.Comprehensive Monitoring CapabilitiesIdeally, organizations want a tool that covers most, if not all, of their monitoring requirements. For example:  Real-time monitoring, including uptime monitoring.  An array of performance metrics such as response time, latency, throughput, and error rates.  Detailed insights into API performance and usage trends.  A detailed breakdown of network timing data for faster root cause analysis.  Response time by location to pinpoint issues more efficiently.  Robust alerting functionality to quickly handle and debug API errors.  The ability to separate network latency from API response time in its alerts.Integration with Existing SystemsWhen you integrate an API monitoring tool with existing systems, pay attention to minimizing disruptions and streamline workflow. The right API monitoring tool should integrate with existing tools and platforms for API development and testing. API monitoring tools should support seamless integration with the frameworks and programming languages the APIs use.Moesif offers plug-and-play compatibility with popular platforms like AWS, Kong, and Azure API Management. This allows engineering teams to integrate API analytics and monitoring without reworking existing infrastructure—streamlining observability while minimizing development friction.You don’t want to pick a tool that doesn’t work well with your existing development and operations platforms. Moreover, if you can integrate your API monitoring tool with other monitoring systems, you can get a more comprehensive overview of system health. It can likely narrow down your focus area and provide a more unified analytical view into your entire system as a whole.ScalabilityAPI monitoring tools must scale to accommodate growing API traffic and complex systems as a business expands. When selecting an API monitoring tool, assess its ability to scale with your organization’s growth and increasing API traffic. A scalable API monitoring tool should handle higher loads and increased complexity without compromising performance.Real-time Monitoring and API ObservabilityReal-time monitoring and API observability are crucial components of modern software development, enabling developers and operations teams to gain deep insights into the behavior and performance of their APIs. By collecting, analyzing, and visualizing relevant data, teams can identify and address issues promptly, ensuring the reliability, availability, and performance of their APIs.Real-time monitoring tools provide continuous visibility into API operations, allowing teams to detect anomalies and performance bottlenecks as they occur. This proactive approach helps in maintaining optimal API performance and minimizing downtime, ultimately enhancing user experience and satisfaction.Importance of Real-time Data CollectionReal-time data collection is essential for effective API observability. It involves collecting data from various sources, such as logs, metrics, and tracing, to provide a comprehensive view of API performance. Real-time data collection enables teams to detect issues quickly, reducing the mean time to resolution (MTTR) and improving overall system reliability.By continuously gathering data on key metrics like response times, error rates, and throughput, teams can monitor API performance in real-time and make informed decisions. This real-time data collection helps in identifying trends and patterns that may indicate potential issues, allowing for timely interventions and optimizations.Analyzing Log Data for InsightsLog data is a critical component of API observability, providing valuable insights into API behavior and performance. By analyzing log data, teams can identify trends, patterns, and anomalies, enabling them to optimize API performance, troubleshoot issues, and improve overall system reliability. Effective log analysis requires well-defined response procedures, ensuring that teams can respond quickly and effectively to issues.Logs capture detailed information about API requests and responses, including timestamps, status codes, and error messages. By systematically analyzing this log data, teams can pinpoint the root causes of performance issues and implement targeted solutions. Additionally, log analysis helps in monitoring compliance with security protocols and detecting potential breaches.API Analytics and Performance OptimizationAPI analytics and performance optimization are closely related, as analytics data provides valuable insights into API performance. By leveraging data from API analytics, teams can identify areas for optimization, improving API performance and reducing latency.API analytics involves collecting and analyzing data on various performance metrics, such as response times, error rates, and usage patterns. This data-driven approach helps in understanding how APIs are being used and identifying potential bottlenecks. By continuously monitoring and analyzing API performance, teams can implement optimizations that enhance efficiency and user experience.Performance optimization strategies may include refining API endpoints, optimizing database queries, and improving server configurations. By using API analytics to guide these efforts, teams can ensure that their APIs remain performant, reliable, and scalable, meeting the demands of modern applications and services.Top 15 API Monitoring ToolsHopefully, the preceding section have given you the core guidelines to help you choose the best tool for your use case. To further help you make an informed decision, here we curate a list of the top 15 API monitoring tools. We briefly discuss each with its own unique features and capabilities to give you a head start in your decision-making process.MoesifMoesif provides the following features:  Detailed insights into API usage patterns, user behavior, and product-level metrics.  Tracking individual user actions and API usage.  Real-time API event data, providing instant feedback on any issues, while enabling businesses to visualize and investigate live API traffic.  Advanced, granular, and real-time alerting based on both technical performance and business metrics, with immediate notification of anomalies or critical issues.  Advanced anomaly detection to identify unusual patterns or behaviors in API usageApart from these features, Moesif stands out by supporting API governance. Directly from the same platform, you can monitor and enforce compliance with legal, security, or rate-limiting rules.DatadogDatadog APM (Application Performance Monitoring) offers:  AI-powered code-level distributed tracing from browser and mobile applications to backend services and databases.  Correlation of traces with logs, metrics, RUM data, security signals, and other telemetry.  Faster detection and resolution of root causes.New RelicNew Relic provides comprehensive monitoring and optimization for infrastructure and applications. Here are some of its features:  APM: offers in-depth visibility into application performance  Infrastructure monitoring: monitors the supporting infrastructure  Real user monitoring: tracks user experience in real-time  Synthetic monitoring: simulates user interactions to test application performance.PostmanPostman, a popular API testing platform, also comes with comprehensive monitoring options. Here are some of the key features it offers:  Scheduled monitoring to automate and continuously check API health.  Custom assertions in JavaScript to validate API responses.  Regional testing to identify performance issues across different locations.  Detailed reports and alerts with insights and real-time notifications for API failures or thresholds.Better StackBetter Stack help developers troubleshoot API-related issues in real-time, serveing as a unified observability platform. It offers integrations with popular third-party tools and platforms:  Heroku  Datadog  New Relic  AWS CloudWatchSome of Better Stack’s key features include the following:  Integration with incident management tools to notify teams of API issues instantly through multiple communication channels.  Testing from multiple global regions for detection of location-specific performance issues.  Comprehensive performance reports, including latency, uptime, and response time metrics.SigNozSigNoz is an open-source APM tool designed specifically for monitoring API performance. It provides detailed metrics and tracing data, allowing users to capture and visualize tracing data. As an open-source tool, SigNoz offers flexibility for customization and extension based on specific needs.PrometheusPrometheus is designed to track the performance, health, and behavior of APIs, among other components. It utilizes PromQL, a powerful query language, for different features:  Selecting and aggregating time series data  Creating custom metrics and alerts  Visualizing data in real-time  Generating detailed performance reportsGraphiteGraphite is a prominent API monitoring tool known for tracking and visualizing time-series data. It specializes in storing numeric metrics over time, allowing for detailed performance monitoring.It also integrates with Grafana, among many other tools, to enhance your monitoring experience by providing rich, interactive visualizations of collected data.Sauce LabsSauce Labs is an all-in-one platform providing comprehensive tools for both functional and performance monitoring. It supports thorough functional testing to ensure API correctness and in-depth performance monitoring to track API responsiveness and load handling.Sauce Labs also supports cross-browser and device monitoring so you can verify your API’s functionality across different environments.SematextSematext offers a comprehensive monitoring solution that supports infrastructure, databases, and applications. It provides real-time insights and monitoring capabilities across different environments. You can configure alerts and send notifications through various channels like email, Slack, or webhooks. Sematext also integrates with other monitoring solutions and dashboards, making it easy to track API health alongside other infrastructure metrics.AssertibleAssertible simplifies functional API testing and monitoring by allowing users to create tests. These tests define the API response expectations using standard validation patterns.It supports collaborative monitoring so that teams can create tests, debug errors, and track API performance together. Assertible also offers codeless API monitoring, eliminating the need for writing code to validate APIs.RapidAPIRapidAPI centralizes various API operations, providing a singular platform for managing and interacting with APIs. It offers a hub containing a wide array of APIs that have already gone through testing and are ready for use. Like some other tools we’ve already discussed, it offers real-time alerts, detailed metrics reports, and more.AppDynamicsAppDynamics offers these key features:  Comprehensive visibility into API performance, tracking response times, throughput, and error rates across distributed systems.  Anomaly detection in API performance through machine learning and automatic alerts when issues arise.  Correlation of API performance with business transactions, helping teams understand the impact of API performance on end user experience.SmartBear (AlertSite)AlertSite by SmartBear offers codeless synthetic monitoring to ensure smooth and efficient API transactions. It monitors API availability, performance, and functional correctness. Users can easily create advanced API monitors from the dashboard or by reusing existing OpenAPI or Swagger definitions or SoapUI tests.Dotcom-MonitorDotcom-Monitor provides tools to assess web performance in different ways:  Simulating user interactions  Ensuring websites perform optimally  Offering detailed performance reports with actionable insights to optimize website functionality.The multi-step monitoring feature can simulate complex user journeys across web applications to identify performance issues.How to Implement API MonitoringTo implement your API monitoring system or pipeline, the first step consists of setting up API tests. These API tests must reflect different aspects such as verifying different functionalities, performance monitoring, and availability inspection. A robust test suite can thereby make sure your API operates efficiently.You can simplify the process by using tools like APIMetrics. It allows for easy setup and configuration through a user-friendly interface.Let’s discuss the implementation process briefly.Setting Up Monitoring ToolsBegin by identifying the key metrics, such as response time and uptime, that are vital to your API’s performance. Then integrate your monitoring tool with CI/CD pipelines for continuous testing and monitoring. We recommend that you set up automated testing in every stage of the CI/CD pipeline.Finally, set up alerts in your monitoring tools to receive notifications when API performance metrics breach predefined thresholds.Analyzing DataInspect the monitoring data and reports your tool generates. For example, trends in response time can reveal patterns suggestive of performance decline.Another thing to focus on is maintaining comprehensive documentation of API responses and errors. This immensely helps with troubleshooting and debugging sessions since you may already have the relevant piece of information close at hand. When prospecting new clients or working on POCs, this documentation can also come in handy.Continuous ImprovementRegularly update and refine your monitoring configurations. Always remember to refer to historical data insights. Incorporate feedback from monitoring data into the development cycle to address performance issues proactively and improve API reliability.  A combination of these approaches can lead to continuous improvement in API performance.Best Practices for Effective API MonitoringSo you’ve finally selected the perfect monitoring tool for your use case. Now if you want to maximize it and the resources API monitoring gives you, you have to abide by some best practices.Regular Testing and ValidationRegular API testing offers several benefits. For example:  Identification and resolution of issues before they impact end users.  Compatibility of application code with new schema versions of third-party APIs.  Continuous monitoring for feature changes to ensure compatibility with external services after updates or bug fixes.Frequent validation of APIs against their specifications ensures they meet the required standards for performance and security.Proactive Alert Systems with Real Time Monitoring ToolsAlerts can inform users about any deviations from predefined thresholds. It allows you to proactively resolve issues and maintain optimal API performance. You can’t expect better user experience by keeping users in the dark about issues, specially if they can impact the users directly. It can cause catastrophic issues and disruptions in their lives that can potentially lead to you losing valuable customers and revenue.Effective alerting regimens ensure the right people get notified of problems as soon as they emerge. Tools with strong alerting capabilities can notify you immediately of API errors. They help you address issues before they impact end users and escalate to serious levels. If they do impact end users, you get the time to inform them beforehand.Documentation and ReportingMaintaining detailed documentation of API metrics such as response time, error rate, and uptime enhances transparency and effectively monitors progress. Thorough documentation of API monitoring processes and results help you track progress and maintain transparency. Regular reporting on API performance and issues can help in making informed decisions and planning improvements.SummaryAPI monitoring has become an indispensable practice to maintain the health, performance, and security of today’s digital infrastructure. Through effective implementation of API monitoring systems, you can guarantee your product a stable trajectory towards sustainable growth.We’ve briefly discussed Moesif and some of the features it offers as an API monitoring solution. But Moesif stands out in the market as a “comprehensive platform” that can cover your needs throughout the entire API lifecycle.Our AI-enhanced platform gives you user-centric API observability and monitoring, powered by robust analytics tools. We’ve also built powerful monetization features that integrates seamlessly with popular providers like Stripe and Recurly.But you don’t have to take our words for it. Sign up today for a free trial and try Moesif for yourself, no credit cards required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue from your APIs?            Monetize your APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/15-Best-API-Monitoring-Tools-for-API-Observability/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-setting-up-a-free-paid-plan-with-moesif-and-stripe": {
          "title": "Setting Up a Free/Paid Plan With Moesif and Stripe: A Guide to Frictionless Onboarding of Customers to Your Platform",
          "content"	 : "This guide walks you through setting up a Free/Paid plan for your customers using a supported payment platform integrated with Moesif, focusing on tracking usage and managing upgrades to paid tiers. While the steps in this guide are specifically for Stripe, they can be adapted for other supported payment platforms.You can manage your free/paid plans directly within Moesif’s Product Catalog, which provides seamless integration with Stripe, or configure them directly in Stripe. Product Catalog provides a simple, user-friendly interface to create, manage, view, and archive plans & prices across your billing providers.By using this, you can manage Stripe prices and products directly within Moesif, which keeps your subscription tiers and usage limits synchronized across both platforms.You can also automate setting up plans and prices in the product catalog using API Access.Prerequisites  A Stripe account.  A Moesif account configured to track API usage.  Webhooks configured between Stripe and Moesif to sync customer and subscription information automatically.  It is crucial to set up Moesif webhooks before customer creation or subscription setup to ensure automatic syncing of customer data. For instructions, refer to Moesif’s guide on configuring Stripe webhooks.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Setting Up and Managing Free Plans with Moesif and StripeYou can set up your free/paid plan either directly through Moesif’s Product Catalog or in Stripe.Step 1: Set Up a Free Plan in Moesif Product Catalog or StripeUsing Moesif’s Product Catalog  Log in to your Moesif dashboard.  Navigate to the Product Catalog section.  Create a new product for your free tier (e.g., “Free” or “Onboarding”).  Add a pricing plan for the free tier, setting the price to $0, and define any credits (e.g., API usage or feature credits) for the free plan.  Specify the maximum usage allowed under the free tier, and set up any usage limits for features or API calls that will be tracked by Moesif.  Plans and prices created in Moesif’s Product Catalog will automatically flow through to Stripe via configured webhooks, syncing across both platforms to ensure consistent pricing and subscription management.Using Stripe’s Product CatalogAlternatively, you may use Stripe’s Product catalog to accomplish the same task.The following steps demonstrate how to create a product, define its pricestructure, and create a billing meter for the price in Stripe.  Go to your Stripe Dashboard.  Go to Product Catalog and then select Create product.  Select Recurring and then select More pricing options.  Select Recurring and choose Usage-based as the pricing model. You canchoose the usage amount as package, unit, or tier.  Define your price amounts, for example, $0.01 for each 1k API requests.      In the Meter section, select the plus icon + to create a billing meter to associate the price with.    a. Enter the meter name.    b. Enter the event name for which the billing meter reports usage.    c. Select the aggregation method for meter events. Moesif supports the Sum and Last aggregation methods.    d. Expand Advanced Options and make sure that Event Time Window is set to Raw.    In the Advanced section, enter a description for the price.  Select Next and then select Add product to finish.Step 2: Onboard Customers and Subscribe Them to the Free PlanCustomer CreationImplement an onboarding process that automatically creates new customers in Stripe when they sign up. You can accomplish this using either the user interface (UI) or Stripe’s API.Using the UI  Navigate to the Customers section in your Stripe dashboard.  Click + Add customer to manually add customer details.Using the APIExample API call for customer creation.curl https://api.stripe.com/v1/customers -u "sk_test_51M9ck5LMAM0dDzVTwZqbPj5V5ZijtrncXI6bx73g3JhwjI6LQ0IzKHfBMNMkzmZkUW7acuEm8vrjJeRC7cIqfpM00dpCISo7e:" -d name="Jenny Rosen" --data-urlencode email="jennyrosen@example.com"Subscribe Customers to the Free PlanOnce the customer is created in Stripe, you can subscribe to the free pricing plan either using the Stripe dashboard UI or via the API.Using the UI  In the Customers section, select the customer and click Add Subscription.  Choose the free tier plan/price you created and create a subscription.Using the APIExample API call for subscribing the customer to the free plan.curl https://api.stripe.com/v1/subscriptions -u "sk_test_51M9ck5LMAM0dDzVTwZqbPj5V5ZijtrncXI6bx73g3JhwjI6LQ0IzKHfBMNMkzmZkUW7acuEm8vrjJeRC7cIqfpM00dpCISo7e:" -d customer="cus_Na6dX7aXxi11N4" -d "items[0][price]"="price_1MowQULkdIwHu7ixraBm864M"Step 3: Monitor Usage in Moesif Using Billing MetersSet Up a Billing Meter for Free Tier  In Moesif, navigate to the Billing Meters section and create a new meter to track the usage of the free plan, such as API calls or other relevant metrics associated with the free tier.Step 4: Notify Customers to UpgradeIf you have access to Moesif’s Governance Rules, you can configure policies to either inform customers or block access once they have reached their free tier limits.If you don’t have access to governance rules, you can manually track usage through Moesif reports or billing meters and notify customers to upgrade or block their access when they exceed their free tier limits.Set Up Governance Rules/Behavioral Emails for Upgrade NotificationsRegardless of tier, all Moesif customers can use the Behavioral Emails feature to send automatic email alerts, prompting users to upgrade when they approach the end of their free credits.Managing Upgrades and Paid Subscriptions with Moesif and StripeStep 1: Set Up a Paid Plan in Moesif Product Catalog or StripeUsing Moesif’s Product Catalog  Log in to your Moesif dashboard.  Navigate to the Product Catalog section.  Create a new product for your paid tier (e.g., “Pro Plan” or “Enterprise Plan”).  Add a pricing plan for the paid tier, specifying the appropriate price (e.g., monthly or annual subscription price).  Define any usage credits or limits (e.g., API usage or access to premium features) for the paid plan.  Specify the maximum usage allowed under the paid plan, and set up usage limits for features or API calls that will be tracked by Moesif.  Plans and prices created in Moesif’s Product Catalog will automatically flow through to Stripe via configured webhooks, ensuring that your pricing and subscription data is consistently synced for billing and customer management.Using Stripe’s Checkout SystemAlternatively, you can use Stripe’s Checkout System.  Log in to your Stripe dashboard.  Navigate to the Product Catalog section.  Create a new product for your paid tier (e.g., “Pro Plan” or “Enterprise Plan”).  Add a pricing plan, specifying the correct price for the paid tier (e.g., monthly or annual subscription fee).  Set up usage limits for the paid plan, such as API calls or feature access, based on the credits set in Stripe.  Plans and prices created in Stripe will automatically sync with Moesif via configured webhooks, ensuring that Moesif can track API usage, billing, and subscription management seamlessly.Example API call for upgrading to a paid plan via Stripe:curl https://api.stripe.com/v1/subscriptions -u "sk_test_51M9ck5LMAM0dDzVTwZqTbPj5V5ZijtrncXI6bx73g3JhwjI6LQ0IzKHfBMNMkzmZkUW7acuEm8vrjJeRC7cIqfpM00dpCISo7e:" -d customer="cus_Na6dX7aXxi11N4" -d "items[0][price]"="paid_plan_price_id"Step 2: Monitor Usage in Moesif Using Billing MetersSet Up a Billing Meter for Paid TierIn Moesif, navigate to the Billing Meters section and create a new meter to track the usage of the paid tier, such as API calls or other relevant metrics associated with the paid tier. This usage is then synced over to Stripe in periodic intervals.Step 3: Notify Customers About Paid Plan Usage and UpgradesOnce customers are subscribed to a paid plan, you can monitor their usage and communicate with them to manage further upgrades or notify them about important plan details.Set Up Governance Rules/Behavioral Emails for NotificationsIf you have access to Moesif’s Governance Rules, you can configure policies to notify customers when they are approaching the limits of their paid plan. You can also set rules to automatically block access to premium features if they exceed their limits, prompting them to upgrade to a higher tier if necessary.If you don’t have access to Governance Rules, you can manually track customer usage through Moesif reports or billing meters, and notify them when they approach or exceed their plan limits. You can also send them upgrade options if their usage indicates they may benefit from a higher plan.All Moesif customers, regardless of their plan, can use Behavioral Emails to send automated email alerts. These emails can notify users when they are nearing their usage limit or if there are any upcoming subscription changes, encouraging them to consider upgrading to a more suitable paid plan or premium features.By utilizing Governance Rules and Behavioral Emails, you can ensure timely communication with customers, helping them manage their subscription usage effectively while promoting potential upgrades to meet their growing needs.By following the steps outlined above, you can effectively onboard customers into a free plan, monitor their usage, and seamlessly transition them to a paid tier using Stripe and Moesif. This flow ensures that you can track API usage, apply credits, and provide a smooth upgrade path, improving the customer experience and optimizing your monetization strategy. Sign up today for a free trial today to try it yourself, no credit card required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue from your APIs?            Monetize your APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Setting-Up-a-Free-Paid-Plan-With-Moesif-and-Stripe/",
          "author": "Venkat",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-ai-customer-experience": {
          "title": "Enhancing AI Customer Experience: A Practical Guide",
          "content"	 : "Organizations are harnessing the power of AI to revolutionize products and services across industries. But AI-powered solutions have been getting more sophisticated. We need to redesign and amend our approach to understanding how customers experience these solutions.Unlike traditional products, AI solutions are dynamic, continuously learning and adapting. Traditional metrics may fall short of capturing the nuances of how users interact with AI. We need better ways to understand and anticipate customer needs.This article delves into why AI customer experience (CX) is different, explores the key metrics and customer data you should be tracking, and outlines strategies for building a comprehensive CX program.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Table of Contents  Why AI Customer Experience is Different (and Why it Matters)          The Ever-Evolving Nature of AI      The Black Box Problem      The Impact of Bias      The Emotional Connection      The High Stakes of Error        Why It Matters  Key Metrics for Tracking AI Customer Experience          Usage Metrics                  Time Spent Using the AI          Features Most Utilized          Frequency of Interactions                    Performance Metrics                  Accuracy of AI Responses          Speed of Processing          Task Completion Rates                    User Satisfaction Metrics                  Surveys and Ratings          Feedback Forms          Sentiment Analysis                    Behavioral Metrics                  Drop-Off Points          User Flow Analysis          Feature Adoption Rates                    Error Analysis                  Identifying and Categorizing Errors          Root Cause Analysis                      Building a Comprehensive AI CX Strategy          Data Collection: The Foundation of Your Strategy                  Track Across All Touchpoints in the Customer Journey          Capture Qualitative and Quantitative Data          Leverage User Analytics Platforms          Integrate with CRM Systems                    Feedback Loops: The Voice of Your Customers                  Active Solicitation          Real-time Feedback          Incentivize Participation          Act on Feedback                    Experimentation: The Path to Optimization                  A/B Testing          Multivariate Testing          Data-Driven Decision Making                    Transparency: Building Trust with Users                  Explainable AI (XAI)          Transparent Data Practices          Open Communication                      ConclusionWhy AI Customer Experience is Different (and Why it Matters)The rise of artificial intelligence has ushered in a new era of customer experiences. AI products demand a fresh perspective and approach. Let’s discuss this in more detail.The Ever-Evolving Nature of AIUnlike static products, AI solutions are dynamic. They constantly learn and adapt from new data inputs and interactions. Some solutions can analyze customer behavior to change over time. This perpetual evolution means that the customer experience with AI can shift and improve—or sometimes regress—over time. Therefore, you must monitor customer experience with continuous attention and adjustment. Companies must invest in tools and practices that allow them to track these changes effectively, ensuring that the AI systems continue to meet customer needs and expectations.The dynamic nature of AI means that the experience doesn’t maintain uniformity across users or time. Different customers may have varying experiences based on their interactions, contexts, or even device differences. This variability requires businesses to optimize every interaction for the individual user. To implement this personalized approach, you need advanced analytics and feedback mechanisms to capture and respond to customer sentiments in real-time.The Black Box ProblemAI decision-making processes are often complex and opaque, sometimes even to the developers who created them. This lack of transparency can lead to mistrust or confusion among users. To address this challenge, AI customer experience must prioritize explainability and transparency so that users understand the how and why behind the decisions. Clear, understandable explanations can build trust and confidence, helping users feel more comfortable with AI interactions.Customers understanding the rationale behind AI decisions leads to more positive and constructive engagement. Therefore, try developing user-friendly interfaces and communication strategies that demystify AI processes. By focusing on explainability, companies can create a more trustworthy relationship with their customers.The Impact of BiasAI models have as much value as the data you train them on. Unfortunately, this data can sometimes carry biases that lead to unfair or discriminatory outcomes. This can result in a compromised customer experience, particularly for marginalized or underrepresented groups. Companies must actively monitor their AI systems for bias and implement strategies to so that their AI solutions are fair and equitable for all users.Addressing bias in AI not only poses technical challenges but also an ethical imperative. Organizations must commit to ethical AI practices. They must recognize the fact that without unbiased AI, you cannot build trust and maintain a positive reputation.By prioritizing fairness and inclusivity in their AI customer experience strategies, companies can foster a more equitable environment where all users feel valued and respected. This commitment to ethical AI can also set a company apart in the marketplace, enhancing brand loyalty and customer satisfaction.The Emotional ConnectionAs AI becomes more sophisticated, it can evoke a wide range of emotions in users, from delight to frustration, and even fear. You can craft a positive customer experience by understanding the emotional impact of AI interactions. AI customer experience strategies must consider these emotional dynamics, striving to build trust and rapport with users. This involves designing AI systems that have empathy and responsiveness, capable of recognizing and adapting to users’ emotional states.Creating an emotional connection with users goes beyond functionality. It requires a deep understanding of human behavior and psychology. In that regard, companies can leverage emotional intelligence in AI design and natural language processing (NLP). This allows them to create personalized service and more meaningful interactions that resonate with users. It will also assist and compliment customer service agents.The High Stakes of ErrorIn sensitive domains like healthcare or finance, AI errors can have significant consequences. Mistakes can lead to severe financial or personal repercussions for users.Therefore, we must ensure the reliability and accuracy of AI systems in these domains. AI customer experience must prioritize error detection and correction, implementing robust protocols to mitigate the impact of any mistakes. A comprehensive approach to quality assurance, involving rigorous testing and validation processes, can ensure that AI solutions perform as intended.In addition to technical safeguards, companies must also develop effective communication strategies for handling errors. You want to maintain transparency and accountability when addressing AI mistakes. Users feel assured that you are considering their issues seriously and working on promptly resolving them.By proactively addressing errors and learning from them, organizations can enhance trust and confidence in their AI systems. This ultimately improves the overall customer experience and in the long run ensures the success of AI-driven initiatives.Why It MattersAI customer experience (CX) influences how companies interact with their customers and leverage AI technology to drive business success. AI CX builds trust—one of the key reasons it matters.In this era, AI’s presence dominates our everyday lives. Therefore, transparent and explainable AI systems foster trust among users. Customer understanding of how AI makes decisions results in long-term adoption and engagement. Trust, the foundation of any successful relationship, gives users confidence and security in AI-driven products and services.Mitigating risk is another critical aspect. Proactively addressing potential biases and errors within AI systems can safeguard brand reputation and avoid negative consequences for users. This involves implementing rigorous testing and monitoring processes to identify and rectify issues before they escalate. A proactive approach to risk management can prevent incidents that can damage customer trust and loyalty. Moreover, demonstrating a commitment to ethical AI practices enhances a company’s reputation as a responsible and trustworthy brand.AI CX also drives innovation. The data generated through AI customer interactions provides valuable insights into user behavior and preferences. By analyzing this data, companies can identify opportunities for developing new and improved AI features that better meet customer needs. You can use predictive analytics to analyze customer sentiment and behavior to prevent churn and facilitate meaningful interactions. This iterative approach ensures that AI solutions remain relevant and effective, continuously enhancing the customer experience.Excelling at AI CX also gives you a competitive advantage. Companies that master the nuances of AI customer experience can attract and retain customers better in an increasingly AI-driven marketplace.As AI continues to reshape industries and consumer expectations, businesses that prioritize exceptional AI CX will differentiate themselves from competitors. This involves not only delivering superior products and services but also providing seamless and personalized experiences that resonate with users.Key Metrics for Tracking AI Customer ExperienceTo effectively measure and improve the AI customer experience, you want a multi-faceted approach that combines quantitative and qualitative data. It helps capture a comprehensive view of how users interact with AI solutions and provides insights into areas that need enhancement. By focusing on the right metrics, organizations can gain valuable insights into user behavior and satisfaction. Here are the key metrics you should be tracking:Usage MetricsTime Spent Using the AIThis metric provides insight into how engaging and valuable users find your AI solution. Longer interaction times often indicate a positive experience. It suggests that users are finding the tool beneficial and engaging. Conversely, short interaction times might suggest confusion, frustration, or a lack of perceived value, highlighting areas for improvement. By analyzing this data, companies can identify patterns and make informed decisions to enhance user engagement and satisfaction.Features Most UtilizedUnderstanding the most popular features can help prioritize development efforts and optimize the user interface.  By identifying high-usage features, organizations can focus on refining and expanding these areas to maximize user satisfaction. Additionally, this metric can reveal underutilized features that may need better visibility or improvement.Frequency of InteractionsFrequent interactions suggest that your AI is meeting a genuine need for your users and has become an integral part of their workflow. This metric indicates that users are consistently finding value in the AI solution, leading to habitual use. Analyzing interaction frequency and customer inquiries can help identify trends and patterns in user behavior, providing insights into how the AI solution fits into users’ daily routines.Performance MetricsMeasuring the performance of AI systems ensures that they meet user expectations and deliver value. Performance metrics focus on the technical capabilities of AI solutions, which directly impact user satisfaction and trust. By continuously monitoring these metrics, companies can make necessary improvements to maintain high standards of service. Here are the key performance metrics to consider:Accuracy of AI ResponsesInaccurate responses can quickly erode trust and lead to user frustration. Users rely on AI systems to provide reliable information and solutions. Any deviation from this expectation can have significant consequences. Continuously monitoring and improving your AI’s accuracy maintains user confidence and satisfaction. This involves regular updates and training of AI models to ensure they remain aligned with user needs and current information.Speed of ProcessingIn today’s fast-paced world, users expect quick responses from AI systems. The speed of processing directly affects user experience, as delays can lead to frustration and decreased engagement. To meet user expectations and keep them engaged with your solution, monitor processing times and optimize your algorithms for speed. A fast and efficient AI system can differentiate your product in a competitive market and enhance overall user satisfaction.Task Completion RatesThis metric measures the effectiveness of your AI in helping users achieve their goals. High task completion rates signal a successful AI solution that effectively guides users through processes and assists them in reaching their desired outcomes. By tracking task completion rates, organizations can identify areas where users may struggle and make improvements to enhance the AI’s effectiveness. This focus on successful task completion reinforces the value of the AI solution and contributes to positive user experiences.User Satisfaction MetricsUser satisfaction reflects how well the AI solution meets users’ needs and expectations. User satisfaction metrics provide organizations valuable insights into the strengths and weaknesses of their AI products. Here are the key metrics to track user satisfaction:Surveys and RatingsTo gauge overall user satisfaction, regularly collect feedback through surveys and rating scales. These tools allow users to provide structured feedback on various aspects of the AI experience, such as usability, accuracy, and efficiency. By asking specific questions about different elements of the AI interaction, companies can identify areas that require improvement and areas that are performing well. This quantitative data provides a clear picture of user sentiment. Ultimately, it can guide strategic decisions towards better customer experience by enhancing customer engagement through  interactions and data analytics.Feedback FormsProviding open-ended feedback forms allows users to express their thoughts and suggestions in detail, offering qualitative insights that structured surveys might miss. These forms give users the opportunity to share unique experiences, highlight specific pain points, and suggest improvements. By encouraging detailed feedback, organizations can gain a deeper understanding of user needs and preferences, enabling them to tailor their AI solutions more effectively. This direct line of communication with users fosters a sense of involvement and engagement, enhancing overall satisfaction.Sentiment AnalysisUtilizing AI-powered sentiment analysis tools to analyze user comments and reviews can help identify areas of positive and negative sentiment. These tools can process large volumes of text data, extracting valuable insights into how users perceive the AI solution. By pinpointing common themes and emotions in user feedback, companies can quickly identify strengths to build upon and weaknesses to address. Sentiment analysis provides a comprehensive view of user attitudes and helps prioritize areas for improvement, ensuring that the AI product evolves in line with user expectations.Behavioral MetricsBehavioral metrics provide insights into how users interact with AI solutions, revealing patterns and tendencies that can inform design and development strategies. Understanding user behavior helps organizations optimize the user journey and enhance the overall experience. Here are key behavioral metrics to track:Drop-Off PointsAnalyzing where users abandon interactions with your AI can help identify areas for improvement in the user journey. Drop-off points indicate where users encounter obstacles, confusion, or frustration, prompting them to exit the process prematurely. By pinpointing these critical junctures, companies can investigate underlying causes and implement changes to streamline the experience, reduce friction, and encourage users to complete their interactions. Addressing drop-off points can improve retention and ensure users derive maximum value from the AI solution.User Flow AnalysisTracking how users navigate through your AI solution provides valuable insights into user behavior and preferences. AI should complement human interaction to optimize customer engagement. User flow analysis helps identify bottlenecks or areas where users might get stuck. This allows companies to enhance navigation and optimize pathways. This analysis also helps identify the most accessed and the most overlooked features. As a result, you get a basis for targeted enhancements and prioritization.Feature Adoption RatesAdoption rates of new AI features and their usage frequencies can help you understand engagement and product relevance. High adoption rates suggest that features are meeting user needs and are effectively communicated. Low adoption rates may indicate a lack of awareness or perceived value. Feature adoption tracking allows organizations to assess the impact of new releases, refine their product roadmap, and prioritize features that resonate most with users. Understanding adoption trends also opens up data-driven decision making.Error AnalysisError analysis helps identify and rectify issues that can negatively impact user satisfaction and trust. By systematically analyzing errors, organizations can improve their AI systems. Here are the key components of effective error analysis:Identifying and Categorizing ErrorsCompanies should implement robust error tracking mechanisms that can log errors in real-time and classify them based on their nature, frequency, and impact. By categorizing errors, companies can prioritize which issues to address first, focusing on those that have the most significant effect on user experience. This systematic approach allows organizations to pinpoint specific areas in their algorithms that require improvement.Root Cause AnalysisConducting root cause analysis involves digging deeper into the underlying causes of AI errors to identify patterns and trends. Rather than simply addressing surface-level problems, root cause analysis seeks to uncover systemic issues that may be contributing to recurring errors. By analyzing the data collected from error tracking, organizations can identify common factors or conditions that lead to failures. This comprehensive understanding enables companies to implement long-term solutions that prevent similar issues from arising in the future, enhancing the robustness and accuracy of their AI systems.By tracking these key metrics, you’ll gain a comprehensive understanding of how users are experiencing your AI product. This data can then be used to inform product development decisions, enhance user satisfaction, and ultimately build a more successful AI solution.Building a Comprehensive AI CX StrategyCrafting a robust AI customer experience strategy requires a holistic approach that encompasses data collection, feedback loops, experimentation, and transparency. A well-rounded strategy ensures that AI solutions meet user needs effectively and adapt to changing expectations. Here’s how to create a winning AI CX strategy:Data Collection: The Foundation of Your StrategyTrack Across All Touchpoints in the Customer JourneyImplement tracking mechanisms across all user interactions with your AI, including websites, apps, chatbots, voice assistants, and customer service interactions. Comprehensive tracking allows you to gather insights from every stage of the customer journey, helping you understand how users engage with your AI solutions in different contexts. This allows organizations to identify areas for improvement and ensure a seamless, consistent experience across all platforms and devices.Capture Qualitative and Quantitative DataTo gain a complete picture of the user experience, gather both numerical data (for example, usage metrics, performance metrics) and qualitative data (for example, user feedback, sentiment analysis). Quantitative data provides insights into user behavior and system performance, while qualitative data offers deeper understanding into user perceptions and emotional responses. By combining these data types, companies can identify strengths and weaknesses in their AI solutions, making informed decisions to enhance customer satisfaction and engagement.Leverage User Analytics PlatformsUtilize user analytics platforms like Mixpanel, Amplitude, and Google Analytics to collect, analyze, and visualize user data. These tools offer powerful features for tracking user interactions, measuring key performance indicators, and identifying trends. By leveraging analytics platforms, organizations can gain actionable insights into user behavior, helping them optimize their AI solutions and drive continuous improvement.Integrate with CRM SystemsConnect your AI CX data with your customer relationship management (CRM) system to gain deeper insights into individual customer journeys and preferences. Sales teams can leverage this integrated data to gain insights into customer preferences and behaviors, allowing them to tailor their outreach and improve conversion rates. Integration with CRM systems allows companies to combine AI interaction data with customer profiles.Feedback Loops: The Voice of Your CustomersActively engage with users and incorporate their insights into your development processes. This way, you can create AI solutions that better meet user needs and expectations. Here are our tips to implement successful feedback loops:Active SolicitationDon’t wait for users to complain. Actively seek feedback through surveys, feedback forms, in-app prompts, and social media. By reaching out proactively, you can gather a wide range of opinions and insights, helping you understand both positive aspects and areas needing improvement. This proactive approach demonstrates to users that you value their opinions and have the commitment to deliver a superior experience.Real-time FeedbackIncorporate feedback mechanisms that allow users to provide feedback in the moment, during their interactions with your AI. Real-time feedback tools, such as in-app surveys or feedback buttons, capture users’ thoughts and feelings as they occur. This provides you immediate insights into their experiences. Such a timely information can highlight issues or successes as they happen, allowing for quicker adjustments and improvements.Incentivize ParticipationOffer incentives like discounts or early access to new features to encourage users to share their feedback. Incentives can boost participation rates and ensure you gather a diverse range of perspectives. By rewarding users for their input, you achieve two things:  Increase the quantity of feedback.  Demonstrate appreciation for their time and effort.In the end, you foster a positive relationship between your brand and its customers.Act on FeedbackAnalyze user feedback regularly and use it to inform product development decisions, prioritize bug fixes, and refine your AI models. Close the loop by showing users that their feedback leads to tangible improvements. By communicating updates and changes based on user input, you reinforce the value of their contributions. Moreover, you further solidify their trust in your commitment to continuous improvement.Experimentation: The Path to OptimizationExperimentation is a critical component of developing and refining AI solutions, allowing companies to test hypotheses, evaluate changes, and optimize user experiences. By systematically experimenting with different aspects of AI products, organizations can make informed decisions that enhance performance and user satisfaction. Here’s how to implement effective experimentation:A/B TestingConduct A/B tests to compare different versions of your AI models, features, or user interfaces. A/B testing involves presenting two or more variations to different user groups and analyzing which version performs better. This helps you identify the changes taht resonate best with users so that you have a clear evidence of what works and what doesn’t. A/B testing is a straightforward and effective method for optimizing specific elements of the AI customer experience.Multivariate TestingFor more complex scenarios, use multivariate testing to evaluate multiple variables simultaneously and identify the optimal combination. A/B testing focuses on one variable at a time. On the other hand, multivariate testing examines how different variables interact with each other. It helps you understand the combined effects of changes and finding the best configuration for enhancing user experience. By testing various combinations, companies can uncover insights that simpler testing methods might not reveal.Data-Driven Decision MakingBase your product decisions on the results of your experiments, not just gut feelings or assumptions. Data-driven decision-making ensures that your choices are supported by empirical evidence, reducing the risk of costly mistakes and improving the likelihood of success. By leveraging the insights gained from testing, organizations can prioritize changes that have a proven impact on user satisfaction and business outcomes. This approach fosters a culture of continuous improvement, where data informs every stage of the product development process.Transparency: Building Trust with UsersTransparency builds trust and fosters positive relationships with users in the context of AI. By being open about how AI systems function and you handle user data, organizations can alleviate concerns and encourage greater acceptance and engagement with their AI solutions. Let’s discuss!Explainable AI (XAI)Implement XAI techniques to help users understand how your AI makes decisions. This can involve providing explanations for recommendations, predictions, or actions the AI takes. By offering clear, understandable insights into AI processes, users can gain confidence in the system’s reliability and fairness. Explainable AI not only builds trust but also empowers users to make informed decisions based on the AI’s outputs.Transparent Data PracticesClearly communicate how you collect, store, and use user data to improve your AI. Address any privacy concerns upfront and give users control over their data. Without transparency in data practices, you cannot build trust, especially as users become more aware of data privacy issues. Provide users with clear information about data handling policies and offer options for data control and consent. These steps reassure users that you’re using their information responsibly and ethically.Open CommunicationMaintain open channels of communication with users to address any questions or concerns they may have about your AI. Regular updates, user forums, and responsive customer support can all contribute to a more transparent relationship. By engaging users in dialogue and being receptive to their feedback, organizations can build stronger, more trusting relationships.By incorporating these key elements into your AI CX strategy, you can create a feedback loop that continuously drives improvement and innovation. Stay adaptable, listen to your users, and be willing to iterate and evolve your approach over time.ConclusionArtificial intelligence has an ever-evolving environment. Therefore, tracking customer experience has become a strategic imperative. The companies that excel at AI customer experience will thrive in the AI-driven future. Don’t just build AI products; build AI products that your customers love. And for that, you need to prioritize CX tracking and focus on the user experience.In a future article, we’ll explore some of the best tools and technologies that can help you collect, analyze, and act on AI customer experience data. But if you want to significantly set yourself apart in your AI CX strategy, consider adding Moesif to your stack of tools.Moesif offers a comprehensive suite of powerful analytics and monitoring tools and robust monetization features. Moesif gives you the best of both worlds: a customer-centric analytics platform for first-class AI CX, and native monetization features to grow your AI product,So sign up today for a free trial to try it yourself, no credit card required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue from your AI APIs?            Monetize your AI APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/AI-Customer-Experience/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-ai-product-analytics": {
          "title": "Maximizing Business Impact: Best Practices of AI Product Analytics",
          "content"	 : "According to Gartner, 87% of organizations are classified as having low business intelligence and analytics maturity, meaning they struggle to extract value from their data.This alarming statistic highlights a common struggle—turning raw data into actionable insights. Product teams often find themselves overwhelmed by the sheer volume of information they collect. Extracting meaningful patterns, deciphering user behavior, and predicting market trends from this sea of customer data can seem daunting.AI product analytics offers a powerful solution, transforming complex datasets into clear, actionable insights. However, to truly harness its potential, teams must go beyond mere implementation and adopt best practices that align with their unique business goals.In this article, we’ll guide you through the most effective strategies to bridge the gap between data and decision-making. Whether you’re a beginner developer trying to understand the basics or a seasoned data professional looking to refine your approach, this article provides valuable insights to elevate your product strategy.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Table of Contents  What is AI Product Analytics?  Understanding AI in Product Analysis          The Role of AI in Modern Product Analysis        Key Elements of AI Analytics          Data Collection and Processing        The AI-Driven Product Analysis Process          Analyzing Historical Data      Real-Time Analysis      Predictive Modeling      User Segmentation      A/B Testing and Experimentation        Benefits of AI-Enhanced Product Analysis          Improved Accuracy and Efficiency        Use Cases for AI Product Analytics          Product Development and Design      Marketing and Sales      Customer Support and Success      Fraud Detection and Prevention      Competitive Analysis        Implementing AI Product Analytics          Data Quality and Security      Choosing the Right Solution      Collaborative Implementation      Ongoing Optimization        Best Practices for AI Product Analytics          Aligning AI Analytics with Business Goals      Choosing the Right Metrics      Building a Data-Driven Culture      Investing in Talent and Technology      Continuous Improvement and Iteration      Ensuring Data Privacy and Compliance        Future Trends in AI-Enhanced Product Analysis  Measuring the Impact of AI Product Analytics  ConclusionWhat is AI Product Analytics?At its core, AI product analytics leverages the power of artificial intelligence to analyze and interpret data from product interactions. This data encompasses a wide range of information, including how users interact with your product, their purchasing behavior, and feedback. AI product analytics transforms this raw data into actionable insights, revealing patterns, trends, and opportunities that traditional methods might miss.Unlike traditional analytics, AI product analytics employs machine learning algorithms and statistical models. These tools can handle massive datasets, uncover complex relationships, and generate predictions with remarkable accuracy. As a result, businesses gain a deeper understanding of their users, products, and market, allowing them to make informed decisions that drive growth.Natural language processing further enhances AI product analytics by enabling users to interact with data through intuitive, conversational queries. It allows platforms to interpret questions in plain language and deliver relevant insights. This way, data analysis becomes more accessible to non-technical team members and improves overall decision-making capabilities.Through proper use, AI-powered product analytics can bring significant benefits. It empowers product teams to pinpoint areas for improvement, personalize user experiences, optimize pricing strategies, and even predict future trends. Businesses can move from reactive decision-making to proactive strategies. This gives businesses a competitive edge in today’s fast-paced digital landscape.AI product analytics also excels at processing both structured data and unstructured data. It analyzes quantitative metrics like sales figures and qualitative data like user reviews and social media sentiment. This comprehensive view provides a nuanced understanding of user behavior and market dynamics.Understanding AI in Product AnalysisAI transforms how businesses approach product analysis. AI algorithms interpret data to derive insights that aid in decision-making processes. Predictive analysis can forecast future trends and behaviors through the analysis of historical and real-time data. It automates complex workflows to uncovers hidden patterns, allowing teams to make faster and more data-driven decisions. AI product analytics tools act as tireless data analysts. A company that has adopted such tools can process and analyze data in vast amounts to extract meaningful insights.The Role of AI in Modern Product AnalysisAI helps businesses maintain deeper insights into user needs and behaviors. Traditional analytics might explain what users are doing, but AI reveals why they do it. This comprehensive understanding allows for the creation of products and experiences that resonate with your target audience.Staying ahead in the competitive landscape means driving your business toward sustained growth. AI product analytics identifies emerging trends before they become mainstream. It allows you adapt your product strategy proactively and keep your offerings relevant and appealing to users.A significant challenge in product analysis stems from handling large volumes of data. AI excels at processing massive datasets. It can draw data-driven insights from historical data, real-time interactions, and external market trends quickly. This speed provides a competitive advantage that manual analysis can’t match.The power of AI in product analysis lies in real-time insights. You no longer have to wait weeks to analyze data. AI can help you track user behavior, monitor product performance, and identify issues as they happen. You can easily afford to make quick adjustments that enhance user experience and drive business growth.Key Elements of AI AnalyticsAI analytics relies on several key elements to transform raw data into meaningful insights. The process starts with data collection and processing, which forms the foundation for everything that follows.Data Collection and ProcessingEffective AI analytics depends on thorough data collection that involves gathering relevant and accurate data from multiple sources. These sources include user interactions, website analytics, customer feedback, social media sentiment, and external market data. A diverse set of data streams offer a comprehensive view of your product and its environment.After collection, the raw data undergoes processing to prepare it for analysis. This stage includes cleaning the data, transforming it into a usable format, and correcting inconsistencies or errors. Effective data processing ensures that AI algorithms access high-quality, reliable information, leading to accurate and meaningful insights.AI algorithms excel in analyzing both structured data, like sales figures and demographics, and unstructured data, like text reviews and social media posts. This capability allows businesses to extract insights from a wide range of sources, offering a more holistic understanding of products and users.Analyzing unstructured data sets AI analytics apart from traditional methods. While conventional techniques struggle with text, images, and audio, AI algorithms can extract valuable insights from these formats. This ability helps you understand user sentiment and identify patterns that reveal emerging trends.Effective data collection and processing lay the groundwork for AI to reveal valuable information.The AI-Driven Product Analysis ProcessAI-driven product analysis involves several interconnected steps that together provide a comprehensive understanding of your product, users, and market.Analyzing Historical DataHistorical data offers valuable insights into past user behavior, product performance, and market trends. AI algorithms interpret data to identify patterns and correlations that might not seem obvious. Understanding the past helps you make informed decisions for the future.Real-Time AnalysisReal-time analysis is a powerful feature of AI product analytics. As users interact with your product, AI tracks their behavior, identifies potential issues, and predicts future actions. This enables proactive adjustments to improve user experience and reduce churn.Predictive ModelingAI product analytics not only reviews past events but also predicts future outcomes. By analyzing historical data and current trends, AI algorithms interpret data to generate forecasts on user behavior, product adoption, and market shifts. This foresight helps you anticipate challenges and seize opportunities ahead of your competition.User SegmentationAI product analytics enables the segmentation of your user base into distinct groups based on behavior, preferences, and demographics. This segmentation supports targeted marketing, personalized user experiences, and a product roadmap tailored to different user needs.A/B Testing and ExperimentationAI enhances A/B testing and experimentation by analyzing variations and identifying the changes that improve engagement, conversions, and overall success. This evidence-based approach grounds product optimization decisions in concrete data.Benefits of AI-Enhanced Product AnalysisAI-enhanced product analysis offers significant advantages, transforming how businesses understand their products and users.  AI algorithms analyze large datasets quickly and precisely, reducing the risk of human error and bias. By automating repetitive tasks, AI allows your team to focus more on strategic initiatives.  AI product analytics uncovers hidden patterns and motivations behind user behavior. It goes beyond surface-level metrics to interpret data, revealing deeper insights that allow you to create personalized experiences, targeted marketing, and products that resonate with your audience.  AI-powered insights help you anticipate user needs and market trends. This include identifying churn risks or predicting what features most appeal to your users.  AI product analytics can optimize product development, marketing, and customer support. This significantly boosts your return on investment (ROI). You can allocate resources more effectively, target the right users, and drive revenue and growth.  AI product analytics provides a crucial edge in a competitive landscape. By leveraging AI for deeper insights and smarter decisions, you can outperform rivals, attract and retain more users, and position your brand as an industry leader.Use Cases for AI Product AnalyticsThe applications of AI product analytics span across the entire product lifecycle, from development and design to marketing and customer support. Let’s explore some of the most impactful use cases.Product Development and DesignAI product analytics guides development and design decisions by analyzing user feedback and usage patterns. You can identify areas for improvement, discover unmet needs, and prioritize features that matter most to users. This ensures your product evolves in line with user expectations and market demands.Marketing and SalesAI product analytics enhances marketing and sales efforts by understanding user behavior and preferences. This allows you to conduct targeted campaigns for specific audience segments. Moreover, you can optimize pricing strategies and identify cross-selling and upselling opportunities. Personalizing the customer journey like this increases conversions and revenue.Customer Support and SuccessAI product analytics improves customer support and success by analyzing customer behavior, interactions, and feedback. It identifies common pain points so you can address them proactively, boosting user satisfaction and reducing churn. AI also personalizes support interactions, offering solutions tailored to each customer.Fraud Detection and PreventionAI product analytics plays a crucial role in detecting and preventing fraud. It can identify unusual patterns and anomalies in user behavior. Having a proactive and automatic threat detection system protects your business and your users from potential threats in real time.Competitive AnalysisAI product analytics helps you stay ahead of competitors by tracking their performance and analyzing their strategies. It identifies strengths and weaknesses and benchmarks your product against industry standards. Consequently, you can make informed decisions that differentiate your product and capture market share.Implementing AI Product AnalyticsImplementing AI product analytics requires more than selecting the right tools. If you want to succeed, you must also invest heavily in careful planning, execution, and ongoing optimization. Here we briefly discuss some key areas that we recommend you consider when implementing AI-powered product and data analytics.Data Quality and SecurityEnsure data quality in AI product analytics by collecting relevant, complete, and accurate data. Validate and clean data regularly to maintain integrity and avoid skewed results.Protect sensitive user information by implementing encryption, access controls, and conducting regular audits. Strong security measures build and maintain user trust.Choosing the Right SolutionSelect the right AI product analytics solution by evaluating vendors and platforms on ease of use, scalability, integration, and support. Choose a solution that aligns with your business needs and technical requirements.Collaborative ImplementationSuccessful implementation requires collaboration among data scientists, product managers, engineers, and stakeholders. Promote open communication and cross-functional collaboration. This makes sure that everyone understands and utilizes insights for product improvement.Ongoing OptimizationMonitor and evaluate AI models continuously. Refine data collection and processing strategies, and adapt as business and user needs evolve. Embrace a culture of learning and experimentation to maximize the value of AI product analytics.Best Practices for AI Product AnalyticsTo fully harness the power of AI product analytics, adopt best practices that align with your business objectives and ensure effectiveness.Aligning AI Analytics with Business GoalsStart by clearly defining your business goals. Determine whether you aim to increase user engagement, boost conversions, or improve customer retention. With clear objectives in mind, tailor your AI analytics strategy to support these goals.Choosing the Right MetricsSelect metrics that directly measure progress toward your business goals. Key performance indicators (KPIs) should align with your objectives, such as user retention rate, conversion rate, average revenue per user, or customer satisfaction scores. These metrics guide the evaluation of your AI product analytics initiatives.Building a Data-Driven CultureFoster a data-driven culture to maximize the benefits of AI product analytics. Encourage your team to make decisions based on data at all levels. Provide training and resources to help them interpret AI-generated insights and apply these insights to their work.Investing in Talent and TechnologySuccessful AI product analytics requires skilled talent and the right technology. Invest in hiring or training data scientists and analysts with AI expertise. Choose AI product analytics tools that are user-friendly, scalable, and compatible with your existing technology stack.Continuous Improvement and IterationAI product analytics requires ongoing learning and improvement. Regularly monitor and evaluate the performance of your AI models. Continuously refine your data collection and processing strategies, and adapt your approach as business and user needs change.Ensuring Data Privacy and ComplianceAI product analytics often involves handling sensitive user data. Therefore, make sure that your data collection, storage, and processing practices comply with relevant data privacy regulations, such as GDPR or CCPA. Regularly review your data practices to maintain compliance and protect user privacy. This not only safeguards your business legally but also builds trust with your users.Future Trends in AI-Enhanced Product AnalysisAI product analytics continues to evolve through technological advancements and changing business needs. Let’s briefly look at some of the trends that can impact its trajectory in the days to come.  AI product analytics will become even more powerful as algorithms grow more sophisticated. These advancements will unlock deeper insights and predictive capabilities, integrating AI into every phase of the product lifecycle.  Real-time personalization represents a significant future trend. AI will allow businesses to tailor product experiences instantly, based on user behavior, preferences, and context. This immediate personalization will boost user engagement and satisfaction.  Predictive analytics will take center stage in the future of AI product analysis. AI will help businesses anticipate user needs and market trends, enabling proactive decisions that drive growth and innovation. This might include predicting churn or identifying new product opportunities.  As AI models become more complex, the demand for explainable AI will grow. Businesses will need transparency in how AI models generate insights. This transparency will build trust and help teams understand the factors influencing user behavior and product performance.  The importance of ethical AI in product analytics will continue to rise. Ensuring AI models are fair, unbiased, and respectful of user privacy will be crucial factors. Businesses must implement responsible AI practices to benefit both the company and its users.Measuring the Impact of AI Product AnalyticsSo you’ve incorporated AI into your product analysis or thinking about doing so. You likely want to understand how much value it adds to your business. This may likely decide the next steps you want to take.The primary goal of AI product analytics is to drive positive business outcomes. These outcomes may include increased revenue, improved user satisfaction, reduced churn, or enhanced operational efficiency. Track key performance indicators (KPIs) aligned with your business goals to quantify the impact of your AI initiatives.Choosing relevant KPIs is crucial for effective measurement. Select metrics that directly reflect the impact of AI product analytics on your business. For instance, if you want to increase user engagement, track metrics like time spent in-app, feature adoption rates, and user retention.Before implementing AI product analytics, establish a baseline for your chosen KPIs. This baseline allows you to compare performance before and after implementation, providing a clear picture of AI’s impact on your business.Monitor your KPIs regularly and generate reports that highlight the impact of AI product analytics. Share these reports with key stakeholders to demonstrate the value of your initiatives and secure ongoing support.Use insights from your measurements to refine strategies and optimize your approach. Identify areas where AI delivers the most value and areas needing improvement. Continuously iterating and adapting ensures AI product analytics remains a strong driver of business success.ConclusionWith the democratization of AI, AI tools and services have become more accessible. We have seen traditional product analytics tools add artificial intelligence support over the years as well, for example, Mixpanel. Like many other aspects of modern business landscape, AI will likely dominate product analysis as well. Small to large companies alike will lean heavily into bespoke AI solutions when it comes to product-related data analysis in the coming years.AI adoption paints only half of the picture. The rest depends on choosing the right tools to monitor, analyze, and monetize your AI product. And we’ve built Moesif from the ground up as a comprehensive platform that can maximize the value of your product’s entire lifecycle.Moesif offers a comprehensive suite of powerful analytics and monitoring tools and robust monetization features so you can confidently grow your AI product.Sign up today for a free trial today to try it yourself, no credit card required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue from your AI APIs?            Monetize your AI APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/AI-Product-Analytics/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-openapi-tools": {
          "title": "Top OpenAPI Tools for Efficient API Documentation and Development",
          "content"	 : "Application programming interfaces (APIs) power numerous applications and services. In today’s fast-paced digital landscape, seamlessly accessing digital services across devices and platforms have become the defining trait of modern software systems. APIs also play an important role in defining this connectivity.When you begin developing an API, you may not pay much attention to API documentation or the development tools you employ. But as API grows in complexity, its efficient documentation and development no longer stays a secondary set of requirements. They ensure the seamless integration we all desire as customers, along with faster deployment, reduced maintenance costs, and other benefits.To make API documentation and development efficient, you need robust tools. OpenAPI tools provide the structure, functionality, and automation necessary to create, manage, and share APIs with precision. This article explores how these tools enhance API management, helping teams deliver high-quality, scalable APIs with confidence.This article explores key OpenAPI tools that elevate API workflows and how they enhance API management. These tools offer comprehensive solutions for every stage of the API lifecycle. Learn how to leverage these tools to optimize your development process and deliver high-quality, scalable APIs with confidence.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Table of Contents  Table of Contents  Introduction to OpenAPI Tools  OpenAPI Documentation Tools          Apidog      Swagger UI      Postman      Apiary      ReDoc        OpenAPI Code Generation  API Testing and Mock Servers          Mock Servers      API Testing      OpenAPI Generator        OpenAPI Spec Validation and Security          OpenAPI Spec Validation      Security        Server Implementations and SDKs          Server Implementations      SDKs      Integration        Top OpenAPI Tools          OpenAPI Generator      Swagger UI      Postman        Best Practices for OpenAPI Adoption          Embrace OpenAPI Early      Standardize with OAS      Leverage Code Generation      Prioritize Collaboration      Continuously Validate      Test Thoroughly      Secure Your APIs      Keep it Up-to-Date        Common Use Cases for OpenAPI Tools          API Design and Prototyping      Documentation Generation      Code Generation      Testing and Validation      Collaboration and Communication      API Governance and Standardization      Microservices Architecture        Choosing the Right OpenAPI Tool  ConclusionIntroduction to OpenAPI ToolsOpenAPI tools offer a comprehensive suite of features that streamline every aspect of API design, documentation, and development. These tools adhere to the OpenAPI Specification (OAS).The OAS is a standardized and language-agnostic format for describing RESTful APIs. OpenAPI documents are machine-readable, as well as interpretable by people. This means you can consistently define an API that both humans and software can understand.OpenAPI tools provide a centralized platform that adheres to the OAS. You can design your API visually or through code, with built-in validation to ensure compliance with the specification. This standardized approach simplifies API development. Moreover, it ensures that you can easily integrate the API with other tools and services that support the OAS.Many OpenAPI tools provide robust documentation platform that automatically generate and host interactive API documentation based on your OpenAPI definition. You can explore endpoints, request-response formats, and even test API calls directly from the documentation. This makes it easier for teams and external users to understand and interact with your API, significantly reducing the learning curve and support burden.Beyond API documentation, OpenAPI tools enhance collaboration with real-time editing, automate code generation across various programming languages, and generate interactive API references. They address critical challenges throughout the API lifecycle. These challenges include initial design and validation, testing, mock server creation, and deployment.Moreover, OpenAPI tools integrate seamlessly with CI/CD pipelines. You can easily set up advanced security testing, and leverage robust analytics and monitoring. These tools have been designed to improve workflows and deliver high-quality, well-documented APIs that adhere to industry standards.The following sections dive deeper into these features and the corresponding tools.OpenAPI Documentation ToolsOpenAPI documentation tools transform complex API specifications into interactive, user-friendly references. They help developers understand endpoints, request-response formats, and authentication methods for efficient integration. If you’re looking for a documentation platform for your needs, consider the following tools: ApidogApidog excels in creating detailed API documentation with minimal effort. It supports multiple API specification formats, including OpenAPI, and automates the documentation generation process. Apidog provides interactive API consoles, enabling users to test endpoints directly from the documentation. It also allows real-time collaboration.Swagger UISwagger UI can generate interactive documentation from OpenAPI specifications. Developers can explore API endpoints, submit requests, and view responses directly within the browser. Swagger UI’s documentation adopts a highly visual approach, making it easy to understand the structure and functionality of an API. The tool also supports customization, allowing users to tailor the documentation interface to better suit their needs or branding requirements.PostmanPostman  offers strong documentation features integrated with its API development platform. It converts OpenAPI specifications into comprehensive and interactive documentation that you can share in different ways. The generated API documentation includes detailed descriptions of endpoints, parameters, and authentication methods. The interactivity allows users to execute API calls and view real-time responses within the documentation itself. The platform also boasts robust sharing and collaboration features. Teams using Postman can work together on maintaining accurate and up-to-date API documentation.ApiaryApiary focuses heavily on API documentation and design. It generates interactive documentation that not only describes the API’s endpoints but also allows users to test them directly from the documentation. Apiary structures its API documentation around API blueprints, making it easy to maintain consistency across multiple versions or iterations of an API. The tool also supports collaborative editing. This allows you to quickly make updates as changes occur during development.ReDocIf you want to generate aesthetically pleasing and responsive API documentation, consider ReDoc. It focuses on readability and user experience, and produces documentation that users find visually appealing and easy to navigate. ReDoc can handle large and complex API specifications, presenting them in a way that remains clear and accessible. The tool offers customization options to align the documentation’s look and feel with brand identity. ReDoc’s ability to render detailed and well-organized documentation makes it a strong choice for teams looking to showcase their APIs effectively.These are just a few of the many OpenAPI documentation tools available. The ideal choice depends on your specific requirements, team size, and workflow. Well-documented APIs lead to faster adoption, better integration, and happier developers.OpenAPI Code GenerationOpenAPI code generation tools help developers create client libraries, server stubs, and documentation directly from OpenAPI specifications. These tools save time, reduce manual errors, and automate repetitive tasks so that developers can focus on core API logic.OpenAPI Generator supports code generation in over 40 programming languages.. It generates client libraries, server stubs, API documentation, and even API mock servers. This versatility allows developers to use the same tool across different projects and platforms. OpenAPI Generator also supports various templating options, giving developers the flexibility to customize the generated code according to their specific needs.The tool’s SDK generation capabilities provide pre-built libraries for specific programming languages, such as Java, Python, C#, and TypeScript. These SDKs include all necessary functions and configurations to interact with the API. Developers can use these SDKs to handle authentication, data serialization, error handling, and other common tasks.With client libraries, OpenAPI Generator enables developers to generate fully functional libraries for popular languages like Java, Python, JavaScript, Ruby, and more. These libraries take care of the low-level details of API communication, such as setting up HTTP requests, managing authentication tokens, and parsing JSON responses.Server stubs that OpenAPI Generator generates provide a foundational codebase for building API servers. These stubs include all the necessary endpoints defined in the OpenAPI specification, giving developers a starting point for implementing the API logic. Teams that need to develop both the client and server sides of an API may find this feature particularly useful, ensuring that both parts align with the API specification and are consistent.OpenAPI Generator also supports customization and extensibility through its templating system. Developers can modify existing templates or create new ones to tailor the generated code to their project’s requirements. This flexibility allows teams to enforce coding standards, integrate with specific frameworks, or optimize performance based on their unique needs.API Testing and Mock ServersWhen talking about OpenAPI tools and how they benefit API development, we cannot ignore its two core elements. Even before fully implementing an API, developers strive to identify bottlenecks and errors as early as possible. Same goes for validating API behavior. API testing and mock servers both play an integral part in foolproof API design and implementation.Mock ServersMock servers simulate API behavior based on the OpenAPI specification. A mock server creates a virtual environment that replicates expected responses and interactions. This allows developers to test their applications against a realistic API environment without relying on a fully functional backend. In agile development, where frontend and backend teams often work in parallel, mock servers enable frontend developers to integrate and test their interfaces while the backend is still under development. This parallel work speeds up the development process and reduces dependencies between teams.Advanced mock servers can simulate various scenarios, such as different response codes, latency, and error conditions. These features help ensure that applications handle all possible API responses correctly. Popular choices for creating and managing mock servers include Postman, Prism, and WireMock.API TestingAPI testing tools execute requests and validate responses in real-time, ensuring that the API behaves as expected according to the OpenAPI specification. These tools identify discrepancies between expected behavior and actual implementation, helping teams catch issues before they reach production.Several types of API testing play different roles in this process:      Unit testing focuses on individual endpoints or functions within the API, ensuring each component works as intended in isolation.        Integration testing evaluates how different parts of the API work together, validating that data flows correctly across endpoints.        Contract testing ensures that the API adheres to the structure defined by the OpenAPI specification, maintaining consistency and backward compatibility.        Performance testing assesses how the API performs under various loads, identifying bottlenecks and ensuring stability under stress.  OpenAPI GeneratorThe OpenAPI Generator enhances both API testing and mock server creation. For its CLI tool, the OpenAPI Generator image gives you a standalone Docker image that you can run from the command line anywhere.OpenAPI Generator can generate mock servers and testing scripts directly from the OpenAPI specification. This automation supports consistent, repeatable, and reusable testing across different environments.You can integrate OpenAPI Generator CLI into continuous integration and deployment (CI/CD) pipelines to automate testing and mock server creation. By generating these resources from the command line, you can easily incorporate them into workflows, making sure that APIs go through thorough testing before deployment.OpenAPI Spec Validation and SecurityValidation tools help you adhere to the OpenAPI standard, while security tools identify potential vulnerabilities that could expose your API to attacks and potential exploits. Therefore, make sure your API strategy includes proper validation and security measures.OpenAPI Spec ValidationValidation tools scrutinizes your OpenAPI specification for accuracy and compliance. They ensure that your API definition aligns with the OpenAPI standard, catching errors and inconsistencies that might disrupt functionality or integration.      Error detection: Validation tools scan your OpenAPI document for syntax errors, such as incorrect data types, missing required fields, or malformed JSON or YAML structures.        Inconsistency identification: These tools also identify inconsistencies in your API specification, such as mismatched parameter names, conflicting data types across endpoints, or incompatible response formats. Consistency ensures a predictable and reliable API that consumers can easily integrate with.        Compliance checks: By validating your API against the OpenAPI standard, these tools ensure that your specification follows best practices and industry standards. This compliance makes for your API’s interoperability so that it can seamlessly integrate with other tools, services, and platforms that rely on the OpenAPI standard.  Popular validation tools include the Swagger Validator and Spectral. You can integrate these tools into your development workflow for automatic validations of your API specifications as part of your continuous integration (CI) processes. This integration ensures that your API remains compliant and error-free throughout its lifecycle, reducing the risk of issues arising during deployment or use.SecurityOpenAPI security tools are designed to help you identify and mitigate potential vulnerabilities within your API specification. These tools analyze your OpenAPI document to uncover weaknesses that malicious actors can exploit, helping you protect your API and the data it handles.      Weak authentication mechanisms: Security tools check for proper implementation of authentication methods, such as OAuth2, API keys, or JWT tokens. They ensure that sensitive endpoints are protected and that authentication mechanisms can prevent unauthorized access.        Authorization gaps: These tools also identify missing or inadequate authorization checks. Proper authorization ensures that even authenticated users can only access the resources they have permission to use, preventing privilege escalation and data breaches.        Sensitive data exposure: Security tools analyze how sensitive data, such as user information or financial details, flows within your API. They look for unencrypted data transmissions, insufficient masking of sensitive data in logs, or exposure of critical information through overly verbose error messages.        Injection vulnerabilities: Tools like OWASP ZAP or 42Crunch can detect potential injection vulnerabilities in your API specification. These vulnerabilities occur when user-supplied data is improperly handled, allowing attackers to execute malicious code or SQL queries. Addressing these issues can safeguard your API against common attack vectors.  Many security tools integrate seamlessly into CI/CD pipelines, automating the security assessment process. For example, 42Crunch offers continuous security validation, providing real-time feedback on potential vulnerabilities as developers modify the API specification.Server Implementations and SDKsThe utility of OpenAPI tools extend beyond API design and documentation. They also accelerate server-side implementation and client-side integration.Server ImplementationsTools like OpenAPI Generator and Swagger Codegen generate server-side code from your OpenAPI specification. For example, if you have an OpenAPI document that defines your API, OpenAPI Generator can produce a complete server stub in languages like Java, Python, Node.js, or Go. It creates the necessary routes, controllers, and models based on your API endpoints and data structures. This automation helps developers start with a working codebase that aligns with the API specification, allowing them to focus on implementing core business logic rather than boilerplate code.OpenAPI Generator supports popular frameworks like Spring Boot (Java), Express (Node.js), Flask (Python), and many others. This process reduces setup time and ensures that the server’s behavior remains consistent with the API specification, minimizing discrepancies between the design and the implementation.SDKsOpenAPI tools also generate SDKs for various programming languages. Postman, for example, allows developers to export their collections as SDKs in languages like Python, JavaScript, and Ruby. These SDKs include pre-built functions that handle API requests, authentication, and error handling.Another powerful tool, OpenAPI Generator, as we discussed previously, can produce SDKs in over 40 programming languages. These include Swift for iOS, Kotlin for Android, and TypeScript for web applications. For example, generating an SDK in TypeScript provides developers with ready-made functions for each API endpoint. These functions abstract away the details of HTTP requests, allowing developers to call API methods directly within their application code, streamlining integration and reducing the chance of errors.SDKs generated by OpenAPI tools also handle authentication mechanisms, such as OAuth2 or API keys, out of the box. This means developers don’t need to manually implement these security features, which reduces the potential for mistakes and enhances the security of the client application.IntegrationBoth server-side code and SDKs generated by these tools integrate smoothly into continuous CI/CD pipelines. Developers can automate the generation of server stubs and SDKs as part of their build process so that any changes to the API specification immediately reflects in the codebase.Top OpenAPI ToolsAmong many OpenAPI tools that exist, these three stand out for their versatility, ease of use, and widespread adoption:OpenAPI GeneratorWe’ve already touched on OpenAPI Generator a number of times in this article. It goes to show how much value it provides to developers.OpenAPI Generator automates code generation for APIs. It supports over 40 programming languages, including Java, Python, JavaScript, and C#. Developers at companies like IBM and Microsoft use OpenAPI Generator to streamline the creation of client libraries and server stubs, significantly reducing manual coding time and repetitive tasks. For example, IBM’s API Connect platform integrates OpenAPI Generator to produce SDKs and server stubs. The customizable templates allow teams to tailor generated code to meet their project’s unique requirements. Integration with CI/CD pipelines helps teams keep their code generation aligned with evolving API specifications, making sure that every update syncs with the implementation.Swagger UISwagger UI provides a visual interface for interacting with OpenAPI specifications. It generates interactive API documentation that developers can use to explore endpoints, test requests, and view responses in real-time. Companies like Slack and Atlassian embed Swagger UI into their developer portals, allowing external developers to interact with their APIs easily. For example, Slack’s API documentation leverages Swagger UI, allowing developers to experiment with API calls directly within the browser. Swagger UI’s customization options allow these companies to align the documentation’s look and feel with their branding..PostmanPostman excels in API testing, collaboration, and documentation. It integrates with OpenAPI to import API specifications, generate detailed documentation, and create reusable test collections. Many organizations rely on Postman for its powerful environment management features. These features simplify testing across different setups, such as development, staging, and production.Using Postman, teams can seamlessly collaborate on API testing and development by sharing collections and environments. Postman also automates testing workflows and runs tests in CI/CD pipelines, helping companies like Stripe catch issues early and maintain the reliability of their payment APIs.These OpenAPI tools cater to a wide range of needs and preferences. Whether you prioritize code generation, interactive documentation, or comprehensive API development capabilities, these tools provide a solid foundation for efficient API development and management.Best Practices for OpenAPI AdoptionSuccessful OpenAPI adoption hinges on following best practices that maximize its benefits throughout the API lifecycle. Here are some that we recommend:Embrace OpenAPI EarlyIntegrate OpenAPI from the beginning of your API design process. Doing so ensures that all teams—developers, product managers, and stakeholders—start with a shared understanding of the API’s structure and functionality. This early adoption establishes clear communication, reducing the chances of misalignment as the project progresses. By starting with a well-defined OpenAPI specification, you establish a strong foundation that guides the API’s development, testing, and documentation. Early integration also helps in identifying potential issues before they become costly to fix.Standardize with OASAdhere strictly to the OpenAPI Specification (OAS) when defining your APIs. Remember that the OAS provides a standardized, machine-readable format that ensures consistency across all your APIs. Therefore, it makes your APIs easier to maintain and scale. Satisfying the OpenAPI spec also simplifies the creation of documentation and gives you a wide range of OpenAPI-compatible tools, some of them we’ve already discussed throughout the article. By sticking to OAS, you enable interoperability between different systems and platforms. You have less risk of integration issues down the line. Standardization also ensures that your APIs stay future-proof, as the developer community has widely adopted and supports OAS.Leverage Code GenerationUse tools like OpenAPI Generator to automate the creation of API client libraries, server stubs, and documentation. Automating these processes saves significant development time and reduces the likelihood of manual errors. Generated code also promotes consistency across different API implementations so that all parts of the system work together seamlessly. By relying on code generation, developers can focus more on building core business logic rather than writing repetitive boilerplate code. This approach not only accelerates development but also helps maintain a high level of quality across the entire API ecosystem.Prioritize CollaborationUse OpenAPI tools to share API specifications with all relevant stakeholders, including developers, testers, and business analysts. This sharing improves feedback and makes sure that everyone has a clear understanding of the API’s capabilities and limitations. Collaboration tools like SwaggerHub or Stoplight allow teams to work together in real-time, making it easier to track changes, discuss design decisions, and resolve issues quickly. Encouraging collaboration early in the process helps prevent misunderstandings and ensures that the final API meets the needs of all users.Continuously ValidateRegularly validate your OpenAPI specification. Tools like Spectral or Swagger Editor can automatically check your API definition for errors, inconsistencies, and deviations from the OpenAPI Specification. Continuous validation ensures that your API remains compliant with industry standards and that any changes during development do not introduce new issues. By integrating validation into your CI/CD pipeline, you can catch problems early, before they affect the broader system. This proactive approach helps maintain the reliability and stability of your APIs.Test ThoroughlyThorough testing ensures that your API behaves as expected under different conditions. Use API testing tools like Postman or RestAssured to create and execute test cases that cover various scenarios, including edge cases and error conditions. Mock servers can simulate API behavior before the actual implementation is complete. Regular testing helps identify potential issues before they reach production, improving API quality and reducing time-to-market. Testing also provides confidence that the API will perform well in real-world conditions and provide better experience for end-users.Secure Your APIsMake security a top priority in API development. Use OpenAPI security tools like 42Crunch or OWASP ZAP to analyze your API specification for vulnerabilities. These tools can identify issues such as weak authentication mechanisms, missing authorization checks, and potential data exposure risks. By addressing these vulnerabilities early, you protect sensitive data and maintain user trust. Implementing strong security measures from the start also reduces the risk of breaches and ensures compliance with industry regulations. Regular security audits and updates to the OpenAPI specification help keep your API secure as it evolves.Keep it Up-to-DateAs your API evolves, keep your OpenAPI Specification updated. This ensures that the documentation, generated code, and other artifacts remain accurate and relevant. An up-to-date specification also facilitates smoother integration and usage by providing developers with the most current information about the API’s functionality. Regularly revisiting and updating the specification as you make changes ensures that your API continues to meet user needs and remains aligned with business goals. Keeping the specification current also helps reduce the likelihood of errors due to outdated information.Common Use Cases for OpenAPI ToolsOpenAPI tools empower developers and organizations across a wide range of scenarios:API Design and PrototypingOpenAPI tools enable collaborative design and prototyping by allowing teams to define endpoints, request-response formats, and other key details in a standardized format. This ensures clear communication and alignment from the start. Tools like SwaggerHub help teams collaborate in real time, refining API designs before development begins.Documentation GenerationThese tools allow you to automatically generate interactive API documentation that evolves with your API. This helps developers understand capabilities, test requests, and integrate smoothly. As discussed previously, Redoc and Swagger UI are popular tools for creating user-friendly, interactive documentation that you can embed in developer portals.Code GenerationAnother use case is generating client libraries and server stubs in multiple languages, saving time and ensuring consistency across implementations. OpenAPI Generator supports over 40 programming languages, allowing developers to automate the creation of code that matches the API specification, reducing the risk of manual errors.Testing and ValidationOpenAPI tools have built-in support for validating API specifications. They also let you use mock servers and testing tools to catch errors, and ensure API behavior meets expectations. Postman and RestAssured are widely used for testing, while Spectral helps with validating API specifications against the OpenAPI standard.Collaboration and CommunicationMany OpenAPI tools provide a central platform for collaboration. You can share API specifications, collect feedback, and track changes among stakeholders. For example, tools like Stoplight and SwaggerHub let you collaborate in real time.API Governance and StandardizationYou can enforce design standards and best practices to ensure consistency, maintainability, and ease of integration. Establishing governance rules with tools like Spectral ensures that all APIs across the organization adhere to the same standards.Microservices ArchitectureSimplify microservices management by using standardized definitions and documentation for APIs across services. OpenAPI tools help maintain a clear and consistent approach to defining and documenting APIs, so it becomes significantly easier to manage complex microservices architectures.Choosing the Right OpenAPI ToolSelecting the ideal OpenAPI tool depends on your specific needs, team size, and workflow. Consider these factors when making your decision:      Features and capabilities: Evaluate the tool’s features to ensure they meet your needs. Whether you require code generation, interactive documentation, or both, prioritize the features most critical to your API development. For example, OpenAPI Generator excels in code generation, while Redoc is great for creating interactive documentation.        Ease of Use and learning curve: Select a tool with an intuitive interface and clear documentation. Consider how quickly your team can become proficient. Tools like Postman have user-friendly interfaces, making them easier for teams to adopt.        Integration and compatibility: Ensure the tool integrates well with your development environment and supports your programming languages and frameworks. SwaggerHub, for instance, integrates seamlessly with CI/CD pipelines and various development platforms, making it a strong choice for many teams.        Scalability: If your API ecosystem will grow, choose a tool that scales with your needs. Consider performance, collaboration features, and support for large API specs. SwaggerHub and Stoplight offer robust collaboration features that can support growing teams and complex API architectures.        Community and support: Choose a tool with an active community and accessible support. This ensures you can get all the help you need, when you need, and keeps you current with updates. Tools like Postman have large, active communities and extensive documentation, making it easier to find solutions and best practices.        Cost: Evaluate the pricing and choose a tool within your budget, considering both upfront and ongoing costs. Some tools, like Swagger UI, are open-source and free, while others, like SwaggerHub, offer tiered pricing based on your needs.  ConclusionOpenAPI tools are indispensable for modern API development. They empower developers and data professionals to design, document, develop, and manage APIs with efficiency and precision. By adopting the OpenAPI Specification (OAS) and leveraging the capabilities of these tools, teams can deliver high-quality APIs that meet the needs of their users.If you think growing your API product requires strong API analytics, monitoring, observability capabilities, then consider adding Moesif alongside your OpenAPI tools.Moesif perfectly compliments the community-driven and robust ecosystem of OpenAPI with its powerful comprehensive suite of powerful analytics and monitoring tools. Moesif has also built robust monetization features from the ground up so that you can grow your product with confidence.To try Moesif yourself, sign up today for a free trial, no credit card required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            You’ve built the API. Now let's generate revenue!            Monetize your APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/OpenAPI-Tools/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-implementing-usage-based-billing-for-enterprise": {
          "title": "Implementing Usage-Based Billing for Enterprise",
          "content"	 : "IntroductionEnterprises are continually seeking innovative ways to optimize their pricing models and drive revenue growth. Usage-based billing, where customers pay based on their actual consumption, has emerged as a compelling strategy. Unlike traditional flat-fee models, usage-based billing aligns more closely with customer needs and behaviors, offering flexibility and scalability that are crucial in a competitive market. This billing model represents a fundamental shift in how enterprises approach pricing, moving from a product-centric model to one focused on continuous value delivery. For enterprises, implementing usage-based pricing as a crucial strategy for businesses can be transformative, providing a pathway to sustainable revenue growth and enhanced customer satisfaction.Benefits of Usage-Based Billing for EnterprisesUsage-based billing models offer significant advantages, particularly in enhancing customer satisfaction and driving revenue growth. By linking charges directly to the value received, these models allow enterprises to be more flexible and customer-centric in their pricing strategies, which is essential in today’s competitive market. Additionally, usage-based billing can lead to greater cost efficiency by enabling more effective cost management.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Increased Revenue GrowthOne of the most compelling benefits of usage-based billing is its potential to boost revenue. SaaS companies are increasingly adopting usage-based billing to align revenue with customer engagement. By aligning charges with actual usage, enterprises can capture more value from high-usage customers without alienating lower-usage segments. This model supports incremental revenue opportunities as customer usage patterns evolve, particularly in industries such as SaaS, cloud services, and telecommunications. Moreover, this approach fosters a stronger relationship between the enterprise and its customers, as clients see a direct correlation between their spending and the value they receive, contributing to optimized revenue streams.Improved Customer Retention and LoyaltyCustomer retention is another area where usage-based billing excels. Transparency in billing ensures that customers feel they are paying fairly, which enhances satisfaction and reduces churn. This pricing strategy also fosters trust, further enhancing customer loyalty, as customers appreciate the fairness and alignment of costs with actual usage.Flexibility and ScalabilityUsage-based billing provides enterprises with the flexibility to adapt to diverse customer needs and market conditions. As enterprises grow, this model allows them to scale their pricing strategies efficiently, aligning charges with the value delivered to customers. For businesses aiming to stay competitive and responsive to market changes, adopting a usage-based billing model is a strategic move toward long-term success.Understanding Usage-Based Pricing ModelsUsage-based billing offers enterprises various pricing models, including the usage-based pricing model, which provides real-time usage metrics and addresses billing complexities. By selecting the appropriate model, enterprises can align their pricing strategies with their goals and customer expectations, thereby optimizing revenue and satisfaction.Pay-As-You-Go ModelThe metered billing model charges customers based on the exact amount of a service they consume, making it straightforward and transparent. This model is particularly effective in industries like cloud services, telecommunications, and utilities, where usage can vary significantly. It provides a flexible pricing structure that attracts a broad customer base and encourages increased usage, as customers know they’re only paying for what they use.Tiered Pricing ModelIn the tiered pricing model, customers are charged based on predefined levels of usage, with each tier corresponding to a specific price point. This model is effective for businesses that want to cater to different customer segments with varying levels of demand. It allows enterprises to offer scalable solutions that grow with the customer’s needs, providing options that align with different usage patterns.Volume-Based Pricing ModelThe volume-based pricing model offers discounts as usage increases, making it attractive to high-consumption customers. This model is common in industries like manufacturing and large-scale SaaS platforms, where economies of scale are a significant factor. It incentivizes higher usage and enhances customer loyalty by providing cost savings to those who consume more.Understanding the nuances of different usage-based pricing models is essential for enterprises aiming to implement a successful billing strategy. Aligning the chosen model with business goals and customer needs can enhance revenue growth and customer satisfaction.Enterprise-Ready Usage-Based Billing: Meeting the Needs of Large-Scale OperationsAs enterprises grow, their billing needs become more complex, requiring a usage-based billing system that integrates seamlessly with existing infrastructure, scales to support large transaction volumes, and complies with stringent regulatory standards.Integration with Existing SystemsEnterprises often rely on sophisticated billing and account management systems like SAP, Zuora, or Stripe. Moesif provides seamless integration with these platforms and even legacy billing systems through custom webhooks. This ensures that all usage data is accurately captured and reflected in customer invoices, maintaining operational efficiency and supporting the scalability needs of large enterprises.Scalability and FlexibilityScalability is critical for enterprises. A usage-based billing system should handle large transaction volumes without compromising performance. As businesses expand, their billing systems must support increasing data and usage, offering flexible pricing configurations that remain competitive and aligned with market demands.Compliance and SecurityCompliance and security are non-negotiable for enterprises, especially those handling sensitive customer information. Moesif addresses these critical needs by offering robust client-side encryption, ensuring that sensitive data is always protected. Additionally, Moesif’s solutions are designed to comply with industry regulations such as SOC 2, HIPAA, and GDPR, making it a reliable choice for enterprises that prioritize data security and regulatory adherence.Implementing a usage-based billing system in an enterprise setting requires addressing integration, scalability, and compliance challenges. By choosing a solution that meets these needs, enterprises can fully leverage the advantages of usage-based billing, driving revenue growth while ensuring operational efficiency and regulatory compliance.Tracking and Measuring Usage: The Backbone of Usage-Based BillingTracking customer usage is a crucial component of usage-based billing architecture. Accurate tracking and measurement of customer usage data are crucial for effective usage-based billing. This goes beyond ensuring accurate billing—it’s about gaining deeper insights into customer behavior, optimizing services, and driving growth. Moesif excels in this area by offering advanced analytics capabilities, including the ability to create custom metrics through scripted fields. This flexibility allows enterprises to tailor their tracking to specific business needs, ensuring that every aspect of customer usage is captured and analyzed effectively. Implementing robust systems for tracking and measuring usage is essential for any enterprise looking to maximize the benefits of usage-based billing.The Importance of Accurate TrackingAccurate data tracking ensures customers are billed correctly, which is essential for maintaining trust and satisfaction. This involves monitoring various units of service, such as API calls, data storage, or processing power. The precision of this data forms the basis for advanced analytics, revealing patterns and trends in customer behavior that can inform business strategy.Leveraging Metering and Rating EnginesMetering and rating engines are vital tools for capturing usage data in real time and applying the correct pricing models. Metering engines monitor the actual usage, while rating engines assign value based on predefined pricing. Together, they ensure every unit of consumption is accounted for and billed correctly. For enterprises with high transaction volumes, these engines must be scalable, reliable, and capable of processing large amounts of data quickly.Utilizing Data Analytics and Visualization ToolsThe data collected through usage tracking is a powerful asset for enterprises. Advanced analytics and visualization tools can help businesses analyze usage trends, predict future consumption, and identify upselling opportunities. These insights can also inform product development, helping enterprises refine their offerings based on actual usage data.Tracking and measuring usage accurately is crucial to the success of a usage-based billing system. Implementing robust metering and rating engines, leveraging data analytics, and integrating with APIs ensures accurate billing and valuable customer insights.Implementing Usage-Based Billing: Best Practices for EnterprisesImplementing usage-based pricing as a crucial strategy for businesses requires careful planning and execution to ensure that the system functions effectively and delivers the intended business outcomes. Here are some best practices for enterprises looking to implement usage-based billing.Determine Your Value MetricIdentifying the appropriate value metric is the first step in implementing a usage-based billing system. This metric should reflect the core value your service provides to customers, such as API calls, data usage, or transactions processed. It should be easy to measure and align with customer expectations, forming the foundation of your billing system.Align Pricing with Customer NeedsOnce the value metric is established, it’s crucial to align the pricing model with customer needs and behavior. Analyze customer usage patterns to determine the most appropriate pricing structure, whether it be pay-as-you-go, tiered, or volume-based pricing. Aligning pricing with customer usage ensures fairness, transparency, and satisfaction, which can improve retention.Invest in Reliable Tracking ToolsAccuracy is critical in usage-based billing. Investing in reliable tracking tools, such as metering and rating engines, is essential to capture every unit of usage accurately. These tools should handle large volumes of data in real-time and integrate seamlessly with existing billing systems. Minimizing errors and discrepancies is vital to avoid customer dissatisfaction and revenue loss.Ensure Scalability and FlexibilityAs enterprises grow, their billing needs will evolve. Choose a usage-based billing solution that is scalable and flexible enough to adapt to changes in customer demand and market conditions. This might involve integrating with cloud-based platforms that offer scalability or ensuring that the system can accommodate new pricing models as your business diversifies.Implementing a usage-based billing system requires careful consideration of the value metric, alignment with customer needs, investment in reliable tracking tools, and ensuring scalability and flexibility. These best practices help enterprises build a customer-centric and growth-oriented billing system.Overcoming Challenges in Usage-Based BillingWhile usage-based billing offers clear advantages, its implementation can present significant challenges for enterprises, particularly in managing pricing complexity, revenue unpredictability, compliance, and cost control. Effectively addressing these challenges is crucial to ensuring the success of a usage-based billing system.Managing Pricing ComplexityUsage-based billing often involves complex pricing structures, with prices fluctuating based on customer usage. This complexity can make it difficult to forecast revenue and manage cash flow, especially during the initial stages. To overcome this, enterprises should invest in advanced billing software capable of handling multiple pricing tiers and volume discounts. Regular audits and pricing reviews can also help maintain competitive and aligned pricing structures.Addressing Revenue UnpredictabilityRevenue fluctuation is a common challenge with usage-based billing, as revenue can vary greatly from one billing cycle to the next. This unpredictability can be mitigated by using predictive analytics to forecast usage trends and revenue changes. Enterprises might also consider hybrid pricing models that combine fixed and variable elements, offering more stable revenue streams.Ensuring Compliance and Cost ControlCompliance with regulatory standards is critical for enterprises, particularly those in regulated industries. Usage-based billing systems must adhere to laws like GDPR and industry-specific regulations like HIPAA. Enterprises should work closely with legal teams to ensure compliance and may seek certifications like SOC 2 or ISO 27001. Additionally, managing the costs associated with implementing and maintaining a usage-based billing system requires leveraging scalable, cloud-based platforms and automating billing tasks to reduce overhead.Overcoming the challenges of usage-based billing involves managing pricing complexity, ensuring revenue predictability, maintaining compliance, and controlling costs. By addressing these issues proactively, enterprises can implement a successful usage-based billing system that drives growth and customer satisfaction.Benefits and Value Alignment: Why Usage-Based Billing Is the Future for EnterprisesUsage-based billing is not just a trend; it is a strategic shift that aligns pricing with customer value and market dynamics. As businesses evolve and customer expectations change, this model ensures that pricing reflects the true value delivered to customers, driving long-term growth.Flexibility in PricingOne of the most significant advantages of usage-based billing is the flexibility it offers. Unlike traditional subscription models, usage-based billing allows enterprises to tailor pricing to individual customer needs and usage patterns. This flexibility is particularly valuable in industries like cloud computing and SaaS, where usage can vary widely. By offering adaptable pricing, enterprises can attract a broader customer base, from small businesses to large enterprises.Scalability and GrowthUsage-based billing inherently supports scalability. As customer usage increases, so does revenue, creating a direct link between consumption and profitability. This model is ideal for enterprises looking to expand or enter new markets, as it allows for rapid adaptation of pricing and operations to meet growing demands.Improved Customer SatisfactionAligning pricing with actual usage enhances customer satisfaction and retention. Customers appreciate the transparency and fairness of paying for what they use, which reduces the likelihood of billing disputes and increases trust. Enterprises that adopt usage-based billing often see higher customer loyalty, as the pricing model offers clear value and flexibility.Usage-based billing represents a forward-thinking approach to pricing that aligns enterprise success with customer value. Its flexibility, scalability, and customer-centric nature make it an ideal choice for enterprises aiming to grow and innovate in a dynamic market. By adopting this model, businesses can create a sustainable revenue stream while fostering stronger customer relationships.ConclusionUsage-based billing is more than just a pricing strategy; it’s a pathway to sustainable growth that aligns closely with customer needs. By adopting this model, enterprises can offer flexible pricing, improve customer satisfaction, and remain competitive in a rapidly changing market.While challenges like pricing complexity and revenue unpredictability exist, the benefits—such as enhanced customer loyalty, increased revenue, and the ability to adapt to market changes—make the transition worthwhile. With the right tools and a customer-focused approach, enterprises can successfully implement usage-based billing and secure long-term success. When customer expectations are constantly evolving, usage-based billing provides the agility and alignment needed to thrive. It’s a forward-thinking approach that positions enterprises for success both now and in the future.Moesif stands out as a powerful solution for enterprises looking to implement usage-based billing with precision and security. What sets Moesif apart is its advanced analytics capabilities, offering multiple ways to create and customize metrics, including the use of scripted fields for deeper insights. This allows businesses to not only track usage accurately but also gain actionable insights that drive growth. Moesif also prioritizes security, providing client-side encryption to ensure that sensitive data is always protected. Additionally, Moesif’s seamless integration with enterprise systems like Zuora, SAP, Stripe, and even legacy billing platforms through custom webhooks ensures that your billing infrastructure is both robust and flexible. By choosing Moesif, enterprises can confidently adopt a usage-based billing model that enhances customer satisfaction, drives revenue, and scales with their growth. Sign up today for a free trial today to try it yourself, no credit card required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue from your APIs?            Monetize your APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Implementing-Usage-Based-Billing-for-Enterprise/",
          "author": "Dylan",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-top-ai-apis": {
          "title": "Top 5 AI APIs For Developers",
          "content"	 : "Artificial Intelligence (AI) technology has been transforming industries and our day-to-day lives alike. Its undeniable impact has led to significant effort and investment into making AI more accessible to everyone, everywhere. Open-source AI technology and AI APIs are two examples of our commitment to AI democratization. AI APIs democratize AI by providing access to pre-trained AI models, even for developers without extensive machine learning expertise. These standardized interfaces connect applications to powerful AI capabilities like speech recognition, natural language processing (NLP), image analysis, and data-driven predictions.This blog explores the leading AI APIs, their advantages, and how to select the ideal one for your projects.Table of Contents  What are AI APIs?          Brief History and Evolution of AI APIs        Types of AI APIs          Natural Language Processing (NLP) APIs      Computer Vision APIs        Top AI APIs for Developers          OpenAI      Google Cloud AI      Microsoft Azure Cognitive Services      IBM Watson      Clarifai        Benefits of Using AI APIs          Increased Efficiency      Enhanced Capabilities        Choosing the Right AI API          Evaluating API Features and Pricing      Integration and Support        Security and Performance Considerations          Data Security and Compliance                  Robust Security Measures          Regulatory Compliance          Data Policies                    Performance Optimization                  Response Time and Throughput          Data Handling Efficiency          Caching and CDN                      Best Practices for Working with AI APIs          Understanding AI API Limitations      Staying Up-to-Date with AI API Developments      Additional Best Practices        Conclusion                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        What are AI APIs?Artificial Intelligence APIs connect powerful AI capabilities with the applications you build. They provide a way for your software to interact with and leverage pre-built machine learning models, often hosted in the cloud. Think of them as ready-to-use AI building blocks that you can integrate into your projects.These APIs abstract away the complexities of AI. Developers can focus on implementing features without needing deep machine learning expertise. For example, you can build an app that can generate images, analyze images, and identify objects in images. You can add functionalities like natural language understanding, sentiment analysis, and facial recognition to your application with just a few lines of code. AI APIs handle the heavy lifting, processing data, and returning results that your application can then use.For pre-trained models, many artificial intelligence APIs have customization options as well. These options can fine-tune models based on specific use cases or datasets. This involves providing additional data to the API that adapts the model’s parameters to better suit the use case. Some providers also allow you to build custom models.This accessibility democratizes AI and makes it available to a wider range of developers and businesses. It accelerates development, reduces costs, and opens doors to innovation that otherwise requires significant AI research and development.Brief History and Evolution of AI APIsAI APIs did not appear overnight; their development mirrors the broader evolution of artificial intelligence. Initially, AI APIs relied on rule-based systems, where programmers defined explicit behavior rules. While useful for specific tasks, these systems lacked the adaptability and learning capabilities of modern AI.The advent of machine learning in the 1990s marked a significant turning point. AI APIs began using machine learning algorithms that learn from data, such as decision trees and support vector machines, improving performance over time. This shift allowed them to tackle more complex tasks, opening doors to new applications.In the 2010s, deep learning techniques revolutionized AI APIs with neural networks inspired by the human brain’s structure. Frameworks like TensorFlow and PyTorch allowed developers to use these models for AI tasks such as natural language processing (for example, Google Translate’s advancements) and computer vision (self-driving cars owe much to this).Today, cloud infrastructure powers AI API accessibility. Companies like Google, Amazon, and Microsoft offer cloud-based AI APIs. Developers have easy access to advanced machine learning models and computational resources now more than ever. Thanks to AI services like AWS SageMaker, Google Cloud AI, and Microsoft Azure Machine Learning, AI APIs can scale automatically to process vast amounts of data and serve millions of users.Types of AI APIsBefore we talk about the top AI APIs, let’s briefly discuss the types of AI APIs and capabilities they offer.Natural Language Processing (NLP) APIsNatural Language Processing (NLP) allows machines to process and respond to human language. Using NLP APIs, developers can integrate language processing capabilities into their applications. These applications can analyze text, detect sentiment, summarize articles, translate languages, and respond to voice commands. We see these features across many of the digital tools we use daily. For example, chatbots that handle customer inquiries, voice assistants that manage schedules, and translation tools that connect people across the globe.NLP APIs power many modern applications, such as virtual assistants like Siri and Alexa. These applications demonstrate how NLP is transforming how we interact with technology. Tasks like ordering a pizza, booking a flight, or controlling smart home devices have become as simple as speaking a command.While NLP has advanced significantly, machines don’t truly “understand” language in the way humans do. They process and generate text based on patterns in data. NLP APIs can perform tasks like sentiment analysis, summarization, and translation with varying accuracy, depending on the context and complexity of a specific language.Computer Vision APIsComputer Vision allows machines to interpret and understand visual information from the world around them. Computer Vision APIs can process images and videos to identify and classify objects, detect and recognize faces, and even interpret scenes and emotions. Providers of computer vision APIs typically categorize them based on the use case. For example, an image recognition API supports detecting objects in the image. A facial recognition API, for example, allows machines to identify individuals based on their unique facial features. Many smart devices vendors support unlocking your device through this feature. Same goes for security systems that don’t allow someone to enter unless their facial scan matches an entry in a database of authorized personnel.Another powerful feature is optical character recognition (OCR). It allows machines to extract text from images and documents. Using OCR, you can convert a handwritten note into a digital document or extract information from a scanned invoice.Top AI APIs for DevelopersComprehensive AI platforms offer a suite of machine learning APIs with pre-trained models. They scale automatically on the cloud with varying degrees of customization options. Developers can easily find an AI API that suits their use case and integrate it, worrying about building the infrastructure from scratch or scalability.Here are five top comprehensive AI platforms for you to consider:OpenAIOpenAI is known for their sophisticated large language models (LLMs) capable of advanced natural language processing. OpenAI has APIs for natural language processing, code generation, creating content generation, and more. You can use these APIs to build applications that understand and generate human-like text.Google Cloud AIGoogle Cloud AI provides AI services like vision, language, and structured data processing. They offer a number of APIs across various use cases. All of them are powered by Google Cloud’s scalable infrastructure and robust documentation.Microsoft Azure Cognitive ServicesAzure AI Services, formerly Azure Cognitive Services, provides a suite of APIs for computer vision, speech, language, and decision-making. Azure AI Services focuses on ease of use and integration. Some popular APIs include Speech service APIs and the Face API.IBM WatsonA pioneer in AI, IBM Watson offers APIs for natural language processing, speech recognition, and discovery, such as Watson Assistant and Watson Discovery. Their robust and accurate services are suitable enterprise-level applications and developers alike.ClarifaiClarifai provides AI and machine learning services in advanced computer vision, natural language, and generative AI technologies. Their API supports integrating video recognition, image recognition, and advanced natural language processing features. The API has SDKs for various programming languages so developers of different backgrounds can integrate AI into their apps.Benefits of Using AI APIsIntegrating AI APIs into your applications can transform your work and interactions with technologies. Let’s discuss the two key advantages.Increased Efficiency  Rapid Development: You can leverage pre-trained AI models and ready-to-use functionalities to reduce development time and focus on core features.  Task Automation: AI-powered apps can automate mundane and repetitive tasks, freeing your team to focus on strategic and creative work.  Scalability: AI APIs can handle growing data volumes and complex processes. This ensures smooth operations as your business expands. However, scalability also depends on the underlying infrastructure and application design. Without proper support from these components, you may not enjoy the full benefits of using AI APIs for scalability.Enhanced Capabilities  Complex Task Handling: AI-enhanced apps can perform tasks like natural language processing and image analysis that humans alone may find challenging or impossible.  Intelligent Interactions: You can build applications that understand human language, analyze visual data, and make data-driven predictions.  Personalized Experiences: AI-driven insights allow you to fine-tune user experiences. You get better engagement and satisfaction, both of which positively impacts your product growth.Incorporating AI APIs into your development toolkit helps you stay current with trends. Moreover, you gain a competitive edge, optimized processes, and deliver delightful user experiences.Choosing the Right AI APILet’s discuss two crucial aspects we recommend you consider when choosing an AI API.Evaluating API Features and PricingIdentify the type of AI functionality your project needs, such as NLP or video analysis. Try to exactly pinpoint the types of services you need and whether the vendor provides them. For example, let’s say you need sentiment analysis features. Despite an API offering natural language processing, it may not provide sentiment analysis. Choose an API that aligns with your requirements and has a proven track record.Examine the pricing model carefully. Some APIs offer pay-as-you-go plans, while others use subscription models. Consider any usage limits or additional fees and evaluate cost-effectiveness relative to your budget.Ensure the API can handle your current and future data volumes. Look for performance metrics or benchmarks to assess its ability to process large datasets and manage concurrent requests efficiently.Integration and SupportEvaluate how easily the API integrates with your existing systems and applications. Look for clear documentation, SDKs, and code examples. Test integration in a development environment before full deployment.Assess the level of support the API vendor provides. Comprehensive documentation, tutorials, and responsive customer support provide immense value during development and troubleshooting. They guide you through the integration process, help you understand the API’s functionalities, and offer solutions when you encounter challenges. Community forums and user groups can also offer helpful resources.Ensure the API supports the programming languages and frameworks you prefer. This compatibility will save time and effort during integration.Security and Performance ConsiderationsData security and performance optimization drive successful AI integration. Rising cyber threats and growing user expectations demand active measures to protect data and ensure seamless performance. Addressing these concerns directly prevents data breaches, legal complications, and customer dissatisfaction.Data Security and ComplianceAs AI gets more adoption, we observe companies adopting data security and regulatory measures with a non-negotiable mindset. This speaks volumes about the collective concern for transparency and risk mitigation while we employ AI in sensitive areas.Let’s discuss this topic in more detail and some key aspects we recommend you consider.Robust Security MeasuresEncryption serves as the first line of defense in protecting data. Implement end-to-end encryption to secure data both at rest and in transit. Even if someone intercepts, this safeguard makes sure that  the data remains unreadable to unauthorized parties.Define strict access controls to limit who can access the API and the data it processes. Consider role-based access control (RBAC). It assigns permissions based on a user’s role within the organization so that users have minimum necessary permissions, reducing the risk of accidental or malicious misuse.Use strong authentication methods such as OAuth 2.0 or API keys to ensure that only authorized users can interact with the API. Multi-factor authentication (MFA) significantly reduces the risk of unauthorized access by requiring more than one form of verification.Conduct regular security audits and vulnerability assessments to identify and fix potential weaknesses in the API infrastructure. Staying proactive helps prevent security breaches before they occur.Regulatory ComplianceWhen deploying AI solutions globally, ensure compliance with global regulations like GDPR (General Data Protection Regulation) in Europe, HIPAA (Health Insurance Portability and Accountability Act) in the U.S., and CCPA (California Consumer Privacy Act). Each regulation defines specific requirements for how the system must handle, store, and process personal data.Ensure that the API vendor maintains transparency about their data handling practices. Understand where they store data (data residency), how it’s processed, and whether third parties have access. This transparency helps avoid legal complications and builds trust with users.Employ techniques like data anonymization and minimization to protect sensitive information. By anonymizing data, you reduce the risk of exposing personal information, even in the event of a breach.Data PoliciesScrutinize the API vendor’s data policies to understand how they might use your data beyond the primary application. Ensure that the vendor does not use your data for purposes that conflict with your organization’s policies, such as selling data to third parties. If the API vendor shares data with third parties, make sure these parties also comply with relevant regulations and maintain high security standards.Understand the vendor’s data retention policies. Determine how long they store data and the processes in place for secure data deletion when no longer needed.Performance OptimizationA high-performing API ensures a better user experience and efficient operations. Pay attention to the following considerations when you choose an AI API:Response Time and ThroughputWithout low latency in API response times, you cannot deliver a smooth user experience, especially in real-time applications like voice recognition or interactive chatbots. Aim for an API that consistently delivers sub-second response times.High throughput ensures that the API can handle a large number of requests simultaneously without degrading performance. Evaluate the API’s capacity to manage peak loads, and consider load balancing solutions if necessary.Implement monitoring tools to track the API’s performance metrics in real-time. These tools can alert you to potential bottlenecks and allow you to adjust quickly to maintain optimal performance.Data Handling EfficiencyThe API should use efficient algorithms that can process data quickly, particularly when dealing with large datasets. Evaluate how the API handles data processing tasks such as batch processing versus real-time streaming, depending on your needs.Consider the API’s ability to manage concurrent requests without slowing down. This includes managing thread pools effectively and ensuring that resource allocation scales with demand.Rate limiting helps prevent any single client from overwhelming the API with too many requests at once. This ensures consistent performance across all users, especially during peak times.Caching and CDNCaching frequently accessed data can significantly reduce the API’s response time. Regularly review and adjust caching strategies to match changing usage patterns to maintain optimal performance.A content delivery network (CDN) distributes API responses across multiple geographically dispersed servers. By serving content from the server closest to the user, a CDN reduces latency and speeds up data delivery.Best Practices for Working with AI APIsAfter you’ve chosen an API for your use case, you want to maximize its utility. For that, you must understand the limitations and stay current with developments. Here are some best practices we recommend you follow:Understanding AI API LimitationsAI APIs, while powerful, have limitations that you must recognize and address to build reliable applications.AI models may inherit biases from their training data and produce errors, especially with edge cases or unfamiliar data. Be proactive in identifying and mitigating these issues, such as through data augmentation or re-training with diverse datasets.Test the API’s accuracy and reliability across different data types and use cases before full implementation. Identifying potential weaknesses early can prevent issues later.Opt for APIs that offer transparency and insights into their decision-making processes. Especially, regulated industries uphold explainability as a crucial factor for compliance and trust.Staying Up-to-Date with AI API DevelopmentsThe AI landscape evolves rapidly. If you stay informed, you can confidently leverage the best tools and techniques.Regularly check for API updates and new features, which may include performance improvements, added functionalities, and bug fixes. Subscribe to vendor newsletters or follow their release notes to stay informed.Choose vendors with a strong commitment to research and development. Evaluate their track record in delivering updates and innovations to ensure long-term support and access to cutting-edge AI capabilities.Select APIs that can adapt to your evolving business needs. Ensure the API can scale and grow with your application as your requirements change.Additional Best Practices  Continuously monitor and log API usage to identify issues early and optimize performance over time.  Utilize well-documented APIs and engage with community support forums. This helps you troubleshoot and learn from others’ experiences.ConclusionAI APIs have transformed the way we build applications. Developers are building more efficient, intelligent, and innovative apps and systems. Whether you want to automate tasks, enhance user experiences, or gain a competitive edge, AI APIs offer a powerful toolkit to achieve your goals.As AI continues to advance and dominate our lives, we’ll see more push for AI democratization. We’ll see more and more businesses and developers adopt AI. So if you have the next big idea, it wouldn’t be wrong to consider how AI can fit into that. But incorporating AI into your product alone doesn’t set you up for success. You must continuously monitor, understand, and conduct research to tackle the challenges that lie ahead.That’s where Moesif comes in with a comprehensive suite of powerful analytics and monitoring tools. Moesif has also built robust monetization features from the ground up so that you can grow your AI product.Sign up today for a free trial today to try it yourself, no credit card required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue from your AI APIs?            Monetize your AI APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Top-AI-APIs/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-monetizing-ai-apis-with-billing-meters-in-moesif": {
          "title": "Monetizing AI APIs with Billing Meters in Moesif",
          "content"	 : "You’ve built an incredible AI API and are ready to release this functionality to your users. The issue is that you’re not sure exactly how to monetize it. Generally, monetizing APIs is challenging at scale, but monetizing AI APIs can be even more difficult. Some AI APIs may be charged on a “per API call” basis, but many AI APIs require charging users for input and output tokens used within an API call. Others may charge per unique user or API key.Monetizing APIs based on these metrics becomes increasingly challenging as you move from the most straightforward metric, charging per API call, to the more complex task of aggregating token usage. In this blog post, I will show you how to implement a Billing Meter in Moesif to expedite monetizing your AI APIs. We will first review what a Billing Meter is and then walk through a step-by-step guide on implementing monetization for AI APIs. Let’s get started!                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        What is a Billing Meter?A Billing Meter is a functionality offered within Moesif that allows you to aggregate API usage on a per-subscription basis and send this usage data to billing providers to charge users for the usage. Within a billing meter, the three main areas are:Billing Provider and Subscription Item to Associate Usage ToWhen you meter your usage, you want it reported to your billing provider, such as Stripe, Zuora, Chargebee, etc. Each billing provider usually has plans and prices that you want this usage counted towards. This part of the Billing Meter allows you to precisely report the usage to a particular price so a user can be charged accordingly.Filter CriteriaThe filter denotes what you want to monetize. For example, you may have a filter that says, “I want to monetize every API call to the /generate route that returns a 200 OK response”. A filter can be high-level, like the example just discussed, or could go down to a deep level, such as only monetizing API calls to a specific route where a field within the body contains a particular string. Anything that does not match the filter will not be eligible to be monetized by that particular meter.Billing MetricThe metric is how you will monetize the API calls that match your filter. For example, you may monetize your APIs by metering event count (each API call is charged for) or more commonly for AI APIs, have your metric set to input or output tokens for that specific meter. This will be the scenario we will demonstrate below in the practical portion of this blog.Once the Billing Meter is live, these different components work together to seamlessly monetize your AI APIs. In the next section, let’s walk through exactly how to set up a billing meter to monetize your AI APIs quickly.Monetizing Your AI API With MoesifStep 1: Create a Moesif Account and Integrate Your AI APIsOnce you’ve signed up for a Moesif account or are logged in as an existing user, our first step is integrating our AI APIs to stream usage data into the Moesif platform. For this, you can use one of our SDKs or plugins for many of the most popular languages, frameworks, and gateways to create AI APIs.Step 2: Integrate With Your Billing ProviderAfter creating an account and integrating your APIs, the next step will be configuring your selected billing provider so that usage can be synced. There are a few out-of-the-box options to choose from:  Stripe  Recurly  Chargebee  ZuoraYou can follow the links in the entries to walk you through how to set up each of these platforms with Moesif. If you don’t currently use one of these providers, you can also set up custom integrations using either the Moesif API or Custom Webhook functionality.Step 3: Create Your Billing MeterNext, you will go to the Billing Meters screen in Moesif by selecting Billing Meters from the left-side menu. On the next screen, click Add Billing Meter in the top-right to create a new meter.On the Create New Billing Meter screen, add the name of your meter and select the billing provider, plan, and price that you’d like the usage to be synced over to. Here’s an example of a plan and price in Stripe being added to a meter.From here, let’s dial in the filter. For this demonstration, in the Filter section, we will make it so that we monetize any API call to the /v1/text/generate route that receives a 200 OK response. Additionally, I’ve also added another criterion where the response.body.generated_text.usage.total_tokens body field is greater than 15. Any API calls that use less than 15 tokens will not be included in the monetized APIs. This condition type is optional but possible since Moesif allows you to use values in the request and response header and body fields. Here’s what that example filter would look like.For this particular meter, I want to aggregate the number of output tokens (known as completion_tokens in the response payload for this particular API) and charge for this usage. To do this, we will need to use Moesif’s Custom Metric functionality and select the Select Field… option from the Metrics dropdown.With the Custom Metric type selected, we will click on the field selector dropdown and choose the response.body.generated_text.usage.completion_tokens field and set the aggregation type to “sum”. This will add all the output tokens used in API calls that match our filter criteria. Here’s what that custom field would look like in Moesif.Below the meter configuration, you’ll see a Preview of Historical Usage table showing how the filter and selected metric would have picked up on past events that have streamed into Moesif. This is a great way to do a quick logic check to ensure the meter is doing what you intended. Here’s an example of what that output would look like.With our Billing Meter configuration complete, we can click the Create button in the top-right to create and activate the meter. At this point, the meter is active, and will begin monetizing our APIs based on what we’ve dialed in. Optionally, but suggested, is to run through a test of the meter, ensuring that each component is running as expected. You can do this by clicking the Test Meter button on the meter you just created.Step 4: Drive RevenueOur last step is to start getting some API traffic so that it can be monetized and drive revenue! As API traffic flows in, if it meets your meter’s criteria, the usage will be logged and synced over to your billing provider based on your selected metric. This is all required to begin monetizing your AI APIs with Moesif.ConclusionMonetizing AI APIs can be complex, but with Moesif’s Billing Meter feature, you can seamlessly track usage and charge users based on specific metrics like token consumption. By integrating Moesif with your billing provider and setting up customized filters and metrics, you can ensure accurate and transparent monetization for your AI services. Ready to unlock the revenue potential of your AI APIs? Sign up for Moesif today and start building Billing Meters to monetize your AI APIs in minutes!                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Monetizing-AI-APIs-with-Billing-Meters-in-Moesif/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-the-ultimate-guide-to-ai-cost-analysis": {
          "title": "The Ultimate Guide to AI Cost Analysis",
          "content"	 : "Artificial intelligence (AI) has quickly become integral to businesses across industries. Whether utilizing Large Language Models or other Machine Learning platforms, AI-powered tools offer immense potential for growth and efficiency. However, as AI adoption surges, so do the associated costs of building and supporting AI apps. For companies that use AI as part of their product offering, understanding and managing the costs associated with AI capabilities is crucial to ensuring AI initiatives deliver a sustainable return on investment.This comprehensive guide will explore AI cost analysis from many angles. We’ll explore what it entails and why it’s important and provide you with a step-by-step approach to calculating and optimizing your AI expenses. We’ll also discuss how leveraging tools like Moesif can streamline the process and accuracy of AI cost analysis, giving you a clear view of your AI spending. Let’s start by looking at what AI cost analysis is and why it’s essential to keep track of.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        What is AI Cost Analysis?AI cost analysis evaluates and understands the financial implications of developing, deploying, and maintaining AI systems. It involves breaking down the various cost components associated with AI, from initial research and development to ongoing operational expenses. Much of the time, the main reason for tracking this is to ensure that profit margins from an AI platform are in line with expectations and, more importantly, to ensure that the cost of your AI offering does not exceed the revenue being generated.A comprehensive AI cost analysis typically includes the following elements:      Infrastructure Costs: This encompasses the cost of hardware (servers, GPUs, etc.), cloud computing resources, and storage needed to train and run AI models.        Data Costs: Acquiring, preparing, and labeling data for AI models can be a significant expense, especially for complex projects requiring large datasets.        Development Costs: This covers the salaries of AI engineers, data scientists, and other specialists involved in building and refining AI models. It also includes the cost of software tools and licenses.        Operational Costs: These are ongoing expenses associated with running and maintaining AI systems, such as energy consumption, monitoring, and updates.        Unexpected Costs: AI projects can encounter unforeseen challenges, such as model retraining or debugging, which can add to the overall cost.  AI cost analysis gives organizations the insight they require to make informed decisions about their AI investments. The outcome of this type of analysis helps them ensure that AI projects are technically successful and financially sustainable.Why AI Cost Analysis is ImportantUnderstanding the financial implications of your AI initiatives, or any technology initiative, is essential. As much as we would like to think that the goal of our latest AI project is to change the world, we can’t get things to scale if they’re not making the money they need to sustain themselves and drive further innovation. Compared to prior generations of technology, AI is extremely expensive to deploy and manage, requiring massive amounts of computing power and data. This underscores the importance of making AI cost analysis a regular part of reviews for the business and technical teams involved. Here are a few reasons why AI cost analysis is important:1. Ensuring Profitability:AI can be a powerful tool for driving revenue and reducing costs. However, the initial investment and ongoing operational expenses can quickly escalate. By analyzing costs, you can identify areas of inefficiency, optimize resource allocation, and ensure that your AI projects generate a positive return on investment.2. Resource Optimization:AI projects often involve significant resources, including computing power, data storage, and specialized personnel. Cost analysis allows you to track resource utilization, identify bottlenecks, and optimize resource allocation to maximize efficiency and minimize waste.3. Risk Mitigation:AI projects are not immune to risks like unexpected technical challenges, model drift, or data quality issues. These risks can lead to cost overruns and project delays. Cost analysis helps you anticipate potential risks, allocate contingency budgets, and take proactive measures to mitigate their impact.4. Data-Driven Decision Making:Cost analysis provides valuable data insights that can inform strategic decision-making. By understanding the cost-benefit tradeoffs of different AI approaches, you can make informed choices about which projects to prioritize, which technologies to adopt, and how to scale your AI initiatives.5. Transparency and Accountability:AI projects often involve multiple stakeholders, including executives, engineers, and financial teams. Conducting a transparent and comprehensive cost analysis can ensure that all stakeholders clearly understand the financial cost and impact of AI initiatives, fostering accountability and trust.AI cost analysis goes beyond saving money. These analyses help everyone involved make informed decisions, maximize the value of your AI investments, and ensure an AI initiative’s long-term success and sustainability.How to Do an AI Cost AnalysisNow that we understand the fundamentals of AI cost analysis let’s examine the exact steps to uncovering AI costs. Below, we will cover three steps required to calculate your AI costs and then an additional step to outline ways to improve them.1. Identify Cost ComponentsIn this first step, you’ll need to identify various costs associated with building and supporting your AI product. The areas you’ll want to focus on are:      Infrastructure: Estimate the cloud computing costs for running the API infrastructure (servers, load balancers, databases, etc.). Consider the volume of API requests across all clients, the average request processing time, and the required computing resources per request.        Model Inference Costs: If you’re using cloud-based AI models (e.g., GPT-3), calculate the costs associated with each API call based on the number of input and output tokens and any additional charges for context size or model complexity.        Data Costs: Factor in any ongoing costs for model training or fine-tuning, especially if you’re offering customization options to your clients.        Development: Calculate the salaries (or hourly rates) of your AI engineers and developers responsible for building and maintaining the API, including ongoing feature development, bug fixes, and performance optimizations.        Operational: Include the ongoing costs of running the SaaS API platform, such as server maintenance, software licenses, customer support, monitoring tools, and API documentation updates.        Client-Specific Costs:                  API Usage: Track the number of API requests made by each client, as this will directly correlate with your infrastructure and model inference costs.                    Support: Consider the cost of providing technical support to clients for API integration and usage issues.                    Customization: If you offer customized models or features through the API, track the costs associated with each request.            Some of these aspects may be hard to quantify, however, getting somewhere in the ballpark or an estimate is generally sufficient. This is especially true if you’re trying to do some predictive cost analysis.2. Quantify Costs and UsageNext, if we are looking at a historical period, we will need to figure out the actual usage of our AI product for the period we are analyzing. If you are trying to forecast usage, use estimates for these factors. Since our example is focused on an AI API, we can leverage API analytics tools like Moesif to gather detailed usage data for each client. Metrics to focus on include:      Number of API requests        Input/output tokens per request        Context size (if applicable)        Error rates        Latency  Once you have actual or estimated usage metrics, use the data to allocate infrastructure, model inference, and other relevant costs to each client based on their API usage.3. Calculate Cost per Token/API Call/UserWith a clear breakdown of your costs, you can begin digging deeper into the specifics. Although you could look at attributing costs from various angles, popular ways to do so are by token, API call, or user.In the case of calculating cost per token, one of the most common ways to calculate costs for AI platforms is to aggregate all of your costs and divide them across the total amount of tokens consumed. This will give you an average cost per token, allowing you to dial in your pricing and anticipated margins more easily. When it comes to AI cost, this is the most granular unit that makes sense when calculating the cost of AI services.Cost per API call, a less common way to calculate costs for AI APIs can be done similarly to tokens. In this case, you’d add up the costs for your API and then the number of API calls made in the same period. From here, you would divide the costs by the amount of API calls, resulting in your cost per API call. Of course, this will give you a less granular look than assessing costs by tokens used, but this can be useful if it makes sense to price your AI API by API call. An example of this, based on some popular examples, might be a content creation API that creates social or blog posts. In this case, you may charge $2 per social post the platform creates, and that functionality is exposed through a single API. If you calculate that the average cost per API call for that endpoint is $0.25, then you can calculate your average profit as $1.75. So, although a bit less common than per-token pricing, for specific applications, especially those targeted at less technical users, pricing and analyzing costs this way may make more sense.Lastly, you could also take a look at costs per user. For some platforms, users may pay by the user/seat. To calculate the cost of AI in this case, you’d want to once again add up all of your costs for a certain period of time and find the total number of users on the platform. Then, you’ll divide your total costs by the number of users to get your average cost per user. An excellent example of this might be a platform that has added AI capabilities, such as Notion. In the case of Notion, AI capabilities are available to users but are covered by charging per AI-enabled seat via their Notion AI add-on. To calculate the cost per user/seat, once again, you’ll take your total costs to build and support the AI capabilities and divide it by the total number of subscribed users. This will land you at the average AI cost per user. For example, let’s say that the price charged per seat is $8 per month and, on average, the AI cost is $2 per user per month; then we can quickly determine that the profit is around $6 per month for each user that is subscribed.These are the most common ways to calculate the cost and profit of AI platforms. When it comes to increasing profit, organizations usually have two options: increase prices or decrease costs. Let’s move on to how to analyze and optimize cost and profit.4. Analyze and OptimizeOnce you have metrics around AI cost and profits, you can consider optimizing using some of the following methods.      Usage-Based Pricing: Design API pricing plans based on usage tiers, considering the cost per API call or other relevant metrics.        Rate Limiting: Implement rate limiting to prevent excessive usage by individual clients, protect your infrastructure, and ensure fair access for all users.        Performance Optimization: Continuously monitor API performance and optimize your infrastructure and models to reduce latency and improve response times, enhancing the user experience.        Client Profitability: Analyze each client’s revenue generated compared to their API usage costs to assess their profitability. This can guide your sales and marketing efforts, pricing strategies, and customer support prioritization.  By meticulously tracking and analyzing costs associated with your API-based AI chatbot service, you can gain valuable insights into your business operations, make data-driven decisions to optimize your pricing and resource allocation, and ultimately ensure the long-term sustainability and profitability of your SaaS offering.How to Leverage Moesif for AI Cost AnalysisCost analysis is deeply routed in metrics. Whether your AI platform is used through a UI or API, you’ll need an API analytics platform that can be a powerful ally in your quest for efficient AI cost management. While traditionally used for API monitoring and analytics, its capabilities extend to tracking AI usage and associated costs, especially when your AI models are exposed through APIs. Here’s how Moesif can assist you:      Granular Usage Tracking: Moesif allows you to track API calls made to your AI models at a granular level. You can see which models are used most frequently, by whom, and how much data they’re processing. This data is invaluable for understanding usage patterns and identifying cost drivers.        Cost Allocation: Moesif enables you to associate specific costs with individual API calls. This means you can accurately attribute infrastructure costs (e.g., cloud computing) to the specific models and users responsible for them. This level of cost allocation is crucial for understanding the true financial impact of each AI model.        Real-Time Monitoring: With Moesif’s real-time monitoring, you can track AI usage and costs as they occur. This allows you to detect anomalies, such as sudden spikes in usage or unexpected cost increases, and take corrective action promptly.        Customizable Dashboards: Moesif provides customizable dashboards that allow you to visualize AI usage and cost data in a meaningful way for your organization. You can create dashboards that track specific metrics, compare costs across different models, and identify trends over time.        API Monetization: If you’re offering your AI models as a service, Moesif can help you monetize them through usage-based billing. You can define pricing tiers based on usage metrics like the number of API calls or the amount of data processed. Moesif can then automate the billing process, ensuring you capture the total value of your AI investments.        Anomaly Detection: Moesif can be configured to alert you to unusual patterns in AI usage or costs. This proactive approach helps you identify potential issues before they escalate into major problems, saving time and money.  Now, let’s take a closer look at how we can use Moesif to calculate the cost of our AI APIs by looking at cost per token, API call, and user.Tracking Cost Per TokenOne common way to break down AI costs, especially when discussing LLMs, is per-token. This is a standard unit of measurement on platforms such as OpenAI and others. If your AI platform is leveraging one of these third-party platforms under the hood, the cost per token is easy to calculate since it will be shown in your subscription or in the pricing of the model you use. Cost per token becomes more complicated once you try to calculate this within a custom AI engine. While it may require a bit more effort and calculation than using a third-party AI provider, it’s possible to determine the cost per token for your own AI platform. Here’s more particulars on exactly how to calculate this for your platform:1. Track Total CostsFirst, you’ll need to calculate how much your AI API costs to build and support. Here are the areas to consider when calculating this:      Infrastructure: Sum up all your monthly infrastructure costs, including servers, GPUs, cloud services, storage, and networking.        Software: Calculate your monthly software expenses, including licenses for deep learning frameworks, development tools, and data management tools.        Personnel: Total your monthly personnel costs, including salaries, benefits, and training expenses.        Data: Estimate your monthly data costs, including acquisition, cleaning, labeling, and maintenance.        Research and Development: Factor in any monthly R&D costs related to your AI platform.  2. Track Total Token UsageNext, we can use Moesif to track how many input, output, and total tokens have been processed within a specific time period. For this calculation, if applicable (some systems may only have a field for total tokens, etc), we will need the following data points:      Input Tokens: Sum the total number of input tokens processed by your AI models over a period (e.g., a month).        Output Tokens: Sum the total number of output tokens generated by your models over the same period.  To find this data in Moesif, you can create a Time Series report. To do this, click the + New button in the top-left corner of Moesif and select Time Series from the modal that appears. On the Time Series screen, Let’s set the following filter and metrics:  Depending on your API response, this value may be in your body or header field. In this example, we use a value from the headers for X-Input-Token-Count and X-Output-Token-Count.In the above filter, we look at all API events. For each event, we plot the input tokens (with the value in the X-Input-Token-Count header), the output tokens (from the value in the X-Output-Token-Count header), and the sum of these two amounts to show the total tokens used.In the output of this Filter and Metrics, you can see the total amount of tokens used in the chart. In the output below, you’ll be able to see the input, output and total tokens used.In our example, the total number of tokens for the first period we are looking at, July, is 21,787 tokens. We can now take this amount and move to the next step.3. Calculate Cost Per TokenNow we have all of the metrics and data we need, we can calculate our cost per token. Continuing with our example, let’s say the total monthly costs for your platform are $1,000 (the sum of the costs found in step 1 above), and your models processed 21,787 tokens (input and output combined) in that month (calculated in step 2). Your cost-per-token calculation would look like this:$1,000 / 21,787 tokens = $0.04589 per tokenThis will give us our average cost per token, which is $0.04589 per token. If our pricing structure is based on a “per token” price, we can now easily calculate our profit margins at a per-token level.Tracking Cost Per API CallAnother way we can look at AI costs is the average cost per API call. As explained above, although less common, specific AI platforms may use this pricing. From the user’s perspective, this may look like “$X per [ACTION]”, for example, “$2 per social post generated”, instead of being explicitly considered an API call externally. However, internally, this functionality is delivered via API call, so pricing per API call may make sense. Here’s precisely how to calculate this:1. Track Total CostsLike what we discussed in the previous section, you’ll need to calculate how much your AI API costs to build and support. Here are the areas to consider when calculating this:      Infrastructure: Sum up all your monthly infrastructure costs, including servers, GPUs, cloud services, storage, and networking.        Software: Calculate your monthly software expenses, including licenses for deep learning frameworks, development tools, and data management tools.        Personnel: Total your monthly personnel costs, including salaries, benefits, and training expenses.        Data: Estimate your monthly data costs, including acquisition, cleaning, labeling, and maintenance.        Research and Development: Factor in any monthly R&D costs related to your AI platform.  2. Track API Usage by API CallNext, we can use Moesif to track how many API calls have been processed within a specific time period. To find this data in Moesif, you can create a Time Series report. To do this, click the + New button in the top-left corner of Moesif and select Time Series from the modal that appears. On the Time Series screen, Let’s set the following filter and metrics:In the above filter, we look at all API events. In the resulting chart, we will see all of the API calls per month displayed. In this specific example, we are looking at all API calls but you can also refine this by endpoint as well to get a more precise cost.In the output of this Filter and Metrics, you can see the total amount of API calls that occurred in the previous month (July) in the chart.In our example, the total amount of API calls in the first period we are looking at, which is July, is 9,956 tokens. We can now take this amount and move to the next step.3. Calculate Cost Per API CallNow we have all of the metrics and data we need, we can calculate our cost per API call. Continuing with our previous example, let’s say the total monthly costs for your platform are $1,000 (the sum of the costs found in step 1 above), and your APIs processed 9.956 calls in that month (calculated in step 2). Your cost per API call calculation would look like this:$1,000 / 9,956 tokens = $0.1004This will give us our average cost per API call, which is $0.1004 per call. If our pricing structure is based on a “per API call” price, we can now easily calculate our profit margins at a per-API call level.Tracking Cost Per UserLastly, for instances where you are charging so much per seat, you may be interested in figuring out how much your AI costs per user. For instance, if you charge $15.99 per seat for all-you-can-eat access on your platform, you’ll want to know the average cost per user to ensure you meet your expected profit margins. For this, we will need to understand our user count and spread our cost over the number of users on the platform. Here’s precisely how to do this:1. Track Total CostsLike what we discussed in the previous sections, you’ll need to calculate how much your AI API costs to build and support. Here are the areas to consider when calculating this:      Infrastructure: Sum up all your monthly infrastructure costs, including servers, GPUs, cloud services, storage, and networking.        Software: Calculate your monthly software expenses, including licenses for deep learning frameworks, development tools, and data management tools.        Personnel: Total your monthly personnel costs, including salaries, benefits, and training expenses.        Data: Estimate your monthly data costs, including acquisition, cleaning, labeling, and maintenance.        Research and Development: Factor in any monthly R&D costs related to your AI platform.  2. Track API Usage by Unique UsersNext, we can use Moesif to track how many users are active on the platform within a specific time period. To find this data in Moesif, you can create a Time Series report. To do this, click the + New button in the top-left corner of Moesif and select Time Series from the modal that appears. On the Time Series screen, Let’s set the following filter and metrics:In the above filter, we look at all API call events that have come into Moesif. From here, we further refine this by selecting Unique Users as our value selected from the Metrics dropdown. This will let us know how many users used our platform.In the output of this Filter and Metrics, you can see the number of unique users that have accessed the platform per month. In the output below, you’ll see the exact amount of users that we can use to calculate the AI cost per user.In our example, the total number of unique users in the first period we are looking at, which is July, is 601 users. We can now take this amount and move to the next step.3. Calculate Cost Per UserNow we have all of the metrics and data we need, we can calculate our cost per user. Continuing with our example, let’s say the total monthly costs for your platform are $1,000 (the sum of the costs found in step 1 above), and we have had 601 unique users on the platform in that month (calculated in step 2). Your cost per user calculation would look like this:$1,000 / 601 users = $1.6638 per userThis will give us our average cost per user, which is $1.6638 per user. If our pricing structure is based on a “per user/seat” price, we can now easily calculate our profit margins at a per-user level.Notes for Further RefinementAlthough the above examples are great and will get you a good idea of your average costs, there are a few ways to dial things in a bit more and a few factors to consider.First, you may want to narrow down your cost analysis to specific endpoints since the underlying AI may cost more to run on one API versus another. To do this, you would simply change any of the use cases outlined above and scope the analysis to specific endpoints. This would include everything from calculating the total costs to making sure that your filter in Moesif is set to only look at data for a specific endpoint. This would be done in the Filter for the Time Series, specifying “URI Route is /X” with “X” being the API endpoints you want to scope your results to.Secondly, sometimes licenses or seats may be granted at the company level so it may make sense to look at the Unique Companies versus Unique Users metric that we used in our per-user calculation. This will allow you to calculate your cost at a higher level if it is useful to your pricing strategy.Lastly, we looked at average costs, which tends to be more effective and smooths out cost calculations a bit more. However, there may be some reason for being more specific and calculating costs at an exact amount for each AI interaction. This can be somewhat challenging, especially for high-volume services. If your AI platform spins up infrastructure per client, then this would likely be possible and may be advantageous, however, with shared infrastructure average cost is the most effective way to do a cost analysis for AI platforms.ConclusionIn the era of AI-powered innovation, understanding and managing costs is not just a financial necessity; it’s a strategic advantage. AI cost analysis empowers you to make informed decisions, optimize your resources, and ensure that your AI initiatives are not only cutting-edge but also financially sustainable.By identifying cost drivers, tracking usage patterns, and optimizing resource allocation, you can unlock the full potential of AI while keeping your budget in check. Remember, the journey of AI cost analysis is ongoing. As your AI projects evolve and scale, so too will your costs. Continuous monitoring and optimization are key to long-term success.If you’re looking for a powerful tool to streamline your AI cost analysis, Moesif is a great platform for collecting and analyzing data around various aspects of your customer’s AI usage. With its granular tracking, real-time monitoring, and customizable dashboards, Moesif provides invaluable insights into your AI spend and revenue. Whether you’re a startup experimenting with AI or an enterprise deploying complex AI systems, Moesif can help you gain control of your AI costs and maximize the return on your AI investments.Ready to take the next step in understanding your AI costs? Sign up for a free trial of Moesif today and discover how easy it is to understand and optimize your AI costs.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/The-Ultimate-Guide-to-AI-Cost-Analysis/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-how-to-track-the-usage-of-ai-apis-the-ultimate-guide": {
          "title": "How to Track the Usage of AI APIs: The Ultimate Guide",
          "content"	 : "When it comes to exposing artificial intelligence (AI) services, APIs (Application Programming Interfaces) have become the building blocks for integrating AI capabilities into software and applications. These APIs provide access to various AI models, from natural language processing to image recognition, opening up many possibilities for developers and businesses.However, with the growing adoption of AI APIs comes the need to track usage effectively. Whether you’re an AI provider managing API access for customers or a developer integrating AI into your projects, understanding how your APIs are utilized is crucial.In this guide, we’ll explore tracking your AI API usage, explain its relevance, and provide you with how to leverage Moesif’s API analytics capabilities to gain deep insights into your API traffic. By the end of our guide, you’ll have the knowledge and tools to monitor, analyze, and optimize your AI API usage effectively.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        What is an AI API?Before we get into tracking AI API use, let’s simplify the term “AI API”. An AI API is a software interface that lets you interact with pre-trained AI models. These models have been “taught” to perform various forms of content, such as language translation, sentiment analysis, or creative content generation.When you use an AI API, you send it input (like a sentence you want to be translated or an image you want to be analyzed), and the API processes it using the underlying AI model. It then returns the output (the translated text, the identified objects in the image, etc.). This all happens smoothly and hides the detailed formulas happening in the background.AI APIs have become popular because they allow developers to add advanced AI capabilities to their applications without building and training their own models from scratch. This saves time and resources and opens AI to a wider range of users. Many companies are now exposing their AI capabilities to the world through AI APIs.Why Track the Usage of AI APIs?Tracking the use of AI APIs allows you to uncover dominant trends that drive your AI initiatives forward. Here are some reasons why tracking is essential:  Cost Management: AI APIs often operate on a pay-per-use model. Tracking usage helps you understand your spending patterns, identify potential cost optimizations, and budget more effectively.  Performance Monitoring: By monitoring your API response times, error rates, and latency, you can ensure that your AI-powered applications are running smoothly and delivering a positive user experience.  Security and Anomaly Detection: Usage tracking can help you identify unusual activity patterns and indicate potential security breaches.  User Behavior Analysis: Understanding how users interact with your AI-powered features allows you to tailor your offerings, boost user interaction, and prioritize future development efforts.  Business Intelligence: Usage data can provide valuable information for making data-driven decisions, identifying your most popular AI features, understanding peak usage times, or measuring the ROI of your AI investments.  Billing and Usage-Based Pricing: If you’re an AI provider, accurate usage tracking ensures fair and open billing for your customers.  Debugging and Troubleshooting: In case of errors or issues, having detailed logs of API requests and responses can assist with diagnosing and resolving problems.Tracking your AI API usage enables you to make informed decisions, optimize your resources, enhance security, and ultimately deliver more value to your users and customers.What Should You Be Tracking?Now that we understand the “why” behind tracking, let’s explore the “what.” Here are the metrics that you should be monitoring to gain a deeper understanding of your AI API usage:  API Call Volume:          The total number of API requests made over a given period.      Monitor for spikes, trends, and anomalies to identify peak usage times and potential bottlenecks.        Token Usage (for Language Models):          The number of input and output tokens processed by the API.      This is essential for cost management, as many AI providers charge based on token usage.        Unique Users/Companies:          The number of distinct users or companies accessing your API.      This helps you understand your customer base and identify your most active users.      By tracking these key metrics, you’ll understand all aspects of your AI API usage, enabling you to make informed decisions, optimize your performance, and power the success of your AI projects.How to Track AI API Usage with MoesifMoesif is a comprehensive API analytics platform that gives you deep insights into your API traffic, including your user’s AI API usage. Here’s how you can leverage Moesif for effective tracking:  Track Usage by API Call Volume:  Moesif automatically captures every API call to your AI endpoints.  You can view detailed logs of these calls, including timestamps, request/response payloads, status codes, and response times.  Analyze trends in API call volume, identify the most frequently used endpoints, and detect anomalies or sudden usage spikes.  Track Usage by Input/Output Token:  Large Language Models (LLMs) like GPT-4 and Gemini often measure their usage in tokens (pieces of words). Moesif can track the number of input and output tokens for each API call.  This is essential for understanding your user’s token consumption and optimizing your pricing and infrastructure costs.  Track Usage by Unique User or Company:  Moesif can associate API calls with specific users or companies, allowing you to monitor usage on a deeper level, identify your most active users, and understand usage patterns across different customer segments.  This can provide crucial insights into how your user base is growing and if your marketing, sales, and onboarding processes are effective.As a next step, let’s dive deeper into each of these tracking methods and how to explore these metrics in Moesif.Track Usage by API Call VolumeFirst, we will use a Time Series chart in Moesif to analyze our usage by API call volume. To open up a new Time Series chart, from the Moesif dashboard, click the + New button in the top left of the screen and select Time Series.To configure the Time Series chart, we will need to dial in the Filter and Metrics sections. First, add a filter for the particular AI API route(s) you want to explore and potentially add additional criteria to the filter, such as HTTP Verb/Method, Response Status Code, etc.Once your Filter is set, set the Metrics dropdown to Event Count. This means that the metric displayed on the chart will be the API call volume or overall event count. Here’s an example of what that may look like:Once our chart criteria are complete, we can see the resulting chart as a line graph. This will allow us to visually understand the trends in our user’s AI API usage.Alternatively, if you’re looking for an easier way to check out the exact numbers, you may want to look at the results as a table, which you can do by selecting the Table View selector in the top-left corner of the chart pane. Here’s an example of the data above shown as a table instead.Track Usage by Unique Users or CompaniesJust like viewing our usage data by API call volume, we will again use a Time Series chart in Moesif by clicking the + New button in the top left of the screen to analyze our usage by unique user or company.Again, we will add a Filter for the particular AI API route(s) we want to explore and potentially add additional criteria to the Filter, such as HTTP Verb/Method, Response Status Code, etc. Once your Filter is set, we will set the Metrics dropdown to either Uniques > Unique Users or Uniques > Unique Companies, depending on which metric we want to view. This means that the metric displayed on the chart will be the total number of unique users or companies accessing our API within the set time period. Here’s an example of what that may look like:Once our chart criteria are complete, we can see the results in a line graph showing the number of unique users or companies accessing the API. This will allow us to understand trends in AI API usage visually. The chart below shows the number of unique users per day over the last seven days.By selecting Table View, we can view this same data as a table, which can be easier to view if you want to see the exact numbers more easily.Track Usage by Input/Output TokenJust like viewing our other example, to analyze our usage by input and output tokens, we will again use a Time Series chart in Moesif by clicking the + New button in the top left of the screen.This time, we will add a Filter for the particular AI API route(s) we want to explore and potentially add additional criteria to the Filter, such as HTTP Verb/Method, Response Status Code, etc. Unlike previous charts, this chart will contain three metrics: one for Input Tokens, one for Output Tokens, and one for Total Tokens. We will click the Metrics dropdown and select the Select Field… option to add our metrics. From here, we will:  Select the element of the API response that represents the input tokens consumed. This will be the response.body.generated_text.usage in the case below.prompt_tokens field.  Set the field’s aggregation to “sum”. This will aggregate the usage and sum up the total usage across all API calls.  Click the Add Metric button.  Like input tokens, select the field in your API response denoting the output tokens used and set the aggregation to “ sum”.  Click the Add Metric button again and select Enter Formula… from the options.  In the Formula field, add “a + b”. This will take metric “a” (our input tokens), add it to metric “b” (our output tokens) and total them up.  Optionally, you can add a custom name to each metric by clicking the ellipsis and checking off Custom Name.Here’s an example of what that may look like, including a Custom Name for each field:Once our chart criteria are complete, we can see the results in a line graph showing the number of input tokens, output tokens, and total tokens consumed by API users. This will allow us to understand the trends in our AI API token usage visually. The chart below shows the number of tokens used over the last seven days.Like the other examples, these metrics can also be viewed in chart form for easier reading when it comes to the exact numbers.With that, we’ve shown three common ways to track AI API usage within Moesif. Of course, you can explore your data further and use Moesif to monetize your APIs using the exact metrics we showed today.ConclusionIn today’s world, understanding how your APIs are utilized is necessary. You gain valuable insights into cost management, performance monitoring, user behavior, and more by tracking your AI API usage. These insights let you make data-driven decisions, optimize your resources, and set your AI initiatives up for success.Moesif provides you with a comprehensive platform for tracking your AI API use, providing a detailed breakdown of your API traffic. With the ability to track usage by API call, user, and token, Moesif gives you the tools to monitor, analyze, optimize, and monetize your API usage effectively.Are you ready to take control of tracking your user’s AI API use? Sign up for Moesif today and unlock the full potential of your AI APIs.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/How-to-Track-the-Usage-of-AI-APIs-The-Ultimate-Guide/",
          "author": "Tara",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-how-to-monetize-ai-apis-choosing-the-right-metric": {
          "title": "How to Monetize AI APIs: Choosing The Right Metric",
          "content"	 : "Regarding charging for API usage, we usually gravitate towards charging per API call. While this can work for many use cases, it’s not optimal for everyone. This is where choosing the right metric to bill upon and finding a platform that supports it is crucial. In this blog, we will talk about choosing the right metric to bill upon and how to implement it in Moesif. Let’s begin by digging into what a billing metric is and how to decide which one is best to use with your AI APIs and services.What is a Billing Metric?In API monetization, including when monetizing AI APIs, a billing metric is the fundamental unit you use to measure how you will charge a customer for receiving value from your API. This metric is the foundation of your pricing model and directly determines how much you charge customers for using your API. In its simplest form, you may decide to charge a user based on the volume of events or API calls being made. This would mean measuring the number of API calls the user or company made to establish their billable amount. A more common approach with AI APIs would be to charge based on tokens used (input, output, or combined) and in this case, you would meter the tokens used in every API request.Here are a few key points to know about when trying to understand and decide on a billing metric:  Usage-Based: Billing metrics are usually tied to how the customer uses your API. This could be the number of API calls, the volume of data transferred, or the amount of tokens consumed.  Pricing Alignment: Your API’s billing metric should align with its value. For example, if your API provides valuable data insights, your metric might be tied to the number of data records accessed.  Flexibility and Granularity: Good billing metrics offer flexibility. You might have multiple tiers or pricing plans based on different usage levels. The granularity of the metric (e.g., per API call vs. per thousand calls) allows you to adjust pricing based on various factors.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        AI Billing Metric ExamplesAlthough every AI platform and API is unique, the way companies bill for usage tends to be similar. Later on we will chat more on exactly how to determine which metrics might be most suitable; however, here are a few examples of billing metrics that you might see when it comes to how you can charge for your AI APIs.By TokenThis is the most granular billing unit for AI services, particularly for Large Language Models (LLMs). Each token represents a text, such as a word or subword. For example, OpenAI’s API-accessible models are priced per 1,000 tokens, with different rates for input and output tokens. This metric is ideal for applications where the length of input/output text varies significantly, such as chatbots or content generation. Tracking token usage can help you keep a tighter coupling on usage and your underlying cost for running the platform. The end result by going this route is that you and your users can more accurately estimate and control costs.By API CallThis metric is less granular than token-based billing and charges per API request made to the AI service. For example, some image generation APIs charge a fixed price per image generated, regardless of the complexity of the prompt. This metric suits applications with predictable or fixed-length input/output, such as image classification or sentiment analysis. It also might be easier for non-technical people to understand the pricing compared to pricing based on tokens, which would likely be confusing for less technical users. This approach does simplify billing but may not be as cost-effective for customers with high-volumes or if the underlying cost of the API call can fluctuate wildly, leading to potentially lower margins.By UserThe last billing metric we will explore is charging based on the number of users that are accessing the platform, also referred to as “pricing by seat”. This model charges based on the number of users or “seats” who have access to the AI service, often monthly or annually. A real-world example of this is how Notion offers the Notion AI add-on for AI capabilities, charging per AI-enabled seat. As you can see from our Notion example, this metric is common for collaborative AI tools or platforms, providing predictable pricing for businesses. The downfall to this approach is that it may not be the most cost-effective way to bill infrequent or low-volume users, leading to a cost-to-value ratio that could lead to churn or downgrades.Although there are other ways to charge for AI API usage, these tend to be the most common metrics to bill on. Knowing what the options are, your next step will be to figure out which metric is best for your product or service. This is exactly what we will dig into next!How Do You Decide on a Billing Metric?The billing metric you choose for your AI API can significantly impact your revenue model, customer satisfaction, and the overall success of your product. It’s an extremely important decision so selecting a metric that aligns with your service’s value proposition, usage patterns, and target audience is crucial. Of course, you can always switch things up later or offer multiple models/metrics but this rework can sometimes be a significant lift. Getting as close as possible to the optimal end state is the goal in the first iteration. Here are some key factors to consider when making your decision:Nature of the AI ServiceThe type of service you are offering makes a big impact on which billing metric you’ll want to go with. Looking at similar products or competitors can give you a good idea of how potential users currently pay for other services. Here are a points to consider:  Variability of Input/Output: If the length of input data or the volume of generated output varies significantly (e.g., chatbots, translation services), token-based billing offers a more granular and fair way to charge customers based on their actual usage.  Predictability of Usage: If your AI API has a more predictable usage pattern (e.g., image classification, sentiment analysis), a simpler model like API call-based billing might be sufficient.Target AudienceYour billing metric and pricing should match the target audience. For instance, if you are offering a B2C application mainly targeted at mobile users who are non-technical, pricing “by token” might lead to confusion or stop users from signing up. These are the factors to consider:  Technical vs. Non-Technical Users: Technical users might be more comfortable with token-based billing, while non-technical users might prefer a simpler model like per-API call or per-user pricing.  Budget Sensitivity: Consider your target audience’s budget constraints and willingness to pay. Some users might prefer a fixed monthly fee (like user-based billing) for predictable costs, while others might be willing to pay per use or go with usage-based pricing for flexibility.Business ModelYour business model will play a big role in how you approach your chosen metric. For example, if your business model is dependent on making $X in revenue per token, then aligning your pricing and billing metric at the token level is likely advantageous. Here are a few things to consider in this regard:  Value Proposition: Align your billing metric with the value your AI service delivers. If the core value lies in the complexity of the output (e.g., high-quality generated content), token-based billing might be more appropriate.  Revenue Goals: Determine whether you want to optimize for revenue per user, revenue per API call, or total revenue. Your billing metric should support your chosen revenue model.Flexibility and TransparencyCertain billing metrics may allow more flexibility than others. Balancing flexibility, transparency, and revenue generation is a key aspect of deciding which metric is the best fit for your customers and business. When looking at this factor, consider this:  Pricing Tiers: Consider offering different pricing tiers based on usage levels, features, or customer segments. This can attract a wider range of customers with varying needs and budgets.  Transparent Communication: Communicate your billing metric and pricing structure to your customers to avoid confusion or frustration. Use tools like Moesif to provide detailed usage reports and insights.Monitoring and IterationAs mentioned at the start, nothing is set in stone when it comes to billing metrics, even if it might be a bit of a lift to change things up. That being said, as with any piece of the revenue puzzle, you should be monitoring how your chosen billing metric is performing from an adoption and revenue standpoint.  Track Usage Data: Continuously monitor your API usage data to understand how your customers use your service. This will help you identify trends, optimize pricing, and refine your billing model.  Be Adaptable: The AI landscape is constantly evolving, so be prepared to adjust your billing metric or pricing strategy to stay competitive and meet your customers’ needs.By carefully considering these factors and leveraging tools like Moesif to gain deep insights into your API usage and costs, you can choose the most appropriate billing metric for your AI API, maximize the value you provide to your customers, and ensure the financial sustainability of your business.Implementing Billing Metrics in MoesifNow that you know your options and which is best for your use case, it comes down to figuring out how to implement your billing metric so that you can actually start driving revenue! This is where Moesif comes in handy. Within the Moesif platform, we use our Billing Meters feature to help implement the mechanisms needed to gather data about API calls and measure usage against your desired metric.  For a deeper dive into Meosif’s API monetization capabilities, check out our docs!First, let’s look at what it takes to implement billing based on a per token billing metric. For this, you will create a new billing meter, dial in the filter to determine which API calls you’d like to monetize, and then create a Custom Metric. For most AI APIs that consume tokens, the amount of tokens consumed by the query will usually be in either a field in the body or header of the response. For example, if you’re charging on output tokens and this value is in the X-Output-Token-Count header, in Moesif you would click the Metrics dropdown and under Custom Metric click Select Field…. In the case that you want to get a total of all the tokens used by this particular user, you’d select sum as your aggregation method. In Moesif, this is how this would look on the billing Meter screen.Next, we will do the same initial steps to create a meter, add in the necessary filter, and then select Volume > Event Count as the metric from the Metrics dropdown (this is the default metric selected when you create a meter). This is an example of what this would look like in Moesif:Lastly, in the case where you want a subscriber to pay a charge per user of your AI API, you’ll follow the same steps as earlier: creating a meter, dialing in the filter to meet your criteria, and then selecting Uniques > Unique Users from the Metrics dropdown. This is what it would look like in Moesif:With this, we’ve used Moesif Billing Meters to implement three of the most common billing metrics for metering and charging AI API usage using Moesif. These meters will then aggregate the usage depending on the filter and metric you’ve specified and then report it to the billing provider so the user can be charged. These simple steps set you up to take full advantage of easily creating revenue streams from your AI APIs.ConclusionChoosing the right billing metric is a critical step for successfully monetizing your AI API. By understanding the unique nature of your service, your target audience, and your business model, you can align your pricing strategy and billing metrics with the value you deliver. This can help create a win-win scenario for both you and your customers. Of course, the ideal billing metric will depend on the specific context of your AI offering. Even after you’ve chosen a billing metric and implemented the mechanisms to support it, continuous monitoring and adaptation are key to ensuring your pricing model remains relevant and profitable as your API and customer base evolve.Ready to streamline your AI API monetization? Sign up for a free Moesif account today and discover how easy it is to implement flexible billing metrics, gain valuable insights into user behavior, and unlock the full revenue potential of your AI platform.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/How-to-Monetize-AI-APIs-Choosing-The-Right-Metric/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-apis-for-machine-learning": {
          "title": "10 Best APIs for Machine Learning",
          "content"	 : "Machine learning APIs provide developers with powerful tools to integrate complex algorithms and models into applications without building them from scratch. These APIs simplify the development process by offering pre-trained models and standardized methods for different tasks. These include image recognition, natural language processing, and predictive analytics. This accessibility democratizes machine learning so that developers of varying expertise can leverage cutting-edge technology efficiently. When you use an API for machine learning to host models in modern web infrastructure, it greatly improves the adoption and integration of AI/ML features.In this article, we’ve curated a list of the best APIs for machine learning. We also touch on how ML APIs benefit us, their real-world use cases, and how to achieve scalability while tackling current challenges in the landscape.Table of Contents  Key Takeaways  Understanding Machine Learning APIs  Key Benefits of Using Machine Learning APIs  Top Machine Learning APIs for Developers          OpenAI API      Google Cloud AI/ML APIs      IBM Watson Discovery API      Microsoft Azure AI Services      NLP Cloud        Specialized Machine Learning APIs          SkyBiometry      Roboflow Universe      Perspective API        Integrating Machine Learning APIs into Your Applications  Managing and Scaling Your Machine Learning Models  Real-World Use Cases of Machine Learning APIs  Common Challenges and Solutions  SummaryKey Takeaways  Machine learning APIs allow seamless integration of AI-powered features into applications. With little to no expertise in the field, you can get access to powerful AI models.  Machine learning APIs accelerate product development, enhance scalability, and promotes modularity. It becomes easier to deploy AI-driven solutions across various applications.  Leading machine learning APIs, such as OpenAI, Google Cloud AI/ML, IBM Watson Discovery, Microsoft Azure AI Services, and NLP Cloud, offer specialized tools and services. They can carry out tasks like natural language processing, image analysis, and data extraction.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding Machine Learning APIsMachine learning APIs provide standardized interfaces to powerful pre-trained models and algorithms. The standardization eases the integration process and efforts by a large margin, no matter what type of model you require or application you have. Applications can also easily establish uniform communication with different parts of software systems.These APIs typically operate over the internet, where developers send requests to an API endpoint with data that needs processing. The API then processes this data using its machine learning models and returns a response with the desired results. This process abstracts the complexity of machine learning, allowing developers to leverage AI without delving into the details of model training or data pre-processing.For example, in image recognition tasks, a developer can send an image file to an API endpoint designed for image analysis. The API uses its pre-trained models to identify objects or features within the image and returns the results in a structured format, such as JSON or XML. Similarly, in sentiment analysis, a developer can send a text snippet to an API that analyzes and returns the sentiment score, indicating whether the text carries positive, negative, or neutral sentiments. These APIs handle the heavy lifting of data processing and model inference so that developers can focus on the application side, integrating the results.Apart from providing pre-trained models, many of these ML APIs offer customization options. These options allow developers to fine-tune models based on specific use cases or datasets. This customization involves providing additional data to the API, which adapts the model’s parameters to better suit the application’s needs.Key Benefits of Using Machine Learning APIs  You get easy access to powerful ML functionalities without having to build, train, or deploy models on your own. You don’t have to spend resources on data collection, model training, and optimization. The standardized interfaces allow for easy integration. Moreover, recycling and reuse of models significantly cuts down the time and effort to build AI-driven solutions.  These APIs often run on cloud-based platforms. This ensures scalability according to demand and that applications can handle varying workloads without performance degradation.  Machine learning APIs offer access to state-of-the-art models and techniques that their providers continuously update and maintain. Applications benefit from the latest advancements in AI without requiring in-depth knowledge or manual updates.  As discussed in the preceding section, many ML APIs allow for customization. Developers to fine-tune models to suit specific needs or datasets.ML APIs provide uniform endpoints that can interact with machine learning models. This simplifies the integration of AI functionalities into pre-existing systems.Furthermore, you can easily establish interaction between independent components and the models without any deep integration. Such modularity enables diverse applications and users to efficiently access and utilize the AI models.Top Machine Learning APIs for DevelopersLeading providers of machine learning APIs include:  OpenAI  Google Cloud AI/ML  IBM Watson Discovery  Microsoft Azure AI Services  NLP CloudEach platform offers unique capabilities, In the following sections, we discuss these platforms in more depth to help you decide the perfect one for your own applications.OpenAI APIOpenAI provides some of the most cutting-edge artificial intelligence models available today. ChatGPT and DALL-E stand out for their versatility and performance as two of the most popular generative AI tools at present.ChatGPT is a conversational AI model capable of generating human-like text. It’s widely used in applications such as customer support, content creation, and virtual assistants. This model’s expertise in natural language processing (NLP) tasks has made it an attractive option to developers building NLP solutions. The OpenAI API allows complete and customized access to ChatGPT’s power that developers have used to build breakthrough solutions.DALL-E, on the other hand, excels in image generation. It can create high-quality images from textual descriptions. This has opened up new possibilities in digital art, advertising, and visual content creation.Google Cloud AI/ML APIsGoogle Cloud offers an extensive suite of AI and machine learning products designed to cater to a wide range of needs.For example, the Vertex AI Platform offers an all-inclusive toolset for the creation, training, testing, monitoring, tuning, and deployment of AI models, including an API. Platforms like this supports developers throughout the entire machine learning lifecycle. It also includes Vertex AI Notebooks for rapid prototyping and secure experimentation.Additionally, Google Cloud’s AI/ML suite comprises specialized APIs like the following:  The Video Intelligence API can analyze videos to identify more than 20,000 objects, places, and actions.  Using the Dialogflow API, developers can build natural conversational experiences into their applications.  Speech-to-Text API provides high-accuracy speech recognition.  Text-to-Speech API offers natural-sounding speech synthesis.Some other AI services that Google Cloud Platform offers include the following:  Vision AI provides pre-trained models for image recognition and analysis.  Natural Language AI offers pre-trained models for sentiment analysis, entity recognition, and language translation.  Translation AI provides pre-trained models for language translation/  Document AI offers pre-trained models for data extraction and document processing.Google Cloud gives you a unified platform where everything works out-of-the-box. Regardless of your use case, you can likely find a solution that fits. You also benefit from a pay-as-you-go pricing model that requires no upfront investment.IBM Watson Discovery APIIBM Watson Discovery API has sophisticated data extraction and pattern analysis capabilities, making it a valuable tool for data scientists. It can also efficiently process unstructured data. Businesses use it to analyze large volumes of text and uncover hidden patterns and insights.IBM Watson Discovery uses custom NLP models and LLMs to deliver powerful natural language processing. This makes the API a suitable tool for sophisticated text analysis—for example, in customer service applications that interpret and respond to customer queries. Furthermore, researchers and development sectors use this API to extract valuable information from extensive datasets.Microsoft Azure AI Services[Microsoft Azure AI Services] offer a dependable and scalable platform for an array of AI solutions. The Azure AI Language service, formerly Text Analytics service, for instance, offers pre-built models for sentiment analysis, key phrase extraction, and language detection.These services have been designed to handle large-scale text and computer vision workflows with scalability and flexibility through cloud deployments.NLP CloudNLP Cloud specializes in natural language processing tasks. The platform offers powerful tools for sentiment analysis, content moderation, and text summarization. This natural language API can classify parts of speech and analyze text to deduce language-specific metadata, making it a versatile tool for handling unstructured text.Developers can employ NLP Cloud for generating summaries of text and web pages. This simplifies content management and improves user experiences.Specialized Machine Learning APIsWhile general-purpose machine learning APIs offer broad functionalities, specialized APIs tailor to specific tasks. They can significantly enhance the performance and accuracy of applications in niche areas.For example:  SkyBiometry offers advanced facial recognition and feature detection capabilities for applications that require precise facial analysis.  Roboflow Universe’s Image Similarity API can analyze images to determine their visual similarities.  Perspective API can detect toxic, obscene, insulting, or threatening text for use cases such as content moderation and user protection.Let’s learn a bit more about these APIs.SkyBiometrySkyBiometry’s facial recognition API can detect faces in images with remarkable precision. You can detect facial features and achieve real-time face tracking. This makes it a powerful tool for security, surveillance, and user authentication applications.Roboflow UniverseRoboflow Universe’s Image Similarity API creates a feature print between two images. A feature print represents an image’s visual features mathematically. By calculating the distance between feature points, the API can determine the similarity between two images. Developers working on projects that involve large image datasets may find this API particularly useful.Perspective APIThe Perspective API, an NLP tool, has been designed to identify and flag potentially toxic, obscene, insulting, or threatening text. It utilizes advanced algorithms to analyze language patterns and determine the level of potential harm in the text. If you want your application to moderate user-generated content and maintain a safe online environment, Perspective API can prove very useful.Integrating Machine Learning APIs into Your ApplicationsThe integration process begins with selecting the right API for your project requirements. For example, if you need image recognition capabilities, you may choose an API like Google Cloud Vision or Microsoft Azure’s Computer Vision API. After selection, thoroughly review the documentation to understand how to use the API, including available endpoints, authentication methods, encryption, and request-response formats. Standardized endpoints ensure consistency in how apps interact with the API.Next, you will implement the API in your application that sends and receive data from the endpoints. This involves constructing HTTP requests with necessary parameters and authentication tokens. For example, for a text analysis API, you send a POST request to the API containing text data. Your implementation must gracefully addresses any errors that may occur. For successful responses, you must properly extract the useful information upon such as sentiment scores or classification labels.Most importantly, you need to prepare and format the input data so that it matches the ML API’s requirements. You may need to profile, cleanse, validate, and transform your data to guarantee the data’s suitability for analysis and best results.Pay attention to standardization of endpoints for model interactions. Platforms like UbiOps provide an easy-to-use deployment layer for data science code, maintaining endpoints in a standardized format, even when you upload new code. This standardization simplifies the integration process and ensures that applications can consistently interact with machine learning models.Managing and Scaling Your Machine Learning ModelsManaging machine learning models effectively ensures performance and reliability of your AI apps against real world problems. Here are some key points to consider:  Tools like MLflow and TensorBoard help track key metrics such as accuracy, precision, recall, and latency, offering insights into model behavior in production environments.  Teams can implement version control with platforms like DVC or Git to manage model changes and updates. This allows for easy rollbacks if issues arise.  Logging and alerting systems, such as from Datadog or Prometheus, help detect anomalies or performance degradation.  It’s also important that models remain relevant and effective as data patterns evolve. Therefore, retrain models with fresh data using automated pipelines in tools like Kubeflow.For scaling ML models, here are some strategies to consider:  Make sure models perform well under varying demands and increased workloads.  Engineers can use cloud-based solutions like Kubernetes or Amazon SageMaker to orchestrate and manage resources efficiently.  Load balancing becomes necessary to prevent any single model instance from becoming a bottleneck. To that end, tools like NGINX can distribute incoming requests across multiple instances.  Developers can optimize models for speed and resource efficiency using techniques like quantization or model pruning. These are often implemented with frameworks such as TensorFlow Lite or ONNX.  For custom models, storing model artifacts in a Cloud Storage bucket within the same Google Cloud project ensures accessibility and security.Real-World Use Cases of Machine Learning APIsMachine learning APIs have played a crucial role in resolving real-world issues across diverse industries. For example:  In healthcare, organizations like Highmark Health use AI to improve efficiency in care delivery, drug discovery, and clinical trial planning. The U.S. Dept. of Veterans Affairs leverages AI for enhanced accuracy in cancer detection at remote military facilities.  Snapchat’s My AI chatbot uses OpenAI’s GPT series of large language models (LLM).  Khan Academy’s Khanmigo offers personal tutoring and teaching assistance that uses OpenAI’s GPT-4.  In the transportation sector, the Central Texas Regional Mobility Authority utilizes Vertex AI to modernize operations, improving efficiency and service delivery.  In customer service, companies like Best Buy and Magalu have created AI-powered virtual assistants to enhance customer engagement and support.  Additionally, Woven, a Toyota investment, employs AI for autonomous driving.Common Challenges and SolutionsThe implementation of machine learning solutions presents its own unique set of challenges.  ML models rely on large, diverse, and high-quality datasets. Otherwise, models perform poorly and yield inaccurate results. Thorough data cleaning, preprocessing, and augmentation techniques can ensure data quality and address imbalances. Investing in data collection and labeling efforts also help improve data availability.  Feature selection, a process that involves pinpointing the most pertinent features from an extensive dataset, can be quite a time-intensive process. Effective feature selection is necessary for building accurate models, and various techniques and tools can aid in this process.  Optimizing ML models require tuning hyperparameters to adjust learning rates and performance. Proper data preprocessing and optimization techniques can ensure that models receive proper training and perform well in real-world applications.SummaryThroughout this blog post, we have discussed how machine learning APIs work and the different types that exist for different use cases. The AI space has been evolving fast. The future seems promising to see AI democratization get more mainstream appeal in tandem with these new innovations. Trends such as open-source AI and machine learning APIs corroborate the global community’s commitment to making AI simpler and more accessible to users.Now it’s possible more than ever to build impactful products utilizing the robust ecosystem of machine learning and AI APIs. But for the critical challenges that lie ahead, it’s important to continuously monitor, understand, and research different aspects of an AI product.For AI products or APIs, Moesif makes some of these challenges very easy to tackle by providing in-depth analytics of your product’s journey.Moesif gives you a comprehensive suite of powerful analytics and monitoring tools. You also get access to robust monetization features so that you can grow your AI product with confidence. You can integrate Moesif easily with your favorite APIM platform or API gateway through one of our easy-to-use plugins. You can also directly embed Moesif into your API code using our SDKs. With Moesif at your disposal, your API’s lifecycle becomes robust, accelerating product growth.To try it yourself, sign up today for a free trial, no credit card required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue from your AI APIs?            Monetize your AI APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/APIs-For-Machine-Learning/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-gen-ai-chatbot-api-openai-token-usaged-based-monetization": {
          "title": "Monetizing a Gen-AI-Based ChatBot API by Metering Token Usage",
          "content"	 : "Motivation:For many Gen AI-based applications, usage-based billing is crucial. It is especially important if you are using third-party models that charge you for tokens used. You want to ensure that your cost of tokens used is covered by your customers, in addition to charging for your own value add.In this tutorial, we will build a quick Chat Bot API using OpenAI’s ChatGPT, and then use Moesif and Stripe to meter the number of tokens used and report the usage to Stripe to be charged.Building Simple Chat APICreate a file called requirements.txt and add the following:Flask==3.0.0openai==0.27.0python-dotenv==1.0.1moesifwsgiThen install the dependencies.pip install -r requirements.txtCreate a file called .env, where we’ll put your API keys. You can create an account with OpenAI, and you may need to put in at least $1 in your OpenAI account. For the Moesif Application ID, you can obtain it from a free account.OPENAI_API_KEY="Obtain from your Open AI Account"MOESIF_APPLICATION_ID="Obtain from your Moesif Account"Create a new file called app.py, the code for the Chat API is very simple:from flask import Flask, request, jsonifyimport openaifrom dotenv import load_dotenvimport osfrom moesifwsgi import MoesifMiddleware# Load environment variables from .env fileload_dotenv()# Set your OpenAI API key hereopenai.api_key = os.getenv('OPENAI_API_KEY')app = Flask(__name__)@app.route('/chat', methods=['POST'])def chat():    user_input = request.json.get('message')    # Define the onboarding conversation    conversation = [        {"role": "system", "content": "You are a customer onboarding agent."},        {"role": "user", "content": user_input}    ]    response = openai.ChatCompletion.create(        model="gpt-3.5-turbo",        messages=conversation,        max_tokens=150    )    # Extract the response text and token usage    response_text = response.choices[0].message['content'].strip()    tokens_used = response.usage.total_tokens    response_obj =  jsonify({        'response': response_text,    })    # Add the token usage to the response headers    response_obj.headers['X-Tokens-Used'] = tokens_used    # Adding the value to header is just one of many approaches.    # Since Moesif can do metering on almost any field, you can add the    # value to Header, Body, or even Metadata.    return response_objThe API is a simple facade for ChatGPT, but we added a response header X-Tokens-Used. There are otherapproaches to capture this data which is briefly discussed in the last section.Add Moesif Middleware## Moesif Middleware Setup:def identify_user(app, environ, response_headers=dict()):    # Your custom code that returns a user id string    user_id = "my-chat-user"    return user_iddef identify_company(app, environ, response_headers=dict()):    # Your custom code that returns a company id string    # hardcoded to this value for now.    company_id = "my-chat-company"    return company_idmoesif_settings = {    'APPLICATION_ID': os.getenv('MOESIF_APPLICATION_ID'),    'DEBUG': False,    'LOG_BODY': True,    'IDENTIFY_USER': identify_user,    'IDENTIFY_COMPANY': identify_company,    'CAPTURE_OUTGOING_REQUESTS': False}# flaskapp.wsgi_app = MoesifMiddleware(app.wsgi_app, moesif_settings)if __name__ == "__main__":    app.run(debug=True)In this example, we didn’t implement authentication, but when you implement authentication, you can easily replace the placeholder user_id and/or company_id with the actual ids. (Btw, see Moesif Entity Diagram to see relationships model for users, companies, and subscriptions.That is basically all the code you need. Let’s run it.python app.pyPlease send a few API requests using the curl command below.curl --request POST   --url http://localhost:5000/chat   --header 'Content-Type: application/json'   --data '{"message": "How are you? What do you do?"}'You should be able to see your API calls captured in Moesif Event Stream.Now, we just need to configure your Moesif and Stripe accounts.Connect Moesif to StripeFollow detailed instructions on Moesif Docs to connect Moesif to your Stripe account..For the purpose of this demo, when configure the id mapping, map the Moesif Field company_id to Stripe Field customer.company_idThis mapping helps Moesif identify the Stripe customer that the company_id associated with the API event.Create Plans/Price with Moesif’s Product CatalogueFor usage-based billing, you need a plan which has one or more prices. Each price represents one resource you are charging usage on.Even though you can create plans and prices in Stripe directly, Moesif’s Product Catalogue feature makes it easier and creates synchronized plans in Stripe for you.Go ahead Create a Test Plan and Price for Per Unit of tokens, see image example below.  Create a Billing Meter.The Billing Meter ties a Metric to the Plan/Price and then reports that metric value to Stripe or other billing providers. Here, we’ll create a billing meter for the SUM of the response.Headers.X-Token-Used header we set earlier.In Moesif, click on Billing Meter and click create new, and in the billing meter form:  Select the Billing Provider Stripe, and the Plan and Price we just created earlier.  (Optional) Set Filters so that request.URI Route is /chat. (You can add additional criteria such as status is 200.).      For metric configuration, select Custom Metric. Then use Field Select to select response.headers.X-Tokens-Used, and use sum as the function to add them together.                  For more details, see documentation for Billing Meter.Test and VerifyAbove is basically all the set up you need. Let’s test and verify.Create a Customer and Subscription in Stripe.Typically, a customer and a subscription are created automatically in Stripe’s checkout flow. But for this purpose, create both objects manually in Stripe’s dashboard.      1: Create a customer.                        2: Add metadata on the customer, and set company_id to the value my-chat-company, which is the “identify_company” hook currently hard coded to in MoesifMiddleware example above. This is how the stripe’s customer id is tied to a company_id.                        3: Add a subscription to the customer, using the plan we created earlier in Moesif which should be synced to Stripe.  Send more API Request again.Now, the API calls send to your test server should be metered and reported to Stripe for payment collection.Conclusion & Where to Go from Here:Gen-AI products can get expensive, so metering usage of various resources will be very important. Above is an example showing how you can set up Usage Billing quickly and tie the cost to the value that you created for your customer.The code in this example is on Github.Next step, please explore the Moesif Docs to see how flexible and powerful the Billing Metering solution can be.  Scenario: You are not using Flask or Python.          Explore the Moesif Middleware for various SDKs and Platforms, and the options you can use to set Metadata, identify_user, and identify_company).        Scenario: You are using a different Billing Provider than Stripe:          See the supported billing providers.      Or use a Custom Webhook to receive usage reports.        Scenario: Your usage data isn’t tied to an API, but some backend processing tasks.          Moesif supports two type of Events: APIs and Custom Actions. In your backend processing tasks, you can send Custom Actions to Moesif.        Scenario: Our usage metric isn’t tied to an incoming API, but to an outgoing API, e.g. the out going API to OpenAI.          Solution: Many of our SDKs support “captureOutgoing” option. You can capture those API calls and send to Moesif, and then create metrics on them.        Scenario: You need to meter on a different metric:          Please see doc on choosing a billable metric.      For more advanced scenarios, see the powerful Scripted Field feature where you can compute new values using any other fields.      Implement GET_METADATA hook in most Moesif SDKs to pre compute any value and add to the event.      ",
          "url": " /technical/gen-ai/ChatBot-API-OpenAI-Token-Usaged-Based-Monetization/",
          "author": "Xing",
          "categories": "technical, gen-AI"
        }
      
    ,
  
    
        "technical-api-development-top-10-api-performance-monitoring-tools-to-boost-efficiency": {
          "title": "Top 10 API Performance Monitoring Tools to Boost Efficiency",
          "content"	 : "API performance monitoring ensures your APIs are reliable and efficient. By tracking key API metrics, you can identify issues early and protect user experience. This guide reviews the top tools for effective API performance monitoring.Key Takeaways      Effective API performance monitoring is crucial as APIs account for a significant portion of web services and traffic. Metrics such as uptime, response time, latency, error rate, and throughput are key indicators of an API’s efficiency, API success, resource utilization, and system performance.    The benefits of robust API performance monitoring include early problem detection, improved user experience, and enhanced security, which collectively help protect organizations from revenue losses and ensure competitive advantage.  Choosing the right API performance monitoring tool involves assessing its feature set, integration capabilities, and cost-effectiveness to ensure it aligns with business goals, scales with the API lifecycle, and provides actionable performance data.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding API Performance MonitoringImagine a bustling airport, where air traffic control monitors the safe and punctual arrival and departure of flights. In the digital world, API performance monitoring serves a similar function, vigilantly tracking the health and functionality of APIs. It’s not just about keeping the engines running; it’s about ensuring a seamless user experience and maintaining the reliability of complex, interdependent systems. An effective API strategy incorporates detailed metrics and analytics to optimize performance and business outcomes.In this context, API testing plays a vital role in validating the performance and functionality of these APIs. Performance issues, data validation, user experience, application performance, and much more can be tracked through a comprehensive analysis program, unlocking valuable insights and improving fundamental success metrics.With external APIs and internal APIs combined now accounting for a staggering 83% of web traffic, the stakes are high, and the performance of these digital linchpins, including key api calls and api requests, is not to be taken lightly.Key Metrics for API Performance MonitoringIn the vast sea of digital interactions, how do we gauge the strength of the currents? Key API metrics are our navigational tools, providing a comprehensive understanding of API performance and the value of each API metric. These key fundamental metrics include items such as:  Uptime of the API  Response time for API Calls  Latency (and other network timing data)  Error rates (and accompanying log data)  Throughput  Memory usage/CPU usage  Other real user monitoring data points (including telemetry data, origination data, etc.)These metrics act as critical indicators of an API’s efficacy across key features, guiding us through the ebb and flow to ensure our APIs are not just afloat but sailing smoothly. The combination of these data points can give us insights into other metrics such as end user experience, performance issues, and more.Uptime of the APIUptime is the lighthouse guiding ships safely to harbor, representing the percentage of time an API is operational and accessible, and is a critical indicator of an API’s performance. It’s a beacon of reliability, inspiring trust in users who depend on consistent and uninterrupted service. API Endpoints are only as good as they are accessible, and monitoring tools often focus on uptime as a metric pointing towards accessibility - if the API can be contacted and used, and the uptime is high, this suggests the service was available as intended.In a digital landscape where downtime can spell disaster, maintaining high uptime is not just desirable; it’s indispensable.Response Time for API CallsWhen users interact with an application, they generally anticipate a swift response, as if conversing in real-time. Even API models that are asynchronous, where a delay is expected, should result in some sort of response - even if that response is just a “successfully received” message. Response time measures the speed of an API’s reply to a request, making it a crucial performance metric for understanding user satisfaction. This metric is essentially the difference between a conversation that flows smoothly and one that’s riddled with awkward pauses.Timely responses are the cornerstone of operational efficiency and user satisfaction, making this metric a priority in API monitoring.LatencyLatency, the subtle pause between a request and its response, can be the silent saboteur of a user’s experience. It lurks in the shadows, affecting the perceived speed and efficiency of an API. High latency can turn a user’s seamless digital journey into a frustrating series of waits, undermining the very purpose of quick and efficient digital services. Latency can also be quite expensive - high enough latency looks an awful lot like a failed request, and can result in duplicate requests and failure from API clients.Error Rates (And Accompanying Log Data)The error rate is the storm on the horizon, indicating the percentage of requests that result in failure. It’s the measure of turbulence in the API’s operations, a warning sign of potential trouble that could shake the foundations of user trust and system stability. This metric, along with accompanying data logs, can help providers understand where in the path things went wrong, identifying potential avenues of improvement.Keeping a watchful eye on the error rate is akin to steering the ship clear of the tempest, preserving the integrity of the API’s service.ThroughputThroughput is the current carrying data through the API, reflecting the volume of successful requests handled over time. It’s an indicator of the API’s capacity and efficiency, a measure of its ability to manage the demands placed upon it.Understanding throughput is essential for scaling services to meet the users’ needs without compromising performance.Memory Usage/CPU UsageThis metric is focused principally on the health of the system itself, and is almost like checking the integrity of the boat weathering the storm. CPU and memory usage can help identify issues of efficiency, pointing towards opportunities for bettering the end user experience and reducing overhead costs at scale.Real User Monitoring Data Points (Telemetry Data, Origination Data, etc.)This data helps provide context to the rest of the metrics, helping the provider understand not only that an error occurred, and why it occurred, but to whom it occurred. This can help direct fixes and improve the overall system performance quite dramatically at scale.Benefits of Effective API Performance MonitoringHarnessing the power of effective API performance monitoring is like having a skilled navigator at the helm. It enables:  The proactive identification of issues  Ensuring third-party APIs meet service level objectives  Protecting organizations from significant revenue losses.By enhancing user experience, bolstering security, and providing a competitive edge, effective monitoring is not just a tool; it’s a strategic asset for any technology-driven business. By leveraging these benefits, organizations can optimize API performance and ensure their digital services remain competitive.Early Problem DetectionEarly problem detection is the lookout perched in the crow’s nest, scanning the horizon for the first signs of trouble. With routine API monitoring, teams can:  Identify and address issues before they manifest as full-blown emergencies  Take a proactive approach to nip problems in the bud  Ensure smooth sailing and avoid the costly consequences of system failures.Improved User ExperienceAn optimized user experience is the reward for meticulous API performance monitoring. Like a well-oiled machine, a monitored API ensures that all the moving parts of an application work together harmoniously. It’s the difference between a jarring and disjointed journey and a seamless, enjoyable digital experience that keeps users coming back for more.Enhanced SecurityEnhanced security is the fortified stronghold guarding against digital threats. Through vigilant API monitoring, organizations can identify vulnerabilities swiftly, pre-empting attacks before they compromise the system. It’s not just about building walls; it’s about intelligent defense systems that evolve to meet emerging security challenges.Challenges in API Performance MonitoringNavigating the complex currents of API performance monitoring presents its own set of challenges. From the intricate web of diverse ecosystems to the deluge of data and the ever-shifting sands of dynamic environments, the task requires not just vigilance but versatility. These challenges demand robust solutions and strategies, as well as effective API monitoring tools, to ensure that API programs remain both effective and efficient.Diverse EcosystemsThe diverse ecosystems of modern architectures, such as microservices and serverless systems, add layers of complexity to API monitoring. Each system speaks its own language, follows its own rules, and presents unique challenges that require specialized monitoring approaches. It’s a puzzle with many pieces, and fitting them together is crucial for a comprehensive view of API health.High Data VolumeThe sheer volume of data generated by API telemetry can be overwhelming, like a flood threatening to breach the levees. Efficient data management and storage solutions are the sandbags holding back the tide, enabling organizations to sift through the flood of information without losing sight of critical insights.Dynamic EnvironmentsDynamic environments are the shifting winds that demand a flexible sail. With continuous changes in applications, monitoring strategies must adapt to maintain consistent API performance.It’s about being as dynamic as the environments themselves, adjusting tactics to navigate the ever-changing conditions.Best Practices for API Performance MonitoringTo steer the ship of API performance monitoring towards success, certain best practices must be followed. Regular testing, comprehensive alerts, and detailed reporting form the triad of a robust monitoring strategy. These practices, when implemented effectively, can transform the performance of APIs, ensuring they deliver value and maintain their vital role in the digital ecosystem.Regular TestingRegular testing is the compass guiding API performance, an essential practice that ensures the accuracy and efficiency of APIs throughout their lifecycle. By incorporating automated testing into the CI/CD pipeline, teams can detect and address issues swiftly, maintaining the integrity of the API’s services.Comprehensive AlertsComprehensive alerts act as the sentinels, keeping a vigilant watch over API performance. With customizable thresholds and real-time notifications, teams can respond promptly to potential disruptions, ensuring that the digital machinery operates without interruption.Detailed ReportingDetailed reporting is the map charting the course of API performance, providing invaluable insights through API usage metrics into the API’s operations. It highlights areas for improvement and guides strategic decisions. Without thorough reporting, navigating the complexities of API performance would be akin to sailing without a compass.From Moesif to APIMetrics, each tool offers unique strengths, tailored to different monitoring needs. These top-tier tools are the instruments that help organizations maintain a steady course, ensuring their APIs perform optimally and efficiently.MoesifMoesif provides deep insights into API usage and performance. Its advanced analytics capabilities and real-time monitoring help organizations understand their APIs’ behavior, allowing them to optimize performance and maintain efficiency. Moesif also provides detailed insights into infrastructure API metrics, helping organizations optimize their server setups and resource allocation. Understanding how APIs behave in real-world scenarios is critical for optimizing performance and ensuring a seamless developer experience. Platforms like Moesif enable organizations to gain deep visibility into API usage and infrastructure metrics, offering advanced analytics and real-time monitoring that go beyond surface-level data. By tracking user behavior and performance trends, Moesif helps teams pinpoint bottlenecks, fine-tune resource allocation, and make informed decisions about server configurations—all essential for maintaining efficient, scalable systems.DatadogDatadog stands as a vigilant watchdog, alert to the performance of APIs. With its ability to standardize testing and provide a unified platform for observability, Datadog aids in quick issue identification and resolution, ensuring APIs run smoothly. Datadog’s ability to standardize testing and provide a unified platform for observability makes it an excellent choice for tracking various performance metrics.New RelicNew Relic offers a telescope to the stars, with its full-stack observability that connects API endpoint failures to the overall health of applications and services. It provides a holistic view that helps troubleshoot issues swiftly, maintaining a high level of performance.PrometheusPrometheus, with its focus on reliability and detailed metrics, is the sextant measuring the coordinates of API performance. Its robust alerting system ensures that teams are notified of issues in a timely manner, enabling effective response and maintenance of service levels.PostmanPostman is the Swiss Army knife of API development and testing, offering a suite of tools that ensure APIs are robust and reliable. With its comprehensive monitoring capabilities, Postman is an essential asset for developers looking to track and improve API performance.SigNozSigNoz is the lighthouse beam cutting through the fog, with its open-source approach and detailed tracing capabilities. It provides clear visibility into API transactions, enabling teams to pinpoint performance bottlenecks and optimize for better outcomes.SematextSematext offers a bird’s-eye view of digital operations with its cloud-based monitoring solution. It ensures that every layer of an organization’s stack is observed, providing comprehensive visibility and centralized management.SmartBear AlertSiteSmartBear AlertSite offers the following benefits:  Acts as an early warning system, proactively identifying issues that could impact end-users  Provides real-time monitoring to ensure optimal performance  Offers detailed analytics to track and analyze user experienceWith these capabilities, AlertSite serves as a guardian of optimal performance and user experience.Better StackBetter Stack integrates various monitoring tools to offer a comprehensive view of API performance and operations. It streamlines incident management processes, helping teams quickly identify and resolve issues.With Better Stack, users can combine uptime monitoring with other critical metrics to maintain high service availability.APIMetricsAPIMetrics simplifies API monitoring by offering the following features:  Users can set up monitoring with just a URL and test HTTP calls  It provides global monitoring from multiple locations, offering a comprehensive view of API performance  It helps to identify region-specific issuesHow to Choose the Right API Performance Monitoring ToolChoice is a luxury, but in the realm of API performance monitoring, it’s a strategic decision that can impact the efficiency and success of digital operations. When selecting the right tool, consider the feature set, integration capabilities, and cost-effectiveness.The ideal tool is one that:  Aligns with your API monitoring strategy  Scales with your business needs  Integrates seamlessly into your workflows  Provides actionable performance dataFeature SetThe compass for navigating the selection of an API monitoring tool is its feature set. A comprehensive tool should support various telemetry types, such as metrics, traces, and logs, to provide a holistic view of API health. Strong alerting capabilities and multi-step load testing are invaluable for simulating real-world interactions and ensuring that the tool offers a robust solution for monitoring API performance.Integration CapabilitiesSmooth integration is the keel that keeps the ship steady, and the same goes for API monitoring tools. The ability to seamlessly integrate with CI/CD pipelines and existing systems is essential for continuous monitoring and immediate detection of issues.A tool that updates monitoring configurations automatically when APIs change is invaluable for maintaining accurate and current monitoring.Cost and ScalabilityWeighing anchor on cost and scalability ensures that your chosen API monitoring tool is not only affordable but can also grow with your business. It should be capable of handling an increasing number of APIs and more complex systems without imposing prohibitive costs. Consider a pay-as-you-go pricing model, like that offered by New Relic, to align expenditures with growth and usage.Implementing an API Monitoring StrategyCrafting and implementing an API monitoring strategy is like charting a course for a voyage. It begins with defining clear objectives that align with your business goals, choosing the right tools for the journey, and setting up a framework for continuous monitoring.Establishing alerting mechanisms and regularly reviewing and refining the process ensure that your API monitoring strategy remains effective and responsive to new challenges.Define ObjectivesSetting sail with clear objectives is crucial for a successful API monitoring strategy. These should reflect your business goals, whether it’s enhancing customer experiences, facilitating seamless third-party integration, or improving operational efficiency. By clearly defining what you want to achieve with API monitoring, you can tailor your strategy to track response times, ensure correct data payload, and verify API availability. Tracking API usage growth should be a key objective, as it provides insights into adoption and performance trends over time.Select ToolsSelecting the right tools is akin to equipping your ship with the best navigation instruments. The tools you choose should not only align with your defined objectives but also facilitate continuous monitoring and immediate detection of issues.An API management platform that includes features such as an API gateway, analytics, security tools, and API endpoints can greatly aid in providing a comprehensive API monitoring solution, which is a crucial part of a comprehensive API monitoring strategy. By using this platform to monitor APIs, businesses can ensure the optimal performance and security of their applications.Continuous ImprovementThe journey doesn’t end with setting up your API monitoring; it requires continuous course correction for improvement. Here are some key steps to take:  Regularly review historical monitoring data to identify patterns and trends.  Adapt your monitoring strategy to address new challenges and changes in your API environment.  Update configurations based on incident feedback to optimize your monitoring setup. By following these steps, you can refine your monitoring strategy and ensure the long-term health of your APIs.SummaryIn the vast and ever-evolving digital expanse, API performance monitoring stands as the compass by which businesses navigate to ensure that their services are not just operational but exemplary. From understanding key metrics and leveraging best practices to selecting the right tools and crafting a dynamic monitoring strategy, the journey to effective API monitoring is both complex and critical. Embrace these insights and tools, and set sail toward a future where your APIs are synonymous with reliability, efficiency, and user satisfaction.Organizations looking for the best tools to support their API management can leverage Moesif’s powerful API analytics and monetization capabilities. Moesif easily integrates with your favorite API management platform or API gateway through one of our easy-to-use plugins, or embed Moesif directly into your API code using one of our SDKs. To try it yourself, sign up today and start with a 14-day free trial; no credit card is required.Frequently Asked QuestionsWhat is API performance monitoring?API performance monitoring involves tracking the health, functionality, and efficiency of APIs to ensure optimal operation and user experience. It is essential for maintaining a high-performing system.Why are uptime and response time important metrics in API monitoring?Uptime is crucial as it measures the availability of an API, and response time significantly affects user experience by reflecting the speed of request processing.Can API performance monitoring enhance security?Yes, effective API monitoring can quickly identify and address security vulnerabilities, preventing potential threats and ensuring secure data exchange.What challenges might I face in API performance monitoring?You might face challenges in API performance monitoring, such as handling diverse ecosystems, managing high data volumes, and adapting to dynamic environments that impact API performance.How do I choose the right API performance monitoring tool?To choose the right API performance monitoring tool, consider its feature set, integration capabilities with existing systems, cost-effectiveness, and scalability to meet your business needs. Avoid overlooking any of these key factors.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design and Build Better AI Products!            Use Moesif to build better AI apps with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Top-10-API-Performance-Monitoring-Tools-to-Boost-Efficiency/",
          "author": "Kristopher",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-optimizing-profits-calculating-and-reporting-cogs-for-your-openai-powered-api": {
          "title": "Optimizing Profits: Calculating & Reporting COGS for Your OpenAI-Powered API",
          "content"	 : "The AI (Artificial Intelligence) gold rush is on; if you’ve built an API on top of OpenAI’s platform, you’re in the thick of it. Using OpenAI’s powerful AI solution to handle AI-related tasks is a trendy route for many API developers and a way to quickly integrate AI capabilities into your offering instead of going too far into custom AI development. But amidst the excitement of AI development and building cutting-edge applications with third-party tools, a crucial financial metric can make or break your business: the Cost of Goods Sold (COGS).For those unfamiliar with the term, COGS is the direct cost of producing the goods or services you sell. This applies to physical goods, software-related offerings, and any product that can be sold. For AI API providers, it includes everything from the fees you pay OpenAI to the infrastructure that keeps your API up and running.If you’re not tracking and optimizing your COGS, you could be losing money—or worse, running your business into the ground without even realizing it. This guide will look at critical knowledge and tools to accurately calculate and report COGS for your AI API, ensuring a clear picture of your profitability and a solid foundation for long-term growth in your AI applications. Let’s begin by looking at critical factors in calculating COGS, starting with direct costs.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Examining Direct Costs: The Backbone of COGSWhen it comes to AI, Cost of Goods Sold calculations usually start by figuring out direct costs. The direct costs of an AI system are the most tangible and often the easiest to quantify. If you’re using OpenAI’s APIs, then this can be broken down into two pieces: the OpenAI costs and the cost to host other infrastructure components needed for your APIs. These are the costs that you will pay out each month to various providers. Let’s break down the two primary components a bit further:OpenAI API UsageThis is the lifeblood of your API and where your AI functionality is derived from. The costs for using OpenAI’s APIs can vary significantly depending on the models you use, the volume of requests, and the complexity of your tasks. Here’s what you need to keep in mind when managing this factor:  Billing Structure: OpenAI typically charges per token (a piece of a word that is contained in the prompt or the response), and different AI algorithms and models have different token rates. Familiarize yourself with their pricing structure to estimate costs accurately and determine which model is best for functionality and price, depending on your needs.  Usage Monitoring: Monitor your API usage closely. Are there peak times when you incur higher costs? Can you optimize your code to reduce unnecessary API calls to the OpenAI APIs you are using?  Cost Optimization: Look for ways to minimize OpenAI API usage without sacrificing performance. This might involve caching results, batching requests, or using more efficient models for specific tasks.InfrastructureThe hardware and software that power your API are essential. As you scale, there may be more efficient ways to manage this cost; however, all routes also have a price tag. Hosting infrastructure on-premise, through a managed service, or on a public cloud are all options here. Mixing and matching these options can save money, depending on exactly what performance and functionality you need. Here are the main infrastructure costs to consider:  Servers: Whether you use cloud-based servers (like AWS or Azure) or run your own hardware, these costs can add up quickly.  Cloud Storage: If your API involves storing data, you’ll need to factor in the cost of cloud storage solutions like Amazon S3 or Google Cloud Storage.  Bandwidth: The more data your API transmits, the higher your bandwidth costs will be.  Additional Services: Depending on your API’s functionality, consider the cost of databases, monitoring tools, or other third-party services.These costs will evolve, so monitoring and reviewing them regularly is critical to maintaining the revenue generated from your AI APIs. Of course, these aren’t the only costs that you need to factor into COGS. Next, we must add another piece of the puzzle: indirect costs.Unmasking Indirect Costs: The Hidden Contributors to COGSWhile direct costs are relatively straightforward, indirect costs can be sneakier and easily lost in the fog of operating a business. Although not always paid attention to as closely as direct costs, indirect costs are almost always visible in your financial statements. It’s extremely important to not ignore or underestimate their impact on your COGS. Let’s take a closer look at these hidden contributors to COGS:Development & MaintenanceYour AI API won’t generally tend to build and maintain itself. A team of developers, product owners, DevOps, and other staff are likely required to get things up and running and keep them going smoothly. We also can’t forget the tools these folks need to manage and build the AI APIs. The ongoing effort to develop and keep the APIs running smoothly and up-to-date incurs costs, including:  Salaries/Wages: The compensation for developers, engineers, data scientists, and other personnel who build and maintain your API. This is one of the significant factors in AI software development cost.  Software Licenses: The cost of software tools, libraries, frameworks, and other licenses used in the development process.  Subscriptions: Subscriptions to cloud services, APIs, and other platforms that support your development workflow.  Ongoing Support: Costs associated with bug fixes, updates, security patches, and other ongoing support activities.Marketing & SalesOnce built, you’ve got to get the word out about your awesome new AI API! Unfortunately, this generally doesn’t come for free. Getting the word out about your AI API and attracting paying customers requires investment. Although the exact marketing and sales activities needed to get your AI API into the hands of customers will vary, consider these marketing and sales costs as potentially impacting the COGS for your AI API:  Advertising: Costs for online ads, social media campaigns, content marketing, and other promotional activities.  Public Relations: Expenses related to press releases, media outreach, and other PR efforts.  Sales Team: Salaries, commissions, and other expenses for your sales team.  Customer Acquisition: Costs of acquiring new customers, such as lead generation, demos, and trials.Customer SupportIf you’re selling to customers, even if they are developers, you will need a way to support them. Generally, this will require additional staff, tools, and training to keep the revenue engine and your API running smoothly. Providing excellent customer support is essential for retaining users and building a loyal customer base. When building out this function, you’ll want to factor in these costs:  Support Staff: Salaries and benefits for your customer support team.  Support Tools: The cost of helpdesk software, knowledge base platforms, and other tools used to manage customer inquiries.  Training: Costs associated with training your support staff on your API and customer service best practices.Acknowledging and accounting for direct and indirect costs will help you understand your COGS comprehensively. With this information, you can now make informed financial decisions and sustain your AI API business’s growth. Let’s move to the next step of calculating COGS using the information gathered about the direct and indirect costs of your AI API.Calculating COGS: Putting the Pieces TogetherNow that we’ve identified the direct and indirect costs that make up your COGS, it’s time to crunch some numbers. Although there may be different variants and approaches to calculating and forecasting COGS, the basic framework to guide your calculations is this:COGS = Direct Costs + Indirect CostsBased on what we discussed above, let’s break down how to apply this formula:  Direct Costs:          OpenAI API Usage: Gather your usage data from OpenAI’s billing dashboard and calculate your total API costs.      Infrastructure: Add up your expenses for servers, cloud storage, bandwidth, and any other infrastructure-related services.        Indirect Costs:          Development & Maintenance: Determine a reasonable percentage of your development team’s time dedicated to your AI API. Multiply this percentage by their salaries and add software licenses, subscriptions, and support costs.      Marketing & Sales: Calculate your total marketing and sales expenses. If you’re unsure how to allocate these costs, you can use a percentage of your overall revenue.      Customer Support: Add up the salaries of your support team, the cost of support tools, and any training expenses.        Total COGS: Add your direct and indirect costs to derive your total COGS for a given period (e.g., monthly or quarterly).To see this formula in action, let’s look at an example of what an AI API might cost to build and support. Suppose your AI API generated $10,000 in revenue in a month. Your direct costs were $2,000 for OpenAI API usage and $1,000 for infrastructure. Your indirect costs were $3,000 for development and maintenance, $1,500 for marketing and sales, and $500 for customer support.Based on these numbers, your COGS calculation would look like this:COGS = $2,000 (API) + $1,000 (Infrastructure) + $3,000 (Dev & Maintenance) + $1,500 (Marketing & Sales) + $500 (Customer Support) = $8,000This would mean that our profit from the AI API would be around $2,000 for the month.Important ConsiderationsAs a note, there are two important considerations to be aware of when calculating COGS. These include:  Allocation: Allocating indirect costs can be tricky. Getting these costs 100% accurate can be tricky, especially if your AI API is only one component of your company’s overall product offering. Use your best judgment and consider seeking advice from an accountant to do a sanity check on the numbers you’ve come up with.  Time Period: Choose a consistent time period for your calculations (monthly, quarterly, etc.) to track your COGS over time and identify trends. This helps to ensure you are comparing “apples-to-apples,” allowing you to make more accurate decisions on improving your COGS, profit, and overall strategy when it comes to your AI APIs.By carefully and periodically calculating your COGS, you’ll unlock valuable insights into your API’s financial performance. This will empower you to make data-driven decisions and optimize your profitability.Reporting COGS: Transparency for Financial HealthCalculating and monitoring COGS is crucial for every aspect of your business. Accurately reporting your COGS goes beyond simply number-crunching and seeing a number on a page; it’s a fundamental practice for maintaining the financial health of your AI API business. Monitoring COGS and establishing a reporting cadence is essential to an API business model. Here are a few angles to consider when it comes to COGS and reporting:Frequency of Reporting  Monthly: This is ideal for monitoring your expenses, especially if your API usage or costs fluctuate significantly.  Quarterly: This is a typical reporting period for many businesses and allows you to identify longer-term trends.  Annually: Annual COGS reporting is often used for tax purposes and provides a big-picture overview of your AI API’s financial performance.Importance of Tracking  Identify Trends: Regularly tracking COGS helps you spot patterns, such as seasonal fluctuations or spikes in usage, that could impact your profitability.  Optimize Spending: By analyzing your COGS, you can identify areas where you overspend and take corrective action. For example, you might discover that a particular model is driving up your OpenAI API costs and consider switching to a more cost-effective alternative.  Pricing Strategy: Understanding your COGS is essential for setting appropriate pricing for your API. You must ensure that your pricing covers costs and generates a healthy profit margin. If OpenAI costs increase and margins shrink, the pricing strategy should be pivoted to reflect this.  Financial Forecasting: Accurate COGS data is crucial for forecasting future revenue and expenses, allowing you to make informed business decisions and plan for growth.Regulatory Compliance  Accounting Standards: Depending on your location and business structure, you might be required to follow specific accounting standards for reporting COGS. Consult with an accountant or financial professional to ensure compliance anywhere you need to report your company’s COGS.  Tax Implications: COGS is a deductible expense for tax purposes. Proper calculations and reporting can help you maximize your deductions and minimize your tax liability.Transparent and accurate COGS reporting goes beyond meeting regulatory compliance obligations; it offers a strategic advantage in maintaining revenue currently and into the future. By keeping a close eye on your COGS, you’ll be well-equipped to navigate the financial aspects of building and supporting AI APIs and able to steer your business toward long-term, data-driven success.Leveraging Moesif: Simplifying COGS Calculation for Your AI APIWhile the principles of calculating COGS remain the same, the tools you use to gather the data you need for the calculations can significantly affect accuracy and efficiency. At Moesif, we offer an API analytics platform that is geared towards AI APIs. Our platform can help monetize and track metrics crucial to calculating the direct costs and revenue driven by AI APIs. Here are some features AI API companies can utilize that are specifically designed to help AI API providers find the data they need to calculate COGS and revenue:  Granular Usage Tracking: Moesif provides in-depth insights into your OpenAI API usage, breaking down costs by factors such as tokens consumed, endpoint, and individual requests. This level of granularity allows you to pinpoint exactly where your spending is going and identify opportunities for optimization.  Cost Allocation: Moesif can assist with cost allocation by automatically attributing OpenAI API costs (based on token usage) to specific users, companies, or endpoints within your API offering. This helps you understand the profitability of different business segments and make data-driven pricing decisions.  Real-Time Monitoring: With Moesif’s real-time dashboards, you can keep a constant pulse on your API costs. This enables you to detect anomalies, such as sudden spikes in usage, and take proactive measures to prevent unexpected expenses.  Business Governance on Auto-Pilot: Using Governance Rules, Moesif can easily block users from using your AI API if they fit a specific criteria such as having an overdue invoice or have burned through all of their pre-paid credits. This can help to prevent any unnecessary losses that can impact COGS.  Customizable Reporting: Moesif allows you to create customized reports tailored to your COGS reporting needs. You can track factors within your customer’s API usage that impact your COGS over time, compare them to revenue, and generate reports for stakeholders or financial audits.  Integration with Billing Systems: Moesif seamlessly integrates with popular billing and accounting platforms such as Stripe and Recurly, making it easy to monetize your AI APIs and incorporate COGS data into your overall financial reporting.By leveraging Moesif’s powerful analytics and automation capabilities, you can simplify the COGS calculation process, gain deeper insights into your API’s financial performance, and ultimately make more informed decisions that drive your business forward.ConclusionUnderstanding and managing your Cost of Goods Sold (COGS) is not just a financial exercise; it’s the key to unlocking the full potential of your AI API venture. By diligently tracking your direct and indirect costs, you gain invaluable insights into your profitability, allowing you to make informed decisions about pricing, resource allocation, and growth strategies.The COGS of your AI project is not a static figure and requires constant and proactive monitoring. It will evolve as your API usage grows, your infrastructure changes, and your business model adapts. Regular monitoring and analysis of your COGS will empower you to stay ahead of the curve, optimize your operations, and maintain a healthy financial position when supporting your AI APIs.If your business is reliant on APIs and AI solutions, Moesif API analytics and monetization platform is a critical piece of the puzzle. Whether you need to monetize your AI APIs or track metrics to help with COGS calculations, Moesif’s platform easily integrates with your existing API infrastructure to get you up and running quickly. To try it out for yourself, sign up today for a 14-day free trial with no credit card required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Optimizing-Profits-Calculating-and-Reporting-COGS-for-Your-OpenAI-Powered-API/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-mastering-api-rate-limiting-strategies-for-efficient-management": {
          "title": "What Is Rate Limiting? A Practical Guide for API Developers",
          "content"	 : "What Is Rate Limiting?Rate limiting caps how many requests a client can send to a server inside a fixed time window. When the cap is hit, further requests are rejected (usually with an HTTP 429 status) or queued until the window resets. APIs use rate limiting to protect backends from abuse, share capacity fairly, and keep response times predictable under load.The rest of this guide covers how the cap is counted, how the server tells clients they have been throttled, and how clients should react.                Monitor REST APIs With Moesif              14 day free trial. No credit card required.              Try for Free        Why Rate Limiting MattersA public API with no rate limiting is a bug waiting to happen. One misconfigured cron job, runaway script, or leaked credential can saturate your database or run up your cloud bill in an afternoon.Preventing abuse and DDoSA botnet pointed at /login will try millions of credential pairs unless something stops it. Per-IP and per-account limits push brute force into the noise; at 10 attempts per minute, it becomes impractical against a decent password policy. OWASP lists this as API4:2023, Unrestricted Resource Consumption.Rate limiting at the edge does not replace a DDoS scrubbing service, but it is the first layer deciding whether a request reaches your application logic.Protecting backend stability and costMost APIs have one or two expensive endpoints: a fan-out search, an LLM completion, a PDF render. Without per-endpoint limits, a handful of clients can pin your CPU and inflate your bill. A cap of 10 requests per minute on the expensive route keeps the rest of the API responsive when one customer gets aggressive.Rate limiting connects to broader API governance: limits encode a contract for what the platform promises to serve, and what it will refuse.Ensuring fair access for legitimate usersWhen one client floods the API, every other client sees slower responses and more timeouts. Rate limits keep shared capacity from being monopolized by a noisy minority, and matter most on free tiers, where unmetered consumers would otherwise compete with paying customers.How Rate Limiting WorksEvery limiter answers three questions: who is this request from, how many have they made, and what to do when they exceed the cap?Identifying clients (IP, user ID, API key)A limiter needs a key. The usual choices, in increasing order of trust:  IP address, works for unauthenticated traffic but breaks behind NATs and is trivially defeated by a botnet.  API key, the standard for authenticated APIs. Tie the limit to the key so customers can rotate without losing budget.  User ID or account ID, useful when one customer has many keys and you want an aggregate limit per organization.  Combination, production systems usually layer all three: per-IP for abuse, per-user for fairness, per-endpoint for cost control.Pick the most specific identifier the request carries. For a walkthrough of issuing and rotating keys at the gateway layer, see our guide to API keys in AWS API Gateway.Counting requests against a quotaOnce you have a key, increment a counter on every request. The counter lives in a fast store (Redis is a common choice) because the limiter sits on the hot path and has to answer in single-digit milliseconds. The counter resets according to whichever algorithm you picked.Returning HTTP 429 responses and Retry-After headersWhen the counter exceeds the limit, the server replies with 429 Too Many Requests (RFC 6585) and a Retry-After header so the client knows how long to wait:HTTP/1.1 429 Too Many RequestsContent-Type: application/jsonRetry-After: 30RateLimit-Limit: 100RateLimit-Remaining: 0RateLimit-Reset: 30{  "error": "rate_limit_exceeded",  "message": "You have exceeded the limit of 100 requests per minute.",  "retry_after_seconds": 30}The current IETF draft for RateLimit header fields has evolved from the older RateLimit-Limit, RateLimit-Remaining, and RateLimit-Reset fields toward consolidated RateLimit and RateLimit-Policy fields. The work is still a draft, not a published RFC. Many APIs still expose older X-RateLimit-* headers, and some gateways continue to emit the earlier three-header shape. GitHub, for example, uses lowercase x-ratelimit-limit, x-ratelimit-remaining, x-ratelimit-reset, and x-ratelimit-used and returns either 403 or 429 on excess. If you support more than one format, document exactly which headers your API returns and what each value means.Common Rate Limiting AlgorithmsFour algorithms show up in most production systems. They differ on burst tolerance, accuracy at the window boundary, and memory cost.Fixed window counterBucket every request into a fixed window (say each calendar minute). Increment a counter; reject anything past the limit; reset at the next window.Fixed window is dead simple and uses one counter per key. The catch is the boundary: a client can send the full quota at 12:00:59 and the full quota again at 12:01:00, doubling the effective rate for two seconds. Acceptable for internal services, problematic for a strict public cap.Sliding window log and sliding window counterThe sliding window log stores a timestamp for every request and counts how many fall inside the current window on each new request. Exact, but memory grows with request rate.Sliding window counter is the practical compromise. It keeps two fixed-window counters (current and previous) and computes a weighted average based on where in the window the request lands. The math is approximate but the boundary spike disappears and memory stays small. Many modern API gateways default to sliding window counter for simple cases, though fixed window, token bucket, and leaky bucket remain common depending on the gateway and use case.Token bucketA bucket holds N tokens and refills at R tokens per second. Each request consumes one token. Empty bucket means the request is rejected; otherwise it goes through.Token bucket allows bursts. A client that has been idle can spend the full bucket immediately, then settle into the refill rate. AWS, Stripe, and many other production APIs default to token bucket because real traffic is bursty.Leaky bucketLeaky bucket is token bucket’s inverse. Requests join a queue (the bucket), and the server drains the queue at a fixed rate. A full queue drops new requests.The result is a smooth output stream, useful when the downstream service cannot handle bursts at all. The cost is latency: a queued request waits even when the system has capacity.Algorithm comparison            Algorithm      How it works      Burst handling      Memory footprint      Best for                  Fixed window      Counter resets every N seconds      Allows 2x burst at window boundary      Lowest (one counter per key)      Quick wins, internal APIs, when boundary spikes are acceptable              Sliding window      Weighted blend of current and previous window counters      Smooths out boundary spikes      Low (two counters per key)      Public APIs that need an accurate cap without log overhead              Token bucket      Bucket of N tokens, refilled at R per second      Allows bursts up to bucket size      Low (token count and last-refill timestamp)      Bursty real-world traffic, most public REST APIs              Leaky bucket      Queue drained at fixed rate      Smooths bursts into a steady output      Medium (queue size)      Protecting fragile downstream systems that need a flat rate      Sliding window counter is the safest default for most public APIs. Switch to token bucket to be generous with idle customers, leaky bucket when downstream cannot absorb bursts.Rate Limiting vs. API ThrottlingThe terms get used interchangeably but describe different behaviors.Rate limiting rejects requests over the cap; the client gets a 429 and has to back off. Throttling slows requests instead, usually by queuing or adding delay. The client still gets a response, just later than it asked for.Most API gateways offer both. Use rate limiting at the edge to fend off abuse and protect capacity. Use throttling deeper in the stack when refusing a request costs more than making it wait, for example on async batch jobs where a few seconds of queueing is invisible to the user.Rate Limiting in APIs, Practical PatternsPer-user vs per-IP vs per-API-key strategiesLayer them. A typical stack:  Per-IP, low limit on unauthenticated traffic, stops scrapers and credential-stuffing bots before they reach auth.  Per-API-key, generous limit after authentication, the contract customers see in your docs.  Per-endpoint, only on expensive routes; search or LLM proxy gets a tighter ceiling than /health.Each layer protects something different. Drop one and the others usually surface a problem it would have caught.Tiered limits for free vs paid customersRate limits double as a pricing lever. Free gets 100 requests per hour, Pro gets 10,000, Enterprise is negotiated. Tiered limits are a low-friction way to introduce usage-based pricing. For guidance on setting these ceilings without burning your best accounts, see Best Practices for API Rate Limits and Quotas.Real-world examples make the trade-offs concrete:  GitHub REST API: 60 requests per hour unauthenticated, 5,000 per hour for authenticated users with a personal access token, 15,000 per hour for GitHub Apps owned by an Enterprise Cloud org.  X (formerly Twitter) API v2: publishes endpoint- and tier-specific limits that change frequently, so the official rate-limit table and developer portal are the source of truth.  Stripe API: documents multiple limits, including account-level, endpoint-specific, concurrency, and read-allocation limits. Because Stripe’s limits vary by endpoint and account activity, link to Stripe’s current rate-limit docs rather than hard-coding a number.All three publish their limits so customers can plan for them.Communicating limits via headersDocument the headers you return and keep them stable. The IETF RateLimit header work standardizes three:  RateLimit-Limit, the cap for the current window.  RateLimit-Remaining, requests left before the client is throttled.  RateLimit-Reset, seconds (delay-in-seconds) until the limit resets. The legacy X-RateLimit-Reset header uses a Unix timestamp instead.If you already shipped the older X-RateLimit-* form, return both during a migration. Whatever you choose, document it.Handling Rate Limit Errors as a ConsumerIf you call someone else’s API long enough, you will see 429s. Retrying immediately just piles load onto an overloaded server. Use exponential backoff with jitter: wait, retry, double the wait on the next failure, and add randomness so retries do not hit the server in lockstep.A minimal Python implementation:import randomimport timeimport requestsdef get_with_backoff(url, max_attempts=6, base_delay=1.0, cap=60.0):    for attempt in range(max_attempts):        response = requests.get(url, timeout=10)        if response.status_code != 429:            return response        # Prefer the server's Retry-After if present.        retry_after = response.headers.get("Retry-After")        if retry_after is not None:            try:                wait = float(retry_after)            except ValueError:                wait = base_delay * (2 ** attempt)        else:            wait = base_delay * (2 ** attempt)        # Cap the wait and add jitter to spread retries.        wait = min(cap, wait) * (0.5 + random.random())        time.sleep(wait)    raise RuntimeError(f"Gave up after {max_attempts} attempts for {url}")Two details matter. First, honor Retry-After if the server sent one. Second, cap the wait, otherwise exponential backoff eventually sleeps for hours and looks like a hung process.For high-throughput producers, pair backoff with a client-side limiter that pre-empts 429s. Track your quota using RateLimit-Remaining and queue work locally when the budget runs low.Implementing Rate Limiting, Tools and ApproachesWhere should the limit live? Three layers, each with trade-offs.At the API gatewayMost teams put the first line of defense in the gateway: Kong, Apigee, AWS API Gateway, Azure API Management, Tyk. Gateways ship pluggable rate-limit modules, shared Redis backing stores, and central policy management. If you already run a gateway, configure rate limiting there first. The trade-off is that gateway limits are usually per-route and per-key, not deeply aware of business context.In application codeWhen you need rules that depend on the request body, the user’s plan, or a feature flag, push the limiter into the application. Standard libraries:  Node.js: express-rate-limit, rate-limiter-flexible for Redis-backed counters.  Python: slowapi for FastAPI, django-ratelimit for Django.  Java/Spring: Bucket4j on Spring’s filter chain.  Go: golang.org/x/time/rate.App-level limits cost a little latency and require coordination across instances, but they let you express any rule you can write in code.Observability, knowing whether your limits are workingThe limit you ship is rarely the limit you keep. Real traffic exposes whether the cap is too tight (legitimate customers getting throttled), too loose (one account still saturating a backend), or misaligned with how customers actually use the API.That is where API analytics earns its keep. Our platform sits on top of your gateway, ingesting every request, attributing it to a customer or pricing plan, and surfacing who is hitting 429s, on which endpoints, and how often. Across the API traffic we observe, a common pattern after a new rate limit ships is that 5–10% of customers bump into it within the first week, and roughly a third of those are paying customers who should have been on a higher tier. Catching that early is the difference between a quiet rollout and a Monday-morning support queue.We do not enforce limits, that is the gateway’s job. We tell you whether the limits are doing what you intended, surface upsell triggers when a customer consistently exceeds quota, and give you per-customer breakdowns for contract renegotiation. The Moesif platform can unify the picture across Kong, AWS, Apigee, Azure APIM, and others; the same usage data feeds policy enforcement and monetization when limits become pricing tiers.Wrapping UpRate limiting is high-leverage work. A few hundred lines at the gateway shuts down categories of abuse, protects your backend from runaway clients, and turns capacity into tiered pricing. The four algorithms cover nearly every traffic shape, the 429 contract is standardized, and the libraries are mature.Once limits are live, you need visibility into who is hitting them and whether your tiers fit how customers actually use the API. Pair whatever gateway enforces your limits with an analytics layer that watches the result.Frequently Asked QuestionsWhat is the meaning of rate limiting?Rate limiting controls how many requests a client can send inside a fixed time window. Over the cap, the server either rejects further requests (HTTP 429) or queues them until the window resets.What does it mean when something is rate-limited?You have hit the maximum number of requests the server will accept in the current window. Wait for the period in the Retry-After or RateLimit-Reset header before retrying. Hammering a server that already told you to back off usually extends the lockout.What is rate limiting in API, with an example?A typical rule is “100 requests per minute per API key.” The 101st request returns 429 Too Many Requests with a Retry-After header. GitHub allows 5,000 authenticated REST requests per hour; Stripe publishes account-level and endpoint-specific limits that vary by activity.How do you choose the right rate limiting algorithm?Start with sliding window counter for most public APIs. Move to token bucket if your traffic is bursty. Use leaky bucket when downstream cannot tolerate bursts. Fixed window is fine for internal services where simplicity beats precision.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /technical/api-development/Mastering-API-Rate-Limiting-Strategies-for-Efficient-Management/",
          "author": "Kristopher",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-mastering-api-modeling-essential-concepts-and-practices": {
          "title": "Mastering API Modeling: Essential Concepts and Practices",
          "content"	 : "An API model acts as a blueprint for building scalable and maintainable software interfaces. In this article, you’ll discover how to design, implement, and validate a robust API model, including the use of AI models. We’ll cover key concepts like API modeling, essential components, user identification, and best practices.Key Takeaways  API modeling is crucial for creating scalable, maintainable, and interoperable systems, offering a clear architecture for developers and easing user interactions.  Key components of an API model include data structures, endpoints, and request/response formats, all of which ensure smooth interoperability and secure access.  Identifying and understanding the API’s target users is essential to tailor the API’s design and documentation to effectively meet their specific needs and objectives.  Incorporating AI models can enhance the functionality and capabilities of your API, providing advanced features like natural language processing and predictive analytics.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding API ModellingIn the software development world, API modeling parallels an architect’s blueprint for a building. It is a process that translates product specifications into a high-level API architecture, providing a smooth interface for both developers and end users. As you sketch your API’s design, you are laying the groundwork for scalable, maintainable, and interoperable systems that can communicate effectively.API modeling extends beyond drafting a preliminary sketch before mapping it onto HTTP and constructing the API. This vital discipline lends structure to the design and implementation of APIs, including the integration of AI models for advanced functionalities. The types of modeling, including REST and GraphQL, cater to different needs yet share a common goal: simplicity and clarity to ensure ease of use and understanding for developers.Key Components of an API ModelAn API model forms the basis for reliable and effective communication between software components. Key components of an API model include meticulously designed data structures, endpoints, and request/response formats that together facilitate smooth interoperability. These data models are not standalone; they are integrated into the very fabric of the API, defining how entities relate to each other and how data is categorized and accessed, including the integration of AI models for enhanced data processing.Endpoints serve as the gateway for interactions, while authentication mechanisms exist to validate and ensure secure access to API functionalities. When things go awry, it is the error handling that comes to the rescue, providing meaningful feedback via standardized error codes and messages. It’s the careful assembly of these components that enables the API to function as intended.Identifying Your API UsersGrasping your API’s target audience is essential. This involves recognizing a range of users—from internal developers to external developers, system administrators, and account administrators—who engage with your API in different ways. Identifying API users goes beyond mere acknowledgment; it involves delving deep into their unique needs and requirements to tailor an API design that speaks directly to their objectives, potentially incorporating AI models to meet advanced user needs.As you list the personas and map their desired outcomes, you’re not just identifying users; you’re differentiating end users and the subtle nuances in how each user group will engage and interact with the project and API. This insight is crucial, as it informs both the functionality of the API and the documentation that will guide its users.Defining Desired OutcomesThe set of desired outcomes serves as the guiding compass for API design, ensuring the API meets users’ needs and solves their problems effectively through a well-crafted user interface. How does one define these outcomes? By employing techniques like Jobs-to-be-Done (JTBD) and Job Stories, we generate definitions which capture the essence of what users aim to accomplish with the API.The process of describing and defining desired outcomes is thoughtful, often involving a deep dive into why the API exists, the problems it intends to solve, and the various ways it can provide solutions to users. This step is not just about description or about defining—it’s about aligning every aspect of the API design with the user’s vision and ensuring that the final product resonates with its intended audience, including the use of AI models for advanced problem-solving.Mapping Out ProcessesWhen embarking on the journey of API modeling, it’s necessary to chart the processes required to achieve the defined outcomes. This involves more than just sketching steps; it requires a collaborative effort with operations engineers and other subject matter experts to inject domain knowledge into the modeling process. Techniques like Event Storming come into play here, fostering a shared understanding of system processes as experts capture domain events and map out the complex choreography of operations. When embarking on the journey of API modeling, it’s necessary to chart the processes required to achieve the defined outcomes, potentially incorporating AI models to enhance these processes.Event Storming is particularly effective because it:  Starts near the end of the process, allowing participants to work backward and ensure that each step is accounted for  Identifies any hotspots that may need additional attention or decisions  Creates a blueprint that accurately represents the pathways to achieve desired outcomesBy incorporating this technique, you’re not just mapping out processes; you’re creating a blueprint that accurately represents the pathways to develop and achieve desired outcomes.Creating and Validating Your API ModelAfter laying the groundwork in the design and development phases of a new project, the subsequent step involves creating and validating the API model. This stage is about capturing the essence of what the API will offer—its methods and interfaces—and ensuring that it aligns with known requirements, including the integration and validation of AI models. It’s a dual-phase process where the API model is first drafted and then rigorously tested against use cases and business requirements to confirm and demonstrate its efficacy.Validation is not a cursory glance at AI model; it’s a thorough investigation to ensure no stone is left unturned. You must examine the API model for any missing participants, outcomes, properties, or steps and make the necessary adjustments to perfect the model. This meticulous approach is what ensures good quality assurance, allowing the API to serve its purpose effectively and meet the expectations of its users.Drafting the API ModelThe drafting of the API model is an intricate process that identifies the primary resources and actions shaping the structure of the API specification. It requires a discerning eye and ability to pinpoint the essential elements that the API will interact with and define the operations that will be performed on these resources. The drafting of the API model is an intricate process that identifies the primary resources and actions shaping the structure of the API specification, potentially incorporating AI models for advanced functionalities.Once these elements are identified, they are mapped to the appropriate HTTP methods defined in the following categories:  GET  POST  PUT  DELETEThis crucial step ensures a clear and logical relationship between the resources, actions, objects and properties and their corresponding HTTP methods, laying the foundation for a robust API model.Validation TechniquesValidation techniques serve as the definitive test for the drafted API model. Utilizing tools like wireframes and user stories, developers can simulate how the API will function in real-world scenarios. Test cases and criteria help verify that every user’s needs are met and that the API behaves as intended. Validation techniques serve as the definitive test for the drafted API model, including the validation of integrated AI models.Interactive tools and mock servers also play a pivotal role in validation, allowing for the testing of API schemas and the return of sample data and object objects in response to requests. Moreover, collaborative review sessions with stakeholders are instrumental in uncovering potential issues and ensuring that the API model aligns with user expectations.Proper documentation solidifies the validation process, ensuring that every resource definition, object, method, and path is clearly defined and easily understandable.Best Practices for API ModellingIn the realm of REST API modeling, best practices guide developers in crafting a successful API. One such practice is the use of reusable components, which promotes consistency and reduces duplication across the API model. Endpoint paths are best expressed in nouns rather than verbs to maintain clarity and reflect the hierarchical relationships between resources.Additionally, embracing JSON as a standard data transfer format ensures compatibility with a wide range of networked technologies and services. Coupled with Readme-style documentation and security measures like SSL/TLS, these best practices form a comprehensive approach to API modeling that prioritizes performance, security, and ease of use.When integrating AI models into your model API call, it’s important to follow best practices such as ensuring data privacy, maintaining model accuracy, and providing clear documentation for users. Utilizing Foundation Model APIs provided by Databricks can help access and query state-of-the-art open models from a serving endpoint. This can be particularly useful for querying a generalized LLM, building a chatbot, replacing proprietary data models with open alternatives, and developing LLM applications for both development and production environments.Tools for API ModellingA plethora of platforms, each with its unique features, enrich the toolbox for API modeling. Some notable platforms include:  Stoplight: a powerhouse of API design and testing, offering a plethora of functionalities that support every stage of API development.  Postman: another powerhouse of API design and testing, offering a plethora of functionalities that support every stage of API development.  SwaggerHub: stands out for its intuitive design platform and collaborative environment.  Redocly: shines with its interactive API documentation capabilities.Some tools also offer functionalities for integrating and testing AI models, providing a comprehensive solution for API development.Other tools such as Slate and apiDoc leverage the simplicity of Markdown and code comments to generate clear and interactive project documentation. Readme and DocFX are also noteworthy for their ease of use and support for input from multiple programming languages, catering to a diverse developer ecosystem.Real-world Examples of API ModelsObserving API modeling in action attests to its transformative potential. Take, for example, the APIs that drive weather applications and social media platforms, enabling seamless user authentication and real-time data access. The utility of Twitter’s API in enabling automated bots and PayPal’s API in facilitating secure payments on eCommerce sites are further demonstrations of API modeling’s versatility.In the travel and hospitality industry, APIs are the linchpins that connect booking platforms with suppliers, while Google Maps’ API empowers search applications with rich location data. AI models enhance these APIs with predictive analytics and personalized recommendations for search. These examples, among others, showcase the breadth of API modeling applications and highlight the industry-specific best practices that contribute to their success.Preparing for API DesignPreparation for API design is a critical step to undertake before delving into the coding phase. It’s about having a firm grasp of the users’ needs and ensuring that communication practices are well-established. A design-first approach begins by identifying the capabilities that the API will offer and defining the API contract, which includes the necessary resources, data formats, and methods. Preparation for API design is a critical step to undertake before delving into the coding phase, especially when integrating AI models for advanced functionalities.Stakeholder alignment on the API’s business use case is the first hurdle to clear, followed by a detailed definition and application of the principles of HTTP and the chosen API style(s), based on the model created. The Align–Define–Design–Refine (ADDR) process serves as an example and a framework to guide teams through this journey, ensuring that the resulting API aligns with stakeholders’ needs and expectations.SummaryAs we wrap up this expedition into API modeling, it’s clear that the process is much more than a technical necessity—it’s a strategic approach that underpins the success and performance of digital products and services. From understanding the fundamental concepts to applying best practices and leveraging the right tools, including AI models, API modeling is a multifaceted journey that shapes the way software interacts within our digital world.With this knowledge in hand, may your future API endeavors be as seamless and robust as the models you’ve learned to create. Take these insights, apply them with confidence, and watch as your APIs transform the user experience and drive innovation in your projects.Organizations looking for the best tools to support their API management can leverage Moesif’s powerful API analytics and monetization capabilities. Moesif easily integrates with your favorite API management platform or API gateway through one of our easy-to-use plugins, or embed Moesif directly into your API code using one of our SDKs. To try it yourself, sign up today and start with a 14-day free trial; no credit card is required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Mastering-API-Modeling-Essential-Concepts-and-Practices/",
          "author": "Preet",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-mastering-api-governance-best-practices-for-success": {
          "title": "Mastering API Governance: Best Practices for Success",
          "content"	 : "API governance sets the rules and practices to manage APIs efficiently, ensuring they remain discoverable, consistent, and secure. It’s crucial for businesses aiming to control API sprawl, treat APIs as strategic business assets, and enhance collaboration.This guide explores API governance, its role in effective API management, its benefits, and best practices for successful implementation.Key Takeaways  API governance ensures APIs are discoverable, consistent, secure, and collaborative across an organization’s landscape, promoting a cohesive and innovative ecosystem. Governance can be built in the early stages of the API development process, but APIs at any lifecycle stage will benefit from an effective governance planning effort.  Distinguishing between API governance and API management is crucial; governance sets the overarching policies and procedures, while management handles the day-to-day execution of these policies across diverse API usage patterns.  Effective API governance strategies hinge on centralization, standardization, automation, and continuous monitoring, leading to enhanced API performance, security, and developer satisfaction.  API governance rules must be clear and communicated to the end user - they should be part of a cohesive API strategy centered around the API product, the security risks it faces, the business processes underlying the offering, and the end user’s experience.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding API GovernanceIn the ever-expanding digital universe, API governance shines as the compass that guides the creation, deployment, and management of application programming interfaces. It’s a comprehensive approach encompassing policies, procedures, and practices which ensure that APIs—essential conduits for modern software and crucial in api development—are:  Discoverable  Consistent  Secure  CollaborativeUnderstanding why API governance matters is essential for businesses to maintain a competitive edge in today’s digital landscape. Many organizations are adopting API governance to maintain a competitive edge. By weaving together an organization’s API landscape, governance paves the way for products that not only meet current demands but are also primed for future evolution in the organization’s API landscape.The goals of API governance are twofold: to maximize value across the API ecosystem and to refine the developer experience. It’s about creating an environment where developers can easily navigate and connect with the entire API landscape, thus fostering an ecosystem rich in quality and innovation.This harmonization of the API lifecycle with the developers’ needs translates into applications built with greater ease and operated with heightened security and reliability.Why API Governance MattersAPI governance emerges as a beacon of structure in the chaotic growth known as API sprawl—where APIs multiply without oversight, leading to a tangled web that stifles rather than stimulates progress. Without governance, this sprawl can obscure APIs, diluting their quality and complicating their consumption for developers.By setting a governance strategy in place, organizations can circumvent these pitfalls, ensuring their APIs remain a cohesive force rather than a fragmented array.This strategic framework is not merely about control; it’s about empowerment. It ensures each API is scalable, secure, and cost-efficient, which is indispensable in an API-first world. Governance fosters collaboration and consistency, enabling organizations to extract the maximum value from their API portfolio.Moreover, for enterprises with vast arrays of APIs, governance is the linchpin that aligns API strategy with broader business objectives.Key Differences: API Governance vs. API ManagementDistinguishing between API governance and API management is akin to differentiating between the rules of the road and the vehicles that abide by them. Governance is the comprehensive establishment of policies and procedures that ensure APIs fulfill their intended roles within the organization, with an emphasis on consistency, security, and compliance. It’s the overarching framework within which management operates, setting the stage for APIs to thrive across the entire landscape.API management, on the other hand, is the hands-on execution of these policies through dedicated tools and platforms. It’s the day-to-day operation that includes designing, deploying, and maintaining APIs, ensuring they remain secure, scalable, and reliable throughout their lifecycle. While governance provides the vision and standards, management is the vehicle that drives APIs toward their destinations, underpinned by robust security and a focus on quality.Core Components of an Effective API Governance StrategyAn effective API governance strategy is built upon a few core principles. Centralization is key, creating a nexus where policies are formulated and enforced, fostering best practices throughout the organization. This central point becomes the dashboard from which potential bottlenecks and utilization patterns are identified, enabling informed decision-making to optimize API performance.Standardization of API design is another cornerstone, ensuring a uniform approach across all teams and services within the organization. Coupled with automation, which streamlines the governance process by handling routine checks and documentation, these components significantly enhance efficiency, reliability, and API quality, as well as improve the reliability of its API governance.Metric tracking is indispensable, offering insights into API usage and interactions, thereby ensuring that APIs align with the business’s evolving needs.Best Practices for Implementing API GovernanceNavigating the complex terrain of API governance requires a compass of best practices.Some best practices for API governance include implementing centralized API governance rules, allowing for flexibility and responsiveness in governance processes, and automating governance checks to ensure consistency and efficiency. These rules may change depending on specific use case, but following general guidelines can help optimize API management and get most of the way to “good”.Centralized Governance RulesA centralized governance framework acts as a single source of truth, a repository where governance rules are stored, actively enforced, and controlled at scale. This centralization ensures that exceptions and rule sets are managed transparently, fostering trust and clarity among stakeholders.Furthermore, leaders in API governance must actively support their teams, providing guidance through design and code reviews, training sessions, and advocacy to ensure these policies are not only understood but embraced.Central to the effectiveness of this strategy is the technology itself. By cataloging existing technologies, promoting new ones, and setting deadlines for outdated systems, organizations can ensure that their API governance is both current and comprehensive. This forward-thinking approach ensures that APIs remain a driving force behind the organization’s digital transformation.Flexibility and ResponsivenessFlexibility in API governance is not about bending the rules, but adapting them to the unique contours of the business landscape. It means crafting a governance model that is responsive to the diverse needs of various lines of business, regions, and API types—essentially tailoring governance to fit like a glove. This adaptability extends to the finer details, such as customizing headers and response codes for different API types to ensure they meet specific needs and requirements. Additionally, ensuring governance models are adaptable to compatible devices is crucial for supporting a variety of technologies and maintaining scalability.Such a flexible approach allows for the creation of exception pathways, ensuring that governance does not become a straitjacket but a tool that enhances the adaptability of the organization. It’s the embodiment of a governance model that evolves in tandem with the organization, staying responsive to the ever-changing digital landscape.Automating Governance ChecksAutomation is the engine that drives efficient API governance, replacing the manual grind with a system that’s both faster and more reliable. By automating governance checks, organizations free up precious developer time, allowing them to focus on innovation rather than getting bogged down in red tape. This automated oversight ensures that APIs adhere to set standards without the need for tedious manual reviews, keeping the systems up and running smoothly.In essence, automation in API governance is like a well-oiled machine—quietly ensuring that all components are functioning as intended while empowering developers to push the boundaries of what’s possible. It’s a testament to how governance, when implemented effectively, can be a catalyst for growth rather than a hindrance.API Security and ComplianceAPI security guards against potential attacks and vulnerabilities, offering critical protection. From internal APIs to those exposed to the world, security measures such as authentication, authorization, and rigorous testing are indispensable for maintaining a secure API landscape.Compliance plays a pivotal role, ensuring that every API adheres to both internal policies and regulatory requirements, safeguarding the organization’s reputation and integrity. It’s not just good enough to have a good governance approach - you must also have an adequate enforcement mechanism.Governance extends its reach to ensure compliance with a variety of regulations, be they corporate or regulatory, with continuous scanning and monitoring to preempt any potential issues. In industries like finance, healthcare, and government, where regulations are particularly stringent, governance is the compass that ensures navigation within the bounds of legal and ethical standards.Applying Governance Throughout the API LifecycleEmbracing governance as a continuous thread throughout the API lifecycle is like laying a strong foundation before building a house. It prevents development roadblocks and ensures that the journey from conception to market is smooth and swift, without compromising on quality. This lifecycle approach means governance is present at every turn, from:  the initial design  the development and testing phases  the deployment and monitoring stages  the final deprecationEnsuring the conformity of new APIs to enterprise standards during their deployment and lifecycle is critical. Automated governance checks should be integrated into the development process, enhancing productivity and potentially reducing security risks associated with API sprawl.This creates a streamlined process that aligns with the organization’s broader objectives.By embedding governance early in the development process, issues are identified and addressed before they can take root, allowing for parallel development and faster time to market. Versioning and deprecation policies further contribute to this streamlined process, offering predictability and stability for API consumers and maintaining the integrity of the API ecosystem.Enhancing Developer Experience Through GovernanceAt the heart of any successful API initiative is the developer experience. Effective governance enhances this experience by ensuring APIs are not just functional but also a pleasure to work with. By aligning APIs with corporate standards and ensuring their ease of use and security, governance acts as a catalyst for consistency and smooth integration. Centralized governance rules play a pivotal role in this by ensuring that important metadata fields are checked for discoverability and reusability.Moreover, exposing an API Catalog offers several benefits:  It increases utilization by making it easier for developers to find and reuse existing APIs  It fosters an ecosystem where innovation thrives  By adopting familiar experiences and leveraging AI to automate API requests, governance can significantly improve developer satisfaction and reduce friction.Monitoring and Iterating on Governance PoliciesThe landscape of API governance is not static; it’s a dynamic terrain that requires continuous monitoring and iteration to remain effective. Leaders in this space must be vigilant, using governance policies as living documents that evolve alongside the organization’s needs and the broader API ecosystem. Continuous research is essential in improving governance policies, ensuring they adapt to new challenges and opportunities. By starting with initial standards and gradually refining them through monitoring and feedback, governance policies can be honed to near perfection.Monitoring API performance and utilization is crucial, as it reveals usage patterns and identifies areas for improvement. By utilizing tools to track and analyze API behavior, organizations can proactively address issues and optimize their API portfolio for increased efficiency and better user experiences. This cycle of monitoring and iteration ensures that governance remains a relevant and effective force within the organization.Tools and Platforms for API GovernanceIn the realm of API governance, tools and platforms are the trusty sidekicks that make the hero’s journey easier. These technological allies automate processes, standardize design, and manage common models, ensuring APIs stay true to governance strategies.For instance, the Postman API Platform equips organizations with the features needed to implement governance at scale, while SwaggerHub’s built-in style validation and domains provide the consistency and reusability that are hallmarks of effective governance.API discovery tools are equally essential, allowing for the easy identification of deployed APIs and the governance tooling associated with them. Capital One’s service discovery portal exemplifies how such tools can simplify the search and discovery of APIs and their governance mechanisms.Moreover, API marketplaces enforce governance rules automatically, ensuring that both internal and external stakeholders adhere to the organization’s standards.SummaryAs we draw this exploration to a close, it’s clear that mastering API governance is not just about enforcing rules—it’s about fostering an ecosystem where APIs deliver maximum value, security, and a seamless developer experience. By embracing the pillars of centralization, standardization, automation, and monitoring, organizations can navigate the complex API landscape with confidence. Staying updated with fresh ideas is essential for evolving governance practices, enhancing efficiency, and ensuring effective processes. Let this be the catalyst for your API governance journey, inspiring you to weave these practices into the fabric of your digital strategy.Organizations looking for the best tools to support their API management can leverage Moesif’s powerful API analytics and monetization capabilities. Moesif easily integrates with your favorite API management platform or API gateway through one of our easy-to-use plugins, or embed Moesif directly into your API code using one of our SDKs. To try it yourself, sign up today and start with a 14-day free trial; no credit card is required.Frequently Asked QuestionsWhat is the primary goal of API governance?The primary goal of API governance is to treat APIs as strategic business assets to maximize the value they create and improve the Developer Experience (DX) by ensuring APIs are discoverable, consistent, compliant, reusable, secure, and collaborative.How does API governance differ from API management?API governance focuses on setting policies and procedures for effective API management, while API management implements these policies using dedicated tools and platforms to design, deploy, and maintain APIs. It ensures they remain secure, scalable, and reliable. Sharing best practices among other organizations can provide benchmarks for efficiency and effectiveness, emphasizing the collaborative nature of these practices.Why is automation important in API governance?Automation in API governance is important because it increases productivity, speeds up the governance process, and frees up developer time for other tasks by replacing manual reviews with automated checks, ensuring adherence to governance rules without tedious manual processes. This automation can lead to optimal efficiency and smoother operations.How does API governance enhance security and compliance?API governance enhances security and compliance by implementing measures such as authentication, authorization, and rigorous testing to protect against attacks, as well as ensuring adherence to internal corporate policies and regulatory requirements through continuous scanning and monitoring. Additionally, transparency in governance policies is crucial to avoid hidden fees, ensuring that users are not surprised by unexpected costs and can rely on clear, straightforward pricing structures.Can API governance tools help with API discovery?Yes, API governance tools can help with API discovery by facilitating the search and identification of deployed APIs, making it easier to manage and apply governance rules across the entire API landscape. Maintaining consistent quality across APIs ensures they are at the same level, which is crucial for providing a seamless and reliable user experience.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            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          "url": " /technical/api-development/Mastering-API-Governance-Best-Practices-for-Success/",
          "author": "Kristopher",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-open-source-ai": {
          "title": "What Is Open Source AI? A Practical 2026 Guide to OSAID, Open Weights, and the Models You Can Actually Run",
          "content"	 : "Last updated: May 25, 2026“Open source AI” is one of the most contested phrases in software right now. A company can stamp it on a model that ships with downloadable weights and a restrictive license, and the term sticks in the headlines. The Open Source Initiative (OSI), the same body that has stewarded the Open Source Definition for software since 1998, published the Open Source AI Definition (OSAID) 1.0 on October 28, 2024 to draw a sharper line. Not everyone agrees with where they drew it. Meta still calls Llama “open source.” Stallman and the Free Software Foundation say OSAID is too soft on training data. Most working developers just want to know what they can actually fine-tune, ship, and not get sued over.This guide answers the practical version of “what is open source AI.” It defines the term against OSI’s OSAID 1.0, explains the four freedoms, separates open source from open weights, surveys the models that come up in OSAID discussions, and walks through how teams put these systems into production.What Is Open Source AI?Open source AI is an artificial intelligence system released under terms that grant users the freedoms to use, study, modify, and share the system for any purpose, with access to the preferred form for making modifications. The Open Source Initiative codified this in the Open Source AI Definition (OSAID) 1.0, released on October 28, 2024.That definition treats an AI system as more than just code. To satisfy OSAID 1.0, a release must include:  The source code used to train and run the system, under an OSI-approved license.  The model parameters (weights and any required configuration) under terms that allow free use, study, modification, and redistribution.  Data information, enough detail about the training data, its provenance, processing, and how to obtain or license it, that a skilled person could substantially recreate the system.OSAID 1.0 does not require shipping the full training dataset itself. OSI made that compromise because much of the data used to train modern foundation models is encumbered by copyright, contracts, or privacy law. The Free Software Foundation and the Software Freedom Conservancy publicly objected to this compromise, more on that in the debate section below.The Four Freedoms of Open Source AIOSAID 1.0 reuses the structure of free software’s four freedoms and applies them to an AI system:  Use the system for any purpose, without asking permission.  Study how the system works and inspect its components.  Modify the system, including changing its output.  Share the system, with or without modifications, for any purpose.A field-of-use restriction (for example, “you cannot use this model for X industry”) violates freedom 1. A clause that revokes your rights above a usage threshold (Meta’s 700-million-monthly-active-users clause in the Llama license) violates freedom 4. A weights-only release with no training code and no data information violates freedoms 2 and 3 because you cannot reproduce or meaningfully modify the system.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Open Source AI vs Open Weights vs Open Models vs Closed AIMost of the public confusion about open source AI comes from conflating four different release patterns. They are not the same.            Category      Source code      Weights      Training data info      Commercial use      Modify & redistribute                  Closed AI (GPT-5, Claude, Gemini)      No      No      No      API-only, per terms      No              Open models / source-available      Partial      Sometimes      Sometimes      Restricted (RAIL, custom)      Restricted              Open weights (Llama, Gemma, Mistral research releases)      Partial or none      Yes, downloadable      Partial or none      Allowed with carve-outs      Allowed with carve-outs              Open source AI (OLMo, Pythia, T5)      Yes      Yes      Yes (per OSAID)      Unrestricted      Unrestricted      Closed AIOpenAI, Anthropic, and Google’s frontier models are reachable only by API. The weights stay in the vendor’s data center. Closed AI is the easiest to start with, the easiest to invoice, and the hardest to audit. You cannot inspect the model, fine-tune the weights, or migrate without rewriting.Open models (source-available)These releases publish code or weights but attach usage restrictions through licenses such as the Responsible AI Licenses (RAIL family). The artifacts are visible, sometimes modifiable, but field-of-use clauses keep them outside both OSI’s Open Source Definition and OSAID.Open weightsThis is one of the largest and most marketed categories today. The weights are downloadable from Hugging Face or the vendor’s site. You can run the model locally, fine-tune it, and ship products built on it, subject to license terms that often include acceptable-use policies, attribution requirements, or commercial caveats. Meta’s Llama family is distributed under the Llama Community License, a custom license, not a permissive one like MIT or Apache 2.0, with restrictions including an acceptable-use policy and a clause requiring commercial users above a defined monthly-active-user threshold (700 million in recent versions) to negotiate a separate license. The license also forbids using Llama outputs to improve other LLMs. Google’s Gemma family ships under the Gemma Terms of Use, which include a separate acceptable use policy. Both Meta and Google describe these releases as “open source.” OSI does not.Open source AI (OSAID-compliant)A small number of releases are widely discussed as meeting the full OSAID 1.0 bar: source code, weights, and data information, all under terms that grant the four freedoms. OSI’s validation discussions and community examples have pointed to systems like OLMo (Ai2), Pythia (EleutherAI), Amber and CrystalCoder (LLM360), and T5 (Google Research) as closer fits to the OSAID definition, often because of their open training data, code, and weights. DeepSeek’s MIT-licensed releases and several Qwen variants under Apache 2.0 are license-aligned, but their training-data disclosure is partial, so they should not be treated as clear OSAID-compliant examples without further review.Models like Llama and Mixtral are typically discussed as open-weight rather than OSAID-compliant due to license restrictions, missing training data disclosure, or both. Phi-2 and Grok come up in the same conversations for similar reasons. OSI does not currently certify individual models, so treat any “compliant” or “non-compliant” list as a moving discussion rather than an official seal.What Makes an AI Model Open Source? The Four ComponentsOSAID 1.0 expects four artifacts to ship together. Missing any one of them moves a release out of “open source AI” and into one of the categories above.1. Source codeThe training code, data-processing code, and inference code must be available under an OSI-approved license such as Apache 2.0, MIT, or BSD. This is the same bar that has applied to open source software for decades. The new wrinkle: the data-processing scripts matter as much as the model code, because they encode the assumptions baked into the system.2. Model parameters (weights)The trained weights, tokenizer files, and any required configuration files must be downloadable under terms that allow modification and redistribution. A weights file under a non-OSI license is still useful, it is just not open source AI by OSI’s definition.3. Training data informationThis is the OSAID-specific addition. OSI does not require you to ship the dataset; it requires enough information about the dataset that a skilled third party could substantially recreate it. That means data provenance, filtering rules, deduplication steps, tokenization details, mixture ratios, and a clear path to obtain or license the underlying corpora. Meta’s Llama releases do not publish this. Ai2’s OLMo releases do, including the Dolma dataset itself.4. LicenseThe license over the combined release must grant the four freedoms without field-of-use restrictions, downstream user limits, or output restrictions. Apache 2.0, MIT, and BSD-3 satisfy this. The Llama Community License, Gemma Terms of Use, RAIL licenses, and the OpenRAIL-M family do not.Notable Open Source AI Models in 2026This list focuses on widely deployed models. The OSAID-aligned column reflects community and OSI-led discussion of each release’s license and data-information posture against OSAID 1.0, not a formal certification.            Model family      Maintainer      License      OSAID-aligned?                  OLMo 2      Allen Institute for AI (Ai2)      Apache 2.0      Commonly cited as a close fit              Pythia      EleutherAI      Apache 2.0      Commonly cited as a close fit              Amber, CrystalCoder      LLM360      Apache 2.0      Commonly cited as a close fit              DeepSeek (recent releases, including R1)      DeepSeek      MIT      License-aligned; data info partial              Qwen family (most recent variants)      Alibaba      Apache 2.0 (most)      License-aligned; data info partial              Mistral 7B, Mixtral 8x7B / 8x22B      Mistral AI      Apache 2.0      Typically discussed as open-weight; data info partial              Mistral research-licensed releases      Mistral AI      Mistral Research License      No, restricted              Llama family (recent versions)      Meta      Llama Community License      Typically discussed as open-weight, not OSAID-aligned              Phi family      Microsoft      MIT      License-aligned; data info partial              Gemma family      Google      Gemma Terms of Use      No, usage restrictions              Granite (recent versions)      IBM      Apache 2.0      License-aligned; IBM publishes training detail              Falcon family      TII      TII Falcon License 2.0      Discussed as a candidate that would fit with a different license              BLOOM      BigScience      OpenRAIL-M      Discussed as a candidate that would fit with a different license              StarCoder2      BigCode      BigCode OpenRAIL-M      Discussed as a candidate that would fit with a different license              T5      Google Research      Apache 2.0      Commonly cited as a close fit      Two notes. OSI has stated it does not certify individual AI systems; it stewards the legal definition. So entries in the OSAID column reflect how each release is discussed against OSAID 1.0, not an official pass or fail. And model versioning matters: Llama releases have all shipped under the Llama Community License, while DeepSeek and Qwen sit in a middle ground where the license is permissive but training-data information falls short of what OSAID demands.Open Source AI Frameworks and ToolsThe model is only half the story. A production open source AI stack pulls together training frameworks, serving runtimes, orchestration libraries, and tooling for fine-tuning.  PyTorch (Meta, BSD-3) is one of the most widely used training frameworks for foundation models.  TensorFlow and Keras (Google, Apache 2.0) remain widely deployed in tabular ML and on-device inference.  Hugging Face Transformers (Apache 2.0) is a widely used interface for loading open weights, and the Hub is where many releases are distributed.  vLLM (Apache 2.0) is a popular high-throughput inference engine for self-hosted LLMs.  llama.cpp (MIT) and Ollama (MIT) cover local and edge inference on Apple Silicon and consumer GPUs.  LangChain and LlamaIndex orchestrate retrieval-augmented generation and agent workflows.  TRL, PEFT, and LoRA tooling from Hugging Face handle reinforcement learning and parameter-efficient fine-tuning.  Axolotl and Unsloth wrap the fine-tuning loop into a managed-feeling local pipeline.These projects are themselves open source software and predate OSAID. They are not what OSAID governs, OSAID is about the AI system itself: weights, data, and the code that produces them.Benefits of Open Source AIThe case for open source AI is partly philosophical and partly operational. Both sides matter when you are picking a model for a roadmap that runs several years.Auditability. When the weights, training code, and data information are public, third parties can replicate evaluations, probe for bias, and test for safety failures. OSI frames transparency as the precondition for AI safety research. Closed models cannot be audited the same way.Cost predictability. Self-hosted open weights replace per-token vendor pricing with fixed infrastructure cost. For a high-volume internal workload, the inflection point typically arrives between a few hundred million and a few billion tokens per month, depending on context length and model size.Data sovereignty. Running the model on your own infrastructure keeps prompts, completions, and customer data inside your perimeter. This is decisive for regulated industries and for any deployment that touches GDPR-class personal data.Customization. Fine-tuning, distillation, quantization, and merging are all gated on access to weights. Closed models offer constrained variants of these via vendor APIs; open weights let you do them yourself, on your own evaluation criteria.Vendor independence. An open weights release is forkable. If a maintainer shuts down, raises prices, or changes terms, your existing checkpoint keeps working. Closed APIs deprecate on the vendor’s schedule.Polyculture. OSI’s framing, more models from more authors reduces concentration risk in the AI supply chain, is a structural argument rather than a feature checkbox, but it shows up in procurement reviews more often than it used to.Risks, Limitations, and the Open Source AI DebateOpen source AI carries real trade-offs, and the definition itself is contested. An honest answer to “what is open source AI” has to include both sides.The OSAID controversy. OSI’s OSAID 1.0 accepts “data information” rather than the dataset itself. The Software Freedom Conservancy published a critique in October 2024 describing OSAID as eroding the meaning of “open source,” and the Free Software Foundation argues that without the actual training data a downstream user cannot meaningfully modify the system. Meta, separately, maintains that Llama is open source and that the OSAID bar is too narrow. OSI’s counter-position is that the alternative, refusing to release any system whose training data cannot be fully published, would cede the conversation to vendors that release nothing at all. Both sides are documented; pick the one you find most defensible and cite it.Misuse and dual use. Open weights remove a vendor’s ability to enforce safety guardrails at the API boundary. Researchers and regulators have raised concerns about deepfakes, voice cloning, malware generation, and biosecurity scenarios. Most open weight releases ship with acceptable use policies, but those policies are unenforceable once the weights are on a third party’s machine.Regulatory pressure. The EU AI Act, in force since August 2024 and rolling into application through 2026 and 2027, gives “free and open source” general-purpose AI models a partial carve-out from some documentation obligations, but not from the systemic-risk obligations that attach to the largest models. The carve-out’s scope depends on how regulators interpret “open source” in practice, which is exactly why OSAID 1.0 matters to policy work.Operational cost. Open weights are free to download and expensive to run. Self-hosting a 70B-parameter model requires multi-GPU servers, MLOps capacity, monitoring, and a patching discipline that most teams underestimate the first time.License opacity and openwashing. “Open source” branding without OSAID compliance creates procurement risk. Legal review teams that approved a model under one set of assumptions can find themselves out of policy when the vendor changes terms, Meta’s Llama Acceptable Use Policy and the Gemma Terms of Use have both been revised since initial release.Training data provenance. Litigation over the data used to train foundation models is unresolved as of mid-2026. The New York Times v. OpenAI case and the consolidated authors’ suits against multiple model vendors continue to work through the courts. Open releases that publish data provenance are easier to defend; releases that obscure provenance carry an unmeasured liability.How to Use Open Source AI in ProductionProduction deployment falls into three rough patterns: self-hosted, managed inference, and cloud-hosted.  Self-hosted with vLLM, TGI, or Text Generation Inference on your own GPUs. Highest control, highest operational load.  Managed inference through Hugging Face Inference, Together AI, Fireworks, Replicate, or Groq. The vendor runs the weights; you call an API. Per-token pricing, but no infrastructure overhead.  Cloud-hosted through AWS Bedrock, Azure AI Foundry, or Google Vertex AI. The hyperscaler offers a curated catalogue of open weight models alongside their own.Choosing a model means matching license posture, parameter count, hardware, context window, and benchmark performance to your workload. The Hugging Face Open LLM Leaderboard and independent harnesses like LM Eval Harness are the usual starting points. After that, the question is whether retrieval-augmented generation, fine-tuning, or prompt engineering best fits the workload, most production teams end up running all three, layered.Whatever model and hosting path you choose, every AI feature you ship becomes API traffic that needs to be measured, billed, and protected. That is the operational gap we see teams hit most often. At Moesif, we work with platform teams using token-level metering and billing for AI APIs to attribute per-user cost and catch anomalies on top of any model, open source or closed. We see the same primitives reused for observability across MCP servers and for internal chargeback models for AI usage on the finance side.Governance and ComplianceOSAID 1.0 is governed by the OSI Board of Directors, with a published roadmap to revise the definition. OSI has signaled that a 1.1 or 2.0 update is planned through Q4 2026 to address the issues raised during the 1.0 validation phase, most notably the data-information compromise.For regulated deployments, the relevant frameworks are:  EU AI Act. General-purpose AI obligations apply from August 2025; full applicability lands by August 2026. The open source carve-out reduces some documentation duties but does not exempt systemic-risk models.  NIST AI Risk Management Framework (AI RMF 1.0) and the generative AI profile published in 2024. Voluntary in the US, but referenced by federal procurement.  Model cards, datasheets for datasets, and system cards. These are the de facto governance artifacts for any model release, open source or not. OSAID 1.0 effectively raises the bar for what a model card has to disclose.Compliance teams treat OSAID compliance as one input into a license review rather than a substitute for one. The questions that still have to be answered include acceptable use, indemnification, export control posture, and contractual terms with any managed-inference provider.The Future of Open Source AIThree things look likely in the next 18 to 24 months.Performance parity is closing. Recent open-weight releases from DeepSeek and Qwen already match closed frontier models on several reasoning benchmarks at a fraction of inference cost. Expect the gap to narrow further over the next two years, particularly for code, math, and structured reasoning workloads.Smaller, specialized OSAID-compliant releases will multiply. The economics of training a 7B-to-30B model are within reach for academic groups and well-funded startups, and OSAID 1.0 gives those groups a credible label to ship under. Expect more Ai2-style releases, full pipeline, full data, OSI-aligned license, alongside the open weight releases from larger vendors.Regulatory weight will shift toward training data. The litigation around training data, the EU AI Act’s documentation requirements, and OSI’s own roadmap all point at the same pressure point. A future OSAID revision is likely to tighten the data-information requirement; vendors that publish provenance now will be better positioned when it does.Frequently Asked QuestionsWhat does open source mean for AI?Open source AI applies the same idea as open source software, freedoms to use, study, modify, and share, but extends the requirements to model weights and training-data information, not just code. OSI’s OSAID 1.0, published October 28, 2024, is the reference definition most discussions point back to.Is ChatGPT open source AI?No. ChatGPT is a product built on closed OpenAI models. The weights are not published, the training code is not released, and access is API-only.Is open source AI free?The license is free, but running the system is not. Self-hosting an open weights model costs GPU infrastructure, MLOps engineering, and monitoring. Managed inference providers charge per token. Open source AI removes vendor lock-in and per-token pricing for closed models; it does not remove compute cost.What are examples of open source AI?Examples commonly cited as close fits to OSAID 1.0 include OLMo from Ai2, Pythia from EleutherAI, Amber and CrystalCoder from LLM360, and T5 from Google Research. Recent DeepSeek releases and most Qwen variants ship under permissive licenses (MIT, Apache 2.0), but their training-data disclosure is partial ,  they’re license-aligned rather than clear OSAID-compliant examples.What is the difference between open and closed source AI?Closed source AI runs behind a vendor API. You cannot see the weights, modify the model, or move it to your own infrastructure. Open source AI ships the weights, the code, and enough information about the training data that you can run it locally, fine-tune it, and redistribute your modifications.What is the difference between open source AI and open weights?Open weights means the model parameters are downloadable. Open source AI, under OSAID 1.0, requires the weights plus the training code, data information, and a license that grants the four freedoms without field-of-use or downstream restrictions. The Llama, Gemma, and Mistral research-licensed releases are open-weights. None of them meet OSAID 1.0.Which AI models comply with OSAID?OSI does not formally certify individual AI systems, so there is no official compliance list. In OSI’s validation discussions and community write-ups, systems like OLMo (Ai2), Pythia (EleutherAI), Amber and CrystalCoder (LLM360), and T5 (Google Research) are commonly cited as close fits to OSAID 1.0. Systems like Llama, Mixtral, Phi-2, and Grok are typically discussed as open-weight rather than OSAID-aligned due to license restrictions, missing training data disclosure, or both. Treat these lists as a moving discussion, not an official seal.The short version: open source AI, as OSI defines it, means OSAID 1.0 compliance, the four freedoms, applied to weights, code, and data information together. Open weights, open models, and closed AI are useful categories too, just not the same one. Pick the model that fits your license posture and your hardware, plan for the operational cost, and instrument every call so you know what you are spending and who is spending it.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /technical/api-development/Open-Source-AI/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-ai-api-tools-for-developers": {
          "title": "Top 7 Must-Try AI API Tools for Developers",
          "content"	 : "Upon close inspection of the growing adoption of artificial intelligence across different industries, it becomes apparent that a lot it rests on the shoulders of API-powered systems. A survey by McKinsey found that 56% of respondents reported AI adoption in at least one function within their organizations, with significant increase in the use of AI APIs for tasks like natural language processing and computer vision.AI APIs have improved the accessibility to AI and simplified the process of adding AI features to applications. Consequently, it’s also made deployment and maintenance of AI systems easier as well since the AI analytics part and the software code aren’t tightly coupled anymore.In this article, we cover the top tools to quickly integrate functionalities like natural language processing and image recognition into your applications. We start by understanding what artificial intelligence APIs are. Then we discuss the different types of AI APIs. Finally, we briefly explain how you can choose the most perfect tool for your use case and integrate it into your application.Table of Contents  Table of Contents  Key Takeaways  Understanding AI APIs          What is an AI API?      How AI APIs Work      Benefits of Using AI APIs        Types of AI APIs          Computer Vision APIs      Speech Recognition APIs      Natural Language Processing APIs      Translation APIs        Top AI API Tools          Google Cloud AI Products      OpenAI API      IBM Watson AI      Hugging Face APIs      DeepAI      Stream’s Auto Moderation      Imagga        Choosing the Right AI APIs for Your Project          Factors to Consider      Comparing AI API Providers        Integrating AI APIs into Applications          Obtaining API Keys      Setting Up API Calls      Handling API Responses        Future Trends in AI APIs          Advances in Machine Learning Models      Enhanced Natural Language Processing      Integration with IoT and Edge Computing        SummaryKey Takeaways  AI APIs enable developers to incorporate advanced AI functionalities into applications without needing deep AI expertise. For example, you can add natural language processing, image recognition, and speech processing to enhance your apps.  Top AI API tools include Google Cloud AI Products, OpenAI API, IBM Watson AI, Hugging Face APIs, DeepAI, Stream’s Auto Moderation, and Imagga. Each offers various specialized capabilities from text analysis and image recognition to content moderation.  When selecting AI APIs, you must take some key considerations into account. For example, understanding specific project needs, ensuring compatibility with existing systems, evaluating cost-effectiveness, and examining the provider’s reliability and security measures.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding AI APIsAPIs form the foundation of modern software development. They give developers many benefits:  Leveraging existing functions and services. This eliminates the need to build everything from scratch and thus saves significant time and effort.  Facilitating efficient data access and sharing across various platforms. It makes interactions seamless and therefore greatly enhances user experiences.  Supporting interoperability between diverse software applications, making systems more modular and scalable.With AI APIs, developers can incorporate sophisticated AI functionalities into their applications, bypassing the need for in-depth AI or machine learning expertise. AI integration provides applications with advanced functionalities like natural language understanding, image recognition, and speech processing.So, how do AI APIs function? They leverage advanced machine learning models and algorithms to provide intelligent functionalities. These models, including those for natural language processing (NLP), continuously learn and improve from new data. This means that APIs remain accurate and their performances improve over time. This dynamic improvement allows applications to become smarter and more efficient without additional development efforts.What is an AI API?AI APIs, just like traditional APIs, act as intermediaries. They allow you to integrate AI services into software applications without the need to build these services from scratch. With APIs, various software systems can interact with one another and exchange information effortlessly. Modern applications often need to pull in data from multiple sources and provide seamless user experiences. As our digital experiences have been shifting over to AI-powered features, AI APIs have naturally become a critical component of software systems.Integrating an AI API into your application essentially bridges your app to powerful AI models and data processing features. For example, tasks such as text analysis, image recognition, and speech processing. When specialized AI services handle these tasks for your application, you get a more efficient and capable application that can offer advanced features to users.How AI APIs WorkThe secret of AI APIs rests in the advanced technologies they use under the hood. Some of these technologies include:  Natural language processing (NLP). It allows APIs to understand and generate human language by analyzing syntax, semantics, and context.  Text analysis. It allows APIs to analyze and extract information from text.  Sentiment detection. It helps APIs determine the sentiment or emotion in text.  Entity recognition. This allows APIs to identify and classify different entities mentioned in text.AI APIs also act as interfaces to access and utilize machine learning models. Powerful machine learning algorithms drive these ML models that often require large workloads and powerful machines. Having AI/ML models run as services behind API endpoints so that applications can freely call those endpoints marks significant progress toward AI democratization.An AI API gives you a clean interface between the analytics and the apps that use them. You don’t have to worry about running and hosting the models. The end result brings faster development cycles and reusability.Benefits of Using AI APIsAI APIs significantly increase the efficiency of development processes. Developers can focus on building new features and improving user experiences without having to start from scratch. The scalability of AI APIs works as a compliment, handling growing amounts of data and user interactions as a product grows.AI APIs also make applications more flexible and interconnected. Real-world implementations of AI APIs show significant advancements in user experience through automation and personalization. For example:  Companies like Amazon and Netflix utilize AI APIs for recommendation engines. These APIs analyze user behavior, purchase history, and preferences to suggest products or content tailored to individual users.  Platforms like Google Translate leverage AI APIs for real-time translation of text and speech.  AI-powered chatbots, for example in Intercom or Drift, use natural language processing (NLP) APIs to understand customer inquiries and provide instant support. This automates routine tasks, improves response times, and improves customer satisfaction.Types of AI APIsVarious types of AI APIs exist to fulfill specific needs and functionalities. For example:  Computer vision APIs for image recognition.  Speech recognition APIs for converting audio to text.  Natural language processing APIs for understanding and generating human language.  Translation APIs for breaking down linguistic barriers.Each type of AI API offers unique capabilities that you can leverage to enhance your applications in specific ways.Computer Vision APIsComputer vision APIs, including optical character recognition can analyze and understand visual content in images and videos. These APIs can perform tasks such as image classification, object detection, and facial recognition. In applications ranging from security to social media, these features open up a lot of possibilities. For instance, Google Cloud Vision API can classify images into thousands of categories and detect individual objects and faces.Beyond basic image recognition, these APIs also support advanced features like text detection and video analysis. Google’s Video Intelligence API, for example, can recognize objects, places, and actions in both stored and streaming video, making it a versatile tool for video content analysis.Speech Recognition APIsSpeech recognition APIs have several key features:  They convert audio files into text, so applications can understand and respond to spoken language.  They support real-time transcription for live audio streams, making them ideal for applications like live captioning and voice-controlled interfaces.  They offer speaker identification. In scenarios where multiple speakers are present, this can prove very useful.  They provide language detection, allowing applications to identify different languages people speak.These features make Speech recognition APIs a powerful tool for a wide range of applications.Developers can integrate speech recognition APIs to create applications that offer voice control features and support for multiple languages. This allows you to create accessible applications for users with disabilities or in enabling hands-free operation in various contexts.Natural Language Processing APIsNatural language processing (NLP) APIs leverage natural language processing models to analyze and understand human language. These machine learning models can perform a variety of tasks, including sentiment analysis, text classification, and entity recognition. Applications that require deep language understanding often use NLP models through APIs. For instance, OpenAI’s models can translate languages, summarize text, and answer specific questions.NLP APIs also support custom models that you can tailor to specific use cases. Whether you need to analyze customer feedback, automate content moderation, or build conversational agents, NLP APIs provide the tools necessary to extract valuable insights from text data.Translation APIsTranslation APIs bridge linguistic barriers by supporting multiple languages. They offer features like dynamic results and domain-specific customization. They use advanced neural machine translation models to provide high accuracy and fluency for applications that require reliable and nuanced translations.In professional settings, these APIs become invaluable tools for their ability to accurately translate specialized jargon.Top AI API ToolsLet’s explore the top seven AI API tools that every developer should consider.Google Cloud AI ProductsGoogle Cloud offers a suite of AI products that include:  Translation API  Speech-to-Text API  Natural Language API  Video Intelligence APIThese tools, including large language models, provide powerful capabilities such as real-time translation, sentiment analysis, and image classification.Google Cloud’s free AI tools make it accessible to developers to experiment and integrate advanced AI features to their applications.OpenAI APIOpenAI API is known for its general-purpose “text in, text out” interface that supports a wide range of tasks. These include language translation, content generation, and sentiment analysis. The API leverages models from the GPT-3 family. These models possess superior speed and throughput, to generate human-like text based on given prompts. The OpenAI API has become a versatile tool for developers looking to integrate advanced NLP capabilities into their applications.Furthermore, users can ‘program’ the OpenAI API by providing examples of desired outcomes. This allows the API to learn from human feedback and improve its performance.IBM Watson AIIBM Watson AI offers robust tools for developers including natural language processing, speech-to-text and text-to-speech conversion, and creating custom models. Its Natural Language Understanding (NLU) API, for instance, analyzes text to extract valuable insights such as entities, keywords, and sentiment.If your applications require deep text analysis and understanding, you can’t go wrong with IBM Watson.Hugging Face APIsHugging Face’s Inference API supports a variety of NLP tasks, including:  Text generation  Text classification  Named entity recognition  Zero-shot classification  Table question answeringIt also handles advanced tasks using NLP, audio, and computer vision models through API calls, making it a versatile tool for developers.DeepAIDeepAI offers a text-to-image API that allows you to generate images based on text input, supporting 29 different styles. The API supports several programming languages, including:  JavaScript  Python  Ruby  C#This makes it accessible to a wide range of developers.Stream’s Auto ModerationStream has designed its Auto Moderation API for content moderation in real time. It can flag prohibited content, and remind users about community standards. It provides valuable information for moderators to review and take appropriate action, ensuring a safe and respectful community environment.ImaggaImagga’s API supports a wide range of image processing capabilities, such as:  Visual search  Face recognition  Tagging  Cropping  Coloring  Categorizing imagesThis allows users to efficiently utilize various tools for enhancing and analyzing their image content. Developers can try the free version to check for compatibility and response rates before committing to a pricing plan.Choosing the Right AI APIs for Your ProjectTo select the most appropriate AI API for your project, you must carefully consider your unique needs, scalability requirements, and compatibility with pre-existing systems. You also need to evaluate how an AI API can meet your project’s objectives and ensure it can handle future growth.Factors to ConsiderWhen selecting an AI API, consider the following factors:  The specific tasks your project needs to accomplish, such as natural language processing or computer vision  The flexibility of the API when it comes to customization to meet your unique requirements, including the ability to fine-tune and adjust parameters.  The pricing models and financial planning, to effectively manage your budget.You must also consider the reliability and security factors. Ensure the AI API provider offers robust documentation and support for smooth integration and troubleshooting. To protect sensitive project and customer data, carefully evaluate the data security measures as well.Comparing AI API ProvidersTo simplify the process of comparing AI API providers, focus on your key criteria.Cost-effectiveness becomes crucial for projects with tight budgets. Providers like Llama 3.1 (8B) offer competitive pricing, making them a cost-effective choice for various applications.Evaluate how the features of each AI API align with your project’s needs. Depending on specific use cases, each API provides value differently.In addition to cost, consider the unique features and benefits each provider offers. Determine which AI-powered APIs would drive the most value for your niche and audience, prioritizing user safety, convenience, and satisfaction.By aligning the provider’s strengths with your project’s requirements, you can make an informed decision that maximizes both performance and budget.Integrating AI APIs into ApplicationsThe integration process of an AI API into an application depends on the provider. However, the integration process contains some best practices and general steps across a majority of the providers. Let’s discuss those.Obtaining API KeysAPI keys act as unique identifiers for authentication and authorization when accessing AI APIs. Users typically obtain these keys through the service provider’s developer portal or API management console. Developers need to sign up for a developer account and register their project to receive an API key.You must store these keys securely, often using environment variables, to prevent unauthorized access.Setting Up API CallsSetting up API calls involves the following steps:  Make HTTP requests to the API endpoints using the obtained API key.  Include necessary headers and parameters in your requests to authenticate and specify the data required.  Utilize libraries or packages specific to your development environment to simplify the process of making API calls and handling responses.Handling API ResponsesWhen handling API responses, implementing error-handling mechanisms ensures that your application gracefully handles issues such as rate limits and service unavailability. Your end users expect a meaningful error message. As developers, any response error should contain enough log information and an error code that informs exactly what went wrong. This makes debugging fast and easier.For successful API responses, you want to parse the data appropriately to extract the useful bit of information and integrate it into your applications.Future Trends in AI APIsAI APIs, powered by artificial intelligence, have a promising future. Some of the key trends to stay updated on include:  Improved machine learning models.  Enhanced natural language processing.  Advanced computer vision capabilities.  Increased automation and efficiency.  Integration with IoT devices.Advances in Machine Learning ModelsAdvancements in machine learning models have been driving significant improvements in AI APIs. Technologies like Google’s AutoML produce high-quality models for natural language processing tasks. Federated learning, which trains models on multiple devices without sharing data, is gaining traction for maintaining data privacy in sensitive fields like healthcare and finance.TinyML enables machine learning on low-power devices. It’s transforming applications in wearable technology and IoT by enabling real-time processing and decision-making. The focus on ethical AI is also growing, with an emphasis on developing transparent and accountable machine learning models.Enhanced Natural Language ProcessingResearchers and developers are pushing for better quality control and accuracy in natural language processing (NLP). Enhanced NLP capabilities are reducing errors and improving user experiences, making applications smarter and more reliable. We observe a growing number of applications that rely heavily on language understanding and generation. Therefore, advancement in the NLP space can further drive innovation forward.Integration with IoT and Edge ComputingThe integration of AI APIs with IoT and edge computing is revolutionizing real-time decision-making and enhancing privacy. Some benefits of this integration include:  Reducing latency and power consumption in IoT devices.  Enabling immediate data processing and action.  Improving real-time responses in applications such as smart homes, industrial automation, and healthcare.Applications in smart homes, industrial automation, and healthcare, use these features to deliver real-time responses.Federated learning’s decentralized approach is highly compatible with IoT. This ensures data privacy while leveraging diverse datasets. This combination of technologies allows us to build more secure and efficient AI applications that can operate independently of centralized cloud services.SummaryAI APIs are transforming the landscape of software development by providing powerful capabilities in software systems. From computer vision and speech recognition to natural language processing and translation, the types of AI APIs available today offer developers unprecedented opportunities to enhance their projects.However, choosing the right API remains a challenge that we need to sensibly tackle. It not only involves considering project requirements, scalability, and compatibility but also the growing concerns around the ethical use of AI. Nevertheless, we see progress and significant efforts behind ethical AI systems. The adoption of ethical frameworks, ethical boards and committees, and the development of tools that mitigate AI risks have been growing positively.As AI technology continues to evolve, the potential for AI APIs will only grow. So leveraging them to the fullest will become essential for a sustainable future. Developers need to stay informed about the latest tools and trends and be mindful of the impact of the tools they choose.For AI products, it’s very important to identify the metrics relevant to success andthen effectively track them. Monetizing AI APIs also comes with challenges.AI APIs are inherently complex compared to traditional APIs. How they relate to different layers of an organization also follows sophisticated patterns. Therefore, you want a dedicated tool formonetization that addresses those challenges.Moesif has been designed from the ground up to provide a unified platform to address these concerns. It comes with a comprehensive suite of powerful analytics and monitoring tools, along with robust monetization features. With these two, you can grow your AI product with confidence.To try it yourself, sign up today for a free trial, no credit card required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue from your AI APIs?            Monetize your AI APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/AI-API-Tools-For-Developers/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-essential-guide-to-product-analytics-metrics-strategies-tools": {
          "title": "Essential Guide to Product Analytics: Metrics, Strategies & Tools",
          "content"	 : "Product analytics helps you understand how users interact with your product, offering insights that drive growth and improve user experience. In this guide, you’ll learn about essential metrics, strategies, and tools for effective product analytics. By leveraging these insights, businesses can make informed decisions, optimize product features, and enhance overall user satisfaction. Additionally, understanding user behavior through product analytics can reveal hidden opportunities for innovation and growth, allowing companies to stay competitive in a rapidly evolving market. Whether you’re a product manager, marketer, or part of a customer success team, mastering product analytics is crucial for achieving your business objectives.Key Takeaways  Product analytics is essential for understanding real user behavior, aiding in data-driven decision-making, and optimizing product features and user experiences.  Critical product analytics metrics include user engagement, retention rate, and conversion rate, which help businesses measure and enhance product performance and customer satisfaction.  Effective implementation of product analytics involves setting clear objectives, promoting cross-functional collaboration, and ensuring data governance to handle challenges like data silos, data quality issues, and analysis paralysis.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding Product AnalyticsProduct analytics is the practice of examining user engagement with a product or service. It provides insights into user behavior and preferences, offering a glimpse into the actual user experience by revealing how various user segments interact with your product. This data-driven approach helps businesses improve user experience, optimize features, and drive growth.Unlike self-reported data from surveys or interviews, product analytics captures real user behaviors, offering a more accurate and detailed understanding of user interactions. This is crucial for identifying trends, analyzing feature adoption, and understanding customer behavior, which are all key to enhancing the product over time.Product analytics can answer a wide range of questions, from identifying which features are most popular to visualizing user experiences and understanding overall customer behavior. Companies can utilize product analytics platforms to track metrics, analyze key performance indicators (KPIs), and visualize the user journey, rendering it a vital tool for product teams. Implementing a product analytics solution can greatly enhance the decision-making process and optimize product development.Importance of Product AnalyticsProduct analytics is important as it fosters data-driven decision-making. By providing actionable insights into user preferences and behaviors, product analytics tools help businesses collect valuable data around funnel analysis, customer journey maps, and user segments. This information is critical for identifying product features that deliver high value, boosting user engagement and satisfaction.Moreover, product analytics benefits both businesses and customers by improving branding and customer loyalty. By deciding to implement product analytics, companies can instrument their products with analytics events, allowing them to prioritize modifications based on user interactions, thereby aligning the product’s evolution with customer needs and preferences.Key Terms in Product AnalyticsGrasping key terms in product analytics is fundamental for deploying an effective analytics strategy. In-product analytics provides a quantitative understanding of what users are doing with the product, offering insights into user engagement and behavior. Attribution analysis helps identify the customer touchpoints associated with success by interpreting user flow data and analyzing touches in reverse.Funnel analysis plays a vital role in pinpointing new opportunities for enhancement and growth. It shows the conversion of the most important steps of the user journey, helping businesses understand what percent of users stay or churn at each stage. Churn analysis, on the other hand, reveals how many people are sticking with or abandoning the product over time, providing insights into customer loyalty and retention.Essential Product Analytics MetricsBusinesses aiming to optimize their products and services should concentrate on critical product analytics metrics like user engagement, retention rate, and conversion rate. These metrics provide a comprehensive view of how users interact with the product, helping teams identify areas for improvement and growth.Product metrics can be categorized into the following categories:  Acquisition metrics: These metrics help track how users discover the product and become new customers.  Activation metrics: These metrics measure how users engage with the product for the first time and experience its core value.  Engagement metrics: These metrics track how users continue to interact with the product and derive value from it over time.  Retention metrics: These metrics measure how well the product retains its users and keeps them coming back.  Monetization metrics: These metrics help track the revenue generated by the product and its profitability.Tracking these metrics empowers companies to comprehend user behavior more effectively and make well-informed decisions to boost product performance.One example of a valuable analysis that gives insights into customer loyalty is retention analysis. It helps to analyze the rate at which users continue to engage with a product over multiple periods. Similarly, funnel analysis provides visibility into the conversion rates of the most important steps of the user journey, helping to identify drop-off points and optimize the user experience.User EngagementUser engagement metrics measure how often users interact with a product, providing insights into their level of interest and satisfaction. Some key user engagement metrics include:  Monthly active users (MAUs): This metric tracks how frequently users engage with the product on a monthly basis.  Feature usage: This metric measures how often users engage with different features of the product.  Time in product: This metric refers to the average session duration an active user spends with the product, indicating their level of engagement.These metrics can help businesses understand how users are interacting with their product and make informed decisions to improve user engagement.Product stickiness, measured by the ratio of daily active users (DAUs) to monthly active users (MAUs), helps to understand how regularly users are returning to the product. Monitoring event properties such as time, duration, and user demographics enables product teams to gain a more profound understanding of user interactions and pinpoint areas for enhancement.Retention RateRetention rate is a critical metric that indicates the percentage of customers who continue using a product over time. A high retention rate signifies that users consistently find value in the product, reflecting their satisfaction and loyalty. Retention metrics, such as free-to-paid conversions and churn rate, gauge the return rate of users over time, providing insights into customer loyalty and engagement.Retention analysis helps businesses understand user engagement over time, compare metrics across different periods, and analyze feature adoption. Recognizing behaviors that contribute to overall product retention allows companies to devise effective retention strategies, ensuring ongoing user engagement and satisfaction.Conversion RateConversion rate metrics track the effectiveness of turning prospects into active users or paying customers. Conversion analysis involves interpreting data related to specific, desirable actions taken by users, such as signing up for a newsletter or making a purchase. Businesses consider conversions as a fundamental metric for enhancing revenue generation, customer retention rates, and product growth. Hence, it’s vital for companies to concentrate on optimizing their conversion strategies.Funnel analysis helps businesses:  Identify drop-off points in the user journey  Optimize the steps along the customer journey  Improve conversion rates  Concentrate on ‘micro-conversions’  Pinpoint the ‘Aha! moment’  Elevate the overall customer experience  Stimulate revenue growthPractical Applications of Product AnalyticsProduct analytics has a multitude of practical applications, ranging from enriching user experience to tailoring customer journeys and spotting growth opportunities. Businesses can utilize user engagement and behavior data to identify areas for improvement. This can help them optimize their products to drive growth.Analyzing feature adoption and trends provides insights into how customers use different features over time, helping teams prioritize developments and enhancements. Moreover, product analytics can track customer interactions to tailor the user experience and provide personalized content and offers unique to each user.Personalization and increased product appeal lead to greater customer satisfaction and loyalty. Recognizing the actions undertaken by high-value customers, businesses can motivate more users to pursue the same path, thereby unveiling new growth opportunities.Enhancing User ExperienceEnhancing user experience involves analyzing user engagement data to improve product design and functionality. Week 1 engagement, for example, measures user interaction within the first week of trial or paid usage, providing early insights into user satisfaction. Feature adoption rate tracks how often users utilize specific features, helping to identify popular and underused aspects of the product.Heat maps can reveal how users engage with the product, highlighting areas that may need improvement. Blending qualitative data with quantitative data assists teams in understanding how users engage with features, enabling informed decision-making to enrich the user experience.Personalizing Customer JourneysPersonalizing customer journeys involves:  Using behavioral data to create tailored experiences for different user segments  Identifying and segmenting the best customers  Monitoring the performance of ads, emails, and in-app experiences  Delivering personalized messaging that enhances individual user experiencesAutomated personalized messaging for different customer cohorts can further enhance the user experience. Analyzing user feedback and reviews helps understand customer preferences, which can be used to tailor their journey and improve satisfaction.Identifying Growth OpportunitiesIdentifying growth opportunities involves:  Comparing your product to similar offerings on the market to understand competitive positioning and unique selling points  Conducting customer interviews  Using business intelligence toolsBy conducting these activities, businesses can identify trends and areas for improvement, driving product development and growth.Comprehending your competitors and distinguishing your product is crucial for sustaining long-term success. By leveraging product analytics data, companies can make informed decisions to enhance their offerings and capture new market opportunities.Who Benefits from Product Analytics?Product analytics benefits various roles within an organization, from product managers to marketing teams and customer success teams. Product analytics, through its data-driven insights, assists these teams in making superior decisions, enriching user experiences, and propelling product and business growth.UX designers, data science teams, and development team leaders all benefit from a product analytics platform, as it provides a comprehensive view of user interactions and product performance. Growth strategists rely on product analytics to track digital interactions within apps, websites, and devices, enabling them to identify opportunities for improvement and growth.Product ManagersUsing product analytics data, product managers can:  Prioritize enhancements  Curtail technical debt  Understand which features have little to no use  Focus on enhancing high-value aspects of the product  Eliminate unnecessary complexityFunnel analysis allows them to track levels of user drop-off at each step across specific features and pages, providing valuable insights into user behavior and areas for improvement.Marketing TeamsProduct analytics can significantly aid marketing teams by:  Refining campaign targeting and messaging based on user behavior and conversion rates  Informing marketing teams about user journeys  Helping them personalize messaging and tailor strategies to improve user engagement and conversion.Additionally, product analytics allows marketing teams to measure the outcomes of hypotheses related to user adoption and engagement, making informed decisions to enhance marketing efforts and drive better results.Customer Success TeamsWith product analytics, customer success teams can:  Monitor long-term customer engagement and retention metrics  Identify friction points in the user journey  Proactively address issues  Improve the overall user experienceThis enhances their support strategies.Tracking onboarding processes helps reduce support tickets and improve user satisfaction, ensuring that customers receive the support they need to succeed with the product.Top Product Analytics ToolsSeveral top product analytics tools are available to help businesses gain insights into user behavior and product performance. These tools offer features such as advanced data analysis, real-time user insights, and robust visualization tools.MoesifMoesif is a robust product analytics tool designed to provide deep insights into user behavior and product performance. It offers advanced analytics capabilities that allow businesses to track and analyze user interactions in real-time. Moesif is particularly notable for its support of AI APIs and monetization features, making it a versatile tool for modern digital products. Some key features of Moesif are:  Advanced Analytics: Moesif provides detailed analytics on user behavior, helping teams understand how users interact with their products. This includes tracking API usage, monitoring performance, and identifying trends that can drive product improvements.  AI API Support: Moesif excels in supporting AI APIs, allowing businesses to monitor and analyze the performance of their AI-driven services. This is crucial for companies that rely on machine learning and AI technologies, as it provides insights into how these services are utilized and their impact on user experience.  Monetization Features: Moesif offers robust monetization features that help businesses optimize their revenue streams. This includes tracking billing metrics, understanding usage patterns, and identifying opportunities for upselling and cross-selling. By leveraging Moesif’s monetization capabilities, companies can enhance their pricing strategies and maximize revenue.Moesif offers numerous benefits for businesses, including real-time insights into user behavior and API performance, enabling data-driven decisions to be made quickly. By analyzing user interactions and API usage, Moesif helps teams identify areas for improvement, leading to a better user experience and higher customer satisfaction. Its monetization features provide valuable data on billing and usage, helping businesses refine their pricing models and boost revenue. For companies utilizing AI technologies, Moesif’s support for AI API monitoring ensures that these services are performing optimally and meeting user needs. In summary, Moesif is a comprehensive product analytics tool that not only offers deep insights into user behavior and product performance but also supports AI APIs and provides powerful monetization features. This makes it an essential tool for businesses looking to optimize their digital products and drive growth.AmplitudeAmplitude is a comprehensive and powerful product analytics tool designed to track user behavior and product performance. It offers:  Advanced data analysis  Real-time user insights  Robust visualization tools  Powerful segmentation capabilitiesAmplitude’s powerful segmentation capabilities allow you to break down data by various user attributes and behaviors, providing deep insights into user interactions.Key features of Amplitude include its Conversion Drivers, which analyze in-product actions between a starting point and a successful endpoint, indicating which steps are correlated with conversion or drop-off. This helps product teams understand customers and provides insights for proactive retention.MixpanelMixpanel is a product analytics software that helps teams learn from user data and innovate to create winning products. It provides interactive reports and allows building retroactive funnels to analyze conversion rates, offering a comprehensive view of user interactions. Mixpanel’s features include retroactive funnel analysis, which helps product teams track user behavior and product performance, providing valuable insights into how users interact with the product and identifying areas for improvement. Additionally, Mixpanel offers segmentation capabilities to break down data by various user attributes and behaviors, enabling a deeper understanding of user interactions. These features can help product teams make data-driven decisions, optimize their product, and ultimately enhance user satisfaction and engagement.Google AnalyticsGoogle Analytics is a widely-used tool for monitoring website traffic and user behavior. It offers detailed reports around user behavior, customer location, and per-click quotas. Google Analytics is particularly valuable for its integration capabilities, allowing businesses to enhance their analytics and connectivity with other platforms.This tool provides a comprehensive view of user interactions, helping businesses understand how users navigate their websites and where they can optimize for better engagement and conversions.Implementing Product Analytics SuccessfullySuccessfully implementing product analytics involves adopting a strategic approach. It also requires a commitment to cultivating a data-driven company culture. Setting clear objectives, promoting cross-functional collaboration, and ensuring data governance are key components of this process.Setting Clear ObjectivesThe process of setting clear objectives entails utilizing the SMART framework (Specific, Measurable, Achievable, Relevant, Time-bound) to formulate actionable goals and align teams with the overarching company objectives. Clear objectives provide focus, direction, and motivation, essential for prioritizing resources and efforts.Well-defined objectives enable the measurement of progress and success, driving innovation and ensuring that everyone is working towards the same outcomes. Choosing the right product metrics is crucial for forming hypotheses, adjusting variables, and measuring results effectively.Promoting Cross-Functional CollaborationFostering cross-functional collaboration requires:  Enhancing communication  Leveraging collaboration tools to boost teamwork across various departments  Including more people in the process of asking product questions and getting answersThese steps help companies move faster and build better products.Using collaboration tools and centralized databases can enhance teamwork and make data more accessible across departments. Improving data literacy across all employees ensures effective use of product analytics and avoids misinterpretation of data.Ensuring Data GovernanceEstablishing data governance requires the creation and maintenance of a framework for data collection, storage, and upkeep, which guarantees data quality, security, and compliance. Robust data governance practices ensure data quality, security, and compliance with regulations.Organizations need robust data governance processes and the right data management platforms to maintain high data quality for reliable product analytics. Ensuring data security and privacy is especially critical for businesses with strong regulatory requirements.Common Challenges in Product AnalyticsDespite its advantages, product analytics presents its unique challenges such as:  Data silos  Data quality issues  Analysis paralysis  Vanity metricsAddressing these challenges necessitates efficient data management, collaboration, and a concentration on significant metrics.Data SilosData silos occur when different departments within an organization generate and manage their own data without centralized storage or sharing. This lack of technological interconnectivity and company structure issues can prevent the full benefits of product analytics from being realized.Dismantling data silos requires the aggregation of data from various business sectors to establish a single source of truth. Using integrated software solutions that unify customer data in one place can help eliminate data silos and foster regular communication between teams.Data Quality IssuesEnsuring data quality and consistency is crucial, as poor data quality can lead to incorrect or suboptimal business decisions. Data quality refers to the:  Accuracy  Completeness  Consistency  RelevanceOf the data used in data analytics for product analytics.Maintaining high data quality necessitates organizations to implement robust data governance processes and employ appropriate tools. This ensures that the data used for product analytics is reliable and trustworthy.Overcoming Analysis ParalysisAnalysis paralysis occurs when teams become overwhelmed by vast amounts of data, preventing them from focusing on actionable insights and making decisions. One approach to counter analysis paralysis is to concentrate on deriving actionable insights instead of being overwhelmed by the data.Abstracting the implementation helps in the tracking process by preventing double-tracking actions and automating the tracking process, ensuring that product teams can focus on meaningful metrics and insights.SummaryIn summary, product analytics is a powerful tool that provides valuable insights into user behavior and engagement. By focusing on essential metrics like user engagement, retention rate, and conversion rate, businesses can optimize their products and drive growth. Practical applications of product analytics include enhancing user experience, personalizing customer journeys, and identifying growth opportunities.Implementing product analytics successfully requires setting clear objectives, promoting cross-functional collaboration, and ensuring data governance. Despite the challenges, overcoming issues like data silos, data quality, and analysis paralysis can lead to significant benefits for product managers, marketing teams, and customer success teams. Embrace product analytics and unlock the full potential of your digital products.Empower your API management with Moesif’s cutting-edge governance and monetization features. Govern user access and enforce quotas efficiently, while unlocking new revenue streams through flexible, usage-based billing models. Seamless integration ensures your API’s security and financial growth. Start revolutionizing your API strategy today by signing up for Moesif’s free trial, and take the first step towards optimized control and monetization of your digital services.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Essential-Guide-to-Product-Analytics-Metrics-Strategies-Tools/",
          "author": "Dylan",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-methods": {
          "title": "API Methods: GET, POST, PUT, PATCH, DELETE Explained",
          "content"	 : "Whether you are fetching weather updates for a smart home or processing a payment for an online purchase, being able to read and write data over an API is critical. At the heart of that data exchange are API methods: the HTTP verbs that tell the server what you want to do with a resource. GET to read, POST to create, PUT and PATCH to update, DELETE to remove.                Monitor REST APIs With Moesif              14 day free trial. No credit card required.              Try for Free        This guide covers what each API method does, the difference between safe and idempotent methods, how to choose between similar methods like PUT and PATCH, and how method choice changes in 2026 with AI agents that retry and MCP endpoints that need idempotency keys. If you have not yet built a REST API, our REST API tutorial walks through the implementation side with Node.js and Express.What are API methods?API methods are the actions a client can request against a resource exposed by an API. In REST APIs (which are the most common type of API in production), these methods are the standard HTTP verbs: GET, POST, PUT, PATCH, DELETE, HEAD, and OPTIONS. The method is set on the request, and it tells the server whether to retrieve data, create something new, update an existing resource, or remove one.You will sometimes hear “API method” used loosely to mean an endpoint or operation. In strict usage, the method is the verb (GET, POST, etc.) and the endpoint is the URL.HTTP methods at a glance            Method      Use      Has body?      Safe?      Idempotent?                  GET      Retrieve a resource or list      No      Yes      Yes              POST      Create a new resource      Yes      No      No              PUT      Replace an existing resource      Yes      No      Yes              PATCH      Partially update a resource      Yes      No      Sometimes              DELETE      Remove a resource      No      No      Yes              HEAD      Retrieve headers only      No      Yes      Yes              OPTIONS      Discover supported methods      No      Yes      Yes      “Safe” means the method does not modify server state. “Idempotent” means calling the method multiple times has the same effect as calling it once.Safe vs. idempotent methodsThese two characteristics shape almost every method decision.Safe methods (GET, HEAD, OPTIONS) are read-only. They retrieve data without modifying the state of the server, and they should have no side effects. A client, proxy, or browser can call them speculatively without worrying about consequences.Idempotent methods (GET, HEAD, OPTIONS, PUT, DELETE) can be called multiple times without changing the result beyond the initial application. DELETE /users/123 is idempotent: the user is gone after the first call, and additional calls do not delete more users. PUT /users/123 replaces the user with the same data on each call, ending in the same state.POST is the odd one out: it is neither safe nor idempotent. Two identical POST requests create two different resources by default. This is why retry logic and idempotency keys (covered below) matter most for POST.PATCH is conditionally idempotent: it depends on how you implement the patch. JSON Patch and JSON Merge Patch can both be made idempotent, but a custom patch format (“increment counter by 1”) is not.GET, POST, PUT, PATCH, DELETE in depthGETThe GET method retrieves data from a server without modifying it.  Client request: The client sends a GET request to a specific endpoint (for example, /users/123).  Server response: The server processes the request, locates the resource (if it exists), and returns a representation of the resource’s current state in the response body.  Example: A GET request to a /products endpoint might return a list of all available products in JSON format.GETs should never change state. If your endpoint modifies data on a GET, you are violating REST conventions and breaking browser caches, proxies, and prefetch behavior.POSTThe POST method creates new resources on the server.  Client request: The client sends a POST request to a collection endpoint (for example, /orders).  Request body: Contains the data for the new resource.  Server response: The server processes the request, creates the new resource, and usually returns the created resource (with its assigned ID) in the response body, alongside a 201 Created status code and a Location header pointing to the new URL.  Example: A POST request to /users with user details in the body creates a new user account.POST is not idempotent. Two identical POSTs create two separate resources. If you want clients to retry safely, give them an Idempotency-Key header to send (see the 2026 section below).PUTThe PUT method replaces an existing resource with the data provided.  Client request: The client sends a PUT request to a specific resource endpoint (for example, /products/456).  Request body: The complete representation of the updated resource.  Server response: The server replaces the existing resource with the new data and returns the updated resource or a success status code.  Example: A PUT request to /users/123 with updated user information overwrites the existing user data entirely.PUT is idempotent. Calling it twice with the same body leaves the resource in the same state.PATCHThe PATCH method partially updates an existing resource. Instead of replacing the whole thing (which is what PUT does), PATCH lets clients modify specific fields.  Client request: The client sends a PATCH request to a resource endpoint (for example, /users/123).  Request body: A set of instructions describing the changes. Most APIs use JSON Merge Patch (the partial object) or JSON Patch (an explicit operations array).  Server response: The server applies the changes and returns the updated resource or a success code.  Example: A PATCH request to /users/123 containing { "email": "new@example.com" } updates only the email field, leaving other fields untouched.PATCH is the right choice when the client only wants to change a subset of fields. Use PUT only when the client is sending the entire resource.DELETEThe DELETE method removes a resource from the server.  Client request: The client sends a DELETE request to a specific resource endpoint (for example, /users/123).  Server response: The server removes the resource and typically returns 204 No Content (empty body) or 200 OK with a confirmation.  Example: A DELETE request to /orders/789 removes the order.DELETE is idempotent: the second call returns the same outcome as the first, because the resource is already gone. Many APIs return 404 Not Found on subsequent calls; some return 204 regardless. Either is acceptable, but pick one and document it. Our HTTP status code reference covers the full set of response codes you will return from these methods.HEAD and OPTIONSTwo methods you will see less often but should know.HEAD is identical to GET but returns only the headers, not the body. Useful for checking whether a resource exists or has changed, without downloading the full payload. Common for HTTP caching and link checkers.OPTIONS asks the server what HTTP methods it supports on a given endpoint. Browsers send OPTIONS requests automatically as part of CORS preflight checks. You rarely call OPTIONS manually in your application code.How to choose the right API methodThe decision rules that come up most often:  Reading data? Use GET. Always. If the request modifies state, you picked the wrong verb.  Creating a new resource the server assigns an ID to? POST to the collection endpoint (POST /orders).  Creating or updating a resource at a URL you know? PUT to the specific resource (PUT /users/123).  Changing some fields on an existing resource? PATCH.  Removing a resource? DELETE.  Performing an action that does not fit CRUD (search, login, send-email, run-job)? POST to a named endpoint (POST /searches, POST /sessions). REST purists will tell you to model it as a resource; in practice, naming the endpoint after the action is fine for non-CRUD operations.The mistake people make most often is using POST for everything. POST works for any operation the server understands, but it gives up the safety guarantees and caching behavior the more specific verbs provide. Following these defaults is one of the core API design principles that pays off across the lifetime of an API.API methods beyond RESTThe HTTP verbs above are the REST vocabulary. A few other API styles use different conventions.  SOAP APIs rely on XML for data exchange and use a strict set of rules and protocols. SOAP APIs are still common in enterprise applications where security and reliability are paramount.  GraphQL APIs typically expose a single POST endpoint and use a query language in the request body. There is no per-method semantic distinction; the query itself describes what to do.  Webhook APIs invert the pattern: instead of you calling an API, the API calls you. The provider sends a POST to a URL you provide when an event occurs.  RPC APIs (including gRPC) let clients call remote functions. The “method” in RPC is the function name, not an HTTP verb.Most public APIs are REST, so the HTTP methods above are what you will use 95% of the time.API methods in 2026: idempotency, agents, and MCPA few wrinkles that did not exist five years ago.Idempotency keys for POST. When a client (especially an AI agent or a serverless function) retries a request, you do not want a second resource created. The convention that emerged from Stripe’s API and is now widely used across payments and infrastructure APIs: clients send an Idempotency-Key: <unique-string> header on POST requests, and the server returns the same response on retry. This makes POST safe to retry without becoming truly idempotent at the protocol level. An IETF draft is working toward standardizing the header.AI agent retries. AI agents retry aggressively when they get errors. Two practical defenses: accept Idempotency-Key on every write endpoint, and return 429 Too Many Requests with a Retry-After header when you are overloaded, rather than 5xx. Agents respect 429 better than 5xx.MCP-exposed APIs use the same methods. When you expose your REST API as an MCP server (which the WSO2 AI Gateway can auto-generate from any OpenAPI spec), the same HTTP methods still apply. The agent runtime translates the model’s intent into the same GET, POST, PUT, PATCH, DELETE calls you already support. Designing for idempotency and clean status codes pays off twice: for human integrators and for agent traffic.Next stepsAPI methods are the vocabulary of REST. Picking the right verb makes your API readable to every HTTP client in the world and unlocks decades of tooling. Once your methods are right, the next question is whether the codes you return match what your consumers see in practice. Start a 14-day Moesif free trial to watch method-by-method usage on your own API in real time. No credit card required.Frequently asked questionsWhat are the main API methods? GET (read), POST (create), PUT (replace), PATCH (partial update), and DELETE (remove). HEAD and OPTIONS exist for HTTP metadata and CORS but are less common in application code.What is the difference between PUT and PATCH? PUT replaces the entire resource with the request body. PATCH modifies specific fields without touching the rest. Use PATCH when you only want to change a subset of fields; use PUT when you are sending the full resource.What is an idempotent API method? A method that can be called multiple times with the same effect as calling it once. GET, HEAD, OPTIONS, PUT, and DELETE are idempotent. POST is not.Can you use POST instead of PUT or PATCH? Technically yes, but you lose the semantic benefits. PUT and PATCH tell HTTP clients, proxies, and tooling what your endpoint actually does. Using POST for everything works but throws away signal.Do API methods only apply to REST APIs? HTTP methods (GET, POST, etc.) are an HTTP concept. REST APIs use them with specific meanings. GraphQL, gRPC, and SOAP have different conventions for what an “API method” looks like.What HTTP method should I use for search? POST to a /searches endpoint is fine when the search parameters are too large for a query string, or when the search creates a saved record. Use GET with query parameters when the search is simple and stateless.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /technical/api-methods/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-the-cost-of-building-ai-understanding-ai-cost-analysis": {
          "title": "The Cost of Building AI: Understanding AI Cost Analysis",
          "content"	 : "Technology generally comes with a cost. At scale, this cost can become exponential and quickly be challenging to manage. Artificial intelligence (AI) has come to the forefront of almost every vertical, finding ways to somehow improve the lives of nearly everyone on the planet. Everyone and every company is either using AI or building it.However, as with any technology, building and deploying AI systems often comes with significant costs. The underlying hardware and expertise that make cutting-edge AI platforms possible is quite pricey. With so much at stake, understanding the intricacies of AI cost analysis is crucial for organizations to make informed decisions and ensure a positive return on investment (ROI) for their AI initiatives. This blog will look at the key factors contributing to the cost of building AI, explore cost-saving strategies, and outline pricing models for different AI solutions. We’ll also discuss essential steps for successful AI implementation and how tools like Moesif can assist in analyzing AI costs. Let’s begin by understanding the costs associated with AI.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding AI CostsThe cost of building and implementing AI systems can vary significantly. Are you leveraging an existing AI platform to power your application’s AI functionality or building something from the ground up? Are you using pre-trained models or training them independently with a large data set? These factors lead to a better understanding of the initial costs of building AI applications and the long-term support costs. Let’s dig further into the critical elements that influence these costs:Factors Affecting AI CostSome factors that affect AI cost are generic to all technology initiatives. Others are hyper-specific to AI solutions. Here are a few factors to focus on when considering the costs of an AI project:Type of AI Software or SolutionDepending on the type of AI solution you want to build and how you build it will have a massive impact on the cost. This factor encompasses some of the other factors below, such as expertise, since creating a custom solution will require more expertise than integrating with a pre-built solution. Overall, this likely has the most considerable impact on the cost of your project, especially in the beginning stages. The two routes most companies take include:  Building a Custom AI Solution: Developing tailor-made AI solutions to address specific business needs typically involves higher costs due to extensive research, development, and customization. These solutions offer maximum flexibility and control but require significant investment in time and resources.  Using a Pre-built AI Solution: Off-the-shelf AI solutions, like chatbots or sentiment analysis tools, often come with lower upfront costs. However, they may lack the customization options and scalability required for complex AI projects.Project ComplexityThis factor examines the scope and scale of your AI platform. Does it require a lot of data from different integrations or sources, or is the functionality limited? If the project needs to scale to massive volumes of users and data, this can also affect costs as operations ramp up. Here are a few particulars to focus on when project complexity is taken into consideration:  Features and Integrations: The more features and integrations an AI system requires, the more complex and costly it becomes. Each additional functionality demands development effort, testing, and ongoing maintenance.  Scalability: Scalability becomes a significant cost factor if the AI system needs to handle increasing workloads or expand to new areas. Building scalable AI infrastructure often involves investment in additional hardware, software, and cloud resources.Expertise of Data Scientists and DevelopersUnsurprisingly, AI and ML developers are among the most costly to hire in the current market. The more expertise they have, the higher the price tag. Do you need an entire team of AI experts? It’s time to open up the pocketbook. Here are a few factors to consider when looking at the cost impact of the team you need to build and maintain your AI application:  Skill Level: Experienced data scientists and AI developers with specialized skills often require higher salaries. Their expertise is crucial for complex projects but can contribute to increased labor costs.  Team Size: The size of the development team directly impacts project costs. Larger teams may accelerate development but also increase the amount spent on payroll.Data RequirementsAI models require data, and generally, lots of it. Moving data around, getting it into the proper format, and all the infrastructure costs for these data pipelines can be expensive. Although data infrastructure costs have come down quite a bit, the increase in volume and velocity of data sometimes makes any savings a wash. Here are a few ways that the data side of AI can impact costs:  Data Collection and Annotation: Acquiring and labeling large volumes of high-quality data for training AI models can be expensive, especially if manual annotation or specialized data sources are required.  Data Storage and Management: Storing and managing massive datasets can incur significant costs, particularly if cloud storage or specialized data management tools are required.Algorithm Accuracy and FluencySome models are more expensive to run than others. Some are more expensive to run, others to train, etc. Depending on the algorithms and models you require for your AI platform, cost will likely be affected. When it comes to algorithm accuracy and fluency, here are some ways this can influence cost:  Model Training: Developing AI models with high accuracy and fluency often involves iterative training processes, consuming computational resources and time.  Model Optimization: Fine-tuning AI models for optimal performance can require additional development effort, further impacting project costs.Of course, factors beyond these affect cost, but this covers the big-ticket items you should consider when beginning your AI cost analysis. Your next question is how you can save money when considering these factors. That’s precisely what’s next on our agenda.Cost Savings StrategiesWhile building AI systems can be expensive, organizations can look to various strategies to optimize costs. Many general cost-saving methods work well in AI projects, but there are also a few AI-specific cost-saving strategies to consider, such as using pre-trained models. Let’s look at four ways to reduce the costs of AI projects.  Thorough Planning: As with all software projects, careful planning is essential to minimize unexpected expenses. Before starting development, define clear project goals, identify potential risks, and create a detailed budget.  Minimum Viable Product (MVP): Another fundamental aspect of software cost savings is starting with a smaller, functional version of your AI solution (an MVP). This can help test its feasibility and gather feedback so you aren’t wasting money on development that doesn’t matter to users. This approach helps validate the concept before investing in full-scale development.  Pre-trained AI Models: Whenever possible, utilize pre-trained AI models as a foundation for your project. This can significantly reduce development time and costs compared to building models from scratch. Many companies offer pre-trained models that are incredibly efficient for most use cases.  Iterative Development: Embrace an iterative development process where you continuously refine and improve your AI solution based on user feedback and real-world data. This lets you identify and address issues early, avoiding costly rework later.By understanding the factors influencing AI costs and implementing as many cost-saving strategies as possible, organizations can navigate the complexities of AI development and maximize their return on investment.AI System Cost FactorsUnderstanding an AI system’s components and associated costs is critical for keeping projects fiscally efficient. Not considering the labor costs associated with designing and building AI systems, data and infrastructure are the most significant costs within an AI system. When budgeting for building and supporting an AI system, it’s essential to consider the costs associated with both data and infrastructure:Data-Related CostsAI is not possible without massive amounts of data. Unfortunately, not all data is AI-ready since many systems require data to be in specific formats and stored on specific platforms. This can get quite expensive to ramp up and maintain. Here are a few data-related costs that you should factor into the potential cost of your AI system:  Data Collection and Annotation: Gathering the necessary data for training AI models can be a significant expense. This includes collecting raw data from various sources, cleaning it to remove errors and inconsistencies, and annotating it (labeling it for machine learning).  Data Storage and Management: Storing and managing large datasets requires extensive infrastructure, which can incur costs for cloud storage, database management systems, and data security measures.  Data Quality and Preprocessing: Ensuring high-quality data is crucial for training accurate AI models since models are only as good as the data they are trained on. This involves preprocessing data to remove noise, handling missing values, and standardizing formats, which can require specialized tools and expertise to implement.  Historical Data Costs: Accessing and utilizing historical data for training AI models may involve licensing fees or subscriptions to data providers.Infrastructure CostsAs Nvidia has learned, infrastructure costs for creating AI systems are high. These platforms require a massive amount of heavily-optimized computing power. While companies developing the tech for this are seeing record profits, those using the tech are seeing significant bills, whether from self-hosted servers or soaring cloud computing costs. Here are some areas to consider when assessing infrastructure costs:  Hardware Costs: AI model training often requires powerful hardware, particularly graphics processing units (GPUs), which can be expensive to purchase and maintain. Specialized hardware like Tensor Processing Units (TPUs) may also be necessary for specific AI workloads.  Software Costs: Developing and deploying AI systems require specialized software tools and frameworks, such as TensorFlow or PyTorch, which may have licensing or subscription costs.  Cloud Infrastructure Costs: Many organizations leverage AWS, Microsoft Azure, or Google Cloud Platform for AI development and deployment in the cloud. These platforms offer scalable infrastructure and pay-as-you-go pricing models, but the costs can quickly increase depending on usage. Thankfully, if you understand where user demand is heading, its relatively easy to forecast spend on these platforms.  IT Infrastructure Costs: If your AI system is deployed on-premises, you’ll need to factor in the costs of servers, storage, networking equipment, and other IT infrastructure components.To accurately forecast the costs of your AI system, you must carefully consider these data and infrastructure cost factors. These are critical components in helping organizations develop a realistic budget for their AI initiatives, ensuring they have the resources necessary to build and deploy successful AI systems.Understanding the Total Cost of Ownership of AI SystemsIn addition to the factors mentioned earlier, understanding the cost breakdown of different components involved in building AI systems is crucial for effective budgeting and resource allocation. Often, you’ll hear a business talk about the Total Cost of Ownership, or TCO, of an application they are building or buying. Looking at TCO requires looking at every possible expense a system incurs when building, deploying, and maintaining it. When we are talking about building AI systems, we want to factor TCO into our cost analysis to ensure that revenue is generated and not lost by adding AI.Let’s look further at the typical cost elements that should be factored into the TCO of an AI application:Personnel CostsThese are the dollars you put out in terms of salaries, contractors, and consultants. Generally, these costs are highest as a platform is being developed versus when it is deployed and goes into support mode. The ebbs and flows of these costs should be calculated based on the application roadmap. For AI applications, here are the buckets of personnel to be aware of:  Data Scientists and AI Engineers: These experts play a pivotal role in developing and fine-tuning AI models. Their salaries and associated benefits can constitute a significant portion of the budget, especially for complex projects.  Software Developers: Skilled software developers are needed to create the infrastructure and applications that interact with AI models and integrate them into existing systems.  Project Managers and Business Analysts: These professionals oversee project planning and execution and ensure alignment with business goals, contributing to project management costs.Hardware and Software CostsAs mentioned previously, hardware and software costs (infrastructure) can significantly contribute to the cost of developing and maintaining AI applications. These costs include:  Computing Resources: Powerful hardware, such as GPUs or specialized AI accelerators, is essential for training and running AI models. The cost of acquiring and maintaining this hardware can be substantial, especially as the demand for this hardware increases.  Software Licenses and Subscriptions: AI development and deployment often require licenses for specialized software frameworks, libraries, and tools. Additionally, ongoing subscriptions to cloud services or AI platforms may be necessary for your AI system.Data CostsAI needs data, and a lot of it. Acquiring and storing this data can cost a lot of money, especially for real-time AI systems that require breakneck read/write speeds and more specialized database technologies, such as graph or vector databases. Not to mention the ETL costs of getting data from one place to another in the correct format. Here are the areas to consider when budgeting this out:  Data Acquisition: Gathering and cleaning relevant data can be costly, especially when acquiring external datasets or manual annotation.  Data Storage: Storing and managing large volumes of data for AI training and inference requires cutting-edge and potentially expensive storage solutions.Operational CostsOnce deployed, AI systems will require ongoing operational budgets to ensure the applications meet user demands. This may impact the areas we already touched on, such as updating a database to the latest version or deploying a monitoring solution on its own infrastructure. These are the areas to take into consideration when budgeting for the current and future operational costs:  Maintenance and Updates: AI systems need ongoing updates to ensure optimal performance, security, and compliance with evolving regulations.  Monitoring and Support: Real-time monitoring and technical support may be required to address any issues that arise during AI system operation.By understanding these cost components, organizations can create a comprehensive budget for their AI initiatives, appropriately allocate resources, and make informed decisions throughout the development and deployment to keep the project on budget.AI Cost Analysis ExamplePutting this into practice, let’s create a simple AI cost analysis for a fictitious Text-to-Speech (TTS) API. Remember, this is for demonstration purposes only, so the numbers I’m using may vary depending on what part of the world you are building this in and the scope of what you are creating. Here’s what a simple AI cost analysis would look like for such an API:Project Name: AI-Powered Text-to-Voice APIGoal: To develop and deploy a high-quality, customizable Text-to-Voice API that converts written text into natural-sounding speech for various applications.Timeline: 9 monthsCost Breakdown:  Personnel Costs:  Machine Learning Engineer (2 FTEs): $220,000 annual salary (combined)  Software Engineer (2 FTEs): $180,000 annual salary (combined)  Linguistics Expert (0.5 FTE): $50,000 annual salary  Project Manager (0.5 FTE): $60,000 annual salary  Total Personnel Costs: $460,000  Hardware and Software Costs:  High-Performance Servers (2): $20,000 (one-time cost)  GPUs for Model Training (4): $16,000 (one-time cost)  Deep Learning Frameworks (TensorFlow, PyTorch, etc.): $5,000 annual subscription  Cloud Computing Resources (AWS/Azure): $20,000 estimated annual cost  API Development and Management Tools: $8,000 annual subscription  Total Hardware and Software Costs: $69,000 (first year)  Data Costs:  Voice Dataset Acquisition and Licensing: $30,000 (one-time cost)  Text Corpus for Training (Public Domain/Scraped): $0  Data Cleaning and Preprocessing: $8,000 (estimated)  Data Storage (Cloud-based): $4,000 annual cost  Total Data Costs: $42,000 (first year)  Operational Costs:  Ongoing Model Refinement and Updates: $15,000 estimated annual cost  API Monitoring, Scaling, and Security: $12,000 estimated annual cost  Customer Support and Documentation: $6,000 estimated annual cost  Total Operational Costs: $33,000 (annual)Total Estimated Project Costs:  Year 1: $574,000  Year 2 Onwards: $33,000 (annual maintenance and operational costs)Additional Considerations:  This cost breakdown is simplified and may not capture all potential expenses.  Voice dataset acquisition and licensing can be a significant variable cost depending on the quality, language coverage, and desired accents.  The costs of cloud computing resources can fluctuate based on usage and scaling needs.  Consider potential revenue streams through API usage fees or subscription models.Expected Benefits (ROI):  Potential for high demand for TTS APIs across various industries (e.g., accessibility, education, entertainment).  Recurring revenue from API usage or subscriptions.  Competitive advantage by offering high-quality, customizable TTS voices.  Reduced development costs for businesses that would otherwise need to build their own TTS solutions.For ROI, we would likely want to dig in a bit further with revenue projections, but this is more involved than what we will get into in this blog. That being said, by carefully analyzing costs and potential revenue streams, organizations can make informed decisions about developing applications and APIs, such as an AI-powered Text-to-Voice API, and position themselves for success in the current AI goldrush.Using Moesif for AI Cost AnalysisManaging and optimizing AI costs can be complex, but tools like Moesif offer valuable insights that can help determine your AI solution’s actual cost and revenue. Moesif is an API analytics platform that provides insights into AI system usage, performance, and associated costs. Here’s how Moesif can assist in AI cost analysis:  Detailed Usage Tracking: Moesif allows you to track API calls made to your AI systems, providing granular data on usage patterns, peak hours, and resource consumption (if it’s included as part of the API request or response). This data helps identify areas where optimization is possible, potentially helping to bring down AI API costs.  Cost Allocation: Moesif enables accurate cost allocation by associating API calls with specific users and companies. This transparency helps identify cost drivers and make informed resource allocation and feature development decisions.  Anomaly Detection: Moesif can detect unusual spikes in API usage or factors leading to unexpected cost increases, such as an increase in outgoing API calls to services you are paying for. By alerting you to potential issues or inefficiencies in your AI systems, Moesif can help teams remedy issues in real-time.  Performance Monitoring: By monitoring API response times and error rates, Moesif helps ensure that your AI systems perform optimally and deliver value to users.  Customizable Dashboards: Moesif offers customizable dashboards to visualize AI usage and cost data in a way that aligns with your organization’s or project’s specific needs and goals.  Monetization Capabilities: AI platforms need to drive revenue. With Moesif, organizations can enable usage-based monetization of their AI apps with support for pre-paid and post-paid billing schemes. AI usage can be aggregated at a fine-grained level and then passed to popular billing providers such as Stripe, Chargebee, and many more.By leveraging Moesif’s analytics and monetization capabilities, organizations can gain a deeper understanding of their AI costs, identify areas for optimization, make data-driven decisions to improve the efficiency and ROI of their AI investments, and drive revenue.ConclusionBuilding and implementing AI systems involves complex costs, ranging from data acquisition and infrastructure to model development and ongoing maintenance. Understanding these cost factors and employing effective strategies to optimize expenses is crucial for organizations aiming to harness the power of AI while ensuring a positive return on investment.Organizations can reduce costs and accelerate time-to-market by carefully planning projects, starting with minimum viable products, leveraging pre-trained models, and embracing iterative development. Tools like Moesif can provide valuable insights into AI usage, performance, and costs, enabling data-driven decisions and continuous improvement.Ready to take control of your AI costs and maximize the value and revenue of your AI investments? Sign up today to discover how Moesif can empower your organization as a key part of your AI cost analysis and optimization efforts. Explore our features and see how we can help you unlock AI’s full potential while keeping costs in check.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/The-Cost-of-Building-AI-Understanding-AI-Cost-Analysis/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-the-challenges-of-ai-api-monetization": {
          "title": "The Challenges of AI API Monetization",
          "content"	 : "  Last month, at Collision Conf 2024, Moesif’s CEO, Derric Gilling, discussed the challenges of monetizing APIs. Based on this talk, we’ve put together some key points to consider when monetizing AI APIs.Building and consuming Application Programming Interfaces (APIs) has become an essential skill for developers. APIs are the glue that enables seamless integration and interaction between different software systems, including those that leverage AI. As the demand for artificial intelligence (AI) grows, the monetization of AI APIs has emerged as a critical concern for businesses looking to capitalize on their technological investments. Almost all AI functionality that companies build is exposed as an API. This means that effectively monetizing these APIs is critical for revenue generation.AI APIs present unique challenges compared to traditional APIs. Their complexity, high operational costs, and varied usage patterns mean that organizations and consumers must deal with sophisticated pricing models and complex management strategies. This blog explores the critical challenges associated with AI API monetization and how businesses can move past these potential hurdles to inch closer to sustainable AI API growth and profitability. Let’s begin by taking a closer look at AI API monetization and what it is.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding AI API MonetizationAt a high level, API monetization involves selling functionality through one or more APIs to generate revenue. In some instances, companies build APIs specifically for this reason, offering APIs as a product. In other cases, companies have built APIs for their own usage or applications and then have decided that other companies may also pay for this functionality. When it comes to AI APIs, both of these scenarios ring true.I often think about OpenAI, which offers ChatGPT as a standalone UI service for consumers. However, the underlying tech, the GPT model, is also exposed via API so that other organizations can leverage this functionality. Users who consume the OpenAI APIs are charged based on tokens for their API usage. This is AI API monetization in action.Of course, there are various approaches to monetizing APIs. Depending on your AI API’s function, internal costs, and other factors, specific monetization strategies might make more sense than others.Different Monetization Models for AI APIsEach monetization model for AI APIs has its benefits and challenges. Here’s a quick overview of the different monetization models and the specific challenges that AI APIs might face when implementing them.Subscription-Based Pricing  Users pay a recurring fee to access the API.  Pros: Predictable revenue stream, easier to manage.  Cons: May not reflect actual usage, potentially overpricing or underpricing for different users.Usage-Based Billing  Users are charged based on their actual usage of the API.  Pros: Aligns cost with usage, which is fair for both provider and user.  Cons: Revenue can be variable and more challenging to predict.Key Challenges in AI API MonetizationAPI monetization can be challenging. It becomes even more complicated once you throw in the factors of metering AI API usage and the nature of AI APIs. Let’s look at four key challenges to look out for when monetizing AI APIs:Varying Usage VolumesAI API usage can vary significantly among different users and applications. Some might use the API sporadically, while others may have high and continuous demand. This variation makes it challenging to predict revenue and to set a pricing strategy that accommodates all users reasonably. Usage-based billing can help address this by ensuring that users pay for what they use, but it requires sophisticated metering and billing infrastructure to manage effectively.High Inference CostsAI APIs incur high inference costs, especially those involving complex models and large datasets. These costs can be a significant portion of the total operating expenses, affecting the API’s overall profitability. Providers must balance these costs with competitive pricing to attract and retain users without eroding profit margins. High inference costs also necessitate careful monitoring and optimization to ensure efficiency.Risk of AbuseThere is a potential for misuse or abuse of AI APIs, where users might exceed reasonable usage limits or exploit the API in unintended ways. Implementing safeguards such as rate limiting, quotas, and user authentication is essential to mitigate these risks and protect both the API and its legitimate users.Complex Input VariablesAI APIs often have multiple input variables that affect the cost of providing the service. These can include the number of input tokens, output tokens, context size, and other factors. Managing and accurately metering these variables is complex but crucial for fair and effective billing. Providers need robust systems to track these inputs and convert them into understandable and billable units for users.These challenges underscore the need for a thoughtful approach to AI API monetization. By understanding and addressing these issues, organizations can develop effective strategies that ensure sustainable revenue while delivering value to their customers. The following section will examine the benefits of usage-based billing and how it can be used to overcome some of these challenges.Why Usage-Based Billing for AI APIs?Usage-based billing has emerged as a highly effective model for monetizing AI APIs, addressing several of the challenges associated with this space. Most major AI API providers are already using this model, such as OpenAI charging for usage based on the tokens that users have consumed in their API requests. This model is particularly well-suited for AI APIs.Benefits of Usage-Based BillingUsage-based billing brings many benefits to AI API monetization compared to other methods. Although usage-based billing can be more challenging to implement than subscription-based or flat-rate billing, AI costs per API call can vary drastically. Here’s how usage-based billing can help tackle some of the challenges:Supports a Variety of Usage VolumesAI APIs often serve a diverse range of users with varying needs. Usage-based billing allows for flexibility, accommodating light and heavy users without imposing a one-size-fits-all pricing structure. This model aligns costs directly with the level of service consumed, making it a fairer approach for providers and users.Leverages Prepaid CreditsPrepaid credits allow users to pay for a certain amount of API usage upfront. This approach helps providers manage cash flow and reduce payment risk. By requiring users to purchase credits in advance, businesses can secure revenue before service delivery, which can be particularly useful for managing operational costs associated with high inference.Enforces Quotas and Balance LimitsUsage-based billing can include quotas and balance limits to prevent overuse and manage resources effectively. Quotas help control the maximum usage allowed within a specific period, while balance limits prevent users from exceeding their prepaid credits, thereby avoiding unexpected costs.Metering of Input and Output TokensFor AI APIs, billing based on input tokens, output tokens, and context size provides a granular and transparent pricing model. This level of detail helps accurately reflect the costs associated with providing the service. It ensures that users are billed for the amount of resources they consume, which can be more fair and precise than flat-rate pricing.Flexibility and Revenue ManagementUsage-based billing offers greater flexibility compared to fixed or subscription-based models. Providers can adapt their pricing strategies to match better the evolving needs of their users and the costs of providing the API. It also facilitates easier revenue recognition and management. By charging based on actual usage, providers can more accurately forecast revenue and adjust pricing strategies in response to market changes and usage patterns.Usage-based billing is a powerful tool for AI API monetization, addressing many challenges associated with varying usage volumes, high inference costs, and complex input variables. By aligning pricing with actual usage, this model provides a fair and transparent approach to billing, supports a range of user needs, and helps manage operational risks effectively. In the next section, we will explore how to align pricing to customer value and further implement strategies to optimize AI API monetization.Aligning Pricing to Customer ValueOne of the most critical aspects of AI API monetization is ensuring that the pricing model reflects the value delivered to customers. By aligning pricing with customer value, businesses can foster stronger user relationships, improve customer satisfaction, and drive sustainable growth. Here’s how to approach aligning pricing to customer value when it comes to AI APIs:Aligning Pricing with Transaction VolumeWhen it comes to APIs, many API consumers want to be charged based on the actual transactions they perform. This could come in various forms, such as pricing per API call or per tokens consumed. However, there can also be a secondary factor, such as discounts, which make the API more cost-efficient for consumers who are doing a large amount of transactions with the API.Transaction-Based PricingFor APIs that are heavily transaction-oriented, aligning pricing with the volume of transactions can make a lot of sense. This model ensures that customers are charged based on the actual value they receive from the API. This approach can encourage higher usage, as customers only pay for what they use as they scale up, making it an attractive option for both startups and enterprises.Revenue/Cost Share ModelsSometimes, the value proposition of an API isn’t just about individual transactions but about the broader outcomes it enables. In such cases, revenue or cost-sharing models can be effective.Revenue SharingIn a revenue-sharing model, the API provider takes a percentage of the revenue generated from the API usage. This model is particularly effective when the API directly contributes to generating revenue for the user. It aligns the interests of both the provider and the user, ensuring that both parties benefit as usage and revenue grow.Cost SharingAlternatively, a cost-sharing model involves passing on a portion of the operational costs to the user. This approach can be used when the API usage incurs significant costs, such as high inference or data processing expenses.It ensures that users are aware of the underlying costs and are billed accordingly, which can help in managing high operational expenses.Input/Output Token BillingWith the rise of AI and large language models, tokens have become central to API pricing. This is one of the most popular ways to meter the usage of AI APIs.Granular Billing Based on UsageAI APIs often involve multiple input and output variables, such as the number of input tokens, output tokens, and context size. Billing based on these granular metrics ensures that users are charged accurately for the resources they consume. This level of detailed billing can be more equitable and precise, providing users with transparency and control over their costs.User-Centric Pricing ModelsUnderstanding your users and their diverse needs is crucial for successful API monetization. Sometimes, taking a more customized approach that is more personal to the business using the API makes sense.Personalized Pricing PlansDeveloping user-centric pricing models involves creating plans that cater to the specific needs of different user segments. This could include tiered pricing, volume discounts, or custom plans for enterprise users. By offering various pricing options, providers can attract more users and accommodate diverse usage patterns.Resource Usage AlignmentAligning pricing with actual resource usage ensures that users are billed based on the value they derive from the API. This could involve tracking CPU usage, memory consumption, or other relevant metrics. This approach can help manage high operational costs and ensure that pricing is fair and reflective of the service provided.Strategies for Successful AI API MonetizationSuccessfully monetizing AI APIs requires a robust pricing strategy and effective methods to attract and retain users. Here are some strategies to gear your AI APIs up for sustainable growth and maximize revenue potential.1. Land Lots of Users FirstThe best way to grow a paying user base is to get as many people in the door as possible right away. There are various ways to do this, and it will depend on the internal cost of your API and your target customer.Attracting Developers and UsersThe initial phase of monetization should focus on acquiring a large user base. This can be achieved through various tactics, such as offering free trials, freemium models, or low-cost entry points. Creating a large adoption funnel is crucial. The goal is to get as many users as possible to realize the value of the AI API. This can involve developer-friendly documentation, easy integration processes, and a supportive community. For example, providing free or low-cost access initially helps users understand the API’s capabilities and encourages widespread adoption.Creating Value QuicklyEnsure that users quickly see the value of the AI API. This can be achieved by providing clear documentation, easy integration, and excellent customer support. The faster users can integrate and start seeing results, the more likely they will continue using and paying for the API.2. Selling Through Existing UsersOften called “land-and-expand,” selling through organizations that already use your API is a great way to increase overall usage and revenue.Expanding Usage Among Existing UsersOnce a user base is established, the focus should shift to expanding usage among these users. Identify new use cases and offer additional features to existing users. Upselling higher tiers of service or additional features can significantly increase revenue. Users already familiar with the API are more likely to invest in expanded capabilities.Identifying Enterprise RequirementsIt is crucial to meet the specific requirements of enterprise users. This might include enhanced security, compliance, and support features tailored to their needs. Enterprise customers often have larger budgets and more complex needs, making them valuable targets for expanded services and higher-tier plans.Sales and Usage-Based Expansion FlywheelA key component of successful AI API monetization is creating a self-sustaining growth cycle driven by user engagement and increasing usage. The Product-Led Growth (PLG) sales flywheel is an effective model that leverages user behavior to drive expansion and revenue growth. Here’s an in-depth look at how this model works and the critical steps involved.Explanation of the Sales-Driven Expansion ModelThe sales-driven expansion model focuses on increasing user engagement and usage, which drives sales opportunities and growth. By continuously engaging users and understanding their needs, businesses can identify new use cases and expand their services. This model is particularly effective for AI APIs, where usage patterns and customer needs vary widely.Steps in the PLG Sales FlywheelWhen talking about a PLG sales motion, we refer to users who prefer a self-service approach, exploring and using the API independently. Providing excellent self-service resources, such as comprehensive documentation, FAQs, and user forums, is vital. This approach reduces the need for direct sales interactions while still supporting user growth and engagement. In an optimized environment, this flywheel in action will look like this:1. Aha MomentThe “aha moment” is when users first experience the core value of the AI API. This moment is crucial as it hooks users and encourages them to explore further. Ensuring that users reach this moment quickly is essential. This can be achieved through easy onboarding processes, clear documentation, and immediate access to key features.2. Increasing UsageOnce users experience the value, they are likely to increase their usage. This phase encourages users to explore more features and integrate the API deeper into their workflows. Providing additional resources, such as tutorials, case studies, and community support, can help users maximize their usage.3. Sales EngagementAs usage grows, sales teams can engage with users to identify new use cases, offer higher-tier plans, and address specific needs. This proactive engagement helps in converting high-usage self-service users into paying customers. Sales teams should focus on understanding user needs, providing tailored solutions, and highlighting the value of advanced features and higher-tier plans.4. Identifying New Use CasesContinuous engagement with users helps them discover new ways to use the API. By understanding user needs and market trends, businesses can identify new use cases and opportunities for expansion. This ongoing discovery process drives innovation and helps keep the API relevant and valuable to users.5. Accelerated UsageAs users become more familiar with the API and see its benefits, their usage accelerates. This phase involves supporting users in scaling their usage and handling more complex tasks. Ensuring the API can handle increased demand and providing robust support is critical to maintaining user satisfaction during this phase.Following the approach in this flywheel, businesses can scale up their API revenue and continue to expand their APIConclusionMonetizing AI APIs presents unique challenges, from varying usage volumes and high inference costs to the risk of abuse and complex input variables. However, by adopting usage-based billing and aligning pricing with customer value, businesses can create fair and effective pricing strategies. Additionally, leveraging the Product-Led Growth sales flywheel can drive sustainable growth by focusing on user engagement and continuous expansion.Businesses can create a self-sustaining growth cycle by initially attracting a large user base and then expanding usage among these users. The key is to ensure users quickly see the value of the API, support their increasing usage, and continuously engage to identify new opportunities. This approach not only maximizes revenue potential but also ensures that the AI API remains relevant and valuable to users in the long term.Are you getting started with monetizing your AI APIs? At Moesif, we have worked with a wide array of AI companies and helped them quickly and effectively monetize their APIs. Every aspect we’ve touched on in this blog can be easily implemented and scaled with Moesif, and additional observability and analytics features can help you optimize your AI API portfolio and customer experience. Want to try it out for yourself? Sign up for Moesif today to monetize your AI APIs in minutes with your favorite platforms like Stripe, Chargebee, Zoura, and more.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/The-Challenges-of-AI-API-Monetization/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-5-ai-product-metrics-to-track-a-guide-to-measuring-success": {
          "title": "5 AI Product Metrics to Track: A Guide to Measuring Success",
          "content"	 : "Not long ago, the thought of Artificial intelligence (AI) products becoming mainstream seemed far off. Fast forward to the widespread adoption of ChatGPT, Gemini, and other large-scale apps, and it’s easy to see that the world has changed drastically. AI isn’t just the future anymore – it’s the present, and every business is hopping on the bandwagon. From chatbots streamlining customer service to algorithms predicting market trends, AI is reshaping how businesses operate. Those who don’t infuse at least an AI component into their products risk getting left behind. But with this rapid integration of AI comes a critical question: How do you know if your AI initiatives are genuinely successful? This is where the focus on AI product metrics is essential.This blog post will cover how to measure AI product performance from multiple angles and the five metrics you should be tracking to measure success. Whether you’re a seasoned AI veteran or just dipping your toes with your first AI initiative, these metrics will equip you with the insights you need to navigate the AI landscape and drive meaningful results for your organization. Let’s begin by taking a deeper dive into understanding AI product performance.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding AI Product PerformanceLike any technical product, the title “AI product” covers many products. An AI product is a technology-based solution that leverages AI algorithms and techniques to perform tasks that typically require a human to complete. AI products can range from chatbots that automate customer service interactions to sophisticated recommendation engines that personalize user experiences and everything in between.Some companies build net-new AI platforms, while others leverage existing technologies by directly using AI platforms like OpenAI to inject AI into their apps with less lift. Regardless of what they are built on, AI products are designed to learn from data, adapt to new information, and make decisions or predictions autonomously. The result is to ultimately deliver enhanced efficiency, productivity, and decision-making to users.To build and refine AI products that meet users’ needs, you need cold, hard data. That’s where performance metrics come in. These quantifiable metrics can help guide you toward a deeper understanding of how your AI is performing in the real world, how users leverage it, and potential areas for refinement and growth.Why Performance Metrics MatterWhen building a product, performance metrics are the key to making calculated decisions concerning building and improving it. Instead of going by gut feel, performance metrics give organizations direct data to back up any assumptions they have about their target market or user base. Here are a few reasons why you want to make sure you monitor performance metrics with your AI product:  Data-Driven Decision Making: Forget guesswork. Performance metrics provide concrete evidence of what’s working and what’s not. As mentioned, this empowers your team to make informed decisions based on real-world data, not just gut feelings.  Continuous Improvement: Building AI products is an iterative process. By tracking metrics over time, you can pinpoint areas for enhancement, optimize algorithms, and fine-tune your AI platform’s capabilities for ongoing success.  Alignment with User Needs: Are your users benefiting from your AI product? Performance metrics like user engagement and satisfaction reveal whether your product delivers on its promises and meets user expectations.  Business Impact: AI isn’t just about technology but driving business results. Metrics like revenue generation and ROI tie your AI products’ performance directly to your bottom line, proving its value to stakeholders.The right metrics act as a bridge between your AI’s technical capabilities and your business goals. They provide the insights you need to ensure your AI products are innovative and impactful for users and your organization. By focusing on the capability to monitor critical metrics, you can answer almost any question about your AI product’s performance and adoption quickly. But what metrics should you be looking at? Next, let’s look at five key performance indicators for your AI products.Five Key AI Product Metrics to WatchNow that we understand the importance of measuring AI performance let’s explore the specific metrics that will give you a comprehensive view of your AI product’s success. The health of a product requires monitoring metrics from various angles. This becomes more complex when it comes to AI products since you need to look at factors such as adoption, technical performance metrics, and operational impacts. There are many moving parts within AI products, and an exponential number of factors influence their success. Here are a few key areas to focus on when looking at AI product metrics:User Engagement MetricsA product needs to be adopted for it to be successful. User engagement metrics are a great way to ensure that users are interested in and see value in your product. Here are three user engagement metrics to watch:  Active Users: The heartbeat of any AI product. This metric reveals how many users are actually interacting with your AI within a given timeframe (e.g., daily, weekly, monthly). A growing number of active users indicates strong adoption and increasing interest.  Session Duration: This measures the average time users spend engaged with your AI per session. Longer sessions suggest users find your AI valuable and engaging, while shorter sessions might indicate usability issues or a lack of compelling features.  Retention Rate: How many users keep coming back for more? Retention rate measures the percentage of users who return to your AI after their initial experience. High retention is a sign of long-term user satisfaction and a product that truly delivers value.Performance MetricsIn order for an AI product to be effective, it needs to be performant. Making sure that the product is accurate and responsive is critical to keeping users happy. Performance can be a key differentiator, especially if your AI product is competing with similar products. Here are a few performance metrics to track:  Accuracy/Error Rate: Accuracy is essential for AI models that make decisions or predictions. This metric reveals how often your AI gets it right versus wrong. A high accuracy rate (and low error rate) demonstrates your AI’s effectiveness and reliability.  Response Time/Latency: Nobody likes to wait for a service to respond. This metric tracks the time it takes for your AI to react to user input. Faster response times enhance the user experience and keep users engaged.Business Impact MetricsWe often think about technical products in terms of technical metrics. That said, organizations become successful through revenue and other factors that matter to the underlying business objectives. Your AI product should positively impact various areas of the business and drive value internally and externally. Here are a few metrics to pay attention to from the business side:  Revenue Generation: An AI product isn’t just about building cool technology; it’s about generating real business value. This metric measures the revenue directly attributable to your AI product through increased sales, cost savings, or new revenue streams.  Customer Satisfaction: Happy customers are loyal customers. By measuring customer satisfaction before and after implementing your AI, you can gauge its impact on the user experience. Tools like surveys, user feedback forms, and online reviews are invaluable for gathering this data.  Return on Investment (ROI): This crucial metric calculates the financial gains from your AI relative to its development and deployment costs. A positive ROI signifies your AI investment is paying off and delivering tangible value to the users who have adopted it.Operational Efficiency MetricsWhen organizations introduce a new tool, it’s usually to save on costs, employee time, or both. AI products have a significant advantage in this department, sometimes drastically reducing the time it takes to complete simple and highly advanced tasks. Tracking how much time an AI product saves customers and the cost savings it can bring is a great metric to be aware of. This can help with marketing, pricing, and many other factors. Here are the two key metrics to focus on in terms of operational efficiency:  Time Saved: Time is money. This metric quantifies how much time your AI saves users completing tasks. Whether automating customer service inquiries or accelerating data analysis, the time saved translates to increased productivity and efficiency within an organization.  Cost Reduction: AI has the potential to streamline operations and reduce expenses. This metric tracks how your AI is impacting your bottom line by lowering costs related to labor, resources, or operational inefficiencies.Ethical and Fairness MetricsLastly, you want to ensure that your AI product delivers ethical and fair outputs. Ethical and fairness metrics are a category that is genuinely unique to AI. This is critical to ensuring that your AI product is steering users in the right direction and not creating issues, as we have seen with even large players who have biased outputs that have negatively impacted customer experience. Tracking these specific metrics through real-time analysis or testing is still an emerging area. However, if you’re working with AI, you must be aware of these issues and watch for the latest tools to track and monitor for any negative impacts. The best metrics to look at for this include:  Bias Detection: Unintended biases can creep into AI models, leading to unfair or discriminatory outcomes. Regular assessments for biases, especially those related to sensitive demographics like race, gender, or age, are essential for building ethical and trustworthy AI.  Explainability: Transparency is vital. Ensuring your AI platform’s understandable and explainable decision-making process helps build trust with users and stakeholders. It also lets you identify potential issues and ensure your AI operates as intended.By monitoring these key metrics, you’ll gain a holistic understanding of your AI product’s performance, impact, and areas for improvement. This knowledge can help you make data-driven decisions about your product’s technical and business aspects, refine your AI strategy, and ultimately drive more significant success in the rapidly evolving AI landscape in which your AI product is competing.ConclusionThe key to success with AI is understanding your products’ performance from every angle. By carefully tracking the five key metrics outlined in this guide – user engagement, performance, business impact, operational efficiency, and ethical considerations – you equip yourself with the insights needed to refine, optimize, and ultimately unleash the full potential of your AI initiatives.Unlock the Full Power of AI with MoesifWant to make tracking these metrics a breeze? Moesif, the leading API analytics platform, offers powerful tools to effortlessly monitor your AI product’s performance, identify bottlenecks, and uncover hidden opportunities. AI applications rely heavily on APIs to deliver their functionality. This means monitoring AI APIs can help track many areas mentioned in this blog. By harnessing Moesif’s comprehensive insights, you can:Gain a deeper understanding of user behavior by tracking how users interact with your AI API endpoints, identifying usage patterns, and optimizing the user experience.Monitor performance in real-time and receive alerts for errors, latency issues, and other anomalies. This allows you to address problems before they impact your users proactively.Monetize your AI APIs using Moesif’s API monetization features. These features enable you to quickly implement usage-based pricing models, opening up new revenue streams for your AI products.Want to explore Moesif’s capabilities with your AI product? Sign up today to begin tracking key AI product metrics in minutes.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/5-AI-Product-Metrics-to-Track-A-Guide-to-Measuring-Success/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-top-api-versioning-strategies-for-seamless-integration": {
          "title": "Top API Versioning Strategies for Seamless Integration",
          "content"	 : "API versioning is key to updating your API without breaking clients’ integrations. It ensures smooth transitions and maintains compatibility. This guide explains the best strategies and practices for effective API versioning.API versioning is not just about the technicalities of managing different versions; it also involves a strategic approach to communication, documentation, and support. Properly implemented API versioning can significantly enhance the developer experience and foster a robust relationship between API providers and consumers. By ensuring that updates and improvements are rolled out seamlessly, developers can focus on innovation without the constant worry of breaking existing integrations.API versioning serves as a crucial tool for long-term API maintenance and scalability. As APIs evolve to incorporate new features and improvements, versioning allows for a structured and predictable approach to handling changes. This predictability is essential for businesses that rely on APIs for critical operations, as it minimizes the risk of unexpected disruptions and helps maintain operational continuity.In addition to the technical benefits, effective API versioning can also contribute to building a strong brand reputation. Companies that manage their APIs well are often seen as reliable and professional, which can attract more developers to use their services. In a competitive market, this can be a significant advantage, helping to build a loyal user base and driving long-term success.Ultimately, the goal of API versioning is to balance innovation with stability. By carefully planning and executing versioning strategies, companies can ensure that their APIs remain robust, reliable, and ready to meet the evolving needs of their users.Key Takeaways  API versioning is essential for managing and tracking changes to APIs, ensuring seamless transitions and preserving customer trust.  There are several API versioning strategies, including URI path versioning, query parameter versioning, custom request header versioning, and Accept header versioning, each with its own advantages and use cases.  Effective API versioning involves careful planning, including choosing the right strategy, updating documentation, gradual deployment, and systematic deprecation of old versions to minimize disruptions and build user trust.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding API VersioningAPI versioning aids in managing changes seamlessly, thus fostering a more transparent process. It is a common practice in API development. It involves making improvements to the API while ensuring that existing consumers are not adversely affected. The ultimate goal of proper API versioning is to take as much burden off the customer as possible.The implementation of API versioning plays a critical role in preventing disruptions, preserving trust, and guaranteeing compatibility. With correct management, API versioning aids in conveying API changes, equipping consumers with the knowledge of what to expect and how to prepare for updates.Definition of API VersioningAn API, or Application Programming Interface, is a shared boundary that allows for the exchange of information between different software systems. API versioning is the process of managing and tracking changes to an API to ensure that these changes are rolled out successfully, maintaining consumer trust and API performance.At the heart of API versioning is the data contract, which defines the agreement regarding the ‘shape’ of the data exchanged between the API and its consumers. This data contract is crucial as it outlines the expected request and response data, ensuring that any modifications do not disrupt the existing functionality.Importance of API VersioningMaking even a minor change to the API has the potential to disrupt client applications, impacting their business and undermining their trust in the service. This highlights the importance of careful planning and communication when making any adjustments. API versioning helps build trust and reputation by providing transparency and strengthening the organization’s reputation.A well-implemented API versioning strategy allows clients to continue using the existing REST API while transitioning their applications to the new version at their convenience, thus reducing disruptions and facilitating a smooth migration process. This approach helps handle updates to the API contract without forcing clients to update their applications immediately, ensuring a smoother transition.Benefits of API VersioningAPI versioning offers numerous benefits that help manage the growth and changes in an application as it evolves over time. Correct versioning of an API can:  Prevent detrimental impacts on a business or product  Allow for seamless compatibility  Enhance the customer experience  Avoid issues that could push customer loyalty away.Moreover, API versioning enables developers to:  Manage changes in a structured way, avoiding disruption to applications that rely on the API  Minimize breaking changes  Enhance communication  Build trustIn the following subsections, we will explore how API versioning achieves these benefits.Minimizing Breaking ChangesEffective change management in an API involves:  Continuing support for existing properties and endpoints to avoid forcing consumers of an API to make changes  Avoiding breaking changes, which can place the burden on consumers to adapt and lead to a lack of trust in the API  Versioning to manage breaking changes, allowing for new features to be released without affecting existing functionalities.Versioning through content negotiation allows for more granular control by versioning resource representations instead of the entire API. Stripe, for instance, uses API versioning to ensure backward compatibility and smooth transitions for developers when new versions are released.Enhancing CommunicationEffective communication of breaking changes is imperative to prevent unnecessary disruptions during API versioning. Versioning helps communicate changes effectively to API consumers, allowing them to adapt at their own pace.Using Semantic Versioning (SemVer) helps in distinguishing between major, minor, and patch updates, aiding in clear communication about the nature of changes. This structured approach ensures that clients are well-informed about what to expect from each new version.Building TrustMaintaining user trust in APIs necessitates consistent versioning practices. API versioning ensures that users are not taken by surprise with unexpected changes, fostering a stable and reliable environment.Well-documented and predictable API updates enhance user trust and adoption. Together, these practices contribute to higher user confidence and greater adoption rates.When to Version an APIAPI versioning becomes mandatory when changes necessitate consumers to alter their codebase for continued API usage. Unavoidable circumstances for versioning include altering or removing attributes, endpoints, or payload structure. It’s considered a best practice to version an API only in the event of a breaking change.Factors to consider before deciding to roll out a new API version include the scope and impact of the change, backward compatibility, and whether it constitutes a breaking change. Non-breaking changes do not warrant versioning on their own.Identifying Breaking ChangesAny change to the API that has the potential to cause client applications to fail is considered a breaking change. This type of change can impact the functionality and stability of the applications using the API. Breaking changes in the context of API versioning are changes that force clients to modify their applications. These changes can be made to an API’s input and output data structures, success, and error feedback mechanisms.Assessing the scope and impact of changes helps in deciding if versioning is necessary. Identifying breaking changes ensures that any modifications are handled carefully to avoid disrupting clients.Avoiding Unnecessary VersionsConfirming the necessity of a new version by evaluating the scope and impact of the change is a vital step. Opting to add a new operation instead of modifying an existing one can avoid unnecessary versioning.By carefully evaluating changes and opting for backward-compatible updates whenever possible, you can prevent the proliferation of unnecessary API versions. This approach helps maintain a streamlined and efficient API ecosystem.Types of API Versioning StrategiesThere are several approaches to API versioning, including:  URI path versioning  Query parameter versioning  Custom request header versioning  Accept header versioningEach strategy has its advantages and use cases, and choosing the right one depends on your specific needs and priorities.In the following subsections, we will explore these strategies in detail, discussing their implementation, benefits, and potential drawbacks.URI Path VersioningThe most common way to version an API is in the URI path, where the version number is included in the URI path to point to a specific version of the API. This method, used by platforms such as Facebook, Twitter, and Airbnb, allows for a clear distinction between different API endpoint versions, making it easier to identify and work with the default version.One of the main advantages of URI path versioning is that it allows clients to cache resources easily by changing cache keys based on the version number in the URI. However, it can be less flexible, more resource-intensive, and may violate a key principle of API design.Query Parameter VersioningQuery parameter versioning involves including the version number as a query parameter in the URI. This method adds the version of the resource to the request and is straightforward to implement.However, query parameters are less ideal for routing requests to the proper API version compared to other methods and can be less clear and harder to manage compared to URL-based versioning.Custom Request Header VersioningCustom request header versioning entails:  Setting the version number using custom headers in requests and responses  Avoiding the need to include version information in the URI  By including the version number in the header, it keeps versions hidden and flexible.This approach is recommended for flexibility in managing API versions without altering the URI space. However, it can be challenging to determine whether the version refers to the endpoint or the API itself.Accept Header VersioningDevelopers can version individual resources using the Accept request HTTP header, which specifies content types the client can process. Setting up APIs with the Accept header is more complex to implement and test. It is less accessible due to the requirement of HTTP headers with media types, making it difficult to test and explore using a browser.However, it allows for granular control over versioning with a smaller footprint in the codebase.Implementing API VersioningEffective API versioning relies on careful planning and methodical execution to ensure seamless integration and minimal disruption. The process starts during the API design phase, where choosing the appropriate versioning strategy is crucial. The steps to ensure a smooth transition to a new version include:  Updating the API documentation to reflect the changes in the new version.  Gradually deploying the new version, allowing users to transition at their own pace.  Communicating with users about the upcoming changes and providing support during the transition.By following these steps, you can ensure that users can seamlessly integrate the new version of your API into their applications.Finally, deprecating old versions in a systematic manner is vital to maintain a reliable and stable API ecosystem. In the following subsections, we will explore each of these steps in detail.Choosing a Versioning StrategySelecting an appropriate versioning strategy is key to ensuring client compatibility and effective update management. During the API design phase, it’s essential to align on a realistic roadmap for API evolution.Semantic versioning provides a clear and consistent way of communicating changes with major, minor, and patch version numbers. The SemVer specification helps in updating the API major version for breaking changes, while minor and patch versions address added functionality and backward-compatible updates.Updating API DocumentationComprehensive documentation aids users in understanding and transitioning between different API versions. Updating API documentation with release information, reasoning behind changes, and migration instructions is vital when versioning an API.Including a changelog or release notes in the documentation helps clients understand what changes each API version introduces. Providing examples and code snippets for each version can aid developers in implementation.Gradual Deployment of New VersionsDeploying a new API version in stages enables verification of its functionality and offers insights into user interactions. Start by releasing the new version to a small user group to identify and fix initial issues before a broader rollout.Use feature flags to control access to new features and ensure they don’t disrupt existing integrations. GitHub employs a unique approach by allowing any API change to be previewed before permanent adoption.Deprecating Old VersionsSystematic deprecation of older versions by developers aids in maintaining the API’s reliability over time. Monitoring API usage helps identify which versions are still in use and which can be deprecated.Notify users well in advance about the deprecation of older API versions to give them time to transition. Offering support to users of the old version during the transition period is crucial. Provide clear documentation and support to help users adapt to newer API versions.Best Practices for API VersioningAPI success is directly influenced by its versioning; adhering to best practices to avoid potential pitfalls can guarantee the success of the strategy. Ensuring backward compatibility, clear communication, and monitoring and support are key practices for effective API versioning.In the following subsections, we will explore these best practices in detail, providing actionable insights on how to implement them successfully.Ensuring Backward CompatibilityAPI versioning helps maintain backward compatibility, allowing newer versions to work seamlessly with applications built on older versions. Carefully managing API changes and maintaining backward compatibility helps build trust among users, especially when using the latest API version.Backward-compatible updates should only involve adding new features or fixing bugs without altering existing functionality. Avoid removing or renaming existing API endpoints or fields to maintain backward compatibility. Provide new fields with updated data types or formats while keeping old ones during a transition period.Clear CommunicationTransparent communication of changes in API versions is vital to user success. API documentation should use a consistent naming scheme for version identifiers to avoid confusion.Notifications about upcoming breaking changes should be sent well in advance to allow clients sufficient time to adapt. Providing detailed documentation and migration guides helps clients transition smoothly between API versions. Monitor API usage to track how changes are being adopted and to identify areas for improvement.Monitoring and SupportBefore announcing a timeline to deprecate the old version, it’s important to monitor the adoption rates of the new version. Offering support during the transition phase can help clients troubleshoot issues and adopt new versions more easily.By actively monitoring and providing support, you ensure that clients feel supported and confident in adopting the new API version, leading to a smoother transition and higher satisfaction rates.Real-World Examples of API VersioningStudying real-world examples of API versioning sheds light on how different companies adeptly manage their versioning strategies to cater to varied needs and challenges. Some examples include:  Google  Spotify  Facebook  Twitter  AirbnbThese companies use various versioning strategies, illustrating that different companies adopt various approaches based on their specific needs.In the following subsections, we will explore how these companies implement URI path versioning, query parameter versioning, and custom header implementation.URI Path Versioning in PracticeFacebook, Twitter, Airbnb, Google’s YouTube Data API, and xMatters use URI path versioning for their APIs. Google’s API versioning strategy involves specifying versions in the URL, such as /v1/resource.Examples of URI path versioning include YouTube Data API with /v3/ and Twitter’s API with https://api.twitter.com/1.1/. This method helps ensure that clients can easily access and cache resources based on the version number in the URI.Query Parameter Versioning SuccessQuery parameter versioning involves adding a version number as a parameter in the API query string, allowing for the simultaneous maintenance of multiple API versions. Facebook’s Graph API utilizes query parameter versioning by including the version number in the query string, for example, ?version=v2.0.Google Cloud APIs also support versioning through query parameters, enabling them to maintain different API versions simultaneously. This method is straightforward and allows for easy management of multiple API versions.Custom Header ImplementationSeveral companies effectively use custom headers for API versioning. GitHub uses a custom header, X-GitHub-Api-Version, for specifying its API version. Airbnb’s API uses custom headers for versioning, specifying the version number in the request header like API-Version: 2.PayPal incorporates versioning in their APIs using custom HTTP headers to define the specific version being used. By using custom headers for versioning, these companies can seamlessly integrate multiple API versions, maintaining backward compatibility and minimizing disruption.SummaryIn summary, API versioning is a critical component of modern API management. It helps manage growth, prevent negative impacts, enhance customer experience, and maintain trust. Whether through URI path versioning, query parameter versioning, custom request header versioning, or accept header versioning, choosing the right strategy and following best practices ensures seamless integration and minimal disruption. By learning from real-world examples and implementing effective versioning strategies, you can navigate the complexities of API evolution with confidence.Empower your API management with Moesif’s cutting-edge governance and monetization features. Govern user access and enforce quotas efficiently, while unlocking new revenue streams through flexible, usage-based billing models. Seamless integration ensures your API’s security and financial growth. Start revolutionizing your API strategy today by signing up for Moesif’s free trial, and take the first step towards optimized control and monetization of your digital services.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Top-API-Versioning-Strategies-for-Seamless-Integration/",
          "author": "Dylan",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-what-is-an-api-endpoint": {
          "title": "What Is an API Endpoint? A 2026 Beginner's Guide",
          "content"	 : "An API endpoint is the specific URL where an API receives a request for a particular resource or function. When you “call an API,” what you actually do is send an HTTP request to one of its endpoints. GET https://api.stripe.com/v1/charges/ch_123 and POST https://api.openai.com/v1/chat/completions are both endpoint calls; different APIs, same idea.                Monitor REST APIs With Moesif              14 day free trial. No credit card required.              Try for Free        If you have ever wondered what makes one URL an endpoint and another just a regular link, this guide is for you. We will start with a clean definition, break down the parts of an endpoint, walk through how to call one, and finish with the production concerns most beginner’s guides skip: security, rate limits, and observability.What is an API endpoint?  An API endpoint is the URL where an API receives a specific request. Each endpoint corresponds to one resource or one action: /users/123 retrieves a specific user, /orders creates a new order. Each endpoint accepts certain HTTP methods (GET, POST, etc.) and returns a defined response shape.Two things make a URL an endpoint:  The API is listening for it. The server has a route handler that matches the URL pattern. Hitting any other path returns 404 Not Found.  It performs one job. Good endpoints do one thing: return a user, create an order, cancel a subscription. Endpoints that try to do everything (/api/do-stuff) are a design smell.You will see “endpoint” used loosely in conversation to mean either the URL itself or the resource the URL targets. In practice the two are interchangeable.API endpoint vs. API: what is the difference?People conflate these constantly. Quick version: an API is the whole contract (every endpoint, every authentication rule, every response shape). An endpoint is one address inside that API.Think of the API as a restaurant and the endpoints as menu items. The menu (API) describes everything you can order; each line item (endpoint) is one specific thing you can request. You do not “call the menu,” you order a specific dish.Anatomy of an API endpointMost public API endpoints look something like this:https://api.example.com/v1/users/123?include=orders└──────────┬─────────┘└┬┘└──┬──┘└┬┘└──────┬──────┘       base URL    version path  path     query                              parameter parameterFour parts to recognize:  Base URL. The root of the API, typically https://api.<domain>.com. Stripe uses api.stripe.com, OpenAI uses api.openai.com. Many APIs use a separate subdomain for their API surface to isolate it from the marketing site.  Version. Most public APIs carry a version in the URL (/v1, /v2). This lets the provider ship breaking changes without breaking existing customers, since old versions stay live while new ones roll out.  Path. The resource identifier. /users is a collection of all users; /users/123 is one specific user. Good APIs use plural nouns for collections and predictable nesting for relationships (/users/123/orders).  Path parameter. The dynamic part of the path, the 123 in /users/123. The route pattern in the server would look like /users/:id.  Query parameters. The ?key=value part after the path. Used for filtering, sorting, pagination, and field selection. ?include=orders tells the API to include related orders in the response.A well-designed endpoint URL is readable: you can guess what GET /v1/customers/cust_42/subscriptions?active=true does without consulting the docs.HTTP methods every API endpoint should supportThe HTTP method tells the endpoint what to do with the resource. Five methods cover almost every endpoint you will encounter:            Method      Purpose      Idempotent?      Request body?                  GET      Retrieve a resource or list      Yes      No              POST      Create a new resource      No      Yes              PUT      Replace a resource entirely      Yes      Yes              PATCH      Update part of a resource      Sometimes      Yes              DELETE      Delete a resource      Yes      No      “Idempotent” means calling the endpoint multiple times has the same effect as calling it once. DELETE /users/123 is idempotent: the user is gone after one call, and additional calls do not delete more users. POST /users is not idempotent, because each call creates a new user.If you are designing endpoints yourself, the rule is simple: use the verb that matches the action, and do not invent endpoints like POST /createUser. The HTTP method already says “create”; the URL just identifies the resource.How to design API endpoints (5 quick rules)This is the short version of our full API design principles guide:  Plural nouns for collections, singular for resources. /orders for the list; /orders/42 for one.  HTTP methods do the action; URLs identify resources. No /createOrder or /deleteUser.  Predictable nesting, two levels maximum. /users/123/orders is fine; /users/123/orders/456/items/789 is too much. Use a top-level resource with filters instead.  Consistent casing. kebab-case in URLs (/order-details), camelCase or snake_case inside JSON. Pick one for JSON and stick with it.  Return the resource you just changed. A POST /orders should return the created order, not just { "success": true }. Saves the consumer a follow-up GET.API endpoints in REST, GraphQL, and gRPCThe shape of an endpoint is not universal. The dominant style in 2026 is REST, but the question “what is an endpoint” has a slightly different answer depending on the protocol the API uses.REST endpoints are what this guide has been describing: a URL maps to a resource, and the HTTP method (GET, POST, PUT, PATCH, DELETE) maps to the action. One endpoint per resource per action. Stripe’s /v1/charges (list all), /v1/charges/{id} (get one), POST /v1/charges (create one) is the textbook REST pattern.GraphQL APIs collapse endpoints into a single URL. A GraphQL API typically exposes one POST endpoint (often /graphql), and the request body carries a query that specifies exactly which fields the client wants. “Endpoints” in REST terms become “queries” and “mutations” in GraphQL. Where a REST API might have 200 endpoints, a GraphQL API has one endpoint that handles 200 queries. The trade-off: GraphQL gives clients fine-grained control over response shapes, but it also collapses the per-endpoint observability that REST gives you natively (per-query metrics replace per-endpoint metrics).gRPC endpoints are method calls, not URLs. gRPC uses HTTP/2 under the hood, but the developer-facing model is method invocations defined in a .proto file. Each method is the gRPC equivalent of a REST endpoint, but the wire format is Protocol Buffers rather than JSON, and the client is generated code rather than handcrafted HTTP calls. gRPC is dominant for service-to-service traffic inside a service mesh; rare for external public APIs because the tooling is less consumer-friendly.SOAP endpoints are operations on a service. SOAP APIs expose a WSDL that lists operations; calling an “endpoint” means POSTing a SOAP envelope to a single URL with the operation name in the envelope. New SOAP APIs in 2026 are vanishingly rare, but enterprises with existing SOAP backends sometimes wrap them in REST gateways.MCP “endpoints” are tool definitions. The Model Context Protocol (MCP) exposes existing APIs to AI agents through a different runtime surface. Each REST endpoint typically maps to one MCP “tool” with a description the agent reads to decide whether to call it. The underlying API is usually REST; MCP is the agent-facing wrapper. Platforms like the WSO2 AI Gateway auto-generate MCP servers from OpenAPI specs, which keeps the two surfaces in sync.In short: when developers say “endpoint” in 2026, they almost always mean REST. The other protocols use different vocabulary for the same concept of “one specific thing the API can be asked to do.”How to call an API endpointTwo ways most developers call an endpoint for the first time: a curl command and a fetch from JavaScript.With curl:curl -X GET "https://api.example.com/v1/users/123"   -H "Authorization: Bearer YOUR_TOKEN"   -H "Accept: application/json"The -X GET is the HTTP method. The two -H flags add headers: Authorization proves who you are, Accept tells the server what response format you can handle. The endpoint URL is the rest.With JavaScript fetch:const response = await fetch('https://api.example.com/v1/users/123', {  method: 'GET',  headers: {    'Authorization': 'Bearer YOUR_TOKEN',    'Accept': 'application/json',  },});const user = await response.json();For exploring an API interactively, most developers reach for Postman or an open-source equivalent (Bruno, Insomnia, Hoppscotch). Once you know how an endpoint behaves, you embed the call in your code.A few things to expect the first time you call an endpoint:  Authentication failures are the most common first error. Most public APIs return 401 Unauthorized if your token is missing, malformed, or expired. Check the Authorization header format the API documents (Bearer <token> is the most common, but Stripe uses HTTP Basic auth and AWS uses signed requests).  The Content-Type matters on writes. For POST and PATCH calls that send a JSON body, include Content-Type: application/json and pass valid JSON in the request body. Forgetting this returns 415 Unsupported Media Type on most APIs.  CORS only matters from a browser. If you are calling the API from a server-side script, curl, or Postman, CORS is irrelevant. CORS errors appear when the call is made from a browser JavaScript context against an API that has not added your origin to its allowed list.API endpoint response structureThree layers tell you what happened on every API response:Status code. The number returned with the response: 200 OK, 201 Created, 400 Bad Request, 404 Not Found, 500 Internal Server Error. Our full HTTP status code reference walks through all of them.Response body. Usually JSON in 2026. On success: the requested resource (for GET) or the created/updated resource (for POST/PUT/PATCH). On error: a machine-readable error code, a human-readable message, and (where useful) the field that failed validation.Headers. Content-Type tells you the body format. X-Request-Id (or Request-Id) is a unique identifier for that specific request, useful to quote back to API support when debugging. X-RateLimit-Remaining and X-RateLimit-Reset tell you how many calls you have left.A well-designed response answers the question without making you parse the body. A 404 Not Found is faster to act on than a 200 OK containing {"error": "not found"}.API endpoint security and rate limitingTwo production concerns most beginner’s guides skip.Security. Every public endpoint needs authentication. The common options in 2026:  API keys. Simplest, a long random string in a header. Fine for server-to-server. Easy to leak.  OAuth 2.0 and OIDC. The standard for user-facing flows. The endpoint receives a short-lived bearer token; the token proves who the user is and what they are allowed to do.  mTLS (mutual TLS). Both client and server present certificates. Higher friction; standard for partner integrations and financial APIs.Two non-negotiables: every endpoint runs over TLS 1.2 or higher (no plain HTTP, even for “internal” APIs), and sensitive data never goes in the URL (URLs leak to server logs and Referer headers).Rate limiting. Every public endpoint should enforce a request limit. When the limit is hit, the endpoint returns 429 Too Many Requests with a Retry-After header telling the client how long to wait. Without this, one badly-behaved customer can take down the API for everyone. Our rate-limit best practices post covers the algorithms (token bucket, leaky bucket, sliding window) and where each one fits.How to monitor and observe API endpoints in productionWhen your endpoint is live, three questions matter:  Is it fast? P50 and P95 latency, broken down per endpoint, per region, per customer.  Is it correct? Error rates, broken down by status code. A spike in 400s is a client problem; a spike in 500s is yours.  Who is calling it? Usage by customer, by endpoint, by feature, so you can see which customers are pushing limits and which endpoints are getting traction.These three questions are why API observability exists as a category separate from infrastructure monitoring. Server-level metrics (CPU, memory) cannot tell you that customer X is getting 500s on /v1/orders while everyone else is fine. Moesif API monitoring gives you per-endpoint, per-customer, payload-level visibility across any gateway (WSO2, Kong, AWS API Gateway, Azure APIM, Envoy).Endpoint observability also matters increasingly for AI agent traffic. When an LLM application calls your API through MCP, you want to see which agent triggered the call, which user the agent was acting on behalf of, and whether the agent’s retry pattern is causing duplicate writes. Without per-endpoint attribution, agent traffic looks identical to human-driven traffic in your dashboards, and the distinction matters both for capacity planning and for billing.What is an endpoint in cybersecurity?If you searched “what is an endpoint” and arrived here, you may have meant the cybersecurity sense of the word, which is a different concept entirely.In cybersecurity, an endpoint is a physical device that connects to a network: a laptop, phone, server, or IoT device. “Endpoint security” (also called endpoint protection or EDR, Endpoint Detection and Response) is the discipline of securing those devices against malware, intrusion, and data exfiltration. The “endpoints” are the devices; the security tools live on them.It is the same word, completely unrelated concept. If you came here looking for that meaning, Microsoft Security and Cloudflare both have strong overviews. The rest of this article is about API endpoints, the URLs your application calls.Next steps for building API endpointsIf you are designing endpoints right now, the API design principles guide linked above is the next read. If you are running endpoints in production and need to see what is actually happening, start a 14-day Moesif free trial and get per-endpoint latency, error rate, and customer-level analytics within an hour of integrating. No credit card required.Frequently asked questionsWhat is an API endpoint in simple terms? A specific URL inside an API that does one job. For example, /users/123 returns a specific user, /orders creates a new order. Calling an API means sending an HTTP request to one of its endpoints.What is an example of an API endpoint? Real examples: https://api.stripe.com/v1/charges (Stripe’s endpoint for creating a charge), https://api.openai.com/v1/chat/completions (OpenAI’s chat completion endpoint), https://api.github.com/users/octocat (GitHub’s endpoint to fetch user info).What is the difference between an API and an endpoint? The API is the whole contract: all the endpoints, all the auth rules, all the response shapes. An endpoint is one specific URL inside that API. Restaurant analogy: API is the menu; endpoint is one menu item.Is an endpoint a URL? Yes. An API endpoint is a URL that an API listens for. The API server has a route handler that responds when that URL receives a request. URLs that the API does not handle return 404 Not Found.What are the 5 API methods? GET (read), POST (create), PUT (replace), PATCH (partial update), DELETE (remove). HEAD and OPTIONS are also part of the HTTP spec but are used less often in normal application development.Why do API endpoints have versions in the URL? Versioning (/v1, /v2) lets API providers ship breaking changes without breaking existing customers. The provider keeps the old version running while new customers adopt the new one, then deprecates the old version with a public notice period (typically six to twelve months for paid APIs).                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /technical/what-is-an-api-endpoint/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-moesif-aws-stripe-ai-api-part-3": {
          "title": "Using Moesif, AWS, and Stripe to Monetize Your AI APIs Part-3: Managing Customer Credit",
          "content"	 : "  This is the third part of a four-part series about AI API monetization. We recommend you go through the first and second part of the series before proceeding with this article.In the previous two articles, we completed the following steps in our AI API monetization journey with Moesif, AWS, and Stripe:  Set up the AI API with AWS Lambda and Gateway.  Integrated the AI API with Moesif.  Connected Stripe with Moesif. We now have the infrastructure to begin billing for API usage.  Set up the pricing for the APIs.  Set up API usage metering with Moesif Billing Meter.  Set up API Access rules using Moesif Governance Rules.In this article, we set up a pre-paid credit management for customers in Moesif and Stripe. Moesif can track and meter customer credits in near real-time. This allows you to fully leverage Moesif for API monetization.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Table of Contents  Table of Contents  Background  Create a Payment in Stripe  Add a Credit to Moesif  Optional: Automating Account Top-up In Moesif          Webhook-only App      API-only App      Combining Webhook and API        ConclusionBackgroundTo implement pre-paid or pay-as-you-go type billing for monetized APIs, you need to consider these aspects:  The ability to add credits to an account.  The ability to burn down those credits.  The ability to block users from accessing the API once they run out of credits.Most billing providers allow you to create payments that you can add to a user’s account as a pre-paid credit. Moesif Billing Meter can burn down those credits as we discussed in the previous post. However, the third aspect of the problem gets increasingly more complex.Billing Providers usually impose a rate limit for reporting usage. This means that real-time balances become much harder to achieve. Real-time balance tracking requires you to feed the billing provider usage information in real time. The billing provider then burns down the balance according to the usage and must report back to the system that controls API access.Up until recently, this problem had no solution. Luckily, we at Moesif have created a way to achieve near real-time pre-paid API access using our latest Pre-Paid Credits and Balance features.This set of features allows Moesif to be the book of record for the user’s balance. This means two things:  You don’t need any round-trips to the billing provider.  The rate limits billing providers put on reporting usage no longer matters.As a result, as Moesif meters the usage, Moesif can burn the user’s credits accordingly to achieve a real-time balance. The governance rule we demonstrated in the second part of this blog series will work in near real-time, blocking users when they run out of credits.For more information on pre-paid credit features in Moesif, see Moesif’s Credit Tracking and Consumption documentation.In today’s article, we will examine how to charge for API credits in Stripe and then add those credits to Moesif. Let’s begin by looking at adding a payment to Stripe.Create a Payment in StripeSince we are implementing pre-paid API access, we must first charge the customer for a certain amount of credits. We can do this by manually creating a payment in Stripe. You can also make a top-up mechanism in your code or UI. Everything we do here is also available through the Stripe APIs. For this guide, we focus on manually adding the balance top-up.We assume you already have a customer in Stripe and Moesif. If you do not, you can revisit this section once a customer is added to both platforms. Adding customers will be covered in the following guide, Part 4.  Go to your Stripe Dashboard and then to the Customers page.  Select the customer you want to add the top-up to.  Select Create Payment. A modal appears.  Select your currency, enter the amount, and select the payment method.  Select Create Payment.Stripe will process the payment and add the amount to the customer’s Stripe account. You can see the entry under Payments on the customer’s profile if the payment has been successfully completed.Add a Credit to MoesifAfter creating the Payment in Stripe, we can create the corresponding entry in the Moesif ledger so that Moesif can control access to the API in real time.To add the amount to Moesif, you must go to the customer’s Company profile page:  In Moesif Portal, select Companies  in the navigation menu.  In the Lookup table, identify your company and select its comapny ID.Now add the credits to the company in Moesif by following these steps:  In the Profile View screen of the company, select ✐ Edit beside Current Balance.      In the Add Transaciton modal that appears, set the Type to Credit and enter 10.00 in the Amount field.    If you have multiple subscriptions for the customer, ensure that the Subscription Id field value matches the subscription you want to apply the credits to.    Select Add Transaction.If you follow these steps, the Add Transaction modal looks like this before finalizing the transaction.Within a few minutes, the result from this transaction appears in the company’s profile. It shows that the company has $10.00 in its Current and Available Balance.Each value represents a few aspects of the company or user’s balance:  Current Balance  Represents the current balance without factoring in any pending activity or transactions that have not been processed yet.  Pending Activity  Represents the activity or transactions (credits or debits) that have not yet been posted. Therefore, this value can change until the transaction completes. Once the transaction completes, Moesif updates the Current Balance and removes the corresponding amount from the Pending Activity value.    For example, if API usage or credits have been added to an account, you can see the corresponding amount here until it has been posted.    Available Balance  From a usage perspective, this amount bears the most significance as it dictates what credits a user has left to spend in a pre-paid scenario. This value corresponds to the amount of credits left for the user to consume. This amount equals the formula (Current Balance + Pending Activity).    For example, if your current balance is $1000 and you have a $250 debit transaction pending, your available balance is $750.  So far, we’ve seen how to manually add the balance in Moesif. However, you may also want to have a top-up in Stripe automatically applied to the balance in Moesif.In the next sections, we discuss how you can automate top-ups using Stripe’s APIs and webhooks.Optional: Automating Account Top-up In MoesifUsing the Credit Consumption API, you can add pre-paid credits to Moesif. To intercept a payment event in Stripe and apply it to the Moesif balance, you have three options:  Using webhook  Using API only  Using a combined approach that leverages both webhooks and API.We’ve created some examples that demonstrate how you can implement these options.Webhook-only AppWith this approach, whenever a user’s balance changes in Stripe, a corresponding debit or credit adds to the Moesif balance. Using the Stripe webhook, when a change occurs, the record will be created and sent to Moesif through the Moesif Credit Consumption API.This approach works well for applications that already have an existing top-up flow and allows for minimal changes. This automated approach enables both balances to stay in sync between the two systems.For an example, see the sample webhook application webhook-app.js on Github. This application receives a message from Stripe when a user has added funds to their Stripe account. To start this example, clone the Github repo, populate the .env file, and then run the following command:node webhook-app.jsFor this application to work, you must deploy the webhook somewhere. For example, for testing, you can use ngrok or deploy on your existing API infrastructure. Make sure that the webhook is publicly accessible. After deploying the webhook, add the webhook to Stripe with the following steps:  In Stripe, go to the Developers Dashboard.  Select the Webhooks and then select + Add endpoints.  In the next screen, add your webhook endpoint URL in the Endpoint URL field.  Select + Select events and then select the payment_intent.succeeded event for the webhook.  Optionally, select the events you want to subscribe to without using Payment Intents.  Lastly, select Add endpoint.You can test your webhook by going through your top-up logic and making sure that the webhook fires and successfully creates the credit or debit entry in Moesif. You can see the debugger output in the console or logs where your webhook is running as well.API-only AppYou may have an existing top-up flow that leverages an API. Therefore, instead of having the webhook execute the logic to do the top-up in Moesif, you can make a direct API call to Moesif’s Credit Consumption endpoint as the payment intent is created.This example application allows users to call a /top-up endpoint with their Stripecustomer ID and the amount they want to credit to their account. In this approach, the endpoint creates a Stripe PaymentIntent that adds the amount to the user’s Stripe account. At the same time, this endpoint creates a corresponding transaction on the Moesif side, adding the amount to the available balance in Moesif.You can deploy the endpoint independently or on your existing API infrastructure. However, this endpoint requires users to have a credit card on file and creates a Stripe Payment Intent for the amount they have requested to be charged against that card.To run the API, clone the Github repo, populate the .env file, and run the following command:node api-app.jsCombining Webhook and APIThis approach offers the best of both solutions. Here’s how this approach works:  Create an endpoint to initiate the top-up transaction.  Then use the webhook to apply that credit on the Moesif side once the PaymentIntent has succeeded in Stripe.The webhook and the API require the same configuration as the initial webhook and API examples we discussed in the preceding sections. In this solution, the /top-up REST API endpoint and the webhook run in the same node project. However, you can separate your concerns as you need for both.You need to host the node app publicly. To test the functionality in your local environment, clone the Github repo, populate the .env file, and run the following command:node webhook-api-app.jsUsing any of these three approaches, you can automatically apply top-up payments processed through Stripe to the balance in Moesif. This gives you automation and more scalability than manually adding these transactions by hand in Moesif.ConclusionIn this article, we’ve successfully added a balance to customers in both Stripe and Moesif. This will help to enable pay-as-you-go billing in real time and leverage the infrastructure we created in the second part of this series. If you’ve followed along so far, you now have a complete pre-paid system that allows you to meter usage, burn down credits, and manage API access based on customers having an available balance of credits to use.In the next and last part of this series, we focus on onboarding customers. We cover how to register customers in Moesif, Stripe, and AWS so that they can use the APIs. At the end us, you’ll have a complete end-to-end API monetization setup to start driving revenue from your AI APIs (or any API, for that matter!). Until next time, stay tuned for the final part of this AI API monetization With AWS, Stripe, and Moesif series!                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue from your APIs?            Monetize your AI APIs in minutes with Moesif, AWS, and Stripe.            Try it out!            No credit card required            ",
          "url": " /technical/api-development/Moesif-AWS-Stripe-AI-API-Part-3/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-python-rest-api": {
          "title": "Python REST API: Build One with FastAPI in 2026",
          "content"	 : "If you are building a Python REST API in 2026, the default stack has shifted. FastAPI now sits at the top of Python web framework adoption per JetBrains’ State of Python 2025 survey, with growth that outpaced every other framework in the last two years. Flask is still strong for small services; Django REST Framework remains the enterprise pick when you are already on Django. For a new public REST API, though, the path of least resistance is FastAPI plus Pydantic, hosted behind an API gateway with proper observability.                Monitor REST APIs With Moesif              14 day free trial. No credit card required.              Try for Free        This guide walks through that path. Working FastAPI code (verified against the official docs at the time of writing), Pydantic validation, JWT authentication, pytest testing, deployment patterns, and the production observability layer most tutorials skip. Every code snippet here is a working example you can paste and run.Why Python is a strong choice for REST APIs in 2026Python’s relevance for REST APIs grew, not shrunk, in the last few years. Three reasons.First, async support stabilized. FastAPI is async-native from the first line; Flask added async view support in Flask 2.0; Django has substantially expanded async ORM support in recent releases. The “Python is slow” objection rarely holds for I/O-bound API work in 2026.Second, type hints became table stakes. FastAPI uses Python type hints to validate requests and generate OpenAPI specs automatically. Pydantic’s data model layer catches a class of bugs that used to live in production. The combination produces tighter APIs with less code.Third, Python became the default language for ML serving. A meaningful share of new APIs in 2026 wrap an LLM call, a vision model, or a recommendation system. FastAPI’s async runtime and clean integration with PyTorch and TensorFlow made it the standard for serving those models through HTTP. The same patterns work for non-ML APIs.If you want a deeper framework comparison before picking one, our Python REST API frameworks guide goes through FastAPI, Flask, Django REST Framework, aiohttp, and Falcon in detail.Picking a Python framework: FastAPI vs Flask vs Django RESTA short decision matrix for the three frameworks most teams compare.            Framework      Best for      Async      Batteries                  FastAPI      New APIs, ML serving      Native      Light              Flask      Small services, prototypes      Optional (Flask 3+)      Light              Django REST Framework      Existing Django apps, enterprise      Partial      Heavy      This guide builds with FastAPI because it is the strongest default for new APIs and because its type-hint-driven validation and auto-generated OpenAPI spec produce a cleaner tutorial than either alternative. The same general patterns translate to the other two frameworks.Setting up your environmentCreate a fresh project directory and a virtual environment.mkdir python-api && cd python-apipython -m venv venvsource venv/bin/activate  # On Windows: venvScriptsactivatepip install "fastapi[standard]"The fastapi[standard] install pulls Pydantic, Starlette, Uvicorn (the ASGI server), and the FastAPI CLI in one step. The quoted form ensures the brackets are parsed correctly on every shell.Verify the install:python -c "import fastapi; print(fastapi.__version__)"Create a file called main.py. That is the file we will fill in across the next few sections.Designing your API before writing codeThe temptation with FastAPI is to start writing @app.get decorators immediately. Resist it for thirty minutes. The decisions you make in the first half hour about resource model, endpoint shape, and status code choices are the ones that get hardest to change later, because every consumer integration locks them in.A useful sequence for designing a Python REST API before any code:Identify the resources. A resource is a noun the API exposes. For an order management API: orders, customers, products, refunds. For an analytics API: events, sessions, reports. Resources are usually plural in the URL (/orders, not /order), and a single resource has a stable identifier (/orders/{order_id}). If you find yourself reaching for verbs (/getOrders, /processRefund), step back; REST works best with noun-based URLs and HTTP verbs (GET /orders, POST /refunds).Map endpoints to operations. For each resource, decide which CRUD operations the API supports and which it does not. A list-orders endpoint (GET /orders) might be paginated, filtered, and sorted; a delete-order endpoint (DELETE /orders/{id}) might require admin auth. Write these out as a flat table before coding; it surfaces the cases where you intended an operation that the resource model does not actually support.Choose status codes deliberately. REST APIs that return 200 OK for every response (including errors) make consumer parsing miserable. The right defaults for a CRUD API: 200 OK for successful reads, 201 Created for successful creates (with a Location header pointing to the new resource), 204 No Content for successful deletes, 400 for malformed requests, 401 for missing auth, 403 for forbidden, 404 for missing resources, 409 for conflicts, 422 for validation failures, 429 for rate limits, 500 for server errors. Our HTTP status codes guide covers the full set.Design the error shape early. Every endpoint will eventually return errors. Decide the error envelope once ({"error": {"code": "...", "message": "...", "field": "..."}} is a common pattern) and apply it consistently. Changing the error format after consumers integrate is a breaking change even if the success format stays the same.Decide on naming conventions. Field names in JSON bodies: snake_case (most APIs, including Stripe, OpenAI, GitHub) or camelCase (most JavaScript-native APIs)? Both are valid; pick one and apply it across every endpoint. The same applies to URL paths (/customers/{customer_id}/orders vs. /customers/{customerId}/orders); pick a style and never mix.Plan for pagination upfront. A list endpoint that returns “all orders” works in development with 50 rows and falls over in production with 50,000. Decide whether you use offset/limit pagination (simpler, struggles at very high offsets) or cursor pagination (slightly more complex but scales arbitrarily), and apply it to every list endpoint from the start.Write the OpenAPI spec or generate it. FastAPI generates the OpenAPI spec from your type hints automatically, which is one of its strongest selling points. The discipline is to look at the generated spec before treating the API as done; if the spec is wrong or incomplete, the API design is wrong or incomplete. The spec is also what feeds downstream tools: SDK generators, MCP server generators (via the WSO2 AI Gateway), contract testing, and documentation portals.The thirty minutes spent here save days later. Most API redesigns we see were avoidable with a sharper initial resource model.Building your first endpointA minimum working FastAPI server:from fastapi import FastAPIapp = FastAPI()@app.get("/")def read_root():    return {"message": "Hello, World"}@app.get("/items/{item_id}")def read_item(item_id: int, q: str | None = None):    return {"item_id": item_id, "q": q}Run it with the FastAPI dev server:fastapi dev main.pyThe server starts on http://127.0.0.1:8000. Call the second endpoint:curl "http://127.0.0.1:8000/items/42?q=test"# {"item_id":42,"q":"test"}Three things just happened automatically:  FastAPI validated that item_id is an integer. Call /items/abc and you get a clean 422 Unprocessable Entity with the validation error in the body.  FastAPI registered the endpoint in an OpenAPI spec, which you can view at http://127.0.0.1:8000/docs (Swagger UI) or /redoc (ReDoc).  The query parameter q is optional because its default is None. Without = None it would be required.The str | None syntax is PEP 604 (Python 3.10+). Older tutorials use Optional[str] from typing; both work, but the union syntax is the modern default.Adding request validation with PydanticThe endpoint above only validated path and query parameters. For request bodies, define a Pydantic model.from fastapi import FastAPIfrom pydantic import BaseModel, Fieldfrom typing import Listapp = FastAPI()class Order(BaseModel):    customer_id: int    items: List[str] = Field(min_length=1)    notes: str | None = None@app.post("/orders", status_code=201)def create_order(order: Order):    # In real code, persist to a database and return the saved order    return {"order_id": 1, "customer": order.customer_id, "items": order.items}A POST to /orders with a missing customer_id or an empty items list now returns a structured 422 automatically. The error body tells the caller exactly which field failed.curl -X POST http://127.0.0.1:8000/orders   -H "Content-Type: application/json"   -d '{"customer_id": 42, "items": ["widget", "gadget"]}'# {"order_id":1,"customer":42,"items":["widget","gadget"]}This is what makes FastAPI different from Flask in 2026. You declare types and constraints once; you get validation, error handling, and OpenAPI documentation by construction.Handling create/read/update/deleteA working CRUD set for orders, with an in-memory store to keep the example focused.from fastapi import FastAPI, HTTPExceptionfrom pydantic import BaseModel, Fieldfrom typing import List, Dictapp = FastAPI()class Order(BaseModel):    customer_id: int    items: List[str] = Field(min_length=1)    notes: str | None = Noneorders_db: Dict[int, Order] = {}next_id = 1@app.post("/orders", status_code=201)def create_order(order: Order):    global next_id    order_id = next_id    next_id += 1    orders_db[order_id] = order    return {"order_id": order_id, **order.model_dump()}@app.get("/orders/{order_id}")def read_order(order_id: int):    if order_id not in orders_db:        raise HTTPException(status_code=404, detail="Order not found")    return {"order_id": order_id, **orders_db[order_id].model_dump()}@app.put("/orders/{order_id}")def replace_order(order_id: int, order: Order):    if order_id not in orders_db:        raise HTTPException(status_code=404, detail="Order not found")    orders_db[order_id] = order    return {"order_id": order_id, **order.model_dump()}@app.delete("/orders/{order_id}", status_code=204)def delete_order(order_id: int):    if order_id not in orders_db:        raise HTTPException(status_code=404, detail="Order not found")    del orders_db[order_id]    return NoneA few patterns worth noting:  status_code=201 on POST for “Created”; status_code=204 on DELETE for “No Content.” These match the HTTP status code conventions that REST APIs are expected to follow.  model_dump() is Pydantic v2’s method for serializing models. Older tutorials use .dict(), which is deprecated in v2.  HTTPException carries the status code and a detail field that becomes the response body’s detail. FastAPI formats the JSON.In a real application the orders_db dictionary is replaced with a database layer (SQLAlchemy is the common choice). The endpoint shape stays the same.Authentication and authorizationThe two patterns most public APIs use in 2026 are API keys (server-to-server) and OAuth 2.0 (user-facing). FastAPI supports both through fastapi.security.A minimal API key check:from fastapi import FastAPI, Header, HTTPExceptionapp = FastAPI()API_KEYS = {"key_demo_123": "demo-customer"}def verify_api_key(authorization: str | None = Header(default=None)):    if not authorization or not authorization.startswith("Bearer "):        raise HTTPException(status_code=401, detail="Missing Bearer token")    token = authorization.removeprefix("Bearer ")    if token not in API_KEYS:        raise HTTPException(status_code=403, detail="Invalid API key")    return API_KEYS[token]@app.get("/orders/{order_id}")def read_order(order_id: int, customer: str = Depends(verify_api_key)):    # 'customer' is now the API key's owner, set by verify_api_key    return {"order_id": order_id, "customer": customer}The Depends(verify_api_key) pattern is FastAPI’s dependency injection. The function runs before the endpoint; if it raises, the request never hits your handler. For OAuth 2.0 and JWT, FastAPI ships built-in helpers documented in the security section of the official docs.The non-negotiables for any production API: TLS 1.2 or higher on every endpoint, no API keys in URLs (they leak to logs), and rate limits enforced at the gateway. Picking these defaults at design time is one of the nine API design principles.Idempotency for AI agent trafficThis is the section most Python REST tutorials skip and the one that matters most in 2026.AI agents retry. When an LLM application calls your POST /orders endpoint as part of a multi-step task and the request times out, the agent retries the same call. Without idempotency, you create duplicate orders.The convention popularized by Stripe and widely adopted across payments and infrastructure APIs: clients send an Idempotency-Key header on POST requests, and the server returns the same response on retry.A minimal implementation:from fastapi import FastAPI, Header, HTTPExceptionfrom typing import Dict, Anyapp = FastAPI()idempotency_cache: Dict[str, Any] = {}@app.post("/orders", status_code=201)def create_order(order: Order, idempotency_key: str | None = Header(default=None)):    if idempotency_key and idempotency_key in idempotency_cache:        return idempotency_cache[idempotency_key]    # ... create the order, get an order_id ...    response = {"order_id": next_id, **order.model_dump()}    if idempotency_key:        idempotency_cache[idempotency_key] = response    return responseIn production, replace the in-memory cache with Redis or a database table keyed by idempotency_key. Set a TTL (24 hours is typical) so the cache does not grow forever.The same pattern works for any framework. The point is that POST endpoints should be safe to retry, which is also useful for human-driven clients that retry on network errors.Structured error responsesDefault FastAPI errors look like {"detail": "Order not found"}, which is fine for quick prototypes but rough for production. Real consumers (and AI agents) want a machine-readable error code, a human-readable message, and where applicable the field that caused the failure.A consistent error envelope across all endpoints:from fastapi import FastAPI, Requestfrom fastapi.responses import JSONResponsefrom fastapi.exceptions import RequestValidationErrorapp = FastAPI()class APIError(Exception):    def __init__(self, code: str, message: str, status: int = 400, field: str | None = None):        self.code = code        self.message = message        self.status = status        self.field = field@app.exception_handler(APIError)async def api_error_handler(request: Request, exc: APIError):    body = {"error": {"code": exc.code, "message": exc.message}}    if exc.field:        body["error"]["field"] = exc.field    return JSONResponse(status_code=exc.status, content=body)@app.exception_handler(RequestValidationError)async def validation_error_handler(request: Request, exc: RequestValidationError):    # Map Pydantic validation errors into the same envelope    first = exc.errors()[0] if exc.errors() else {}    field = ".".join(str(p) for p in first.get("loc", [])[1:])    return JSONResponse(        status_code=422,        content={"error": {"code": "validation_failed", "message": first.get("msg", "Invalid request"), "field": field}},    )Routes now raise APIError instead of plain HTTPException:@app.get("/orders/{order_id}")def read_order(order_id: int):    if order_id not in orders_db:        raise APIError(code="order_not_found", message="Order not found.", status=404)    return orders_db[order_id]The result is one response shape across success and failure paths. Consumers parse error.code to handle errors programmatically (without string-matching the message), and your support team can grep for a specific code across logs.Testing your APIFastAPI ships a TestClient (built on httpx) that lets you exercise endpoints without running a server.# test_main.pyfrom fastapi.testclient import TestClientfrom main import appclient = TestClient(app)def test_create_order():    response = client.post(        "/orders",        json={"customer_id": 42, "items": ["widget"]},    )    assert response.status_code == 201    body = response.json()    assert body["customer"] == 42def test_read_missing_order():    response = client.get("/orders/9999")    assert response.status_code == 404def test_idempotency():    payload = {"customer_id": 1, "items": ["a"]}    headers = {"Idempotency-Key": "abc-123"}    first = client.post("/orders", json=payload, headers=headers)    second = client.post("/orders", json=payload, headers=headers)    assert first.json() == second.json()Run with pytest:pip install pytestpytestThe third test is the agent-retry case. If your idempotency implementation is wrong, the second POST creates a duplicate order and the assertion fails.For contract tests (verifying the live implementation matches the OpenAPI spec), Schemathesis is the standard tool in 2026. It reads the spec, generates property-based test cases, and runs them against the API.Background tasks and async workSome endpoints kick off work that should not block the response: sending an email, processing an uploaded file, calling a slow downstream API. FastAPI ships a BackgroundTasks helper for the simplest case.from fastapi import BackgroundTasks, FastAPIapp = FastAPI()def send_welcome_email(email: str):    # In real code, this would call an email provider    print(f"Sending welcome to {email}")@app.post("/signups", status_code=201)def create_signup(email: str, background_tasks: BackgroundTasks):    background_tasks.add_task(send_welcome_email, email)    return {"status": "accepted"}The background task runs after the response is sent. For anything that needs durability (must survive a crash), reliability (must run even if the request handler died), or scale (offload to a worker pool), reach for a real job queue. Celery and Dramatiq are the standard choices in Python; both work with Redis or RabbitMQ as the broker.For long-running jobs the consumer should be able to poll, return 202 Accepted with a job ID:@app.post("/reports", status_code=202)def create_report(background_tasks: BackgroundTasks):    job_id = create_job_in_database()    background_tasks.add_task(generate_report, job_id)    return {"job_id": job_id, "status_url": f"/reports/{job_id}"}The pattern (POST to create the job, GET to poll status, optional webhook on completion) is what every modern async API converges on. It is simpler to implement than holding HTTP connections open for minutes.Performance and scaling: caching, pagination, connection poolingMost FastAPI tutorials end after “it works locally.” The interesting performance work begins after that, and the patterns are the same regardless of which Python framework you picked.Use the right ASGI server. fastapi dev is the development server (auto-reload, debug-friendly, single-process). In production, run Uvicorn with multiple workers behind a process manager, or use Gunicorn with the uvicorn.workers.UvicornWorker worker class:gunicorn main:app -w 4 -k uvicorn.workers.UvicornWorker --bind 0.0.0.0:8000The right worker count is roughly (2 * CPU cores) + 1 for I/O-bound APIs, lower for CPU-bound workloads. Tune by load-testing, not by guessing; the cost of one extra worker is small but the cost of running with too few is unbounded latency.Pool your database connections. A FastAPI endpoint that opens a new database connection per request will hit connection limits at low traffic. Use SQLAlchemy’s connection pooling (sqlalchemy.create_engine with pool_size and max_overflow) or asyncpg’s pool directly. For PostgreSQL specifically, set the pool size below the database’s max_connections minus a safety margin for other clients.Cache aggressively at the gateway and at the application layer. Cacheable GET responses can be served by a gateway cache (Cloudflare, Varnish, the gateway’s own cache, or a CDN) before they ever hit your Python process. For per-request computation that is too dynamic for HTTP caching, use Redis (or cachetools for in-process caches) keyed by the inputs that drive the computation. Always include a Cache-Control header on responses so downstream caches behave predictably.Paginate every list endpoint. A GET /orders that returns 50,000 records on every call is a denial-of-service vector on yourself. Use offset/limit for simple cases and cursor pagination for large datasets. Default to a sane page size (50-100), and cap the maximum (don’t let a client request ?limit=10000).@app.get("/orders")def list_orders(limit: int = 50, cursor: str | None = None):    limit = min(limit, 100)    query = select(Order).order_by(Order.id).limit(limit + 1)    if cursor:        query = query.where(Order.id > int(cursor))    rows = await session.execute(query)    items = rows.scalars().all()    next_cursor = str(items[-1].id) if len(items) > limit else None    return {"items": items[:limit], "next_cursor": next_cursor}Batch endpoints for chatty consumers. When clients need to make many small calls (especially agent runtimes that loop over collections), expose a batch endpoint that processes a list of operations in one request. A batch shape (one POST /batch taking an array of sub-requests, each with method/path/body, and returning an array of sub-responses) saves network round-trips and reduces per-call overhead. This is a common pattern across modern APIs but the exact endpoint name and request shape varies, so check the docs of any specific provider you plan to model after.Async all the way down. Mixing async def endpoints with synchronous database drivers blocks the event loop and defeats the point of async. If your endpoint is async def, your database driver, HTTP client (use httpx not requests), and cache client should all be async. A single synchronous call in an async path can turn a 100-RPS service into a 5-RPS service.Stream large responses. Returning a 50MB JSON blob in one go loads the whole thing into memory and delays the response. FastAPI’s StreamingResponse lets you yield rows as you fetch them from the database:from fastapi.responses import StreamingResponseimport json@app.get("/export/orders")async def export_orders():    async def iter_rows():        yield "["        first = True        async for order in stream_orders_from_db():            if not first:                yield ","            yield json.dumps(order.model_dump())            first = False        yield "]"    return StreamingResponse(iter_rows(), media_type="application/json")Profile before optimizing. py-spy and scalene are the standard Python profilers in 2026. Run them against your live API under realistic load before adding caches or rewriting endpoints. The bottleneck is almost never where you expect (it is usually the database, then JSON serialization, then everything else).Set timeouts everywhere. A downstream API that hangs for 60 seconds will hang your Python worker for 60 seconds. Set httpx.Timeout(5.0) on every outbound call, and configure Uvicorn’s --timeout-keep-alive and --timeout-graceful-shutdown for the server side.Deploying to productionA FastAPI app deploys to any platform that runs Python. The common 2026 paths:  Managed platforms. Vercel (Python support), Railway, Render, Fly.io. Push code; the platform handles the rest.  Container runtimes. AWS ECS, Google Cloud Run, Azure Container Apps. Build a container, push to a registry, deploy.  Kubernetes. Required only when you already operate a cluster.  FastAPI Cloud. The team behind FastAPI launched a managed deploy service (fastapicloud.com); private beta with a public waitlist at the time of writing. Deploys via a single fastapi deploy command.A minimal Dockerfile for the container path:FROM python:3.13-slimWORKDIR /appCOPY requirements.txt .RUN pip install --no-cache-dir -r requirements.txtCOPY . .EXPOSE 8000CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]In front of the application sits an API gateway: rate limiting, authentication, routing, request transformation. The WSO2 API Manager handles this layer at enterprise scale across any cloud; lighter alternatives include Kong, AWS API Gateway, and Azure API Management.Observing the API in productionOnce the API is live, server metrics (CPU, memory) tell you whether the box is healthy. They do not tell you whether a specific customer is getting 500s on /orders/{id}. That is what API observability solves.Moesif API monitoring integrates with FastAPI through a middleware SDK and provides per-endpoint, per-customer, payload-level analytics. The data feeds back into product and platform decisions: which customers to support more closely, which endpoints to deprecate, which integrations are stalling at first-call.For AI/MCP traffic specifically, the WSO2 AI Gateway auto-generates an MCP server from your FastAPI app’s OpenAPI spec, so the same endpoints become agent-consumable without a separate build. The MCP and LLM proxy components inside the AI Gateway handle inbound agent calls and outbound LLM calls respectively.Next stepsA working Python REST API in 2026 is the FastAPI tutorial above plus three production layers: an API gateway, an authentication scheme, and an observability stack. The framework is the easy part. The platform around it is where most teams underinvest and end up paying for it later.If you want to see per-endpoint, per-customer analytics on your own Python API within an hour of integrating, start a 14-day Moesif free trial. No credit card required.Frequently asked questionsHow do you create a REST API in Python? Install FastAPI (pip install "fastapi[standard]"), create an app = FastAPI() instance in a main.py file, define route handlers with @app.get, @app.post, @app.put, @app.delete decorators, run fastapi dev main.py. Verify the API at http://127.0.0.1:8000/docs.Can you build a REST API with Python? Yes, and Python is one of the most common languages for it in 2026. The dominant framework choices are FastAPI for new APIs, Flask for small services, and Django REST Framework for existing Django applications.How do I call a REST API in Python? Use the requests library for synchronous calls or httpx for async. Example: import requests; response = requests.get("https://api.example.com/orders/42"); print(response.json()).Is FastAPI better than Flask for REST APIs? For new APIs, generally yes. FastAPI is async-native, type-safe, and auto-generates OpenAPI documentation. Flask is still excellent for small synchronous APIs and prototypes where you want minimal opinion.What is the best Python framework for building REST APIs? FastAPI for new projects (currently the top-ranked Python web framework in the JetBrains survey). Django REST Framework if you are already on Django. Flask for small or prototype services. aiohttp for highly async-heavy or WebSocket-heavy applications.How do I deploy a Python REST API? A managed platform (Vercel, Railway, Render, Fly.io) is the simplest. A container runtime (ECS, Cloud Run, Container Apps) is the standard for production at scale. FastAPI Cloud is a newer option built by the FastAPI team (private beta with a waitlist at fastapicloud.com at the time of writing).                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /technical/python-rest-api/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-acronym": {
          "title": "API Acronym: What Does API Stand For? (2026 Guide)",
          "content"	 : "The API acronym stands for Application Programming Interface. It is the contract that lets one piece of software ask another for data or for an action to be performed. When you tap “Pay with PayPal” on a shopping site, your browser is calling PayPal’s API. When ChatGPT looks up the weather, it is calling a weather API. The term has been around since the 1960s, but APIs as we know them today, HTTP, JSON, self-service signup, public documentation, are mostly a product of the last twenty years.                Monitor REST APIs With Moesif              14 day free trial. No credit card required.              Try for Free        This guide walks through what the acronym actually means, where the term came from, how an API call works end-to-end, the types of APIs you will encounter, the difference between APIs and the things they get confused with, and how the conversation changed in 2026 with the arrival of AI agents and the Model Context Protocol.What does the API acronym stand for?  API stands for Application Programming Interface. The Application is the software that exposes the interface; Programming signals that the interface is meant to be consumed by other code, not by a human clicking buttons; and the Interface is the contract that defines how the asking and answering happens.Each word in the acronym is doing work, and reading them in order is the cleanest way to remember the concept:  Application: the system being talked to. Could be a payments service, a maps server, a language model.  Programming: the consumer is code, not a person browsing a UI.  Interface: a documented contract: send these requests, get these responses.The acronym is sometimes spelled out as “Application Program Interface” (older usage from the 1960s and 1970s). The modern form, and the one used universally in 2026, is “Application Programming Interface.”Where the term “API” came from (the actual history)The phrase “application programming interface” first showed up in computer science literature in the 1960s, referring to the boundary between an application and the operating system it ran on. For decades it meant exactly that: OS-level function calls in C or assembly, with examples like the POSIX API on Unix and the Win32 API on Microsoft Windows. There was no HTTP involved.The term broadened in the 2000s as the web matured and services started talking to each other over HTTP. Three milestones from that era anchored the modern usage:  2000: Roy Fielding published his dissertation on Representational State Transfer (REST), defining the architectural style behind most modern web APIs.  2000: Salesforce launched what is widely cited as the first commercial web API, allowing partners to integrate against its CRM data over the internet.  2002: Jeff Bezos issued the “Bezos API Mandate” inside Amazon, requiring every team to expose its data and functionality through service interfaces. This is what eventually made Amazon Web Services possible.The third wave came in the 2010s when API-first companies showed that a business could be built entirely around exposing a single API to outside developers. Twilio launched its Voice API in 2008 (with SMS following in 2010) and grew into a multi-billion-dollar company. Stripe followed in 2011 with payments. By the mid-2010s, the assumption shifted: when someone said “API,” they almost always meant an HTTP-based interface returning JSON.That assumption holds in 2026, with one new wrinkle (covered later): some APIs are now also exposed through the Model Context Protocol for AI agent consumption.How an API actually works (with verified examples)The simplest API call has three parts: a client (your code), an endpoint (a URL), and a response (usually JSON).A working example you can run from any terminal:curl https://api.github.com/users/torvaldsThat single line is a complete API call. It sends an HTTP GET request to GitHub’s user API, asking for information about the user torvalds. GitHub’s server processes the request and returns a JSON document containing the user’s profile data: name, public repositories, follower count, account creation date.A more complex call adds authentication and a body:curl -X POST https://api.stripe.com/v1/charges   -u sk_test_YOUR_KEY:   -d "amount=2000"   -d "currency=usd"This calls Stripe’s API to create a $20 charge. The structure is the same, client, endpoint, response, but with more parameters. The -u flag adds an API key for authentication; the -d flags add fields in the request body.In both examples, your client sends an HTTP request to a documented URL. The server’s code receives it, runs the business logic (look up a user, charge a card), and returns a JSON document. That is the whole pattern. Everything else, auth schemes, rate limits, request validation, schemas, is implementation detail layered on top.For the deeper anatomy of what an API endpoint actually is, we have a dedicated guide.The types of APIs you’ll encounter in 2026The “API” acronym applies to a handful of shapes that share the same idea. The five most common in 2026:            Style      Default protocol      Best for                  REST      HTTP/JSON      Public web APIs, most B2B integrations              GraphQL      HTTP/JSON (single endpoint, query language)      Client-driven data fetching, mobile apps              gRPC      HTTP/2 binary      Service-to-service inside a mesh              SOAP      HTTP/XML      Regulated enterprise (banking, insurance)              Webhook      HTTP (inverted: provider calls you)      Event notifications      REST is the dominant style by a wide margin. If a 2026 article says “API” without further qualification, it almost certainly means REST/JSON over HTTP.A sixth shape that became relevant in 2025-2026 is the MCP server, which exposes an existing REST API in a form AI agent runtimes can consume directly. That is not a separate API style; it is a wrapper around an existing one. We come back to it in the 2026 section.API vs SDK vs libraryThese three terms get conflated constantly. They are related but distinct.  API: the interface itself. The contract that says “send these requests, get these responses.” The API lives on the server (or as a public spec).  SDK: Software Development Kit. A package of tools, libraries, code samples, and documentation that helps developers in a specific language call the API more easily. Stripe ships SDKs for Node.js, Python, Ruby, Go, PHP, and others, each wrapping the same underlying REST API.  Library: a reusable piece of code you import into your application. An SDK is usually built from one or more libraries plus auth helpers, retry logic, type definitions, and tutorials.In practice: the API is the thing being called; the SDK is what you import to make calling easier; the library is the actual code inside the SDK. A developer who has integrated Stripe will say “I use the Stripe SDK”, what they mean is “I import Stripe’s Node.js library, which wraps Stripe’s REST API.”The distinction matters because not every API has an SDK. Some APIs publish only a REST spec and let consumers use whatever HTTP client they want. Others ship SDKs in twelve languages. Both are legitimate.Real-world examples developers use every dayThe APIs you almost certainly touch in a normal workweek:  Stripe: payments. Charge cards, manage subscriptions, handle disputes.  Twilio: communications. Send SMS, place voice calls, run conversational flows.  OpenAI, Anthropic, Google Gemini: LLM completions. Generate text, classify content, run agentic workflows.  GitHub: code, issues, pull requests, and CI/CD events.  AWS, Google Cloud, Azure: infrastructure provisioning. Spin up servers, manage databases, schedule jobs.  Google Maps / Mapbox: geolocation, routing, geocoding.  Salesforce: CRM data, customer records, sales pipeline.  Slack: messaging integrations, app installations, bot interactions.Every company above exposes documented public APIs, ships SDKs in major languages, and operates a developer portal where outside engineers register, get credentials, and start integrating. Several of them are multi-billion-dollar businesses in part because the API itself is the product.What changed about APIs in 2026Two shifts that did not exist five years ago changed how the acronym is used in conversation.AI agents are first-class API consumers. When a model like Claude or GPT completes a task that involves looking something up, making a purchase, or writing a record, it is calling APIs. A meaningful share of API traffic in 2026 is non-human; the agent runtime translates a user’s natural-language intent into specific endpoint calls. APIs designed for human integration still work, but a few patterns matter more now: idempotency keys on POSTs (because agents retry), clear operationId and description fields in the OpenAPI spec (because agents read them to choose endpoints), and per-agent attribution in observability (because tracing traffic to a customer is no longer enough).The MCP protocol gave APIs a new surface. The Model Context Protocol, introduced in late 2024 and now broadly adopted, exposes existing REST APIs to agent runtimes in a semantically richer form. The WSO2 AI Gateway auto-generates an MCP server from any OpenAPI spec, so the same API a company publishes for human developers becomes agent-consumable without a separate build. The acronym still stands for Application Programming Interface; the “application” doing the programming is increasingly a model.These shifts do not change the definition. They change which design and operational decisions matter most.Tools you’ll use to work with APIs (a 2026 starter set)If you are new to APIs, the ecosystem of tools that surround them is its own learning curve. The set most working developers reach for in 2026:For exploring an API interactively. Postman is the most widely-used tool for poking at unfamiliar APIs; paste a URL, set headers, see the response. The open-source alternatives (Bruno, Insomnia, Hoppscotch) cover the same ground without the cloud sync. For terminal-first developers, curl and httpie are the command-line equivalents.For documenting and designing APIs. The OpenAPI specification (formerly Swagger) is the de facto standard for describing REST APIs in 2026. The tooling around it (Swagger UI, Redoc, Stoplight, Mintlify, ReadMe) turns the spec into interactive documentation, code samples, and try-it consoles. If you are designing an API, the spec comes first; almost every downstream tool consumes it.For generating client code. OpenAPI Generator (open-source) generates SDKs in 50+ languages from an OpenAPI spec. Commercial tools like Stainless and Speakeasy generate higher-quality SDKs with hand-tuned ergonomics for specific languages. The output is real code you can ship, not just scaffolding.For running APIs in production. An API gateway (WSO2, Kong, Apigee, AWS API Gateway, Azure API Management, Envoy) sits in front of your services and handles cross-cutting concerns: auth, rate limiting, transformation, logging. Pick the one that fits your cloud and team; the patterns are similar across them.For monitoring APIs in production. Server-level metrics (Datadog, New Relic, Prometheus + Grafana) tell you whether the system is healthy. API-specific observability platforms (Moesif is purpose-built for this) tell you what your customers are actually doing on a per-endpoint, per-customer basis; the views you need to make product and business decisions rather than just operational ones.For testing APIs. Postman includes a test runner; pytest with requests or httpx is the standard Python pattern; Jest with supertest is the standard JavaScript pattern. For contract testing (verifying the live implementation matches the spec), Schemathesis is the open-source standard.For agent-facing APIs. If your API will be called by AI agents through MCP, the WSO2 AI Gateway auto-generates an MCP server from your OpenAPI spec. The same spec drives the REST surface for human developers and the MCP surface for agents, with no second build to maintain.Most working developers in 2026 will touch the first three categories (explorer, OpenAPI tooling, SDK generation) regularly, the operational categories occasionally, and the agent-facing ones increasingly as MCP adoption grows.How to use APIs as a developer (practical onboarding)If you have never integrated an API before, the standard path from first sign-up to first successful call:  Read the docs. Find the endpoint you want to call and the auth scheme it uses.  Sign up. Create an account on the provider’s developer portal. Most are self-service and free for low volume.  Get credentials. Generate an API key, or complete an OAuth flow to receive a token. Keep credentials out of version control.  Make the first call. Most providers include a runnable curl example. Copy it, paste your key, run it.  Move into code. Install the language-specific SDK if one exists; otherwise use your language’s HTTP client (requests in Python, fetch in JavaScript).  Handle errors. Check the status code. 2xx is success; 4xx means your request was wrong; 5xx means the server failed.  Respect the rate limits. Most APIs publish a per-minute or per-hour ceiling. Build with backoff and retry from day one.The patterns above are what every API onboarding looks like in 2026, regardless of provider. They are also the patterns that show up across our broader API design principles guide.Next stepsThe API acronym is shorthand for the contract that powers most of the modern internet. If you are about to build or consume one, start a 14-day Moesif free trial to see per-customer analytics on every API call from the day you ship. No credit card required.Frequently asked questionsWhat does the acronym API stand for? Application Programming Interface. The Application is the software exposing the interface; Programming signals that the interface is for code, not humans; the Interface is the documented contract for asking and answering.Is API a verb or a noun? A noun. People sometimes verb it informally (“we API’d into Stripe”), but in writing it stays a noun.What does the acronym API stand for in Java? Same as everywhere: Application Programming Interface. In Java contexts the term often refers to the Java Class Library (the set of classes the JDK exposes), but the acronym’s meaning is unchanged.Is API the same as a web service? A web service is an API delivered over HTTP. All web services are APIs; not all APIs are web services. The OS-level APIs in Windows and Linux are not web services.Are all APIs free? No. Many APIs are free for low volume and paid above a quota. Some are paid from the first call (typically enterprise data or AI APIs). The acronym describes the interface, not the price model.What is an API key? A long secret string an API issues to identify which customer is making the call. Most public APIs use API keys for server-to-server authentication; OAuth tokens are common for user-facing flows.What is the difference between API and AI? API stands for Application Programming Interface, a contract between software components. AI stands for Artificial Intelligence, a category of computation. AI systems are often consumed through APIs (OpenAI’s API, Anthropic’s API), but the two acronyms refer to different things.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /technical/api-acronym/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-mastering-rest-api-for-test-essential-methods-and-tools-for-quality-assurance": {
          "title": "Mastering REST API for Test: Essential Methods and Tools for Quality Assurance",
          "content"	 : "Testing REST APIs is crucial for reliable software. This article zeroes in on harnessing REST API for test, focusing on the practicalities of verifying API performance and security. Learn the steps and techniques for testing REST API, including the tools and methods used, the challenges faced, and the significance of REST API testing in web applications. Skip the guesswork and discover surefire tools and techniques that fortify your testing protocol.Key Takeaways  A solid understanding of REST API and thorough preparation of the testing environment are crucial starting points for effective testing REST API, focusing on API documentation and detailed planning of test cases.  Mastering REST API testing requires testers to execute varied test scenarios and HTTP methods, validate response codes and data, and ensure comprehensive coverage across functional, integration, security, and load testing.  Employing the right combination of manual and automated testing tools, such as Postman and JMeter, allows testers to efficiently handle testing tasks, supporting continuous integration workflows and ensuring API reliability and performance.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding REST API in the Testing LandscapeIn the bustling urban landscape of modern web applications, REST APIs stand as the intricate network of pathways enabling swift and efficient communication between diverse web services. Just as a city planner meticulously considers every road and alleyway, a tester must approach REST API testing with strategic precision. It’s this vital cog in the wheel that ensures applications not only meet expectations but exceed them, delivering experiences that are as seamless as they are robust. To master this skill, one should consider following a REST API testing tutorial. Understanding the steps and techniques for testing REST API is crucial, as it involves using various tools and methods to ensure the reliability and performance of web applications.However, before diving into the testing process, it is imperative to set up the testing environment with the diligence of an architect reviewing blueprints. The API specifications act as the contract between the service and its users, detailing every nook and cranny of the expected outcomes. Like any expert craftsman, a tester’s first step is to understand the materials at hand, which, in this case, involves combing through API documentation and meticulously preparing the testing environment. This foundational knowledge is critical, serving as the beacon that guides every subsequent action in the testing arena.Crafting Test Cases for REST APIsWith the testing stage set, it’s time to sculpt our REST API test cases. Crafting test cases for testing REST API is an art in itself, demanding a comprehensive understanding of the API’s intended behavior and the possible deviations from it. It is through these test cases that we validate the REST API against its requirements, safeguarding against regressions and ensuring that the API’s functionality stands the test of time.In this crucible of validation, we employ a variety of test types, from the precision of functional unit testing to the rigor of integration and load tests. Each test type is a brushstroke that adds depth and detail to the overall picture, ensuring no aspect of the API’s performance or security is left to chance. As we delve deeper, we find that the success of REST API testing hinges on a series of well-defined actions, including verifying HTTP status codes, examining response payloads, and establishing performance benchmarks, all of which act as critical checkpoints along the way. In this context, it becomes essential to test REST APIs effectively, making testing REST APIs a crucial aspect of the process.Defining Test ScenariosAs our testing journey unfolds, we encounter a spectrum of test scenarios, each presenting its own set of challenges and opportunities. These scenarios range from basic positive tests that operate within the golden path of anticipated outcomes to negative tests that probe the application’s resilience against valid and invalid inputs alike.It is within these edge cases and negative scenarios that the REST API reveals its true mettle, handling unexpected inputs with grace and robustness. The API tester meticulously outlines scenarios that push the boundaries of the API’s capabilities, ensuring that when the unexpected strikes, the API stands ready to respond with steadfast reliability. Defining these test scenarios is a crucial part of testing REST API, as it helps in identifying potential weaknesses and ensuring comprehensive coverage.Choosing HTTP MethodsThe choice of HTTP methods in REST API testing is much like selecting the right tool for a job – each method is tailored to a specific action within the API’s repertoire. The HTTP methods commonly used in REST API testing are:  GET: This method functions as the observant eye, retrieving data without alteration.  POST: This method is the creator, birthing new resources into existence.  PUT: This method acts as the shaper of the API world, updating resources to reflect changes.  DELETE: This method removes resources from the API.By understanding and utilizing these HTTP methods effectively, you can ensure the proper functioning and behavior of your REST API. Choosing the correct HTTP methods is a fundamental aspect of testing REST API, as it ensures that each action within the API is executed correctly and efficiently.Understanding which API testing tool – or HTTP method – to wield is crucial, for it dictates the ebb and flow of the testing process. It is through these methods that the API tester orchestrates the symphony of request and response, ensuring that each note resonates with the intended purpose and outcome.Some common HTTP methods used in API testing include:  GET: retrieves information from the server  POST: sends data to the server to create a new resource  PUT: updates an existing resource on the server  DELETE: removes a resource from the serverPOST, for instance, is not merely about creation but about communicating information through the server in a manner that aligns with the REST API’s overall narrative.Validating Response Codes and DataWithin the realm of REST API testing, validating response codes and data is similar to tuning an instrument – each adjustment brings harmony to the ensemble. It’s essential to ensure that the correct HTTP status code accompanies each API request, be it the triumphant 201 status heralding a successful resource creation or the stern 403 forbidding unauthorized access. Testing REST API involves validating these response codes and ensuring the data returned is accurate and meaningful.But the melody doesn’t end with the status code; the response body must also sing the right tune. The JSON payload must be meticulously validated for correct field names, types, and values, ensuring that even in the face of errors, the API responds with grace and clarity. Furthermore, adherence to the established data formatting schema is paramount, as any discordance can lead to a cacophony of miscommunication and potential breaches.And let’s not forget the HTTP headers, those unsung heroes that must carry their load without revealing sensitive information, safeguarding the integrity of the response.The Art of Manual Testing REST APIsStepping into the world of manual testing is like embracing the craftsmanship of old – where each request is sent and each response recorded with the careful attention of an artisan. Manual testing of REST APIs requires an eye for detail, observing response codes, messages, and bodies as one would scrutinize a fine tapestry. It is in this deliberate process that the functionality, reliability, performance, and security of RESTful web services are truly revealed. Testing REST API manually involves various steps and techniques to ensure thorough validation.Manual testing is not merely about observation; it’s also about interaction. Functional testing demands a deep dive into the API’s compliance with its documentation, while usability testing ensures that the interface is as intuitive as it is effective. As with any masterpiece, the maintenance of REST API health and performance is achieved through monitoring and logging practices, tracing patterns and anomalies with the diligence of a historian preserving history’s greatest works.Tools of the Trade: REST API Testing ToolsIn the toolbox of a REST API tester, one finds an array of tools, each offering unique features and capabilities to suit the varied landscapes of testing requirements. Renowned tools like Katalon Studio, Postman, JMeter, and their contemporaries stand ready to assist the tester in conquering the vast expanse of REST API testing. These tools are the allies in our quest, providing the means to manually test APIs, run automated sequences, and validate responses with precision and efficiency. Additionally, testing REST API involves understanding the tools and methods used, the challenges faced, and the significance of this process in web applications.Yet, among these stalwarts, some tools shine for their simplicity and directness. Command-line utilities such as cURL and HTTPie strip away the complexities, offering a straightforward approach to sending and receiving HTTP requests. Meanwhile, Resty serves as a trusted companion for those who seek the elegance of cURL with added ease of use, all from the command line. These tools are the trusty steeds that carry us through the testing terrain, whether we’re navigating the familiar pathways of manual testing or venturing into the uncharted territories of automation.Postman: An All-in-One SolutionEnter Postman, the Swiss Army knife of REST API testing, embodying the versatility and comprehensiveness that testers yearn for. With its Collections, Workspaces, and Built-in Tools, Postman serves as the command center from which requests are dispatched, responses analyzed, and automated tests orchestrated. Its features include:  Collections: organize and group requests for easy management  Workspaces: collaborate with team members and share collections  Built-in Tools: generate code snippets, test scripts, and monitor APIs  Documentation: create detailed documentation using Markdown  Postman Echo: a sandbox environment to craft and validate sample API callsWith Postman, testers can perform API testing with the finesse of a seasoned artisan. It is an essential tool for testing REST API, providing a robust platform to ensure the reliability and performance of web applications.Collaboration in Postman transcends the solitary confines of manual testing, fostering a community of testers sharing workspaces, commenting on progress, and building upon each other’s expertise. Automated test scripts, written in JavaScript, run in the Post-response tab, providing immediate feedback and insights that sharpen the tester’s edge.And then there’s Postbot, the AI that suggests and generates tests from interactions, a true testament to Postman’s commitment to innovation and efficiency in REST API testing.Advanced REST Client: Streamlining Chrome-Based TestingFor those who prefer the familiar confines of a browser, the Advanced REST Client emerges as a beacon of simplicity. As a Chrome extension, it provides a welcoming interface for sending HTTP requests and delving into the depths of server responses. The installation is a breeze, and with a few clicks, the tester is equipped to embark on the REST API testing journey, armed with nothing more than their browser and a keen sense of curiosity. It is particularly useful for testing REST API, allowing users to explore various testing techniques and tools.The process is straightforward yet powerful – specify the request URL, choose the HTTP method, and if needed, configure headers and parameters before sending off the request into the digital ether. The response, once received, becomes a canvas for analysis, revealing truths about the API’s behavior and performance. This tool, though unassuming, is a mighty ally in the manual testing arsenal, offering clarity and convenience to testers of allAutomating Your REST API TestsAs the digital landscape evolves, so too does the need for efficiency in testing. Automating REST API tests is not just a convenience; it’s a strategic move that aligns with the agile and DevOps ethos of rapid deployment and continuous integration. With REST API automation testing, automation transforms the monotonous into the dynamic, allowing for more comprehensive and consistent coverage of functional, integration, and regression tests, thus elevating the quality of software to new heights. Testing REST API in an automated fashion ensures that the process is streamlined and effective.Automated validation of API responses is a symphony of checks and balances, ensuring correctness in status codes, response payloads, and application state, all while maintaining a keen eye on performance. It’s a data-driven approach, where complex parameters and functionalities are examined with the finesse of a maestro, ensuring that the API’s behavior aligns with the grand composition of user expectations and business requirements.The journey from manual to automated testing is a metamorphosis that promises not just speed but also the precision and reliability that modern-daySelecting Automation ToolsThe quest for the perfect automation tool is akin to a knight’s search for the Holy Grail – a pursuit filled with consideration and discernment. Key to this quest is understanding the specific needs of the project at hand, and assessing features against the intricate tapestry of testing requirements. The chosen tool must offer a suite of capabilities that cater to the various hues of REST API tests, from the broad strokes of functionality to the fine lines of integration, all the while ensuring seamless incorporation into the existing development milieu. Selecting the right tool is crucial for effectively testing REST API, ensuring robust and reliable web applications.Take for instance Telerik Test Studio, a paragon of versatility, supporting UI, REST API, and load testing with a focus on integration that makes it a coveted asset in any tester’s toolkit. The decision to adopt an automation tool, therefore, must be made with a clear vision of the project’s landscape, considering the nuances of ease of use and the ability to meld into the continuous flow of CI/CD processes. It’s a decision that shapes the very fabric of the testing process, and one that must be made with the utmost care and foresight.Writing Automated Test ScriptsThe creation of automated test scripts is a craft that marries logic with creativity. Built-in libraries like Moment.js, Lodash, and Faker.js serve as the palette from which testers draw the colors of functionality – be it date manipulation, data generation, or iterating over collections. Tools such as ReadyAPI offer the canvas, with features like data source loops and assertions that enable the artist to validate and manage test data with precision. Testing REST API is an essential part of writing these automated test scripts, ensuring that web applications function correctly and efficiently.The Postman setNextRequest method is a brushstroke that defines the sequence of request execution, crucial for managing the flow of automated tests. Through Postman’s API, the tester can perform CRUD operations on collections and environments, managing dynamic data and state between calls with the deftness of a maestro. For those who speak the language of Java, REST-assured is the instrument of choice, harmonizing with industry best practices to simplify the testing of REST services.Load Testing REST APIs for PerformanceThe crucible of load testing for REST APIs is akin to a theater where every performance is scrutinized under the spotlight of user demand. The objective is to push the stage – the API – to its limits, assessing its response time and behavior under various scenarios. This rigorous testing ensures that no timeout errors occur when the audience – the users – crescendo, demanding more from the system. Performance testing is the dress rehearsal for the API, exposing it to the rigors of a live environment and pinpointing the exact moment when the curtain might fall, thus preventing any unwelcome surprises during the main act. Additionally, testing REST API under load conditions helps identify potential bottlenecks and ensures the system’s robustness.In the hands of a seasoned tester, tools like:  Apache JMeter  Loadmill  Artillery  Gatling  Blazemeterbecome the instruments that orchestrate the simulated loads. These tools echo the diverse voices of potential users, each with their unique requests and expectations, ensuring that the REST API can handle the chorus without missing a beat.Monitors in Postman act as the vigilant audience, keeping a close watch on the API’s health and performance, ensuring that each show – or release – maintains the high standards set by previous performancesSecurity Testing: Protecting Your REST APIsIn the fortified realm of REST API testing, security is the gatekeeper, steadfastly guarding against the ever-present threat of invasion. Uncovering security vulnerabilities is not just about patching holes; it’s a preventative measure that secures the citadel from future assaults, ensuring the effectiveness of authentication and the robustness of authorization controls. Security testing is a meticulous sweep of the castle grounds, scrutinizing every potential point of entry, from input validation to error handling, and erecting barriers such as rate limiting to repel would-be attackers and abuse. Testing REST API for security vulnerabilities is crucial to ensure the integrity and safety of web applications.Modern techniques like feedback-based fuzz testing and white-box automation are the siege engines of API security testing, efficiently breaching the walls to reveal weaknesses that might otherwise go undetected. Adhering to industry standards and engaging in continuous monitoring are akin to the ongoing training of the royal guard, a commitment to excellence that ensures the security measures in place are not just adequate but exemplary. It is in the segregated testing environment, far from the prying eyes of the public domain, that these measures are honed to perfection, safeguarding the production systems while allowing for rigorous and unfetteredNavigating REST API Parameter CombinationsNavigating the vast sea of REST API parameter combinations requires the precision of a seasoned cartographer. The challenge lies not just in charting a course through familiar waters but in anticipating the currents and eddies that arise from untested combinations of parameters. The validation process becomes a voyage of discovery, where incorrect data types or values beyond predefined ranges are like hidden reefs that threaten to undermine the API’s journey. Testing REST API involves navigating these parameter combinations to ensure robust and secure web applications.The permutations of parameters are a constellation of possibilities, each combination offering a different glimpse into the REST API’s behavior. It is the tester’s mandate to explore these realms, ensuring that no sequence or dependency leads to an undesired state that could compromise the security or functionality of the system. Coverage-guided testing methods are the stars by which testers navigate, providing insightful metrics that illuminate the path forward and generate error reports that serve as the logbook for the journey.Implementing dependency management principles infuses this exploration with the additional foresight needed to monitor the process effectively, ensuring that the voyage not only reaches new horizons but does so with unparalleled efficiency and security.Sequential and Dependency Testing for REST APIsIn the tapestry of REST API testing, each thread must be woven with care to maintain the integrity of the overall design. Sequential and dependency testing ensure that the narrative of API calls unfolds as intended, with each API’s function critically examined to avoid the unraveling of the application’s storyline. Dependency management becomes the guide to seamlessly integrate third-party APIs and resolve any tangled threads that may arise, ensuring the tapestry remains intact and scalable across enterprise projects. Additionally, testing REST API involves various steps and techniques to validate the functionality, performance, and security of the APIs.Chaining REST APIs in end-to-end workflow tests is akin to following the plot of a novel, where each chapter builds upon the last to create a cohesive and engaging story. This approach simulates the complete use cases of the application, validating the integration between different software modules and ensuring that the user’s journey through the application is both logical and seamless. In this intricate dance of APIs, it is often wise to first test those that perform critical, single-function tasks, as they lay the foundation upon which more complex sequences are built. It simplifies debugging and ensures that the core functionalities are robust before more elaborate testing begins.Ensuring REST API Reliability and ConsistencyAt the heart of every REST API lies the promise of reliability and consistency, a pledge that every request will be met with the same level of quality and precision. Reliability testing is not just a routine check; it’s a commitment to the user that the application will perform admirably under all circumstances, a beacon of trust in an unpredictable digital sea. The goal is to ensure that the REST API delivers consistent connections and results, much like a dependable friend who is always there when needed. Testing REST API is crucial to ensure reliability and consistency in web applications.Maintaining this level of service requires that the REST API operates consistently under a variety of conditions, weathering the storms of peak traffic and the lulls of inactivity. It is this steadfast operation that fosters user trust and cements their satisfaction with the application. Consistency is the thread that weaves through every aspect of the API, from the predictability of its responses to the maintenance of data formatting, ensuring that the user’s experience is never disrupted by missing functionality or unexpected behavior.REST API Testing Checklist: A Tester’s CompanionAs the journey of REST API testing draws to a close, a trusty checklist becomes the compass that guides the tester through the final checks and balances. This checklist is the distillation of the testing process, a tool that ensures no stone is left unturned, and every parameter combination is thoroughly explored. It begins with the validation of business logic at the code level, steering clear of the user interface’s siren song to focus on the underlying structure of the API.Functional testing should commence with positive scenarios, the sunlit paths of the API’s capabilities, before venturing into the thicket of negative and edge cases. This progression from certainty to uncertainty ensures that the API can handle not just the expected but also the unexpected with equal aplomb. The checklist is a testament to the tester’s journey, a record of all the trials and triumphs that come with ensuring an API’s functionality and reliability. It serves as a constant reminder that while the path of REST API testing is intricate, with the right tools and a methodical approach, it is a path that leads to triumph. Including steps and techniques for testing REST API in the checklist ensures comprehensive coverage and thorough validation.SummaryAs we reflect on the journey through the multifaceted world of REST API testing, it’s clear that this is a domain where precision meets creativity, and rigor meets innovation. We’ve traversed the landscape from crafting test cases to ensuring reliability, equipped with an arsenal of tools and methodologies that turn the daunting into the attainable. REST API testing is not merely a task; it’s an art form that requires the tester to be part detective, part craftsman, and part visionary. This article also delves into the steps and techniques for testing REST API, highlighting the tools, methods, and challenges involved.Let this guide be the beacon that illuminates your path in the realm of REST API testing. Embrace the challenges and complexities with confidence, knowing that each step taken is a stride towards excellence. As you continue to explore and master the intricacies of REST APIs, may your applications not only function but flourish, delivering experiences that resonate with quality and reliability.Organizations looking for the best tools to support their API management can leverage Moesif’s powerful API analytics and monetization capabilities. Moesif easily integrates with your favorite API management platform or API gateway through one of our easy-to-use plugins, or embed Moesif directly into your API code using one of our SDKs. To try it yourself, sign up today and start with a 14-day free trial; no credit card is required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Mastering-REST-API-for-Test-Essential-Methods-and-Tools-for-Quality-Assurance/",
          "author": "Preet",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-top-benefits-of-api-observability-for-modern-applications": {
          "title": "Top Benefits of API Observability for Modern Applications",
          "content"	 : "API observability is essential for enhancing performance, speeding up issue resolution, and tightening security. An API observability tool is crucial for understanding and managing the complex web of API interactions within modern enterprise applications. With the benefits of API observability, you can dive deep into API behavior, ensuring reliability and better user experiences.In today’s digital landscape, where APIs act as the backbone of many applications, having robust observability is more critical than ever. It allows organizations to monitor, debug, and improve their APIs continuously. This proactive approach not only minimizes downtime but also enhances the overall efficiency of the API ecosystem. By leveraging advanced observability tools, businesses can gain actionable insights into API performance metrics, identify potential bottlenecks before they escalate, and ensure seamless integration between various services.API observability fosters a culture of collaboration among development, operations, and security teams. By providing a unified view of system health and performance, it enables cross-functional teams to work together more effectively, ensuring that APIs are not only high-performing but also secure and compliant with industry standards. This holistic approach to API management ultimately drives innovation and supports the achievement of broader business objectives.Key Takeaways  API observability improves performance by identifying bottlenecks, optimizing resource allocation, and ensuring smooth running, giving developers the visibility to predict and prevent issues.  Observability tools speed up issue resolution with real-time error detection, root cause analysis, and reduced downtime, so you can identify and fix issues in API-driven applications quickly.  Strengthening API security with observability means anomaly detection, data breach prevention, and compliance monitoring, so you have secure, compliant API environments that protect sensitive data and user trust.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Enhancing API PerformanceAPI observability is a total game changer in software development. By giving you a full view of API performance and user experience, observability tools let you understand, manage, and optimize your APIs for performance. This goes beyond traditional API monitoring, giving you request flow, transaction traces, and logs to troubleshoot complex issues and continuous improvement and scalability in API-driven applications. With an observability tool and API management, you can ensure your APIs run smoothly and improve the overall user experience by drilling into the API’s performance and providing insights into the API’s performance and usage patterns.The building blocks of API observability, structured logs, metrics, and traces let you predict and prevent issues so your APIs can do their job efficiently and reliably. By looking at API data and monitoring API calls you can proactively identify expected and unexpected issues and have a smoother operation and better user experience.The following sections will go into more detail on API performance, identifying bottlenecks, optimizing resource allocation, and monitoring API performance.Identifying Performance BottlenecksPinpointing the main source of performance problems in modern API applications can be challenging due to the increasing complexity of these systems. Traditional monitoring tools often fall short, but API observability tools, with their ability to track API requests from start to finish, provide a clear picture of where delays occur. Distributed tracing, a key feature of observability, visualizes the journey of a request through various services, helping identify bottlenecks and inefficiencies.By representing the lifecycle of a request as it travels through different components, traces help developers follow the request’s journey and pinpoint delays caused by components outside of direct control, such as network errors. This detailed sequence of events within an API request is invaluable for optimizing performance and ensuring that APIs perform at their best.Optimizing Resource AllocationOptimizing resource allocation is a crucial aspect of maintaining high API performance. API observability enables better resource management by analyzing high-cardinality data, which helps identify the busiest API endpoints and allocate resources accordingly. Insights gained from this analysis provide information on how to allocate resources more effectively, ensuring that APIs perform optimally under varying loads.Distributed tracing further aids in visualizing the complete journey of a request, helping to optimize resource allocation in microservices architectures.Ensuring Smooth OperationRunning your APIs smoothly means continuous monitoring and predictive issue detection. By detecting issues early continuous monitoring prevents issues and ensures information delivery is reliable. Enhanced observability reduces downtime by giving you fast response times and real-time analytics which gives you instant insight into API performance and allows you to make quick decisions. Continuous monitoring also helps you track performance benchmarks and drill into the API’s performance.Centralized dashboards in observability tools like Prometheus and Grafana give you real-time data visualization so you can stay informed and react to issues as soon as they happen. Telemetry data from observability tools also lets DevOps and site reliability engineers see the internal state of the API so they can make better decisions and run smoothly.Accelerating Problem ResolutionIn the fast-paced world of API-driven applications, the ability to quickly resolve problems is crucial. API observability tools provide detailed insights into errors, allowing teams to:  View real-time data  Spot issues as they happen  Facilitate swift resolutions By correlating logs, traces, and metrics, these tools enable rapid root cause analysis, reducing the time needed to identify and resolve problems.Advanced observability tools can even predict potential issues before they escalate, further reducing downtime and ensuring a reliable API ecosystem. The following subsections will explore the specific benefits of real-time error detection, root cause analysis, and reduced downtime in more detail.Real-Time Error DetectionReal-time error detection is a key part of API observability so you can identify and resolve issues immediately. Observability tools give you immediate alerts for performance deviations so you can take a closer look and fix them fast.By giving you real-time data visualization and alerting these tools simplify the communication of issues and system status so errors are detected before customers report them.Root Cause AnalysisRoot cause analysis in API observability streamlines troubleshooting by correlating different data points and events to pinpoint exact issues. Automated root cause identification leverages structured logging to quickly address API problems, facilitating effective problem-solving.Observability tools can trace system changes, such as delays, by examining various factors like database queries or new code deployments, providing detailed insights needed for accurate root cause analysis.Reduced DowntimeReduced downtime is achieved through:  Proactive issue detection  Quick resolution of issues  Real-time error detection  Addressing issues the moment they are identified  Using historical data to alert teams of potential future problems  Allowing for quick rollbacks or disabling features to minimize service interruptionsThis approach ensures a seamless user experience and optimal performance.Strengthening API SecurityAPI security is paramount in modern software development, and observability plays a crucial role in strengthening it. By monitoring abnormal API usage patterns in real-time and utilizing predictive algorithms, observability tools can detect security threats and forecast potential breaches before they occur. This proactive approach ensures a secure and compliant API environment, protecting sensitive data and maintaining user trust, while also contributing to overall API health.Improved observability enables continuous monitoring of API access and usage, using logs and traces to track user behavior and identify potential unauthorized access attempts. This also strengthens compliance by ensuring a proactive approach to monitoring and addressing any irregularities. The following subsections will delve into specific aspects of strengthening API security, such as anomaly detection, preventing data breaches, and compliance monitoring.Anomaly DetectionAnomaly detection in API observability identifies unusual patterns that may signal security threats. Through continuous monitoring, observability tools provide a holistic view of system performance, helping in early detection of anomalies.Advanced anomaly detection tools use machine learning algorithms to identify and flag unusual API behavior, ensuring that potential security issues are addressed promptly.Preventing Data BreachesPreventing data breaches is a critical aspect of API security. Continuous monitoring plays a vital role in early detection of potential security threats, allowing for quick identification of unauthorized access attempts.By continuously analyzing log data and monitoring APIs, observability tools facilitate the early detection of data leaks, significantly reducing the risk of data breaches.Compliance MonitoringCompliance monitoring ensures adherence to regulatory requirements by tracking API access and usage. Observability platforms facilitate compliance by logging and analyzing API interactions, providing real-time compliance reports.This continuous monitoring helps ensure that APIs meet regulatory requirements consistently, maintaining a secure and compliant API environment.Improving User ExperienceImproving user experience is a key goal of API observability. By understanding user behavior and tailoring API responses to meet user needs, observability tools enhance user satisfaction and engagement. Analyzing API usage patterns can highlight parts of the user journey that may be causing frustration or high drop-off rates, allowing for targeted improvements.Additionally, API observability helps identify potential issues impacting user experience before they become actual problems, ensuring a seamless interaction with applications. The following subsections will explore how understanding user behavior, tailoring API responses, and enhancing reliability contribute to improving user experience.Understanding User BehaviorUnderstanding user behavior through observability data helps you tailor APIs to user needs. By analyzing logs and metrics observability tools give you insights into how users interact with APIs, and what usage patterns and features they prefer.API logs + user behavior tracking gives you a complete view of user interactions so you can see usage patterns and preferences.Tailoring API ResponsesTailoring API responses based on user preferences and contextual data enhances relevance and personalization. Observability data helps in understanding user preferences, enabling the customization of API responses to deliver more relevant and personalized content or actions.This approach leverages contextual data to offer a more relevant user experience.Enhancing ReliabilityReliability through consistent API performance means smooth user interaction with applications. Observability tools contribute to higher system reliability by fixing issues before they cause extended downtime.By giving you a complete view of how software components work together observability means users experience minimal downtime.Supporting Business GoalsSupporting business goals with API observability means making informed decisions, optimizing API ecosystems and driving innovation. By giving you insights into usage patterns and trends observability tools help you align your API strategy with your organization’s goals. Connecting observability metrics to CRM or marketing automation tools gives you a complete customer journey map so you can make decisions that grow your business.Understanding how APIs work and interact is key to smooth digital experiences and business goals. The following sections will go into more detail on how making informed decisions, optimizing API ecosystems and driving innovation supports business goals.Informed Decision MakingInformed decision-making is achieved through deep insights into API performance and user behavior. Data from observability tools offers a detailed understanding of how APIs function and interact, leading to more informed strategic planning.User analytics provide essential information on user behavior, helping businesses make data-driven decisions that align with their goals.Optimizing API EcosystemOptimizing API ecosystems through observability data means you can streamline operations and overall performance. Traditional API monitoring tools are limited but API observability gives you a mature monitoring capability by actively exploring API data to understand both known and unknown issues.Data correlation in observability links related data points to give you a complete view of API operations and how they impact the system.Driving InnovationDriving innovation by identifying trends and patterns in API usage and interactions is a key benefit of observability. Insights gained from observability encourage continuous improvement in API development, helping businesses innovate based on how APIs are used and interacted with.This approach fosters innovation and keeps businesses ahead of the curve in modern software development.Facilitating CollaborationCollaboration between development, operations and security teams is another big benefit of API observability. By giving everyone a shared view of the system and issues observability promotes synergy and aligned efforts across teams. This common ground means everyone is on the same page so you can solve problems and make decisions faster.Observability facilitates collaboration by quickly spotting and responding to potential issues before they hit customers. The following sections will go into more detail on cross functional insights, streamlined communication and single troubleshooting.Cross-Functional InsightsCross-functional insights from observability data enable teams to work together effectively in problem-solving efforts. Shared observability data provides valuable insights into system behavior, leading to unified troubleshooting and more efficient resolutions.By leveraging advanced analytics, teams can gain deep insights into system performance and collaborate more effectively to address issues.Streamlined CommunicationCentralized data and dashboards from observability tools means everyone has access to the same information so communication is more efficient and teams can work together seamlessly.This gives teams the ability to make decisions fast and together.Unified TroubleshootingUnified troubleshooting is achieved through integrated observability platforms that aggregate diverse data points and insights. Tools like OpenTelemetry bring together operational data, providing a holistic view necessary for effective troubleshooting.This integrated approach supports joint troubleshooting sessions, significantly enhancing efficiency and collaboration among teams.SummaryAPI observability is a game changer for modern API-driven applications. By improving API performance, speeding up issue resolution, strengthening security, user experience, business goals, and collaboration observability gives you complete and proactive API management. This gives you the ability to predict and prevent issues, optimize resource allocation, and run smoothly so you can have a more reliable and efficient API.In summary, API observability is not just about monitoring your APIs; it’s about getting insight, continuous improvement, and innovation. The benefits are clear: performance, uptime, security, user experience, and collaboration. With API observability you can align your API strategy to your business goals and make decisions that drive growth.Organizations looking for the best tools to support their API management can leverage Moesif’s powerful API analytics and monetization capabilities. Moesif easily integrates with your favorite API management platform or API gateway through one of our easy-to-use plugins, or embed Moesif directly into your API code using one of our SDKs. To try it yourself, sign up today and start with a 14-day free trial; no credit card is required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Top-Benefits-of-API-Observability-for-Modern-Applications/",
          "author": "Preet",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-moesif-aws-stripe-ai-api-part-2": {
          "title": "Using Moesif, AWS, and Stripe to Monetize Your AI APIs Part-2: Setting up Metering and API Access",
          "content"	 : "  This is the second part of a four-part series about AI API monetization. We recommend you go through the first part of the series where we integrate the platforms before proceding with the second part.In the previous article, we set up the AI API with AWS Lambda and Gateway, integrated it with Moesif, and then connected Stripe with Moesif. We now have the infrastructure to begin billing for API usage.In this article, we move on to configuring Moesif with the following steps in the API monetization journey:  Pricing  Metering  Access control and governanceFirst, let’s set the prices we want to charge for API usage in Moesif.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Table of Contents  Table of Contents  Create Your API Pricing in Moesif          Create the Plan      Create the Prices                  Create Prices for Input Tokens          Create Prices for Output Tokens                      Metering API Usage With Moesif          Create the Billing Meter for Input Tokens      Create the Billing Meter for Output Tokens        Enforcing Pre-Paid Access to the API With Moesif Governance Rules  Blocking API Access For Users With Overdue Invoices  ConclusionCreate Your API Pricing in MoesifIn Moesif, you can configure the plans and prices for APIs through the Moesif Product Catalog. Product Catalog allows you to configure your plans and prices conveniently in Moesif, and Moesif automatically creates them in Stripe. This enables you to do everything within the Moesif platform instead of bouncing between the two.For our AI API example, we want to create two distinct prices for our AI API Plan. If you have multiple plans or tiers, you can duplicate what we do here for each plan you want to create. To simplify things, we create a single plan with two prices, one for each token type: input and output.In this section, we create a Plan for the AI API and then two prices for each token type. A Plan in Moesif translates to Product in Stripe. For more information, see Provider Mapping.Create the PlanTo create the plan in Moesif, follow these steps:  Log into Moesif Portal.  Select Product Catalog in the navigation menu.  In the Plans screen that appears, select Create New.      Then add the following details about the new Plan:    a. Enter a name for your Plan.    b. Select Stripe from the Billing Provider dropdown.    Lastly, select Create.For example, here we create a plan “My AI API Plan”:Next, we define the prices for input and output tokens.Create the PricesAfter selecting Create and creating a plan, a dialog appears confirming that you’ve created the Plan successfully. The dialog also prompts you to create a price for the plan by selecting Create Price. Alternatively, you can select Product Catalog and then select Prices in the navigation menu.Create Prices for Input TokensIn the Create Price screen, follow these steps:  Enter a price name..  In Linked Plan, select the plan you created in the preceding section.  Select Per Unit in Pricing Model. If you have another pricing model you want to use, you can select it from the dropdown menu and configure it for your use case.  In Usage Measurement Method, select Stripe Price Meter. Then select from an existing Stripe meter or create a new one. Select Month as the period of time for aggregating usage and Sum as the aggregation method.  In Price Structure, set $0.50 for the unit  price, 1000000 (one million) for the number of units, and up as the rounding direction.  Keep Tax Structure at Auto.  Select Create.For example, here we create a price called “Input Tokens”:  Once you save the price, a workflow dialog appears to create a Billing Meter or Governance Rule. Since we create these components later, skip this for now and exit the dialog.Create Prices for Output Tokens  Select Product Catalog and then select Prices in the navigation menu.  Select Create New.  Enter a price name.  In Linked Plan, select the plan you created.  Select Per Unit in Pricing Model. If you have another pricing model you want to use, you can select it from the dropdown menu and configure it for your use case.  In Usage Measurement Method, select Stripe Price Meter. Then select from an existing Stripe meter or create a new one. Select Month as the period of time for aggregating usage and Sum as the aggregation method.  In Price Structure, set $1.50 for the unit  price, 1000000 (one million) for the number of units, and up as the rounding direction.  Keep Tax Structure at Auto.  Select Create.You now have a plan and two associated prices for input and output tokens. Let’s begin to meter API usage so that Moesif can report usage statistics to Stripe.Metering API Usage With MoesifIn this section, you’ll learn to use Moesif’s Billing Meter to meter API usage. The Billing Meter will define these requirements for metering usage:  The plan and price the API usage you want to report to or bill against.  The traffic you want to meter—for example, URI routes and response status.  How you want to meter the traffic—for example, per API call and unique user.Let’s create two billing meters for the two token types.Create the Billing Meter for Input Tokens  Select + Create New in the left navigation menu and then select Billing Meter.  Enter your meter name.  Select Stripe from the Billing Provider dropdown.  Moesif then automatically shows you the products and prices you have associated with the billing provider. Select the Plan and associated price for input tokens that you created earlier.  Set the reporting period to Every 5 minutes.      In the Filters pane, add the following criteria:    a. Set Request.URI Route to /ai-chat.    b. Set Response.Status Code to 200 OK        In the Metrics pane, select Event Count and then select Select Field… from the dropdown. This lets you define a custom metric field.    a. From the field selector, select response.body.request_payload.usage.prompt_tokens.    b. Set the aggregation function to sum (Unweighted).    Select Create to create the meter.  Optionally, you can test the meter by selecting Test Meter afterwards. For more information, see Testing Billing MetersFor example, here we create the meter “Input Token Meter” for input tokens.Create the Billing Meter for Output Tokens  Select + Create New in the left navigation menu and then select Billing Meter.  Enter your meter name.  Select Stripe from the Billing Provider dropdown.  Moesif then automatically shows you the products or plans and prices you have associated with the billing provider. Select the Plan and associated price for output tokens that you created earlier.  Set the reporting period to Every 5 minutes.      In the Filters pane, add the following criteria:    a. Set Request.URI Route to /ai-chat.    b. Set Response.Status Code to 200 OK        In the Metrics pane, select Event Count and then select Select Field… from the dropdown. This lets us define a custom metric field.    a. From the field selector, select response.body.request_payload.usage.completion_tokens.    b. Set the aggregation function to sum (Unweighted).    Select Create to create the meter.  Optionally, you can test the meter by selecting Test Meter afterwards. For more information, see Testing Billing Meters.At this point, the billing meters we have created will meter usage based on the criteria we set for each API call. Then, they sync this usage to Stripe to accurately charge the user.In the next steps, we put some restriction policies on access. These will allow you to block users from accessing the API when they run out of pre-paid credits, or have an overdue invoice if they are post-paid users.Enforcing Pre-Paid Access to the API With Moesif Governance RulesTo block API users from accessing the API when they have run out of credits, we use a Moesif Governance Rule. Moesif gives you several templates to create a rule, including one where Moesif blocks the user when their balance reaches zero.To make this Governance Rule from Moesif Portal, follow these steps:  Select Quotas & Governance from the navigation menu and then select + Add New. Alternatively, select + New and then select Gov Rule/Quota.  Select the template Block when Zero Prepaid Balance Remaining.  The next dialog shows a prompt to create a cohort for the governance rule automatically. Select Continue to create the cohort.     After this point, the rule becomes active. Since we haven’t specified what to block users from, the rule requires that users have credits to access any endpoint.  Select Block What to show the Regex Criteria pane.  Select Request Route from the Select Field list and enter "/ai-chat" as the regular expression criteria.  Select Save.If you follow these steps, the governance rule looks like this:This governance rule currently blocks any user who tries to access the /ai-chat endpoint with zero prepaid credits. If you only have a single endpoint in your API, you don’t need to explicitly tell Moesif what to block. However, if you have multiple routes, you may want to ensure that this only applies to specific routes.You may also have users with post-paid subscriptions. If you want block them from accessing the API when they have overdue invoices, you have to take a slightly different approach. Let’s look at how to implement that next.Blocking API Access For Users With Overdue InvoicesFor blocking API users from accessing the API when they have an overdue invoice, Moesif also offers a governance rule template.  Select Quotas & Governance from the navigation menu and then select + Add New. Alternatively, select + New and then select Gov Rule/Quota.  Select the template Block on Unpaid Invoices.  The next dialog shows a prompt to create a cohort for the governance rule automatically. Select Continue to create the cohort.     After this point, the rule becomes active. Since we haven’t specified what to block users from, the rule requires that users have credits to access any endpoint.  Select Block What to show the Regex Criteria pane.  Select Request Route from the Select Field list and enter "/ai-chat" as the regular expression criteria.  Select Save.This governance rule currently blocks any user who tries to access the /ai-chat endpoint with overdue invoices. This rule only makes sense to implement if you use a post-paid billing model. For pre-paid, you can make use of the previous rule. In the case that users can use either post-paid or pre-paid, you can enforce both rules.Using Governance Rules, you can also enforce pre-paid quotas or keep post-paid subscribers within the ranges they have selected, stopping them from bumping to the following usage tier. For more information on how to use governance rules to enforce tier-based billing, see Implementing Subscription Tiers with Moesif and Stripe.ConclusionIn this article, we’ve achieved quite a lot in terms of monetizing our AI API with with some robust toolings:  The pricing for the APIs.  API usage metering with Moesif Billing Meter.  API Access rules using Moesif Governance Rules.Now we can confidently bill users for their usage and block them when they run out of credits or have overdue invoices. At this point, we can push the API and billing infrastructure to production. In the next tutorial in this series, we will cover how to add credits to a user’s account so that they can use the API and burn through credits.Want to follow along as we build out this billing infrastructure to monetize APIs? Sign up for a free trial of Moesif and follow this article series for a step-by-step path for implementing API monetization. Until next time, stay tuned for the next part in this series on monetizing AI APIs!                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            You’ve built the API. Now let's generate revenue!            Monetize your AWS Gateway powered APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Moesif-AWS-Stripe-AI-API-Part-2/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-cloudflare-api-observability-5-metrics-to-monitor": {
          "title": "Cloudflare API Observability: 5 Metrics To Monitor",
          "content"	 : "Building APIs is a fact of life for most modern developers. When we aren’t building them, we integrate them within our applications. Regardless of how you look at it, modern businesses are built on top of the foundation that APIs have built. They’re the bridges that allow different software systems to talk, share data, and work together seamlessly. But with great power comes great responsibility. As APIs become more complex and critical to the business, ensuring they’re reliable, performant, and secure is vital.So, how does one ensure their APIs operate with peak performance from a technical and business perspective? The answer is API observability. It’s the practice of getting deep visibility into your APIs, so you can see what’s going on, spot issues, optimize performance, and deliver outstanding user experiences. In the broader context of cloud computing, API observability becomes even more crucial as it allows developers to deploy code to a cloud service and have it run at scale near end users without worrying about regions and multi-region architectures. In this post, we’ll dive into various aspects of API observability, why it matters, and 5 key metrics to monitor. We’ll also examine how Moesif and Cloudflare can work together to help with API observability. Let’s begin by looking closer at our main focus: API observability.                Monetize in Minutes with Moesif and Cloudflare              14 day free trial. No credit card required.              Try for Free        What is API Observability?API observability gives you a central place to look at all facets affecting API performance, adoption, error rates, etc. It’s the practice of collecting, analyzing, and understanding data from your APIs in real time. This data gives you valuable insights into how your APIs perform, behave, and interact with other systems.Applying API observability to your APIs allows you to keep a pulse on critical metrics coming from your APIs. By capturing logs, you can generate metrics and enable monitoring and troubleshooting.By monitoring metrics and logs, you can:  Detect issues early: Catch errors, bottlenecks, and anomalies before they affect your users.  Optimize performance: Implement better monitoring to find areas where your APIs can be faster and more efficient.  Understand usage: See how your APIs are being used, who is using them, and for what.  Troubleshoot faster: Diagnose issues when they do happen.  Make data-driven decisions: Use the insights to inform future API development and management initiatives.API observability goes beyond monitoring. For instance, when using API observability to detect errors, it’s not just about knowing something went wrong but also why it went wrong. This level of understanding lets you take proactive measures to ensure your APIs are always reliable, responsive, and secure.The three critical parts of API observability are:  Metrics: Quantifiable measurements like request rate, latency, error rate, and success rate.  Logs: Detailed records of API events, including request/response data, timestamps, and error messages. Monitoring logs, especially HTTP request logs, is crucial for identifying and troubleshooting issues. These logs help in examining end user requests, detecting malicious activity, and correlating related events.  Traces: These are end-to-end views of API requests as they flow through your systems, showing bottlenecks and dependencies.Combining these different types of data gives you a complete picture of your API’s health and performance. These insights let you deliver improved user experiences to your API users, optimize your infrastructure, and drive business success through your APIs.Why is API Observability important?With APIs being so ubiquitous, it’s apparent that API observability is no longer a nice to have but a must-have. Here are a few reasons why API observability is a critical component for developers and organizations that are building APIs:  Reliability and Uptime: APIs are the glue that holds your applications together. Observability lets you detect and fix issues proactively, reduce downtime, and ensure smooth operation. Cloudflare’s global network plays a crucial role in maintaining this reliability by providing a robust infrastructure.  Performance Optimization: Slow or unresponsive APIs frustrate users and lead to lost revenue. Observability helps you find bottlenecks, optimize resource usage, and deliver lightning-fast experiences.  Root Cause Analysis: When issues happen, observability provides the forensic tools to quickly diagnose the root cause, reduce the mean time to resolve (MTTR), and minimize the impact on your business.  Customer Satisfaction: Great API performance translates directly to happy users. Observability lets you understand and anticipate user needs and deliver a seamless experience.  Security: APIs are often the gateway to sensitive data. Observability helps you detect unusual activity, identify potential threats, and protect your systems from attacks. Cloudflare’s global network secures internet properties, ensuring that web properties and applications are protected from threats.  Cost Optimization: By understanding how your APIs are used, you can optimize your infrastructure spend, reduce waste, and ensure you get the most out of your resources.  Data-Driven Decision Making: Observability data provides valuable insights into usage patterns, trends, and customer behavior. This information can inform product development, marketing strategies, and business decisions.  Competitive Advantage: In today’s fast-paced digital world, businesses that prioritize API observability have a big advantage over their competitors. They can iterate faster, provide better experiences, and stay ahead of the curve.API observability is a game changer whether you’re a startup or an established enterprise. Investing in the right tools and practices for API development and support can unlock many benefits and move your business forward.5 API Observability Metrics to TrackThere are numerous metrics that businesses should look at when it comes to their APIs. That being said, there are some critical metrics that should always be on the radar of companies that are developing and publishing APIs. To get a complete picture of your API’s health and performance, here are 5 key metrics to keep an eye on:Request Rate (Throughput)This measures the number of requests your API receives per unit of time (e.g. requests per second). Monitoring request rate helps you:  Understand overall API usage and identify peak traffic periods.  Scale infrastructure to meet increased demand.  Detect sudden spikes or drops in traffic that could indicate issues or anomalies.Latency (Response Time)Latency is the time it takes your API to respond to a request. It’s a critical metric for user experience. By tracking latency, you can:  Ensure your API is responding quickly and efficiently.  Identify slow endpoints or bottlenecks in your system.  Optimize your code and infrastructure to improve response times.Error RateThis measures the percentage of API requests that cause errors. A high error rate means issues need to be addressed immediately. Monitoring error rate helps you:  Detect issues early and prevent them from impacting your users.  Identify the types of errors occurring (e.g. 500 Internal Server Error, 404 Not Found).  Troubleshoot and resolve errors quickly.Success Rate (Uptime)This is the percentage of successful API requests. It’s a key indicator of your API’s overall reliability and uptime. Monitoring success rate lets you:  Ensure your API is available and functioning as expected.  Identify periods of downtime or degraded performance.  Set service level objectives (SLOs) and track your progress towards meeting them.SaturationThis metric measures how well your API uses its available resources (e.g. CPU, memory, network). High saturation levels can cause performance degradation or even crashes. By monitoring saturation, you can:  Ensure your API has enough resources to handle current and future demand.  Identify potential bottlenecks and optimize resource allocation.  Prevent performance issues caused by resource constraints.Tracking these 5 metrics gives you a complete picture of your API’s health and performance. This will enable you to make informed decisions, optimize your infrastructure, and deliver great user experiences.Using Moesif and Cloudflare for API ObservabilityBy combining Moesif and Cloudflare, users can explore in-depth API analytics, user behavior insights, custom dashboards, and real-time alerts. This allows developers to see critical metrics reflecting how APIs are performing and being used.Cloudflare acts as a secure gateway for your API traffic, with features like rate limiting, caching, and web application firewall (WAF) protection. It also provides logs and analytics for traffic patterns and security threats.The Cloudflare dashboard provides visibility and customization for different use cases.Through Moesif’s integration with Cloudflare, analytics from the Cloudflare API gateway can be moved into Moesif, enriching it with insights. This integration allows you to:  Monitor API traffic in real-time: Track request volume, latency, and errors.  Detect and troubleshoot issues fast: Identify anomalies, bottlenecks, and other issues before they hit users.  Monetize API traffic: Charge API users for their API calls based on subscription or usage-based billing.  Visualize data to identify patterns and insights: Use analytics to understand traffic and performance trends.  Optimize API performance: Use Cloudflare’s performance features and Moesif’s insights to fine-tune your API’s speed and reliability.  Get deep user behavior insights: Understand how users interact with your APIs and where to improve.  Secure your APIs: Protect your APIs from attacks with Cloudflare’s security features and Moesif’s governance rule/quota capabilities.Moesif’s observability platform and Cloudflare’s security and performance combine to create a complete API observability solution that delivers great user experiences, optimizes your infrastructure, and protects your API assets.ConclusionAPI observability is the key to unlocking the value within your APIs. You can achieve optimal performance, reliability, and security by monitoring, understanding, and fixing issues within your APIs. Tools like Moesif and Cloudflare make it easy to do that with insights and actions.As we covered in the blog, API observability isn’t just a nice to have; it’s a must-have. With the right tools and strategies, you can deliver great user experiences, grow your business, and stay ahead of the competition. As cloud services evolve, serverless architectures such as Cloudflare and API observability tools like Moesif will play a crucial role in the future of API observability, offering scalable and efficient solutions.Looking to get started with Moesif and Cloudflare? Sign up for Moesif today and quickly integrate with your Cloudflare API gateway in minutes.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Cloudflare-API-Observability-5-Metrics-To-Monitor/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-contract": {
          "title": "API Contract: What It Is and How to Write One (2026)",
          "content"	 : "An API contract is the agreement between an API and the code that calls it. It says: send these requests, get these responses; this field is required, this field is optional; this version is supported until this date. When the contract is clear and enforced, integrations stay stable across releases. When it is vague, every release breaks something for somebody, and the support load grows in proportion to how many integrations you have shipped.                Monitor REST APIs With Moesif              14 day free trial. No credit card required.              Try for Free        This guide covers what an API contract actually contains, the difference between a contract and the OpenAPI spec people sometimes confuse it with, the seven steps for writing one from scratch, and how contract testing keeps the implementation honest. The 2026 wrinkle is that AI agents now consume APIs through MCP, which raises the stakes on contract clarity in a way that did not exist five years ago.What is an API contract?  An API contract is the formal agreement that defines how an API behaves. It covers the endpoints, request and response shapes, authentication, error formats, rate limits, and versioning policy. Both the provider and the consumer build to the contract, and contract tests verify the implementation matches what the contract promises.The contract is not the same thing as the source code, and it is not the marketing description on your developer portal. It is the precise, machine-readable plus human-readable specification of behavior. A team can change the implementation behind the contract without breaking anything as long as the contract holds; conversely, a tiny change to the contract (a required field becoming optional, a status code changing) can break every consumer in production even if the code looks the same.What an API contract includes (the 8 required pieces)Most articles list three or four pieces. A complete contract covers eight:  Endpoints and methods. Every URL the API exposes and which HTTP methods it accepts on each path. Stable paths matter; renaming an endpoint is a breaking change.  Request shapes. Required fields, optional fields, types, validation rules, and header expectations for every method on every endpoint.  Response shapes. What comes back on success, what comes back on each error class, and which status codes are used in which situation.  Authentication. API keys, OAuth scopes and flows, mTLS, JWT structure. Cover both how to get credentials and how to rotate them.  Rate limits and quotas. Per-endpoint and per-customer ceilings, the response behavior when exceeded (429 Too Many Requests with a Retry-After header is the standard), and how customers see their current usage.  Error catalog. Every error code the API can return, each tied to a machine-readable identifier, a human-readable explanation, and the field or condition that caused it. A contract without an error catalog forces consumers to handle errors by string matching, which is fragile.  Versioning policy. How versions are named (URI versioning like /v1/orders is the most common), how long each version stays live, how breaking changes are signaled, and how customers will be notified about deprecation.  Service-level expectations. Uptime target, latency expectations, support response times. If you do not publish these, customers assume the worst, and your enterprise sales conversations stall.Some of these belong in a machine-readable spec (OpenAPI). The rest belong in human-readable documentation. Both halves together are the contract.API contract vs OpenAPI spec vs API documentationPeople conflate these three terms constantly, which causes real confusion in API reviews. Here is the disambiguation:                   API contract      OpenAPI spec      API documentation                  What it is      The full agreement      A machine-readable description of endpoints and shapes      Human-readable explanation for developers              Format      Spec + docs + SLA together      YAML or JSON      HTML, Markdown, rendered docs              Source of truth for      Behavior      Structure      Adoption              Tested by      Contract tests      Schema validators      Manual review and analytics              Audience      Provider, consumer, agent runtime      Code generators, validators, agents      Human developers      The cleanest setup in 2026 is to write the OpenAPI spec, generate the documentation from it, and treat the spec plus the documentation plus the deprecation policy as the contract together. The contract is broader than the spec; the spec is the machine-checkable subset of the contract.How to write an API contract from scratch (step-by-step)The seven-step sequence that holds up across teams:  Start from the consumer. Write three to five example calls you expect a real developer (or AI agent) to make. If the example is awkward, the contract is awkward, and no amount of polishing the spec later will fix it.  Write the OpenAPI spec. Define paths, methods, request and response schemas, security schemes. Pick the spec version (3.0 or 3.1) and stick with it across the API. Use semantic field names, not abbreviations.  Document the auth scheme separately. OAuth flows, token TTLs, scope semantics, key rotation policy. The OpenAPI securitySchemes block describes the shape; the human-readable doc explains why and how.  Define the error catalog. Every endpoint can return certain error codes. Each gets a stable machine-readable identifier (e.g., payment_method_declined) and a one-paragraph explanation.  Write the versioning policy. Pick URI versioning (/v1/orders) or header versioning. State explicitly how long deprecated versions stay live. Twelve months is the standard for paid public APIs.  Publish the SLA. Uptime target, latency expectations, support response times by tier. If you do not publish these, prospects will not trust the API for production workloads.  Get the contract reviewed before code starts. Have one external consumer (real or stand-in) try to implement against the contract on paper. Their questions are the questions the contract failed to answer; rewrite the contract until the questions go away.The contract becomes the artifact engineering implements against. Changes to it require change-control, not a casual pull request. This is also where API design principles start paying off, because most design mistakes are visible in the contract before any code exists.Contract testing (Provider vs Consumer-driven)A contract is only useful if you can verify the implementation matches it. Contract testing does that mechanically, in CI, on every change.There are two flavors, and serious teams run both:  Provider contract tests run against the API server. They send realistic requests, capture the responses, and assert that everything matches the OpenAPI spec. The mature tools in 2026 are Dredd (sends every request in your spec and validates responses), Schemathesis (property-based: generates thousands of valid and edge-case requests from the spec), and the OpenAPI validators built into most API gateways. These run in CI before deployment.  Consumer-driven contract tests flip the direction. Each consumer team defines what they need from the provider, and the provider runs those tests to ensure changes do not break known consumers. The standard tools are Pact and Spring Cloud Contract. These are the antidote to “we did not know team X was using that field.”The discipline that has moved up the priority list since 2024: idempotency testing on POST and PATCH endpoints. AI agents retry aggressively, often without the developer’s explicit instruction. If your contract says POST /payments is idempotent when given an Idempotency-Key header, your contract test should send the same request twice and assert that the second call returns the same response (and the same resource ID), not a duplicate charge.Contract versioning and deprecation (with IETF RFC 8594 and RFC 9745)Contracts evolve, and the only real question is how visibly that evolution gets communicated to the consumers who depend on them.Two non-negotiables when a contract changes:  Breaking changes get a new version. If you change a field’s type, remove an endpoint, or alter required fields, you bump the major version and run old and new in parallel for the published deprecation period.  Non-breaking changes are documented in a changelog. New optional fields, new endpoints, and new error codes are additive. They do not require a version bump, but they belong in the changelog so consumers can take advantage of them.The HTTP-native way to communicate deprecation is a pair of standard response headers: Sunset (defined in IETF RFC 8594, published May 2019) and Deprecation (defined in IETF RFC 9745). When set, SDKs and well-behaved clients warn developers automatically. Sunset carries an HTTP-date for when the endpoint goes away; Deprecation indicates the endpoint is deprecated as of a given date. Use both: one says “this is going to be removed,” the other says “here is the exact moment.”Sensible deprecation periods by category:            API category      Standard deprecation period                  Paid public API      12 months              Free public API      6 months              Partner / B2B API      6-12 months by contract              Internal API      90 days      Versioning is one of the parts of the contract that compounds the most over time. A contract without a deprecation policy in writing accumulates versions until engineering velocity collapses under the weight of supporting all of them. State the policy on day one of the contract, before you have shipped your first version.API contracts in 2026: AI agents, MCP, and idempotencyThree things shifted in 2025-2026 that put more weight on the API contract than ever before.Agents read the contract more carefully than humans do. When an LLM application chooses which endpoint to call from your OpenAPI spec, it reads the summary, description, and operationId fields literally. Vague or marketing-flavored descriptions cause wrong tool selection. A description: "Manages payment objects" is not enough; the agent needs description: "Creates a charge against a saved payment method. Idempotent when called with an Idempotency-Key header." Treat those fields as user-facing copy, because they now are.Agents retry by default, so idempotency moves up the priority list. The Idempotency-Key header pattern, popularized by Stripe and widely adopted across payments and infrastructure APIs, belongs in the contract for every POST and PATCH. Without it, agent retries produce duplicate resources in production. With it, retries are safe by construction. Document the key TTL, the conflict response, and the supported HTTP methods. The IETF has an ongoing draft “The Idempotency-Key HTTP Header Field” that aims to standardize the convention.MCP exposes the same contract to a different runtime. The WSO2 AI Gateway auto-generates an MCP server from an OpenAPI spec, so the same API a company already publishes for human developers becomes agent-consumable without a separate build. The MCP and LLM proxy components inside the AI Gateway handle inbound agent calls and outbound LLM calls respectively, but both rely on the source contract being precise. The clearer the OpenAPI spec, the better the agent experience, with no duplicated work and no second source of truth to keep in sync.Common API contract mistakesThe failure modes that show up most often in real reviews:  Treating the OpenAPI spec as the entire contract. The spec covers structure; it does not cover auth flows, error semantics, rate limit policy, deprecation period, or SLA. Teams that ship “the spec” and call it done create gaps consumers fill with guesses.  No error catalog. Returning 400 Bad Request with a free-text message field means consumers must string-match to handle errors. Within a year, the support load is dominated by “what does this error mean?” tickets.  Implicit versioning. No version policy means every change is a potential breaking change. The first time a customer is broken by a “harmless tweak,” trust is gone.  Marketing language in description fields. “Powerfully manages your payments” tells an agent (or a human) nothing. Descriptions should be operational: what the endpoint does, what side effects it has, what makes it idempotent.  No idempotency on POST. Acceptable in 2018; not acceptable in 2026 when half your traffic might be coming from an agent that retries on any transient error.  Contract drift in production. The contract says one thing; the live API returns something else. Without contract observation in production, drift accumulates silently until a major consumer breaks.Most of these are caught by either an external-consumer review (step 7 above) or production observability that compares live traffic to the spec.How Moesif and WSO2 enforce API contracts in productionA contract is only as good as the runtime that enforces it. WSO2 API Manager applies governance policies against the OpenAPI spec at design time and merge time, so contract violations get caught before the deployment that would have shipped them. Once the API is live, Moesif records every request and response per customer, which gives the data needed to detect contract drift in production: endpoints returning shapes the spec does not describe, status codes that do not match the catalog, deprecated endpoints still being called from named customers.The combined stack covers contract design and enforcement at the gateway (WSO2) plus contract observation in production traffic (Moesif), which is the loop most API teams are missing. See our API lifecycle guide for how the contract fits into the broader development cycle, from initial design through deprecation.Next stepsA clear API contract is the cheapest way to keep integrations stable as your API evolves. Once your contract is in production, the next question is whether the implementation actually matches it. Start a 14-day Moesif free trial to monitor live traffic against your contract per endpoint and per customer, and surface drift before it costs you a customer. No credit card required.Frequently asked questionsWhat is an API contract in simple terms? It is the agreement between an API and the code that calls it: the endpoints, request shapes, response shapes, authentication, errors, rate limits, and versioning policy. Both sides build to it, and contract tests check that the running API matches what was promised.Is an OpenAPI spec the same as an API contract? No. The OpenAPI spec is the machine-readable structural part of the contract. The full contract also includes the auth scheme, error catalog, versioning policy, and SLA in human-readable documentation.How do you write an API contract? Start from three to five example consumer calls. Write the OpenAPI spec. Document auth, errors, versioning, and SLA. Get the contract reviewed by an external consumer before any production code is written, and rewrite until their questions go away.What is contract testing? Automated verification that the API server’s actual behavior matches the contract. Provider contract tests run against the API and check spec compliance using tools like Dredd or Schemathesis; consumer-driven tests using Pact or Spring Cloud Contract verify that the provider has not broken known consumers.What is contract-first API development? The practice of writing the API contract (OpenAPI spec plus docs) before any server code. The spec generates server stubs, client SDKs, and documentation by construction, so all three artifacts stay in sync as the contract evolves.How long should a deprecated API version stay live? Twelve months for paid public APIs, six months for free public APIs, ninety days for internal APIs. State the deprecation period in your contract on day one, and use the Sunset (RFC 8594) and Deprecation (RFC 9745) HTTP response headers to communicate it programmatically.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /technical/api-contract/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-top-tools-for-heroku-analytics-in-2024": {
          "title": "Top Tools for Heroku Analytics in 2024",
          "content"	 : "Heroku analytics helps you monitor and optimize your application’s performance and data integration. In this article, we highlight the top tools for Heroku analytics in 2024, covering data integration, real-time monitoring, error tracking, performance optimization, data-driven decisions, business intelligence, and more.With the rapid advancements in technology, the landscape of Heroku analytics tools has evolved significantly. These tools not only aid in maintaining the health of your applications but also provide deep insights that can drive strategic business decisions. By leveraging these analytics tools, businesses can identify performance bottlenecks, optimize resource allocation, and ensure seamless user experiences. The integration of advanced analytics and monitoring solutions is crucial for staying competitive in today’s fast-paced digital environment. Furthermore, the ability to visualize data through custom dashboards and receive real-time alerts ensures that teams can respond proactively to any issues, minimizing downtime and enhancing overall operational efficiency.Key Takeaways  Several powerful tools are available for Heroku analytics in 2024, including Talend, Integrate.io, Sisense, Spotfire, and Microsoft Power BI, which cater to various data integration, cleansing, and visualization needs, enabling data-driven decisions and enhancing business intelligence.  Setting up and integrating Google Analytics, performance monitoring tools like New Relic, real-time monitoring, and error tracking solutions such as Sentry are essential steps for optimal application performance on Heroku.  Real-time data analysis and custom dashboards with tools like Kafka, Redis, Grafana, and alerting systems like AppDynamics and PagerDuty are crucial for monitoring, providing actionable insights, and maintaining the high availability and reliability of Heroku applications.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Top Tools for Heroku Analytics in 2024Navigating the world of Heroku analytics can be daunting, but with the right tools, you can streamline this process significantly for your website. In 2024, several standout tools can help you manage and optimize your Heroku applications, ensuring you’re always aware of when they were last updated.These tools support data-driven decisions and enhance business intelligence, providing valuable insights for your operations through real-time monitoring and error tracking.Talend offers comprehensive data integration and cloud pipeline design features, focusing on data governance and quality across various operations. Integrate.io supports integration with data stores like Heroku Postgres, MySQL, and Google Cloud Storage, making it a versatile choice for complex data environments. For those needing advanced data cleansing capabilities, WinPure Clean & Match stands out, providing sophisticated algorithms for identifying data anomalies.IBM Infosphere Quality Stage is tailored for cleaning extensive datasets, featuring around 200 pre-configured data quality rules. For interactive dashboards, Sisense is a top choice, used by organizations like eBay and NASA for combining various data sources. Additionally, Spotfire offers natural language search and AI-driven data insights, ideal for users seeking affordable yet powerful analytics solutions.Some popular tools for data analysis and visualization include:  Microsoft Power BI: offers flexibility with plans starting from $10 per user/month and the ability to connect to various data sources.  Heroku Add-ons: provides over 200 managed services, allowing developers to easily extend their application capabilities.  Databox: offers centralized data monitoring with plans ranging from free to $319/month, making it accessible to companies of all sizes.  DashThis: integrates with over 30 data sources and provides a customizable dashboard experience.These tools can help you understand, analyze, and visualize your data effectively.What is Heroku?Heroku is a cloud platform designed to simplify the process of building, delivering, monitoring, and scaling applications. By abstracting away the complexities of infrastructure management, Heroku allows developers to focus solely on writing code and delivering value to customers. The platform supports a variety of programming languages, including:  Node.js  Ruby  Java  Python  Scala  PHPThis makes it versatile for different development needs, and it’s important to note that versatility is crucial in today’s ever-changing landscape. Additionally, Heroku empowers businesses to make data-driven decisions and leverage business intelligence to enhance their operations.Leveraging Heroku’s capabilities involves utilizing a range of tools to monitor and optimize your application performance. From setting up analytics to integrating with powerful data tools, this article will guide you through the essential steps needed to create a robust monitoring setup on the Heroku platform.Setting Up Heroku AnalyticsSetting up analytics for your Heroku app is a crucial step toward making data-driven decisions and understanding your application’s performance. Selecting the right tools and integrating them with your Heroku app can provide valuable insights into user behavior, system performance, and potential issues.Tools like Databox are excellent for centralizing data and monitoring company performance, offering both free and paid plans to suit various needs. To start, you need to choose tools that align with your specific requirements. For instance, if real-time data visualization is crucial, Grafana, known for its leading dashboard capabilities, might be the right choice. Once you’ve selected your tools, integrating them with your Heroku app ensures that you have a comprehensive view of your application’s health and performance.Google Analytics IntegrationIntegrating Google Analytics with your Heroku app is a straightforward process that can provide deep insights into user interactions and behaviors. To begin, follow these steps:  Create a Google Analytics account.  Set up a new GA4 property, ensuring you select ‘Web’ as the platform type.  Once your property is set up, obtain your Measurement ID, which will be formatted as ‘G-XXXXXXXXXX’.Google Analytics is a powerful tool for business intelligence, helping you make data-driven decisions.For web applications, embed the Google Analytics tracking code snippet into the <head> tag of every HTML file. If you’re working with single-page applications like those built with React, use the ‘react-ga’ library by importing and initializing it with your Measurement ID.After integrating Google Analytics, commit and deploy your changes to Heroku. You can verify the integration by checking the ‘Realtime’ report in your Google Analytics account after deployment.Performance Monitoring ToolsUnderstanding and improving your application’s performance is critical, and Application Performance Monitoring (APM) tools are essential for this task. These tools help analyze and optimize slow-performing parts of your application, including external services like APIs and databases. Performance monitoring tools are crucial for making data-driven decisions.New Relic APM offers end-to-end transaction tracking for web and microservices, supporting multiple programming languages and providing detailed observability from a single dashboard. AppOptics provides continuous performance monitoring with custom dashboards and supports distributed tracing across Dynos and Apps, making it an excellent choice for complex application environments.Other notable tools include Atatus, which offers full visibility into application performance by monitoring front-end, back-end, slow transactions, external requests, and database issues in real time. Raygun Real User Monitoring (RUM) supports performance monitoring for web, single-page, and mobile applications, providing detailed waterfall charts for load time analysis. These tools collectively ensure that your application performs optimally, delivering a seamless user experience.Error Tracking SolutionsError tracking is vital for maintaining a smooth user experience and quickly addressing issues as they arise. Sentry is a powerful tool that can be integrated with Heroku to monitor application errors, capture exceptions, and provide detailed stack traces for quick troubleshooting. Error tracking is also a crucial component of business intelligence, helping organizations make informed decisions based on application performance data.To start using Sentry with Heroku, follow these steps:  Add the Sentry add-on using the command heroku addons: create sentry.  For Ruby or Rails integration, add the sentry-ruby gem (and sentry-rails gem if you’re using Rails) to your Gemfile.  Unhandled exceptions in Rails are automatically sent to Sentry, while other platforms require manual logging.For Python, initialize Sentry with your DSN during library start-up, and in Django, configure it in the settings.py file. This setup ensures that errors are captured and reported, allowing for timely resolution.Database Analytics with Heroku PostgresAnalyzing your database performance is crucial for maintaining application efficiency, and Heroku Postgres offers a suite of tools to help with this. The ‘Expensive Queries’ feature identifies the most time-consuming queries in your database, allowing you to optimize them for better performance. Business intelligence relies heavily on effective database analytics.The ‘pg:diagnose’ tool performs health and diagnostic checks, providing a comprehensive report for analysis and optimization. Regular checks for table bloat and index hit rates ensure that your database runs efficiently without wasting space or frequently hitting the disk. Additionally, the ‘Blocking Queries’ check identifies queries that take locks and potentially block other queries from running. These features collectively help maintain a high-performing database environment.Real-Time Data AnalysisReal-time data analysis is essential for applications that require immediate data processing and insights. Apache Kafka on Heroku enables seamless integration with your applications, allowing producers and consumers to run as Heroku apps with secure connections. Kafka’s distributed architecture supports the transformation of high-volume event streams into manageable partitions for real-time processing.Real-time data analysis is crucial for making data-driven decisions.Kafka acts as a durable buffer for high volumes of inbound events, facilitating incremental processing of immutable event streams. Similarly, Redis on Heroku provides in-memory data structures to support real-time analytics and fast data processing for applications. Together, these tools enable efficient real-time data analysis and processing.Custom Dashboards and VisualizationsCreating custom dashboards and visualizations can significantly enhance your ability to monitor application performance in real-time. HostedGraphite offers infrastructure and application monitoring using open-source tools like Prometheus or Graphite, with real-time visualization in Grafana Dashboards. These tools are essential for business intelligence.To create a dashboard in Grafana, follow these steps:  Click ‘Dashboards’ in the menu.  Select ‘New Dashboard’.  Add visualizations.  Set up new data sources.  Write queries.  Choose from multiple visualization types to create informative and visually appealing dashboards.  Grafana allows you to move, resize, and configure panels, providing a flexible and customizable monitoring experience.Once your dashboard is set up, you can visualize and share real-time monitoring data and alerts effectively.Alerting and NotificationsEffective alerting and notification management ensure that you are promptly informed about critical issues. AppDynamics offers intelligent alerting with dynamic baselining, ensuring no false alarms and integrating with tools like ServiceNow, PagerDuty, and JIRA for comprehensive incident management. This is crucial for maintaining business intelligence.PagerDuty can be integrated with Wavefront to receive notifications when Service Level Objectives (SLOs) are in danger of being breached. Dead Man’s Snitch monitors Heroku Scheduler, cron jobs, or any periodic processes, alerting you of any errors via integrations like Slack or PagerDuty. These tools ensure timely alerts and efficient incident management, helping you maintain application reliability.Moesif leverages advanced anomaly detection algorithms to identify unusual patterns and deviations from normal API behavior. This proactive approach alerts you to potential issues even before they reach critical thresholds, allowing for early intervention and mitigation. Moesif enables you to set up alerts at a granular level, monitoring individual customers or specific segments. This is invaluable for identifying issues impacting specific users or detecting unusual usage patterns that might signal security threats or abuse. Moesif integrates with a wide range of communication channels, including email, Slack, PagerDuty, webhooks, and more. This ensures that alerts reach the right teams or individuals promptly, regardless of their preferred communication platform.Uptime and Availability MonitoringMonitoring uptime and availability is crucial for ensuring your application remains accessible to users. Pingdom focuses on external availability monitoring, providing continuous testing from over 100 global probe servers to verify your application’s accessibility. Issues detected by Pingdom are double-checked from a second probe before triggering an alert, ensuring accuracy. Uptime and availability monitoring are essential for making data-driven decisions.Pingdom provides actionable and contextual alerts, detailing the nature of the problem and affected users. Better Stack offers uptime monitoring with frequent checks from multiple locations, ensuring quick detection of issues. These tools help you maintain high availability and quickly address any accessibility problems.Utilizing Heroku StatusHeroku Status is an invaluable resource for real-time information on platform incidents. You can sign up to receive email alerts for platform issues by subscribing on the Heroku Status site. Apart from email alerts, you can follow updates via their Twitter feed, RSS feed, or API. Utilizing Heroku Status is part of maintaining business intelligence.The Heroku Status site provides incident reports and the current status of apps, data services, and tools, updating every 30 seconds with the latest information. The Heroku Status API supports client-side JavaScript requests, offering endpoints for current status and incident details. Utilizing these resources ensures you stay informed about any platform-related issues.Documentation and Best PracticesMaintaining a robust Heroku analytics setup requires referring to the official Heroku documentation and adopting best practices. Key documentation links include guides on integrating third-party services, monitoring performance, and configuring error trackers. Documentation and best practices are essential for business intelligence.Adopting best practices for maintaining a high-performing and secure analytics setup includes:  Setting up continuous monitoring  Automating alerts  Regular configuration reviews  Keeping your Heroku analytics tools updated to the latest versionsThese practices ensure that your AWS setup remains efficient and effective in taking action, following the best guidelines, and maintaining sync across your resources.SummaryIn summary, making data-driven decisions and utilizing business intelligence through the right tools for Heroku analytics can significantly enhance your application’s performance and reliability. From integrating Google Analytics to using powerful APM tools like New Relic and AppOptics, each tool offers unique benefits that contribute to a comprehensive monitoring setup. Error tracking with Sentry, database analytics with Heroku Postgres, and real-time data analysis with Kafka and Redis further solidify your application’s robustness.By adopting best practices and keeping your tools updated, you can ensure your Heroku analytics setup remains efficient and secure. As you continue to optimize your applications, these insights and tools will help you stay ahead, delivering seamless and reliable experiences to your users.Organizations looking for the best tools to support their API management can leverage Moesif’s powerful API analytics and monetization capabilities. Moesif easily integrates with your favorite API management platform or API gateway through one of our easy-to-use plugins, or embed Moesif directly into your API code using one of our SDKs. To try it yourself, sign up today and start with a 14-day free trial; no credit card is required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Top-Tools-for-Heroku-Analytics-in-2024/",
          "author": "Preet",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-access-control-allow-origin": {
          "title": "Access-Control-Allow-Origin: The CORS Guide for APIs",
          "content"	 : "Access-Control-Allow-Origin is the HTTP response header that tells a browser whether a web page running on one origin is allowed to call your API on a different origin. It is the central piece of the Cross-Origin Resource Sharing (CORS) protocol, and it is the header you change when a frontend developer pings you with the message: “I’m getting a CORS error.”                Monitor REST APIs With Moesif              14 day free trial. No credit card required.              Try for Free        This guide covers what the header actually does, how to set it correctly on your API, the wildcard trap that breaks credentialed requests, and the common errors you will see in the browser console. It is written for backend engineers who own the API and have a frontend team that depends on getting CORS right.What is Access-Control-Allow-Origin?Access-Control-Allow-Origin (often abbreviated as ACAO) is a response header your server sends back to the browser. Its value tells the browser which origin (scheme + host + port) is allowed to read the response.Two valid values:  A specific origin, like https://app.example.com. The browser will let only pages served from that exact origin read the response.  The wildcard *. The browser will let any origin read the response, but with a major restriction: credentialed requests (cookies, HTTP auth, client certificates) are blocked.That is the whole header. Everything complicated about CORS comes from the interactions between this header, the browser’s Same-Origin Policy, and the rest of the CORS protocol.Why CORS exists (the Same-Origin Policy in one paragraph)By default, the browser enforces a Same-Origin Policy: JavaScript running on https://app.example.com cannot read responses from https://api.different.com. This protects you against a malicious site reading your bank balance just because you happen to be logged in. CORS is the standardized way an API can say “I trust this specific other origin, let it read my responses.” The Access-Control-Allow-Origin header is how the API says it.“Origin” in CORS terms is a specific tuple: scheme + host + port. https://app.example.com and http://app.example.com are different origins (different scheme). https://app.example.com and https://app.example.com:8443 are different origins (different port). https://app.example.com and https://www.example.com are different origins (different host). All three pairs require explicit CORS allowance to call each other from the browser. The strictness is deliberate: the moment the browser starts treating “close enough” origins as the same, the security model breaks.CORS is the W3C standardization of what was previously a patchwork of cross-domain workarounds (JSONP, document.domain manipulation, postMessage relays). The current spec lives at the Fetch Standard at WHATWG and the relevant W3C CORS specification. Mozilla’s MDN documentation is the canonical reference for day-to-day work and is what most teams cite in code reviews.How a CORS request actually worksThere are two flavors of CORS requests: simple and preflighted.A simple request is a GET, HEAD, or POST with a few specific content types (application/x-www-form-urlencoded, multipart/form-data, or text/plain) and no custom headers. The browser sends the request directly. The server includes Access-Control-Allow-Origin in the response. The browser checks the header against the page’s origin and either lets the JavaScript read the response or blocks it.Everything else triggers a preflight. Before sending the actual request, the browser sends an OPTIONS request to the same URL with these headers:  Origin: https://app.example.com  Access-Control-Request-Method: PUT  Access-Control-Request-Headers: Authorization, Content-TypeThe server responds (without a body) including:  Access-Control-Allow-Origin: https://app.example.com  Access-Control-Allow-Methods: GET, POST, PUT, DELETE  Access-Control-Allow-Headers: Authorization, Content-Type  Access-Control-Max-Age: 86400 (optional, caches the preflight response)Only if the preflight succeeds does the browser send the actual request. If the preflight fails (wrong origin, method, or header in the allowlist), the actual request never happens and the JavaScript sees a CORS error.This matters because CORS errors are almost always preflight failures. The browser console will tell you which preflight check failed; reading the message carefully usually points you straight at the misconfiguration.A request triggers a preflight when any of the following is true: the method is PUT, PATCH, DELETE, CONNECT, TRACE, or OPTIONS (or POST with a non-standard content type); the request includes a Content-Type other than the three “safe” ones above; the request sets any custom header (including the common Authorization and X-Requested-With); the request includes credentials (cookies, basic auth, client certificates) while using a wildcard origin. In modern apps using JSON bodies and bearer tokens, essentially every non-GET call triggers a preflight, so designing the preflight response correctly matters more than the original spec’s “simple request” optimization suggests.How to set Access-Control-Allow-Origin correctlyThe right configuration depends on your framework. A few common patterns.Express (Node.js):import cors from 'cors';app.use(cors({  origin: ['https://app.example.com', 'https://admin.example.com'],  credentials: true,  methods: ['GET', 'POST', 'PUT', 'PATCH', 'DELETE'],  allowedHeaders: ['Content-Type', 'Authorization'],}));FastAPI (Python):from fastapi.middleware.cors import CORSMiddlewareapp.add_middleware(    CORSMiddleware,    allow_origins=["https://app.example.com", "https://admin.example.com"],    allow_credentials=True,    allow_methods=["GET", "POST", "PUT", "PATCH", "DELETE"],    allow_headers=["Content-Type", "Authorization"],)Spring (Java):@CrossOrigin(  origins = {"https://app.example.com", "https://admin.example.com"},  allowCredentials = "true",  methods = {RequestMethod.GET, RequestMethod.POST, RequestMethod.PUT, RequestMethod.DELETE})@RestControllerpublic class ApiController { /* ... */ }The patterns to follow regardless of framework:  List explicit allowed origins, not *, for any production API that handles authenticated requests. The wildcard is a development convenience that does not survive contact with credentialed traffic.  Cache the preflight with Access-Control-Max-Age. Without it, the browser sends an OPTIONS before every actual request, doubling your request volume. The maximum useful value is 86400 (24 hours) on Firefox and 7200 (2 hours) on Chrome; values higher than those are clamped by the browser.  Apply CORS at the gateway, not in every service. API gateways (the WSO2 API Gateway, Kong, AWS API Gateway, Envoy) all handle CORS centrally. Doing it per-service is how teams end up with inconsistent CORS behavior across endpoints.  Avoid reflecting the origin without an allowlist. A surprisingly common pattern is Access-Control-Allow-Origin: <whatever Origin header the request sent>, which effectively makes your API accept any origin while pretending not to. Always check against an explicit allowlist before reflecting.  Do not forget Vary: Origin when the response varies by origin. Without it, CDNs and HTTP caches can serve a response built for origin A to a request from origin B, which silently breaks CORS for the second caller.Preflight caching with Access-Control-Max-AgeThe preflight OPTIONS request is the biggest hidden cost of CORS. Without caching, the browser sends an OPTIONS before every cross-origin request that triggers preflight, which in modern apps means before every non-GET call. Two requests for every actual call doubles the request volume against your API gateway and adds a round-trip of latency to every user-visible action.Access-Control-Max-Age is how you tell the browser to cache the preflight response. Set it on the OPTIONS response:Access-Control-Max-Age: 7200That tells the browser it can skip the preflight for the next 7,200 seconds (2 hours) for the same origin / method / header combination. The browser caps the effective value: Chromium-based browsers (Chrome, Edge) cap at 7,200 (2 hours), Firefox at 86,400 (24 hours), and historical Chromium versions before v76 capped at only 600 seconds. Setting Access-Control-Max-Age: 86400 is fine (every browser will clamp it down to its own ceiling) and that is the value most production APIs use.A few things to know about how this caches. The cache is keyed by origin + method + header set, so if your frontend sometimes calls PUT /orders and sometimes calls DELETE /orders, each method needs its own preflight cached separately. Adding a custom request header (a tracing header, a feature-flag header) invalidates the cache for that combination. The cache lives in the browser, not in your CDN; switching browsers or clearing browsing data drops it, and the cache scope can vary across browsers (per-profile, sometimes per-context), which is why you sometimes see preflights re-fire from what looks like the same session.When you change CORS configuration, the cached preflight is what makes the change slow to take effect for existing users. New users see the new config immediately; users with a cached preflight will keep using the old policy until the cache expires. For high-impact CORS changes (tightening an allowlist), this matters: a cached Access-Control-Allow-Origin: * will keep working for hours after you switch to a specific origin server-side.Multiple origins: why you cannot list more than oneThe CORS specification only allows one origin or * in the Access-Control-Allow-Origin header. You cannot return Access-Control-Allow-Origin: https://app.example.com, https://admin.example.com and have it work.The standard workaround is to check the incoming Origin request header against your allowlist server-side, and reflect it back if it matches. Most framework CORS libraries (the Express and FastAPI examples above) do this automatically: you give them a list of origins, and they reflect whichever one matches the incoming request.When you reflect the origin, you should also send Vary: Origin so caches do not serve the wrong response to the wrong origin.Credentialed requests and the wildcard trapThis is the single most common CORS mistake.If your API uses cookies, HTTP authentication, or client certificates for authentication, the browser treats those requests as credentialed. For credentialed requests, the CORS spec has stricter rules:  Access-Control-Allow-Origin cannot be *. It must be a specific origin.  Access-Control-Allow-Credentials: true must be present in the response.If either of those is wrong, the browser blocks the response. The developer sees a CORS error that mentions credentials.The symptom: a GET request that works fine in tools like Postman fails from the browser with a credentials-related CORS error. The fix is almost always replacing * with the specific origin and adding the credentials header.A related trap: the frontend must also opt into credentials. With fetch, that means credentials: 'include'; with axios, that means withCredentials: true. If only one side opts in (browser sends credentials but server does not allow them, or vice versa), the browser blocks the response and the error message is often misleading. Both sides have to agree.For modern applications using cookie-based session auth, also set the cookie’s SameSite attribute correctly. A cookie marked SameSite=Strict will not be sent in cross-origin requests at all, no matter what CORS says. Use SameSite=None; Secure for cross-origin cookies, and recognize that this requires HTTPS.CORS vs. CSRF: same problem space, different protectionCORS gets confused with Cross-Site Request Forgery (CSRF) often enough that it is worth separating them, because the protections do not overlap.CSRF is the attack where a malicious page tricks a user’s browser into making a state-changing request to your API while the user is authenticated. The classic example: the user is logged into their bank with a session cookie, visits a malicious page, and that page’s JavaScript submits a form to bank.com/transfer; the browser sends the user’s cookie along with the request, and the transfer goes through because the bank cannot tell the difference between this request and a legitimate one.CORS is a browser policy that controls who can read responses from your API. CORS does not block the request from being sent. A relaxed CORS policy makes some classes of CSRF easier to exploit (because an attacker can now read responses), but a strict CORS policy does not, by itself, prevent CSRF. The browser will still send the cookie and the server will still execute the request before CORS kicks in to block the response.The protections that actually defend against CSRF:  SameSite cookie attribute. SameSite=Strict blocks the cookie on all cross-site requests, including top-level navigation. SameSite=Lax (the modern default) blocks it on cross-site sub-requests like form POSTs and image loads, but still sends it on top-level navigation, which is enough to defeat the classic form-submission CSRF vector. Either setting is a substantial improvement over the older default of sending the cookie on every cross-site request.  CSRF tokens. A per-session token that the server issues and the legitimate frontend includes in each state-changing request. The malicious cross-origin page cannot read the token (CORS blocks that), so it cannot include it. Frameworks like Django and Rails generate these by default.  Double-submit cookies. A pattern where the server sets a token in both a cookie and a header, and the API validates that they match. Works in stateless APIs that cannot store per-session state server-side.  Bearer-token auth in headers. APIs that authenticate via Authorization: Bearer ... instead of cookies are not vulnerable to CSRF the same way, because the browser does not auto-attach the header on cross-origin requests.The right way to think about it: CORS controls response reading and SameSite/CSRF tokens control request authorization. If your API uses cookies, you need both. CORS alone does not stop CSRF, and CSRF protections alone do not stop the cross-origin scripting attacks that CORS was designed to prevent.Advanced CORS configurations: subdomains, dynamic origins, regexA few patterns come up often enough in production that they are worth calling out.Allowing a subdomain wildcard. The CORS spec does not understand *.example.com as a value for Access-Control-Allow-Origin. To support all subdomains, your server has to parse the incoming Origin header, check that it matches a subdomain of your apex domain, and reflect it back. Every CORS library worth using (Express’s cors, FastAPI’s CORSMiddleware, Spring’s @CrossOrigin) supports this via a regex or function-based origin check.Allowing different origins for different routes. A single API may serve a public, unauthenticated set of endpoints (wide-open CORS, * is fine) alongside an authenticated set (strict allowlist, credentialed). The cleanest pattern is to configure CORS per-route at the gateway, not as a single global policy. Both Kong and the WSO2 API Gateway support per-route CORS plugins; AWS API Gateway supports per-resource CORS configuration.Local development with localhost. http://localhost:3000 is a different origin from https://app.example.com, and your production allowlist usually does not include localhost. The pattern is to add localhost only in development and staging environments, never in production. A common subtle bug is forgetting that 127.0.0.1 and localhost are different origins in the CORS sense; whitelist both if your dev environment uses either.Dynamic origins from an allowlist database. SaaS multi-tenant apps where each tenant has a custom domain need the allowlist to be loaded from a database rather than a static config. The CORS middleware then takes a callback function that checks the incoming Origin against the dynamic list. Cache the lookup aggressively; the CORS preflight runs on the hot path for every cross-origin request.Reverse proxies and the Origin header. Some reverse proxies (older NGINX configurations, some Cloudflare setups) strip the Origin header from forwarded requests. If your CORS logic depends on reading Origin, this breaks it silently. Verify the header is reaching your application before debugging the CORS config itself.Common CORS errors and how to read themThe browser console messages are verbose but informative. The patterns to recognize:  “No ‘Access-Control-Allow-Origin’ header is present on the requested resource.” The server did not send the header. Either CORS is not configured, or it is configured but the request did not match (wrong origin, wrong path).  “The ‘Access-Control-Allow-Origin’ header has a value ‘X’ that is not equal to the supplied origin.” Your allowlist does not include the calling page’s origin. Add it.  “The value of the ‘Access-Control-Allow-Origin’ header in the response must not be the wildcard ‘*’ when the request’s credentials mode is ‘include’.” Credentialed-request wildcard trap. Switch to an explicit origin.  “Request header field X is not allowed by Access-Control-Allow-Headers in preflight response.” The frontend is sending a header your CORS config does not list. Add it to Access-Control-Allow-Headers server-side.  “Method X is not allowed by Access-Control-Allow-Methods in preflight response.” Same thing for HTTP methods. Add PUT, PATCH, or DELETE if your API uses them.The first place to look when debugging is the browser’s Network tab, specifically the response headers on the failed OPTIONS preflight. Eight times out of ten, the missing header is right there. Pairing the browser tab with a real-time view of HTTP status codes and headers your API is returning makes this even faster, because you can see exactly which preflight requests are failing without re-creating the issue.CORS in 2026: AI agents, MCP servers, and same-origin questionsA few new questions came up in the last two years.Do AI agents respect CORS? Not the way browsers do. CORS is a browser-enforced policy. An AI agent calling your API directly from a server (Python, Node, a serverless function) is not subject to CORS at all. The Same-Origin Policy is a browser concept; server-to-server calls do not have origins in the CORS sense.Does MCP traffic care about CORS? Mostly no. MCP servers expose APIs to AI agents through a server-side runtime, not the browser. If you are designing an API endpoint to be consumed both by browser JavaScript and by MCP-mediated agent calls, configure CORS for the browser path and treat the MCP path as a separate, authenticated channel.Does the LLM-fetcher pattern trigger CORS? When a model calls your API as part of a chain inside an LLM provider’s runtime, the call originates from the provider’s servers, not the user’s browser. No CORS preflight. Authenticate it like any other server-to-server call.The short version: CORS is still important for the browser-facing slice of your API. The growing agent and MCP traffic does not have a CORS concept, but it does have its own auth and rate-limiting concerns, which are worth a separate guide.How to test CORS configuration before shippingCORS bugs that survive code review tend to be ones that work locally and fail in production because the production origin is different. A few practical checks before pushing a CORS change:  Test from at least two origins. A configuration that works from https://app.example.com may fail from https://staging.example.com if your allowlist is hardcoded. Run the smoke test from every origin that is supposed to work.  Test both credentialed and non-credentialed requests. A wildcard configuration that “works” for unauthenticated GETs will fail the moment an authenticated POST hits the API. Cover both paths in your test suite.  Use the browser, not just curl. curl does not enforce CORS. A request can pass curl and fail the browser. The only meaningful end-to-end test is a real browser fetch from a real origin.  Check the preflight response in the Network tab. The OPTIONS preflight is the first request the browser sends. If its response is missing any required CORS header, the subsequent real request never happens. Most CORS bugs are visible in the preflight response alone.  Verify Vary: Origin on responses if you reflect the origin. Without it, the first cached response will serve every subsequent caller, including ones from blocked origins.Where to take this nextOnce your CORS configuration is right, the only way to know whether browser clients are actually getting what they need is to watch real production traffic. Start a 14-day Moesif free trial to see per-origin, per-endpoint request volume and any 4xx responses coming back to browsers. No credit card required.Frequently asked questionsWhat does Access-Control-Allow-Origin do? It tells the browser which origin is allowed to read responses from your API. Without it, the browser blocks cross-origin JavaScript from accessing the response.Can I set Access-Control-Allow-Origin to multiple origins? Not in one value. The spec allows exactly one origin or the wildcard *. To support multiple origins, check the incoming Origin header against your allowlist server-side and reflect whichever one matches.What is the wildcard in CORS? Setting Access-Control-Allow-Origin: * allows any origin to read the response. It breaks credentialed requests (cookies, HTTP auth), so it is fine for public read-only APIs but inappropriate for anything authenticated.Why does my CORS work in Postman but not the browser? Postman does not enforce CORS because it is not a browser. The Same-Origin Policy is browser-only. If something works in Postman and fails in the browser, your CORS configuration is incomplete.How do I fix the “No ‘Access-Control-Allow-Origin’ header is present” error? Configure your server or gateway to send the Access-Control-Allow-Origin header on responses, with either a specific origin matching the calling page or * for fully public APIs.Where should CORS be configured: the application or the gateway? The gateway, if you have one. Centralized CORS configuration at the API gateway prevents drift between services and makes the policy visible to your security team. API design principles lists this as the default for the same reason.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /technical/access-control-allow-origin/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-embracing-the-future-how-the-api-first-approach-is-revolutionizing-software-development": {
          "title": "API-First Approach: What It Is and How to Adopt It",
          "content"	 : "An API-first approach is a development methodology where teams design the API contract, usually as an OpenAPI spec, before writing any application code, then build the implementation against that agreed contract. The payoff is parallel work across teams, a cleaner developer experience, and fewer expensive surprises late in the lifecycle.That definition is short on purpose. The longer version, with examples, comparisons, and the steps to actually do it, is what the rest of this guide is for.What is an API-first approach?API-first treats the API itself as the product you ship, not as a side effect of shipping an application. Before the backend writes a controller, before the frontend writes a fetch call, somebody opens an editor and writes an OpenAPI document. That document describes every path, every request body, every response schema, every error. It gets reviewed by the people who will build against it. Only then does the code follow.This sounds obvious, and yet most APIs in the wild are built code-first: a service grows, somebody bolts an HTTP layer on top, and the “spec” is whatever the framework happened to generate from the existing handlers. API-first inverts that order. The spec leads; the code follows.API-first vs. API design-firstThese two terms get used interchangeably, and most of the time that’s fine. Postman draws a useful distinction, though: API design-first focuses narrowly on writing the spec before the code, while API-first describes the broader organizational stance, APIs are products, they have owners, they have a lifecycle, and decisions about them rise above the level of a single team. If you’re a solo developer or a small team, you can be design-first without being fully API-first. The reverse is harder.API-first vs. code-firstCode-first means the implementation comes first and the spec is generated from it, often after the fact. Tools like FastAPI or Springdoc make this easy: write your handlers, annotate your models, get a Swagger UI for free. The trade-off is that the spec becomes a downstream artifact. Any breaking change in the code immediately ships to clients. Reviewers don’t see the API until the implementation is already written.                Monitor REST APIs With Moesif              14 day free trial. No credit card required.              Try for Free        Why teams adopt an API-first approachThe arguments for API-first land differently depending on who you ask. Here are the ones that actually move budget.Parallel development across teamsOnce the contract is agreed and frozen, the iOS team, the web team, and the backend team can all start work on Monday. The mobile engineers point their app at a mock server generated from the spec. The backend writes the real implementation. Nobody is blocked on anybody. In a code-first world, the mobile team waits for a sandbox environment, and that wait is usually weeks.Better developer experience (DX)API consumers, internal or external, get documentation, type-safe SDKs, and predictable error shapes from day one because all of those are generated from the same source of truth. Postman’s 2024 State of the API report flagged DX as the top differentiator for APIs that get adopted versus APIs that get ignored, and API-first is the cleanest path to delivering it.Faster time to marketFewer integration surprises means fewer re-plans. When a frontend team discovers in week 8 that the response shape doesn’t match what they assumed, you lose a sprint. Catching that mismatch at the spec review stage costs a 30-minute meeting.Lower integration and rework costsThe cheapest bug to fix is the one caught during contract review. The next-cheapest is caught in mock testing. The most expensive is the one your enterprise customer reports after they’ve already integrated. API-first pushes detection left.A foundation for AI agents and automationThis one is newer and worth dwelling on. LLM-powered agents, function calling in OpenAI, tool use in Claude, MCP servers, custom orchestrators, work by reading an API’s specification and deciding which calls to make. If you don’t have a clean, accurate, machine-readable spec, your APIs are effectively invisible to that whole category of consumer. Contentful’s 2025 update to their own API-first guide is one example of vendors flagging this shift, but the underlying point holds across the category: agents don’t read your marketing site, they read your OpenAPI document.How API-first works in practiceThe lifecycle has five concrete steps. The exact tooling varies, but the order doesn’t.Step 1, Define the API contractOpen an editor. Write an OpenAPI 3.x document. Most teams use Stoplight, Redocly, or just a YAML file in a repo. The minimum viable spec for a single endpoint looks like this:openapi: 3.1.0info:  title: Orders API  version: 1.0.0paths:  /orders/{id}:    get:      summary: Get an order by ID      parameters:        - name: id          in: path          required: true          schema:            type: string      responses:        '200':          description: OK          content:            application/json:              schema:                $ref: '#/components/schemas/Order'        '404':          description: Not foundcomponents:  schemas:    Order:      type: object      required: [id, total, currency]      properties:        id: { type: string }        total: { type: number }        currency: { type: string, example: USD }Two things to call out. First, components/schemas/Order is defined once and referenced from every operation that uses it, that’s how you keep the spec DRY. Second, the spec is detailed enough that a mock server can serve fake responses tomorrow morning, before any real code exists. For more on writing solid specs, see our guide to the API contract and our OpenAPI specification primer.Step 2, Review and validate the contractTreat the spec like you treat application code: pull request, reviewers, comments, approvals. The reviewers should include at least one consumer of the API, a frontend engineer, a partner team, an SDK author, not just the backend folks writing it. Lint the spec with Spectral or a similar tool to enforce house style rules (kebab-case paths, mandatory error responses, version prefixes).Step 3, Generate mocks, stubs, and SDKsThis is where API-first stops feeling like overhead and starts paying back. From the OpenAPI document, you can generate:  A mock server (Prism, Stoplight, Postman) that returns realistic responses immediately  Client SDKs in a dozen languages (openapi-generator, Speakeasy, Fern)  Interactive documentation (Redoc, Swagger UI, Mintlify)  Type definitions for TypeScript, Go, PythonFrontend and partner teams work against the mock. Backend works against the contract. Both meet in the middle.Step 4, Build the implementation against the contractThe backend team implements handlers that satisfy the spec. Contract testing tools (Dredd, Schemathesis) run the real implementation against the OpenAPI document and fail the build if the response shape, status codes, or error structure drift from what was agreed.Step 5, Test, monitor, and iterateShip it, then watch what actually happens. Which endpoints get called? Which return errors? Which customers are on deprecated versions? This is the step most teams treat as an afterthought, and it’s where Moesif lives, more on that below.  Diagram placeholder: API-first workflow vs. code-first workflow. Side-by-side flowchart. API-first column: Define spec → Review → Mock + Generate SDKs → Build implementation (parallel: frontend builds against mock) → Contract test → Deploy → Observe. Code-first column: Build implementation → Generate spec from code → Document → Frontend integrates → Discover mismatches → Refactor → Re-document.API-first vs. other approachesThe four terms below get mixed together a lot. Here’s how they actually differ.            Approach      When you design      Primary artifact      Best for      Trade-offs                  API-first      Before any code      OpenAPI spec, agreed across teams      Multi-team orgs, public APIs, platform plays      Upfront design cost; needs cultural buy-in              Code-first      Implementation drives the design      Generated spec (derived from handlers)      Small teams, internal services, rapid prototypes where shipping speed matters most      Spec can drift from implementation; breaking changes are easier to introduce without review              Design-first      Spec first, but at one team’s level      OpenAPI spec      Single product teams shipping quickly      No org-level governance; APIs as products is implicit              Contract-first      Before code, often with consumer contracts (Pact)      Consumer-driven contracts      Microservices with many consumers      Tooling overhead; CDC discipline required      API-first is the umbrella; design-first and contract-first sit underneath it. Code-first is a different philosophy with its own trade-offs, faster early on, harder to govern as the surface grows. If you want a deeper read on the contract-first variant, our piece on contract-first API development covers it in depth.Common challenges (and how to handle them)Nothing here is a deal-breaker. All of them are predictable.Cultural resistance from backend-first teamsEngineers who built their careers shipping code resent being asked to “write YAML for a week” before they can open their IDE. The fix isn’t to argue, it’s to show the receipts. Track the rework rate on your last three integration projects. Compare to the first API-first project. The numbers usually argue better than you can.Keeping the contract and implementation in sync (drift)The spec says the field is called customer_id. Someone refactors the handler and renames it to customerId. The spec doesn’t get updated. Three weeks later, a partner integration breaks. The defense is automated contract testing in CI: every build runs the implementation against the spec, and any divergence fails the pipeline. Schemathesis is the go-to open-source option; commercial tools like Stoplight and Optic do the same thing with prettier dashboards.Governance at scaleOne team writing OpenAPI is fine. Forty teams writing OpenAPI is chaos unless someone owns the rules, naming conventions, versioning policy, error envelope, auth pattern. Most large API programs land on some version of an API council or platform team that maintains a style guide and a Spectral ruleset. Our guide to API governance walks through how to set this up without bureaucracy.Best practices for adopting API-firstA short list, in rough order of impact:  Pick one spec format and one style guide. OpenAPI 3.1 plus a Spectral ruleset is the default for a reason. Don’t let teams roll their own.  Version from day one. /v1/orders, not /orders. Future-you will thank present-you.  Standardize your error envelope. Same shape every time. Same status code semantics. RFC 7807 (Problem Details) is a reasonable starting point.  Mock before you build. If frontend can’t integrate against a mock the day after the spec is approved, your spec isn’t detailed enough.  Automate contract tests in CI. No exceptions, no “we’ll add it later.”  Make the spec a first-class artifact in the repo. Not in a wiki. Not in a Google Doc. In the same repo as the code, reviewed in the same PRs.  Instrument from launch, not after the first outage. You need to know which endpoints are used, by whom, and how often. See the Moesif section below.For a broader treatment of design fundamentals, naming, pagination, idempotency, pagination, our good API design principles post covers the territory.How Moesif fits into an API-first workflowAPI-first gets you to a shipped contract. It doesn’t tell you what happens after that. Once your API is live, the questions shift: which endpoints are people actually calling? Which customers are still on the deprecated v1? Is the production behavior matching the spec, or has implementation drift crept in? Are the partners you onboarded last quarter hitting the volume thresholds in their contracts?That’s where Moesif comes in. We provide API analytics, observability, and monetization for teams that treat their API as a product:  Per-customer and per-endpoint usage analytics. Slice traffic by user, organization, plan, or any custom dimension. See which endpoints have product-market fit and which are dead weight.  OpenAPI ingest. We import your spec and track compliance, if the live API starts returning fields that aren’t in the contract, you’ll see it.  Usage-based billing. Meter calls, payload bytes, or whatever dimension your pricing model uses, and push it to Stripe or your billing system.  Alerts on schema drift and anomalies. Get paged when a deploy starts returning new status codes or when an enterprise customer’s error rate spikes.For an API-first team, we close the lifecycle: spec → build → ship → observe → iterate on the spec again.API-first FAQWhat is the API-first principle?The principle is that the API contract is the most important artifact in your project, more important than any individual application, frontend, or backend that consumes it. Everything else is designed to satisfy the contract; the contract is not designed to satisfy whatever the code already does.What is an API-first integration strategy?An integration strategy is API-first when every system the company connects, internal services, partner integrations, SaaS connectors, is treated as an API consumer that gets a stable, documented, versioned interface. The alternative is point-to-point integration, where every new connection is a custom project.What is a code-first approach in Web API development?Code-first means you write the implementation in your web framework first (ASP.NET, Express, FastAPI, Spring) and let the framework generate the spec from your code. It’s faster for small projects and harder to govern as the API surface grows.Is API-first the same as API design-first?Mostly yes, sometimes no. Both put the spec before the code. API-first is broader, it also implies APIs are organizational products with owners, contracts, and lifecycle management. Design-first is the narrower technical practice. In casual usage, the two are interchangeable.Which tools do I need to go API-first?A spec editor (Stoplight, Redocly, or just VS Code with an OpenAPI extension), a linter (Spectral), a mock server (Prism), an SDK generator (openapi-generator or Speakeasy), a contract test runner (Schemathesis or Dredd), and an observability layer (Moesif). You don’t need all of them on day one, start with the editor and the linter.Last updated: May 25, 2026. Written by Preet Kaur, Developer Advocate at Moesif, who has spent the last five years helping API teams ship contracts they can defend in production.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /technical/api-development/Embracing-the-Future-How-the-API-First-Approach-is-Revolutionizing-Software-Development/",
          "author": "Preet",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-maximizing-design-and-performance-with-api-observability": {
          "title": "Maximizing Design and Performance with API Observability",
          "content"	 : "Struggling with API issues? API observability may have the solution. It reveals the critical insights that hide within API metrics, logs, and traces, offering you the power to diagnose and fix problems swiftly. In this article, we detail what API observability is, how it benefits your systems, and the ways it supersedes traditional monitoring to keep your digital services operating at peak performance.  Key Takeaways  Unlocking the Potential of API Observability          Understanding API Performance with Observability      The Role of Data in API Observability      API Observability vs Traditional Monitoring        The Core Components of API Observability          Collecting and Analyzing API Metrics      Leveraging API Logs for Deeper Insights      The Significance of API Traces        Enhancing Security Through API Observability          Identifying Vulnerabilities in Real-Time      Collaborative Defense with Development Teams        API Observability Tools: Your Gateway to Better Performance          Selecting the Right Observability Service      Integration with API Management Systems      Tracking User Journeys for Enhanced UX      Aligning API Analytics with Business KPIs        Why you need API Analytics and Observability  SummaryKey Takeaways  API observability provides a detailed, real-time view of API performance and user experience by enabling arbitrary questions about API behavior. In doing so, it facilitates immediate fixes and contributes to improved API design.  API observability tools like Datadog and Moesif go beyond traditional monitoring by offering deep insights into API interactions and end-user impact. These tools also integrate with management systems, and therefore aligns analytics with business KPIs (key performance indicators) for data-driven decision-making.  Advanced API observability aids in real-time threat detection and thus promotes proactive security practices. It fosters collaboration between development and security teams, enhancing the overall security posture of API-driven applications.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Unlocking the Potential of API ObservabilityAPI observability provides an X-ray vision into the API’s operational health. It goes beyond the surface to provide a comprehensive view of performance and user experience. API observatility acts like the superhero of traditional monitoring, with different capabilities:  Answering arbitrary questions about the API behavior, not limited to predefined metrics or logs.  Providing quick fixes.  Improving API performance.  Enhancing user experience.We will explore API performance in greater depth through observability, examine the critical role of data in API observability, and highlight how this approach advances beyond traditional monitoring.Understanding API Performance with ObservabilityImagine driving a car without a speedometer or a fuel gauge. Sounds risky, right? Similarly, tracking latency metrics like response time is integral to enhancing user experience by identifying underperforming endpoints. But that’s not all. Analyzing API logs is like being Sherlock Holmes. The logs enable teams to pinpoint error sources, be it specific endpoints or geographic regions, facilitating targeted debugging and informed API design. In this process, it’s essential to passively log API traffic to ensure accurate data collection.Consider real-time analytics as your fast-acting superhero, akin to The Flash. Tools that offer real-time analytics in API observability grant instant insights into API performance, thus facilitating quick decisions and speedy resolution of issues.The Role of Data in API ObservabilityWithin the domain of API observability, data reigns supreme. It encompasses a vast kingdom that includes logs, metadata, and traces. Thus data offers more context and comprehensive insights than the narrow focus of traditional API monitoring on predefined metrics. API logs are like the knights in shining armor in this kingdom. They offer real-time inspection of API calls, assisting in debugging, auditing, and generating metrics, while preserving the context of the operations.The role of data extends beyond technical insights, though. With API analytics, organizations can do the following:  Pinpoint their most vital digital assets.  Gain an enhanced understanding of digital interactions.  Strategize accordingly.  Pave the way for improved business value.API Observability vs Traditional MonitoringImagine being able to predict and prepare for an issue before it manifests. This is the power of API observability. It provides a proactive approach that allows exploration of API data to identify both anticipated and unanticipated issues. This approach contrasts with the reactive nature of traditional monitoring where response to problems occurs after they manifest. API observability feels much playing an open-world video game. It supports an open-ended analysis, aiding not only technical troubleshooting but also reinforcing business strategy. On the other hand, traditional API monitoring restricts itself to predefined metrics.When interacting with contemporary API-driven applications, traditional monitoring tools can appear outdated, much like an old game console. They were designed for more monolithic systems and struggle with ‘unknown unknowns’, limiting their effectiveness. On the other hand, API observability dives deep into the behavior of APIs and their systemic interactions, offering more than mere performance metrics. Its ultimate aim is the proactive identification and resolution of unknown issues through enriched data insight tools like MELT (metrics, events, logs, and traces). Embracing open standards like OpenTelemetry enhances team collaboration through a common language, improving visibility into transactions across applications for optimization and troubleshooting.The Core Components of API ObservabilityImagine API observability as a well-oiled machine. The fundamental components of this machine include structured logs, metrics, and traces. Each of these components plays a vital role in instantaneous inspection and comprehension of API calls. An effective observability pipeline requires the following:  Setting clear goals.  Selecting appropriate data sources.  Deploying the right tools.  Maintaining ongoing performance monitoring.This acts like the well-oiled gears of the machine, enhancing the collaboration between development and security teams.For cloud-based applications constructed with APIs and microservices, API observability serves as an indispensable component. It functions like the fuel that ensures delivered functionality and performance while upholding security standards. Just like the fuel gauge in a car gives you an in-depth understanding of your fuel levels, API observability aims to provide an in-depth understanding of API activities. Thereby it facilitates quick identification and resolution of issues.Collecting and Analyzing API MetricsAPI metrics function similarly to diagnostic tests for an API’s performance. For example, consider uptime metrics. Uptime represents the operational percentage of an API within a year. Some other important metrics include CPU and memory usage metrics, and request per minute (RPM) analytics. These play fundamental roles for assessing API performance.Now imagine having a tool that can segment and aggregate this API data, providing insights into actual customer API traffic and overall API usage. Tools like Moesif serve this purpose, also including funnel metrics like Time to First Hello World (TTFHW), while monitoring API traffic.Non-200 HTTP status codes as error rates act like the warning signs on your car’s dashboard. They are critical for determining the stability of an API and the efficacy of its design and documentation. But metrics don’t just work as technical indicators. They also inform business decisions by tracking contributions to revenue, development efficiency, and cost savings attributed to API performance.Leveraging API Logs for Deeper InsightsIn API observability, structured logs surpasses the scope of traditional logging. Structured logs preserve the context of each API call, offering a framework for comprehending in-depth API interactions. These API logs encapsulate detailed records of each API interaction, enabling a granular view on API usage, error rates, and user behavior patterns. It’s like having a detailed map, where visualization through real-time monitoring tools, including graphs, charts, and dashboards, facilitates immediate and powerful insights into the API’s operational health.Centralizing log data from a variety of sources within the API ecosystem into a single repository enhances the effectiveness of log analytics and visualization capabilities. It’s like having all your travel destinations marked on a single map, making your journey more efficient and insightful.The Significance of API TracesAPI traces act like the breadcrumbs in the fairy tale of Hansel and Gretel. They provide a detailed sequence of events within an API request, identifying bottlenecks and inefficiencies in the flow of API calls. In a distributed system, each request gets a unique trace ID associated with it and spans represent individual units of work. This allows developers to follow the request’s journey through various services, much like following breadcrumbs in a forest.Engineers employ visualization tools such as flame graphs to decipher API traces. This simplifies the process of identifying bottlenecks or errors and organizing traces through tagging. API tracing is crucial for diagnosing and resolving performance issues, providing insights that lead to improved service relationships and performance optimizations.Enhancing Security Through API ObservabilityIn a digital landscape teeming with security threats, API observability acts as a telescope, identifying threats and ensuring compliance. By utilizing machine learning in API observability, you possess something akin to a smart security system for your home. Mobile apps developers can proactively identify and address vulnerabilities through analysis of user interactions.API observability takes an end-to-end view of applications, aiding in the early detection and prevention of security vulnerabilities and potential cyber threats. Understanding the full scope of API security has become a complex task due to the distributed nature of software teams and the need for extensive observability.Identifying Vulnerabilities in Real-TimeObservability empowers development teams to proactively identify and address security flaws or business logic problems in API-driven applications before they are exploited. Relying on synthetic data can lead to a false sense of API robustness due to its lack of diversity and complexity, much like a security system that only detects specific threats and ignores others.Centralized logs, when combined with advanced analytics, can reveal patterns and anomalies, allowing for a swift response to emerging API security issues. It’s like having a vigilant security guard who always remains on the lookout for suspicious activity and acts promptly to ensure safety.Collaborative Defense with Development TeamsIn the war against security threats, effective collaboration between security and development teams can maximize API security. A shared culture of security across departments helps ensure that we don’t confine accountability solely to the security team. This resembles how every member of a family shares the responsibility of keeping their home safe.Transparent communication and joint decision-making processes are fundamental for maintaining trust between security and development teams. It’s like having an open discussion with your family about safety measures, ensuring everyone is on the same page and trusts each other to do their part.API Observability Tools: Your Gateway to Better PerformanceAPI observability tools are like the magic carpet in Aladdin’s tale, providing more measures than what an API gateway can offer. API observability offers better performance by offering insights into API behavior, facilitating integration with management systems, and tracking user journeys for enhanced user experience.In this section, we cover the following key areas:  How to choose the appropriate observability service.  The significance of integration with API management systems.  The effect of tracking user journeys on user experience.  The importance of aligning API analytics with KPIs.There exists several tools in the market today:  Datadog  Atatus  Userpilot  Catchpoint Tracing  Better UptimeThese tools offer a wide range of capabilities. For example:  Automated site availability.  Geolocation validations.  Extensive infrastructure monitoring.  In-app user behavior tracking.  Multi-location checking and alerts.  Centralized monitoring of global API operations.They offer insights not only into the technical aspects of the API but also contribute to business strategy, thereby optimizing API-driven application performance.Selecting the Right Observability ServiceChoosing the ideal observability service is similar to picking the perfect vehicle for your journey. It should offer features like real-time updates and analytical capabilities. It should also provide customization for dashboards, reports, and alerts to meet specific needs. The service should integrate smoothly with various programming languages, frameworks, CI/CD tools, and cloud services, and offer connections to platforms like GitHub and Slack.Robust alerting systems that notify teams through multiple channels and track abnormal or unexpected behavior are critical for maintaining API health. When selecting an observability service, we recommend you take into account key aspects like the following:  Availability of support and documentation  The tool’s scalability to handle increased traffic or data volume, or both.  An evaluation of ROI (return on investment) for cost-effectiveness.Effective tools deliver high data granularity for precise troubleshooting. They also include pre-built reports that align API analytics with business objectives and compliments business logic.Integration with API Management SystemsIntegrating with API management systems is akin to consolidating all your travel tickets in one location. It ensures seamless monitoring and improved performance within the existing architecture. The tool should be able to integrate with APIM (API management) providers like Kong, Tyk, and WSO2. The tool should also integrate directly through an SDK so that developers can craft innovating and custom solutions for different use cases.Tracking User Journeys for Enhanced UXMonitoring user interactions across APIs is crucial to comprehend the impact on end-users and to identify optimizations for enhancing user experiences. Personalizing user experiences based on user journey analytics leads to better engagement and satisfaction by various means:  Addressing user disengagement points.  Fostering feature adoption.  Recognizing power users.When you interlink API observability metrics with data from CRM (customer relationship management) or marketing automation tools, you can generate a comprehensive customer journey map. This helps you understand and foster informed decisions to drive business growth. Tools like Saucelabs allow for extensive UX testing, providing insights into full functionality, front-end performance, and user experience.Aligning API Analytics with Business KPIsAPI analytics play a critical role in answering product and business-related queries, enabling companies and business teams to make decisions based on data. API observability extends beyond technical infrastructure, integrating with business metrics such as revenue and growth, to enhance decision-making for entire products or business units.Metrics like API usage growth, unique API consumers, and top customers by API usage are essential for gauging the adoption and business significance of APIs. Platforms like Moesif provide API analytics services that help businesses in areas such as customer activation and API monetization.Why you need API Analytics and ObservabilityIf we compare API observability to the stethoscope monitoring the heartbeat of your digital services, then API analytics acts as the ECG. API analytics offers a more detailed and comprehensive view of your API’s performance. Implementing API analytics and observability platforms, such as Moesif, bring significant benefits to organizations. These benefits range include:  Comprehensive insights into API behavior.  Enhanced performance.  Improved security.  The ability to make data-driven decisions for business growth.These platforms provide a 360-degree view of your API’s health, allowing you to monitor various metrics, analyze trends, identify potential issues, and take proactive measures to ensure optimal performance. They create an environment conducive to constant learning and improvement, leading to superior user experience, increased customer satisfaction, and ultimately, business success.SummaryIn a nutshell, API observability and API analytics are game-changers in the digital landscape. They provide valuable insights into API performance, facilitate data-driven decisions, enhance user experience, and ensure security. By embracing these tools and practices, organizations can stay ahead of the curve, effectively manage their APIs, and drive business growth.As you navigate the digital highway, remember that the key to maximizing performance and security lies in the depth of your understanding and the breadth of your vision. API observability and analytics make both of these possible.And Moesif makes all of these super easy and accessible with its comprehensive suite of powerful analytics and monitoring tools. You also get access to robust monetization features. You can integrate Moesif easily with your favorite APIM platform or API gateway through one of our easy-to-use plugins. You can also directly embed Moesif into your API code using our SDKs. With Moesif at your disposal, your API’s lifecycle becomes robust, accelerating product growth.To try it yourself, sign up today for a free trial, no credit card required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            You’ve built the API. Now let's generate revenue!            Monetize your APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Maximizing-Design-And-Performance-With-API-Observability/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-http-status-codes": {
          "title": "HTTP Status Codes: The Developer's Reference (2026)",
          "content"	 : "HTTP status codes are the three-digit numbers a server returns with every response. 200 OK, 404 Not Found, 500 Internal Server Error: those are the ones everyone knows. The full set runs to dozens of codes, but in practice you will use maybe fifteen of them across the entire lifetime of an API.                Monitor REST APIs With Moesif              14 day free trial. No credit card required.              Try for Free        This guide covers what each code actually means from the perspective of someone building or integrating with an API endpoint, what the response body should look like, and the 2026 wrinkles around idempotent retries, webhook delivery, and AI agent traffic. It is not a replacement for the IETF reference at iana.org or the MDN documentation, both of which are excellent. It is the practical version you can keep open while debugging.What HTTP status codes are (and why they matter for API design)A status code is the first signal a client gets about what happened. Before parsing the response body, before reading any headers, the client knows from the status code whether the request succeeded, failed, was redirected, or hit a server problem.For an API designer, this matters because the status code is the universal contract: every HTTP client in every language understands them the same way. If your API returns the right code, libraries handle retries correctly, browsers cache correctly, and proxies behave correctly. If your API returns a 200 OK containing {"error": "not found"} instead of a real 404, you are forcing every consumer to parse the body to figure out what happened. That breaks decades of established tooling.Choosing the right code per endpoint is part of API design principles, and it is one of the design choices that compounds over time. Every consumer of your API will write code that depends on the codes you return, and changing them later is a breaking change.The five families at a glance            Range      Family      What it means                  1xx      Informational      The request is in progress (rarely used in REST APIs)              2xx      Success      The request worked              3xx      Redirection      The resource is somewhere else              4xx      Client error      The request was wrong              5xx      Server error      The server failed to process a valid request      Most APIs in 2026 only use a small subset within each family. The sections below cover the ones developers actually return and consume.1xx informationalYou will almost never see these in a REST or GraphQL API. They exist mostly for low-level HTTP plumbing.  100 Continue. The server is telling the client “I got your headers, go ahead and send the body.” Used in large uploads where the client wants to check the server is willing to accept the request before sending megabytes of data.  101 Switching Protocols. The server is upgrading the connection, usually from HTTP to WebSocket.If you are designing a normal API, you can ignore 1xx.2xx success codes you actually use  200 OK. The default success code. Use it for GET, PUT, PATCH, and DELETE when the request completed and you are returning the resource (or, for DELETE, an empty body).  201 Created. Use this for POST when you created a new resource. Include the URL of the new resource in the Location response header, and return the new resource in the body.  202 Accepted. The request is valid and will be processed asynchronously. Used for long-running jobs (think “this report will be ready in a few minutes”). Pair with a job ID the client can poll.  204 No Content. The request succeeded but there is no body to return. Common for DELETE operations, especially in APIs that prefer no body over an empty {}.  206 Partial Content. The server returned only part of the resource because the client asked for a range (large file downloads, video streaming).The mistake to avoid: returning 200 OK with {"success": false} instead of the appropriate error code. The status code is the contract; do not overload the body to compensate for the wrong code.3xx redirects  301 Moved Permanently. The resource has a new permanent URL. Search engines update their index; browsers cache the redirect. Use for permanent URL restructures.  302 Found. Temporary redirect. The resource is somewhere else right now but the original URL is still the canonical one.  303 See Other. After a POST succeeds, redirect the client to do a GET on a different URL. The classic “post/redirect/get” pattern.  304 Not Modified. Conditional response when the client sent an If-None-Match (ETag) or If-Modified-Since header and the resource has not changed. Saves bandwidth.  307 Temporary Redirect. Like 302 but the client must repeat the same HTTP method (a POST stays a POST after the redirect, which 302 does not guarantee).  308 Permanent Redirect. Like 301 but preserves the HTTP method, the same way 307 does for 302.In modern API design, prefer 307 and 308 over 302 and 301 for redirect responses, because they explicitly preserve the request method. The older codes have historically been treated inconsistently across HTTP clients.4xx client errors (the ones you debug daily)This family is the one developers spend the most time looking at.  400 Bad Request. The request is malformed: invalid JSON, missing required fields, wrong data types. Return a body with a machine-readable error code, a human-readable message, and ideally the field that failed validation.  401 Unauthorized. “You did not authenticate.” The credentials are missing or expired. The right fix is to authenticate.  403 Forbidden. “You authenticated, but you cannot access this resource.” Different from 401, because re-authenticating will not help. Used for permission errors.  404 Not Found. The resource does not exist. Use for missing IDs (/users/9999). Do not use for “the route does not exist,” which is also 404 but for a different reason at the framework level.  405 Method Not Allowed. The resource exists but does not accept the HTTP method you used (you tried to DELETE an endpoint that only supports GET).  409 Conflict. The request would create a conflict with the current state. Common for duplicate-resource errors (“a user with this email already exists”).  410 Gone. Like 404, but with the explicit signal that the resource used to exist and has been intentionally removed. Useful for deprecated endpoints.  413 Payload Too Large. The request body exceeds your server’s limit.  415 Unsupported Media Type. The Content-Type of the request is not one the API accepts.  422 Unprocessable Entity. The request is syntactically valid (parsed cleanly) but semantically wrong (failed business validation). Useful when you want to distinguish “your JSON is broken” (400) from “your JSON is fine, but the data violates a business rule” (422).  429 Too Many Requests. Rate limit exceeded. Always include a Retry-After header telling the client how long to wait.A good 4xx response includes three things: the status code, an error.code machine-readable identifier, and a human-readable error.message. For validation errors, include the offending field. The standard shape most teams converge on:{  "error": {    "code": "invalid_email",    "message": "Email address is not valid.",    "field": "email"  }}5xx server errorsThese are your responsibility, not the client’s.  500 Internal Server Error. The catch-all. The server encountered an unexpected problem. Return a request ID the client can quote to support; never return a stack trace.  502 Bad Gateway. The server is acting as a proxy and got an invalid response from an upstream service. Common when a load balancer cannot reach a backend.  503 Service Unavailable. The server is temporarily unable to handle the request (maintenance, overload, or deliberate circuit breaking). Include a Retry-After header.  504 Gateway Timeout. Same proxy scenario as 502, but the upstream did not respond in time.  507 Insufficient Storage. Used for storage-quota errors on services that have one (uncommon for most REST APIs).For 5xx responses, log enough on your side to debug the issue, and return a request ID in the response so your support team can trace the call. The client cannot fix a 5xx; only you can.HTTP response headers you’ll see alongside status codesThe status code is the headline, but most of the actionable information lives in the response headers. A working API treats the headers as a first-class part of the contract, not a side channel.Location is the destination on a 201 Created (pointing at the new resource) or a 3xx redirect. A POST /orders that returns 201 should include Location: /orders/42 so the client knows where to find the resource it just created.Retry-After is the wait time on 429 Too Many Requests and 503 Service Unavailable. The value is either a number of seconds or an HTTP date. Well-behaved clients (and AI agents) parse this and back off accordingly; servers that return 429 without Retry-After give clients no signal except “try less.”X-RateLimit-Remaining, X-RateLimit-Limit, and X-RateLimit-Reset let clients self-throttle before hitting 429. The convention is not formally standardized (GitHub, Twitter, and Stripe each have slightly different conventions), but pick one shape and apply it across every rate-limited endpoint.Cache-Control and ETag turn on HTTP caching. Cache-Control: max-age=300 lets downstream caches (CDNs, browsers, proxies) reuse the response for 5 minutes. ETag is a resource version string that lets clients ask “has this changed since my cached copy?”; a 304 Not Modified response saves bandwidth and reduces backend load on read-heavy endpoints.Allow is the response header on 405 Method Not Allowed that lists which methods the endpoint does accept. A client calling POST /users/{id} against an endpoint that supports only GET, PUT, DELETE should see Allow: GET, PUT, DELETE in the response.WWW-Authenticate is the response header on 401 Unauthorized that tells the client which authentication scheme the endpoint expects. WWW-Authenticate: Bearer is the modern default; the older Basic realm="..." shows up on systems still on basic auth.Sunset and Deprecation (IETF RFC 8594 and RFC 9745) signal that the endpoint is deprecated or scheduled for removal. Well-behaved SDKs surface these to developers at integration time.Content-Type and Content-Length are the basics every response needs. APIs that omit Content-Type force clients to guess; APIs that omit Content-Length make streaming and progress reporting harder.X-Request-Id (or Request-Id) is the per-request identifier the API should return on every response, success or failure. When a customer reports an issue, the request ID is what your support team uses to find the call in logs. APIs without it slow down every support ticket by a step.Put together: a status code alone tells the client what happened, while the headers tell them what to do next.Status codes for AI agent and webhook scenariosThis is the part of the conversation that did not exist five years ago and that most reference articles still skip.AI agents retry. A lot. When an agent calls your API and gets a 5xx, it will retry, often aggressively. Two practices keep this safe:  Make your POST endpoints idempotent. Accept an Idempotency-Key header from the client and return the same response on retry. Stripe and OpenAI both follow this pattern and it is the de facto standard.  Return 429 with a Retry-After value rather than letting the agent hammer your server when it is overloaded. Agents respect 429 better than they respect 5xx.Webhook delivery is its own game. When your API delivers a webhook to a customer’s endpoint and gets a non-2xx response, the convention is to retry with exponential backoff. Specifically:  2xx: delivered, done.  3xx: follow the redirect, then retry the original logic.  4xx: do not retry. The customer’s endpoint is rejecting the payload on purpose.  5xx and timeouts: retry with backoff (typical schedule: 1m, 5m, 30m, 1h, 6h, 24h).If you are building the webhook publisher side, surface the delivery status to the customer’s dashboard so they can see what was delivered and what failed. Per-customer, per-event visibility is the part that tends to be missing in homemade webhook implementations.How to return status codes correctly from your APIThe framework you are using makes a difference. Express, FastAPI, Spring, ASP.NET, and Go’s standard library all have their own conventions for setting status codes. A few patterns that hold across all of them:  Set the status code explicitly. Frameworks default to 200, which is wrong for most non-GET responses.  Set the status before writing the body. Once bytes are flushing, you cannot change the code.  Match the code to the actual outcome, not the easiest one. A “validation failed” response is 422 or 400, not 200 with an error string.  Return JSON for 5xx responses too, not HTML error pages. Your clients are programs, not browsers.Once your API is live, the question becomes whether the codes you are returning match what consumers see in practice. Moesif API monitoring shows you the status code distribution per endpoint, per customer, in real time, which is how most teams discover they are silently returning the wrong code for a class of errors.Where to take this nextThe right status code is the cheapest part of API design and the easiest part to get wrong. If you are building an API and want to see whether the codes you return match what your customers actually experience in production, start a 14-day Moesif free trial and watch the status code distribution per endpoint roll in. No credit card required.Frequently asked questionsWhat does HTTP status code 200 mean? The request succeeded and the server is returning the requested data. It is the default success response for GET, PUT, PATCH, and DELETE operations.What is the difference between 401 and 403? 401 means you are not authenticated (no credentials, or credentials are invalid). 403 means you are authenticated but you do not have permission to access this resource. Re-authenticating fixes 401; it does not fix 403.Why do I keep getting 429 errors? You are exceeding the rate limit set by the API. Check the Retry-After header in the response, wait that long, and try again. Long-term, implement client-side throttling that respects the X-RateLimit-Remaining header.What is the difference between 500 and 503? 500 is an unexpected server error (a bug or unhandled exception). 503 is a deliberate “we are temporarily unavailable” response, usually for maintenance or overload. 503 implies the server will be back; 500 implies you do not know what just happened.Should I create custom HTTP status codes? No. The 100-599 space is reserved by the IETF, and HTTP clients, proxies, and frameworks treat unknown codes as the default for their range (an unknown 4xx is treated as 400). Stick to standard codes and put any custom information in the response body.What do HTTP status codes beginning with 2xx indicate? Success. The server received the request, understood it, and processed it successfully. The most common is 200 (success with a response body); others include 201 (a new resource was created), 202 (the request was accepted for asynchronous processing), and 204 (success with no response body).How can I get the HTTP status code from a URL? From a terminal, run curl -I <url> to make a HEAD request that returns only the status line and headers. From browser developer tools, open the Network tab, click the request, and look at the Status column. From code, use your HTTP client’s response object (response.status_code in Python requests, response.status in fetch, response.statusCode in Node).What is an HTTP 200 status code? The standard success response. The server is returning the requested data in the response body. For GET requests, the body contains the resource; for PUT and PATCH, it usually contains the updated resource; for DELETE, the body is often empty (in which case 204 is more appropriate than 200).What are the different categories of HTTP status codes? Five families, grouped by the first digit: 1xx (informational, the request is in progress), 2xx (success, the request worked), 3xx (redirection, the resource is somewhere else), 4xx (client error, the request was wrong), and 5xx (server error, the server failed to process a valid request).                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /technical/http-status-codes/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-mastering-contract-first-api-development-key-strategies-and-benefits": {
          "title": "Mastering Contract-First API Development: Key Strategies and Benefits",
          "content"	 : "Contract-first API development involves defining the API contract before any coding begins. This method ensures all teams are aligned on the API’s structure and functionality from the start. In this article, we’ll delve into what contract-first API development is, its benefits, and how to implement it effectively.By starting with a well-defined contract, teams can avoid common pitfalls associated with the code-first approach, such as inconsistent designs and poor documentation. This upfront agreement on the API’s endpoints, data structures, and behaviors acts as a blueprint that guides the development process, ensuring that all stakeholders—from developers to quality assurance experts—are on the same page. Additionally, the contract-first approach facilitates better communication between teams, reduces integration issues, and accelerates the development lifecycle. With the help of tools like Swagger and OpenAPI Specification, teams can create, validate, and maintain API contracts with ease, leading to more reliable and maintainable APIs. Ultimately, adopting a contract-first methodology can significantly enhance the efficiency and quality of your API development projects.Key Takeaways  Contract-first API development ensures consistent API design by defining endpoints, data structures, and behavior before any coding begins, enhancing uniformity and collaboration.  The contract-first approach offers significant benefits including faster iterations, reduced time to market, and improved team collaboration, ensuring all stakeholders work cohesively from a well-defined API contract.  Tools like Swagger and OpenAPI Specification are essential for effective contract-first API development, enabling accurate API documentation, code generation, and rigorous testing against predefined contracts to ensure reliability and consistency.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding Contract-First API DevelopmentContract-first API development, at its heart, revolves around the contract or the detailed agreement between client and server. This contract serves as a blueprint that dictates how different parts of an application interact with one another, ensuring they speak the same language. Initiating the process with a contract provides teams with a robust foundation for the API’s structure and behavior, essential for uniform and collaborative development.In the code-first approach, developers often dive headfirst into coding and retrofitting an API around existing code—a practice that leads to inconsistent designs and poor documentation. However, this methodology flips the traditional script on its head. Instead of following the code-first approach, the contract-first method mandates defining the API endpoints, data structures, and expected behavior before a single line of backend code is written. Think of it as drafting a detailed roadmap before a road trip — it’s not just wise, but also critical for a smooth journey.Key Advantages of the Contract-First ApproachAdopting the contract-first approach reveals numerous advantages capable of revolutionizing the API development process. It’s a strategic maneuver that aligns teams from the outset, ensuring everyone is speaking the same language, metaphorically and literally. This clarity and uniformity pave the way for:  Faster iterations  Reduced time to market  Less wasted effort  Allowing organizations to respond swiftly to changing market demands without getting entangled in the development weeds.Enhanced Team CollaborationWhen it comes to teamwork, a well-defined API contract is a keystone for enhanced collaboration. A clear contract allows various stakeholders, from developers to quality assurance experts, to simultaneously work on their parts of the project. Imagine developers crafting the backend, front-end professionals sculpting the user experience, and quality analysts preparing test cases, all moving forward in tandem without stepping on each other’s toes. This parallel progress is made possible because the contract acts as a common reference point.Involving members from different teams early in the design process, using tools like TypeSpec, minimizes errors and ensures that the API implementation sticks to the agreed-upon contract.Consistency and ReliabilityThe beauty of the contract-first approach lies in its ability to foster consistency and reliability—a crucial part of any robust API. Developers, by upfront defining the API contract, guarantee predictability and stability of the API’s behavior across various developmental stages. This uniformity not only reduces integration issues but also instills confidence in the API’s consumers.A project that adopts a contract-first strategy benefits from:  Early identification of discrepancies, leading to improved reliability in the API’s performance  Consistent object properties, error codes, and array items ensure that data exchanged across the system is understood without ambiguity  Having a reliable compass that safely navigates you through the turbulent seas of software developmentTools and Technologies for Contract-First DevelopmentA set of sophisticated tools and technologies are required to navigate the contract-first API development route. These instruments are not just enablers but catalysts that transform the API development lifecycle. Tools like Swagger and Postman come to the forefront, offering capabilities to mock, prototype, and test APIs against their contracts. These utilities equip teams to validate their API designs frequently and early, securing alignment of the final product with the intended blueprint.While developers implement the API, these technologies facilitate the creation of a shared understanding among all parties involved in the project. Backend developers, frontend artisans, and even non-technical stakeholders come together in the early stages of the development process, thanks to these powerful tools. This collaboration is indispensable for a successful contract-first approach.OpenAPI SpecificationThe Open API Specification, also known as the open API specification, stands as a pillar in the world of contract-first API development. It’s a standardized language that meticulously details HTTP APIs, enabling code generation, infrastructure configuration, and the creation of comprehensive API documentation. Through the OpenAPI lens, developers can describe REST APIs accurately, integrating HTTP methods to ensure a harmonious alignment between the API’s design and implementation.Essentially, OpenAPI serves as the DNA sequence of an API’s contract. It lays out the building blocks of the API in an unambiguous format, fostering an environment where API endpoints and data models are articulated with clarity. This specification is the bridge that connects the conceptual world of API design to the concrete reality of API implementation.AsyncAPIAs we delve deeper into the realm of modern API development, the AsyncAPI specification emerges as the counterpart to OpenAPI for asynchronous communication. It’s the lingua franca for describing and documenting APIs that operate over message brokers like Kafka and MQTT or through WebSockets. AsyncAPI caters to the growing need for event-driven architectures, providing a framework for systems to engage in robust, bidirectional conversations.With AsyncAPI, developers can create blueprints for APIs that are not only powerful but also versatile, enabling different systems to interoperate seamlessly. Think of it as a guide for constructing an extensive network of highways, assuring a smooth journey and reachable destinations, irrespective of the vehicle or route.Implementing Contract-First API DevelopmentThe journey of implementing a contract-first API commences with the formation of a robust API contract. This contract, typically documented in human and machine-readable formats such as YAML or JSON, serves as the backbone of the entire development process. It encapsulates the essence of what the API is and how it behaves, serving as a reference for all subsequent stages—from code generation to testing.Defining the API ContractThe first step in this journey is to define the API contract. This involves detailing the following:  API endpoints  HTTP methods they will respond to  Structure of request and response bodies  Various scenarios that might result in error messagesThe contract acts as a comprehensive design document that captures the API’s intended functionalities and how it will interact with clients. This blueprint serves as the basis for all subsequent development activities.Forming an API contract involves more than just listing specifications — it’s a demonstration of foresight and strategic planning. Using a standard like the OpenAPI Specification ensures that the API design is not only thorough but also adheres to industry best practices. This early investment in defining the API pays dividends throughout the lifecycle of the API, as it becomes the single source of truth that guides developers and stakeholders alike.Generating Code from API ContractsWith the API contract in place, the subsequent step involves bringing it to life via code generation. Instruments like the OpenAPI Generator interpret the API contract and generate boilerplate code for servers and clients. This automated process translates the contract’s specifications into tangible code structures, laying down the scaffolding upon which developers can build the API’s business logic.The beauty of generating code from API contracts is that it allows backend developers to concentrate on implementing the unique features of the API rather than getting bogged down by repetitive coding tasks. Meanwhile, client SDKs can be generated in a variety of programming languages, providing a toolkit for external developers to easily interact with the API. This automation streamlines the development process, making it efficient and error-resistant.Testing Against the API ContractTesting holds a crucial role in the contract-first approach. It’s not just about checking for bugs; it’s about ensuring that the API implementation is faithful to the API contract. Testing tools that are compatible with the OpenAPI Specification, such as Swagger and Postman, enable developers to rigorously validate the API design against the actual behavior. This validation acts as a quality gate, confirming that the API adheres to the predefined contract and delivers the expected functionality.Testing against the API contract is comparable to orchestrating a symphony — every instrument must harmonize with the written score. Any deviation can lead to a discordant performance. Similarly, any discrepancies between the API’s implementation and its contract can lead to inconsistencies, which is why contract testing is an indispensable part of the API development lifecycle.Case Study: Real-World ApplicationThe theoretical benefits of API-first development, also known as contract-first API development, are compelling, but how does it fare in the crucible of the real world? Let’s consider a few examples. A large financial institution embraced contract-first development to enhance collaboration among distributed teams. By establishing clear API contracts, they were able to integrate with legacy systems efficiently, thereby solving one of their most pressing challenges.Similarly, a healthcare technology company implemented contract-first principles to streamline communication between its internal systems and external partners. In the e-commerce realm, a leading retailer adopted an API-first strategy to bolster the flexibility and scalability of their microservices architecture, ensuring they could quickly adapt to market trends and customer needs. These success stories demonstrate that contract-first development isn’t just a theoretical concept but a practical strategy that delivers tangible results.Best Practices for Contract-First API DevelopmentAdherence to certain best practices is vital to maximize the benefits of contract-first API development. These practices are the guiding principles that ensure the API’s design and implementation are robust, clear, and sustainable. They include creating explicit API specifications that serve as high-quality documentation and establishing checks and balances to mitigate errors throughout the development process.Versioning API ContractsJust like software, API contracts evolve. Versioning these contracts is a disciplined way to manage changes, ensuring that updates do not disrupt existing client applications. By maintaining multiple versions, developers can introduce new features while supporting legacy systems, striking a balance between innovation and stability. It’s a strategy similar to keeping a detailed journal of a project’s history, enabling teams to:  Traceback and understand the evolution of the API  Identify and resolve issues that may arise from changes  Communicate changes effectively to clients and stakeholdersAs part of versioning, providing detailed changelogs and maintaining an up-to-date release schedule are essential. They allow API consumers to track changes, prepare for updates, and adapt to new versions without surprises. Backward compatibility remains a key consideration, ensuring that the API can serve a wide array of consumers without causing friction.Documentation and CommunicationDocumentation is the lighthouse that guides developers through the often murky waters of API development. In contract-first development, documentation serves as a crucial resource that outlines every aspect of the API. It’s the comprehensive manual that provides developers and stakeholders with the knowledge they need to understand, implement, and interact with the API effectively.Apart from documentation, effective communication serves as the binding element that keeps the development process intact. Keeping all team members informed about the API’s functionality and updates ensures alignment and cohesion. It’s about ensuring that changes and updates are communicated transparently to API consumers, maintaining a seamless integration experience.Common Challenges and How to Overcome ThemEvery journey has its share of obstacles, and contract-first API development isn’t an exception. One of the common challenges is ensuring all teams adhere to the defined API contracts. To mitigate this challenge, you can:  Conduct regular reviews of the API contracts  Implement automated contract testing to catch discrepancies early  Synchronize efforts between teams to decide on the data and structure that will be transferred between each party.Another challenge lies in handling incompatible data formats and communication protocols. Solutions like middleware can simplify API integration by abstracting technical details and promoting code reusability. Moreover, effective error-handling strategies, such as retries and exponential backoff, are critical for maintaining API reliability in the face of errors. It’s about building a resilient system that can gracefully handle the unexpected.SummaryAs we conclude, it’s clear that mastering contract-first API development is not just about following a set of steps; it’s about adopting a mindset that prioritizes clarity, collaboration, and consistency. By defining the API contract upfront, leveraging powerful tools, and implementing best practices, teams can create APIs that are robust, reliable, and ready to meet the challenges of the digital landscape. Let this be a call to action. Embrace the contract-first approach and watch as your projects transform from disjointed efforts into cohesive, well-oiled machines. The time to innovate is now, and the contract-first methodology is your key to unlocking a future of seamless API development.Organizations looking for the best tools to support their API management can leverage Moesif’s powerful API analytics and monetization capabilities. Moesif easily integrates with your favorite API management platform or API gateway through one of our easy-to-use plugins, or embed Moesif directly into your API code using one of our SDKs. To try it yourself, sign up today and start with a 14-day free trial; no credit card is required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Mastering-Contract-First-API-Development-Key-Strategies-and-Benefits/",
          "author": "Preet",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "api-strategy-api-strategy": {
          "title": "API Strategy in 2026: A Practical Framework",
          "content"	 : "An API strategy is the plan that turns APIs from engineering output into a business asset. It covers which APIs you build, who they are for, how they are governed, how they are exposed and monetized, and how their performance ties back to business outcomes. Most organizations do not have an API strategy. They have an accumulation of APIs that grew organically, and the bill for that comes due about the time they reach a dozen services or their first paying API customer.                Monitor REST APIs With Moesif              14 day free trial. No credit card required.              Try for Free        This guide walks through what a working API strategy actually contains in 2026, why the conversation now has to include AI agents and MCP, how to build a strategy from scratch, and the mistakes that quietly kill API programs.What is an API strategy?An API strategy is the documented set of decisions and policies that govern how an organization designs, builds, exposes, and operates its APIs. It sits above individual API design and below corporate technology strategy. The strategy answers: which APIs exist and why, who consumes them, how they evolve over time, how their performance maps to business goals, and which platform decisions support all of that.An API strategy is not a roadmap of which APIs to ship next quarter. That is a tactical plan. The strategy is the framework that produces those plans.Why API strategy is becoming AI strategy in 2026The dominant conversation in 2025-2026 is that the boundary between API strategy and AI strategy has effectively dissolved. Industry surveys and analyst commentary on API platform adoption increasingly treat them as the same problem, and we see the same pattern across enterprise customers.Three shifts drove it:  AI agents are first-class API consumers. When an LLM application calls your API as part of a chain, the consumer is a model, not a human developer. Your API strategy now has to account for agent traffic patterns, retry behavior, and per-agent attribution.  MCP gives APIs a second runtime. The Model Context Protocol exposes existing REST APIs to agent runtimes through a different surface. Your strategy has to decide whether you maintain the API once and expose it through MCP generated from the same OpenAPI spec, or treat MCP as a separate workstream.  AI cost and governance are API problems. Outbound LLM calls (your application calling OpenAI, Anthropic, etc.) are API consumption from your side. Tracking, governing, and budgeting that consumption uses the same tools and patterns as the APIs you expose to others.The teams that treat AI strategy as a separate workstream from API strategy end up doing the same governance work twice.The 6 components of a working API strategyA complete API strategy has six components.1. Audience and value. Who consumes your APIs, what they need, and what outcome the API enables. External developers integrating commercially, internal teams reducing engineering coordination, AI agents accessing enterprise data, partners under contract. Each audience has different design and governance implications.2. API portfolio model. How APIs are grouped, named, versioned, and discovered. Some organizations run a single API surface; others run dozens. The strategy decides which APIs are public, which are partner-only, which are internal, and how new APIs get proposed.3. Governance and security baseline. The non-negotiables every API must meet: design conventions, authentication, rate limiting, logging, sunset policy. The strategy defines them once; individual API teams inherit them.4. Platform decisions. Which API gateway, developer portal, observability layer, and monetization layer the organization uses. Whether you assemble multiple best-of-breed tools (Postman + Kong + Datadog + Stripe) or run on a single platform (WSO2, Apigee, MuleSoft, IBM API Connect). The trade-off is integration cost versus the flexibility to customize.5. Measurement. How API success ties back to business outcomes. Integrations shipped per quarter, agent calls per month, revenue from consumption, time-to-first-call, support cost per API.6. Lifecycle discipline. How APIs move from design to retirement deliberately. The API lifecycle is the operational expression of strategy.A strategy that misses any of these six components will produce APIs that work in isolation but do not compound into a platform.Security and monetization as strategy decisionsTwo of the six components (governance/security baseline and platform decisions) generate more debate in practice than the other four combined. They are also the two that most directly affect external users, so the strategy decisions show up in customer-facing outcomes.Security as a strategy decision, not a checklist. Most strategy documents treat security as a one-page checklist: “use OAuth, enforce rate limits, validate inputs.” That is the floor, not the strategy. The actual strategic questions are harder. Which authentication patterns do you support across the portfolio? OAuth 2.0, mTLS, API keys for read-only public endpoints, signed requests for partner traffic each fit different audiences. Where does authorization live: in the gateway, in each service, or in a separate policy engine? How do you handle credential rotation and revocation at scale? And critically in 2026, how do you authenticate agent traffic distinctly from human-developer traffic, so that you can apply different rate limits, audit trails, and per-agent attribution? Treating these as five separate decisions made by five different teams is how enterprise API portfolios end up with seven authentication patterns nobody can fully document.Monetization as a strategy lever. Even if your APIs are not directly billed, monetization decisions shape strategy. Free public APIs need different rate-limiting and abuse-prevention than paid commercial APIs. APIs offered to partners on contract need SLA tracking and per-customer reporting that internal-only APIs do not. APIs that consume third-party services (your application calling OpenAI, Stripe, Twilio) need cost attribution back to the customer driving the call. The strategy says: which APIs are revenue-generating, which are cost-recovery, which are commodity/free, and what infrastructure investments each category justifies. Our API pricing strategy guide covers the pricing-model trade-offs (per-call, per-token, tiered, usage-bundle) in more depth, but the strategic question is which APIs warrant the analytical and billing investment at all.The platforms that integrate security policy enforcement, per-customer monetization, and AI/agent traffic governance into one runtime (WSO2 plus Moesif on the analytics-and-billing side) exist because doing this with five separate vendors becomes its own coordination tax. The trade-off is integration cost versus the flexibility to swap pieces.Developer engagement: the portal-as-product ideaThe most-overlooked component of an API strategy is how external (and increasingly, internal) developers actually adopt the APIs. Engineering teams tend to think the API itself is the product. In practice, the developer’s path from “I heard about this API” to “I have a working integration in production” passes through documentation, code samples, SDKs, a developer portal, and support; and any one of those being broken kills adoption regardless of how well-designed the API is.A working strategy treats the developer experience as a product owned by a product manager, not as documentation owned by no one.The components most strategies underinvest in:  A first-class developer portal with searchable docs, interactive try-it consoles, SDK downloads, and self-service auth. The portal is where new developers form their first impression of your platform.  SDKs in the languages your audience actually uses. A REST API with no SDKs forces every consumer to write HTTP-call boilerplate. SDKs in 3-5 dominant languages (TypeScript, Python, Go, Java, Ruby) compress integration time meaningfully.  Sample applications, not just reference docs. A working sample app that demonstrates the common integration patterns is worth more than another 50 pages of endpoint documentation.  A dedicated developer-support channel. Slack/Discord community, ticketed support, or a public forum where developers can get unblocked. Teams that route API support through general customer-support tickets bury the technical questions and frustrate developers.  Time-to-first-call as a tracked metric. How long does it take a brand-new developer to go from signup to their first successful API call? Stripe famously optimizes for this; few enterprises measure it.The strategy decision is how much of this you build in-house versus buying. Developer-portal generators (some bundled with API management platforms, some standalone) take you from zero to functional quickly. SDK generation tools (OpenAPI Generator, Stainless, Speakeasy) cut the SDK maintenance burden meaningfully. The integrated platforms (WSO2, Apigee, MuleSoft) generate the portal from the OpenAPI spec, which keeps it in sync without manual updates.API strategy in enterprise digital transformationDigital transformation is a broader corporate initiative that almost always depends on APIs as the integration layer, but it is rarely run by the same team that owns the API strategy. The disconnect between those two groups is where transformation programs slow down, and where API strategy can either accelerate or block the broader effort.The pattern: a digital transformation initiative announces it will modernize legacy systems by exposing them through APIs. The legacy team builds APIs that reflect the legacy data model. The downstream consumers (internal apps, partner integrations, agent runtimes) find these APIs hard to use because they leak implementation detail. The transformation slows down. The API strategy team gets called in to clean up after the fact.The cleaner pattern: the API strategy owns the consumer-facing contract from day one, and the legacy modernization is the implementation detail. The strategy specifies what the consumer-facing API should look like; the legacy team builds whatever adapter is needed to expose the legacy system through that contract.The strategic questions:  Which APIs are the digital transformation deliverables? Not every modernized system needs an external API; some are internal-only. Define this upfront.  Who owns the contract? Platform engineering owning the contract specification keeps it consumer-facing. Legacy teams owning it produces leaky abstractions.  What is the deprecation path for the legacy interface? Some transformations expose APIs on top of the old system as a permanent compatibility shim; others use the API as a stepping stone to a full replacement. The strategy should be explicit about which.  How does the AI surface fit? Increasingly, digital transformation programs include “make this data accessible to AI agents” as a deliverable. If your API strategy already has an AI/MCP layer, the transformation reuses it. If not, the program creates a parallel AI workstream that duplicates governance and security policy.Most enterprise transformation programs we see start treating API strategy as a separate, optional input and end up either restarting the API contract work or shipping APIs that the API strategy team has to redesign.API-first strategy vs. service-first strategyTwo opposing philosophies show up repeatedly when organizations design their API approach.API-first means designing the API contract before building the service. The OpenAPI spec is the source of truth; the implementation follows. Stripe and Twilio are the textbook examples. Pros: clean consumer experience, parallel client/server development, easier governance enforcement at the spec level. Cons: more upfront design work; less freedom for the implementation team.Service-first means building the service to meet internal needs, then exposing an API on top. Most enterprises grew this way. Pros: faster initial delivery; fits when the business case is internal rather than external. Cons: APIs reflect the implementation rather than the consumer; harder to govern consistently.The 2026 default for new APIs is API-first. Service-first remains common in legacy modernization projects, but rarely chosen on new builds.How to build an API strategy from scratchA practical sequence for organizations starting from no formal strategy:  Inventory what exists. List every API the organization currently runs, who owns it, who consumes it, and what it does. Most teams substantially underestimate this on the first pass, because shadow APIs (services exposed by individual teams without going through the platform group) rarely show up in central documentation, and tracing them down often surfaces several times as many endpoints as the platform team thought they had.  Define the audiences. Who are your API consumers? Be specific. “External developers” is not specific; “fintech platform teams integrating our payments API” is. Each audience surfaces different priorities: external developers care about docs and SDKs, partners care about SLAs, AI agents care about idempotency and clear endpoint descriptions.  Pick the success metrics. Two to four metrics, no more. They should map directly to business outcomes: revenue from API consumption, integrations shipped per quarter, time-to-first-call for new developers, agent calls per month. Vanity metrics (total requests, total endpoints) tell you nothing about strategy effectiveness.  Draft the governance baseline. The non-negotiables every API must meet. Keep it short on the first pass: auth standards, naming conventions, error format, rate limiting defaults, deprecation policy. You can add as you learn what teams keep doing wrong.  Choose the platform stack. Gateway, developer portal, observability, monetization. Document the choices and the reasons. Re-evaluate annually, because the tool landscape changes quickly and an honest 12-month look often surfaces gaps.  Publish the strategy internally. A short document (10-15 pages, not 50). Make it findable by every team that ships APIs, and add it to the platform engineering onboarding so new hires read it in week one.  Audit quarterly. Compare what the strategy says with what teams are actually doing. Update the strategy when reality has moved. A strategy that is never revised becomes folklore that nobody follows.The biggest pitfall is making the strategy too detailed to maintain. A 50-page strategy document goes stale within a year and is never updated. A 10-page strategy that gets revised every quarter survives.For the monetization slice specifically, our API pricing strategy guide covers the unit-of-value and pricing-model decisions in depth.Common API strategy mistakesThe patterns we see across enterprise customers.Treating API strategy as an engineering exercise. APIs are products. Strategy needs product, platform, and business inputs, not just architecture decisions. The strategies that fail are the ones written entirely by the platform engineering team.Skipping the audience definition. Without a clear audience, governance defaults to the lowest common denominator (which protects nothing) or the highest (which over-engineers everything). Pick the audiences first.Choosing the platform before the strategy. Selecting Apigee or Kong or WSO2 before the strategy says what the platform needs to support is how enterprises end up with expensive tools that do not match their actual workflow. The strategy defines requirements; the platform is the answer.Confusing AI strategy with API strategy. They are now the same conversation. Running them separately produces duplicate governance work and contradicting policies.No retirement plan. Most strategies focus on building. The ones that stay coherent over time also have a discipline for retiring APIs: deprecation policy, sunset header usage, sunset date tracking. Without it, three-version platforms become eight-version platforms.Strategy without measurement infrastructure. A strategy that names success metrics but does not invest in the observability to measure them produces quarterly slides full of guesses. If “revenue from API consumption” is a metric, you need per-customer call data tied to billing. If “agent calls per month” is a metric, you need attribution that separates agent traffic from human-developer traffic. Pick metrics you can actually measure, or invest in the tooling to measure the ones you picked.How Moesif and WSO2 fit into an enterprise API strategyMost strategy documents stop at “pick a platform.” The harder question is which tools cover which parts of the strategy without requiring your platform team to integrate five vendors.The integrated WSO2 + Moesif stack covers:  Governance and security baseline (WSO2 API Manager) with policies enforced against the OpenAPI spec at merge time  Multi-gateway runtime (WSO2 API Manager) across WSO2, Kong, AWS, Azure, Envoy  AI surface (WSO2 AI Gateway) for inbound MCP traffic and outbound LLM calls  Developer portal generated from the spec  Observability (Moesif) with per-endpoint, per-customer, payload-level analytics  Monetization (Moesif Billing Meters) with sync to Stripe, Recurly, Chargebee, SalesforceThe alternatives are real: Postman covers design with a lighter platform layer (and lighter governance and analytics depth); Apigee, Kong, MuleSoft, and IBM API Connect each cover the gateway-plus-governance layer with different cloud-fit, pricing, and lock-in trade-offs. The choice depends on which cloud your platform runs on, how much of the lifecycle you want pre-built rather than assembled yourself, and whether AI traffic is a meaningful share of your usage.Next stepsAPI strategy is a living document, not a one-time deliverable. Pick the audiences, define the governance baseline, choose the platform, set the metrics, and revise quarterly.If you are ready to see what your APIs are actually doing in production against the strategy you set, start a 14-day Moesif free trial for per-endpoint, per-customer analytics in production. No credit card required.Frequently asked questionsWhat is an API strategy? A documented set of decisions about which APIs an organization builds, who consumes them, how they are governed, and how their performance ties to business outcomes. It sits above tactical roadmaps and below overall technology strategy.What is API-first strategy? Designing the API contract before building the implementation. The OpenAPI spec becomes the source of truth and the implementation follows. The opposite is service-first, where the service is built first and an API is layered on top.Why is API strategy important in 2026? Because the boundary between API strategy and AI strategy dissolved. Agent consumption, MCP exposure, and outbound LLM governance all use the same tools and patterns as the APIs you expose to others. Treating them separately produces duplicate work.How long should an API strategy document be? Ten to fifteen pages. Anything longer goes stale and stops getting updated. A short, frequently-revised strategy beats a comprehensive one that has not been touched in a year.Who owns the API strategy? A cross-functional working group: platform engineering, product, security, and (increasingly) the AI platform team. Single-owner strategies fail because they miss inputs from one of those four perspectives.How does API strategy connect to API governance? Governance is the enforcement layer of the strategy. The strategy says what the rules are; governance makes sure individual API teams follow them. Without strategy, governance is arbitrary; without governance, strategy is theoretical.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /api-strategy/api-strategy/",
          "author": "Matthew",
          "categories": "API-Strategy, API-Development"
        }
      
    ,
  
    
        "technical-api-development-moesif-aws-stripe-ai-api-part-1": {
          "title": "Using Moesif, AWS, and Stripe to Monetize Your AI APIs - Part 1: Integrating The Platforms",
          "content"	 : "  This is the first part of a four-part series about AI API monetization.As the wave of AI sweeps through the technology landscape, many have hopped on board. Interestingly enough, and often overlooked, is that many AI capabilities are served through APIs. Fancy user interfaces integrate with the actual mechanisms where the magic happens: the APIs. So, when generating revenue through AI platforms, the APIs drive the revenue.This leads to the challenge of controlling access to the APIs, metering API usage, and charging customers for said usage. The overarching term for this is API monetization, but unlike simpler forms of API monetization that only charge based on API calls, AI platforms generally tend to charge on things such as “tokens used” and other AI-specific metrics that are metered upon. Luckily, platforms exist that can expedite this process and make it simple to implement. We will focus on Moesif, AWS, and Stripe to implement API monetization for AI APIs.You’ll learn to use each platform to fulfill a specific role in the setup:  AWS Lambda will power the API backend logic.  Amazon API Gateway will host the API, providing access control and API management.  Then, you will integrate Moesif with AWS to add metering capabilities. This will allow us to send usage data over to Stripe. Stripe will take care of collecting payment or burning down balances based on usage.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Table of Contents  The AI API          POST Request Payload      Response Payload        Setting up AWS Lambda          Step 1: Install the Dependencies      Step 2: Write the Lambda Function      Step 3: Prepare a Zip File Archive      Step 4: Create the Lambda Function in the Console      Step 5: Add Environment Variables in Lambda      Step 6: Deploy the Lambda Function        Setting up Amazon API Gateway          Step 1: Create a REST API      Step 2: Create API Resource      Step 3: Add API Method      Step 4: Deploy the API        Protecting the API with API Gateway          Step 1: Set Up a Usage Plan                  Create a Usage Plan          Associate a Stage to a Usage Plan                    Step 2: Add an API Key                  Create API Key          Require API Key on Your API’s POST Method                      Integrating Moesif and Stripe  ConclusionThe AI APIIn almost all cases, you will expose AI service through an API or leverage existing AI services that will power your API functionality. In our example, we will use OpenAI’s /chat/completions endpoint to demonstrate how to monetize an AI API. This provide an accurate depiction of an AI API. Many companies use OpenAI-compatible API Specifications to allow for drop-in replacement for OpenAI APIs.We will create a single endpoint called /ai-chat that will function similarly to OpenAI’s chat endpoint. Well, exactly like it, since it will leverage the /chat/completions endpoint under the hood! The following two sections show how the request and response will look.POST Request Payload{  "model": "gpt-3.5-turbo",  "messages": [    {      "role": "system",      "content": "You are a helpful assistant."    },    {      "role": "user",      "content": "Hello!"    }  ]}Response Payload{  "id": "chatcmpl-123",  "object": "chat.completion",  "created": 1677652288,  "model": "gpt-3.5-turbo-0125",  "system_fingerprint": "fp_44709d6fcb",  "choices": [{    "index": 0,    "message": {      "role": "assistant",      "content": "nnHello there, how may I assist you today?",    },    "logprobs": null,    "finish_reason": "stop"  }],  "usage": {    "prompt_tokens": 9,    "completion_tokens": 12,    "total_tokens": 21  }}For a complete reference for the API we will be using, see the OpenAI Chat Completions docs.For our monetization efforts, we must accurately meter the usage. In that regard, only the response fields for prompt_tokens and completion_tokens concern us. These two fields give us the input and output token counts for the prompt and response, respectively. For the rest of the tutorial, you can either follow along using your own AI API or use the OpenAI Chat Completions one that we will be using throughout. Either works as long as you have a field in the response that outlines how many input and output tokens the query has consumed.Setting up AWS LambdaTo set up the Lambda function, we assume you have the following prerequisites:  An AWS account  A user with administrative accessStep 1: Install the DependenciesThe Lambda function requires these dependencies:  The Moesif AWS Lambda middleware  The Python Requests libraryInstall these dependencies in a folder:pip install --target ./package moesif_aws_lambda requestsHere we put these dependencies inside the package/ folder.Step 2: Write the Lambda FunctionNext, we write a Lambda function lambda_handler in a file lambda_function.py:from moesif_aws_lambda.middleware import MoesifLoggerimport jsonimport osimport requestsmoesif_options = {}@MoesifLogger(moesif_options)def lambda_handler(event, context):    req_body = json.loads(event.get("body"))    headers = {"Authorization": os.environ["Authorization"]}    ai_res = requests.post(        "https://api.openai.com/v1/chat/completions", json=req_body, headers=headers    )    res_body = ai_res.json()    return {        "statusCode": 200,        "headers": {"Content-Type": "application/json"},        "body": json.dumps({"request_payload": res_body}),    }This simple Lambda function performs the following tasks:  It extracts the request body from the Lambda event object. The request body contains the prompt we want to send to OpenAI, same as the example POST request payload.  It sets the Authorization header to the OpenAI API key.  It then sends an HTTP request to OpenAI API’s /chat/completions endpoint.  Finally, the function returns a response back to the client, containing the response from OpenAI API in the body.request_payload field.In a later section, you will set up AWS Gateway to create an API and integrate the Lambda function. When you send requests to your API, the Lambda function will capture the request data in the event object and process it.Step 3: Prepare a Zip File ArchiveTo deploy the Lambda function with the dependencies, you need to package them into a zip file archive. Consider the following your current directory structure:.├── lambda_function.py└── package/Then you can create a zip file archive function.zip by executing these commands:cd package/zip -r9 ../function.zip .cd ..zip -g function.zip lambda_function.pyStep 4: Create the Lambda Function in the Console  Go to the Functions page of the Lambda console.  Select Create Function.  Select Author from scratch.  Fill out the Basic information section. Make sure you select a Python runtime and choose the 64-bit x86 architecture. This article uses the Python 3.12 runtime.  Select Create Function.Step 5: Add Environment Variables in LambdaWe will add two environment variables for the Lambda function:  A MOESIF_APPLICATION_ID environment variable. The Moesif AWS Lambda middleware expects this variable to be able to connect with your Moesif account and send analytics.  An OpenAI API key. You need to add this key in the Authorization header in the format Bearer YOUR_OPENAI_API_KEY.Follow these steps to add these environment variables:  Go to the Functions page of the Lambda console.  Select the function you’ve created.  Select Configuration and then select Environment variables.  Under Environment variables, select Edit.  Select Add environment variable to enter key and value for an environment variable.  Enter the keys and and values for the environment variables:          For Moesif Application ID, set Key to MOESIF_APPLICATION_ID and Value to your Moesif Application ID.      For your OpenAI API key, set Key to Authorization and Value to Bearer YOUR_OPENAI_API_KEY. Replace YOUR_OPENAI_API_KEY with your OpenAI API key.        Select Save.To get your Moesif Application ID, follow these steps:  Log into Moesif Portal.  Select the account icon to bring up the settings menu.  Select Installation or API Keys.  Copy your Moesif Application ID from the Collector Application ID field.Step 6: Deploy the Lambda FunctionIf you’ve followed the preceding steps, you now have a zip file archive that contains your Lambda function code and its dependencies. Next, follow the instructions in AWS Lambda docs to upload and deploy your Lambda function code as a zip file archive.Setting up Amazon API GatewayTo set up Amazon API Gateway, we assume you have the following prerequisites:  An AWS account  A user with administrative accessStep 1: Create a REST APIAPI Gateway offers several API types. But for our use case, create a REST API.  Go to your API Gateway console.  Select Create API.  Under REST API, select Build.  Select New API.  For API endpoint type, select Regional. Fill out the rest of the fields as you need.  Select Create API.Step 2: Create API ResourceAfter creating API in the preceding step, your API contains only the root / resource. In the following steps, we create the resource /ai-chat.  Go to your API Gateway console.  Go to the Resources page.  Select Create resource  Enter ai-chat for Resource name.  Select Create Resource.Step 3: Add API MethodNext, you must add an HTTP POST request method to the /ai-chat resource. This allows clients to send HTTP POST request to the /beta/ai-chat endpoint, where beta denotes the deployement stage of the API.  Go to your API Gateway console.  Go to the Resources page and select the /ai-chat resource.  In the Methods pane, select Create method.  For Method type, select POST.  For Integration type, select Lambda function.  Enable Lambda proxy integration.  For Lambda function, select the Lambda function you’ve created in the preceding section.  Select Create method.Step 4: Deploy the APIWith the resource and method in place, you can now deploy the API:  Go to your API Gateway console.  Go to the Resources page.  Select Deploy API.  Select *New stage* and then enter a stage name.  Optionally, add a description of your API.  Select Deploy.After deployement finishes, the Stages page appears. In the Stage details pane, Invoke URL shows the URL to call your API.For example, consider the URL https://abcdefgh12.execute-api.ap-southeast-2.amazonaws.com/beta where beta is the stage name. You can send POST requests to https://abcdefgh12.execute-api.ap-southeast-2.amazonaws.com/beta/ai-chat with the following body:{  "model": "gpt-3.5-turbo",  "messages": [    {      "role": "system",      "content": "You are a helpful assistant."    },    {      "role": "user",      "content": "Hello!"    }  ]}The associated Lambda function takes the requst body and calls the OpenAI Chat Completions API. If you’ve followed the preceding instructions properly, you should see a response similar to the following:{    "request_payload": {      {        "id": "chatcmpl-123",        "object": "chat.completion",        "created": 1677652288,        "model": "gpt-3.5-turbo-0125",        "system_fingerprint": "fp_44709d6fcb",        "choices": [{          "index": 0,          "message": {            "role": "assistant",            "content": "nnHello there, how may I assist you today?",          },          "logprobs": null,          "finish_reason": "stop"        }],        "usage": {          "prompt_tokens": 9,          "completion_tokens": 12,          "total_tokens": 21        }      }    }}Protecting the API with API GatewayIf you have followed along so far, you have a functional AI API that can generate appropriate responses to requests. In this section, we implement two basic security mechanisms to protect the API:  A usage plan. This allows you to restrict rate or total number of requests a client can make to the API.  An API key to control access to the API.Step 1: Set Up a Usage PlanBefore you can an API key, you must first create a usage plan and associate it with a stage:Create a Usage Plan  Go to your API Gateway console.  Go to the Usage plans page.  Select Create usage plan  Enter the name and an optional description for the usage plan.  Enter the number of requests per second in Rate.  Enter the number of concurrent requests in Burst.  Lastly, enter how many requests a client can make to your API in a time period.  Select Create usage plan.Associate a Stage to a Usage Plan  Go to your API Gateway console.  Go to the Usage plans page.  Select your usage plan.  Go to the Associated stages tab and select Add stage.  Selet your API and the stage.  Select Add to usage plan.Step 2: Add an API KeySetting up an API key to protect access to your API consists of the following steps:  Creating an API key for a stage of your API.  Requiring API key on API POST method.Create API Key  Go to your API Gateway console.  Go to the Usage plans page.  Select your usage plan.  Go to the Associated API keys tab and select Add API key.  Select Create and add new key.  Enter the name and an optional description for your API key.  Select Generate a key automatically.  Select Add API key.Require API Key on Your API’s POST MethodAfter following these steps, anyone who wants to access the the API must include the API key in the x-api-key request header:  Go to your API Gateway console.  Select your REST API.  Go to the Resources page.  Select the method. In our case, for example, select the POST method for the /ai-chat resource.  Select the Method request tab and then select Edit.  Select the API key required checkbox.  Select Save.  Select Deploy API and deploy API to the stage you have associated your usage plan with.If you’ve followed the steps successfully, you will get the same response back from your API. Otherwise, you API Gateway throws 403 Forbidden status response.Integrating Moesif and StripeIn the preceding steps, we’ve successfully finished the following tasks:  Set up the AI API.  Integrate Moesif with AWS.You should now receive API traffic analytics in Moesif for each call to the API. Since Moesif now receives details about API calls, we’re ready to calculate usage. After calculating usage, we need a way to report the usage to Stripe. Moesif integrates directly with Stripe very quickly.To configure Stripe in Moesif, follow these steps:  Log into Moesif Portal.  Select the account icon to bring up the settings menu.      Select Extensions.    a. Find the Stripe Integration in the extensions and then select Configure.    b. In the dialog that appears, follow the instructions for adding the Moesif Stripe webhook into Stripe.          On the Developers screen in Stripe, select Webhooks and then select Add Endpoint.     Add the Moesif endpoint URL and select Select Events. Select all customer.*, customer.subscription.*, and invoice.events.*. Then, select Add Events.     Lastly, in the original Add Endpoints screen, select Add Endpoints.         Once you add Moesif endpoint URL as a Stripe Webhook, add your Stripe API key in the Stripe Integration dialog in Moesif.    a. In Stripe, go to the Developers screen and select API Keys.    b. Copy a private key for your API in either the Secret key or a generated Restricted keys pane on the screen. You can use either key.    c. In Moesif, paste the API key into the Stripe API Key field.    Scroll to the bottom of the Stripe Integration dialog in Moesif and select Save.  You can optionally customize the ID mapping in Moesif. The default works fine for most purposes and this example uses the same. However, if you need to customize it, you can specify how to map the Stripe Customer objects to Company entities in Moesif. For more information, see Setting the Id Mapping for Stripe.ConclusionAt this point, we have all of the wiring in place to begin monetizing APIs. The AWS Gateway instance can now proxy traffic to the upstream AI API. Moesif tracks that traffic, meters it, and reports to Stripe.In the next part of this tutorial, we cover how to set up a Billing Meter in Moesif. Billing Meter will meter the usage based on the configuration we specify and then report that usage to Stripe. After this, we will also use Moesif’s Governance Rule feature to block users from accessing the API if they have run out of pre-paid credits.Want to follow along as we build out this billing infrastructure to monetize APIs? Sign up for a free trial of Moesif and follow this article series for a step-by-step path for implementing API monetization. Until next time, stay tuned for the next part in this series on monetizing AI APIs!                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            You’ve built the API. Now let's generate revenue!            Monetize your AWS Gateway powered APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Moesif-AWS-Stripe-AI-API-Part-1/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-api-design-first": {
          "title": "API Design-First: Enhance Your Development Process",
          "content"	 : "Why should you prioritize an API design-first approach in your development strategy? This essential methodology promotes early and thorough API specification, fostering a blueprint that guides the entire development process. You can cut straight to improved efficiency, consistency, and quality in your API projects by embracing this approach from the start.This article walks you through the crucial details and benefits of this development model, preparing your team for a future where smart design decisions drive your API toward success.Key Takeaways  API design-first prioritizes the planning and design of APIs before coding and implementation. It fosters collaboration using API contracts as a synchronized reference for all stakeholders. This approach also prevents disconnects and aligns the development with business requirements.  The use of API specification languages and tools enhances the clarity and reliability of API documentation, encourages early feedback, and allows for rapid prototyping and testing. All of this contributes to a more efficient development process.  Implementing API design-first can pose challenges. For example, ensuring organizational buy-in, adhering to API contracts during development, managing API governance, and navigating the learning curve associated with new design tools and languages.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Unveiling API Design-First: The Genesis of a Structured API DevelopmentAPI design-first signifies a paradigm shift emphasizing the design and planning of application programming interfaces (APIs) before any code development. This approach encourages collaboration among developers, analysts, and stakeholders, who use a shared API contract as a reference to synchronize their efforts. By detailing the API’s specifications in advance, all stakeholders stay on the same page with a clearly-defined API structure. The structure supports the software development lifecycle and prevents potential disconnects before coding starts.You may have heard about the API-first methodology. API-first advocates prioritization of building APIs at the inception of a software development project. You can consider the API design-first model as an essential component of API-first approach. Understandably, combining these two approaches together vastly improves the development process and API-first product.However, this approach doesn’t come without its challenges. One of the most common hurdles is securing organizational buy-in. This necessitates enlightening the organization about the pivotal function and value of APIs, positioning them as strategic assets beyond mere technical details. After all, APIs are the backbone of modern software development, enabling diverse systems to communicate and share data.Understanding API ContractsIn the API design-first methodology, API contracts are the foundational agreements defining the behavior between different software components. Developers create these formal contracts using specifications such as OpenAPI. A contract dictates the API’s behavior ahead of implementation. Creating an API contract requires extensive design considerations and collaboration among stakeholders before developers can start coding.API contracts act as a blueprint for developers. They provide a clear guide on how to build each component of the API. Early definitions of these contracts allow developers to preclude misunderstandings and miscommunications, thereby guaranteeing a smooth development process that aligns with the initial design and business requirements.The Role of API Specification LanguagesTo effectively use an API, it must have clear and comprehensive documentation. API specification languages play critical role in this regard. Tools like Swagger UI utilize OpenAPI Specification to create interactive API documentation, improving the API’s usability for developers. This interactive documentation allows developers to explore the API’s endpoints, view sample responses, and even make test requests without needing to write any code.The use of specification languages offers several benefits for API documentation:  Ensures that the documentation always remains up-to-date with the latest changes in the API’s design.  Enhances the developer experience by providing accurate and reliable information.  Makes it easier for teams to collaborate on the API’s development.  Serves as a single source of truth for the API’s design.By utilizing API description language, you can create comprehensive and reliable API documentation, including a detailed API specification.Importance of Early FeedbackA key principle of the API design-first approach is the significance it places on early feedback. By involving stakeholders in the API design process sooner, companies can refine the API from the outset, leading to an enhanced API design and developer experience. This early feedback is crucial for detecting potential architectural inconsistencies or issues.The use of tools to verify API implementation against the API contract aids in improving the API quality in different ways:  Catching errors earlier in the development cycle.  Ensuring the API’s reliability and performance.  Saving developers from the time-consuming and costly process of troubleshooting and fixing issues later in the development process.The Blueprint of API Development: Crafting Your API Before CodingThe process of crafting an API before coding is a key aspect of the API design-first approach. The alternative to this approach is the code-first approach. This practice has been effectively applied in building web applications and services, particularly in microservices architecture, to create decoupled systems that closely align with business objectives.Focusing on API design before code development comes with several benefits:  Devising the API with an emphasis on reducing the necessity for future versioning.  Ensuring effective management of evolution.  Making backward-compatible changes whenever possible.In essence, designing the API before writing code helps to ensure that developers build the API right the first time, saving valuable time and resources in the long run.From Concept to Contract: Laying the GroundworkCreating an API contract comprises a crucial stage in the API design-first methodology. This contract provides a detailed description of the API’s design and endpoints, serving as a blueprint that guides developers throughout the development process. The initial phase of this process involves stakeholders reaching a consensus on the API’s intended operations, data formats, and endpoints.The team identifies key business services and documents use cases, outlining potential endpoints in the API contract drafting process. This strategy facilitates the crafting of APIs that align with client requirements from the start, using documentation to define interactions, data models, and endpoints.Rapid Prototyping and API MockingAPI mocking enables developers to simulate real API behavior. It allows application testing without the need for real data and even before the actual API becomes ready. This practice accelerates development cycles, provides immediate feedback, and allows for systematic validation. In the long run, mocking results in financial efficiency through cost savings.Developers can host mock APIs locally or on servers. These APIs have such structure that they can accurately emulate real API schemas for effective testing and development. This early testing and validation of the API by simulating its endpoints before the development finishes is a form of rapid API prototyping. It allows for early detection and mitigation of issues, ensuring a smooth and efficient development process.Collaborative Dynamics in API DesignThe API-first development approach invites a broader range of contributors, including non-developers such as business analysts and product managers. It encourages whole development team participation in the API design process. This enhances collaboration between developers and clients by offering a visual representation that allows for early feedback.By including both technical and non-technical stakeholders, API documentation becomes more comprehensive and improves the team’s understanding of the APIs. API contracts carry out pivotal roles for inter-organizational collaboration, enabling controlled sharing of APIs for feedback and business opportunities. This approach fosters a shared vision among team members, reinforcing the balance between collaborative dynamics and the governance structure.API Documentation: The Cornerstone of API Design-FirstAPI documentation serves not merely as a manual for developers; it forms a fundamental element of the API design-first approach. It provides a clear understanding of the functionality of an API and how to integrate it effectively. Clear documentation leads to an enhanced developer experience by providing modular and reusable components, reducing the learning curve.Tools like Apidog are instrumental in promoting the API-first approach, aiding developers in creating, visualizing, and documenting APIs. In essence, good API documentation ensures that the API is not only well-designed but also well-understood, leading to effective and efficient API usage.Generating and Maintaining API DocsTools like SwaggerHub makes the process of generating and maintaining API documentation easier. These tools not only generate API documentation automatically, but they also support style validation and API mocking. Other tools, such as SwaggerHub Explore and Apidog, enable the automated generation of OAS-compliant documentation directly from code in both design and debug modes.However, generating the documentation constitutes only half the job. API documentation must receive frequent updates to mirror modifications in the API accurately, maintaining its usefulness for development and stakeholder communication. API contracts, often written in human and machine-readable formats such as YAML or JSON, facilitate the automated generation and maintenance of API documentation.Documenting Beyond Endpoints: Use Cases and ScenariosWhile documenting the API’s endpoints is essential, it’s important to go beyond that by including use cases and scenarios in the documentation. This provides a clear understanding of how the API works in various contexts.For example, consider an e-commerce API. Some common use cases to consider documenting include the following:  Creating a new user account.  Retrieving a list of products.  Updating a customer’s shipping address.  Processing a payment.For each use case, provide code samples and step-by-step instructions to guide developers in implementing the API functions. This enhances clarity and ease of use for developers.The documentation should not only describe what the API does but also how to use it. This context is crucial for developers, as it helps them understand how the API fits within their specific use case. After all, the success of an API is contingent upon its adoption, which is more likely if the API is intuitive and fits within the user’s context.Seamlessly Transitioning from Design to DeploymentIn the API design-first approach, the transition from design to deployment encompasses several key steps. Maintaining design consistency requires enforcing the API contract during the implementation phase to ensure preservation of the original design intent. Developers set up deployment pipelines to automatically integrate API changes, ensuring they are immediately tested and ready for production.Developers also configure API gateways to match new deployments with the API design, keeping the deployment process closely aligned with the design. Continuous integration systems provide instant feedback on the compliance of the implementation with the API design specifications. By managing API endpoints effectively, automated testing frameworks verify that new features and changes do not deviate from the API’s contract, guaranteeing stability and reliability on the API platform.Ensuring Contract Adherence During DevelopmentDuring the development phase, it remains vital to abide by the API contract. Developers must avoid making incompatible changes like modifying or removing existing data structures, fields, or URIs to avoid breaking client applications. This adherence to the contract ensures that the API remains compatible with existing systems and meets the expectations set out in the API’s design.However, ensuring contract adherence can be complex, especially when integrating with existing systems. The implementation of hierarchical relations influenced by Entity Framework can lead to complexities, which might require updates to API contracts. To manage these complexities, investing in education and training can help organizations implement API governance as an automated, flexible, and democratically perceived part of the development process.Version Control and API EvolutionHandling versioning constitutes a vital element of the API design-first methodology. You can manage versioning in different ways and consider various options. For example:  Including the version number in the API’s base URL or header.  Considering versioning when publishing new contracts that should not override previous versions.  Using the semantic versioning pattern of [major]-[minor]-[patch] to delineate the API contract versions.When the externally observable behavior of an API changes, introducing a new major version ensures that existing clients do not face any disruption. With each new API version release, it’s critical to support the older version for a predetermined grace period to facilitate consumer transition before eventually retiring it.Providing clear and proactive communication to API users about new versions, updates, and deprecation schedules is essential for smooth version control and user experience.Real-World Success Stories: API Design-First in ActionThe API design-first approach has led to numerous success stories in the world of software development. Salesforce attributes about 50% of its annual revenues to APIs following an API-First strategy. Similarly, Expedia reportedly generates over 90% of its revenue from its API, emphasizing the financial success of its API-First approach.Even giants like Amazon and Netflix have experienced exponential revenue growth and business expansion after adopting an API-first strategy. For instance, Twilio’s revenue has risen remarkably since its inception, thanks to its API-first strategy that allows for scalable and robust communications platforms.Transact, a company specializing in payment processing, cut 80% of its API development time by adopting the design-first approach to its API program, as opposed to the code-first approach.Efficiency Gains and Enhanced CollaborationAPI-first development encourages collaboration among developers, product managers, and UX designers. By aligning the software development process with business goals and user needs, API-first development synchronizes the efforts of various stakeholders, leading to efficiency gains and enhanced collaboration.In addition to these efficiency gains, the API-first approach also helps you do the following:  Expose potential system issues early, improving development velocity by reducing unexpected blockers.  Proactively solve problems, saving time and resources.  Create better quality APIs that meet the needs of end-users.Improved Developer Experience and Business ValueAPI-first companies develop APIs in advance, often in collaboration with business stakeholders, to ensure seamless integration and expansion of application capabilities. For example, Stripe’s use of APIs has enhanced secure data sharing with other businesses, enabling strategic partnerships and collaborations.Twilio’s focus on API-first has simplified the integration of communication functionalities for businesses, leading to time and cost savings for their customers. By thinking API-first, these companies have not only improved the developer experience but also created significant business value, demonstrating the tangible benefits of the API design-first approach.Navigating the Challenges: When API Design-First Meets ComplexityDespite the numerous benefits of the API design-first approach, it may introduce complexity, especially in extensive and intricate projects. To effectively balance flexibility, functionality, and simplicity, a thorough understanding of business requirements, system architecture, and end-user needs is essential.Integrating API design-first with existing systems presents the following challenges:  Compatibility concerns.  Meeting security, compliance, and performance requirements.  Testing requires a deep understanding of the API’s design and functionality, making it a specialized and potentially time-consuming process.  API governance becomes more complex in larger projects. Reasons for this include varying architectural styles, exception handling, and adhering to diverse lifecycle stages and approval processes.Balancing Flexibility with GovernanceAPI governance applies rules and policies to APIs to ensure standardization and high quality. In large organizations, strong governance and management frameworks are crucial to ensuring APIs are used consistently and securely. Yet, API governance doesn’t advocate imposing rigid rules that stifle creativity. It promotes striking a balance between standardization and creativity by applying enterprise-wide rules flexibly.Constructing an API contract requires setting standards and best practices for API consistency, which includes detailing:  Endpoint names  URLs  Error codes  VersioningAn API-first strategy fosters a shared vision among team members, reinforcing the balance between collaborative dynamics and the governance structure.Addressing the Learning CurveEmbracing the API design-first approach entails a learning phase where development teams acquaint themselves with design tools and API description languages such as OpenAPI. Tools like TypeSpec can facilitate the transition to API design-first methodology. TypeSpec allows for a more enjoyable and maintainable process of creating API specifications.This learning curve can initially seem daunting. But the long-term benefits of a well-designed and easily maintainable API make it a worthwhile investment.SummaryThe API design-first approach has revolutionized the way APIs are developed and implemented. By prioritizing design and planning, this approach fosters collaboration among stakeholders, ensures a clear API structure before coding begins, and minimizes the need for future versioning. Tools like Swagger UI and SwaggerHub have made it easier to generate and maintain API documentation, enhancing the developer experience and promoting the API-first approach.However, the API design-first approach doesn’t come without its challenges. It requires a significant shift in mindset, a learning curve for development teams, and the need to balance flexibility with governance. But the benefits of this approach far outweigh the challenges. The real-world success stories of Salesforce, Expedia, and Amazon illustrate that very fact. So, we hope this article helps you embrace API design-first and revolutionize your software development process.As you embark on your journey to make your API-first idea successful, consider adding Moesif to your stack of tools. We built Moesif from the ground-up to help API-based projects reach their full potential with powerful API analytics and monitoring tools. Moesif also offers powerful monetization features that seamlessly work across different billing providers. If you want to see for yourself how easily Moesif can help you grow and solve problems, sign up today for a free trial, no credit card required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/API-Design-First/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-ultimate-guide-to-api-for-developer-productivity": {
          "title": "What Are API Capabilities? A Guide for Modern API Platforms",
          "content"	 : "For most of the last decade, “what does your API do?” was answered with a list of endpoints. POST /refunds. GET /customers/{id}. PUT /orders/{id}/status. That answer worked when the consumer was a human developer reading a portal at 2 a.m. It works less well when the consumer is an AI agent trying to decide, in 200 milliseconds, whether your platform can refund a $14.99 subscription charge to a customer in Germany whose card was declined last Tuesday.The shift is from endpoints to capabilities, and it changes how platform teams design, document, monetize, and govern APIs.  API capabilities are the discrete, business-aligned functions an API exposes to consumers, describing not just how to call an endpoint, but what the platform can actually do. Modern capabilities are self-describing, composable, governed, and discoverable by humans, machines, and AI agents alike.This guide covers what API capabilities are, the characteristics of a well-defined one, the types you’ll find in a real platform, how capabilities get exposed across REST, GraphQL, and Model Context Protocol (MCP), and how to instrument, monetize, and govern them as first-class units of value.What Are API Capabilities?A capability is the answer to a business question. An endpoint is the mechanism that delivers it. POST /v3/charges/{id}/refunds is an endpoint. “Refund a charge” is a capability. The endpoint is implementation; the capability is intent.The reframing isn’t new. Kin Lane has been writing about capabilities at API Evangelist for years, and the recent surge of interest comes from a confluence of three forces: AI agents that need to discover what tools can do without reading docs, business stakeholders who want to attribute revenue to specific platform functions, and platform teams who are tired of every consumer integration starting from zero. Daniel Kocot, Christian Posta, and Mike Amundsen have each pushed adjacent versions of the same idea: that the unit of design should be the thing the consumer is trying to accomplish, not the HTTP route that happens to expose it.Capabilities vs. endpoints vs. resourcesThe three terms get conflated, but they describe different layers:            Layer      What it is      Example                  Resource      A nouned thing the platform manages      customer, subscription, invoice              Endpoint      An HTTP method + path that operates on a resource      POST /customers/{id}/subscriptions              Capability      A business-meaningful function the platform performs      “Subscribe customer to a paid plan”      A single capability can span multiple endpoints (start checkout, confirm payment, provision access). A single endpoint can serve multiple capabilities (a generic POST /events might handle “track signup,” “track upgrade,” and “track churn”). And a resource often supports several capabilities that aren’t worth equal investment. The “Cancel subscription with prorated refund” capability is worth ten times more attention than the “Update billing address” capability, even though both touch the same resource.Why the shift matters nowThree things changed recently that pushed capability thinking from a niche design topic into a platform-team requirement.AI agents became real API consumers. The Model Context Protocol, introduced by Anthropic in late 2024, gave agents a standard way to discover and invoke tools. An MCP server doesn’t expose endpoints, it exposes capabilities, each with a name, a description, an input schema, and an output schema. If your API’s documentation can’t be machine-read at that level, it isn’t usable by agents.Monetization moved from seat-based to event-based. Usage-based pricing makes “what did this customer do?” a billing question, not just an analytics one. The answer needs to be expressed in capabilities the customer recognizes (“processed 12,000 refunds, made 4M LLM tool calls”), not raw HTTP counts.Governance moved from API gateways to API platforms. The question stopped being “is this endpoint authenticated?” and became “which business capabilities does this consumer have access to, and what’s their consumption profile?” Capability-level governance lets you grant a partner the “read invoices” capability without granting them every GET on the invoice resource.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Core Characteristics of a Well-Defined API CapabilityA capability isn’t just a renamed endpoint. The work happens in the metadata around it. Here’s what separates a real capability from a relabeled route.Business alignment. The capability name is something a non-engineer would recognize. “Process Refund,” not “RefundController.execute().” If a PM can’t say the name in sprint planning without translation, it’s named at the wrong level.Clear inputs and outputs. The capability declares what it needs and what it produces in typed terms. “Process Refund” takes a charge ID, an amount, and an optional reason; returns a refund ID, status, and estimated settlement date. The HTTP shape can change. The capability contract shouldn’t.Composability. A capability either does one thing or composes others. “Cancel subscription with prorated refund” is fine if it composes the two underlying capabilities with declared ordering and rollback. It’s a problem if it secretly does both with no way to invoke either independently.Discoverability and semantic metadata. A capability has a description, category, tags, and version information that something other than a human can parse. An agent reading your OpenAPI contract or MCP capability manifest should be able to decide, without a human in the loop, whether your platform can do what it needs done.Governance. Capabilities have owners, lifecycles, access policies, and audit trails. Endpoint-level governance (“who can call POST /refunds?”) is too coarse for partner tiers and too fine for product packaging. Capability-level governance sits at the right altitude.Observability. Every invocation is countable, attributable, and inspectable. Not just “POST /refunds was called 12,400 times today” but “Process Refund ran 12,400 times, 11,800 for Acme, p95 latency 340ms, 0.3% failure rate.” This is the foundation billing, governance, and prioritization rest on.Monetization unit. The capability is something a customer can be billed for individually. Not every capability needs to be a SKU, but every billable SKU should map to one or more named capabilities. “Unlimited API calls” is a packaging cop-out; “10,000 Process Refund invocations per month” is a price.Types of API Capabilities (with Examples)Real platforms expose four or five flavors of capability, and they don’t all need the same treatment.Data capabilities read, query, or stream information. “List customers,” “Search transactions by date range,” “Stream account events.” They’re often the most-called and cheapest to serve, which makes them candidates for volume-based pricing or rate-limit-as-pricing.Transactional capabilities change state with business consequences. “Process Refund,” “Ship Order,” “Approve Loan,” “Provision Tenant.” These justify the most investment in observability, idempotency, and governance, and they’re where capability-level pricing tends to land first because per-invocation business value is high and discrete.AI and agentic capabilities are tool calls invoked by language models or agent runtimes: “Summarize document,” “Extract entities,” “Run analysis.” The consumer is a model reasoning about which capability to invoke based on metadata, and the unit economics often involve a per-token or per-inference cost the platform passes through. Exposing them through MCP gateways gives agents a standard way to find them.Operational and platform capabilities are cross-cutting functions: authentication, rate limiting, idempotency, webhook delivery. They usually aren’t billed directly, but treating them as capabilities (rather than “infrastructure that happens to exist”) forces quality bars and published SLOs.Monetization and metering capabilities track and price usage: “Report usage event,” “Get current usage,” “Generate invoice.” They turn every other capability into revenue, and they’re often built last, which is backwards.How API Capabilities Are ExposedThe same capability can be exposed over multiple protocols. The shape changes; the contract shouldn’t. Here’s how each surface handles capability exposition in practice.REST. A capability maps to a verb + resource path, with the capability semantics carried in OpenAPI metadata. The operationId, summary, description, and tags fields are where capability identity lives. A REST API that ships rich OpenAPI is already partway to capability-thinking; one that ships a path list and nothing else is not. Good REST API design treats the OpenAPI spec as the capability contract, not an afterthought.GraphQL. Capabilities map to queries, mutations, and subscriptions. The mutation name (processRefund, cancelSubscription) is the capability name; input and output types are the contract. Introspection makes capability discovery easier for agents than REST does, but it shifts the burden onto schema design discipline.gRPC. Service definitions map almost directly to capabilities. A RefundService with a ProcessRefund RPC is, by construction, capability-aligned. The cost is that gRPC is mostly an internal protocol, so capability metadata isn’t usually exposed externally without a translation layer.OpenAPI as the source of truth. Whatever the underlying protocol, OpenAPI (or its gRPC and GraphQL equivalents) is increasingly where capability metadata lives. Tags become categories. Operation summaries become capability names. x- extensions carry custom metadata for billing, governance, and lifecycle. A team that takes OpenAPI seriously gets a capability catalog for free.MCP and agent-facing discovery. MCP servers expose a tools/list method that returns each tool’s name, description, and JSON Schema for inputs. That’s a capability manifest by another name. An MCP wrapper around your existing API is a forcing function: you can’t expose a tool an agent can use unless that tool has a clean capability identity.Developer portals and capability catalogs. Internal platforms increasingly publish a capability catalog separate from the API reference. The reference documents endpoints; the catalog documents capabilities with owners, lifecycle status, dependencies, and SLOs.API Capabilities for Monetization and Business OutcomesCapability-level monetization is where the framing earns its keep. Endpoint-based pricing (“$0.001 per API call”) treats every invocation as equal, which is convenient for the billing system and wrong for the business. A GET /health call and a POST /refunds call have wildly different value, and pricing them identically either overcharges low-value usage or undercharges high-value usage.Tying capabilities to billable units. Declare which capabilities are billable, what the unit is (per invocation, per token, per object), and what the tier looks like. “Process Refund” might be billed per invocation; “Summarize Document” per input token; “Stream Account Events” per delivered event. Each is a meter bound to one or more capabilities.Usage-based pricing on capability events. Once capabilities are first-class, usage-based pricing becomes a configuration exercise instead of an engineering project. Moesif’s Billing Meters watch capability-level events (not raw API calls) and roll them into Stripe or another billing system. When your AI customer asks “why was my bill $4,200 last month?”, “you made 4.2M tool calls” is a worse answer than “your agent invoked the Extract Entities capability 1.8M times and Generate Embedding 2.4M times.”Internal chargeback. The same instrumentation powers internal cost attribution. A platform team can publish an internal chargeback model where each downstream team is billed for the capabilities it consumes, sharpening incentives in a way flat infrastructure budgets don’t.Pricing bundles, not endpoints. When packaging a tier, the question shifts from “how many API calls?” to “which capabilities are in this tier, and what’s the included usage?” Growth includes Process Refund up to 10K/month; Enterprise includes it unlimited plus Multi-Currency Refund. That’s a bundle sales can sell and procurement can understand.Governance, Observability, and Capability MaturityCapabilities only deliver on the promise if you can see them in production. Most platform teams that adopt capability thinking trip on the same step: they design the capability catalog, then never wire it to the actual traffic, so the catalog rots while the API keeps shipping.Capability-level analytics. Knowing that POST /v3/charges/{id}/refunds was called 12,400 times tells you very little. Knowing that Process Refund ran 12,400 times, that 90% of invocations came from three accounts, that Acme is on the Growth tier consuming at 4x its included rate, and that p95 latency spiked 280ms after last week’s deploy tells you everything. The API metrics that matter for platform teams are nearly all capability-level, not endpoint-level. This is the layer we sit at: capability-level analytics attributed to specific accounts, feeding both product (for prioritization) and billing (for monetization).Shadow capabilities. Every long-lived API accumulates capabilities nobody put in the catalog. The legacy /v1/legacy/refund one customer still hits. The internal admin endpoint that quietly got exposed to a partner. The undocumented header that changes behavior. Capability-level observability surfaces these by diffing the declared catalog against actual traffic; anything called in production but missing from the catalog is a shadow capability to document, deprecate, or block.Lifecycle and maturity. Each capability moves through experimental, beta, GA, deprecated, retired. Transitions should be visible in the catalog and enforced in governance. An experimental capability shouldn’t be allowed in a sales contract; a deprecated capability should generate warnings (eventually hard failures) when consumers call it. Tying this to consumption data lets you measure deprecation: “Process Refund v1 is deprecated, 84% of traffic has migrated to v2, three customers account for the remaining 16%.”Maturity models. A rough four-stage view: Level 1 platforms expose endpoints with no capability layer. Level 2 have a catalog but no runtime enforcement. Level 3 instrument capability-level events and use them for analytics and monetization. Level 4 govern access, billing, and lifecycle at the capability layer end-to-end and expose metadata to AI agents through MCP. Most enterprise platforms today sit at Level 1 or 2.How to Build a Capability-First API StrategyAdopting capability thinking on a greenfield API is straightforward. Retrofitting it on a platform with three years of accumulated endpoints is the harder problem. Here’s a sequence that works.1. Map existing endpoints to business capabilities. Take the OpenAPI spec, sit a PM next to an engineer, and for each operation ask: “What’s the business outcome this delivers?” Most teams find 80% of their endpoints collapse into 30-40 capabilities. The remaining 20% reveal either real capabilities that were never named or endpoints that could be deprecated.2. Define capabilities in business language. Write the catalog in the language a customer would use. “Process Refund,” not “RefundService.execute.” This is the artifact sales, docs, and AI agent integrations all read.3. Build the catalog. OpenAPI tags + extensions work fine at small scale; Backstage, port.io, or internal builds make sense larger. The catalog should answer for any capability: who owns it, lifecycle stage, SLO, runbook, which endpoints implement it, what it’s metered as.4. Instrument every capability call. Every invocation should produce a structured event with capability name, consumer identity, input shape, result, and latency. This is the foundation of capability-level analytics and AI-driven API strategy. Without it, the catalog is a wiki page.5. Iterate from data. The catalog you write on day one will be wrong. Capabilities you thought were core will turn out barely used; capabilities you missed will drive most of the value. Usage data tells you what to invest in, deprecate, bundle, and price separately.Order matters. Teams that build the catalog without instrumenting fall into theater. Teams that instrument without a catalog drown in raw event data.API Capabilities FAQWhat is the difference between an API and an API capability?An API is the technical interface: a set of endpoints, schemas, and protocols a consumer uses to interact with a system. An API capability is a discrete business function that the API exposes: “Process Refund,” “Cancel Subscription,” “Summarize Document.” One API typically exposes many capabilities, and a single capability can be exposed through more than one API surface (REST, GraphQL, MCP).What are the 4 types of API?The most common taxonomy is by audience: public (or open) APIs available to any developer; partner APIs available under contract to specific business partners; private (or internal) APIs available only inside an organization; and composite APIs that bundle calls to other APIs into a higher-level operation. Capabilities are orthogonal to this: any of the four types can expose any of the capability flavors described above.What is API capability in cloud computing?In cloud platforms, “API capability” refers to a specific function the cloud service exposes. AWS, Azure, and GCP all describe their services in capability terms (“Run a query,” “Train a model,” “Provision a database”) even though the underlying APIs are larger and more granular. Cloud SDKs typically wrap the raw API into capability-named methods for the same reason: developers think in capabilities, not endpoints.How do AI agents use API capabilities?Agents discover capabilities through machine-readable manifests (OpenAPI documents, MCP tools/list responses) and decide which to invoke based on metadata: name, description, input schema, and tags. The cleaner the metadata, the better agents perform. An API exposing 200 thinly described endpoints is harder for an agent to use than one exposing 40 well-named capabilities, even with identical underlying functionality.The shift from endpoints to capabilities is, ultimately, about treating your API as a product instead of an implementation detail. The teams that get this right build catalogs, instrument capability-level events, and let consumption data drive pricing, packaging, and governance. The teams that don’t end up debugging billing disputes and trying to retrofit observability into platforms that were never designed to be observed.If you want to see what capability-level analytics look like in practice, our API observability tooling is built around this model: every capability invocation, attributed to a consumer, with the data feeding both product analytics and usage-based billing. Worth a look if you’re moving past endpoint-level thinking.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /technical/api-development/Ultimate-Guide-To-API-For-Developer-Productivity/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-heroku-and-moesif-unleashing-deep-api-analytics-for-your-applications": {
          "title": "Heroku + Moesif: Unleashing Deep API Observability for Your Applications",
          "content"	 : "With API-driven applications being increasingly common, understanding how your APIs are performing is crucial for success. That’s where the combination of Heroku and Moesif allows developers and their organizations to step up their observability game. In this blog, we will quickly examine how you can integrate Moesif with your Heroku app to begin monetizing and analyzing your API traffic. Let’s kick things off by taking a brief look at both platforms.What is Heroku?Heroku is a cloud-based Platform as a Service (PaaS) that enables developers to build, run, and scale applications entirely in the cloud. It abstracts away the complexities of infrastructure management, allowing you to focus on writing code and delivering features. Heroku supports many programming languages and frameworks, making it an excellent application development and deployment tool.What is Moesif?Moesif is an API observability and monetization platform that provides deep insights into how your APIs are used and delivers the capabilities to monetize them easily. It captures detailed information about API calls, including request/response payloads, latency, errors, and user behavior. With Moesif, you can:  Monitor API Performance: Identify bottlenecks, track error rates, and optimize response times.  Understand User Behavior: See how users interact with your APIs, which endpoints are most popular, and what features they’re utilizing.  Debug Issues: Quickly pinpoint the root cause of errors and resolve problems impacting your users.  Monetize Your API: Implement usage-based billing models and track revenue generated from your API.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Why Are API Analytics Important?If your app contains APIs, then a specialized API observability platform must be used to truly understand how your APIs are used and what value they deliver. API observability is essential for several reasons:  Improved Performance: Identify and fix performance issues before they affect your users.  Enhanced User Experience: Understand how users use your API and tailor it to their needs.  Data-Driven Decisions: Make informed API development, pricing, and business-level decisions based on usage data.  Increased Revenue: Monetize your API effectively by understanding usage patterns and identifying growth opportunities.API observability allow you to examine not only the engineering side of the puzzle but also derive a large number of business insights.Adding Moesif to Your Heroku Application (Step-by-Step)When using Heroku and Moesif together, the process is straightforward and can be done directly through the Heroku CLI and UI. Below, we will go through how to add Moesif to your Heroku instance, including the steps in the UI or Heroku CLI, depending on your preferred approach.Add Via CLIFirst, we will look at installing the Moesif Add-On through the CLI. For this, we assume that you:  Have a Heroku account and an app running on Heroku  You have the Heroku CLI installed and logged into the application you want to add Moesif to.With these prerequisites handled, you can proceed.Install the Add-onMoesif can be attached to a Heroku application via the CLI:heroku addons:create moesifOnce the command is executed, you should see something similar to the following:-----> Adding moesif to sharp-mountain-4005... done, v18 (free)A MOESIF_APPLICATION_ID config var is added to your Heroku app’s configuration during provisioning. It contains the write-only API token that identifies your application with Moesif. You can confirm the variable exists via the heroku config:get command:heroku config:get MOESIF_APPLICATION_IDThis will print out your Moesif Application ID to the console, confirming it is correctly set in the config file.Add Via UIAlternatively, you can install the Moesif Add-On through the Heroku Dashboard UI. For this, we assume that you:  Have a Heroku account and an app running on Heroku  Are logged into the Heroku Dashboard for the app you’d like to add Moesif toWith these prerequisites handled, you can proceed.Install the Add-OnWhile logged into the dashboard for the app you want to add Moesif to, on the Overview page, click the Configure Add-ons button.This will then bring you to the Resources screen to view your current add-ons. In this instance, we have none. From here, click the Find more add-ons button.On the next screen, where all available add-ons are listed, click Metrics and Analytics on the left-side menu. Locate the Moesif API Observability entry and click on it.On the Moesif API Observability and Monetization overview page, click Install Moesif API Observability in the top-right corner.Next, you’ll be prompted to confirm the installation and submit the order. To confirm and install, click the Submit Order Form button to add Moesif to your Heroku app and activate your subscription.Once complete, you’ll see that Moesif has been added to your Heroku instance and is ready for further configuration.Install the server integrationWith Moesif installed on our Heroku instance and subscription activated, we need to add Moesif to the application running on Heroku. To do this, go to your Heroku dashboard and open Moesif from under “Installed add-ons”Once inside the Moesif application, the onboarding flow that appears will walk you through adding the Moesif SDK to your code.When initializing the SDK, use the environment variable MOESIF_APPLICATION_ID for the application ID. For example, in a Node application, you’d grab the Moesif Application ID by using process.env.MOESIF_APPLICATION_ID. This would be retrieved from the app config variables.Local setupAfter you provision the add-on, you must replicate your config variables locally so your development environment can operate against the service.Use the Heroku Local command-line tool to configure, run, and manage process types specified in your app’s Procfile. Heroku Local reads configuration variables from a .env file. To view all of your app’s config vars, type heroku config. Use the following command for each value that you want to add to your .env file:heroku config:get MOESIF_APPLICATION_ID -s  >> .envCredentials and other sensitive values should not be committed to source control. If you’re using Git, you can exclude the .env file by adding it to the gitignore file with:echo .env >> .gitignoreFor more information, see the Heroku Local article.Using Moesif DashboardsOnce everything is configured, events should begin to flow into Moesif. These events can be used for analytics and monetization directly within the Moesif platform.Key Moesif Features to Leverage:  Live Event Log: See individual API calls in real-time.  Time Series Metrics: Track API traffic, latency, errors, and more over time.  Funnels and Retention: Analyze user journeys through your API.  Alerting: Get notified of critical API issues.  Monetization: Drive revenue from your API calls using post-paid and pre-paid billingCheck out our docs and tutorials pages for all the ways you can leverage Moesif.Open Through The Heroku CLITo open Moesif, you can the following command via the Heroku CLI:heroku addons:open moesifOr, from the Heroku Application Dashboard, select Moesif from the Add-ons menu.Once logged in, you’ll have full access to the Moesif platform, which includes everything needed for extensive API observability and monetization.Try It OutWant to try out Moesif for yourself? You can do so by following the directions above and creating an account through Heroku or sign-up directly. Powerful API analytics and monetization capabilities are just a few clicks away.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            You’ve built the API. Now let's generate revenue!            Monetize your APIs deployed on Heroku in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Heroku-and-Moesif-Unleashing-Deep-API-Analytics-for-Your-Applications/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-gravitee-announcing-moesif-api-analytics-and-monetization-for-gravitee": {
          "title": "Announcing Moesif API Observability and Monetization For Gravitee.io",
          "content"	 : "We are thrilled to announce that Moesif now offers full plugin support for Gravitee.io! This new integration provides Gravitee.io users with advanced API observability and monetization capabilities, empowering you to gain deeper insights into your API usage and optimize your API strategy. Whether you’re looking to monitor performance, understand user behavior, or implement flexible monetization models, Moesif’s robust feature set is now at your fingertips within the Gravitee.io ecosystem.Gravitee.io is known for its flexible and powerful API management solutions, enabling organizations to efficiently design, secure, and deploy their APIs. With the addition of Moesif’s API observability and monetization features, Gravitee.io users can now leverage comprehensive data insights to drive business decisions, enhance user experiences, and unlock new revenue streams. This integration seamlessly blends Moesif’s capabilities with Gravitee.io’s API management platform, making it easier than ever to achieve your API goals.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Unlock Deep API Insights with Moesif and Gravitee.ioWith the integration of Moesif and Gravitee.io, you gain unparalleled visibility into your API ecosystem, providing crucial insights into your business’s health and enabling proactive customer support. Moesif, a leader in API observability, empowers you to effortlessly create detailed charts and dashboards, revealing how customers interact with your APIs and the value they derive from them. By streaming API analytics from Gravitee.io into Moesif, both technical and non-technical users can access real-time data, enhancing their ability to understand and optimize API usage.Moesif’s advanced capabilities extend beyond traditional observability, offering a user-centric approach with deep dives into API payloads. Funnel reports allow you to track the complete user journey, from initial interaction to becoming active or paying customers. Retention reports and cohort analysis provide critical insights into user behavior, helping to identify early signs of churn and take action to improve retention. Together, Moesif and Gravitee.io enable you to elevate your applications and gain comprehensive insights that drive better decision-making, optimize performance, and support business growth.Simplify API Monetization with Moesif and Gravitee.ioAs more companies look to generate revenue from their APIs, they often encounter a common challenge: API monetization is not easy. Handling both simple and complex usage-based billing scenarios requires significant custom coding, which can create overhead for developers. With the integration of Gravitee.io and Moesif, you can monetize APIs effortlessly using Moesif’s Metered Billing features. No coding is required.With just a few clicks, you can create plans for prepaid, postpaid, pay-as-you-go (PAYG), and other usage-based billing models with minimal effort. Moesif can then aggregate usage data and send the correct usage information to billing platforms such as Stripe, Recurly, Chargebee, or a custom billing solution, ensuring users are billed accurately. Regardless of how simple or complex your API billing criteria are, Moesif and Gravitee.io can manage it with ease, streamlining the monetization process and allowing you to focus on delivering value through your APIs.Enhance Customer Success and Security with Automated ActionsLooking to automate customer success outreach or restrict user access based on specific business criteria? Moesif makes this possible with its Behavioral Emails feature, which allows you to send personalized emails to users triggered by specific events. For instance, if a user encounters difficulties integrating with your APIs, Moesif can detect this issue and automatically send the most relevant guide to assist them.Additionally, Moesif’s Governance Rules feature provides another layer of automation by enabling you to block users, such as those with overdue invoices, from accessing certain APIs or features within your application. Pairing Moesif with Gravitee.io unlocks new levels of automation, ensuring that your customer success strategies and security protocols are both proactive and effective.Get Started with Moesif and Gravitee.ioFor more details on how to get started with Gravitee and Moesif, please refer to our documentation. Stay tuned to the Moesif blog as we will have guides for installation, user and company tracking, and monetization in the near future!We are excited to see how you leverage the powerful combination of Moesif and Gravitee.io to transform your API management and monetization strategies. Don’t miss out on the opportunity to enhance your API insights, automate customer success, and streamline your billing processes. Get started today with a 14-day free trial and experience the next generation of API observability and management.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Need API observability and monetization for Gravitee?            Monetize your Gravitee-based APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/gravitee/Announcing-Moesif-API-Analytics-and-Monetization-For-Gravitee/",
          "author": "Dylan",
          "categories": "technical, gravitee"
        }
      
    ,
  
    
        "technical-api-development-understanding-api-types": {
          "title": "API Types: A 2026 Practitioner's Taxonomy (REST, GraphQL, gRPC, MCP)",
          "content"	 : "“API type” gets used to mean two different things in practice. The first is who the API is for (open to the public, restricted to partners, internal to the company). The second is the protocol the API speaks (REST, GraphQL, gRPC, SOAP). Both axes matter, and they answer different questions for different audiences.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        This guide walks through both taxonomies, where the lines are blurry, and the new shape that emerged in 2025-2026: APIs consumed primarily by AI agents through the Model Context Protocol. For the broader fundamentals, see our API design principles and API acronym guide.How API types are categorizedTwo axes are useful:  Audience. Who is allowed to call the API. Drives auth, rate limits, support model, and pricing.  Protocol. What wire format the API uses. Drives client tooling, performance characteristics, and ecosystem.A given API has one audience type and one protocol. The most common combination in 2026 is “open” + “REST”. Other combinations are picked when the workload has specific requirements.By audience: open, partner, internal, compositeOpen APIs (public APIs)Available to any developer who signs up. Usually self-service, with a developer portal, public documentation, and tiered pricing. Stripe, Twilio, OpenAI, GitHub, and most major SaaS APIs fit here.  Best for: product surfaces designed to be extended by third parties; APIs that are themselves the product.  Trade-offs: the broadest possible audience means the most aggressive abuse, the strictest rate limits, and the most public scrutiny on API design choices.Partner APIsAvailable only to approved partners under a contract. Usually require explicit onboarding, dedicated rate limits, and shared SLA commitments.  Best for: B2B integrations, embedded use cases, enterprise integrations where the data sensitivity or contractual scope rules out a fully public surface.  Trade-offs: higher operational overhead (managing partner relationships); smaller blast radius for any single integration breakage.Internal APIs (private APIs)Used only by services and applications within a single organization. Sometimes called private APIs.  Best for: microservices communication, internal tools, line-of-business applications.  Trade-offs: less external pressure on design quality, which can be a problem at scale. Internal APIs that aren’t governed well become as painful to maintain as bad public APIs, just with a smaller audience.Composite APIsBundle multiple underlying API calls into a single endpoint. The client sends one call; the composite API fans out to multiple backends, aggregates the results, and returns a single response.  Best for: reducing client-side complexity, especially on mobile networks where each round trip is expensive; orchestrating workflows across multiple internal services.  Trade-offs: the composite endpoint becomes a single point of failure for the workflow. Designing for partial failure (some backends responding, others not) is non-trivial.By protocol: REST, GraphQL, gRPC, SOAP, WebhookREST APIsHTTP/JSON, resource-oriented, with HTTP methods (GET, POST, PUT, PATCH, DELETE) mapped to CRUD operations. The dominant style in 2026 by a wide margin.  Best for: public APIs, B2B integrations, anywhere broad client compatibility matters.  Trade-offs: fixed response shapes mean clients sometimes over-fetch or under-fetch data. Multiple-resource requests typically require multiple calls.GraphQL APIsSingle endpoint, query language, client specifies exactly which fields it wants. Standardized by Facebook (now Meta) in 2015 and popularized by Apollo’s tooling ecosystem.  Best for: client-driven data fetching, especially mobile apps with bandwidth constraints; APIs serving many different client shapes from the same data.  Trade-offs: more complex caching than REST (single endpoint defeats HTTP-level cache); harder to rate-limit by operation cost; ecosystem smaller than REST.gRPC APIsHTTP/2 with Protocol Buffers as the wire format. Strongly typed, performant, and designed for service-to-service communication inside a network.  Best for: internal microservices, especially in service-mesh environments; cross-language polyglot stacks (gRPC has first-class clients in most major languages).  Trade-offs: not browser-friendly without gRPC-Web; smaller ecosystem for external consumers; harder to debug than text-based protocols.SOAP APIsXML-based, with a heavy specification stack (WSDL, WS-* standards). The dominant style of the 2000s, still in use in regulated industries (banking, insurance, government) where existing systems were built on it.  Best for: integration with legacy enterprise systems, regulated workflows that already require SOAP.  Trade-offs: verbose, slow, and difficult to debug. Almost no new APIs are designed in SOAP in 2026.Webhooks (inverted APIs)The API provider calls the consumer’s endpoint when an event occurs, rather than the consumer calling the provider. Used for event notifications.  Best for: asynchronous integration patterns; “tell me when X happens” use cases.  Trade-offs: the consumer needs a publicly reachable endpoint; delivery semantics (at-least-once vs exactly-once) require careful handling.Web APIs and where they fit“Web API” is a loose umbrella term that covers any API delivered over HTTP, regardless of audience or protocol. All web APIs are APIs; not all APIs are web APIs (OS-level APIs, library APIs, and direct database APIs are not delivered over HTTP).Within the web API category:  Public web APIs are openly accessible (often with rate limits).  Private web APIs require authentication and authorization.  Social media APIs are a specific subcategory (Twitter/X, Reddit, Slack, Discord) that share the platform with end users; they’re public APIs with specific terms of service shaped by the social context.The term is most useful for distinguishing HTTP-delivered APIs from older OS-level or library-level integration patterns.Choosing the right API type for your projectA practical decision tree:  External developers, broad audience, evergreen surface? REST, with OAuth 2.0, JSON responses, tiered pricing.  Mobile app with many UI variants over the same data? GraphQL.  Service-to-service inside a mesh? gRPC.  Real-time events your customers need to consume? Webhooks.  Existing SOAP backend you have to expose? SOAP gateway, with a plan to migrate over time.  Agent-consumable surface in addition to human-facing REST? REST + MCP exposure derived from the same OpenAPI spec.The audience axis usually decides itself based on your business model. The protocol axis is where most of the actual design debate happens, and REST is the safe default unless one of the alternatives explicitly fits better.API types in 2026: MCP servers and AI-agent-consumable APIsThis is the category that did not exist in any meaningful way before 2024-2025 and that is reshaping how API teams think about consumers.Model Context Protocol (MCP) servers expose existing APIs to AI agent runtimes through a specific server interface. The MCP server is not a separate API in the audience sense; it is a different runtime exposure of the same underlying API. An agent calling an MCP server is structurally similar to a human developer calling REST, but with different metadata, different scope models, and different observability needs.In 2026, the practical pattern is:  Maintain one OpenAPI spec for your API  Expose it as REST for human-facing developers  Generate an MCP server from the same spec for agent-runtime consumers  Instrument both with shared observability so per-customer attribution covers both surfacesThe WSO2 AI Gateway auto-generates MCP servers from OpenAPI specs, which keeps the two surfaces in sync without a second build to maintain. Most teams whose APIs are seeing agent traffic in 2026 are converging on this pattern.A growing share of API traffic in 2026 is non-human. The implication for the taxonomy: “audience” now includes “AI agents” as a category alongside open/partner/internal, and “protocol” includes MCP alongside REST/GraphQL/gRPC.Where API types are heading: a 2027-2028 outlookThe API-type taxonomy has been stable for fifteen years. The last two changes (the rise of GraphQL in the late 2010s, the arrival of gRPC for service mesh in the early 2020s) both took roughly five years to move from emerging to mainstream. The shifts visible in 2026 suggest the next five years will move faster, because they are responding to a fundamental change in who calls APIs rather than just how.The trends we see playing out across enterprise customers:Agent-first API design becomes a category. Today, most APIs are designed for human developers and exposed to agents through an MCP shim. By 2027-2028, the pattern reverses for some categories: APIs that primarily serve agent runtimes (workflow APIs, data-retrieval APIs, integration APIs) get designed for agents from day one; clearer descriptions in OpenAPI, idempotency on every endpoint, strong typing, deterministic responses. The human-developer interface becomes the secondary surface for those APIs.Per-token and per-invocation metering converge. Today, REST APIs meter by call, LLM APIs meter by token, and MCP servers meter by tool invocation. As these categories blur (a single application calls all three from a single user action), the metering unit will converge toward “work done on behalf of customer X.” Per-customer attribution becomes the cross-cutting metric; the unit underneath it (call, token, invocation) is implementation detail.Event streaming gets a first-class slot in the taxonomy. WebSockets, Server-Sent Events, MQTT, and Kafka-over-HTTP have always existed but were not usually included in API-type taxonomies. Agent runtimes generate sustained, bursty event traffic that does not fit the request/response model. By 2028, “event API” likely sits alongside REST/GraphQL/gRPC/MCP as a first-class category, with its own observability and metering patterns.Edge APIs and serverless reshape the protocol-choice question. When the API runtime is a serverless function on an edge network (Cloudflare Workers, Vercel Functions, AWS Lambda@Edge), the protocol-choice math changes. Cold starts favor lighter protocols (REST/JSON beats gRPC/HTTP-2 on cold start); the lack of a long-lived process changes how WebSocket-style protocols work. APIs designed for edge runtimes look different from APIs designed for traditional servers.OpenAPI continues to be the gravity well. The trend that does not change: OpenAPI remains the spec-level lingua franca that everything else hangs off of. SDK generators, MCP servers, gateway policies, contract tests, developer portals, AI-readable descriptions; all consume the spec. Investments in OpenAPI hygiene pay off across all of these downstream uses, and that compound effect is why most API platforms in 2026 treat OpenAPI as the source of truth rather than a documentation by-product.SOAP completes its slow exit. SOAP is still in production at financial institutions, government agencies, and large healthcare providers, but new SOAP APIs are essentially zero. The 2027-2028 trajectory is continued attrition rather than disappearance: SOAP gateways translating SOAP to REST/JSON for downstream consumers, with the SOAP origin staying in place until the underlying system is replaced.Composite APIs become a default rather than an optimization. As agent traffic grows, the cost of multi-round-trip integrations rises; agents are sensitive to latency in a way human developers can absorb. Composite endpoints (POST /v1/checkout that handles auth, inventory check, payment, and order creation in one call) become the standard rather than the optimization for high-frequency agent paths.These shifts compound. By 2028, the dominant pattern is likely “a REST + MCP surface, designed agent-first, metered per-customer, served from an edge runtime, with event streams for asynchronous workflows”; which is recognizably an evolution of today’s APIs rather than a replacement, but with the proportions inverted.How API type choice affects observability and monetizationThe type of API you ship affects how you monitor and monetize it.  REST APIs generate the most natural observability stream: per-endpoint, per-customer, per-status-code metrics map directly onto how the API is used. Per-call billing maps cleanly to the metering model.  GraphQL APIs complicate observability because the single endpoint receives a wide variety of queries. Per-query-complexity metering becomes the appropriate model rather than per-call.  gRPC APIs require gRPC-specific tooling on the observability side; not every HTTP-aware observability stack handles gRPC natively.  MCP servers introduce per-tool-invocation as a unit of observability and metering, which is more granular than per-call.Moesif handles REST and gRPC natively, with MCP support through the AI gateway integration. Per-customer attribution holds across the protocols, which means the analytics question stays consistent regardless of which API type sits underneath.Next stepsThe API type you pick is one of the design decisions that compounds across an API’s entire lifetime. Most public APIs in 2026 are REST + OpenAPI + (increasingly) MCP exposure, with audience and protocol choices flowing from that base.To see how API consumption breaks down by endpoint, customer, and increasingly by agent, start a 14-day Moesif free trial. No credit card required.Frequently asked questionsWhat are the main types of APIs? By audience: open (public), partner, internal (private), and composite. By protocol: REST, GraphQL, gRPC, SOAP, Webhook, and now MCP for agent runtimes.What is the difference between an open API and a public API? Often used interchangeably. “Open” sometimes refers specifically to APIs that follow the OpenAPI Specification standard; “public” specifically means available to any developer. In conversation they overlap heavily.Is REST or GraphQL better? Different tools for different jobs. REST is the safe default for public APIs; GraphQL is better when clients need to fetch many shapes of data over the same surface. Neither replaces the other.What is a composite API? An API that bundles multiple backend calls into a single client-facing endpoint. Reduces client round trips at the cost of more complex backend orchestration.Are MCP servers a new type of API? Sort of. An MCP server is a different runtime exposure of an existing API, designed for AI agent consumers rather than human developers. The underlying API is usually REST; MCP is the agent-facing surface generated from it.Which API type should I pick for a new project? REST for almost any new public API. GraphQL for mobile-heavy or multi-shape consumers. gRPC for internal service-to-service. MCP exposure is added on top of REST when agent consumers are part of the audience.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /technical/api-development/Understanding-API-Types/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-decoding-api-keys-essential-uses-and-security-best-practice": {
          "title": "Decoding API Keys: Essential Uses and Security Best Practices",
          "content"	 : "Understanding how to manage and protect an API key is crucial for anyone interacting with web services. This article outlines the process of generating and implementing API keys, along with strategies to secure them against potential threats, ensuring the security of your API key.Key Takeaways  API keys are unique identifiers critical for controlling and monitoring access to API services, but they should not be used for sensitive authorization due to inherent security risks and cannot identify actual users.  It’s crucial to manage API key access and permissions carefully, including revoking and regenerating compromised keys and implementing role-based access control to maintain security and operational efficiency.  Best practices for API key security include secure transmission, encryption, regular rotation of keys, and using user authentication mechanisms. Monitoring API usage and analyzing metrics are essential for detecting misuse and optimizing API performance.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding API Keys: The BasicsThe world of application programming interface (API), and the ‘universal keys’ are the ‘API keys’. These keys are unique identifiers that control access to specific features or data in an application and authenticate users requesting an API service. They are the Access Controllers, the watchful sentinels monitoring API activity, breaking down development silos, controlling API access to software, applications, and websites, and even blocking anonymous traffic during an API call. In this intricate system, API keys play a crucial role in ensuring security and functionality on the API server.But API keys are no quick fixes. For all their might, they have limitations. API keys should not be used for secure authorization, they cannot identify the actual project owners or users, and they possess inherent security risks due to the potential of theft or unauthorized use. It’s like using a skeleton key in a world full of thieves - it opens many doors, but it also has its own set of risks. Understanding how API keys work is crucial to mitigate these risks, and knowing when to use API keys can make all the difference.Generating and Implementing API KeysJust like unique door codes grant access to specific buildings, API keys are created specially for each user or application on a developer platform, such as Google Maps Platform. But crafting the key is just the beginning. The real challenge lies in implementing a specific API key securely in your application.We will now explore the process of creating and embedding API keys in detail.Registering for Unique API KeysDevelopers need to follow these steps to obtain a unique API key:  Sign up for a developer account with the API provider.  Register a project with the API provider and obtain your project API keys.  Log into your developer account with the necessary permissions.  Generate your unique API key.This process is similar to ordering a custom-made key for a specific lock, with the lock being your project and the key being the API key. Just like a locksmith would need your permission to create a key for your lock, the API provider requires you to log into your developer account with the necessary permissions to generate your unique API key.Embedding API Keys in Your Application Usage PatternsWhile creating an API key is a part of the process, securely embedding it into your application is an entirely different challenge. Imagine leaving your house key under the doormat - it’s the first place a thief would look! Similarly, embedding API keys directly into the application’s source code, such as JavaScript, is a recipe for disaster as it increases the risk of unintended exposure, especially when code is shared in public repositories.Instead of placing your key under the digital ‘doormat’, it is safer to use environment variables or secure key management systems to store them. To embed an API key into an application, the key must be included in every API request, conforming to the specific format required by the API provider.Types of API Keys: Public vs. PrivateJust as there are different types of keys for different types of locks, there are different types of API keys. The world of API keys comprises two main types: Public and Private. Public API keys are like keys to a city park - they give developers or users access to public data or features within an application and are typically used for public collaboration.At the other end of the spectrum are the Private API keys. These are like the keys to a vault, crucial for secure server-to-server communications, and used to handle sensitive data. They maintain strict control over who can access the non-public portions of an API. It’s like having a high-security key for a vault - only a select few are granted access.Managing API Key Access and PermissionsOnce we have created and embedded the keys and understood their different types, the next step is their management. Managing API key access and permissions is similar to deciding who gets access to which parts of your building. It involves setting up a security booth, a sign-in procedure, and even assigning keycards for specific floors.Let’s look at how this is achieved in the digital realm.Revoking and Regenerating API KeysMuch like revoking a keycard when an employee leaves an organization, API keys should be revoked upon being compromised. Additionally, new ones should be regenerated as a security measure to prevent unauthorized access. Implementing regular rotation and deletion policies is also essential to reduce the risk of API key exposure and misuse.Revoking access to an API key is like calling a locksmith to change a lock - you’d use permissions like ‘apikeys.keys.delete’. Creating new keys, on the other hand, is similar to making new keys for the new lock, done using permissions like ‘apikeys.keys.create’. But before you delete an API key, make sure it’s not currently in use to avoid disrupting services and remember that regenerating an API key starts a 24-hour grace period before the old key is deleted.Role-Based Access with API KeysIn a large building, not everyone gets access to every floor. The janitor might not have access to the CEO’s office, and the CEO might not have access to the server room. This is role-based access, and it’s a principle that applies to API keys as well. Role-based access control with API keys involves assigning roles to users or groups which define the permissions for API operations, enhancing security and operational efficiency.In the same way that a janitor’s keycard could mistakenly open the CEO’s office, incorrect authorization logic in APIs can let tokens from lower-privilege environments access higher-privilege ones, posing a significant security risk. It’s a flaw in the system that needs to be addressed, just like a faulty security system in a building.Storing and Protecting Your API KeysAfter creating, embedding, and managing your API keys, the next step is storing them. Just as you would store a valuable key in a safe place, API keys should be stored securely and protected similarly to passwords. Environment variables help keep API keys separate from the main codebase, much like a secret compartment in a safe. For an extra layer of security, secure key management systems or services can provide robust secrets management, like a vault within a vault.But just as a key can be copied, API keys can be stolen. Therefore, API keys should be encrypted when stored, to provide additional security against unauthorized access. It’s imperative to keep private API keys confidential and not share them with external parties, just like you wouldn’t share your house key with a stranger.Moreover, using separate API keys for different applications minimizes the impact of compromise, much like having different keys for different locks. For mobile applications, secure key stores or proxy servers should be used, and all API requests should be constructed on the server-side to protect API keys.Monitoring and Analyzing API Key UsageSimilar to a security guard keeping an eye on building entries and exits, constant monitoring of API key usage is vital to block anonymous traffic, detect unusual activities signaling potential breaches or misuse, and debug API issues. API owners can filter by key to view all requests from a specific client, much like a security camera trained on a specific entrance. They can also limit the number of requests a user can make within a certain time period to detect and prevent unauthorized access.Just as a building manager would analyze foot traffic patterns to optimize building operations, standard metrics such as:  request counts  error rates  latencies  API metricsprovide valuable data for troubleshooting and understanding normal behavior for latency and error rates. Logs of API key access should be maintained and regularly reviewed for audits, troubleshooting, and investigating breaches. This review should include identification of unexpected use and ensuring keys are not used beyond their intended services. It’s like maintaining a visitor logbook at the security booth.Best Practices for Secure API Key ManagementMuch like adhering to best practices for building security, one should follow best practices for secure API key management. These include secure transmission, using encryption, and regular rotation to mitigate risks. Developers must treat API keys with the same care as passwords to avoid unauthorized access and should be trained on security best practices to prevent accidental exposure.Regularly rotating, revoking, and deleting old or unused API keys is essential to maintaining secure API integrations and reducing the likelihood of compromised keys. Ensuring that API keys are transmitted over HTTPS is recommended, as it encrypts the data in transit, protecting it from being intercepted by unauthorized parties. It’s like sending a key by courier - you’d want it to be in a secure package that can’t be tampered with.Integrating API Keys with User AuthenticationAPI keys can be integrated with user authentication mechanisms, such as authentication tokens, to boost the security model of APIs. User authorization, similar to how a keycard system might integrate with a biometric system for enhanced security, can also be achieved using API keys. These keys can function as the username or password in Basic Auth, while the other field is left empty, to add an extra layer of security.The Bearer token method with API keys in the Authorization header is commonly used with OAuth tokens, providing secure user-specific access control. Custom headers, like ‘x-api-key’, offer a widely adopted approach to API key integration within the request headers, which enhances security and simplicity in services like AWS API Gateway. It’s like adding a fingerprint scan to a keycard system - each layer adds more security.Common Pitfalls in API Key SecurityDespite adhering to best practices, certain pitfalls in API key security can still occur. One such pitfall is exposing API keys in client-side code, such as JavaScript, or in URL query strings. This can lead to security breaches as these can be easily discovered by attackers. It’s like leaving your key in the lock - anyone passing by could take it.To avoid this, make sure to:  Store API keys securely on the server-side, as this is the best practice for managing store API keys  Use server-side code to make API requests instead of client-side code  Avoid including API keys in URL query stringsBy following these practices, you can ensure the security of your API keys and protect your application from potential attacks.Another common mistake with API keys is not implementing rate limits, which can lead to errors such as exceeding the permitted number of API calls. This is often fixed by adhering to the API’s rate limits. It’s like overusing a key - if you use it too much, it can wear out and break.API Key Security on Cloud PlatformsThe scope of API key security extends beyond individual applications. On cloud platforms, such as the Google Cloud Platform, API keys are primarily used for associating requests with a specific Google Cloud project for billing and quota purposes. To manage API keys on these platforms, users must have the API Keys Admin role which provides the ability to handle key-related administrative tasks. It’s like being the master key holder for a large building complex.These platforms also provide detailed metrics for monitoring API usage. The API Dashboard in Google Cloud console provides an overview of API usage or detailed metrics for a specific API. For a more detailed analysis, Cloud Monitoring allows for custom dashboards and alerts, like a security control room monitoring all entrances and exits. However, there is a limit of 300 API keys per project, so if more are necessary, multiple projects must be used, much like a large building complex might be divided into multiple blocks, each with its own set of keys.SummarySo there you have it, a walkthrough of the world of API keys. We’ve covered everything from understanding their role in digital communication to crafting unique keys, implementing them securely, managing their access, storing them safely, monitoring their usage, and integrating them with user authentication. We’ve also highlighted the common pitfalls and how to avoid them and discussed the best practices for secure API key management. Remember, API keys are like real keys - they unlock access, but if not managed correctly, they can also unlock trouble. So handle them with care!Organizations looking for the best tools to support their API management can leverage Moesif’s powerful API analytics and monetization capabilities. Moesif easily integrates with your favorite API management platform or API gateway through one of our easy-to-use plugins, or embed Moesif directly into your API code using one of our SDKs. To try it yourself, sign up today and start with a 14-day free trial; no credit card is required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            You’ve built the API. Now let's generate revenue!            Monetize your APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Decoding-API-Keys-Essential-Uses-and-Security-Best-Practice/",
          "author": "Preet",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-how-to-easily-deploy-usage-based-billing-with-moesif-and-sbt-aws-for-saas": {
          "title": "How to Easily Deploy Usage-Based Billing with Moesif and SBT-AWS for SaaS",
          "content"	 : "IntroductionAre you building a SaaS application on AWS and wrestling with thecomplexities of implementing usage-based billing?Moesif and Amazon Web Services(AWS) are working together to make it easyfor SaaS architects and developers to implement usage-based billing withthe SaaS Builder Toolkit for AWS(SBT-AWS). Moesif is acloud-based solution for usage-based metering andbilling.SBT-AWS is an open-source developer toolkit provided by AWS to implementSaaS best practices. With the recently released Moesif Module forSBT-AWS, billinginfrastructure can quickly be incorporated in SBT-AWS projects making iteasier than ever to monetize your SaaS application and drivingusage-based revenue.Why Usage Based Billing?Usage-based billing models (also called consumption-based billing) arequickly becoming the preferred way to monetize APIs and SaaSapplications to accelerate adoption time, increase expansion revenue,while improving overall customer experience. For applications with highinfrastructure cost like data APIs and AI-based applications,usage-based billing allows SaaS providers to maximize revenue byaligning the costs with actual usage. Consumers benefit from costcontrol and transparency, paying only for what they use, while gainingthe flexibility to scale usage as needed. This model also fostersinnovation by reducing the financial risk associated with adopting a newAPI or SaaS applicationChallenge with Usage-Based BillingUsage-based pricing requires a flexible reliable analytics system toaccurately meter customer usage in real-time. This requires significanttime and engineering resources to build the needed data infrastructureto meter usage at scale delaying the time to start generating revenue.Tracking granular usage metrics, accurately invoicing customers with theright amounts, and ensuring ASC 606 compliant revenue recognition canintroduce significant complexity. The development to create this billinginfrastructure is time taken away from core development which canprolong monetization plans.Another challenge is accurately predicting and managing costs for SaaSproviders and customers. Usage patterns can be unpredictable, leading tofluctuating bills that are difficult to budget for and create unexpectedcost from overage. Additionally, setting the right price points is abalancing act, as overly high prices deter users while overly low priceslead to revenue loss for providers.Challenge building SaaS ApplicationsBesides investment in billing, building a successful SaaS involves asignificant engineering investment in many “boring” systems that goesunnoticed by the end-user. A substantial amount of effort is dedicatedto creating essential infrastructure. This includes building robustbilling systems, secure authentication flows, reliable provisioningprocesses, and intuitive onboarding experiences. These underlyingcomponents are critical for the smooth operation of the SaaS platformbut can divert valuable engineering resources away from developinginnovative features that directly enhance the product’s valueproposition.The Moesif SolutionMoesif is a turnkey usage-based billingsolution forAPIs and AI-based applications. With powerful metering and analyticscapabilities, you can quickly create complex metering rules on justabout any attribute around your event data such as billing on APItransactions, compute resources, or unique users. You can set upprepaidcredits,tiered discounts, quotas and entitlements, and more. As an openplatform, Moesif can automatically invoice through your preferredinvoicing solutions like Stripe, Zuora, or a custom invoicing solution.With Moesif, you can meter usage in real-time so you can take actionsuch as revoke access when credits run out or payment failed. Moesifalso unifies usage reporting from disparate sources such as Amazon APIGateway, Amazon Data Firehose, internal applications, and more,simplifying metering and rating. This ensures product owners andcustomer facing teams have a good understanding of customer usageensuring customer spend is aligned to value. The SaaS Builder ToolkitLike Moesif, the AWS SaaS Builder Toolkit for AWS (SBT) is anopen-source developer toolkit to quickly build SaaS applications withoutworrying about the “boring” stuff. SBT is a boilerplate to handle thecommon functions most SaaS applications require including billing,authentication, provisioning, onboarding, and more. SBT is built on topof the AWS Cloud Development Kit (CDK) which makes it easy to treat yourSaaS infrastructure as Code (IaaC) to easily encapsulate SaaS and devopsbest practices in your application.Moesif Plugin for SBT-AWSWith the Moesif plugin forSBT-AWS, you can addnative usage-based billing capabilities to your SBT-AWS basedapplications without worrying about the boilerplate logic. The plugin isspecifically tailored for SaaS applications built with the SaaS BuilderToolkit for AWS and provides the following capabilities into your SaaSarchitecture:  Accurate Event Ingestion: The plugin leverages Moesif’s AWSFirehose integrationto collect your raw usage events. These events, which can encompass APIcalls or custom actions within your application, are sent to Moesif’sCollection API for accurate metering and aggregation.  Unified User and Tenant Management: A dedicated AWS Lambda functionensures that core SaaS entities (tenants, subscriptions, and users)remain synchronized across your SBT-AWS deployment, Moesif, and yourselected payment gateway (e.g., Stripe, Zuora).  Automated Usage-Based Invoicing: Moesif’s comprehensive APImonetization features allow you to define billable metrics, streamlinethe invoicing process, and integrate with your preferred paymentprovider (e.g., Stripe, Zuora).Key Use CasesMoesif and the Moesif SBT-AWS plugin empowers you to:  Monetization of APIs: Charge customers based on the volume of APIrequests, sum of payload size, and more.  Monetization AI Applications: Create PAYG pricing models forGenAI-based applications to charge on input tokens, context size, andmore.  Monetization of Infrastructure: Create billing models that accountfor resources such as CPU time, memory usage, or storage consumption.Key Pricing ModelsWith Moesif and the Moesif SBT-AWS plugin, a variety of differentpricing models are supported including:  Prepaid Credits: Customers purchase credits upfront which are thenburned down over time as Pay-As-You-Go (PAYG). A customer’s access canbe revoked when credits run out.  In Arrears: At the end of a billing period (such as a month), acustomer is invoiced for the previous period’s consumption. This can bepaid through credit card, bank transfer, and other payment methods.  Commitment Plans A customer can prepay to commit to a usage tier orquota before service is consumed. Volume discounts can be provided forenabling a customer to pay upfront.How Does it workThe plugin will deploy a set of resources as part of your SBT-AWSproject to support usage-based billing including:  An Amazon Firehose to ingest your raw usage events.  An Amazon Lambda to handle provisioning and deprovisioning subscriptions.The plugin will map Moesif entities to SBT-AWS entities as follows:            SBT Entity      Moesif Entity      Description      Parent                  Tenant      Company      Your customer that you provisioned resources for.      None              Tenant      Subscription      A single subscription for a company/tenant.      Company              User      User      End users of your customer who login to your SaaS.      Company      Getting StartedGetting started with the plugin is straightforward. Follow these stepsto integrate the Moesif SBT-AWS plugin (assumes an existing SBT-AWSproject and Moesif account). The setup takes less than an hour.PrerequisitesIf you don’t already have a SBT-AWS project deployed, followSBT-AWS’stutorialto deploy the sample hello-cdk project with a ControlPlane andCoreApplicationPlane.After your sample project is deployed, follow the steps below.InstallationWithin your SBT-AWS project directory, install SBT-aws-moesif via thefollowing command:npm install --save SBT-aws-moesifAdd Moesif ModuleInstantiate the MoesifBilling construct like below. You will need to setsome properties like the moesifApplicationId and moesifManagementAPIKeyto authenticate with Moesif.export class ControlPlaneStack extends Stack {  public readonly regApiGatewayUrl: string;  public readonly eventBusArn: string;  constructor(scope: Construct, id: string, props: any) {    super(scope, id, props);    const cognitoAuth = new CognitoAuth(this, 'CognitoAuth', {      idpName: 'COGNITO',      systemAdminRoleName: 'SystemAdmin',      systemAdminEmail: '<<Your Admin Email>>',    });    const moesifBilling = new MoesifBilling(stack, 'MoesifBilling', {      moesifApplicationId: '<<Your Moesif Application Id>>',      moesifManagementAPIKey: '<<Your Moesif Management API Key>>',      billingProviderSlug: BillingProviderSlug.STRIPE,      billingProviderSecretKey: '<<Your Billing Provider's Secret Such as for Stripe>>'    }   );    const controlPlane = new ControlPlane(this, 'ControlPlane', {      auth: cognitoAuth,      billing: moesifBilling,    });    this.eventBusArn = controlPlane.eventBusArn;    this.regApiGatewayUrl = controlPlane.controlPlaneAPIGatewayUrl;  }}Ingest EventsNow that you created a tenant, you should ingest some actions in yournewly created firehose. Actions have an action name (like “Signed Up”,“API Request”, or “Finished Job”) which represents the usage event. Youcan also include arbitrary metadata with an action, which enables you tocreate billable metrics, usage reporting, and more. For more info, seedocs on actionsYou’ll want to set a few fields like below:action_name is a string and should include name of the event such as“Processed Payment Transaction”company_id is your tenant identifier. See companiestransaction_id should be a random UUID for this event which Moesif usesfor deduplication. Docs on Moesif idempotency.request.time represents the transaction time as an ISO formattedstring.metadata is an object which includes any custom properties for thisevent. By setting metadata, you can bill on arbitrary metrics, createmetrics on them, etc. For example, if the action name is “ProcessedPayment Transaction”, you can include an amount and the currency to billon the total amount.For full schema and available fields, see Actions APIReferenceAn example action is below:{  "action_name": "Processed Payment Transaction",  "request": {    "time": "2024-04-01T04:45:42.914"  },  "company_id": "12345",  "transaction_id": "a3765025-46ec-45dd-bc83-b136c8d1d257",  "metadata": {    "amount": 24.6,    "currency": "USD",    "time_seconds": 66.3  }}In the above example, the action is created whenever a payment isprocessed. There are also two metrics we are tracking as part of theaction (the amount of the payment and how long the job took). You cancreate billable metrics and usage reports from these attributes.If your events are API calls, we recommend changing theMoesifEventSchema to API_CALL which provides a different schema than theabove actions. See APICallsLet Us HelpThe Moesif SBT-AWS plugin is in preview, with active developmentunderway. For more information, you can find the plugin and detaileddocumentation on GitHub:https://github.com/Moesif/SBT-aws-moesif.If you’d like a personalized demonstration of how the Moesif SBT-AWSplugin can power your SaaS billing strategy, don’t hesitate tocontact us!",
          "url": " /technical/api-development/How-to-Easily-Deploy-Usage-Based-Billing-with-Moesif-and-SBT-AWS-for-SaaS/",
          "author": "Derric",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-essential-open-apis-for-app-development": {
          "title": "Essential Open APIs for Thriving in App Development",
          "content"	 : "An application programming interface (API) defines a set of rules and protocols that allow different software entities to communicate with one another. Open or public APIs offer free access to everyone with little or no restrictions. These public APIs allow developers to build innovative solutions and extend existing ones covering a variety of use cases.In this article, we cover some of the best open APIs from different categories. We also discuss their real-world applications and practical tips for integrating them into your workflow.Key Takeaways  Open APIs fuel efficient and creative application development in wide-ranging areas. For example, social media, geographic location data, and weather information.  Specific industries benefit from bespoke open API solutions. For example, healthcare, finance, and entertainment.  To successfully integrate open APIs, you must carefully evaluate API documentation, performance, security, and privacy. You must also consider the technical aspects of registering API keys, implementing API calls, and handling responses.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding Open APIsAn open API, or a public API, is an application programming interface that allows developers to tap into a proprietary software application or web service. The beauty of these APIs lies in their accessibility and utility; they’re publicly available and come with certain permissions. The practicality of open APIs extends to connecting diverse technologies, including legacy systems, which in turn fosters competitive procurement and potential cost savings.Moreover, the creative and transparent nature encourages innovative use of technology. This leads to new applications and enhanced public trust. Good API design is often simple, enabling developers to kickstart the usage and enhance productivity without extensive reference to documentation.Benefits of Using Open APIsOpen APIs attract developers and companies through its many benefits. For example:Cost and implementation efficiencyOpen APIs inherently comes with more cost-efficiency than the other counterparts. This makes them a favored choice for organizations looking to cut down on tech expenses. But the charm of open APIs doesn’t stop there. They’re cloud-based and incredibly easy to implement, requiring just internet access. This accelerates development process, allowing organizations to bring their visions to life swiftly.Simplicity and interoperabilityOne of the standout traits of open APIs lies in their language-agnostic design. This means they can seamlessly interact with the server’s API, without needing to know the implementation details. Apart from ease of use, open APIs offer universal access. Therefore, the software developers build can integrate and function across various devices. This enhances the functionality of applications, offering a seamless user experience across platforms. Availability on more devices also results in larger user base, more profit, and innovation.AccessibilityOpen APIs greatly compliment the idea of removing barriers from information access through open data. Since you can consume an API programmatically, you can create meaningful ways to collect and present information for different audiences. For example, Open Data initiatives aim to enhance government transparency and accountability by offering public access to government-owned data.Top API Categories for App DevelopersOpen APIs bring more depth and breadth to the available categories developers and startups can innovate and develop in. From marketing to geo data and weather information, you possess a host of possibilities at your fingertips to realize your next great idea, thanks to open data.Marketing APIsSome APIs specifically designed for marketing and content development include the following:  Yahoo Search Marketing API          Provides access to Yahoo marketing data and tools for managing marketing campaigns        Google Ads API          Allows developers to interact with Google Ads services, such as creating and managing campaigns, ad groups, ads, and keywords        Facebook Marketing API          Enables developers to integrate Facebook’s advertising platform into their own applications        Twitter Ads API          Provides programmatic access to Twitter’s (now known as X) advertising platform, allowing developers to create and manage ad campaigns on Twitter      By incorporating these APIs, marketers can enhance their projects and campaigns, leveraging the power of data and automation to drive better results.This kind of strategic integration of APIs can lead to more comprehensive, efficient, and insightful app development.Social Media APIsSocial media have become an integral part of our daily lives. We use them everyday through our phones and computers. Think about your interactions on platforms like LinkedIn, Twitter, and Facebook. Behind the scenes, these platforms offer some of these capabilities:      signing in        data analytics        content publishing        scheduling        integration of marketing tools  Free public APIs and partner APIs power these features, utilizing not only the data that exists but also fake data and dummy data for testing purposes. Most of these APIs fall in the category of RESTful API. Thanks to APIs, we can enjoy seamless user experiences across these digital products and platforms.However, not all APIs have the same access levels and criteria. For example:  Pinterest API offers trial access with a 1,000 calls per day limit and requires standard access application for more extensive features.  Telegram’s API requires creating a bot and presents a unique authorization workflow.  X (formerly Twitter) API mandates registering a project and offers both free tiers for development or testing and paid plans for more extensive use of the Twitter API.  Instagram’s API allows only Business or Creator accounts to use its features and imposes a 24-hour limit on API-published posts.The diverse range of APIs and access levels offers a world of possibilities for app developers to tap into.Geolocation APIsMany location-based services use geographic location APIs. In this digital age, we’ve become accustomed to many applications and services that require precise geographic location and real-time communication. For example:  You may have used Facebook to post a check-in status update when you visit a restaurant.  Uber requires your permission to access your device location so that it can locate nearby drivers for you.Some leading providers of IP geolocation data include the following:  Mapbox          Developers can utilize this data to create custom dynamic apps.        IP2Location          Offers non-intrusive geolocation and accurate IP geolocation lookup.        IPinfo          Provides detailed location data for enhanced app insights.        Ipapi          Offers low-latency IP geolocation lookup.        Ipstack          Provides accurate and detailed location data.        Positionstack          Enhances the app with rich, location-specific insights.      The landscape of geolocation APIs is vast and varied. Some more notable options include the following with different feature sets:  Abstract’s IP geolocation API is recognized for its bulk request capacity.  Maxmind specializes in intelligent fraud detection along with IP geolocation.  Ipdata offers geographic location with proxy detection that aids in GDPR compliance and fraud prevention.  ipgeolocation.io provides VPN and threat detection features.These APIs not only provides basic geographic location data, but also come with extra layers of security and compliance features, making them a valuable asset for any app developer.Weather APIsWeather APIs power applications that work with weather and meteorological information. These APIs typically provide information in formats like JSON and XML. The offerings for such APIs vary from current weather conditions, forecasts, to advanced data like air quality. Several weather data APIs stand out for their unique features:  The Tomorrow.io API offers hyper-local weather data.  AccuWeather API provides minute-by-minute precipitation forecasts.  Weather20/20 specializes in long-range forecasting up to 12 weeks out.  OpenWeatherMap offers free access to current conditions and forecast data.Here are some more weather APIs that may benefit you with their unique services:  Weather Underground delivers hyper-local data via personal weather stations.  AerisWeather provides comprehensive weather information including imagery.  Weatherstack allows access to real-time weather data through REST API calls based on location.These APIs enable developers to create rich, detailed, and accurate weather applications. Against the backdrop of climate change crisis, weather APIs have become an incredible resource for building useful tools and gaining valuable insights. For example, your country’s natural disaster management system uses precise geographic location, weather, and meteorological data to safeguard people, property, and environment from different hazards.Essential Open APIs for Specific IndustriesMoving beyond the broad categories, open APIs also cater to specific industries, offering tailored solutions to meet unique needs. Here are some examples:  In the healthcare sector, APIs streamline the integration of electronic health records and patient data into healthcare apps.  In the financial sector, APIs critical for e-commerce and financial services apps enable processing payments and accessing cryptocurrency rates.  In the travel industry, APIs power reservation systems for flights and accommodations.  Geographic location APIs enhance user experience with location-based services.Finance APIsIn the financial sector, APIs drive various functions that benefit not only service providers but also the users. For example:  Facilitating stock market news coverage.  Supporting trading platforms.  Tracking cryptocurrency markets.Banks and financial service providers often use finance APIs to add advanced features, enhancing their service quality and attracting new customers. These APIs enable customers to conduct deposits, transactions, and transfers remotely, promoting convenience and accessibility. Finance APIs deliver up-to-date market rates and financial information, driving user engagement and loyalty to a brand.Developers can access a range of free finance APIs and services, including but not limited to the following:  Yahoo Finance  Alpha Vantage  Currency Converter  Live metal prices  Latest stock prices  Stock market data  CoinrankingEntertainment APIsEntertainment APIs serve as the backbone of many content-rich platforms, offering diverse content such as:  Movies  Music  Games  Jokes  QuotesThe IMDB movie API, for instance, provides extensive details like cast, synopsis, episodes, and release dates, making it a valuable resource for movie and TV show-focused apps. Spotify’s open APIs grant developers access to music-related data including songs, albums, artists, and playlists. These APIs offer compatibility with numerous programming languages and SDKs, making them versatile tools for a diverse developer audience.Health & Fitness APIsHealth and fitness public APIs help developers create apps in the health and fitness domain, supporting individuals in managing their well-being. Some of the features these APIs provide are the following:  Access to food nutrition databases.  Tools for creating personalized exercise plans.  Connectivity with wearable technology for real-time vital statistics monitoring.These APIs offer more than just data. Users gain invaluable insights about their current state of health and ways to improve. Health and fitness APIs deliver information rooted in extensive research, presenting them in a user-friendly format. Furthermore, these APIs offer compatibility with a variety of programming languages, SDKs, and health demographics. This ensures wide adoption across different types of applications and health landscapes.Enhancing User Experience with Open and Free APIBesides introducing new functionalities, open APIs also enhance the user experiences. By leveraging personalization, natural language processing, and image and video APIs, developers can tailor a product to accommodate individual needs. This creates a more engaging and personalized experience for an entire user base. Some ways to enhance the user experience with APIs include:  Leveraging personalization APIs to tailor content and recommendations to individual users.  Using natural language processing APIs to enable voice commands and chatbot interactions.  Integrating image and video APIs to enhance visual content and media playback.Entertainment APIs, for instance, significantly enhance user experience by streamlining access to vital entertainment data. These APIs reduce the effort consumers need to put into research and enable businesses such as movie theaters and fan sites to easily reach wider audiences.Moreover, open APIs like QuickChart can generate charts and graphs, visually improving an app’s data presentation and making it more understandable and engaging for users.Personalization APIsPersonalization APIs help deliver personalized experiences and engage users. For example, apps like Flipboard and Feedly use open APIs to aggregate different sources of content according to user’s choices. Similarly, the Pocket app incorporates open APIs to compile articles from various publishers, allowing users to save stories for later reading. .News apps like SmartNews leverages open APIs to curate and present news stories tailored to the preferences and behavior of individual users. The Chuck Norris API uses natural language processing to understand user preferences and delivers personalized content. It stands out as a prominent example of how we can deliver light-hearted content to users.Natural Language Processing APIsNatural Language Processing (NLP) APIs carry out syntax analysis, entity recognition, and content classification. They equip developers to extract and interpret information from text, including understanding customer sentiments. Python developers can benefit from open-source NLP tools like Natural Language Toolkit, SpaCy, and TextBlob. These tools offer the flexibility to embed sophisticated language processing capabilities into applications at no cost.Developers have access to some of the most effective NLP APIs for their projects. For example, Google NLP, text processing, and sentiment analysis. These tools have compatible SDKs in programming languages like Node.js, PHP, Python, and Java.Image & Video APIsImage and Video APIs are a critical component of any visual media-centric application. The Pexels API offers a rich library of free high-quality images and videos for enhancing app visual components. Shutterstock provides access to a vast library of royalty-free images, music tracks, and video clips through its API.With the Pexels API, developers can perform searches for specific themes, like nature, across various resolutions to match their design needs. Developers can retrieve curated photo and video collections from Pexels, catering to different screen sizes and orientations.YouTube API integration enables in-app video playback and interactive features like subscription management and live broadcast controls. GIPHY’s API facilitates the creation of visually engaging content, enabling users to search for and share GIFs within apps.Tips for Choosing the Right APIYou must make an informed decision when choosing the right open API from the vast array of options available to you. RapidAPI offers a solution for developers to easily locate and handle their API connections by providing an API Marketplace for this purpose.The marketplace streamlines the process and ensures efficient management. To provide developers with open and free APIs suitable for experimentation, testing, and integration, the platform also curates a list of top free APIs.Evaluating API DocumentationAssessing an API’s documentation is a crucial step in the selection process. API documentation helps developers understand an API’s functionality, syntax, and integration process. It ensures that the developers do not regret their choice later because the documentation shows careful craft and is easily understandable. Documentation must receive regular updates and maintenance to promptly reflect API changes. This keeps the documentation consistent and a reliable resource for developers.Effective API documentation includes the following basic elements:  A comprehensive and up-to-date reference section  Educational guides  Details of the API’s endpoints, request/response fields, and authentication methodsA good API documentation uses clear and plain language and includes practical examples. These examples allow developers of various expertise levels to better grasp the API concepts and quickly start their implementation.Considering API PerformanceAPI performance is another critical factor to consider when choosing an open API. Slow API performance can significantly detract user experience, potentially driving users away from the application. Developers can examine an API’s average response time and uptime to ensure it aligns with the app’s performance needs. Load testing APIs under high demand scenarios aids in understanding their scalability and stability, critical for maintaining app performance.To optimize response times for users worldwide, consider the geographic location of API servers and the availability of global CDN support. Tools like curl, Postman, and cloud-based services like LoadImpact can help a lot to regularly monitor and manage API performance.Assessing Security & PrivacySecurity poses a paramount concern when choosing an API, as data breaches can lead to severe consequences for both users and developers. An API must offer robust authentication methods, such as OAuth, API keys, and tokens, to control access and protect sensitive information.The following best practices can help you foster robust API security:  Following industry standards and guidelines.  Keeping up with evolving threats.  Leveraging security communities.When you choose an API provider, review the following details about the provider:  History of handling security incidents  Transparency in communicating vulnerabilities and patches  Privacy policies  Compliance with regulations like GDPRThese evaluations ensure that the API adheres to legal standards for data protection. Finance APIs, for instance, provide a secure connection when data flows among consumers, databases and transactional servers of financial institutions. With sufficient measures in place, companies can protect the sensitive financial data of users during real-time transactions.SimplicitySimple and modular API designs bring joy to the developers. They drive productivity, reduce costs, and promotes further improvements. Simplicity allows you to better understand the tool you’re using and therefore design better products. Simple, easy-to-understand APIs always attract new users.An unnecessarily complex API may offer more features. But it may also introduce serious pitfalls in your journey. This results in you spending more time addressing issues instead of focusing on your actual product development. In the end, complex APIs can create subpar user experiences both for the developers and the end users.Integrating Open APIs into Your AppOnce you’ve chosen the right open API, the next step is integration. The process involves registering for an API key, implementing API calls, and handling API responses. Follow each of these steps to successfully integrate the API into your app and make sure the app functions as you desire.Registering for an API KeyAn API key is a unique identifier that’s required to authenticate API calls. It acts as a passport for the client application to access the API’s resources. Every API provider documents the steps developers must take to obtain such a key. Each API request contains the API key as a parameter.Since an API key lets you access an API’s resources, pay attention that you handle the key securely. Otherwise, it can fall into the wrong hands, causing both the API and your application to become vulnerable. This can potentially lead to hackers gaining access to private information of your users. Therefore, we highly recommend developers to follow the best practices for securely using, storing, and managing API keys and other credentials.Implementing API CallsImplementing API calls involves these general steps:  Identify the uniform resource identifier (URI) of the external server or program that you want to access.  Include the appropriate HTTP request verb in your call.  Include appropriate HTTP headers in the API call inform the API about the nature of the request and the format of the response expected. Common headers include User-Agent, Content-Type, and Accept.After making an API call, expect a response from the API with a status code. HTTP status codes indicate whether a request has been successfully completed or not. In the latter case, the status code contains the exact error information that have occurred. As a developer, you must address the errors gracefully. Common success codes start with 2XX, while error codes start with 4XX.Before making an API call, ensure that the app checks for internet connectivity to avoid crashes and inform the user if the connection fails.Handling API ResponsesHandling API responses is an equally important part of the integration process. The following key points summarize how to handle responses properly:  Properly manage exceptions that arise in API responses, for example, incorrect or malformed inputs. Failure to do so may expose sensitive information about users or application state, which in turn can give attackers means to exploit.  API response fields typically use camel case (camelCase) for naming, while constants or enumeration values use lowercase. Whatever style you pick, keep consistency and readability in mind.  Parse and validate API responses to ensure that the data structure and type match what the app expects. This prevents errors and potential security vulnerabilities.  Use asynchronous calls to handle API responses to maintain a responsive user interface and avoid blocking the main thread.  Implement retry logic with exponential backoff to handle transient failures when calling an API. This setup ensures a better experience during temporary network issues or server unavailability.Real-Life Examples of Apps Using Open and Public APITo better grasp the utility and potential of open APIs, consider real-life examples of apps that use them. The use of open APIs can significantly expand an app’s content offerings and enhance the user experience.For instance, applications use the Associated Press API to pull in a diverse array of content like the following:  Articles  Lists  Bestsellers  Tags  Movie reviews  Other shared materialWeather AppsWeather apps are a great example of the effective use of open APIs. The Weather Channel app, for instance, uses weather data APIs to provide users with accurate forecasts and weather alerts. AccuWeather integrates various APIs to give minute-by-minute precipitation forecasts along with severe weather warnings.Dark Sky, prior to its acquisition by Apple, offered hyper-local weather information through its own API. The API was a valuable resource for delivering detailed hyper-local weather predictions to users.This showcases how weather APIs can enable the creation of powerful, insightful, and user-friendly weather apps.Travel AppsOpen APIs have made a significant impact in the travel apps sector. Uber’s innovative Ride Requests open API enables users to book a ride directly within external apps without the need to open the Uber app itself. Rentalcars.com’s API facilitates the integration of car rental services spanning approximately 60,000 locations in 163 countries into travel applications.Travelfusion operates as an aggregator with an open API that aggregates real-time flight data and booking options for over 400 low-cost and traditional airlines. Skyscanner’s travel app leverages open APIs to conduct thorough flight price comparisons across various airlines and booking websites, enhancing the search experience for travelers.News AppsNews apps are another category where open APIs have seen increasing usage over the years. Some examples of APIs used in news apps include:  The News API scans news sources and returns relevant content based on a request  The NewsData API provides a free method for developers to integrate news articles from various global sources.  The New York Times API allows integrating content such as articles, lists, bestsellers, tags, movie reviews, and other popular shared material into news apps.These APIs contribute to a wide array of information resources in news apps, including access to valuable twitter data.Personalization APIs in this context can construct news applications that aggregate personalized news content to offer users an experience that aligns with their individual preferences and interests.SummaryTo wrap up, open APIs serve as the backbone of modern app development. They offer a wealth of resources, from social media and geolocation data to health and finance information, and beyond. Open APIs streamline the development process, foster innovation, and enhance user experiences. They’re publicly available and come with certain permissions, facilitating the seamless integration of diverse technologies. Whether you’re a developer looking to enhance your app’s functionality or a business seeking to improve user experience, open APIs are an invaluable tool. The possibilities are endless, and the future of open APIs certainly looks promising.After reading this article, you may already have found your next great idea. If you want to harness the power of APIs, consider adding Moesif to your stack of tools to aid you in the journey. Moesif boasts a comprehensive set of API analytics and monitoring tools that can help you understand your API early on. And if you want to monetize your API, Moesif has you covered with powerful monetization tools that work seamlessly across different billing providers and suit your business strategy. Sign up today for a free trial no credit card required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Creating an open API?            Unlock deep insights into whos using your APIs and how with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Essential-Open-APIs-For-App-Development/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-design-principles": {
          "title": "9 API Design Principles That Hold Up in 2026",
          "content"	 : "Most “API design principles” articles still read like they were written in 2015. The fundamentals matter (REST verbs, status codes, predictable URIs) but the bar has moved. In 2026 your API is also being consumed by AI agents through MCP, governed by platform teams across multiple gateways, and benchmarked against the LLM APIs your customers already know.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        This is the working list we use with WSO2 API Platform customers when they review an API at the design stage. Nine principles, in the order they matter.1. Design for the consumer, not the implementationThe most common mistake in API design is letting the database schema bleed through to the API surface. Resources should reflect the concepts the consumer reasons about, not the tables you happen to store them in. A /customers/123/subscriptions endpoint is more useful than /db/v2/cust_subs_join, even if the second one is closer to your data model.The discipline here is to write three or four example calls before writing any code. If the example call is awkward to write, the API is awkward to use. Stripe famously rewrote its early API after Patrick Collison wrote the first integration himself and discovered that the original shape required four calls to perform what a developer mentally thought of as one transaction. The fix was a single Charges resource that hid the underlying complexity, and that decision still shapes the API a decade later.The practical test: hand a written example to a developer who has never used your API and ask them what they think happens. If they can answer correctly without consulting docs, the design is doing its job. If they hesitate, the resource names or response shapes need work before you write the first line of server code.2. Use the conventions developers already knowA well-designed API is boring to read. Developers should be able to predict your URL structure, request shape, and response codes without consulting the docs. The conventions that have stayed conventional:  Plural nouns for collections, singular for items. /orders for the collection, /orders/42 for one order.  HTTP verbs do the action; URLs identify resources. POST /orders creates an order. Do not invent /createOrder.  Standard CRUD mapping (the verbs you will use almost all the time):            Method      Purpose      Idempotent?      Request body?                  GET      Retrieve a resource or list      Yes      No              POST      Create a new resource      No      Yes              PUT      Replace a resource entirely      Yes      Yes              PATCH      Update part of a resource      Sometimes      Yes              DELETE      Remove a resource      Yes      No        kebab-case in URLs, camelCase or snake_case in JSON. Pick one for JSON and use it everywhere. Mixing styles inside one API is the fastest way to lose developer trust.When you deviate from a convention you owe the consumer an explanation. When you follow it you owe them nothing.3. Make endpoints predictablePredictable endpoints reduce documentation lookups, which is the actual UX of an API.Nested resources should map to real ownership. /users/123/orders is good when an order belongs to one user. /orders/?userId=123 is also good. What is not good is having both: pick one.Avoid more than two levels of nesting. /companies/1/users/2/orders/3/items/4 is a mistake. When you find yourself reaching for a third level, you usually want a top-level resource with a filter parameter instead.4. Make responses self-explanatoryA response should tell the consumer what happened without them parsing the body. Three layers do that work together:Status codes are the universal signal. Stick to standard ones: 200 family for success, 400 family for client errors, 500 family for server errors. Custom 6xx codes are a red flag. We have a full HTTP status code reference if you need a refresher.The response body returns the resource on create and update, returns the deletion outcome on delete. For errors, return a machine-readable code, a human-readable message, and (where appropriate) the field that failed validation.Headers carry the metadata. Include X-Request-Id (or Request-Id) on every response so consumers can quote it back to your support team. Include rate-limit headers (X-RateLimit-Remaining, X-RateLimit-Reset) so consumers can self-throttle.Together those three layers let a developer debug a problem without opening your dashboard.5. Build security in at the design phaseSecurity designed in is cheap; security retrofitted is not. The minimums in 2026:  Authentication. OAuth 2.0 or OIDC for user-facing flows, API keys or mTLS for server-to-server, JWT for short-lived access tokens. Never invent your own scheme.  Transport encryption. TLS 1.2 or higher on every endpoint, including ones you think are internal. mTLS for partner integrations.  Sensitive data handling. Never put PII or secrets in URLs, since they leak to logs and Referer headers. Encrypt log payloads at rest. Mask sensitive fields in any observability tooling you use.  Scopes and granularity. Issue tokens with the smallest scope that gets the job done. “Read all customer data” should be a different scope from “read my own customer record.”Most production breaches we see at the platform layer come from missed basics, not novel attacks. The OWASP API Security Top 10 (last revised in 2023) reflects this directly: the top three risks are broken object-level authorization, broken authentication, and broken object property-level authorization. None of those require an exotic exploit; they require a single endpoint where the implementing engineer forgot to check that the requesting user owns the resource being modified. The fix at the design layer is to make object-level authorization an explicit requirement in your API contract, not an afterthought added during code review.6. Plan for rate limits and quotas before launchRate limiting is part of the API contract. Pick the algorithm before you ship, not after the first incident. The four patterns worth knowing:  Fixed window: N requests per minute, counter resets. Simple, but allows burst at window boundaries.  Sliding window: N requests over the last 60 seconds, rolling. Smoother, more memory.  Token bucket: fill a bucket at rate R, each request takes a token. The de-facto default for public APIs.  Leaky bucket: requests queue and drain at rate R. Useful when downstream is the bottleneck.Whichever you choose, do three things: return 429 Too Many Requests (not 503), include Retry-After in the response header, and surface usage to the customer in real time so they can self-correct. Thresholds that look right on paper rarely hold up after launch, so plan to revisit them after the first month of real traffic.Quota design is a related decision that often gets skipped. Pick the dimension before you ship: requests per minute, requests per day, requests per resource type, requests by token cost (relevant for AI APIs where one call can cost 100x another). Stripe rate-limits by integration; OpenAI by token throughput; GitHub by authenticated user plus IP. The right dimension depends on what the cost of a single call actually is on your infrastructure, and that varies enough by API that there is no single “correct” answer.7. Version deliberatelyAPIs evolve. The question is whether the evolution is visible to your consumers. Three versioning approaches are in common use:  URI versioning (/v1/orders, /v2/orders): easy to implement, easy to route, easy to deprecate. Most public APIs default here.  Header versioning (Accept: application/vnd.company.v1+json): cleaner URLs, harder for the consumer to debug.  Query string (/orders?version=1): simple but blurs the line between version and filter parameter. Avoid for production.The actual discipline is not picking an approach; it is having a deprecation policy. State, in writing, how much notice you give before removing a version. The standard is twelve months for paid public APIs, six months for free public APIs, and ninety days for internal APIs.Communicate deprecation in the response itself, not just in a changelog. Two IETF specs define complementary HTTP response headers for this purpose: RFC 8594 defines Sunset (the date the endpoint goes away) and RFC 9745 defines Deprecation (flags that the endpoint is deprecated as of a given date). Modern SDKs and well-behaved HTTP clients warn developers automatically when they see these headers, so the deprecation message reaches the consumer’s terminal before it reaches their inbox.8. Document as part of the design, not afterThe shortest path to a well-documented API is to make the documentation the source of truth, not the output. An OpenAPI-first workflow (write the spec, generate the server stub from it, generate the docs from it) keeps the three artifacts in sync by construction.This is also where the WSO2 API Platform earns its keep at enterprise scale. WSO2 API Manager applies governance policies against the OpenAPI spec automatically (naming rules, required fields, security minimums), so design-time decisions get enforced before code is merged.What good docs include at minimum:  Authentication walkthrough with a working curl example  Each endpoint with request shape, response shape, status codes, and at least one realistic example  Error catalog keyed by error code  Changelog and deprecation calendar: every version, what changed, when the old version goes away  SDKs or Postman collection, generated from the spec, kept in syncDocs that drift from the API are worse than no docs at all. A consumer who once tried to use an outdated example and got a cryptic error is unlikely to trust the docs again, which means every subsequent call becomes a support ticket. The cost of stale documentation is measured in developer-hours per integration, and at any meaningful scale it dwarfs the cost of keeping the spec-to-docs pipeline maintained.9. Design for observability and AI-agent consumptionThis is the principle that did not exist in 2020 and that matters most in 2026.Observability-by-design. Decide at the API design stage what you will measure in production:  Which fields are interesting for cohort analysis? Mark them in the spec.  What is the “successful business event” (order created, message sent, payment captured)?  Which fields contain PII and need to be masked in any logging pipeline?When those decisions are made at design time, your observability stack (in our case, Moesif API monitoring) can wire up automatically. When they are made later, you end up doing a separate instrumentation project six months after launch.Agent-consumable shapes. A meaningful share of API traffic now comes from AI agents through MCP. Two design choices make your API agent-friendly without code changes:  Self-describing operations. Your OpenAPI spec should have a clear summary, description, and operationId for every endpoint. These become the natural-language interface the agent uses to choose your endpoint over a competitor’s. Treat them as user-facing copy, not as internal documentation; vague descriptions like “manages payment objects” cause wrong tool selection in roughly the same proportion that a poorly named UI button confuses a human user.  Idempotency where it matters. Agents retry. POST endpoints that should be idempotent (create-or-update) should accept an Idempotency-Key header and return the same response on retry. Stripe’s API documents this header explicitly, and the same pattern is widely used across payments, infrastructure, and fintech APIs; using a different name (X-Request-Id, X-Client-Token) breaks compatibility with client libraries that expect the convention. The IETF also has a draft “The Idempotency-Key HTTP Header Field” working toward standardization.The WSO2 AI Gateway converts any REST API into an MCP-compatible server, so the better your spec, the better the agent experience without a second build to maintain.Common design patterns these principles produceThe nine principles above describe the what. The patterns below show how they compose in production APIs, and which decisions repeatedly come up because the principles do not pin them down by themselves.Pagination on every list endpoint. A GET that returns “all orders” works in development with 50 rows and falls over in production at 50,000. Pick between offset/limit (?limit=50&offset=100) and cursor-based (?cursor=abc123) pagination upfront, and apply the same shape to every list endpoint. Cursor pagination survives concurrent writes; offset pagination is simpler but degrades at high offsets. Cap the maximum page size so a client cannot request ?limit=10000.Filtering, sorting, and field selection via query parameters. ?status=paid&customer_id=42 for filtering. ?sort=created_at or ?sort=-created_at for sorting. ?fields=id,name,email for field selection. The shape compounds across the API; once consumers learn it on one endpoint, they assume it everywhere. Inconsistent filter naming (status here, state there) is one of the most common consistency violations in API reviews.A consistent response envelope for both success and failure. Successful responses return the resource (for GET) or the created/updated resource (for POST/PUT/PATCH). Error responses return {"error": {"code": "...", "message": "...", "field": "..."}} with the same shape across every endpoint. Consumers parse error.code to handle errors programmatically; the message is for humans and may change.Content negotiation that matches what clients send. Read Accept and Content-Type headers and respond accordingly. Accept: application/json is the common case; Accept: text/csv for endpoints that support CSV export. Return 406 Not Acceptable if you cannot satisfy the client’s Accept header rather than silently returning a different format.Resource representations that are stable across operations. The shape returned by GET /orders/{id} should match the shape of item entries in GET /orders. The shape returned by POST /orders (creation response) should be the same as the GET shape. Inconsistent shapes for the same resource across operations is a permanent paper cut for SDK authors and a frequent source of agent confusion.Webhook + polling as a pair for asynchronous results. For long-running operations, return 202 Accepted with a job ID and a status URL. Let consumers either poll the status URL or subscribe to a webhook to be notified when the job completes. The pattern scales better than holding HTTP connections open for minutes.Bulk operations for chatty workflows. When consumers regularly need to make N small calls (especially agent runtimes looping over collections), expose a batch endpoint that accepts a list of operations in a single request. Saves network round-trips and reduces per-call overhead substantially.Caching headers that match the resource’s actual mutability. Cache-Control: max-age=300 on GET endpoints whose responses do not change every second. Cache-Control: no-store on responses containing sensitive data. ETag on resources where conditional requests (If-None-Match) would meaningfully reduce bandwidth. The defaults shipped by most frameworks are wrong for both extremes (over-caching sensitive data, under-caching cacheable data).These patterns are not separate principles; they are what the principles produce when applied. Reading them in this concrete form is often the fastest way to spot which principles your API is following only partially.Where to take this nextThe fastest way to put these principles into production is to make them enforceable. Write your OpenAPI spec first, set up automated governance against it, and instrument every call from day one so you can see whether your design is actually serving your consumers.Start a 14-day Moesif free trial to get per-user, per-endpoint observability on the API you are designing. No credit card required. To see how design, governance, runtime, and monetization fit together in one architecture, walk through the WSO2 API Platform with our team.Frequently asked questionsWhat are the principles of API design? Design for the consumer, follow REST conventions, keep endpoints predictable, make responses self-explanatory, bake in security, plan rate limits, version deliberately, document as you design, and design for observability and AI-agent consumption.What are the 7 core API design principles? The most common short lists drop the “AI-agent consumption” and “observability” principles and keep the seven REST fundamentals: consumer-first, conventions, predictability, self-explanatory responses, security, rate limiting, and versioning. Those two extra principles are now table stakes for any API designed in 2026.What are the 4 pillars of REST API? Stateless interactions, a uniform interface (consistent verbs and resources), cacheability where appropriate, and a client-server separation of concerns. These are Roy Fielding’s original constraints, and they still hold.Where do API design and API governance meet? Design says what the API should look like. Governance enforces it across an organization at scale. Platforms like WSO2 API Manager turn design principles into automated checks on every spec.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /technical/api-design-principles/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-master-the-craft-guide-to-develop-an-api": {
          "title": "Master the Craft: A Simple Step-by-Step Guide to Develop an API",
          "content"	 : "This guide provides a streamlined approach to the API development process, detailing the essential phases from the initial concept to the realization of a fully operational API. You’ll discover how to plan, design, and implement your own API and ensure it’s reliable and secure. With practical insights and actionable advice, you’ll learn the nuances of API development, from understanding the needs of your users to creating a scalable infrastructure that can handle growth. We’ll also delve into the best practices for API documentation and versioning, ensuring that your API remains user-friendly and maintainable over time.Key Takeaways  API development is a structured process that includes requirement conception, design, implementation, testing, documentation, deployment, and performance monitoring.  The planning and design phases of API development are crucial, involving the selection of API goals, defining functional and non-functional requirements, choosing an architectural style, and ensuring robust API security.  Proper API monitoring and optimization post-deployment are vital. To maintain API health and user satisfaction, performance metrics must be tracked, user feedback incorporated, and continuous improvement loops implemented.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding API DevelopmentIn web development, APIs connect different software components, enabling them to communicate harmoniously. The art of effective API development is a series of detailed steps:  The initial conception of requirements          This involves understanding the user’s needs and what the API aims to achieve. It’s the stage where you define the scope and purpose of your API.        Designing the API architecture          Here, you select the protocols and standards your API will adhere to. You determine how the API will be structured, the data formats it will use, and how it will handle different requests.        Implementing the API endpoints          At this stage, you write the actual code for the API’s functionalities. This includes setting up the endpoints that will handle requests and defining the actions that will be taken when these endpoints are accessed.        Testing and debugging          Before going live, the API must be rigorously tested. This involves running various test cases to find and fix bugs, ensuring that the API behaves as expected under different conditions.        Documenting the API          Good documentation is crucial for any API. It guides developers on how to effectively use the API and includes information on endpoints, parameters, data formats, and error codes.        Deploying the API          Once the API is tested and documented, it’s time to deploy it to a server where it can be accessed by users. This might involve setting up a scalable infrastructure to handle the load.        Monitoring performance          After deployment, it’s important to monitor the API to ensure it’s performing well and to quickly identify any issues. This includes tracking uptime, response times, and usage patterns.      The journey from blueprint to launch can be seamless or prolonged, tailored to the complexity of the task.The process reaches its peak when an API comes to life, with source code becoming published and accessible to developers.Application Programming Interface (API) BasicsDiving into the core, APIs conduct data flow and functionalities across diverse platforms, modern applications, and services through their API interface. Picture REST APIs and SOAP APIs as two distinguished architects in API design: REST, with its lightweight and agile structure, orchestrates performance and scalability easily, while more precise SOAP weaves complex transactions in its robust XML fabric.The architect you choose depends on the complexity of the narrative you wish to create—whether it is simple and user-centric with REST or more complex with SOAP.Key Components in API DevelopmentA well-structured API clarifies digital development. It stands on the pillars of architectural style and security practices, supporting comprehensive API description and documentation. The API documentation and descriptions guide developers through the API functionalities, leading them to seamless integration and troubleshooting.Crafting every API key and planning each response, including handling API requests, lays the foundation of a robust private API server primed to the tests of time and technology. Managing your API keys effectively is crucial to maintaining security and efficiency in your system.Planning Your API: Goals and RequirementsAs with any grand venture, the blueprint for building an API is drawn with foresight and precision. The planning stage is crucial to structure the value for users and the organization. A deep understanding of the audience, a clear vision of what creates an API’s objectives, and a thorough contemplation of the technical canvas—spanning architecture to limitations.The plan, focused on scalability and security, is taking shape and is ready to evolve into a digital masterpiece.Identifying API GoalsThe quest for a purposeful API begins with aligning its goals with the overarching narratives of business strategy. It’s about:  Storytelling through data exchanges  Crafting experiences that resonate with the users  Presenting information in a way that augments engagement and innovation.Whether the API serves as a gateway to new revenue streams, many tools for internal developers, or a bridge to customer satisfaction, its goals should mirror its creators’ ambitions.Defining Functional and Non-Functional RequirementsAt the heart of an API’s blueprint lie its API functions—the capabilities and data it must adhere to its business capabilities. Yet, beyond functionality and data formats, the non-functional requirements exist as the guardians of performance, integrity, and security. They ensure that the API is resilient against the onslaught of errors, responds with agility, and easily processes data.These requirements maintain the API’s quality as it evolves, guaranteeing its reliability across various platforms and languages.Designing Your API: Structure and SecurityEvery layer of API design must serve its purpose, from usability to security. A consistent architecture serves as a foundation where developers can confidently build, understanding the standard patterns that make APIs intuitive to use. But it doesn’t end with mere structure; it extends to fortifying the API against the ever-present specters of vulnerabilities, ensuring that each transaction is secure.Selecting an Architectural StyleThe selection of an architectural style is a declaration of the one creating an API’s identity. It can be:  REST, with its stateless interactions and cacheable data  GraphQL, catering to precise data queries  Microservices, segmenting the API into a well-defined ecosystem of services, each operating independentlyThe choice reflects the essence of the API’s identity, mirroring its unique needs and aspirations.Ensuring API SecurityThe API gateway acts as the security guard for the API. It checks each request, including various HTTP methods, ensuring that only the worthy pass through its gates and that the data within the server remains untarnished. The API gateway controls traffic flow, preventing overload from sudden spikes or harmful attacks.These measures create an impenetrable shield around the API, showcasing the unwavering commitment to security.Developing and Implementing Your APIThe development of an API is constantly evolving with continuous refinement of form and function. It begins with a foundation of requirements and blossoms into a structure defined by endpoints and API responses, each step closer to the ultimate goal of a seamless, functional API.The API matures through continuous integration, testing, and version control, guaranteeing compatibility and reliability that endure the test of time.Choosing a Preferred Programming LanguageThe alchemy of API development is deeply rooted in the choice of a programming language. This choice shapes the we create an API’s capabilities, influencing everything from performance to the ease of writing and maintaining code. Some popular programming languages for API development include:  Python, favored for its rapid application development  Java, known for its robustness  JavaScript, widely used for web development  Ruby, known for its simplicity and readability  PHP, commonly used for web development  C#, used for Windows development  Go, known for its efficiency and scalabilityDevelopers may call upon these languages (and many others) to bring their APIs to life.Each language holds the potential to bring ideas to digital reality, easily creating them by leveraging its unique strengths and community support.Writing and Testing CodeWriting code for an API balances creativity and precision, where each line of code is a step toward its functionality. As the first build an API works on takes shape, testing becomes vital to ensure each move is graceful and accurate. Tests like unit and integration tests ensure the API is ready.Every test polishes the API for its big launch.Monitoring and Optimizing API PerformanceMonitoring becomes the telescope through which we observe the API’s journey across the digital cosmos. Performance testing and measurement tools such as Postman Monitoring and Amazon CloudWatch help ensure the API remains a steadfast vessel, navigating users’ and businesses’ demands.Moesif steers your API to excellence.Moesif tracks key metrics like performance and response times, helping you navigate for continued improvements. Moesif doesn’t just provide data; it takes action by setting up automated tests that ensure your API meets the highest standards. Finally, Moesif acts as a guide, providing clear alerts and dashboards that guide you through periods of high traffic and changing usage patterns. With Moesif, you can ensure the health and vitality of your API for all users to depend on.Incorporating User FeedbackUser feedback guides the API’s evolution, ensuring it remains attuned to the needs of its audience. API usage insights realistic data are gathered through tools and data models while analysis uncovers patterns and priorities that steer the API towards continuous enhancement.Changes are carefully integrated into the API’s fabric, enriching the user experience without damaging functionality.SummaryOur API journey is now complete! We covered everything from the basics to ongoing improvements. Whether your first API or your fiftieth, the path to creating something extraordinary is paved with intention, innovation, and introspection. May this guide be your light and guide you to API expertise.As you embark on this adventure, remember that each API is a living entity that grows with your business. It requires nurturing through careful planning, vigilant security measures, and thoughtful design choices. Continue to engage with your users, gather feedback, and use it to refine your API. Be ready to adapt and evolve with the ever-changing landscape of technology and user needs. With dedication and foresight, your API will not only meet needs but exceed expectations, becoming an indispensable tool in the digital ecosystem. Here’s to your success in the dynamic world of API development!Empower your API management with Moesif’s cutting-edge governance and monetization features. Govern user access and enforce quotas efficiently, while unlocking new revenue streams through flexible, usage-based billing models. Seamless integration ensures your API’s security and financial growth. Start revolutionizing your API strategy today by signing up for Moesif’s free trial, and take the first step towards optimized control and monetization of your digital services.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Ensure peak performance and ironclad security with Moesif's comprehensive API monitoring            Secure your API ecosystem today with Alerting and Governance- Enhance your security with Moesif!            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Master-the-Craft-Guide-to-Develop-an-API/",
          "author": "Preet",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-transforming-integration-harnessing-the-power-of-api-as-a-product-for-business-growth": {
          "title": "Transforming Integration: Harnessing the Power of API as a Product for Business Growth",
          "content"	 : "Transform your application programming interfaces into dynamic revenue generators—this is the essence of treating ‘API as a product’. This concise guide provides fundamental insights into how APIs transcend their traditional roles to become standalone products, opening new avenues for customer interaction and business growth. By adopting this mindset, organizations can leverage APIs not just as tools for integration, but as strategic assets that contribute significantly to their bottom line. With the right approach, APIs can be packaged, promoted, and monetized just like any other product offering, providing customers with a valuable solution that meets their needs while also creating a sustainable source of revenue for the business. This shift in perspective from utility to product encourages a more holistic approach to API design and management, ensuring that every aspect of the API lifecycle is optimized for success in the marketplace.Key Takeaways  APIs have transitioned from being just middleware to standing as standalone products that contain core functionalities and align with strategic business objectives, managed with a customer-focused approach.  API product managers play a critical role in the lifecycle of an API product, from conception to monetization, ensuring it meets market needs and maintains high standards of performance and security.  Successful API products require a clear value proposition, competitive differentiation, and effective communication strategies, all underpinned by thorough market analysis, realistic goal setting, and adaptive roadmap development.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Defining API as a ProductGone are the days when APIs were considered mere middleware, hidden within the technical layers of digital services. Today, APIs have evolved to become central to business ecosystems, standing proudly as standalone products. An API product encapsulates specific core functionalities and is managed with a steadfast focus on customer service, ensuring it delivers strategic value and aligns with business objectives.The development of such a product demands:  An API product mindset with a customer-focused approach  Strategically linking APIs to the organization’s goals  Meticulously crafting the ‘user interface’ or contract to offer a delightful and value-driven experience to developers, the key users.The Role of the API Product ManagerSteering the ship that carries the API product through the tumultuous waters of the market is the API product manager. This role is not just about overseeing development; it’s about orchestrating a vision, creating a strategic direction, and collaborating with business and IT teams to ensure the API product is a resounding success.From the conception to the final monetization strategy, the API product manager is involved in every phase of the API lifecycle, establishing standardized governance to maintain consistency in performance and security while focusing on creating consumer experiences that align with the ever-evolving market needs.Crafting a Value Proposition for Your API ProductThe cornerstone of any successful API product is its value proposition, serving as the beacon that attracts and retains customers. A well-articulated value proposition not only drives customer adoption but also enhances user engagement, making it fundamental for the API’s market success.Let’s delve into the art of crafting this vital aspect of your API product.Identifying Customer NeedsUnderstanding customer needs is essential to navigating through the mind of the market, a journey that leads to increased retention, sales, and loyalty. The exploration begins with:  Customer journey identification and segmentation  Diving into product usage analysis  Collecting user feedback to uncover the deeper, often unspoken needs of consumers.By listening to the market’s pulse through sentiment analysis and product substitution, you can fine-tune your API to resonate with your customers’ evolving requirements.Competitive DifferentiationIn the crowded marketplace of digital offerings, your API must shine through competitive differentiation. By thoroughly analyzing the competitive landscape, you can position your API to address specific user needs or introduce unique functionalities that set it apart. This involves a keen understanding of your competitors’ strategies and offerings, revealing opportunities to innovate and provide an improved experience for developers.Remember, knowing your competitors’ strengths and weaknesses is the foundation upon which you can build a truly differentiated and competitive API product.Clear CommunicationThe ability to communicate the benefits of your API clearly and compellingly is paramount. It’s about crafting brand messages that resonate with developers and businesses, highlighting the tangible value and benefits they gain from using your API. Effective communication requires outcome-driven storytelling, where the focus is on the positive impacts and experiences enabled by your API, thus creating a message that not only informs but also inspires.Lifecycle Management: Keeping Your API Product RelevantAPI lifecycle management is the disciplined approach that keeps your API product in its prime through every stage of the API product lifecycle: design, implementation, and management. API product managers are the guardians of this lifecycle, vigilantly overseeing every aspect from inception to retirement using an API management platform. API providers play a crucial role in strategizing and implementing API monetization and engagement through developer portals as part of lifecycle management, including considerations for free trials, billing options, and promoting API adoption and use.This management ensures that the API remains a beacon of innovation, drawing on critical operational and usage data for:  continuous improvement  performance optimization  stability  relevance in the fast-paced world of technology.Building Blocks of a Successful API Product StrategyA winning API product strategy is key to a well-constructed edifice, with each block placed just right to support the structure’s integrity. At its foundation lies a deep understanding of the customers, a set of realistic goals, and a detailed roadmap that encourages collaboration and partnerships.This strategy not only drives business results but also catalyzes development and operational efficiency.Market AnalysisPenetrative market analysis is the compass that guides your API product’s journey. It involves:  A granular understanding of the target market  Identifying potential customer segments  Forecasting their needs and preferences  Evaluating market demand  Assessing the competitive landscape  Identifying opportunities for your API to deliver unique valueThis ongoing analysis is crucial for the success of your API product.It’s about positioning your API as a solution to industry-specific challenges, thereby resonating with the target audience’s pain points.Goal SettingSetting goals for an API product is not about shooting arrows in the dark. It’s about defining clear success metrics and objectives that resonate with the organization’s strategy. A strategic goal for the API is set, with tactics developed to achieve it, ensuring that it brings value to the organization and remains adaptable to changes.It’s about aligning with the broader business model and carving a niche for the API within the organizational strategy.Roadmap DevelopmentThe roadmap for an API product is the chart that navigates its evolution. It outlines clear stages and incorporates the key pillars of API work – strategy, design, documentation, and development – ensuring the API grows and evolves with purpose.With planned updates and iterations, the roadmap keeps the API adaptive to market trends and organizational shifts, thus securing its place in the future of the business.Monetization Models for API ProductsMonetization is the moment of truth for API products, where their value translates into revenue. Direct monetization models such as pay-as-you-go systems and bulk cost models allow clients to be billed periodically or purchase a set number of transactions, providing a clear pathway to profitability.Indirect monetization strategies, on the other hand, enhance business value by facilitating digital transformation and streamlining processes like client onboarding, thus embedding the API deeply into the business fabric. Some examples of indirect monetization strategies include:  Offering tiered pricing options  Providing value-added services or features  Partnering with other businesses to create integrated solutions  Offering consulting or support services  Providing training or educational resourcesA tiered pricing strategy is often the key to growth and profitability, requiring a proactive approach to meet the changing market demands.Turn Your API into a Profit Center: Monetization with Moesif and StripeTransform your API into a revenue stream with Moesif and Stripe. Moesif’s comprehensive API analytics unlock valuable insights into developer behavior. Analyze usage patterns and identify popular endpoints to define targeted pricing tiers. Integrate Stripe, the leading payment gateway, to seamlessly handle transactions. Moesif facilitates usage-based billing, allowing you to charge developers based on the number of API calls they make or specific features they access. This flexible monetization strategy ensures fair pricing for developers while generating revenue for your business. With Moesif and Stripe working together, you can unlock the full potential of your API and turn it into a profitable asset.Ensuring Security and Trust in API ProductsIn a world where data breaches are common, security and trust are the lifelines of API products. This trust is built through:  Rigorous access control measures, such as API proxies that enforce credential verification policies  Regular security audits  Penetration testing  Comprehensive logging practices that fortify the API against vulnerabilities.By adhering to the principle of least privilege and employing robust security measures like OAuth, HTTPS, and api key verification, API providers can assure users of the integrity and confidentiality of their data when accessing api resources.Engaging Developers and CustomersEngagement is the key to an API product’s adoption and success. A developer portal serves as the hub where developers can access, test, and understand your API, thereby accelerating their productivity and innovation through effective API development. This portal can also be considered as a communications facilitation platform for developers.Clear documentation, sandbox environments, code samples, and responsive customer support form the pillars of a great developer experience, ensuring that both developers and businesses can derive maximum value from the API.Analytics and Feedback LoopsAnalytics and feedback loops are the sensors that gauge the pulse of your API product, providing invaluable insights into consumption patterns and guiding improvements. By understanding the customer journey through analytics, API providers can optimize performance and make informed decisions that align with user needs and market demands.User feedback, collected through various channels, becomes the catalyst for continuous enhancement, ensuring that the API evolves in parallel with the customers’ experiences. To achieve this, it is crucial to gather user feedback effectively.Integrating API Products with Existing InfrastructureIntegration of API products with existing infrastructure is a dance of precision, ensuring that every step is in sync with the organization’s technological rhythm. It requires:  Clear naming conventions  Efficient load balancing  The ability to manage complex queries  API gateways like Tyk to facilitate the process.This integration allows APIs to become an integral part of the existing digital architecture, enhancing performance and scalability.Case Studies: API Products in ActionThe transformative power of application programming interface (API) is best illustrated through case studies of success stories like Uber, whose API integration of mapping, payment, and communication has redefined the user experience. Google Maps’ API has been utilized by apps to display real estate listings, showcasing the potential of APIs to enhance functionality and user engagement through efficient API calls.These examples, along with the likes of Spotify, Netflix, and eBay, demonstrate the immense potential of APIs in driving user experiences, fostering innovation, and generating substantial revenue.SummaryAs we reach the end of our journey, it’s clear that APIs have transcended their traditional roles to become invaluable assets for businesses. They forge the backbone of digital ecosystems, driving innovation, and growth. By embracing the API-as-a-product approach, companies can unlock new revenue streams, engage customers, and pave the way for digital transformation. The future beckons with opportunities for those who harness the true power of APIs.Organizations looking for the best tools to support their API management can leverage Moesif’s powerful API analytics and monetization capabilities. Moesif easily integrates with your favorite API management platform or API gateway through one of our easy-to-use plugins, or embed Moesif directly into your API code using one of our SDKs. To try it yourself, sign up today and start with a 14-day free trial; no credit card is required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            You’ve built the API. Now let's generate revenue!            Monetize your APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Transforming-Integration-Harnessing-the-Power-of-API-as-a-Product-for-Business-Growth/",
          "author": "Preet",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-guide": {
          "title": "API Development: The 2026 Practitioner's Guide",
          "content"	 : "If you are shipping a new API in 2026, the work is no longer just writing route handlers. You are picking a framework that handles async cleanly, designing a contract that AI agents can consume through MCP, planning a deprecation policy before the API has any users, and choosing an observability stack that attributes every call back to a customer. The decisions you make in the first week shape every quarter that follows.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        This guide walks through the seven stages of modern API development, what to choose at each stage, the mistakes that bite the most, and how to keep the lifecycle coherent once your API is in production. It is the practitioner’s version, not a textbook overview.What API development actually means in 2026API development is the process of designing, building, governing, deploying, and operating an API across its full lifecycle. The phrase covers the technical work (spec, code, tests) and the platform work (governance, security, observability, monetization) that turn a working endpoint into a product real customers integrate against.The 2026 wrinkle: APIs are no longer consumed only by human developers. AI agents now consume APIs through the Model Context Protocol (MCP). LLM applications call APIs as part of multi-step reasoning. That changes some of the foundational decisions about idempotency, versioning, and observability.The seven-stage API development workflow            #      Stage      Primary owner      Output                  1      Identify the consumer and the value      Product + Platform      One-page brief: who, why, how measured              2      Design the contract (OpenAPI-first)      Engineering + Architecture      OpenAPI spec              3      Build the implementation      Engineering      Working API server              4      Test      QA + Engineering      Unit + integration + contract tests passing              5      Govern and secure      Platform team      Approved spec, security review, rate limits              6      Deploy and publish      Platform + DevOps      Live endpoint + developer portal listing              7      Observe and iterate      Platform + Product      Live analytics, deprecation calendar      Stages overlap in practice. A v2 endpoint can be in stage 3 while v1 is in stage 7. The framework helps a team know what they own at each moment. We cover the lifecycle in detail in our API lifecycle guide; this post focuses on the development slice.Stage 1: Identify the consumer and the valueThree questions, answered before any code:  Who is the consumer? External developers integrating commercially, internal teams, AI agents through MCP, or some mix?  What outcome does this API enable? “Customers can settle invoices programmatically” beats “we expose a payments endpoint.”  What is the success metric? Integrations shipped per quarter, agent calls per month, revenue from consumption, ticket reduction. Without a metric, you cannot tell stage 7 whether the API is working.Stage 2: Design the contractModern API development is OpenAPI-first. You write the spec before any server code. The spec generates server stubs, client SDKs, and documentation by construction, which keeps the three artifacts in sync.The design decisions worth making deliberately here:  Resource model that matches the consumer’s mental model, not your database schema  Pagination, filtering, and sorting that work the same on every list endpoint  Versioning policy in writing (URI vs header; how long deprecated versions stay live)  Idempotency keys on every POST (because AI agents retry)The nine principles of good API design (see our broader design guide) cover this stage in depth.Stage 3: Build the implementationThe framework you pick handles routing, request parsing, and response shaping. You write the business logic. For new APIs in 2026:  Python: FastAPI is the default for new APIs; Django REST Framework when you are already on Django; Flask for small services.  Node.js: Express remains the default; Fastify and NestJS are common alternatives.  Go: chi or stdlib net/http for small APIs; Gin or Echo for fuller frameworks.  Java: Spring Boot still dominates.Pick the stack your team already knows. Framework choice is rarely the bottleneck.Stage 4: TestThree test layers matter:  Unit tests for individual route handlers and business logic  Integration tests that hit the API end-to-end with realistic payloads  Contract tests that verify implementation matches the OpenAPI spec (Dredd, Schemathesis)Idempotency is the 2026-specific test. If your POST endpoints will be hit by retrying agents, write a test that sends the same request twice with the same Idempotency-Key header and asserts that the second call returns the cached response.Stage 5: Govern and secureThe platform team enforces a baseline so individual API teams do not each invent their own rules. The minimums:  Naming conventions checked against the OpenAPI spec at merge time  TLS 1.2+ on every endpoint, including ones marked “internal”  OAuth 2.0 / OIDC or mTLS authentication; no homegrown schemes  Rate limits and quotas configured before the endpoint goes live  Audit logging attributable to a customer for incident investigationsAt small scale you can do this manually. At a dozen APIs and up, you need an API platform applying these policies automatically. WSO2’s API Manager handles this layer in production for enterprises, with policy checks running against the OpenAPI spec at merge time.Stage 6: Deploy and publishTwo distinct activities. Deploy moves the code to a runtime. Standard 2026 choices: a managed platform (Vercel, Railway, Render, Fly), a container runtime (AWS ECS, Cloud Run, Azure Container Apps), or Kubernetes if your team operates one. An API gateway sits in front for auth and rate limiting.Publish makes the API discoverable to consumers. For public APIs that means a developer portal with docs, self-service keys, and a changelog. For internal APIs, the equivalent platform listing. Time-to-first-call is the metric that matters here: minutes between signup and a working request.Stage 7: Observe and iterateThe question shifts from “does it work?” to “does it work for the right customers, at the right latency, with the right error rates?” Server metrics (CPU, memory) do not answer those questions. API observability does.Moesif API monitoring sits behind the gateway and provides per-endpoint, per-customer, payload-level analytics across any gateway (WSO2, Kong, AWS API Gateway, Azure APIM, Envoy). The data feeds stage 7’s iterate decisions: which endpoints to deprecate, which customers to support more closely, which integrations are stalling.Choosing your stackThe decision matrix for picking a stack in 2026:            Use case      Recommended language + framework                  New REST API for outside developers      Python / FastAPI or Node.js / Express              Internal microservice in a service mesh      Go / chi or Java / Spring Boot              Serving an ML model behind an HTTP endpoint      Python / FastAPI              API on top of an existing Django app      Python / Django REST Framework              High-throughput event ingestion      Go / Gin or Node.js / Fastify              Enterprise integration with SOAP backends      Java / Spring Boot      Beyond the framework, three pieces span every stack: the gateway (handles auth, rate limiting, routing), the developer portal (handles docs, signup, key management), and the observability layer (handles per-customer analytics). The frameworks above pair with all of them.If you are starting from scratch, our REST API tutorial walks through building an API end-to-end with Node.js and Express.API development in 2026: AI agents, MCP, and AI-assisted spec generationThree shifts changed how mature API teams develop in 2025-2026.AI agents are first-class consumers. When an LLM application calls your API as part of a chain, the consumer is a model, not a human developer. That changes idempotency expectations (agents retry aggressively), versioning expectations (agents follow Deprecation headers when they exist), and observability needs (you have to attribute traffic to specific agents or workflows, not just customers).MCP gave APIs a second interface. The Model Context Protocol exposes existing REST APIs in a shape agent runtimes can consume directly. The WSO2 AI Gateway auto-generates an MCP server from any OpenAPI spec, so the same API you publish for human developers becomes agent-consumable without a second build. The MCP and LLM proxy components sit inside the AI Gateway as the inbound and outbound paths respectively.AI-assisted design is real. Tools generate OpenAPI specs from natural language now. Apigee ships Gemini Code Assist in Apigee for spec generation; WSO2’s API hub does similar work. The discipline shifted from “write the spec by hand” to “review the AI-generated spec carefully before locking it in.”Common API development mistakesThe patterns we see most often in API platforms that are not working:Designing the data model, not the contract. When the URL structure mirrors database tables, every schema change becomes a breaking API change. Design the API surface against the consumer’s mental model, not the storage layer.Skipping the versioning policy. Picking URI versioning (/v1/orders) without writing down how long old versions stay live is how three-version APIs become eight-version APIs. State the deprecation period in the developer portal on day one.No observability from day one. Adding analytics six months after launch means you cannot answer the basic question “which customer is hitting this endpoint?” Instrument from the first deployed call.Sync APIs serving async work. If your endpoint kicks off a long-running job, return a 202 Accepted with a job ID and let the client poll, rather than holding an HTTP connection open for two minutes. Modern async patterns (job + status endpoint) handle this cleanly.Treating MCP as a separate API. Building a separate REST API for human consumers and a separate API for agents doubles your maintenance work and guarantees they drift. Generate the MCP exposure from the same OpenAPI spec.How Moesif and WSO2 cover the full API development lifecycleMost API development stories stop at stage 3 or 4. The harder problems show up later: attributing usage to customers, exposing live consumption, deprecating cleanly, monetizing the API as a product.The integrated WSO2 + Moesif stack covers all seven stages:  Stages 1-2 (identify, design): WSO2 API Manager supports OpenAPI-first design with governance policies that run against the spec.  Stages 3-4 (build, test): Your language and framework choice; WSO2 governance checks run against the spec at merge time.  Stage 5 (govern, secure): WSO2 API Manager handles multi-gateway governance (WSO2, Kong, AWS, Azure, Envoy) with automated security policy enforcement.  Stage 6 (deploy, publish): WSO2 generates the developer portal from the spec; the AI Gateway exposes the same API as an MCP server.  Stage 7 (observe, iterate): Moesif provides per-endpoint, per-customer, payload-level analytics that feed iteration and deprecation decisions.Alternative stacks exist: Postman covers stages 1-4 with a lighter platform layer (and lighter governance and analytics depth); Apigee, Kong, Azure APIM, and AWS API Gateway each cover stages 5-6 with different deployment and pricing models, and typically require additional tools for per-customer analytics and monetization. The right choice depends on which cloud your platform is on and how much of the lifecycle you want pre-built versus assembled.Next stepsAPI development in 2026 is a seven-stage discipline. The platforms that span all seven stages are what enterprises buy; the teams that instrument from day one are the ones that get to stage 7 without surprises.If you are shipping a new API and want per-endpoint, per-customer analytics in production within an hour of integrating, start a 14-day Moesif free trial. No credit card required.Frequently asked questionsWhat is API development? API development is the process of designing, building, governing, deploying, and operating an API. It covers the technical work (spec, code, tests) and the platform work (governance, security, observability, monetization) that turn a working endpoint into a product customers integrate against.How do I develop an API? Follow the seven stages above. Write an OpenAPI spec first, pick a framework that fits your team’s existing skills, test against the spec, govern security and rate limits at the gateway, deploy behind that gateway, publish to a developer portal, and instrument observability from day one.What is the difference between API design and API development? Design is stage 2 of development. It produces the OpenAPI spec that the rest of development implements against. The full development lifecycle includes design plus six other stages.What languages and frameworks are best for API development in 2026? Python (FastAPI for new APIs, Django REST Framework for existing Django apps, Flask for small services), Node.js (Express, Fastify), Go (chi, Gin), Java (Spring Boot). Pick the stack your team already knows.Do I need an API platform? At a single API, no. At a dozen APIs and up, yes, because the alternative is each team inventing its own governance and observability, which leads to inconsistency and security gaps. WSO2, Apigee, Kong, MuleSoft, and IBM API Connect are the platforms enterprises typically evaluate.How does AI change API development? AI agents are now first-class API consumers. That means idempotency keys on writes, careful Sunset and Deprecation headers, and observability that attributes traffic to specific agents. MCP exposes the same API to both humans and agents from a single spec.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /technical/api-development-guide/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-what-is-open-api": {
          "title": "What Is Open API? Definition, Examples & Best Practices",
          "content"	 : "An open API is an API that any developer can use without applying for special access. If you have ever signed up for a Stripe or Twilio account, grabbed an API key, and started making requests within an hour, you have used an open API. The phrase covers a wide range, from fully public weather APIs to commercial APIs gated behind a free tier, but they all share one trait: there is a documented public path from “outside developer” to “first successful call.”                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        This guide walks through what makes an API open, how the term relates to the OpenAPI Specification (which is not the same thing), what open APIs look like in production, and how to design and govern one in 2026.What is an open API?An open API (sometimes called a public API or external API) is an API that a service exposes to developers outside the company that built it. The contract is published, the authentication is self-service, and the endpoints behave the same way for every consumer.Three things distinguish an open API from a private one:  Public documentation. Anyone can read the spec, request/response shapes, and code examples without an NDA or sales call.  Self-service onboarding. A developer can register, get credentials, and make a first call without human intervention.  A stable contract. Breaking changes get versioned and announced in advance, because the customer base is large and unknown.Open APIs are how Stripe, Twilio, OpenAI, GitHub, and most of the modern internet expose their products to other companies. They are also how banks expose account data under Open Banking regulation, and how government datasets reach civic-tech developers.Open API vs. OpenAPI Specification: clearing the confusionThe two terms sound identical and people use them interchangeably, but they refer to different things.  Open API (two words) is a category of API, one that is publicly accessible.  OpenAPI Specification (one word, sometimes “OAS”) is a file format for describing any HTTP API in a machine-readable way. It is maintained by the OpenAPI Initiative under the Linux Foundation. A spec is usually written in YAML or JSON.You can have one without the other. A private internal API can use the OpenAPI Specification to document its endpoints. A public open API does not have to use OpenAPI; some still ship hand-written REST docs or rely on GraphQL introspection.In practice, most new open APIs do use the OpenAPI Specification, because the spec makes it easy to auto-generate docs, client SDKs, and (in 2026) MCP servers for AI-agent consumption.How an open API works in practiceMost open APIs follow the same shape from a developer’s perspective:  Sign up. Visit the provider’s developer portal, create an account, agree to the terms of service.  Get credentials. The portal issues an API key or starts an OAuth flow to issue a token.  Read the docs. Find the endpoint you need, the request shape, and the auth header pattern.  Make the first call. Usually a curl or fetch example you can copy and paste, with your key dropped in.  Move into production. Once the integration works, the developer’s traffic ramps up against the same endpoints, just with higher volume.Behind the scenes, the provider is doing significant work to keep those endpoints stable: an API gateway is enforcing auth and rate limits, a versioning policy is protecting old clients, and an observability layer is recording who called what.Open API vs. closed (private) APIThe opposite of an open API is a closed or private API: one only available to consumers inside the organization, or to a small set of named partners under contract.The main differences:                   Open API      Closed API                  Audience      Any external developer      Internal teams or named partners              Documentation      Public, indexed by search      Internal wiki or partner portal              Onboarding      Self-service (sign up, get key)      Human-mediated (account managers)              Rate limiting      Required, applied per customer      Optional, often unenforced              Versioning policy      Public deprecation calendar      Coordinated with consumers directly              Threat surface      Internet-wide      Internal network or VPN      The two often coexist inside one company. Stripe’s open API is what developers integrate with; their internal admin APIs are closed. Both can use the same gateway and observability stack.Real-world examples of open APIsA few patterns to recognize:  Payment APIs: Stripe, Adyen, Square. The dominant pricing model is revenue share.  Communication APIs: Twilio, Vonage, Plivo. Usage-based pricing, billed per call or message.  AI APIs: OpenAI, Anthropic, Google Gemini. Token-based pricing with cached-input discounts.  Developer infrastructure: GitHub API, Vercel API, Cloudflare API. Often free for low volume, paid above quota.  Open Banking APIs: Plaid, Tink, regulated bank-to-fintech APIs in the EU and UK.  Social and content platforms: Reddit, YouTube, Spotify. Mostly read-only for free tiers, write access on commercial plans.Each model uses a different pricing approach (transaction volume, token volume, revenue share, subscription tiers) but they all share the open-API onboarding pattern above.Benefits of open APIsFor the company exposing the API:  Distribution beyond your direct customers. Every developer who builds on your API becomes a distribution channel.  Faster integration with partners. Self-service onboarding compresses what used to be six-month BD cycles into one afternoon.  A market signal for what to build next. Usage patterns across thousands of integrations are the most honest product feedback you will ever get.For the developer consuming it:  Speed. Skip building from scratch when an existing service already does the job.  Specialization. Use the best provider for each capability (payments, comms, search, ML) without picking a single vertical SaaS.  A common contract. REST and OpenAPI conventions mean a developer who has used one API can usually pick up another in a few hours.Security and governance challengesThe flip side of being publicly accessible: every endpoint is reachable from the internet, and every endpoint can be attacked.The non-negotiables for any open API in 2026:  TLS 1.2 or higher on every endpoint. No exceptions, even for “internal” surfaces that briefly touch public DNS.  Per-customer authentication. API keys or OAuth tokens, with the ability to revoke a single customer without rotating credentials for everyone.  Per-customer rate limiting. Without it, one badly-behaved integration takes down the API for the rest of your customers.  Schema validation at the gateway. Reject malformed requests before they reach your business logic.  Audit logging. Every call attributed to a customer, retained long enough to support incident investigations.The observability layer that makes this possible is the one you reach for after the gateway has done its work. Moesif API monitoring gives per-endpoint, per-customer, payload-level visibility across any gateway, including the WSO2 API Manager, Kong, AWS API Gateway, Azure APIM, and Envoy. When a customer reports a problem, the path from their support ticket to the specific request that failed is one query, not a half-day of log diving.Monetizing and analyzing open API trafficOnce an open API is live and authenticated traffic is flowing, the next questions are about turning that traffic into either business outcomes (revenue, expansion, retention) or operational insight (which integrations are growing, which are stalling, which are at risk).The patterns that compound:Per-customer usage attribution is the foundation. Every call must be tagged with the customer it came from at the gateway layer, before the request hits any backend service. Without per-customer attribution, every downstream analytics and billing decision is impossible.Pricing decisions follow usage shape. Tiered subscriptions work for predictable consumers; per-call (or per-token, per-agent-invocation) usage-based pricing works for variable consumers and AI-mediated traffic. The unit of value is the call’s business outcome, not the call itself. Our API pricing guide covers the trade-offs in depth.Engagement metrics that matter. Daily/weekly/monthly active customers, time-to-first-call, time-to-100-calls, calls per customer, error rate per customer, endpoint adoption per customer. These tell you which customers are growing, which are stalling, and where the product team should invest.Anomaly detection on usage shape. Sudden spikes (10x traffic from one customer, a new IP range hitting auth endpoints, an unusual error pattern) need to be visible without waiting for an incident. Open APIs are public surfaces, so the abuse vectors are real.Cost attribution for downstream services. If your open API calls third-party services (LLM providers, payment processors, identity services), those costs need to be attributable back to the customer that drove them. Otherwise the unit economics get opaque.The tool that integrates with every major gateway and covers these patterns out of the box is the API-specific observability and monetization layer (Moesif in this stack). It instruments the WSO2 API Manager, Kong, AWS API Gateway, Azure APIM, and Envoy through middleware, and presents the data in views built around customer behavior rather than infrastructure health.How to design and manage an open API in 2026The short version of our nine API design principles, adapted for open APIs specifically:  OpenAPI-first. Write the spec, generate the server stub and docs from it. Keeps documentation honest by construction.  Resource-oriented URLs. Plural nouns for collections, predictable nesting, no /createUser endpoints.  Versioning policy in writing. State, on your public site, how much notice you give before removing a version. Twelve months is the standard for paid open APIs.  Idempotency keys on writes. Public APIs get retried by clients you cannot audit. Make retries safe.  Generous error responses. Return an error code, a human-readable message, and the field that failed validation, on a 4xx status. The developer trying to integrate is your customer.  Pagination, filtering, and sorting that work the same way on every list endpoint.  A changelog page that is actually maintained.  Per-customer observability from day one. If you cannot answer “which customer is currently hitting /v1/charges?” your support team will burn out by month six.At enterprise scale, the design-to-runtime governance question is what platforms like the WSO2 API Platform solve. WSO2 API Manager enforces governance policies against the OpenAPI spec automatically (naming rules, required security minimums, deprecation policies), and the WSO2 AI Gateway turns those same APIs into MCP-compatible servers for AI-agent consumers without a separate build.Where to take this nextAn open API is more than a documentation page. It is a contract you keep with developers who chose your service over your competitors, and it lives or dies on the design, security, and observability behind it.Start a 14-day Moesif free trial to see per-customer usage on your open API within an hour. No credit card required.Frequently asked questionsIs the OpenAPI Specification the same as Swagger? They share history. Swagger was the original name of the specification before it was donated to the Linux Foundation and renamed the OpenAPI Specification in 2016. Swagger now refers to the set of tooling (Swagger UI, Swagger Editor, Swagger Codegen) maintained by SmartBear that consumes OpenAPI documents. The spec itself is OpenAPI.Are open APIs free? Some are; most commercial open APIs are free for low volume and paid above a quota. “Open” refers to accessibility, not price. An open API can charge.Do open APIs need authentication? Almost always. A small set of fully public APIs (like some government datasets) do not require credentials, but any open API that performs writes, accesses customer data, or has a cost to serve will require an API key or OAuth token.What is the difference between REST API and open API? REST is an architectural style for designing APIs; open describes the accessibility of the API. A REST API can be open or closed; an open API can be REST, GraphQL, or RPC.Can you monetize an open API? Yes, and most successful public APIs do. The dominant models are pay-as-you-go (Twilio, OpenAI), tiered subscription (most SaaS APIs), and revenue share (Stripe). Choosing among them is its own discipline.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /technical/what-is-open-api/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-moesif-kong-stripe-ai-api-part-3": {
          "title": "Using Moesif, Kong, and Stripe to Monetize Your AI APIs - Part 3: Managing Customer Credit",
          "content"	 : "  This is the third part of a three-part series about AI API monetization. You can check out the first part of the series here and the second part here.Things can get tricky when managing pre-paid, pay-as-you-go billing for monetized APIs. Three mechanisms must be in place for this type of billing to work: first, you need to be able to add credits to an account. Second, you need to be able to burn down those credits. Third, you need to be able to block users from accessing the API once they have run out of credits.The first part is relatively easy since most billing providers allow you to create payments that can be added to a user’s account as a pre-paid credit. The second part is covered by mechanisms such as a Moesif Billing Meter, which we discussed in the last post. The third part is where things get increasingly more complex. This is because Billing Providers, where the customer’s balance and usage are calculated, usually have a rate limit for reporting usage. This means that real-time balances are much harder to achieve since it would require usage to be fed to the billing provider in real-time; the balance burned down and then reported back to the system that controls API access to decide whether the user should have continued access to the API.Up until recently, this was not possible. Luckily, we at Moesif have created a way to achieve near real-time pre-paid API access using our latest Pre-Paid Credits and Balance features. This set of features allows Moesif to be the book of record for the user’s balance, so no round-trip to the billing provider is needed, and rate limits put on usage APIs are no longer a concern. The result: as usage is metered in Moesif, it can then be burned down against the user’s credits to achieve a real-time balance. The governance rule we created in Part 2 of this blog series will work in near real-time, blocking users when they run out of credits.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free          For more info on pre-paid credit features within Moesif, check out Moesif’s Credit Tracking and Consumption docs.In the next part of this tutorial series, we will examine how to charge for API credits in Stripe and then add those credits to Moesif. We will begin by looking at adding a payment to Stripe.Create a Payment In StripeSince we are implementing pre-paid API access, we must first charge the customer for a certain amount of credits. We will do this by manually creating a payment in Stripe. Of course, you can also make a top-up mechanism in your code or UI. Everything we will do here is also available through the Stripe APIs. For this guide, we will focus on manually adding the balance top-up.  This part of the guide assumes you already have a customer in Stripe and Moesif. If you do not, you can revisit this section once a customer is added to both platforms. Adding customers will be covered in the following guide, Part 4.As a first part of our flow to add credits, we will need to add some money into our Stripe account to be consumed when API calls are made. This can be done by:  Logging into Stripe and going to the Customers screen.  Select the customer you’d like to add the top-up to.  Click Create Payment in the top left of the screen.          On the Create a new payment modal, select USD as your currency (or whatever currency you’d like to use), add the amount, and choose the Payment method.      Click Create Payment.      Once complete, the payment will be processed, and the amount will be added to the customer’s Stripe account. You should see the entry under Payments on the customer’s profile if the payment has successfully been completed.Add a Credit to MoesifOnce the amount is added to Stripe, we can create the corresponding entry in the Moesif ledger so that Moesif can control access to the API in real time.To add the amount to Moesif, you must go to the customer’s Company profile. This can be done by going to the Companies page in Moesif and finding the company in the Lookup screen. Once found, click on the customer’s Company ID.To add the credits to the company in Moesif, do the following:  On the Company Profile screen, click the pencil icon beside Current Balance.  In the modal that appears, ensure the Type is set to Credit and add “$10.00” to the Amount field.          If you have multiple subscriptions for the customer, ensure that the Subscription Id field value matches the subscription to which you want to apply the credits.        Once the form is completed, click Add Transaction.Based on the steps above, the Add Transaction screen will look like this before clicking the Add Transaction button.You should see the balance added to the company’s account within a few minutes. The result from our above transaction will now appear in the company’s profile, showing that the company has $10.00 in its Current and Available Balance.Each value represents a few aspects of the company/user’s balance. Let’s examine each a little closer.Current BalanceThis value is the current balance without factoring in any pending activity/transactions that have not been processed yet.Pending ActivityThis is the activity/transactions (credits or debits) that have not yet been posted. This value is mutable and can change until the transaction is posted. Once posted, this will be reflected in the updated Current Balance field and removed from the amount under Pending Activity.For example, if API usage or credits have been added to an account, you will see the corresponding amount here until it has been posted.Available BalanceFrom a usage perspective, this amount is the most important as it dictates what credits a user has left to spend in a pre-paid scenario. This value is the amount of credits/$ left for the user to consume. This amount equals (Current Balance + (plus) Pending Activity).For example, if my current balance is $1000 and I have a $250 debit transaction pending, my available balance will be $750.Now, manually adding the balance in Moesif is one way to do things; however, wouldn’t it be nice to have a top-up in Stripe automatically applied to the balance in Moesif? Let’s take a look at how this (optional) step can be done using Stripe’s APIs and Webhook.Optional: Automating Account Top-up In MoesifPre-paid credits can be added to Moesif via Moesif’s Credit Consumption API. Because of this, there are three potential ways that users can intercept a payment event in Stripe and apply it to the Moesif balance. We’ve created a Github repo that contains all the code you will need to run any of these variants. These include a webhook, API, and combined approach. Let’s look at each approach a bit closer:Webhook-only AppWith this approach, whenever a user’s balance changes in Stripe, a corresponding debit or credit will be added to the Moesif balance. Using the Stripe webhook, when a change is sent, the record will be created and sent to Moesif via the Moesif Credit Consumption API.This approach works well for applications that already have an existing top-up flow built and allows for minimal changes. This automated approach enables both balances to stay in sync between the two systems.The webhook application in this Github example (webhook-app.js) will receive a message from Stripe when a user has added funds to their Stripe account. To start this example, clone the Github repo, populate the .env file, and run:node webhook-app.jsFor this application to work, you must deploy the webhook somewhere (for example, for testing, you could use ngrok or deploy on your existing API infrastructure). It needs to be publicly accessible. Once deployed, add the webhook to Stripe with the following steps:  In Stripe, go to the Developers menu.  Then go to Webhooks > Add endpoints.  On the next screen, you’ll add your webhook endpoint URL in the Endpoint URL field.  Then, you’ll also need to click the Select events button, selecting the following events for the webhook:          payment_intent.succeeded      Optionally add the events you want to subscribe to without using Payment Intents.        Once configured, click Add endpoint.Once the endpoint has been added to Stripe, test it by going through your top-up logic and ensure that the webhook fires and successfully creates the credit/debit entry in Moesif (you will be able to see the debugger output in the console/logs where your webhook is running as well).API-only AppIf you are building or have an existing top-up flow that leverages an API, instead of having the webhook execute the logic to do the top-up in Moesif, you can make a direct API call to Moesif’s Credit Consumption endpoint as the payment intent is created. In this example, there is an API app that allows users to call a /top-up endpoint with their Stripe customer ID and the amount they would like to credit to their account.In this approach, the endpoint creates a Stripe PaymentIntent that adds the amount to the user’s Stripe account. At the same time, this endpoint creates a corresponding transaction on the Moesif side, adding the amount to the available balance in Moesif.The endpoint can be deployed independently or on your existing API infrastructure; however, this endpoint requires users to have a credit card on file and will create a Stripe Payment Intent for the amount they have requested to be charged against that card. To run the API, clone the Github repo, populate the .env file, and run the following command:node webhook-api-app.jsWebhook + API AppFor users who want to use the best of both solutions, this example allows users to create an endpoint to initiate the top-up transaction and then use the webhook to apply that credit on the Moesif side once the PaymentIntent has succeeded on the Stripe side.The webhook and API will require the same configuration as the initial webhook and API examples above. Currently, in this solution, the /top-up REST API endpoint and the webhook run in the same node project. However, both can be split apart as needed.The node app will need to be hosted publicly, but to test functionality locally, clone the Github repo, populate the .env file, and run the following command:node webhook-api-app.jsUsing any of these three approaches, you can have it so that top-up payments processed through Stripe are automatically applied to the balance in Moesif. This is much more scalable and automated than manually adding these transactions by hand in the Moesif UI.ConclusionWith that, we’ve successfully added a balance to customers in both Stripe and Moesif. This will help to enable real-time PAYG billing and leverage the infrastructure we created in Part 2 of this series, giving us a complete pre-paid system that will allow usage to be metered, burned down, and access to the API’s controlled based on them having an available balance of credits to use.Next, and lastly, we will focus on onboarding customers. We will cover how to register customers in Moesif, Stripe, and Kong so that they can use the APIs, giving us a complete end-to-end API monetization setup to start driving revenue from our AI APIs (or any API, for that matter!).                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue from your APIs?            Monetize your AI APIs in minutes with Moesif, Stripe, and Kong.            Try it out!            No credit card required            ",
          "url": " /technical/api-development/Moesif-Kong-Stripe-AI-API-Part-3/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-moesif-kong-stripe-ai-api-part-2": {
          "title": "Using Moesif, Kong, and Stripe to Monetize Your AI APIs - Part 2: Setting Up Metering and API Access",
          "content"	 : "  This is the second part of a four-part series about AI API monetization. You can check out the first part of the series here.Now that we have set up our API, integrated it with Kong, and connected Stripe with Moesif, we have the infrastructure to begin billing for API usage. Now, we will move into configuring Moesif with the pricing, metering, and access control/governance pieces of the API monetization journey. Let’s kick things off by setting up the prices we want to charge for API usage in Moesif.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Create Your API Pricing in MoesifIn Moesif, we can configure the plans and prices for our APIs using the Moesif Product Catalog. This feature allows you to configure your plans and prices conveniently in Moesif, and Moesif will automatically create them in Stripe. This enables you to do everything within the Moesif platform instead of bouncing between the two.For our AI API example, we will want to create two distinct prices for our AI API Plan. Of course, if you have multiple plans/tiers, you can duplicate what we do here for each plan you’d like to create. To simplify things, we will create a single plan with two prices, one for each token type: input and output.To create our plan, in Moesif, you will do the following:  Click on Product Catalog in the left-side menu in Moesif  On the Plans screen that appears, click Create New in the top right corner.  On the Create New Plan screen, add the following details:          Add “My AI API Plan” to the Name field      Select Stripe in the Billing Provider dropdown.        Once the plan is populated, click Create in the top right corner.Before you create your plan, this is how the form should look:With our Plan created, we need to move forward with developing our prices for input and output tokens. After you have clicked Save on the Plan screen, a modal will appear showing that the Plan was successfully created and prompting you to Create a Price. Alternatively, you can click the Prices menu item on the left-hand menu under Product Catalog.Once you are on the Create Price screen, do the following:  Populate the Price Name field with “Input Tokens”.  If not already selected, under Linked Plan, set it to the plan we created in the last step.  Under the Pricing Model, ensure the value is “Per Unit (Package)”.          If you have another pricing model you’d like to use, you can select it from the dropdown menu and configure it as required for your use case.        In Usage Measurement Method, select Stripe Price Meter. Then select from an existing Stripe meter or create a new one. Select Month as the period of time for aggregating usage and Sum as the aggregation method.  In Price Structure, set the values to “$0.50 per 1000000 (One Million) unit(s) (rounded up)”.  Keep Tax Structure set to Auto.  Click Create in the top right corner of the screen to create the price.Here is what the form should look like before you save the price:  Once you save, a workflow modal will appear to create a Billing Meter or Governance Rule. We will skip this for now and create these components later. For now, exit out of the modal.Next, we will create another price for output tokens. While still on the previous screen for the input tokens we just created, click Prices in the left-side menu and then click the Create New button at the top of the Prices Main screen. This will return us to the same screen where we created the input token price.Once you are on the Create Price screen again, do the following:  Populate the Price Name field with “Input Tokens”.  If not already selected, under Linked Plan, set it to the plan we created in the last step.  Under the Pricing Model, ensure the value is “Per Unit (Package)”.          If you have another pricing model you’d like to use, you can select it from the dropdown menu and configure it as required for your use case.        In Usage Measurement Method, select Stripe Price Meter. Then select from an existing Stripe meter or create a new one. Select Month as the period of time for aggregating usage and Sum as the aggregation method.  In Price Structure, set the values to “$1.50 per 1000000 (One Million) unit(s) (rounded up)”.  Keep Tax Structure set to Auto.  Click Create in the top right corner of the screen to create the price.Once the plan and two prices have been created for our input and output tokens, we then need to begin to meter API usage so that it can be reported to Stripe for each of the created plans.Metering API Usage With MoesifThe way to meter API usage through Moesif is to use a Billing Meter. The Billing Meter will define three things:  What plan and price the API usage should be reported to/billed against  What traffic should be metered (routes, response status, etc.)  How the traffic should be metered (per API call, unique user, etc.)To create the first billing meter for input tokens, we will do the following:  Click the New button in the top left of the screen in Moesif and select Billing Meter.  On the Create New Meter screen, configure the meter with the following:          Add “Input Token Meter” as the meter name.      In the Billing Provider dropdown, select “Stripe”.      Once the products and plans are populated from Stripe, select the Product and Plan created in the previous step. If you’re following along, the product will be named “My AI API Plan”, and the price will be “Input Tokens”.      The reporting Period dropdown can be left as “Every 5 Minutes”      Under Filters, we will add the following criteria:                  request.URI Route is /ai/chat          response.Status Code = 200 OK                    Under Metrics, select “Custom Metric” from the dropdown.                  In the field selector in the dropdown to the right, select “response.body.usage.prompt_tokens” and set the aggregation as “sum (Unweighted)”.                    Click the Create button to create the meter.        Optionally, you can test the meter by clicking Test Meter on the workflow modal that pops up after it is created or clicking Test Meter beside the meter name input.Before you create the meter, here is an example of what the Create Meter screen will look like:Next, we will create the meter for the output tokens in the same way. To generate this meter, do the following:  As with the previous meter, click the New button at the top left of the screen in Moesif and select Billing Meter.  On the Create New Meter screen, configure the meter with the following:          Add “Output Token Meter” as the meter name.      In the Billing Provider dropdown, select “Stripe”.      Once the products and plans are populated from Stripe, select the Product and Plan created in the previous step. If you’re following along, the product will be named “My AI API Plan”, and the price will be “Output Tokens”.      The reporting Period dropdown can be left as “Every 5 Minutes”      Under Filters, we will add the following criteria:                  request.URI Route is /ai/chat          response.Status Code = 200 OK                    Under Metrics, select “Custom Metric” from the dropdown.                  In the field selector in the dropdown to the right, select “response.body.usage.completion_tokens” and set the aggregation as “sum (Unweighted)”.                    Click the Create button to create the meter.        Optionally, you can test the meter by clicking Test Meter on the workflow modal that pops up after it is created or clicking Test Meter beside the meter name input.For more information on how to test your meter to make sure that it is working as intended, check out our meter testing docs.At this point, the billing meters we have created will meter usage based on the criteria we set for each API call. Then, they will sync this usage to Stripe so that the user can be accurately charged. Next, we need to put some guardrails on access, blocking users from accessing the API when they are out of pre-paid credits or have an overdue invoice, like when they are post-paid users.Enforcing Pre-Paid Access to the API With Moesif Governance Rules and KongTo block our API users from accessing the API when they have run out of credits, we will use a Moesif Governance Rule. In Moesif, we can use plenty of templates to create a rule, including one where the user is blocked upon a zero-credit balance. To make this Governance Rule in Moesif, you’ll need to do the following:  In Moesif, navigate to the Quotas & Governance screen by clicking on the corresponding menu item on the left-side menu.  On the Quotas & Governance screen, click the Add New button on the top-right of the screen.  Select the template for Block When No Available Credits in the modal that appears.          This will then prompt you to create a cohort for the governance rule automatically. In this next modal, click Continue.      This will then bring you to the configuration screen for the rule you just created. At this point, the rule is active and will enforce that users should have credits to access any endpoint.We will dial in the rule to only apply to the /ai/chat route. The rule currently states that every route will be blocked if the user doesn’t have credits. If we only have a single route, this won’t be a problem; however, if we have multiple routes, we may want to ensure that this only applies to specific routes. To make this rule more specific, we will need to change it slightly. To do this, we will need to do the following:  On the governance rule screen, under the Block What section, expand it so that you can add some input.  Next, under regex criteria, select Request Route from the first dropdown.  In the regex input textbox, add “/ai/chat”.  In the top-right corner, click Save to save the updated regex criteria.Based on the above configuration, the governance rule input screen will look like this:After this, the governance rule will block any users without credit trying to access the /ai/chat route. If users are subscribed to a post-paid subscription and you’d like to block them from accessing the API if they have overdue invoices, this will require a slightly different approach. Let’s look at how to implement that next.Blocking API Access For Users With Overdue InvoicesTo block our API users from accessing the API when they have an overdue invoice, we can also use a Moesif Governance Rule. Once again, we can use a built-in templated rule for this scenario to have the rule up and running quickly. To create this Governance Rule in Moesif, you’ll need to do the following:  In Moesif, navigate to the Quotas & Governance screen by clicking on the corresponding menu item on the left-side menu.  On the Quotas & Governance screen, click the Add New button on the top-right of the screen.  Select the template for Block On Unpaid Invoices in the modal that appears.          This will then prompt you to create a cohort for the governance rule automatically. In this next modal, click Continue.      This will then bring you to the configuration screen for the rule you just created. At this point, the rule is active and will enforce the rule that users with overdue invoices (as defined by the various billing providers) should not have access to the API.We will dial in the rule to only apply to the /ai/chat route. The current rule is similar to the one we created previously: if the user is delinquent on payment, it will block every route. If we only have a single route, this won’t be a problem; however, if we have multiple routes, we may want to ensure that this only applies to specific routes. To make this rule more specific, we will need to change it slightly. To do this, we will need to do the following:  On the governance rule screen, under the Block What section, expand it so that you can add some input.  Next, under regex criteria, select Request Route from the first dropdown.  In the regex input textbox, add “/ai/chat”.  In the top-right corner, click Save to save the updated regex criteria.Based on the above configuration, the governance rule input screen will look like this:After this, the governance rule will block any users with an overdue invoice from accessing the /ai/chat route. Again, this rule only makes sense to implement if you are using a post-paid billing model. For pre-paid, the previous rule will be all you’d want to implement. In the case that users can use either post-paid or pre-paid, you could leave both rules enforced.Governance Rules can also be used to enforce pre-paid quotas or keep post-paid subscribers within the ranges they have selected, stopping them from bumping to the following usage tier. More details on how to use governance rules to enforce tier-based billing can be explored here.ConclusionIn this second tutorial, we went over how to set up pricing for your APIs, how to set up API metering with a Billing Meter, and how to set up access rules using Governance Rules. With this, we have everything in place to ensure that user can be billed for their usage and blocked when they run out of credits or have overdue invoices. At this point, our API and billing infrastructure are ready to be released for the world to use. In the following tutorial in this series, we will cover how to add credits to a user’s account so that they will be able to use the API and burn through credits.Want to try this for yourself? Sign up for Moesif today and enjoy a free trial to help you get your API monetized quickly and efficiently!Next StepsReview adding credit balances and testing in Part 3 of this three part series on monetizing AI APIs with Kong, Moesif, and Stripe.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue from your APIs?            Monetize your AI APIs in minutes with Moesif, Stripe, and Kong.            Try it out!            No credit card required            ",
          "url": " /technical/api-development/Moesif-Kong-Stripe-AI-API-Part-2/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-pre-paid-credit-based-billing-with-stripe": {
          "title": "How to Set Up Pre-paid Credit-Based Billing With Stripe",
          "content"	 : "In today’s subscription-driven economy, flexible billing options are crucial. While traditional post-paid models are widespread, pre-paid credit-based billing is gaining popularity. This approach empowers businesses to bill customers upfront for a set amount of usage, offering an alternative way for customers to pay for your service.Stripe, one of the largest payment processing platforms, can implement pre-paid billing models even though the setup isn’t directly built into its core features. This blog will guide you through everything you need to implement pre-paid credit-based billing using Stripe. With a few simple steps, you can begin to use Stripe to implement pre-paid billing models and governance to make sure that when users run out of credits, they are no longer able to access the service. Let’s begin by quickly looking at what Stripe is.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        What is Stripe?Chances are that if you’re trying to monetize a product or have purchased something recently, you’ve heard of Stripe. The platform is a comprehensive suite of online payment processing tools and APIs, allowing businesses to monetize their products easily. At its core, Stripe offers a robust infrastructure that securely helps businesses accept and manage payments across various channels. For the more technical crowd, developers love Stripe for its ease of integration, well-structured and highly-respected documentation, and a wide range of features that include:  Payment Gateways: Facilitating the secure processing of credit/debit card transactions directly on websites and within applications.  Recurring Billing: Automating subscription-based payments at various intervals (weekly, monthly, annually, etc.).  Fraud Prevention: Advanced tools and machine learning models to mitigate fraudulent activity.  Developer-Friendly: Extensive libraries and SDKs for multiple programming languages, simplifying integration efforts.While Stripe provides an excellent foundation for payment processing, setting up a pre-paid credit-based billing system requires additional steps. Getting pre-paid credit-based billing up and running involves integrations with other tools to track usage and manage customer balances. Even though this may sound complicated, it can be done relatively quickly using Moesif and Stripe.What is Pre-Paid Credit-Based Billing?In a pre-paid credit-based billing system, customers purchase a predetermined amount of usage in advance. These credits could represent anything from API calls, text messages sent, minutes of video streaming, or any quantifiable unit of consumption relevant to your business.For instance, imagine an audio streaming platform where users can listen to audiobooks and pay by the minute. A user may pre-pay for 1000 minutes. As the user listens, these credits are “burned down” until they have no credits remaining. At that point, their service access will be cut off until they add more credits to their account, known as “topping up”.For an even more straightforward example, we can go back to the days of pre-paid, pay-as-you-go phones. You bought a top-up card to add minutes to your account. When those minutes ran out, your service was disrupted, and you had to top up to gain access again. This is pre-paid credit-based billing in a nutshell.To summarize the above concepts, a user’s pre-paid credit balance gradually decreases as the customer utilizes your service.  Here’s how the process/functions break down:  Loading Credits: Customers “top up” their account with a payment covering their desired usage level.  Usage Tracking: The system meticulously monitors resource consumption, deducting credits accordingly.  Access Control: Usage can be restricted or blocked once a customer’s credit balance is depleted.  Invoicing and Reconciliation: While payment happens upfront, invoices detailing usage and the corresponding credit deductions are generated.Pre-paid vs. Post-paid: Key differencesThe fundamental distinction between pre-paid and post-paid billing is when payment occurs relative to service usage. Let’s take a look at how things compared between the two billing models:Post-paid: This is the conventional billing method. Customers are charged after consuming a service, often at the end of a billing cycle (e.g., monthly). This model focuses on measuring usage first and billing later.Pre-paid: Customers pay in advance, purchasing credits that are then consumed as they use the service. This shifts the focus to upfront payment and controlled service consumption based on available credit.There are a few key differences between the two models. First, non-payment risk for pre-paid models is reduced since customers have already paid for their usage. Second, pre-paid offers make revenue predictability more straightforward since payment is received upfront. Lastly, usage control is more straightforward to enforce within a pre-paid model since service access can be tied directly to the availability of credits.Does Stripe Support Pre-Paid Credits and Pre-Paid Plans?While Stripe provides a robust foundation for various payment scenarios, it doesn’t offer out-of-the-box solutions for pre-paid credit-based billing. Stripe’s core strengths lie in managing recurring subscriptions and post-paid billing models, where charges are calculated after the customer uses the service. However, Stripe can easily handle pre-paid billing requirements with additional tools.To get pre-paid billing working with Stripe, you’ll need additional mechanisms to address specific requirements for implementing a pre-paid credit system with Stripe. Firstly, you’ll need a way to track and manage customer credit balances, ensuring accurate decrements as usage occurs. Secondly, you’ll require a system for metering usage precisely,  measuring things like API calls, data transfer, or any other quantifiable unit relevant to your product.  Finally,  you’ll need logic to control access to your service, potentially blocking usage when a customer’s credits are depleted. So, although Stripe does not support pre-paid billing out of the box, it doesn’t mean that it can’t be supported; it just requires additional tools and configuration.When Should I Use Pre-Paid Credits?The debate over whether to use pre-paid or post-paid billing models is always a great one. Some businesses offer one or the other, while others may allow you to use either option depending on what works best for you as a customer. That being said, pre-paid credit-based billing is a better option in several specific use cases. Let’s examine three key factors in why and when pre-paid billing models make sense.Preventing AbuseIf your service is susceptible to abuse or potential overuse that could lead to unexpectedly high bills (e.g., an AI service with variable costs), pre-paid credits help you mitigate risk. Users are restricted by their purchased credits, minimizing the potential for overages.Getting Paid UpfrontPre-paid models ensure payment is received before the bulk of service usage occurs. This can be particularly advantageous for businesses with high upfront vendor costs related to service delivery (e.g., telecoms paying significant fees for SMS delivery).Inconsistent Usage PatternsPre-paid credits offer flexibility if your product experiences unpredictable usage spikes (like sending large volumes of SMS messages in bursts). Users can pre-purchase a buffer of credits to accommodate fluctuations without committing to a rigid subscription tier that may be underutilized in low-usage periods.Of course, there are many other advantages that may factor into your decision to implement a pre-paid option for customers. Choosing one approach over another is a balance of business risk, revenue needs, and many other factors specific to your use case.How Pre-paid Credits Work In StripeWhile Stripe doesn’t have a single button or mechanism to activate “pre-paid billing,” its flexibility allows us to assemble a pre-paid system with Stripe as the payment processor and subscription management platform. Here’s a conceptual outline of the core elements involved in implementing pre-paid billing with Stripe.Pay UpfrontCustomers purchase credits in advance using Stripe’s standard payment processing features. This could be done through a custom portal where users can do a checkout to add credits to their account, an auto top-up process, or even having your team manually process payments through the Stripe UI. Somehow the user gets charged for their pre-paid usage, and Stripe will process that payment.Burn DownAs customers use your service, you’ll have a separate system meticulously tracking usage events. This system calculates the corresponding cost of credits and decrements the customer’s balance accordingly. In the case of our example below, we will use Moesif to do this since it allows us to quickly set up these mechanisms through it’s direct integration with Stripe.Monthly Invoice (with credit adjustments)Stripe can still generate invoices at regular intervals (e.g., monthly). However, instead of charging the customer directly, the invoice will primarily reflect their usage for the period and the deduction of credits from their balance. For instance, if a user has pre-paid for $100 in credits and has had $40 worth of usage within the current billing period, the Stripe invoice will show a charge for $40 in usage and a corresponding $40 credit adjustment applied to the invoice. This will leave the customer with $60 in credits for any following billing periods.It’s important to note that the success of this model hinges on integrating Stripe with a system that can reliably track and manage credit balances, like Moesif. Without such a system, pre-paid billing with Stripe from a credit-based perspective is not possible. To show you how this works in action, let’s look at how we can implement this with Moesif.Setting Up Pre-paid Billing in Stripe and MoesifTo follow along with the example below, you will be required to have the following:  An active Stripe account  An active Moesif accountThese two will allow you to implement all of the mechanisms mentioned below for pre-paid billing and credit management using Stripe.Integrate Stripe With MoesifFirst, we must integrate Stripe with Moesif. This can be done by adding your Stripe API key into Moesif and adding the Moesif-Stripe webhook into Stripe. This facilitates two-way communication between the two platforms, where usage can be sent over to Stripe from Moesif and data, such as subscription status updates, can be passed over to Moesif to keep both systems in sync.To achieve this connectivity, check out the following step-by-step guide.Creating Your Plans and Prices  Go to Product Catalog:          Log in to your account and navigate to the “Product Catalog” page.        Click Create New:          On the Plans page, locate the “Create New” button and click on it.        Fill in details:          Enter the required details for your plan, such as the plan name, billing provider, a unit label,description, and any additional metadata you would like to include..        Click Create:          Once you have filled in all the required details, review your entries for accuracy and click on the “Create” button to save the plan.      Next, you need to establish pricing for your plan. After successfully creating the plan, you can set a price either directly from the workflow modal by clicking on the “Create Price” button or by navigating to the Prices page. To get to this page, go to the left-side menu and select “Product Catalog,” then choose “Prices.” Here, you will find the option to click “Create New.”On the “Create New Price” page, you’ll be asked to provide the specifics of the pricing structure. Make sure to link the price to the plan you just created by selecting it under the “Linked Plan” section.For instance, let’s create a price setup where you charge $0.01 per unit. In this context, the unit will be each API call. This means every time an API call is made, a charge of one cent will be applied.Once the price screen is dialed in as you need, click Create in the top right of the screen.Create a Billing MeterNext, we will create a Billing Meter that will meter API usage as it rolls into Moesif. This will add up the API calls that are being consumed and will report the usage to Stripe as well as burn down the pre-paid balance within Moesif.To create the Billing Meter, you can click on the Create a Billing Meter item in the workflow modal shown after the price is created or go to the Billing Meters screen and click Add Billing Meter in the top right.On the Add Billing Meter screen, we will do the following:  Add a name for the meter in the Name field  Under Billing Provider, we will select Stripe and choose the Plan and Price we just created.  Under the Filter, we will dial which events we want to cover with this meter.  Under Metrics, we will dial in how we want the usage to be metered. Per API call, per unique user, etc.Here is an example of a Billing Meter that uses the created plan and price and filters on a specific route and status code. The metric in meters is Event Count, which means that each API call will count as an event and be charged at the $0.01 rate we put in our created price.Once this is filled out as you’d like, click Create to create the meter and activate it.Create a Governance Rule to Block Users Without CreditsNext, we will implement a Governance Rule that will block users from accessing the metered API when they run out of credits. To do this, we will click the New button in the top-left corner of the screen and in the modal that appears, select Gov Rule/Quota.On the modal that appears, we will choose Start From Template -> Block Zero Prepaid Balance.This will then create a new cohort called Subscriptions with zero prepaid balance. On the creation modal that appears, click Continue. This will create and activate the governance rule that will block users who have run out of credits.  Support for Governance Rules varies across Moesif’s SDKs and plugins. Before using a method, please verify that it supports governance rules.Create a Payment In StripeNext, we will need to add some money into our Stripe account in order to be consumed when API calls are made. This can be done by:  Logging into Stripe and going to the Customers screen  Select the customer you’d like to add the top-up to  Click Create Payment in the top left of the screen          On the Create a new payment modal, select USD as your currency, add in the amount, and choose the Payment method.      Click Create Payment      Once complete, the payment will be processed and the amount will be added to the customers account in Stripe. If the payment has successfully been completed, you should see the entry under Payments on the customer’s profile.Add a Credit to MoesifOnce the amount is added in Stripe, we can then create the corresponding entry in the Moesif ledger so that Moesif can control access to the API in real-time.To add the amount to Moesif, you will need to go to the customer’s Company profile. This can be done by going to the Companies page in Moesif and finding the company in the Lookup screen. Once found, click on the customer’s Company ID.To add the credits to Moesif, do the following:  On the Company Profile screen, click on the pencil icon beside Current Balance.  In the modal that appears, make sure the Type is set to Credit and then add “$10.00” into the Amount field.          If you have multiple subscriptions for the customer, make sure that the Subscription Id field value matches the subscription you want to apply the credits towards.        Once the form is completed, click Add Transaction.Within a few minutes, you should see the balance added to the company’s account.Now, metered API calls immediately deduct from our pre-paid balance within Moesif and are periodically synced to Stripe. Access is automatically restricted through Governance rules for users without a balance.ConclusionThis guide offered a comprehensive overview of how we’ve set up pre-paid billing with Stripe using Moesif. It included instructions on integrating Stripe with Moesif, creating plans and prices, establishing a billing meter, and implementing a governance rule to restrict access for users without credits. We’ve supplemented the guide with detailed steps and examples to facilitate the setup process, making it an ideal resource for businesses looking to implement a pre-paid billing model with Stripe.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Need pre-paid capabilities with Stripe?            Monetize your APIs in minutes with Moesif and Stripe, including pre-paid options!            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Pre-paid-Credit-Based-Billing-With-Stripe/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-moesif-kong-stripe-ai-api-part-1": {
          "title": "Using Moesif, Kong, and Stripe to Monetize Your AI APIs - Part 1: Integrating The Platforms",
          "content"	 : "  This is the first part of a four-part series about AI API monetization.As the wave of AI sweeps through the technology landscape, many have hopped on board. Interestingly enough, and often overlooked, is that many AI capabilities are served through APIs. Fancy user interfaces integrate with the actual mechanisms where the magic happens: the APIs. So, when generating revenue through AI platforms, the APIs drive the revenue.This leads to the challenge of controlling access to the APIs, metering API usage, and charging customers for said usage. The overarching term for this is API monetization, but unlike simpler forms of API monetization that only charge based on API calls, AI platforms generally tend to charge on things such as “tokens used” and other AI-specific metrics that are metered upon. Luckily, platforms exist that can expedite this process and make it simple to implement. We will focus on Moesif, Kong, and Stripe to implement API monetization for AI APIs.We will use each platform to fulfill a specific role in the setup. Kong will be used to control the access to our APIs and be used for its API management capabilities. Then, we will integrate Moesif with Kong to add metering capabilities, allowing us to send usage over to Stripe, where payment can be collected, or a balance can be burned down based on usage.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        The AI APIIn almost all cases, you will have your AI service exposed through an API or leverage existing AI services that will be the backbone of your API functionality. Regardless, we will use OpenAI’s /chat endpoint in our example to demonstrate how to monetize an AI API. This will give us an accurate depiction of an AI API. Many companies use OpenAI-compatible API Specifications to allow for drop-in replacement for OpenAI APIs.In our example, we will create a single endpoint called /ai/chat that will be very similar to OpenAI’s chat endpoint. Well, exactly like it, since it is leveraging that endpoint under the hood! The request and response for the API can be seen below:POST Request Payload{  "model": "gpt-3.5-turbo",  "messages": [    {      "role": "system",      "content": "You are a helpful assistant."    },    {      "role": "user",      "content": "Hello!"    }  ]}Response Payload{  "id": "chatcmpl-123",  "object": "chat.completion",  "created": 1677652288,  "model": "gpt-3.5-turbo-0125",  "system_fingerprint": "fp_44709d6fcb",  "choices": [{    "index": 0,    "message": {      "role": "assistant",      "content": "nnHello there, how may I assist you today?",    },    "logprobs": null,    "finish_reason": "stop"  }],  "usage": {    "prompt_tokens": 9,    "completion_tokens": 12,    "total_tokens": 21  }}For a complete reference for the API we will be using, you can check out the OpenAI Chat Completion docs.For our monetization efforts, our only genuine concern regarding metering is the response fields for prompt_tokens and completion_tokens. These two fields will give us the input and output token counts for the prompt and response, respectively. For the rest of the tutorial, you can either follow along using your own AI API or use the OpenAI Chat Completion one that we will be using throughout; either will work as long as you have a field in the response that outlines how many input and output tokens have been consumed in the query.Protecting the API with Kong KonnectWith an API ready to go, it’s time to integrate it with Kong Konnect. Alternatively, you can follow similar steps using Kong Enterprise, the on-premise offering. We will use Konnect in this demonstration to ensure ease of deployment. For this next part, we will assume that you have the following prerequisites:  An active Kong Konnect account  An active Control Plane  An active Data Plane (in this tutorial, we are running the data plane in Docker on Mac)With these prerequisites in place, we can move on to the first step: creating the API service in Konnect. To do this, select the Control Plane on the Gateway Manager screen to which you want to add the service. For example, I will choose the Default control plane I created in this demo.Create a Service in Kong KonnectOnce you’ve clicked the desired Control Plane, the next thing to do is to select Gateway Services from the left side menu. On the Gateway Services screen we will:  Click on the New Gateway Service button  On the New Gateway Service screen, we will add:          “AIService” as the value in the Name field      Choose Full URL and add “https://api.openai.com/v1/chat/completions” as the value in the Upstream URL field        Click the Save button to save the new service.Create a Route in Kong KonnectNext, we will add a route for the service to be used. To do this, click on the Routes menu item on the left side of the screen and do the following:  Click on the New Route button  Add “AIRoute” into the Name field  Select the “AIService” from the Service dropdown  Under Protocols, select the “HTTP, HTTPS” option  Under HTTP / HTTPS Routing Rules, add the following:          In the Paths input, add “ai/chat”      Under Methods, select “POST”        Click Save at the bottom of the screenTest the Route/Service For ConnectivityAt this point, we’ve now set up a service and a route. Let’s try testing it to see if we get a response. Since I am running the Konnect Data Plane locally using Docker, I will send a simple cURL command to my terminal.curl --location --request POST 'localhost:8000/ai/chat'The response I received is from the upstream OpenAI Chat Completions API:{  "error": {    "message": "You didn't provide an API key. You need to provide your API key in an Authorization header using Bearer auth (i.e. Authorization: Bearer YOUR_KEY), or as the password field (with blank username) if you're accessing the API from your browser and are prompted for a username and password. You can obtain an API key from https://platform.openai.com/account/api-keys.",    "type": "invalid_request_error",    "param": null,    "code": null  }}Automatically Adding The Header To The Upstream RequestNow, I would like to make it so that the OpenAI API can be used without the user needing to pass the required bearer token manually. I will use Kong’s Request Transformer plugin to automatically attach my OpenAI bearer token to the server for each upstream request.To enable the Request Transformer plugin, we will select Plugins in the Gateway Manager menu on the left. From here, we will:  Click the New Plugin button  On the Select a Plugin screen, scroll or search for “Request Transformer” in the list. Once you’ve located the Request Transformer plugin, click Enable  On the Configure plugin: request transformer screen:          Select the Scoped radio button      Under Gateway Service and Route inputs, add our AI service and route we created, respectively.      Under Plugin Configuration, ensure that “http” and “https” are added in the Protocols      Under Add.Headers, add in your Authorization key in this format: Authorization: Bearer sk_my_api_key. Replacing the value with your actual key        Click Save to save the configurationNow, when you send out a request to the API you should no longer receive the API key error that was experienced earlier.With this response, we can verify that our service is successfully being routed through Kong Konnect and to the upstream API, the OpenAI endpoint, or your own if you’ve added your own AI service/API into the Gateway Service that we created earlier.Add Key-Auth to The EndpointNext, we will add an API key requirement to our request process. To do this, we will leverage Kong’s built-in Key-Auth plugin.  In this demo, we will enable the plugin globally, but if you have routes that you want to exclude from requiring an API key, you could add the plugin on a route-by-route basis.To enable the Key-Auth plugin, in the Gateway Manager menu on the left, we will select Plugins. From here we will:  Click the New Plugin button  On the Select a Plugin screen, scroll or search for “Key Authentication” in the list. Once you’ve located the Key Authentication plugin, click Enable  On the Configure plugin: key authentication screen:          Select the Global radio button, if not already selected by default      Ensure that “http” and “https” are added to the Protocols      Check off the Key in Header option (leaving others elected if there is a preference)      Under Key Names, ensure there is an entry for “apikey”        Click Save to save the configurationWe will run the previous cURL command and should receive an error message showing that an API key is needed to process the request.We will leave this as is for now since we will later create a consumer in Kong so API keys can be generated and used to access the API. At this point, we can be happy with knowing that our API endpoint is protected via API key, so unauthorized access is not possible.Add Rate Limiting and Quotas For SecurityAlthough optional, it’s always a good idea to include rate limiting and quotas to ensure that services can’t be inadvertently or maliciously inundated with traffic.  Later, we will add quotas for pre-paid API users, which will be done through Moesif, not Kong’s quota and rate-limiting functionalities.Like the other plugins we have enabled, we will select Plugins to enable the Rate Limiting plugin in the Gateway Manager menu on the left. From here, we will:  Click the New Plugin button  On the Select a Plugin screen, scroll or search for “Rate Limiting” in the list. Once you’ve located the Rate Limiting plugin, click Enable  On the Configure plugin: rate limiting screen:          Select the Global radio button if not already selected by default      Ensure that “http” and “https” are added to the Protocols      In the Limit By field, set the value to “consumer”      In the Seconds field, add “10” as the value      Optionally, add values to Day, Hour, Minute, and Month based on your specific rate-limiting needs        Click Save to save the configurationWith the basic configuration we implemented above, each consumer we create in Kong will be limited to 10 requests per second. In this example, this is an arbitrary number, but you should adjust this plugin to match your expected volume of requests, balancing a wide enough rate limit while still preventing denial-of-service attacks.Integrate Moesif and KongNow that Kong is configured, we can integrate it with Moesif to extend our API monetization functionality. For this, we will use the Kong-Moesif pluginAdd the Kong-Moesif Plugin to Your Konnect Data Plane(s)First, we must bring up a new Konnect Data Plane with Moesif installed and enabled. For this, in the Konnect UI, click on Data Plane Nodes in the left-side menu, and on the screen that appears, click Create a New Data Plane Node.On the next screen, we will select the applicable Gateway Version (the default is fine if you have no particular version you want to run) and choose our platform as Linux (Docker). This will allow us to run the gateway on our local machine or another server using docker.With these options selected, scroll down to the setup script pane and click Generate certificate. This will create a docker command we can run in our terminal. Let’s copy the docker command.Next, we will add a few lines to the command to install and enable the Moesif plugin. Bring up a text editor (or you can do this directly in the terminal) so that we can edit the initial command output in the Konnect UI.In a text editor, paste the Konnect docker command and add the following:-v "/{PATH_TO_DIR}/kong:/tmp/custom_plugins/kong" -e "KONG_PLUGINS=bundled,moesif" -e "KONG_LUA_PACKAGE_PATH=/tmp/custom_plugins/?.lua;;" The {PATH_TO_DIR} is the path to the Kong-Moesif plugin repository on your local machine (or server). You’ll need to ensure you’ve downloaded the repository and fill in this argument in the command.The amended command will look like this:docker run -d -e "KONG_ROLE=data_plane" -e "KONG_DATABASE=off" -e "KONG_VITALS=off" -e "KONG_CLUSTER_MTLS=pki" -e "KONG_CLUSTER_CONTROL_PLANE=85987db54f.us.cp0.konghq.com:443" -e "KONG_CLUSTER_SERVER_NAME=85987db54f.us.cp0.konghq.com" -e "KONG_CLUSTER_TELEMETRY_ENDPOINT=85987db54f.us.tp0.konghq.com:443" -e "KONG_CLUSTER_TELEMETRY_SERVER_NAME=85987db54f.us.tp0.konghq.com" -e "KONG_CLUSTER_CERT=-----BEGIN CERTIFICATE-----123certhere-123-----END CERTIFICATE-----" -e "KONG_CLUSTER_CERT_KEY=-----BEGIN PRIVATE KEY-----123privatekey-123-----END PRIVATE KEY-----" -e "KONG_LUA_SSL_TRUSTED_CERTIFICATE=system" -e "KONG_KONNECT_MODE=on" -v "/Users/user/Documents/GitHub/kong-plugin-moesif/kong:/tmp/custom_plugins/kong"  -e "KONG_PLUGINS=bundled,moesif" -e "KONG_LUA_PACKAGE_PATH=/tmp/custom_plugins/?.lua;;" -p 8000:8000 -p 8443:8443 kong/kong-gateway:3.5.0.1As you can see, we’ve added additional commands to the initial docker script to install and enable the Moesif plugin on the Konnect data plane instance.  If you’re running multiple data planes, you must run this command on each.Then, run the amended docker command in a terminal to bring up the data plane instance and enable Moesif. Once the command has executed successfully, you should see a response in the UI showing “Data Plane Node has been found.”.Then, click Done in the top right corner of the screen. We now have our data planes up and running with Moesif enabled.Add the Kong-Moesif Plugin to the Konnect Control PlaneNext, we will add our Moesif plugin to the control plane. This will allow us to configure the plugin in the Konnect UI and have that config cascaded out to each data plane running the Moesif plugin. To do this, go to the Gateway Manager menu option in Konnect and click on default (or another control plane if you’ve already created one).Then, navigate to the Plugins page from the side menu and click New Plugin.Then, select Custom Plugins.Next, we will upload the schema.lua file to define the custom plugin. This can be found in the Kong-Moesif plugin repo under kong/plugins/moesif/schema.lua. Alternatively, you can download the schema.lua file for the Moesif Plugin directly. Add the file to the Upload Schema File input and click Save in the top right corner.On the Custom Plugins page, locate the Moesif plugin and click enable to enable the plugin on the control plane.On the Moesif config page that appears, let’s add our application ID into the application ID field.To get the required value, you can retrieve your Moesif Application ID from Moesif by going to Settings > API Keys.Copy the value in Your Collector Application Id for {App Name} and back in the Konnect UI, add the value to the configuration in the Application Id input. Once complete, scroll to the bottom of the screen and click Save.At this point, Moesif and any configuration set in the control plane will be propagated to the plugin installed on each data plane.Integrate Moesif and StripeOnce we integrate Kong with Moesif, we will receive API traffic analytics in Moesif from our Konnect instances. Each API call will be tracked in the system, which is how we will calculate usage. Once usage is calculated, we will need a way to report it to Stripe. Luckily, Moesif integrates directly with Stripe very quickly, and once this is done, API usage is reported to Stripe.To configure Stripe in Moesif, click on the Settings menu at the bottom left of the screen (this will be shown as your username), and then click Extensions in the menu. On the Extensions screen, do the following:  Find the Stripe Integration in the Extensions Gallery and click Configure.  In the modal that appears, follow the instructions for adding the Moesif Stripe webhook into Stripe.          On the Developers screen in Stripe, select Webhooks and then Add Endpoint in the top right corner of the screen.      Add in the Webhook URL and click Select Events. You’ll want to select all customer., customer.subscription., and invoice.events. Then, click Add Events.      Lastly, once returned to the original Add Endpoints screen, click the Add Endpoints button at the bottom of the screen.        Once the webhook is added, we will add our Stripe API key to Moesif following the next set of instructions in the modal back in Moesif.          In Stripe, go to the Developers screen and click API Keys.      Copy a private key for your API in either the Secret key or a generated Restricted keys field on the screen. Either key can be used.      In Moesif, paste the API key into the Stripe API Key field        Scroll to the bottom of the screen in Moesif and click Save.  You can optionally customize the ID mapping in Moesif. The default should work fine for most purposes and is what we will use in this example. However, if you need to customize it, you can specify how to map the Stripe customer objects to company entities in Moesif. For more info on this, check out our docs on Setting the Id Mapping for Stripe.ConclusionAt this point, we have all of the wiring in place to begin monetizing our APIs. Our Kong Konnect instance can now proxy traffic to our upstream AI API. That traffic can then be reported and tracked in Moesif, and once it is metered in Moesif, we can report it to Stripe.In the next part of this tutorial, we will cover how to set up a Billing Meter in Moesif that will actually meter the usage based on the configuration we specify and then report that usage to Stripe. After this, we will also use Moesif’s Governance Rule feature to block users from accessing the API if they have run out of pre-paid credits.Next StepsReview setting up PAYG billing in Part 2 of this three part series on monetizing AI APIs with Kong, Moesif, and Stripe.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue from your APIs?            Monetize your AI APIs in minutes with Moesif, Stripe, and Kong.            Try it out!            No credit card required            ",
          "url": " /technical/api-development/Moesif-Kong-Stripe-AI-API-Part-1/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-mastering-the-python-get-api": {
          "title": "Using Python for GET API Calls: A Step-by-Step Guide for Developers",
          "content"	 : "Understanding how to make a GET request to an API using Python is an essential skill for developers. This article will guide you through the process, demonstrating how to use Python’s ‘requests’ library to fetch data, handle the full JSON object in response, and manage API errors efficiently. Step into the practical world of Python GET API calls without any detours.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Key Takeaways      Python is an ideal language for API consumption and development due to its straightforward syntax and extensive library ecosystem, making API interactions simpler and more efficient.        The Python ‘requests’ library is a fundamental tool for API interaction, enabling easy installation, seamless HTTP requests, and the handlings of responses, including parsing JSON data.        Understanding and using HTTP status codes is critical for handling API requests and responses effectively; mastering API documentation is essential for effective API use, and hands-on examples like fetching weather data and accessing social media APIs demonstrate Python’s versatility.  Understanding APIs and Their ImportanceAPIs, or Application Programming Interfaces, serve as a system that bridges the gap between different software applications, enabling a standardized method for data exchange and interaction. Imagine a world where applications could communicate seamlessly, where data could be exchanged without hassles, and where software ecosystems could interact efficiently. That’s the world APIs help create!As a language, Python has found its niche in API consumption and development, thanks to its straightforward syntax and extensive library ecosystem. It serves as a universal translator in the realm of APIs, effortlessly simplifying complex procedures and interactions.API Types and ProtocolsAmong the various types of APIs, REST APIs, which use HTTP for data exchange, are the most popular and flexible. The REST architecture simplifies client/server communication access data together, making data access more straightforward and logical, akin to how a well-organized library simplifies the search for a particular book. By utilizing a REST API, developers can easily integrate various systems and applications, enhancing their functionality and user experience.APIs can be categorized based on architecture, such as SOAP or REST, and by their intended scope of use, like private, public, partner, and composite APIs. It’s like having different routes to reach a destination, each with its unique advantages and challenges.The Role of Python in API DevelopmentThe simplicity of Python’s syntax and its comprehensive library ecosystem make it an attractive choice for developers. It equates to a powerful tool that facilitates tasks effortlessly.Python’s capability is boosted by frameworks such as Django and Flask that aid API development, allowing developers to construct robust API suites with speed and efficiency using python code. This could be compared to owning a power drill that not only drills holes rapidly but also provides a wide range of other functions, much like a versatile python object.Setting Up Your Python Environment for API RequestsWhether you’re a Windows, Mac OS X, or Linux user, setting up your Python environment for API requests is a straightforward process. It’s as simple as downloading and running the Python installer, adding Python to the PATH, and installing the requests package via the command line.The requests library is a common choice for API interaction in Python. To start using it, simply import requests. Think of it as your passport for a smooth and efficient journey through the world of APIs.Installing the Requests LibraryThe Python Requests library is an essential tool in a developer’s arsenal, simplifying the process of making HTTP requests to APIs and web services. Installing it is as easy as using the package manager pip, which works across various operating systems, including Windows, Mac OS X, and Linux.By using the simple console command ‘pip install requests’, you can install the Requests library and initiate your exploration of API interactions. Consider it as pressing the ‘Start’ button on your journey towards mastering APIs.Importing Required LibrariesMake sure to equip yourself with the necessary tools before embarking on your journey. In the realm of Python and APIs, these tools are the libraries you need to import into your Python script.The ‘requests’ library comes recommended for consuming APIs with Python. Once installed, it must be imported into your code to be utilized, much like starting your car’s engine before you set off on a journey.Making Your First API Request with PythonTo make a GET request in Python, you typically use the ‘requests.get()’ method in conjunction with the request library or API’s URL endpoint. It is comparable to dialing a phone number to make a call. The Python Requests library, which simplifies HTTP requests, is frequently used to interact with REST APIs.An endpoint in the context of APIs is a specific URL that specifies the exact requested resource, or data that a developer wishes to retrieve through an API call. By using an API endpoint, API responses include headers and the requested data or payload, often formatted as JSON, making it essential for developers to understand how to handle this format.Analyzing API ResponsesUpon making a GET request to api server to retrieve data, the server offers post request a response object carrying status codes and response data that indicate whether the request was successful or not. This is similar to receiving a delivery confirmation text after ordering a pizza, as it helps in managing response requests.To extract the valuable content from the API response, developers can employ various methods like ‘response.content()’, ‘response.text()’, or ‘response.json()’, the latter being specifically designed for JSON-formatted data. HTTP headers in the request data response can provide extra parameters that govern the exchange data request and response, offering valuable information for developers.Parsing JSON DataJSON, the language of APIs, is a lightweight data-interchange format that is machine-readable and favored for its simplicity, flexibility, and ease of use for humans. It’s like the English language in the world of APIs, widely understood and easy to use. the JSON format, short for JavaScript Object Notation, has become the go-to choice for developers when working with APIs.With Python’s built-in json module, encoding and decoding JSON and data formats becomes simpler, easing the interaction between Python programs and JSON-formatted API data. To readily access and manipulate the JSON data from an API response, the ‘response.json()’ method is used, which converts the whole JSON string of data into a Python dictionary. This is similar to translating text from a foreign language into your native tongue for better comprehension.Working with Query Parameters in PythonQuery parameters are  the secret sauce in your API requests. They are filters that can be sent with an API request to narrow down the responses and refine data requests according required parameters or to specific requirements, analogous to ordering a customized burger with specific instructions.Query parameters empower developers to filter results according to specific criteria. This customizes the behavior of AI models and provides selective access to relevant data subsets when making API requests. It can be compared to using a specific keyword to find relevant results on a search engine.Constructing API Requests with Query ParametersAdding query parameters to API requests can be done using the ‘params’ argument in ‘requests.get()’, which accepts a dictionary of parameters.When constructing API requests with multiple query parameters, in Python, a dictionary can be used to hold varying key-value pairs that represent all the data query parameters. It’s like packing a suitcase with different items for a trip, each item representing a query parameter.Best Practices for Using Query ParametersJust like there are best practices for writing efficient code for asynchronous requests, there are also best practices for using query parameters in API requests. Understanding these practices can help you make the most of your API interactions.Whether it’s understanding the API documentation, using the ‘params’ attribute, or constructing advanced requests with lists of items, these practices will guide you in crafting effective and simple API requests. It’s like learning the rules of a game to play it well.Handling API Errors and Status CodesAs with any journey, the journey through APIs can come with its share of hiccups. Status codes, part of the HTTP protocol, indicate the result of a request, communicating the outcome of API requests such as success, failure, or error types.Comprehending status codes is vital since they guide applications on how to manage responses, indicating success, failure, or the requirement for further action, such as providing missing data. Understanding the meaning of each status code is akin to road signs that aid in your journey, steering you efficiently.Common API Status CodesHTTP status codes categorized under 2xx indicate successful requests, while 4xx codes indicate client errors and 5xx codes indicate server errors. It’s like traffic lights, each color and status code indicating a different action to be taken.A Response [200] means that everything went okay, indicating that an API request was successful.Implementing Error Handling in PythonTo implement error handling in Python, you can follow these steps:      Use the ‘response.raise_for_status()’ method to check for any HTTP errors.        Catch exceptions such as HTTPError, ConnectionError, and Timeout to handle different types of errors.        Handle the errors appropriately, such as displaying an error message or retrying the request.  Implementing error handling in your code is like carrying a first-aid kit during a journey, preparing you for any unexpected situations.To handle API errors, use try-except blocks to catch exceptions like:  HTTPError  ConnectionError  Timeout  RequestExceptionThese exceptions cater to handling different types of request issues including exceeding rate limits. It’s like having different tools to fix different issues in a car, preparing you for any eventuality.Navigating and Understanding API DocumentationUnderstanding API documentation provides guidelines on how to effectively use API services, incorporating crucial information about available endpoints http methods, and resources, and how to navigate them to construct requests and handle responses.Understanding the documentation thoroughly involves obtaining an API key and integrating the API keys with python itself as per the documentation much like learning the rules and strategies of a game before starting to play.Finding API DocumentationAPI reference documentation is typically found on the platform where the API is hosted, under a ‘Documentation’ tab.While the access token visibility of API documentation might be restricted and require an invitation, many APIs have their documentation publicly available and searchable on the developer’s portal. It’s like open-source software, accessible to anyone who wishes to use it.Reading and Interpreting API DocumentationReading and interpreting API documentation can seem daunting at first, but with a little practice, it becomes a breeze. API documentation may include different sections such as an overview, a developer guide, and a user’s guide tailored to the audience.Generated API documentation provides a top-level view of operations, and detailed views filtering data used for each operation, and enables developers to test the API by sending and receiving messages.Practical Examples of Python API RequestsPython demonstrates its versatility in API interaction through diverse use cases, and following a Python API tutorial can help you explore these possibilities. These include fetching weather data and accessing social media data, showcasing the boundless possibilities for developers. This is similar to a Swiss army knife application programming interface, providing a tool for every task.From interacting with the World Bank’s Development Indicators to fetching trending GIFs from the GIPHY website, Python’s Requests library makes these tasks effortless.Fetching Weather DataFetching weather data is a common use case for APIs, and OpenWeatherMap provides weather data services that can be accessed with an API key.To access current weather information from OpenWeatherMap’s API, the ‘requests’ module is used for the API call, while the ‘json’ module manages the json response itself. This is comparable to using a remote control to turn on your TV and tune into your preferred channel.Accessing Social Media DataIn the age of social media, Python can be used to automate social media postings on platforms like Facebook, Twitter, and Instagram using their respective APIs.Whether you’re posting text messages and images on Facebook using the Graph API or scheduling posts for automatic posting at specific times using Python’s ‘schedule’ library, the opportunities are limitless.Effortless Python Integration with Moesif’s Powerful SDKMoesif simplifies API analytics for Python developers. Our Python SDK streamlines integration, allowing you to effortlessly capture valuable API usage data directly within your Python applications. This empowers you to gain deep insights into developer behavior and optimize your APIs for maximum performance and user satisfaction.SummaryAs we wrap up this journey through the world of APIs and Python, it’s clear that APIs are crucial for communication between software applications, and Python is a popular language for API development and consumption. The versatility of Python in API interactions, from fetching weather data to automating social media postings, reveals the extensive possibilities for developers. With the right tools and understanding, you too can master the Python Get API and harness its potential to create powerful, data-driven applications.Organizations looking for the best tools to support their REST API management can leverage Moesif’s powerful API analytics and monetization capabilities. Moesif easily integrates with your favorite API management platform or API gateway through one of our easy-to-use plugins, or embed Moesif directly into your API code using one of our SDKs. To try it yourself, sign up today and start with a 14-day free trial; no credit card is required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            You’ve built the API. Now let's generate revenue!            Monetize your Python APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Mastering-the-Python-Get-API/",
          "author": "Preet",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-mastering-api-document": {
          "title": "Master Documenting Your APIs: Tips for Effective API Documentation",
          "content"	 : "API (application programming interface) document works as a developer’s compass for navigating complex services. In this guide, we provide straightforward insights into crafting excellent API documentation. At the end of this article, you will know how to succeed as both creators and consumers of APIs through effective documentation.Key Takeaways  API documentation helps developers understand an API’s functionality. Quality documentation significantly enhances user experience, adoption, and loyalty.  Comprehensive API guides include overviews, endpoints, parameters, architecture, and code samples in multiple languages. They receive consistent updates to reflect the dynamic nature of APIs.  Technical writers play a crucial role in creating accessible API documentation.  A healthy and robust system always integrates documentation into the development process and caters to diverse audiences with clear, consistent, and inclusive content.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding API DocumentationAPI documentation serves as a set of human-readable instructions about how to effectively use an API and integrate with it. I bridges the gap between the company and the developers or end users. The quality of API documentation dictates how easily you can understand an API’s functionality and get started.A clear, concise , and comprehensive documentation that compliments a good API design minimizes the learning curve for developers. It enhances developers’ overall experience, and fosters loyalty to the platform.Anatomy of API DocumentationAn API documentation typically consists of guides, tutorials, code examples, and an API reference. It also includes an overview that summarizes the API, its purpose, and the unique benefits it offers to potential users.How-to Guides and TutorialsUsers want to know how they can get started interacting with your API quickly to accomplish specific tasks. An effective documentation understands its audience and presents effective how-to guides to onboard users. For example, a product manager doesn’t have the same needs as a software developer. By categorizing users into cohorts and structuring your guides accordingly, you ensure that every user succeeds using your product. Also, a getting started guide that highlights the API’s benefits and demonstrates the most common use case can pave the way for a smooth start for developers.Tutorials with real-world use cases help users understand how different parts of your API work. Effective tutorials contain step-by-step instructions that are easy to follow and understand. When preparing a tutorial, explicitly state any prerequisites that users must meet—for example, having a specific software or API version.Meaningful Code SamplesAn excellent API documentation includes code examples that demonstrate how to handle successful calls, deal with errors, and address common problems developers might encounter. Example responses help developers understand the data API calls return upon an API request. When preparing a piece of sample code, consider using multiple programming languages. This is a critical element to consider when you document APIs.API ReferenceAPI reference contains comprehensive details about everything an API has to offer. An API reference contains information about endpoints, methods, request and response fields, and available authentication methods. API references also include very specific examples of successful calls and responses to demonstrate effective API endpoint usage. For example, a REST API contains several endpoints. Therefore, the documentation must include examples that show how to properly use each API endpoint. In addition, developers often find API references from other sources to be helpful in understanding the nuances of the API, and helps them decide on the one that suits them the best.For developers to access the API’s capabilities, they also need to understand the authentication method the API has. An API can employ multiple authentication schemes. For example, an API can use both API key and OAuth. Therefore, the documentation must also explain each authentication method.After carefully considering these requirements, implement a proper structure for your API’s documentation. This ensures a seamless integration process between the documentation itself and the evolving API. A well-structured documentation directly impacts user experiences and the documentation’s maintainability.Why Quality Documentation MattersThe quality of documentation can significantly impact the developer experience. It serves the dual purpose of attracting potential users and assisting both internal and external users in understanding the API and its capabilities, thereby promoting adoption.For APIs intended for third-party use, comprehensive documentation is crucial for the creation of an ecosystem that depends on the API. It also plays a vital role in training new users and making them aware of the security aspects of the APIs they are working with.Crafting Comprehensive API GuidesA comprehensive API guide should include the following elements:  An overview of the API’s purpose  Core functionalities  Available endpoints with required parameters and potential responsesTo enhance the developer experience and encourage effective API utilization, prepare guides that cater to users at different stages of their journey. In these guides, discuss scenarios and provide common use case guidelines to users. This essentially helps you build an inclusive framework that guides users to their goals and builds confidence in your API.Including tutorials that teach API concepts and demonstrate its functionality can also be a valuable aid to developers.Keeping API Documentation Current and EngagingUp-to-date API documentation provide the best experience for API consumers, attracting new users and satisfying existing ones. Inaccurate or outdated documentation can deter potential users and lead to a decrease in API adoption.Tools such as Postman and SwaggerUI enhance user engagement by generating publishable documentation from API requests and creating interactive API documents.Regular UpdatesAPI documentation, like a living entity, requires regular care and updates. Maintaining and updating the documentation keeps it relevant and reliable, mirroring any changes or new features in the API. Including a changelog or release notes helps communicate modifications, keeping users and stakeholders updated with the latest changes.Providing a clear rationale for API changes in the documentation enhances transparency, fostering user trust, and clarifying the benefits of new features or updates. Automated deployment of up-to-date documentation and incorporating user feedback can support ongoing improvement and user satisfaction.Interactive DocumentationInteractive documentation elevates the user experience by allowing developers to preview API requests, modify values, and view mock or live responses in real-time. Various tools such as Swagger UI, ReDoc, Document360, and DapperDox provide potent features for crafting interactive docs. Some of these features include customization options, live experimentation, and intuitive navigation.Incorporating interactive elements such as API consoles allows users to test endpoints directly within the documentation, enhancing the overall user experience.The Technical Writer’s Role in API DocsIn the realm of API documentation, technical writers act as unsung heroes, converting complex technical details into clear, user-friendly documentation for fellow developers. They provide an essential link between API engineers and developers, creating user manuals that promote understanding and interaction with APIs. Good API documentation improves the developer’s user experience by efficiently guiding them through the API integration process.A technical writer also specializes in understanding their audience. They can understand user perspectives and craft compelling stories that tell how your API can benefit them. Since users possess varying degrees of skills and background, a technical writer practices caution about leaking jargon into their writings to eliminate friction from user experiences. By putting the audience first, they help build robust, inclusive, and accessible documentation.With the expansion of the API market, the role of technical writer is gaining importance, requiring an understanding of development processes, different programming languages, and a technical knowledge base.Visualizing API Data: Layouts and StructuresThe layout significantly influences the effectiveness of API documentation. A popular choice is the three-column layout. It provides distinct sections for navigation, core content, and additional resources or context. This consistency enables the core text to stay the same for each programming language, while users can select from different code examples.To enhance user experience, good API documentation also includes the following pre-built components:  A search bar  Clear navigation aids like a sticky header and sidebar  Thoughtful design of typography and color schemesAddressing Common API QueriesAPI documentation should serve as a comprehensive encyclopedia, catering to all potential user queries. It should outline the status codes and error messages users can expect when making calls, along with descriptions to help users resolve any issues they encounter. You should clearly explain API keys, which facilitate the management of the number of calls made to an API and the analysis of usage patterns.The documentation should include guides for common issues and their solutions. This leads to more efficient API integrations and a better understanding of potential pitfalls.API Documentation Best PracticesHere’s a list of best practices API documentation should follow:  Prioritize clarity, detail, descriptiveness, and accessibility. By eliminating technical jargon and simplifying complex concepts, it can cater to a broader user base.  Incorporate reference materials, guides, and tutorials.  Exemplify good security practices, error handling, rate limits, and API’s data safety and privacy.  Integrate feedback mechanisms into the documentation platform for continuous refinement.  Regularly update API docs to address security vulnerabilities and stay compliant with regulatory requirements.  Treat API documentation like an instruction manual detailing API features, endpoints, and use cases, supported by real-world examples and guides for a hands-on approach to consuming the API.Clarity Over ComplexityIn API documentation, clarity should always prevail over complexity. This enables new users and non-technical readers to understand the API’s essentials. Discarding technical language and explaining technical ideas in simple terms can make API documents more accessible to a wider range of readers.Code tutorials that focus on why a specific code is used can enhance users’ comprehension of the underlying principles.ConsistencyConsistency is key in API documentation. It ensures that all stakeholders have a unified understanding of the API. A consistent style empowers smooth user journey, easy navigation, and helps users focus on the content. A documentation following the same rules everywhere contains recurring patterns that makes locate a topic much easier.If you plan to make your API documentation available in other languages, consistent use of terms makes the translation process much smoother and faster.Documentation GeneratorDocumentation generators serve as powerful tools for crafting API documentation. The OpenAPI Specification (OAS), a widely adopted format for describing API endpoints, supports automated API documentation generation. Tools like Swagger and Postman utilize OpenAPI specifications to enable automation in generating API documentation, handling versioning, and tracking iterations.You can present Swagger documents in JSON and YAML formats, allowing for a wide range of integration possibilities and easy edits.Real-World ExamplesIn API documentation, real-world examples take center stage. Here are some of the best API documentation examples:  Salesforce  Mailchimp  Twilio  Spotify  StripeStudying various resources can serve as inspiration for using an api effectively and understanding what makes the best API documentation effective and noteworthy. Additionally, learning how to write API documentation can further enhance your skills in this area, especially when one writes API documentation regularly.Including diverse guides for different use cases, showcasing example apps that illustrate advanced API application, and providing real-life examples can help users comprehend and leverage the API to its full extent.Integrating API Documentation into Development ProcessRather than being an afterthought, API documentation should form an integral component of the development process. Documentation and the API should develop in tandem to ensure that the documentation stays current with the evolution of the API and new feature releases.Defining clear goals and metrics for API documentation helps in understanding its impact and in measuring success.Writing for Diverse AudiencesAPI documentation must present itself as an inclusive resource for all users. It should be readily accessible, which includes providing translations, enhancing accessibility, and avoiding exclusionary or ableist language to support a wide range of users. Documentation should demonstrate sensitivity to diverse audiences by using inclusive terms, avoiding gendered language, and refraining from unnecessarily violent terms.Examples in API documentation must strive to reflect a global audience and avoid cultural specificity. API documentation content should be accessible for both technical and non-technical users, providing fundamental explanations for newcomers as well as detailed technical information for experienced developers.API Documentation TemplatesAn API documentation template provides an advantageous head start. They should include the following sections:  Introduction to the API’s capabilities  Detailed sections on API endpoints  Parameters  Sample responses  Use cases  Data models  Code examplesTemplates should also emphasize methods for authentication, provide request examples, and include explanations of any domain-specific terminology to aid clarity for new users.A good template incorporates the following interactive elements:  A three-column format  Sticky navigation for usability  Instructions for user feedback  A designated maintainer to ensure regular updatesTools and Platforms for API DocumentationThe usage of dedicated tools and platforms can significantly enhance API documentation. Community-centric support systems, effective search capabilities, and version control are some features that can enhance the quality of API documentation.Some tools for managing the API lifecycle include:  SwaggerHub: manages the full API lifecycle, focusing on scalability and collaboration, while integrating core Swagger tools for interactive reference documentation  Stoplight: provides customizable themes and interactive capabilities in documentation  Theneo: emphasizes ease of use and integrations  DreamFactory: offers automatic Swagger documentation generationThese tools can help streamline the process of managing APIs and creating interactive documentation, as well as creating API documentation.SummaryEffective API documentation can successfully bridge the gap between API developers and end users, providing a detailed map that guides developers through the process of API integration. The role of technical writers, the importance of regular updates, the benefits of interactive documentation, and the value of preparing for diverse audiences – all these aspects play a crucial role in crafting API documentation that is clear, concise, and comprehensive. When writing API documentation, you may face challenges. But with the right tools, templates, and best practices, your work can become a valuable resource and create amazing user journeys.Just implementing an API for your product doesn’t do much in terms of extending your product and bringing in new users. A good documentation testifies your API’s functionalities and ensures that users can leverage the maximum benefit from your API for their use cases.Moesif offers a comprehensive suite of tools that can help you better understand your API and its users. Using powerful analytics and monitoring tools, you can unlock valuable insights that you can incorporate into your documentation and user-facing materials. Your documentation can cover more ground about the different obstacles your users face when consuming your API. This vastly enriches your documentation and improves user experiences through proactive and fast measures. So sign up today for a free trial, no credit card required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            You’ve built the API. Now let's generate revenue!            Monetize your APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Mastering-API-Document/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-rest-api-naming-conventions": {
          "title": "REST API Naming Conventions: A 2026 Reference",
          "content"	 : "A REST API’s naming conventions are the invisible UX that shapes whether your API feels obvious or annoying. The choices look small (plural or singular, camelCase or snake_case, id or userId) but they accumulate. Get them right and developers integrating with your API rarely consult the docs. Get them wrong and every team that touches your API runs into the same friction.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        This reference covers the conventions that hold up across modern REST APIs in 2026: resource paths, HTTP methods, path vs query parameters, JSON field names, versioning, and the additional considerations that matter when AI agents consume the same API through MCP.Why REST API naming conventions matterNaming is the part of API design that compounds the most. A name lives forever in client SDKs, in customer integrations, in your support tickets, in third-party documentation that referenced your API. Renaming a field after launch is a breaking change, which means the cost of fixing a bad name later is roughly the cost of shipping a new major version. Most teams underestimate this until they ship their second major version and realize the migration touched dozens of customer codebases.There is no single “official” REST naming standard. Roy Fielding’s REST dissertation defined the architectural style but not the naming conventions; those evolved through practice over the last two decades. The conventions in this guide are the ones every modern public API converges on (Stripe, Twilio, GitHub, OpenAI), not because they are mandated, but because they minimize surprise for the developer reading your spec. A developer who has integrated three APIs that follow these conventions can integrate a fourth one in half the time.The rules that hold across every REST APISix rules show up in every modern naming convention:  Use nouns for resources, not verbs. /orders, not /getOrders. The HTTP method is the verb. Stripe’s /v1/charges, GitHub’s /repos, and OpenAI’s /v1/chat/completions all follow this pattern.  Plural nouns for collections, singular for items. /orders for the collection, /orders/42 for one order. The exception is singletons (/me, /health) where there is only ever one instance.  Lowercase only in URLs. No camelCase or PascalCase in paths. URLs are technically case-sensitive per RFC 3986, but lowercase is the universal expectation. Stripe’s /payment_intents (snake_case but lowercase) and Twilio’s /messages both honor this.  Hyphens separate words in URLs. /order-details, not /orderDetails or /order_details. Stripe is the notable exception here, using snake_case in paths to match their JSON convention; pick one strategy and apply it consistently.  kebab-case in URLs, camelCase or snake_case in JSON bodies. Pick one for JSON and stick with it. Stripe uses snake_case for JSON keys; OpenAI and Twilio use snake_case; GitHub uses snake_case; AWS uses PascalCase. The choice is convention-driven, not technical.  Be consistent within your API. Whatever choices you make, apply them everywhere. Inconsistency is worse than any specific convention. An API with some camelCase fields and some snake_case fields will produce client SDKs full of awkward conversion code.Following these six rules covers most naming decisions before any deeper thinking.Resource naming (plurals, hierarchy, depth)The two questions that come up most often:Plural or singular? Plural for collections (/customers), singular through the resource identifier (/customers/123). The path /customer/123 is unusual and signals to consumers that the convention is inconsistent.How deep should nesting go? Two levels is the practical maximum. /customers/123/orders is fine. /customers/123/orders/456/items/789 is a design smell. When you reach for three levels, you usually want a top-level resource with filter parameters instead: /items?orderId=456.A few related practices:  Use the resource ID directly in the path: /users/123, not /users/show/123.  For actions that do not fit CRUD (search, login, send-email), POST to a named endpoint: /searches, /sessions, /messages. REST purists will tell you to model these as resources; in practice, naming the endpoint after the action is acceptable for non-CRUD operations.  For relationships that are not strict containment, use a sub-resource path: /users/123/followers is clearer than /followers?userId=123.Resource naming choices show up in our broader API design principles as one of the decisions that compounds across an API’s lifetime.HTTP method conventionsThe method is the verb. The URL identifies the resource. The method tells the server what to do with it. Our API methods reference covers each verb in detail.            Method      Use      Idempotent?                  GET      Retrieve      Yes              POST      Create      No              PUT      Replace entirely      Yes              PATCH      Partial update      Sometimes              DELETE      Remove      Yes      The mistake to avoid: putting the verb in the URL. POST /createUser is wrong; the method already says “create.” POST /users is right.For non-CRUD actions that do not fit cleanly into the method/resource pattern, use POST to a named endpoint. POST /sessions for login is conventional and well-understood, even though “session” is being treated as a resource rather than an action.Path parameters vs query parametersPath parameters identify a specific resource. Query parameters modify a request.  Path parameter (identifies): /orders/42 tells the server which order.  Query parameter (filters or modifies): /orders?status=paid&limit=20 tells the server which subset of orders to return.The rule that follows from this: do not put filters in the path, and do not put resource IDs in the query string. /orders/status=paid and /orders?id=42 are both anti-patterns. The first turns a filter into a fake resource; the second loses the ability to use HTTP caching on a specific resource URL.Path parameter names follow the resource naming convention (lowercase, hyphens, no verbs). Query parameter names typically use camelCase or snake_case to match the JSON field names of the resource: ?customerId=123 or ?customer_id=123. Pick the same convention you use in JSON.A few common query parameter names that have crystallized into convention across major APIs:  Pagination: limit and offset, or page_size and page, or cursor for cursor-based pagination. Stripe uses cursor-based; Twilio uses page-based; choose one and document the choice.  Filtering: the field name being filtered, e.g., ?status=paid&customer_id=123. Stay away from generic ?filter=... query strings; they push parsing complexity onto consumers.  Sorting: sort=created_at or sort=-created_at (the minus sign for descending order is a well-understood convention).  Field selection: ?fields=id,name,email lets consumers request only the fields they need. Useful for mobile and bandwidth-constrained clients.JSON field naming (camelCase vs snake_case)The single biggest unresolved debate in REST naming. There is no objectively correct answer; there are popular conventions:  camelCase is the conventional default in JavaScript and Java codebases. Used in GitHub’s GraphQL API and many JavaScript-first public APIs.  snake_case is the Python and Ruby default. Stripe, OpenAI, and GitHub’s REST API use it, as do Slack, Twilio, and many older APIs from the JSON era.  kebab-case is occasionally seen in JSON keys but causes friction in most languages (it cannot be a property name in JavaScript without quoting), so avoid it in JSON bodies.Pick one and apply it across every endpoint. Mixing camelCase and snake_case in the same API is the kind of inconsistency developers complain about for years.For field names specifically:  Use full words, not abbreviations. description over desc. customer over cust. Abbreviations save four characters and cost years of “what does this field mean?” questions.  Be explicit about types in the field name when ambiguity matters. expiresAt (timestamp) is clearer than expires. isActive (boolean) is clearer than active. priceCents (integer) is clearer than price when you have decided to store money as integer cents.  For IDs, use the parent resource name as a prefix when the field appears in a child resource: customerId on an Order is clearer than id. The plain id field is reserved for the resource the JSON itself represents.  For timestamps, use ISO 8601 strings ("2026-05-22T14:30:00Z"), not Unix epoch integers or human-readable strings. Every modern API and language has a parser for ISO 8601; ambiguous formats produce parsing bugs.  For enum-style fields, use lowercase strings, not integers. "status": "paid" survives schema migrations better than "status": 2. The string is also self-documenting in logs.Versioning, deprecation, and the URLThree conventions hold across the major API styles:  URI versioning (/v1/orders, /v2/orders) is the most common and most consumer-friendly. The version is visible in the URL; routing and caching just work.  Header versioning (Accept: application/vnd.company.v1+json) is cleaner but harder for the consumer to debug. Used by GitHub and a few other large APIs.  Query string versioning (/orders?version=1) is rare and not recommended; it blurs the line between versioning and filtering.For deprecation, use the IETF-standard Sunset response header (RFC 8594) paired with the Deprecation response header (RFC 9745). Well-behaved SDKs warn developers automatically when they see them.The naming decision that compounds the most: state the version naming policy in your developer portal on day one. Without a public policy, every breaking change becomes a negotiation.Naming conventions for AI-agent-consumable APIsThree additional considerations matter when the consumer is an AI agent rather than a human developer.operationId in the OpenAPI spec is now user-facing copy. Agents read the operationId, summary, and description of every endpoint to decide which one to call. Vague or generic descriptions cause wrong tool selection. Treat these fields the same way you treat a feature name in a developer portal: specific, distinctive, unambiguous.Idempotency-Key header support is now naming convention. Agents retry aggressively. For every POST and PATCH, accept an Idempotency-Key header and document it in the OpenAPI spec. The convention name (Idempotency-Key) is what agents look for; using a different header name (e.g., X-Request-Id) breaks compatibility with agent libraries.Endpoint names should reflect the intent the agent will invoke. POST /messages is clearer to an agent than POST /msg-send or POST /communication. The endpoint name should map to a verb the agent might form (“send a message”) without much translation. Stripe’s POST /v1/charges and OpenAI’s POST /v1/chat/completions both pass this test: the path tells the agent exactly what kind of resource it is about to create.Tags in the OpenAPI spec group related endpoints for agents. When an agent loads your spec, the tags field organizes endpoints into navigable groups. Use semantic tags (Payments, Customers, Webhooks) rather than internal-team naming (team-platform-v2). Agents do not have organizational context; they will treat your tags as user-facing categories.The WSO2 AI Gateway auto-generates an MCP server from any OpenAPI spec. The better the names in the spec, the better the agent experience without a separate build. Specs written with vague descriptions and team-internal tags produce agents that pick the wrong endpoint roughly as often as they pick the right one.Common REST API naming mistakes to avoidA short catalog of the patterns that cause the most pain in production. These come up in API reviews repeatedly and are worth scanning for before shipping.Singular collection names. /order instead of /orders, /user instead of /users. The plural form scales to “list of orders” and “one order at /orders/42” without any awkwardness. Singular collection names force you to invent special paths for the list operation.Verbs in URLs. POST /createUser, GET /listOrders, POST /deleteAccount. The HTTP method already says what the operation is; the URL identifies the resource. Verbs in the URL duplicate what the method conveys and break the consistency every REST consumer expects.Mixing casing styles in URLs. /orderDetails next to /order-items next to /order_logs. Pick one URL casing convention (kebab-case is the most common for paths) and apply it across every endpoint. Mixed casing in URLs is the most visible naming inconsistency and frequently the first thing reviewers complain about.Inconsistent JSON field naming. customer_id in one endpoint, customerId in another, CustomerID in a third. Pick one casing for JSON (camelCase or snake_case; both are valid) and never mix them. SDK generators produce ugly output across mixed conventions, and consumers waste time looking up the spelling for each endpoint.Pluralizing the wrong noun. /childrens (the plural of “child” is “children”), /peoples (the plural of “person” is “people”), /datas (the plural of “data” is “data”). Use correct English plurals; the URL is part of the developer-facing surface and odd plurals signal a careless API.Over-deep nesting. /companies/{companyId}/users/{userId}/orders/{orderId}/items/{itemId}/comments/{commentId} is six levels deep. Two levels is the practical maximum (/companies/{companyId}/users/{userId} is the limit). For deeper relationships, use top-level resources with filter parameters (/items?orderId=...).Inconsistent ID formats across endpoints. Some endpoints return integer IDs, some return UUIDs, some return prefixed strings (cust_123). Pick a convention (typed prefixed IDs scale well for multi-resource APIs) and apply it everywhere. Mixed ID formats break SDK type safety and force consumers to write per-endpoint parsing code.Acronyms in inconsistent case. userId vs. userID, apiKey vs. APIKey, URLPath vs. urlPath. Pick a style for acronyms (most modern style guides recommend treating them as words: userId, apiKey, urlPath) and apply it across both URLs and JSON.Reserved-word collisions. class, type, for, default. These are reserved or near-reserved words in many languages. SDK generators handle them, but the generated code looks awkward (class_, _default). Prefer specific names (category over class, kind over type) where the meaning allows.URL paths that leak implementation detail. /api/v1/getOrdersByCustomerIdSorted exposes the implementation; /customers/{id}/orders?sort=... is the consumer-facing shape. The URL should reflect the consumer’s view of the resource model, not the database schema or the internal handler name.Plural-vs-collection ambiguity. /auth and /authentication mean roughly the same thing. /billing and /payment overlap. /usage and /metrics overlap. Pick one canonical name per concept and use it everywhere; allowing two names creates SDK and documentation duplication.Path segments that depend on query parameters to be meaningful. A path like /items that returns wildly different shapes depending on ?type=order_item vs ?type=invoice_item is two resources pretending to be one. Split them into /order-items and /invoice-items.These are not exhaustive, but they cover the bulk of naming-related comments in real API reviews. Catching them at design time (or in spec linting) is the cheapest way to prevent them.How Moesif and WSO2 enforce naming conventions in productionThe hard part of naming conventions is not picking them. It is enforcing them across an organization that ships dozens of APIs.WSO2 API Manager runs governance policies against the OpenAPI spec at merge time. Naming rules (plural collections, kebab-case paths, camelCase JSON, no verbs in URLs) become automated checks that fail builds before the API ships. New APIs inherit the rules; existing APIs flag violations during the next migration.Once the API is live, Moesif observes whether the conventions hold up in practice. When customer code calls endpoints with unexpected case-sensitivity or sends fields with the wrong casing, Moesif’s logs surface the pattern across customers, not just one ticket at a time.The integrated stack covers the design-time enforcement (WSO2) and the runtime observation (Moesif), which is the loop most teams are missing.Next stepsGood naming costs nothing at design time and is impossible to fix after launch without breaking changes. Get the conventions written down before the first endpoint goes live and enforce them with automated governance.If you want to see whether the naming conventions your API ships with are actually being used the way you expect, start a 14-day Moesif free trial for per-endpoint, per-customer analytics in production. No credit card required.Frequently asked questionsWhat are REST API naming conventions? The standard practices for naming resources, endpoints, HTTP methods, path and query parameters, and JSON fields in a REST API. They cover plurals, casing, hierarchy, versioning, and special cases like non-CRUD actions.Should REST API endpoints use plural or singular nouns? Plural for collections (/orders), singular for individual resources accessed by ID (/orders/42). Mixing these is the most common naming inconsistency.Is camelCase or snake_case better for REST API JSON fields? Neither is objectively better. camelCase is the JavaScript and Java default in source code; snake_case is the Python and Ruby default. Several major public APIs (Stripe, OpenAI, GitHub’s REST API, Slack, Twilio) use snake_case for JSON keys regardless of the implementing language. Pick one for your API and apply it everywhere.Should REST URLs be lowercase? Yes. URLs are case-sensitive per RFC 3986, but lowercase is the universal convention. Mixed case in paths is treated as a design error by most API governance tools.How do I name actions that don’t fit CRUD? POST to a named endpoint that describes the action: POST /searches, POST /sessions, POST /messages/send. REST purists prefer modeling these as resources; in practice, both styles are used widely.Should I version REST APIs in the URL or in headers? URL versioning (/v1/orders) is the most common in 2026 because it is the most consumer-friendly. Header versioning is cleaner but harder for developers to debug. Whichever you pick, publish your deprecation policy on day one.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /technical/rest-api-naming-conventions/",
          "author": "Matthew",
          "categories": "technical, api-design"
        }
      
    ,
  
    
        "technical-api-development-api-for-dummies": {
          "title": "What Is an API? A Complete Guide to Application Programming Interfaces",
          "content"	 : "Every time you check the weather on your phone, sign in with Google, or pay for something with Apple Pay, you’re using an API behind the scenes. APIs (application programming interfaces) are the contracts that let one piece of software ask another for data or actions, and they sit underneath almost every modern app, website, and AI agent. This guide explains what an API actually is, the major architectural styles you’ll encounter today, how a typical API call works end to end, and where APIs fit into the new wave of AI tooling.What Is an API?An API (application programming interface) is a set of rules that lets two software programs exchange data and instructions. A client application sends a request to an API endpoint; the server processes that request and returns a response, usually formatted as JSON. APIs power logins, payments, maps, weather widgets, and AI integrations across the modern web.The textbook analogy is the restaurant waiter: you (the client) order from a menu (the API contract), the waiter takes your order to the kitchen (the server), and the kitchen sends back your meal (the response). The analogy holds because the customer doesn’t need to know how the kitchen works, only how to read the menu and place an order. APIs work the same way. They expose a defined surface of operations while hiding everything else about the system on the other side.That separation is what makes modern software composable. A developer building a logistics dashboard doesn’t need to write payments code, mapping code, identity code, or fraud detection. They consume APIs from Stripe, Mapbox, Auth0, and Sift, then focus on the dashboard itself. The result is software that ships faster and gets better as the underlying services improve.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        What Does API Stand For?API stands for application programming interface. The “interface” part is doing most of the work in that phrase: an interface is the agreed-upon way two parties interact. The “application programming” part narrows it to interfaces designed for software-to-software communication rather than human-to-software interaction (a UI).The term predates the web by decades. APIs first appeared in the 1940s and 1960s as subroutine libraries that one program could call from another on the same machine. Web APIs as we know them today were shaped by Roy Fielding’s 2000 doctoral dissertation Architectural Styles and the Design of Network-based Software Architectures, which formalized REST and gave the industry a coherent model for using HTTP as an application protocol. Almost every public API you’ll touch today is downstream of that work.How Do APIs WorkAPIs follow a request-response model. A client (a browser, mobile app, backend service, or AI agent) constructs a request, sends it across the network to an API endpoint, and waits for the server to respond. The full anatomy of a typical web API call has six moving parts:  Endpoint URL. The address of the resource, for example https://api.example.com/v1/users/42.  HTTP method. The verb describing what the client wants to do: GET (read), POST (create), PUT (replace), PATCH (partially update), DELETE (remove). HTTP method semantics are defined in IETF RFC 9110, the current HTTP semantics specification.  Headers. Metadata about the request: authentication tokens, the content type, caching directives, the user agent.  Request body. Optional payload, typically JSON, for methods that send data (POST, PUT, PATCH).  Status code. A three-digit code in the response signaling outcome (200 OK, 201 Created, 401 Unauthorized, 404 Not Found, 500 Internal Server Error).  Response body. The data returned to the client, again usually JSON.A concrete example. To pull a user record from a hypothetical service, you’d send:curl -X GET https://api.example.com/v1/users/42   -H "Authorization: Bearer eyJhbGciOiJIUzI1NiIs..."   -H "Accept: application/json"And get back something like:{  "id": 42,  "email": "ada@example.com",  "plan": "pro",  "created_at": "2026-01-14T09:31:00Z"}That round trip happens in tens to hundreds of milliseconds, and a single user-facing action often fans out into many such calls. Loading a checkout page might trigger calls to a product catalog API, a pricing API, an inventory API, an address validation API, and a session API, all in parallel. Understanding the HTTP status codes the server returns is how you debug what went wrong when one of those calls fails.Types of APIs by AudienceThe first useful way to classify APIs is by who’s allowed to call them. The audience determines how the API is published, documented, secured, and monetized.Public APIsPublic APIs (also called open APIs) are exposed to anyone on the internet. Developers typically sign up for an account, receive an API key, and start calling endpoints under a documented rate limit. Stripe, Twilio, OpenAI, GitHub, and the Google Maps Platform are all public APIs. They’re the engine of the platform economy because they let third parties build on top of a service without negotiating a private contract first.Private APIsPrivate (internal) APIs are used inside a single organization. The microservice architecture most modern engineering teams run on is, in effect, a graph of private APIs calling each other. They’re often the highest-traffic APIs at a company even though no external developer ever sees them. Internal API governance, latency budgets, and schema versioning are usually where most engineering time gets spent.Partner APIsPartner APIs sit between public and private. They’re exposed to a known set of business partners under a contract, usually with tighter authentication and higher rate limits than a public API. A travel booking platform giving major hotel chains direct access to inventory and reservation endpoints is a typical example.Composite APIsA composite API bundles several underlying calls into a single request. Instead of the client making five round trips, it makes one call to the composite endpoint and gets a combined response. Composite APIs reduce latency over mobile networks and are common in BFF (backend-for-frontend) layers.Types of APIs by ArchitectureThe other classification cuts by protocol and data model. Most teams today work with REST and GraphQL, but the other styles all show up in real codebases and each makes different trade-offs.RESTREST (representational state transfer) is the most widely used style on the public web. It treats every resource as a URL, uses standard HTTP methods, and is typically (but not strictly) tied to JSON. REST APIs are easy to cache, easy to debug with browser tools and curl, and have the deepest tooling support across languages. The trade-off is that REST has no built-in schema, so clients often fetch more data than they need and discover breaking changes at runtime.SOAPSOAP (simple object access protocol) is an XML-based messaging protocol introduced in the late 1990s. It’s strictly typed via WSDL, includes built-in standards for security (WS-Security) and reliable messaging, and can run over HTTP, SMTP, or other transports. SOAP is verbose and slow to evolve, but it’s still entrenched in banking, insurance, healthcare, and government systems that need formal contracts and don’t change often.GraphQLGraphQL is a query language and runtime, originally developed at Facebook and now maintained as an open spec (latest release September 2025). Clients send a single query describing exactly which fields they want, and the server returns precisely that shape. The big win is over-fetching: a mobile client on a slow network can ask for the three fields it actually needs instead of pulling a 40-field user object. The cost is server-side complexity. Caching, rate limiting, and query-cost analysis are all harder than with REST.gRPC and RPC variantsgRPC is Google’s open-source RPC framework, built on HTTP/2 and Protocol Buffers (a binary, schema-first serialization format). It supports bidirectional streaming, code generation across roughly a dozen languages, and dramatically lower payload sizes than JSON. gRPC is the default choice for internal service-to-service traffic in performance-sensitive systems. Older RPC variants like XML-RPC and JSON-RPC still appear in legacy integrations and in some blockchain APIs.WebSocket APIsWebSockets give you a persistent, full-duplex connection over a single TCP socket. Once the client and server complete a one-time HTTP upgrade handshake, either side can push messages whenever it wants. That makes WebSockets the right tool for chat, multiplayer games, live trading data, collaborative editors, and live dashboards. They’re the opposite of REST’s stateless request-response model and require different thinking about reconnection, backpressure, and scaling.REST vs SOAP vs GraphQL: How They CompareThe three architectural styles you’ll see most often in production aren’t directly interchangeable. Each is a sensible default for a different problem.            Dimension      REST      SOAP      GraphQL                  Data format      Usually JSON; can be XML, CSV, HTML      XML only      JSON              Schema / typing      Optional (OpenAPI is common but not required)      Strict (WSDL)      Strict (SDL)              Transport      HTTP/HTTPS      HTTP, SMTP, TCP, JMS      Usually HTTP/HTTPS              Caching      Built-in via HTTP semantics      Limited      Hard; needs per-field strategy              Best fit      Public web APIs, CRUD over resources      Enterprise systems with formal contracts      Mobile and rich clients with varying data needs              Common drawbacks      Over-fetching, under-fetching, versioning      Verbose, slow on the wire, harder to debug      Server complexity, caching, query cost      Picking between them usually comes down to who the consumer is. If you’re publishing an API to thousands of external developers, REST is the path of least resistance. If you’re integrating with a bank or insurer that requires WS-Security, you don’t get to choose, you’re using SOAP. If you’re feeding a React Native app or a TypeScript single-page app, GraphQL eliminates a class of frontend pain that REST creates.Real-World API ExamplesA quick way to see what APIs do is to walk through a few you’ve almost certainly used today.  Sign in with Google, Apple, or GitHub. OAuth APIs handle the identity handshake so the application you’re signing into never sees your password.  Stripe and PayPal. Payment APIs accept card details on the merchant’s behalf, run them through fraud and 3DS flows, and return a charge token. Most ecommerce checkouts today are stitched together from at least three different payment-related APIs.  Google Maps Platform. The Maps Embed API and Maps JavaScript API put interactive maps into ride-share apps, real estate listings, and food delivery interfaces.  OpenWeather and the National Weather Service. Weather widgets in most consumer apps call out to weather APIs rather than running their own forecasting models.  Social share buttons. When you click “Share to X” inside another app, that app calls the social network’s posting API.  Travel aggregators like Kayak and Skyscanner. These products are mostly composed of partner APIs from airlines, hotels, and global distribution systems, fanned out and combined at query time.  OpenAI and Anthropic. Generative AI APIs return model completions over HTTP, and they’ve become a dependency for a significant share of consumer software shipped in the last two years.The pattern across all of these: the product team didn’t build the hard part themselves. They composed it from APIs.API BenefitsWhen teams ask whether an investment in API design and tooling is worth it, the upside lands in five buckets.  Faster integration. A well-documented API can be wired into a new application in hours. Building the same capability from scratch takes weeks.  Composability and innovation. Public APIs create the conditions for unexpected products. Twilio enabled an entire generation of two-factor authentication apps. Stripe made it possible to launch a marketplace in a weekend.  New revenue. APIs themselves are products. Usage-based pricing, tiered plans, and metered overages let companies turn their internal services into revenue lines, which is the whole basis of API monetization as a strategy.  Scalability. A clean API boundary lets you swap the implementation underneath without breaking callers. That’s how teams migrate from monoliths to microservices, or from one cloud provider to another, without rewriting the client.  Security, when done right. A single API gateway is much easier to harden, monitor, and rate-limit than a sprawling set of ad hoc endpoints. The same gateway becomes the natural place to enforce auth and detect abuse.The same five benefits create the same five risks if the API is designed poorly. A confusing API slows integration. A leaky one creates security incidents. A bad versioning strategy turns every change into a breaking change.Common API Security ConsiderationsMost API breaches don’t come from exotic zero-days. They come from missing or misconfigured basics. Four controls account for the majority of safe-API patterns:  Authentication. API keys are the simplest scheme and still appropriate for low-risk, server-to-server use. For anything involving end-user data, OAuth 2.0 and OpenID Connect are the standards, with short-lived JSON Web Tokens (JWTs) carrying scopes and claims.  Rate limiting. Limits cap abuse, protect against accidental traffic spikes, and create a basis for usage-based billing. GitHub’s REST API, for example, allows 5,000 requests per hour for authenticated users on the primary rate limit, with separate secondary limits to catch burst patterns.  TLS everywhere. Every endpoint should be HTTPS, with HSTS enabled. Plain HTTP for API traffic is malpractice at this point.  Input validation. Validate types, lengths, and shapes on the server. Never trust that the client sent what your contract said it would send. The OWASP API Security Top 10 consistently lists broken object-level authorization (BOLA) and broken authentication near the top, and most exploits chain together small input-validation gaps.The teams that handle API security well treat it as a layered design problem, not a checklist. Auth at the edge. Validation at the service. Authorization checks on every object reference. Logging and alerting on the analytics layer.How to Use an API: A Practical WalkthroughIf you’ve never called a public API end to end, the workflow is the same regardless of which service you pick. Five steps cover it:  Read the docs. Find the API reference, identify the endpoint you need, and note the required parameters, headers, and rate limits.  Get credentials. Sign up for an account, register an application if required, and copy the API key or OAuth client credentials. Store them in environment variables, never in source control.  Make a test request. Use curl, Postman, or your language’s standard HTTP library. Confirm you get a 200 (or expected) status before writing any application code around it.  Parse the response. Most APIs return JSON. Decode it into a typed structure in your language of choice and pull out the fields you care about.  Handle errors and edge cases. Check for non-2xx status codes, network timeouts, rate-limit responses (429), and partial failures. A robust integration retries on transient errors with exponential backoff and gives up on permanent ones.A working example using the httpbin.org echo service to confirm your setup:curl -X POST https://httpbin.org/post   -H "Content-Type: application/json"   -d '{"plan":"starter"}'Once that round-trips cleanly, you’ve cleared the basic plumbing. For a deeper end-to-end build, our REST API tutorial for beginners walks through designing your first endpoint, defining methods, and choosing a payload format.APIs and AI AgentsThe biggest shift in how APIs are consumed since the move from SOAP to REST is happening right now: AI agents are becoming a first-class API client. Where a 2018 API integration meant a developer reading docs and writing glue code, a 2026 integration increasingly means an LLM reading docs and calling endpoints on its own.Three patterns recur.Function calling and tool use. Major LLM providers expose structured tool-calling interfaces. OpenAI’s function calling and Anthropic’s tool use let a developer hand the model a list of available functions (effectively, API endpoint signatures), and the model decides when to call which one and with what arguments. The model gets the response back as a message and continues the conversation. Building an agent that can book travel, query a database, or take an action in your product mostly means writing the tool specs.Model Context Protocol (MCP). Anthropic introduced MCP in 2024 and it has since been adopted broadly. MCP is an open protocol for exposing tools and data to AI applications in a portable way. MCP is supported by Claude and Anthropic’s APIs directly, by OpenAI through ChatGPT connectors and developer-mode tooling, and by a growing set of IDE and agent frameworks. The support story varies by product surface, so check the specific platform’s MCP docs before integrating. The protocol is shaping up as an emerging standard for the agent layer, similar to how REST became the standard for the web app layer.Long-running agent loops. Agent frameworks orchestrate multi-step tool use against APIs autonomously. That changes traffic patterns dramatically. A single user prompt can fan out into dozens of API calls over several minutes, retries are common, and the “user” hitting your API isn’t a human anymore.For API providers, this shift has real consequences. Agent traffic is bursty, less predictable than human-driven traffic, and harder to attribute. Rate limits designed around human pacing get blown through. Cost attribution and per-customer usage analytics become hard problems. We see teams increasingly need to separate agent traffic from human traffic at the analytics layer, because the two patterns require different alerting thresholds and different billing logic.How Moesif Helps You Build, Monitor, and Monetize APIsOnce an API is live, the questions shift from “how do I expose this?” to “who is using it, how, and is it healthy?” That’s the gap we built Moesif to close.Moesif sits at the API edge (or inside a service mesh, or behind an SDK) and captures every API call as a structured event. From there, our customers use it for four things:  Product analytics on APIs. Funnels, retention, and cohort analysis applied to API consumers instead of webpage visitors. Which endpoints drive activation? Which customers are about to churn based on declining call volume?  Usage-based billing. Meter calls, bytes, compute, or any custom metric, then sync usage to Stripe, Recurly, or Zuora for invoicing. The same metering can power tiered plans and overage logic.  Real-time alerting. Anomaly detection on per-customer error rates, latency, or volume. Catch a broken integration before the customer files a ticket.  Governance and security signals. Detect new endpoints, spot abusive usage patterns, and feed billing-aware rate limits.The common thread: turning API traffic from a black box into a measurable, monetizable product surface. See how teams use Moesif’s API analytics to do this in production.ConclusionAPIs sit underneath almost every modern app, and the same patterns that powered the SaaS era (REST, JSON, OAuth, gateway-mediated traffic) are now powering the agent era too. Whether you’re publishing your first endpoint, comparing GraphQL against REST for a mobile rebuild, or trying to figure out how to bill AI agents that hit your API hundreds of times per task, the fundamentals in this guide are the foundation. The next move is understanding how your specific API is actually being used. Try Moesif free and see what real API traffic looks like in production.Frequently Asked QuestionsWhat is an API in simple terms?An API is a contract that lets one piece of software ask another for data or actions. The requesting side (the client) sends a request in a defined format, and the responding side (the server) returns a defined response. APIs are how apps, websites, and now AI agents get things done without having to build every capability themselves.What are the 4 types of API?The four most common types, grouped by audience, are public APIs (open to any developer), private APIs (internal to one organization), partner APIs (shared with specific business partners under contract), and composite APIs (which bundle several calls into one). A separate four-way split groups APIs by architecture: REST, SOAP, GraphQL, and RPC (including gRPC).What is an example of an API?The Stripe API is a good example. When an online store charges your card, the merchant’s backend calls Stripe’s PaymentIntents API (POST /v1/payment_intents, the modern replacement for the legacy /v1/charges flow) with a payment method ID and amount, and Stripe returns a PaymentIntent object that tracks the lifecycle of the payment through confirmation and capture. The merchant never handles raw card data directly; the API contract isolates that work.Is ChatGPT an API?No. ChatGPT is a consumer product. The underlying API is the OpenAI API, which exposes endpoints like /v1/chat/completions that developers call programmatically to get model responses. Anthropic, Google, and other providers offer similar APIs for their own models. The product is what end users see; the API is what developers integrate against.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /technical/api-development/API-For-Dummies/",
          "author": "Sakib",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-blueprint-to-create-restful-apis": {
          "title": "Step-by-Step Blueprint to Create RESTful APIs for Efficient Web Services",
          "content"	 : "Creating RESTful APIs is essential for efficient web service development. With this article, you’ll learn to create restful apis using Node.js and Express by following a concise, step-by-step approach designed for real-world application. From server setup to endpoint creation and securing data, we’ve got you covered. This comprehensive guide will ensure you grasp the fundamentals while also diving into the more nuanced aspects of RESTful API development. You’ll gain insights into best practices for designing your API for maximum scalability and performance, and learn how to handle common challenges that arise during development. Whether you’re a novice eager to get started or an experienced developer looking to refine your skills, this article will provide valuable knowledge to enhance your web service projects.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding REST ArchitectureDiving into the subject matter, the REST architecture needs to be understood first. As an architectural style for building web services, REST promotes a clear separation between the client and the server, facilitating the independent progression and scaling of each component. This architectural design not only enhances scalability but also boosts reliability and predictability, thanks to each client-server interaction being stateless. That is to say, each request is self-contained with all the required details for its execution.Beyond the client-server dichotomy, a uniform interface is another key aspect of REST. By abstracting the client from the server, it simplifies interactions and makes the system independently evolvable. A REST API, fundamentally, is a collection of standard protocols and guidelines that establish the framework for web services, using universal HTTP methods for interaction.At the core of REST architecture lie six principles:  Uniform interface  Client-server architecture  Statelessness  Cacheability  Layered system  Optional code on demandEach principle is instrumental in the design of REST APIs for efficient, scalable systems. We’ll explore these principles in greater depth, along with their practical applications, in the upcoming segments.Crafting Your First REST API with Node.js and ExpressAs we step into the domain of REST API development, Node.js and Express stand out as the prime frameworks for developing straightforward REST API endpoints. From setting up the server.js file as the starting point to ensuring that the Node.js server is actively running for accessible API endpoints, the journey of building a REST API is an engaging endeavor.The initial phase of this process involves establishing the appropriate environment. This initial setup is akin to laying the foundation for a building, providing a stable base on which to construct our API.Step 1: Setting Up the Development EnvironmentThe initial phase of this process involves establishing the appropriate environment. This initial setup is akin to laying the foundation for a building, providing a stable base on which to construct our API. Before diving into the coding aspect, it’s essential to prepare your development environment. Here’s a step-by-step guide to get you started:Install Node.js and npmEnsure Node.js and npm (Node Package Manager) are installed on your system. Node.js serves as the runtime environment, while npm is the package manager that allows you to install various libraries, including Express.Create a New ProjectOpen your terminal or command prompt, navigate to the folder where you want to create your project, and run:mkdir my-rest-apicd my-rest-apiInitialize your ProjectInitialize a Node.js Project To create a package.json file which holds metadata about the project and its dependencies, run:npm init -yThis command creates a package.json file with default values.Install ExpressInside your project directory, install Express using npm by running:npm install expressThis command adds Express to your project’s dependencies, allowing you to start building your REST API.Step 2: Creating a Simple Server with ExpressWith the environment set up, let’s create a simple server using Express. This involves writing a small amount of code in a file, traditionally named server.js, to start your server and listen for requests.Create server.jsIn your project directory, create a file named server.js.Write Server CodeOpen server.js in your text editor and add the following code:// Import expressconst express = require('express');// Create an instance of expressconst app = express();// Define a port numberconst port = 3000;// Define a route for GET requests to '/'app.get('/', (req, res) => {  res.send('Hello World! This is my first REST API.');});// Start the serverapp.listen(port, () => {  console.log(`Server running on http://localhost:${port}`);});This code snippet does the following:  Imports the Express library.  Initializes an Express application.  Sets up a basic route ('/') that responds with a message when accessed via a GET request.  Starts the server on a specified port (3000 in this example), logging a message to the console once it’s running.Run Your ServerTo start your server, go back to your terminal and run:node server.jsYou should see a message indicating that the server is running. Open a web browser and navigate to http://localhost:3000. You will be greeted with the message, “Hello World! This is my first REST API.”Nice, you’ve just set up your development environment and created a simple server with Express to serve your first REST API endpoint! This forms the foundation upon which you can build more complex APIs, adding more routes and functionality as needed.Defining Endpoints and HTTP MethodsNow that the groundwork has been laid, we can commence the building process. And in REST API development, building starts with defining endpoints and HTTP methods, including handling HTTP requests. To build a REST API, it’s essential to understand the standard HTTP methods - GET, PUT, POST, and DELETE - as these are the tools we use to define endpoints for CRUD (Create, Read, Update, Delete) operations, including handling delete requests. These methods, when combined with clear and intuitive endpoints, provide a structured interface that clients can use to manage information intuitively.Creating a REST API involves more than just setting up a server; it requires defining specific paths (endpoints) and how they respond to different HTTP requests. This is crucial for performing CRUD operations. Let’s dive into how we can define these operations using HTTP methods like GET, POST, PUT, and DELETE, and set up a structured request and response system.GET Request - Reading DataRetrieve All ItemsLet’s say we want to retrieve a list of items. We use a GET request for reading data.   app.get('/items', (req, res) => {     // Assuming 'items' is our data array     res.json(items); // Send all items as response   });POST Request - Creating DataAdd a New ItemTo create a new item, we use a POST request. This involves sending data (in the body of the request) from the client to the server.// Middleware to parse request bodyapp.use(express.json());app.post('/items', (req, res) => {  const newItem = req.body; // Data sent from the client  items.push(newItem); // Add item to our data array  res.status(201).send('Item added.');});PUT Request - Updating DataUpdate an Existing ItemUpdating data can be accomplished with a PUT request, specifying the item’s ID in the endpoint.app.put('/items/:id', (req, res) => {  const id = req.params.id; // Get the item ID from the URL  const updatedItem = req.body; // Data for updating the item  // Logic to find and update the item by ID  const index = items.findIndex(item => item.id === id);  if (index !== -1) {    items[index] = updatedItem;    res.send('Item updated.');  } else {    res.status(404).send('Item not found.');  }});DELETE Request - Deleting DataRemove an ItemFor deleting an item, a DELETE request is used, also specifying the item’s ID.app.delete('/items/:id', (req, res) => {  const id = req.params.id; // Get the item ID from the URL  // Logic to find and remove the item by ID  const index = items.findIndex(item => item.id === id);  if (index !== -1) {    items.splice(index, 1);    res.send('Item deleted.');  } else {    res.status(404).send('Item not found.');  }});By defining endpoints and handling HTTP methods appropriately, we lay the foundation for a robust REST API that allows clients to perform CRUD operations intuitively. This setup not only makes our API more structured but also ensures that it can handle complex data manipulation and interaction seamlessly.Structuring the Request Body and Response PayloadIn a REST API, both the request from the client and the response from the server typically use JSON (JavaScript Object Notation) for the body’s structure. This format is easy to read and write for humans and easy for machines to parse and generate.  Request Body: When clients make POST or PUT requests, they send a JSON object in the request body, which contains the data to be created or updated.  Response Payload: The server responds with a JSON object. This could be the created or updated data, a confirmation message, or, in the case of GET requests, an array of objects.Using JSON ensures consistency in the way data is sent and received, making the API more intuitive to use. Furthermore, setting appropriate status codes (like 200 for success, 201 for created, 404 for not found) in the response helps the client understand the outcome of their request.Preventing processing delays and potential errors can be achieved by evading unnecessary data and prudently utilizing nested data structures. This involves careful consideration of the data that is essential for each API call, ensuring that each JSON object sent in a request or response contains only what is necessary for that particular interaction. Avoiding overly complex or deeply nested structures helps to maintain clarity and aids in the processing speed of the API.It’s important to establish a schema for the request and response data. This schema acts as a contract that defines the format, type, and constraints of the data elements within the API’s exchange. Utilizing schemas not only helps in validating the data but also serves as documentation for the expected data model, which can be extremely beneficial for both the API developers and consumers.In addition to the structure, the way data is handled in the request body and response payload can significantly impact the performance and usability of the API. For example, pagination in the response payload can help manage large datasets by breaking them down into smaller, more manageable chunks. This improves the client’s ability to consume and process the data, as well as the efficiency of data transmission over the network.Overall, thoughtful structuring of the request body and response payload is a critical aspect of RESTful API design that contributes to the creation of robust, scalable, and user-friendly APIs.Designing Intuitive and Scalable REST APIsMuch like any design process, API design aims at developing something that is not only operational but also intuitive and capable of scaling. Horizontal scaling, for instance, is a preferable approach over vertical scaling for maintaining scalable and high-performing APIs.Incorporating cache layers in HTTP and along the API request processing pipeline significantly contributes to sustaining API performance.Data Handling in RESTful APIsData forms the core of APIs, and its efficient management is critical to the success of a RESTful API. JSON (JavaScript Object Notation) is one of the standardized data formats commonly used when interacting with RESTful APIs.Along with data modeling, which is essential for identifying the structure of data and its interrelationships, JSON enables efficient operations in RESTful APIs.Determine Resource RepresentationsWithin the domain of REST API, the representation of resources is of paramount importance. Using plural nouns to represent resources in REST API endpoints makes the API more intuitive for those interacting with it. These endpoints, or URLs, represent resources in a web service, allowing for actions to be performed on these resources like creating, reading, updating, and deleting.By meticulously structuring data, defining clear resource representations, and managing data transfer formats, developers lay the groundwork for powerful web services. It’s a process that demands attention to detail and a deep understanding of web standards, but the rewards—scalable, efficient, and user-friendly APIs—are well worth the effort.Managing Data Transfer and FormatsManaging data transfer and choosing the right data format is as important as determining resource representations. The Last-Modified header, for instance, is used in content negotiation to indicate when the associated resource last changed, which is crucial for data transfer management.Robust Error Handling StrategiesThe foundation of dependable API development lies in robust error management. Implementing proper HTTP status codes and detailed error messages helps engineers quickly identify problems. Moreover, mapping all exceptions in an error payload that describes the origin of the error and provides guidance on how to address it offers clearer feedback during unexpected situations.Securing Your RESTful APISecurity holds utmost importance in every digital undertaking, including API development. Implementing authentication and authorization protocols like OAuth 2.0 is crucial for the security of your RESTful API. Additional security measures such as SSL/TLS encryption and fine-grained access control complement API key usage to protect sensitive information.Enhancing Performance with Caching TechniquesTo boost API performance, caching serves as an influential tool. Using data caching solutions like Redis and apicache improves the overall experience, serves often requested resources more quickly, and reduces querying from the database.Cache-Control headers are used to manage the duration and location of cached responses, including their validation before use.Testing Tools and Practices for REST APIsAPI development is incomplete without the integral process of testing. Automated testing frameworks like Rspec, API Fortress, and Postman can enhance testing procedures, allowing for structured, extendable, reusable, and maintainable tests.Prior to testing REST APIs, understanding API requirements, including query parameter usage, is imperative for proper test data and verification method preparation.Documenting Your API for Maximum UsabilityDocumentation, being the initial interaction point for consumers with an API, aids developers in using the API effectively. Clear and thorough API documentation is crucial for understanding functionalities, endpoints, request/response formats, authentication, rate limits, and error handling. Including practical code examples within the documentation enhances developers’ understanding of how to implement the API.Optimizing REST APIs for Third-Party AppsThe optimization of APIs for third-party apps focuses on facilitating effortless integration and updates. API versioning is essential for allowing service providers to introduce changes without disrupting existing clients. Optimizing API architecture for modularity helps manage complexity and isolates issues, facilitating the integration of superfluous services.SummaryFrom understanding the core principles of REST architecture to the intricacies of crafting, testing, and optimizing REST APIs, we’ve embarked on a comprehensive journey through the world of RESTful APIs. As digital transformation continues to shape our world, the knowledge and skills shared in this blog post will empower you to contribute to this evolution.Empower your API management with Moesif’s cutting-edge governance and monetization features. Govern user access and enforce quotas efficiently, while unlocking new revenue streams through flexible, usage-based billing models. Seamless integration ensures your API’s security and financial growth. Start revolutionizing your API strategy today by signing up for Moesif’s free trial, and take the first step towards optimized control and monetization of your digital services.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Do you know how to generate revenue with your API?            Monetize your APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Blueprint-to-Create-RESTful-APIs/",
          "author": "Dylan",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-ai-api-goldrush": {
          "title": "The AI API Gold Rush: How to Turn Your Models into Revenue With Moesif",
          "content"	 : "So, you’ve built a cutting-edge AI model! Congratulations! With the vast array of AI capabilities coming to market, it may generate realistic product descriptions, analyze customer sentiment, or perform cutting-edge processing. Innovation with AI is helping companies to discover new capabilities and use cases. However, innovation alone isn’t enough. To truly reap the rewards of an investment in AI, companies need a strategy to monetize it.  That’s where AI APIs come in, providing a streamlined way to integrate your AI capabilities into other applications and create new revenue streams.API monetization might seem complex, but it doesn’t have to be.  Regarding AI APIs, monetization involves understanding the value the AI functionality delivers, deciding on a suitable pricing model, and implementing the technical infrastructure to manage usage and billing. Luckily, specialized platforms like Moesif are dedicated to this purpose, making the task far more manageable.In this blog post, we’ll explore AI API monetization. We’ll discuss the fundamental reasons for turning your AI endpoints into revenue sources, how to select the best monetization strategies, and how to use Moesif to manage the process step-by-step. By the end of this blog, you’ll know how to turn your AI innovation into a sustainable revenue stream quickly! Let’s begin by defining an AI API.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding the AI APIAn AI API (Application Programming Interface) is a communication gateway between your AI models or services and external applications. It provides a standardized way for different software components to interact with your AI capabilities. This abstraction allows developers to seamlessly integrate your AI service into their projects without needing a deep understanding of your model’s underlying architecture and implementation.For example, imagine you’ve built a proprietary AI model that excels at analyzing market sentiment based on financial news and social media feeds. By offering this capability through an API, you empower trading platforms or investment tools with valuable real-time insights, giving them a competitive edge. Quite a few AI APIs have been built and used extensively by developers. These include Google’s Cloud Vision API and OpenAI’s popular GPT-3 API.Google Cloud Vision APIThis API exposes Google’s powerful recognition models, enabling classification, object detection, facial analysis, and content moderation tasks. These readily available functionalities save developers significant time and resources compared to building similar models from scratch.OpenAI’s GPT-3 APIThis API provides access to a world-class language generation model. Applications built on top of it can create realistic dialogue for chatbots, generate marketing copy, translate languages, or even power creative writing tools.REST APIs are commonly chosen as a protocol for AI APIs due to their familiarity and scalability. However, technologies like GraphQL are gaining popularity by offering more flexibility in querying data.Overall, AI APIs have become foundational in making AI technologies accessible. They lower the technical barrier for developers, allowing them to focus on building innovative applications powered by cutting-edge AI models.Why Monetize Your AI API?Monetization is a natural step if you’ve invested significant time, expertise, and computing resources into developing a unique and valuable AI model. By beginning to drive revenue from your AI capabilities, your organization can derive the maximum value from the services you’ve built. Here are some common reasons organizations turn their AI APIs into revenue streams.Offset Development and Operational CostsThere are substantial expenses for AI research, model training, and the necessary infrastructure. Monetizing your API helps recoup these investments and ensures the long-term sustainability of continuing to develop AI services.Recurring Revenue ModelAPI monetization enables you to generate a consistent income stream, especially with usage-based pricing models. This predictability is incredibly attractive compared to the often project-based nature of other software development work.Market ValidationWhen users are willing to pay for access to your AI API, it’s a strong signal that your model provides genuine, quantifiable value. This validation can attract further investment and partnerships.Growing the EcosystemBy making your AI capabilities commercially accessible, you foster a developer ecosystem around your models. This accessibility can lead to innovative use cases you may not have envisioned, increasing the overall impact of your AI work.Beyond creating a new revenue stream, monetization often forces organizations to think deeply about packaging their APIs in a user-friendly and reliable manner. This process benefits your users and inadvertently improves the overall quality of your offering. It’s also important to remember that not all AI models are inherently suitable for monetization. Successful monetization hinges on having a model or service that provides a clear and unique value proposition in the marketplace. Most importantly, the service must be something developers are willing to pay for. Next, let’s look at various strategies for monetizing AI APIs.Monetization Strategies for AI APIsOnce you’ve decided to monetize your API, choosing a suitable pricing model is critical to turning raw API access into a profitable venture. Let’s look at a few common strategies and factors regarding API monetization:1. Usage-Based Pricing  How it Works: Users are charged based on the number of API calls they make or the amount of data processed. This can be metered per API call, compute time, output data size, or custom metrics specific to your AI model’s function.  When it’s Ideal: Suits AI models with predictable usage patterns and when costs align well with API call volume.  Example: A processing API might charge per analyzed or resized.2. Subscription-Based Pricing  How it Works: Users pay a recurring fee (monthly or annually) for access to a specific amount of API usage within a defined time frame. Different tiers are often offered with varying usage limits and additional features.  When it’s Ideal: Works well for AI models that provide consistent value and are likely to be used frequently.  Example: A language generation API might offer tiers based on the monthly number of words/characters generated.3. Freemium  How it Works: Offers a basic level of API access free of charge, with usage limits or reduced functionality. Users pay to upgrade to premium tiers that unlock higher limits, more features, or better support.  When it’s Ideal: Excellent for attracting a broad developer base and allowing users to test your API before committing financially, this model is often paired with usage-based or subscription-like pricing for premium tiers.  Example: A sentiment analysis API might offer limited free requests and charge for larger volumes or more complex analyses.You might also want to consider some other considerations when deciding on which pricing model to adopt. In some instances, you may want to offer a more hybrid approach, offering multiple models.**Combining different strategies (like freemium with usage-based) can be highly effective.In order to garner traction, you’ll also want to make sure to define your value proposition very clearly. By clearly defining the problem your AI API solves better than any other solution on the market, developers will be more willing to give your API and AI capabilities an initial go.Lastly, understanding potential customers’ budgets and payment preferences is essential for designing appropriate pricing structures. Choosing the optimal monetization strategy is an iterative process. Start with a clear hypothesis, monitor usage patterns, and be flexible enough to adapt your pricing model as you learn what works best for your specific API and user base.Challenges of API MonetizationMonetizing an AI API, while offering significant benefits, isn’t without its complexities. Monetization is challenging; however, with proper planning and knowledge of the shortcomings of the technologies you use to monetize your APIs, you can still implement it successfully. Here are critical challenges to recognize and strategize for:Pricing Sweet SpotFinding the right price point is crucial when it comes to getting people to consider and buy API access.  Charge too much, and you risk deterring potential users.  Price it too low, and you leave revenue on the table. Depending on the pricing model being used, experimenting with pricing can become complex. This is especially true with usage-based billing, which introduces an added layer of complexity to pricing decisions. Overall, market research and knowing your target audience’s budget will help to narrow down the solution to the pricing problem.Security and Rate LimitingOnce you begin monetizing your APIs, you must implement robust mechanisms to prevent API abuse and unauthorized access. This includes authentication, rate limiting to mitigate denial-of-service attacks, and careful consideration of data privacy issues. By allowing users to overuse their quota, if they are prepaid, or allowing massive amounts of traffic through for postpaid customers, you could leave money on the table or even begin costing yourself money. A good example would be if your API depends on a third-party API you pay for. If users are allowed to use the API freely, this could accumulate a bill on your side, a potential risk if customers do not settle their tab or can blow past their prepaid quota.Superb DocumentationWith a focus on selling your APIs, documentation becomes a key feature in usability for developers accessing the APIs. Excellent documentation is more than just a courtesy; it’s a sales tool. For seamless integration, developers need explicit instructions, examples, comprehensive error-handling explanations, and SDK guides. By covering your API documentation from every angle, you can ensure that developers can easily use your APIs when they decide to use them. Documentation is king when it comes to selling APIs and keeping developers happy.Market CompetitionAs APIs become more abundant, your offering may compete with other monetized APIs offering the same functionality or even competing with free solutions. Because of this, standing out in the increasingly crowded world of AI APIs can be difficult. When it comes to AI APIs, it helps to clearly articulate your model’s unique strengths and tailor your marketing to your target audience’s specific pain points. Keep an eye on competitors and evolve your product offering and pricing with market trends as often as possible.Although the challenges mentioned above are significant and should be considered, dedicated API analytics and monetization platforms can significantly streamline the process of API monetization. They can handle billing and user management and provide deep insights into usage trends, easing the technical and administrative burdens of implementing monetization. To show you how easy it is, let’s look at how you can monetize your AI API with Moesif next.How to Monetize Your AI API with MoesifNow, let’s look at how Moesif can be used to monetize your APIs. Below, we will go through a step-by-step example of how to:  Integrate a billing provider, notably Stripe, with Moesif for billing and account management  Create a billing meter in Moesif to keep track of API calls and report them to a billing provider  Implement a pre-paid model using Moesif’s governance rules to ensure user access is halted once they run out of creditsFirst, let’s look at what our AI API will look like and how we want to calculate usage.The AI APIIn this example, we will use a request/response payload similar to OpenAI’s GPT-3 API. For our purposes, we only really care about the response since that contains the data we want to monetize based on.In this example, we will have an endpoint called /ai-chat that allows users to input prompts and receive a response. We will monetize the API’s response since it will contain information about the number of tokens used in the prompt and the AI response.Here is what the API response for our example AI API looks like:{  "id": "chatcmpl-123",  "object": "chat.completion",  "created": 1677652288,  "model": "gpt-3.5-turbo-0125",  "system_fingerprint": "fp_44709d6fcb",  "choices": [{    "index": 0,    "message": {      "role": "assistant",      "content": "nnHello there, how may I assist you today?",    },    "logprobs": null,    "finish_reason": "stop"  }],  "usage": {    "prompt_tokens": 9,    "completion_tokens": 12,    "total_tokens": 21  }}Our focus will be mainly on the usage payload, which shows the prompt_tokens, completion_tokens, and total_tokens. In this example, we will use the total_tokens amount to burn down a pre-paid balance. However, if your API does not have such a total, you could create a custom metric that will allow you to combine prompt_tokens and completion_tokens.Setting Up Moesif for MonetizationWith our API understood, it’s time to configure Moesif to integrate with Stripe (our billing provider in this example) and the example AI API. To do this next step, you’ll need to make sure that you have the following prerequisites checked off:  An active Moesif account  An active Stripe accountOnce you have handled these prerequisites, you can move forward with API monetization with Moesif! Let’s begin by integrating your API with Moesif.  If you’d like to see end-to-end monetization guides for specific SDKs and Gateways, check out our examples for Node, Django, Go, Kong, Tyk, and AWS API Gateway.Integrate With The AI APIMoesif can integrate with your APIs in various ways. The two main options are via one of our easy-to-use SDKs or a plugin for popular API gateway and API management platforms. To see the complete list, check out our integrations page.You’ll also want to ensure that users are accurately tracked to attribute usage correctly. To do this, you will use Moesif’s user and company tracking. Many plugins that connect Moesif to your gateway will support this by default (as long as calls are authenticated); however, the SDKs may require some custom work but are conversely more flexible. You can check out our docs for more information on user and company tracking.Once the API is integrated, in Moesif’s Live Event Log, you will see something similar to this:Once you also have user and company tracking enabled, you will see that the user is now tagged as a specific user and company in Moesif so that their API calls can be aggregated and billed accordingly.Connect to the Stripe WebhookNext, with traffic streaming into the platform, we can link Moesif to Stripe. This step is simple and requires only a few things.The first step in integration is adding the Moesif webhook to the Stripe configuration. This allows Stripe to send subscription updates to Moesif.  For our video walkthrough of how to do this, check out the video hereTo add the Moesif webhook to Stripe, click on Developers in the upper right-hand side of the Stripe dashboard and then Webhooks in the left-side menu. This will bring you to the Webhooks page, where you can view existing webhooks and add new ones. We will click the Add an endpoint button at the bottom of the screen to add a new webhook.From here, plug in the Moesif API endpoint URL and configure the events to listen to. Copy your Moesif Webhook URL, shown in the Stripe Settings modal displayed in Moesif, into the Endpoint URL field. After this, click the + Select Events button.On the Select events to send screen, Scroll to the Customer section and select the option for Select all Customer events. After this, click the Add events button at the bottom of the screen. You’ll be returned to the original screen where you added the endpoint details. Scroll to the bottom of the screen and click Add endpoint to save the webhook endpoint to Stripe.Plug the Stripe API details into MoesifFor Moesif to add usage quantities to subscriptions in Stripe, we need to add the Stripe API details. This is done in the Stripe configuration screen in Moesif. Currently, Moesif only supports version 2020-08-27 of the Stripe API, so the Stripe API Version field defaults to that.For the Stripe API Key field, you’ll need to retrieve the API key from Stripe to plug it in. In Stripe, from the Developers screen, click on API Keys in the left-side menu. You’ll then see the private key for your API in either the Secret key or a generated Restricted keys field on the screen. Depending on your security requirements and environment, either key can be copied and used.After copying the key from Stripe, paste it into the Stripe API Key field on the Stripe Configuration screen in Moesif. After setting the API key value, scroll down to the bottom of the screen and click Save to save the configuration in Moesif. Your Stripe integration is complete in Moesif, and you can begin to use it to build plans, prices, and billing meters.Create a Plan and PriceAfter logging into Moesif, navigate to the Product Catalog by clicking the corresponding menu item in the left-side navigation. On the Plans page, click the Create New button in the top-right.On the Create Plan screen, you’ll fill out the plan name and select the billing provider. In this case, we will choose Stripe. Once done, click on the Create button at the top right.Next, we will create the Price in Moesif by clicking on the Price menu item under the Product Catalog on the left-side menu. On the Price screen, click the Create New button at the top right.On the next screen, you’ll add the price name and select the Linked Plan from the dropdown. In this case, we will choose “My AI API Plan”, the plan we made in the previous step above.Under Pricing, we will select the Pricing Model as “Per Unit (Package)”. For Price Structure, we will set the value as “USD 0.0001 per 1 unit”. For for usage measurement method, we will select Stripe Price Meter. Then we will either choose an existing Stripe meter or create a new one with the Sum aggregation method. In this case, a unit will be equivalent to one token. Once everything is input, click Create.Create a Billing MeterNow, with our API integrated and our pricing set up, let’s set up the meter that will tally up usage and report it to Stripe. In this case, we will look at the API responses’ total_tokens field and use that to meter the API usage.Alternatively, if you wanted to charge a different amount for input and output tokens, you could set up two prices, one for each token type, under the plan we created, and set up two individual meters that would report the consumption of each token type to each price accordingly. To keep things simple, let’s look at how to create everything based on the total_tokens field.First, click the + Create New button in the top left of the screen and then select Billing Meter under API Monetization. On the Create Billing Meter screen, we will add a name, link the billing meter to the plan and price we created, and set up a filter to include all successful calls to our AI API endpoint. The configuration will look something like this:Then, we will choose the metric on which we will bill. To do this, click the Metrics dropdown underneath the Filter and select Custom Metric. In the dropdown that appears on the right, we will choose Response > body > embeddings > usage > total_tokens. After that is selected, in the following dropdown to the right, choose sum.Once you have the metric dialed in, you’ll see a preview of the company’s usage that matches the criteria of your billing meter.Lastly, click Create in the top right corner. At this point, your billing meter is active and will begin metering usage and sending the usage data over to Stripe. Next, and lastly, we need to put a mechanism in place to stop users from being able to use the API if they have run out of credits.Create a Prepaid Governance RuleNow, our last thing to do is ensure that users are blocked when they run out of pre-paid credits. To implement this, we will use Moesif’s Governance Rule feature. To do so, we will again click the + New button and select Gov Rule/Quota under the API Monetization section.In the wizard modal that appears, select Block when No Available Credits to begin setting up the rule that will block users from accessing the API when they run out of the credits they have purchased.On the following popup that appears, you will see that a new cohort will automatically be created. By clicking Continue, a new cohort will be created that will be able to manage and keep track of users without credits. After the creation of the cohort, the rule will automatically be created and activated. After this, you’ll be moved into the Update Rule screen, where you can dial in the configuration further if needed. By default, you will see the following:Here, you can see that Moesif will apply this rule to any users with a 0 balance, blocking further access to the API(s) and returning an overridden response status and response body, all of which can be amended as needed.  If you only want to apply the block to a single endpoint, you can adjust the cohort’s criteria/filter to include the desired endpoints in your blocking functionality.With this completed, we have an end-to-end prepaid flow for our AI API. We can accurately meter the amount of tokens used in each API call and burn down credits as they are used. Once the user runs out of credits, Moesif will block the user from accessing the upstream AI API using the Governance Rule that we created. Now, it’s time for you to get users in the door and start generating revenue with your API! To make that even more manageable, check out Moesif’s Developer Portal, which helps streamline onboarding users to your monetized APIs (including checkout and API key management capabilities!).ConclusionConsidering the technical and business complexities, monetizing an AI API can seem overwhelming. As you can see from the above example, Moesif streamlines this process, empowering you to focus on delivering innovative AI capabilities rather than wrestling with the intricacies of billing infrastructure.Key benefits of using Moesif:  Faster Implementation: Pre-built integrations and intuitive tools accelerate the path from API creation to revenue generation.  Data-Driven Decisions: Moesif’s analytics provide actionable insights into how your API is used, helping optimize pricing and user experience.  Flexible Monetization: Easily experiment with different pricing models and adapt your strategy based on real-world results.  Developer Experience: Moesif’s focus on API monetization and onboarding means a better experience for your users, which helps drive adoption.Ready to turn your AI innovation into revenue?  Sign up for a free trial of Moesif or chat with our team of API monetization experts and start exploring the possibilities!                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue from your AI APIs?            Monetize your AI APIs in minutes with Moesif            Get started!            No credit card required            ",
          "url": " /technical/api-development/AI-API-Goldrush/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-api-keys-in-aws-gateway": {
          "title": "Securing Access: A Guide to Implementing API Keys in AWS API Gateway",
          "content"	 : "Implementing an API gateway API key is key to securing your APIs. In this guide, you’ll learn to generate and apply these keys, ensuring your AWS API gateway effectively authenticates and processes authorized client requests. We’ll dive into setup procedures, management tips, and security best practices to help you maintain robust access control. Get ready to fortify your services’ entry points.The goal of this post is to help you gain insights into the nuances of API key generation and the pivotal role they play in safeguarding your digital infrastructure. We’ll explore the intricacies of API key management, from creation to retirement, and the importance of a meticulous approach to this process. By the end of this guide, you will not only be equipped with the knowledge to implement API keys but also to oversee their lifecycle with precision. Prepare to delve deeper into the world of API security and emerge with the tools necessary to protect your APIs against unauthorized access and potential threats.Key Takeaways  API Gateways act as centralized gatekeepers to manage and secure API traffic, while API keys serve as unique identifiers that control access to these APIs, preventing unauthorized use.  Setting up an API in AWS API Gateway is a multi-step process involving the creation of the API, defining its resources and methods, and deploying it through stages that act as snapshots for clients to invoke the API.  API key authentication is crucial for securing APIs and should be used with the ‘API Key Required’ option and associated with specific usage plans to control and monitor access. Regularly rotating API keys and limiting access to certain users enhances security.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding API Gateway and API KeysAn API Gateway serves as the virtual gatekeeper for your business, handling incoming API calls, which include routing, authentication, and processing. This critical tool ensures secure and efficient API traffic management, proving its importance for organizations.Conversely, API keys function as VIP passes to your API party. Assigned to a client’s project that interacts with an API, these unique identifiers guarantee appropriate access for authorized individuals, thereby avoiding misuse or overuse. API keys add a layer of security, validating that a user has the key to unlock the connected service.What is AWS API Gateway?AWS API Gateway is like a Swiss army knife for APIs. It’s a service that allows users to create, publish, maintain, and monitor APIs at any scale. It supports various types of APIs, including HTTP, WebSocket, REST, SOAP, and GraphQL, making it versatile and adaptable.AWS API Gateway, positioned as a server between client applications and backend services, oversees tasks like routing, authentication, and traffic management. Its scalability, advanced security features, and capability to expose backend services to external clients contribute to its high value as a solution.The Role of API KeysAPI keys serve as your API’s gatekeepers, while API tokens provide an additional layer of security. Their unique identifier role for each client utilizing the API is integral in securing access. Imagine them as the keys to a private club, controlling and monitoring who comes in and out, and ensuring the club’s rules are adhered to.Prevention is better than cure, and that’s exactly what API keys aim to do. They prevent unauthorized access, keeping your API secure and ensuring it’s used as intended.Creating Your API in AWS API GatewayEstablishing an API in AWS API Gateway resembles building construction. The process includes the following steps:  Select the location, in this instance, the API Gateway console.  Select ‘Create API’, the equivalent of laying the foundation.  Choose the type of building you want to construct, in this case, the ‘HTTP API’ option.  Proceed by clicking ‘Build’.The next step is naming your API in the ‘Name’ field during the setup process, much like naming a building. Once you’ve reviewed your API configurations, you create an API by clicking on ‘Review and create’, followed by ‘Create’. And just like that, your API is constructed and ready for use.Define Your API Resources and MethodsDefining your API resources and methods is akin to designing the interior layout of a building. In API Gateway, resources are organized in a tree structure with a root resource (/) and can include child resources, establishing a hierarchy relative to the API’s base URL. To simplify the creation of multiple resources, the {proxy+} proxy resource can be used, representing any child resources path under it.Defining an API involves creating routes with HTTP methods, where the method ANY can be used to accept any HTTP method at runtime. These routes are like the corridors in a building, directing traffic to the right places. To implement the desired API methods, API method requests require the configuration of parameters such as path variables, headers, and query string parameters, alongside the definition of request models for validation and initialization purposes.Every API, like every building, must have at least one functional route and integration, specifying a backend service to handle the request, such as a Lambda function or an HTTP endpoint.Deploying Your APIDeploying your API is the grand opening of your building. It requires a stage in AWS API Gateway, which serves as a snapshot of the API for clients to invoke. During the API creation process, a default stage is created that is automatically configured to deploy changes, much like a soft opening of a building to test the operations.To manually deploy your API, you choose a stage, review its settings, and then deploy the API to make it accessible through the stage’s invoke URL, like a grand opening ceremony. Stages can have various settings adjusted, including enabling caching, logging, and customizing throttling settings for API requests, ensuring the building operates smoothly.Implementing API Key AuthenticationAPI key authentication is like the security check at the entrance of a building. It’s one of several mechanisms supported by AWS API Gateway for controlling and managing access to an API. Once an API key is created, its value cannot be changed, ensuring that each key remains constant throughout its lifecycle, much like a permanent access pass.Nonetheless, remember that two access passes, despite having different names, are deemed identical if they share the same value. This principle applies to API keys, where keys with different names but the same value are deemed identical by API Gateway. API keys can be sourced from headers, commonly using the X-API-Key header, or verified by a Lambda authorizer in AWS API Gateway, much like a security guard checking passes at the entrance.Enabling the “API Key Required” OptionActivating the “API Key Required” option likens to establishing a security checkpoint at your building’s entrance. To mandate an API key for a method, you simply navigate to the API Gateway console, select your REST API, and pick the method you want under Resources. It’s like setting up a security checkpoint for specific entrances.Within the Method request settings, you can:  Edit the method request  Select ‘API key required’ to enforce the use of an API key for that method, like setting up a sign saying “Access pass required”  If the ‘API key required’ option is not enabled, any associated API key will not be used for that method  After configuring the method to require an API key, make sure the ‘API key required’ option is selected and save the setting to make the requirement effective, much like turning on the security system.Configuring Header-Sourced API KeysSetting up header-sourced API keys parallels the process of instituting a system that demands access passes at the entrance. To use header-sourced API keys, the API key source must be set to ‘HEADER’ within the API Gateway settings. Clients are required to include the API key in the HTTP request header as ‘X-API-Key’ when using header-sourced keys, like showing their access pass to the security guard.Setting the API key source to ‘HEADER’ can be done using the AWS CLI command update-rest-api with –patch-operations specifying op=replace,path=/apiKeySource,value=HEADER. Similarly, to set the API key source via the API Gateway’s REST API, you issue a PATCH request to /restapis/{restapi_id}/ with a patchOperations JSON payload containing the appropriate op, path, and value keys, like setting up a new security protocol.Managing API Keys and Usage PlansOverseeing API keys and usage plans is akin to controlling access passes and operational hours of your building. API keys are used in REST API methods to control access and can be used alongside usage plans to implement tracking and throttling. They can be directly generated within AWS API Gateway or imported from external sources such as a CSV file, much like how access passes can be issued on-site or sent digitally.Think of a usage plan as the building’s operational hours. An API key must be associated with a usage plan to regulate the functionality passed to users or clients, like specifying when access pass holders can enter the building.Creating a New Usage PlanCreating a new usage plan is like setting up new operational hours for your building. Usage plans, in conjunction with API keys, enable service providers to define access levels and monitor the usage of their APIs. A Usage Plan specifies the rate of requests per second, burst capacity, and overall quota for a set time period (day, week, or month), like specifying how many visitors are allowed per day, week, or month.It’s possible to set varying throttling limits and quotas at both the API and the method level within a Usage Plan, like setting different visitor limits for different entrances. AWS enforces default quotas on the number of requests per second and employs the token bucket algorithm for determining burst capacity, ensuring that the building does not get overcrowded. However, these limits are not hard-set, and occasional exceedances are possible, much like how sometimes, more visitors are allowed during special events.Associating an API Key with a Usage PlanAssociating an API key with a usage plan is like linking an access pass with the operational hours. API keys can be associated with multiple usage plans; however, each API key can be linked with only one usage plan per API stage, like an access pass being valid for certain hours at specific entrances.To link an existing API key with a usage plan, you access the ‘Associated API keys’ tab in the AWS API Gateway and utilize the ‘Add existing key’ option to select and associate the intended key, like adding an access pass to the database. For situations where a new API key is required, the process includes creating the new API key via the ‘Create and add new key’ option within the usage plan and completing the association simultaneously, like issuing a new access pass and adding it to the database at the same time.Once an API key is associated with a usage plan, the changes may take a few minutes to propagate, after which the API key can be used to make API calls as per the plan’s limits, much like how an access pass takes a while to get activated.Testing Your Secured APITesting a secured API can be compared to carrying out a security drill in your building. Use tools such as Postman or cURL for this purpose, as they enable the inclusion of API keys in the request header, akin to verifying the functionality of access passes. You can test the API by sending requests to its invoke URL, including the necessary API keys, much like running a mock drill.To test the API with Curl, you can follow these steps:  Use the provided invoke URL.  Pass the API key in the request header.  Check if the access pass scanners are working properly.  The method of testing the API by entering its invoke URL is similar whether using a browser or Curl.  You can also check the access pass at different entrances to ensure it is working correctly.Making API Calls with an API KeyMaking API calls with an API key is like entering a building with an access pass. After the API key value has been configured for header-sourcing, the client can call the API methods by supplying the API key in the request’s X-API-Key header, like showing the access pass at the entrance. This simple yet effective method ensures that only those with the correct credentials—akin to the right access pass—are able to enter and interact with the API, maintaining the integrity and security of the system.When using testing tools like Postman, the API key is added to the request headers by inputting, like inputting the access pass number into the scanner. This process is straightforward and user-friendly, allowing for quick and easy authentication. The key must be included in every request, serving as a consistent checkpoint that validates the identity and access rights of the requestor before any interaction with the API’s resources can occur. It’s a seamless part of the workflow that, while simple, plays a critical role in the overall security and functionality of the API.Handling Errors and Access ViolationsHandling errors and access violations during testing is like handling security breaches during a drill. Requests to AWS API Gateway endpoints that lack a required API key result in a 403 Forbidden error, like an alarm going off when an access pass is not shown.Error messages in the body of the response from AWS API Gateway due to API key violations clarify the cause of the error, like a security guard explaining why the alarm went off. This helps in identifying and rectifying the issue promptly.Best Practices for API Key ManagementEfficient management of API keys mirrors the maintenance of a building’s security system. API keys can be securely monitored using AWS CloudWatch and AWS CloudTrail to help ensure secure access to APIs, like using CCTV cameras and access logs to maintain building security.AWS Security Hub can be used to monitor compliance with security best practices in the use of API Gateway and API keys, like a security audit to ensure all security protocols are being followed. It’s important to establish a policy for regularly rotating API keys, with rotations suggested every 30, 60, or 90 days, to meet various compliance regulations, much like regularly changing security codes.Unused or unneeded API keys should be deleted to minimize the risk of exploitation by malicious actors, like disabling lost or stolen access passes. Cooperation with third-party partners is essential to ensure that any API keys created or managed by them are properly secured, like working with security companies to ensure effective security management.Regularly Rotate API KeysThe regular rotation of API keys equates to the periodic alteration of a building’s security codes. It reduces the risk of theft or compromise, ensuring that only authorized users or applications have access to your data, like changing the access codes to prevent unauthorized access.API key rotation involves:  Replacing an existing API key with a new one that serves the same purpose, like changing the access code but keeping the same level of access.  Choosing the right frequency for API key rotation is crucial and should take into account the risk environment.  It should be clearly communicated to users and app maintainers, like deciding how often to change the access codes based on the level of security risk.Limit Access to Specific Users or ClientsRestricting access to particular users or clients is similar to permitting entry to only a select group of individuals in a building. API keys by themselves do not offer a reliable method for restricting access to specific users or clients within AWS. If a user has a valid API key for one API in a Usage Plan, they have access to all APIs included in that plan, which compromises granular control, like an all-access pass allowing entry to all areas of the building.Therefore, it’s recommended to use an IAM role, a Lambda authorizer, or an Amazon Cognito user pool instead of just API keys to control which users or clients can access your APIs, like using biometric access control or personal identification numbers (PINs) for better access control.SummaryIn conclusion, securing your API with AWS API Gateway and API keys is like having a robust security system for your building. From understanding the role of API Gateway and API keys to creating an API, defining resources and methods, deploying the API, and implementing API key authentication, we’ve navigated the process like a security expert. We’ve also delved into managing API keys and usage plans, testing the secured API, and the best practices for API key management. With this knowledge, you can now confidently secure your API and ensure its efficient and safe operation.Empower your API management with Moesif’s cutting-edge governance and monetization features. Govern user access and enforce quotas efficiently, while unlocking new revenue streams through flexible, usage-based billing models. Seamless integration ensures your API’s security and financial growth. Start revolutionizing your API strategy today by signing up for Moesif’s free trial, and take the first step towards optimized control and monetization of your digital services.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            You’ve built the API. Now let's generate revenue!            Monetize your AWS Gateway-powered APIs in minutes with Moesif.            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          "url": " /technical/api-development/API-Keys-in-AWS-Gateway/",
          "author": "Dylan",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-mastering-api-gateway-auth": {
          "title": "API Gateway Authentication: The 2026 Methods Compared",
          "content"	 : "API gateway authentication is the layer between a caller and your API that confirms two things before any business logic runs: who the caller is, and whether they are allowed to make this specific call. Getting it right is one of the highest-leverage parts of an API platform; getting it wrong shows up later as a security incident or a customer-trust problem.                Monitor and Secure your APIs with Moesif              14 day free trial. No credit card required.              Try for Free        This guide covers the five authentication methods you will see most often in 2026, when to use each, how the gateway layer differs from in-application auth, and the new questions that agent traffic and MCP introduce.Why API gateway authentication mattersThe gateway is the first place every external request lands. Handling authentication there has three benefits over doing it inside each application service:  Centralized enforcement. Auth policy lives in one place, not duplicated across services. A change to the rotation policy applies everywhere automatically.  Defense in depth. Requests that fail authentication never reach the application. Backend services see only authenticated, scope-checked traffic, which simplifies their threat model.  Consistent observability. Auth failures are visible in one log stream. A spike in 401s from a specific customer is easy to spot when it lives next to the rest of the traffic.The trade-off is that the gateway becomes a critical dependency. A misconfigured gateway can block legitimate traffic from every service simultaneously. Most platforms address this with staged rollouts of policy changes and explicit bypass paths for internal health checks.The five common API gateway auth methodsAlmost every API in 2026 uses one of five methods, often combined depending on the audience.API key authenticationA long random string the API issues to each customer. The client sends it on every request, usually in an Authorization: Bearer <key> header or a custom X-API-Key header.  Best for: server-to-server integrations, internal APIs, developer-tool APIs where speed and simplicity matter more than fine-grained scopes.  Trade-offs: keys do not expire by default and rotation requires customer action. If leaked, the key is valid until revoked. Mitigate with short-lived keys, key prefixes that identify the customer (so you can revoke quickly), and rate limits per key.OAuth 2.0The standard for user-facing flows. The client redirects the user to an authorization server, the user grants consent, the authorization server returns a short-lived access token, and the client presents that token on subsequent API calls.  Best for: APIs called on behalf of an end user, third-party integrations where the developer needs scoped permission to act on user data, and any flow where the user (not the developer) is the source of authorization.  Trade-offs: more moving parts than API keys (authorization server, refresh tokens, scope management), but the resulting tokens are short-lived and scoped, which limits blast radius if any single token is compromised.OAuth 2.1 recommends authorization code with PKCE for all flows (web, mobile, and SPA), with client credentials for service-to-service and device authorization for TVs and CLIs. Pick the flow that matches your client’s environment.OpenID Connect (OIDC)An identity layer built on top of OAuth 2.0. OAuth handles authorization (what the client can do); OIDC adds authentication (who the user is) via an ID token, a signed JWT containing user identity claims.  Best for: APIs that need to know the identity of the end user, single sign-on across multiple services, federated identity setups.  Trade-offs: more complex than raw OAuth. OIDC is what most modern enterprise SSO flows use under the hood.Mutual TLS (mTLS)Both the client and server present X.509 certificates during the TLS handshake. The server only completes the connection if the client’s certificate is signed by a trusted CA.  Best for: machine-to-machine APIs in regulated industries (finance, healthcare), partner integrations where both sides control their certificate infrastructure, zero-trust internal networks.  Trade-offs: higher operational overhead than bearer-token auth. Certificate distribution, rotation, and revocation become production concerns. mTLS is the strongest auth method but requires the infrastructure to support it.Basic and LDAP authenticationHTTP Basic sends a username and password on every request, Base64-encoded. LDAP-backed auth resolves credentials against a directory service like Active Directory.  Best for: internal APIs inside a corporate network, legacy systems that already use AD/LDAP, quick prototypes.  Trade-offs: credentials are sent on every request (Basic) or every authentication call (LDAP), which means losing them is worse than losing an OAuth token. Avoid these for external public APIs.How to choose between methodsA practical decision matrix:            Audience      Recommended primary auth                  External developers, server-to-server      API keys (with rotation policy + IP allowlist when feasible)              External developers acting on behalf of end users      OAuth 2.0 with PKCE              Internal application accessing user data      OIDC (SSO)              Partner / B2B integration with sensitive data      mTLS + signed payloads              Internal services on a trusted network      mTLS or service mesh identity              Legacy enterprise integrations      LDAP via the gateway, on the way to OAuth      Most production APIs end up using more than one method. A public API typically offers API keys for server-to-server and OAuth 2.0 for user-acting clients, with mTLS available as an option for the largest enterprise customers.Implementing auth at the gateway vs. in the applicationThe general rule: enforce identity and basic scope at the gateway; enforce fine-grained business rules in the application.The gateway handles:  Token validation (signature, expiration, issuer)  Scope checks at the route level (this endpoint requires read:orders)  Rate limiting per authenticated identity  Logging of every auth decisionThe application handles:  Object-level authorization (does this user own this specific resource)  Field-level authorization (can this user see this PII field)  Business-rule checks tied to the user’s account stateMixing the two layers (object-level checks at the gateway) is the most common pitfall. Gateways do not have application data, so object-level rules there inevitably become stale or wrong. Keep the boundaries clean.API gateway auth in 2026: agent identity and MCPTwo new patterns that did not exist five years ago and that matter for any API expecting agent traffic.Agent identity is becoming a first-class scope. When an AI agent calls your API on behalf of a user, the request needs to identify both: the user (so authorization rules apply correctly) and the agent (so attribution, rate limits, and audit trails work). The 2026 convention is to pass the user identity in the standard OAuth bearer token and the agent identity in an additional claim or header (X-Agent-Id, agent_id in the JWT). Several drafts and vendor-specific extensions add agent-identity claims; the convention is not yet standardized.MCP introduces a different auth surface. The Model Context Protocol exposes APIs to agent runtimes through a server interface that runs alongside the REST API. The auth pattern is typically OAuth 2.0 with refresh tokens, scoped to the specific MCP tools the agent is allowed to invoke. The WSO2 AI Gateway handles this by deriving MCP scopes from the same OpenAPI spec that drives the REST gateway, which keeps the two surfaces in sync.If your API will be consumed by agents, the gateway needs to understand agent identity as a first-class concept, not as a special case of user identity.Common API gateway auth mistakesPatterns that show up across customer reviews:  Using API keys with no rotation policy. Keys that never expire are credentials that leak forever. Set rotation cycles and instrument tooling that makes rotation painless for customers.  Storing tokens in URLs. Tokens in URL query parameters leak to server logs, Referer headers, browser history, and CDN logs. Always pass tokens in Authorization headers.  Wildcard CORS combined with credentialed requests. A 2026-relevant gotcha: setting Access-Control-Allow-Origin: * does not work for credentialed requests. See our CORS guide for the full pattern.  Object-level authorization at the gateway. The gateway lacks application data; resource-ownership checks belong in the application.  One auth method for everything. Public-developer APIs and partner integrations have different threat models. Trying to make API keys work for both ends up worse than offering both options separately.How WSO2 + Moesif handle gateway auth and observationWSO2 API Manager handles the authentication layer at the gateway: OAuth 2.0 authorization server, JWT validation, OIDC, mTLS, API key issuance, scope enforcement, and rate limiting tied to authenticated identity. Policies attach to API proxies declaratively, so a change to an auth requirement rolls out without redeploying services.Once the call is authenticated, Moesif records every request and response with the authenticated identity attached. The combination answers the questions that show up in production: which customer is hitting which endpoint, where auth failures are concentrated, whether agent traffic is being correctly attributed, and how token usage maps to billing.For the design-time decisions that shape the auth layer (and the rest of your API), see our API design principles guide. The auth boundary is one of the parts of API design that compounds the most across the rest of the platform.Next stepsAPI gateway authentication is the most leveraged security decision your API platform makes. Pick the methods that match your audiences, enforce them consistently, instrument the auth decisions in your logs, and revisit the policy annually.If you want per-customer visibility into authentication patterns and failures in your live API, start a 14-day Moesif free trial. No credit card required.Frequently asked questionsWhat is API gateway authentication? The layer at your API gateway that confirms the identity of every incoming request before it reaches your application services. It usually validates a token (API key, OAuth, JWT) or a certificate (mTLS) and enforces basic scope rules at the route level.What is the difference between authentication and authorization? Authentication confirms who is making the request; authorization confirms what they are allowed to do. Gateway authentication validates the credential; the application typically enforces fine-grained authorization based on the validated identity.Should I use API keys or OAuth 2.0? API keys for server-to-server integrations where the developer is the source of authorization; OAuth 2.0 when the request is acting on behalf of an end user. Most production APIs offer both.What is mTLS and when should I use it? Mutual TLS, where both client and server present certificates during the handshake. Use for high-security machine-to-machine APIs, regulated industries (finance, healthcare), and partner integrations where both sides can manage certificate infrastructure.Where should I implement rate limiting? At the gateway, tied to the authenticated identity. Application-level rate limiting is a fallback but should not be the primary defense. Gateway-level rate limiting protects backend services from authenticated traffic spikes.Do AI agents change API gateway auth requirements? Yes. Agent identity is now a first-class concept that the gateway needs to understand, separate from the user identity the agent is acting on behalf of. Most modern OAuth flows have or are adding draft extensions for this distinction.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /technical/api-development/Mastering-API-Gateway-Auth/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-api-gateway-vs-load-balancer": {
          "title": "Decoding the Roles: API Gateway vs Load Balancer",
          "content"	 : "Are you looking to streamline your network traffic but puzzled over whether an API gateway or a load balancer fits your needs? An API gateway centralizes and manages access to your APIs, while a load balancer efficiently distributes incoming traffic across multiple servers. This guide will directly compare api gateway vs load balancer, delineating their strengths to inform your network strategy.Key TakeawaysAPI gateways manage API calls and centralize request routing, providing a single point of entry that implements security, enforces operational policies, and improves scalability and efficiency through features like authentication, caching, and performance enhancement.Load balancers distribute network traffic across multiple servers using intelligent algorithms to prevent overloading, thereby enhancing application performance and maintaining high-availability through server health monitoring and traffic rerouting during failures.While API gateways and load balancers have different functions and routing mechanisms, they are often used collaboratively in microservice architectures to optimize network performance, with load balancers enhancing content delivery and API gateways strengthening security through robust protocols.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding API GatewaysAPI gateways serve as intermediaries between API consumers and microservices. They:  Act as a single point of entry  Manage API calls  Centralize request routing  Implement security  Enforce operational policiesBy managing the entrance to APIs, API gateways create a single entry point for security and operational policies, thereby improving scalability and efficiency.API gateways streamline the process of translating between web protocols and web-unfriendly protocols that are used internally. This ensures that when a client sends a request, the API gateway can interpret it, no matter the format or protocol, and then route it to the appropriate microservice with the correct translation. This not only simplifies the client’s interaction but also decouples the client from the backend services, allowing for independent scaling and evolution of both sides.API gateways provide a layer of abstraction over the microservice architecture, which simplifies the client interface. They can aggregate results from multiple microservices into a single response, reducing the number of round trips required. This is particularly beneficial in mobile applications where network calls can be costly and slow down the user experience. They can offload functionality from individual microservices, which can be significant in cases where common tasks like SSL termination, response caching, or static response handling are needed. By centralizing these tasks, API gateways reduce the complexity of microservices and allow developers to focus on their unique business logic, rather than on boilerplate code.Key Functions of API GatewaysAPI gateways play a crucial role in ensuring security by implementing authentication and authorization, thus preventing the misuse of APIs and allowing only legitimate users to access them. Moreover, as the api gateway receives requests, they actively manage incoming traffic and maintain service availability through rate limiting, preventing system overloads and ensuring consistent performance. One of the essential api gateway functions is to manage these security and traffic-related aspects efficiently.Enhancing performance is another key function of API gateways. By using response caching, API gateways reduce the demand on microservices and the volume of direct requests, thereby improving the overall performance. Furthermore, by monitoring and logging activities within an application’s framework, API gateways maintain efficient API management and contribute to the application’s security and performance.Benefits of Implementing API GatewaysImplementing API gateways offers a myriad of benefits. They improve security by providing centralized control over authentication and authorization, thus ensuring only secure access to APIs and services. By employing caching mechanisms, API gateways enhance performance and reduce the load on backend services by reusing frequently requested data.Furthermore, when comparing API gateway vs other solutions, API gateways offer advanced monitoring and error notification capabilities. These features enable quick detection and resolution of issues across services, thus maintaining high availability and improving user experience.Unraveling Load BalancersIn the digital symphony, load balancers are another type of conductor. They distribute network traffic across multiple servers, ensuring no single server bears too much load. By using various algorithms like round robin, least connections, and least response time for request distribution, they prevent bottlenecks and add redundancy to the system, thereby allowing systems to scale with confidence.Core Functions of Load BalancersAt the heart of load balancers are their core functions, which include efficient client request distribution, server health monitoring, and high-availability management. Load balancers receive incoming requests and distribute them to the best server based on algorithms, ensuring efficient request distribution.Moreover, load balancers offer the following benefits:  Continuously monitor server health to detect server issues  Seamlessly reroute traffic when a problem is detected, ensuring optimal resource utilization and performance  Manage high-availability through mechanisms like active-passive and active-active configurations  Enable uninterrupted operation and minimize downtime, even during server failuresAdvantages of Employing Load BalancersEmploying load balancers comes with a host of advantages. They enhance content delivery by:  Distributing internet traffic between application servers and visitors, thereby improving application performance and speed  Handling gigabytes of traffic and intelligently redirecting them across hundreds of servers  Offering the flexibility to easily scale up or down based on demand  Load balancers are pivotal for scalability and can greatly improve the performance and efficiency of your applications. As the load balancer sits at the forefront of your infrastructure, it plays a crucial role in managing traffic and ensuring optimal distribution of requests.Furthermore, load balancers offer the following benefits:  Redundancy and high-availability are ensured as they prevent any single server from being overloaded, thus contributing to the reliability and uptime of services.  Load balancers allow for customization and are optimized for high-volume, cost-effective traffic handling.  Load balancers offer businesses a competitive advantage in terms of quality and customer satisfaction.Comparing API Gateway and Load BalancerDespite their overlapping roles in network traffic management, API gateways and load balancers serve different purposes and are often used together in a microservice architecture. Differentiating the purposes, intents, and use cases of API gateways and load balancers is crucial to avoid long-term issues across the thought patterns of developers and system designers.While an API gateway acts as the maestro, orchestrating the flow of requests and ensuring that each is treated with the appropriate security checks, rate limiting, and protocol translations, a load balancer operates more like a behind-the-scenes stage manager, diligently working to ensure that the performance runs smoothly by distributing the audience’s demands across a cast of servers. This division of labor is essential in a landscape where the complexity and volume of network traffic continue to escalate.In essence, the API gateway’s role is to provide a unified interface and centralized management of API interactions, while the load balancer’s role is to optimize resource utilization and maximize uptime. Together, they form a powerful duo that enhances the resilience and efficiency of modern network infrastructures.Routing MechanismsAPI gateways and load balancers have significantly different routing mechanisms. Each of them is designed for specific purposes and operates in distinct ways to handle traffic and optimize performance. Global server load balancing is utilized to efficiently distribute application traffic across servers in different geographic locations, redirecting users to a server that is closest to them, unless there is a failure.Amazon’s architecture is a perfect example of how these two components can work together efficiently. It includes the use of Application Load Balancers, Network Load Balancers, and AWS Cloud Map, which assist HTTP APIs in routing traffic to the appropriate endpoints within a Virtual Private Cloud (VPC).Security FeaturesSecurity features of API gateways and load balancers complement each other, enhancing the overall security of the network. Some of the security features provided by load balancers include:  Monitoring traffic patterns to block potentially malicious content  Routing traffic through network firewalls  SSL termination, which allows the secure handling of encrypted traffic and offloads encryption and decryption duties from backend serversThese features work together to ensure the security and integrity of the network. When combined with an API gateway, the security is further strengthened. The API gateway enforces robust authentication protocols and access controls, preventing unauthorized access and providing a secure environment for the network.Combining API Gateway and Load Balancer for Enhanced PerformanceThe symphony of network performance reaches its crescendo when API gateways and load balancers function together. The gateway acts as a bridge between microservices, while the load balancer redirects requests across API endpoints to optimize performance. Utilizing a load balancer with an API gateway improves application performance by evenly distributing traffic among servers, reducing latency, and offloading HTTPS processing.Moreover, this partnership aids in application scalability, facilitating smooth transitions in blue-green deployments and efficient service discovery. By combining a load balancer API gateway with a load balancer, system reliability and fault tolerance are enhanced. This is achieved by rerouting traffic during service failures and providing consistent API endpoints to end-users.Real-World Examples: API Gateway and Load Balancer in ActionIn the real world, companies have successfully implemented API gateways and load balancers to manage network traffic. Company A, for instance, implemented an API Gateway, which centralized the entry points for their microservices, thereby streamlining endpoint management and improving its API ecosystem’s security. Company B’s adoption of a Load Balancer significantly enhanced their user experience by efficiently distributing client requests to the least busy servers, ensuring high availability and consistent application performance.Moreover, Company C integrated an API Gateway with a Load Balancer to manage multiple instances of high volume simultaneous API calls while evenly distributing traffic. This resulted in robust system architecture with the flexibility to handle peak loads, clearly demonstrating the benefits of combining API gateways and load balancers in system design interviews.SummaryIn conclusion, API gateways and load balancers are like the conductors of a digital symphony, each with a unique score. While they may play different instruments, their harmonious performance is integral to managing network traffic effectively. Whether it’s the API gateway’s role as an intermediary between API consumers and microservices or the load balancer’s role in efficiently distributing network traffic, each plays a vital role in maintaining the rhythm of network performance. So, the next time you’re orchestrating your network, just remember - it’s all about harmony!Organizations looking for the best tools to support their API management can leverage Moesif’s powerful API analytics and monetization capabilities. Moesif easily integrates with your favorite API management platform or API gateway through one of our easy-to-use plugins, or embed Moesif directly into your API code using one of our SDKs. To try it yourself, sign up today and start with a 14-day free trial; no credit card required.Frequently Asked QuestionsWhat is the difference between AWS load balancer and API gateway?The main difference between AWS load balancer and API gateway is that load balancers distribute incoming requests, while API gateways authenticate and provide access to data sources or other applications. Load balancers are usually deployed as dedicated physical devices or software running on a set of virtual servers, while API gateways are usually implemented as a service — organizations often deploy an API gateway as a container or VM instance.Can a load balancer be a gateway?Yes, a load balancer can function as a gateway by routing traffic to healthy virtual appliances, centralizing traffic, and enforcing consistent policies across appliances. This capability is particularly useful for high performance and high availability scenarios with third-party Network Virtual Appliances (NVAs).Should I use API gateway or ALB?Based on your transaction volume, if it’s less than 500k per day, the API gateway is effective, but if it’s more than 500k, ALB may be a more affordable solution. Additionally, if you are aiming for feature-rich solutions and want to reduce development hours, the API gateway would be the better choice.What is the main purpose of an API Gateway?The main purpose of an API Gateway is to serve as a single point of entry for managing API calls between API consumers and the microservices of an application.How do load balancers contribute to network management?Load balancers contribute to network management by distributing traffic across servers, preventing overloading on any single server and ensuring system scalability and redundancy. This helps maintain system performance and reliability.                Deep API Observability with Moesif              14 day free trial. No credit card required.              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          "url": " /technical/api-development/API-Gateway-VS-Load-Balancer/",
          "author": "Dylan",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-top-api-managers": {
          "title": "Top 8 API Management Platforms in 2026: A Practitioner's Comparison",
          "content"	 : "API management platforms sit between your APIs and the people (and agents) calling them. They handle the gateway runtime, the developer portal, the analytics, and increasingly the monetization layer. The category has been mature for a decade, but the choices that hold up in 2026 reflect a few shifts: cloud-native deployment, multi-gateway support, and explicit handling of AI agent traffic.                Monitor REST APIs With Moesif              14 day free trial. No credit card required.              Try for Free        This is a practitioner’s comparison of the eight platforms that consistently show up on enterprise shortlists, what each does well, what each does poorly, and how to pick between them. The post does not include vendor marketing language; descriptions are factual and based on customer reviews and live product documentation.What API management actually covers in 2026The category has expanded beyond the original “API gateway plus developer portal” definition. A full API management platform in 2026 covers six capabilities:  API gateway runtime. Routes calls, enforces auth and rate limits, transforms payloads. The runtime sits in front of every backend service.  Developer portal. Hosts documentation, manages signup and credentials, exposes self-service rate limit and usage info.  Lifecycle management. OpenAPI-first design, governance against the spec at merge time, deprecation tooling.  Analytics. Per-endpoint and per-customer usage analytics, latency and error tracking.  Monetization. Usage-based pricing, billing integration, customer-level rate plans.  AI/agent surface. MCP exposure, agent-readable spec output, separate auth model for agents.Not every platform covers all six. The shortlist below is honest about what each one ships and what requires bolt-on tools.How to evaluate API management platformsFive criteria that matter in practice:  Deployment model. Cloud-only, self-hosted, hybrid, or multi-cloud? Match this to your existing infrastructure rather than choosing a deployment model first.  Gateway runtime depth. Throughput per node, latency overhead, policy execution model (declarative vs. code), supported protocols (REST, gRPC, GraphQL, WebSocket).  Developer portal quality. OpenAPI-spec-driven, customizable, multi-tenant for partner programs.  Analytics and monetization layer. Built-in vs. requires-third-party-tool. Most platforms are stronger on operational analytics than on per-customer business analytics.  Pricing model. Per-request, per-environment, per-API, or per-call-volume. Estimate annual cost carefully before committing.The fit-for-purpose question matters more than the absolute capability ranking. A platform that fits your team’s existing stack will outperform a stronger platform that requires architectural change.The 8 platforms comparedKongMaintained by Kong Inc., available in open-source (Kong Gateway) and commercial (Kong Konnect / Enterprise) forms.  Strengths: Lightweight gateway runtime with a large plugin catalogue (auth, rate limiting, observability, transformations) on top of an NGINX/OpenResty core.  Trade-offs: Lighter on lifecycle management, governance, and developer portal compared to full platforms. Typically requires separate tools for per-customer analytics and monetization.  Best for: Teams that want gateway control without the full lifecycle suite; Kubernetes-heavy environments where Kong Ingress Controller is already in use.ApigeeGoogle Cloud’s API management platform; formerly an independent product (and earlier “Apigee X”), now marketed simply as Apigee.  Strengths: Full-lifecycle platform that covers gateway, developer portal, analytics, and monetization. Natural fit if the organization is already committed to Google Cloud at meaningful scale.  Trade-offs: High entry cost (the minimum Google Cloud spend is non-trivial), XML-based policy configuration that takes time to learn, documentation breadth that has historically been uneven. Cross-cloud and on-prem deployments are constrained.  Best for: Google Cloud-committed enterprises that want a single integrated platform and accept the operational and pricing commitment.See our deeper Apigee deep dive for a full teardown.TykMaintained by Tyk Technologies; open-source gateway with commercial dashboard and management portal.  Strengths: Self-hostable in air-gapped environments. GraphQL gateway in the commercial offering. Multi-data-center deployment.  Trade-offs: Smaller community and partner ecosystem than Kong or WSO2. Dashboard and analytics features are paywalled. AI/MCP support is plugin-based rather than native.  Best for: Teams that specifically need air-gapped or hybrid deployment and are willing to invest in commercial Tyk for the management surface.AWS API GatewayAmazon’s managed API gateway service inside AWS.  Strengths: Direct integration with Lambda, IAM, CloudWatch, and the rest of AWS. Per-request pricing is straightforward at low-to-moderate scale.  Trade-offs: Developer portal is minimal; lifecycle management, governance, and monetization features are thin compared to full platforms. AWS-only deployment means real lock-in, and multi-cloud or on-prem use cases need a different gateway. Per-request pricing gets expensive at high volume.  Best for: AWS-native stacks where the API is fronting Lambda or other AWS services, and where cross-cloud portability is not a requirement.Azure API ManagementMicrosoft’s API management platform inside Azure.  Strengths: Direct integration with Azure services (Entra ID/AAD, Functions, Logic Apps). Includes a built-in developer portal.  Trade-offs: Pricing tiers are layered and harder to estimate than other platforms. Per-customer analytics depth is lighter than WSO2 or Apigee. Single-cloud deployment limits portability.  Best for: Azure-native enterprises that already use Entra ID for identity and want minimal new vendor integration.IBM API ConnectIBM’s enterprise API management platform.  Strengths: Compliance feature set (HIPAA, PCI) and direct integration with the rest of the IBM software estate.  Trade-offs: Slower release cadence than cloud-native platforms; developer experience and self-service surface are weaker than newer platforms. Pricing typically requires direct IBM engagement rather than self-service.  Best for: Enterprises with existing IBM commitments or in industries where IBM is already the standard vendor.GraviteeMaintained by Gravitee.io; open-source with commercial Enterprise edition.  Strengths: Multi-protocol (REST, GraphQL, gRPC, async) with an event-native gateway. Open-source license on the core gateway.  Trade-offs: Smaller community and partner ecosystem than Kong, Apigee, or WSO2. AI/MCP support is limited compared to platforms with dedicated AI gateways. Analytics and monetization depth are lighter.  Best for: Teams that need event-driven or async API patterns (Kafka, MQTT) alongside REST and can live with a smaller ecosystem.WSO2 API ManagerMaintained by WSO2; open-source under Apache 2.0 with commercial subscription. Note: Moesif is part of WSO2, so this comparison is not arms-length. Treat the description as factual but verify independently.  Strengths: Full-lifecycle platform covering gateway, governance, developer portal, analytics, monetization, and AI/agent surface. Multi-gateway runtime (deploy across WSO2, Kong, AWS, Azure, Envoy from one control plane) (a capability no other platform in this list offers). Native AI Gateway for inbound MCP and outbound LLM traffic, with MCP servers auto-generated from any OpenAPI spec. Spec-level governance enforced at merge time. Apache 2.0 license on the core gateway, portal, and publisher. Named a Leader in The Forrester Wave: API Management Software, Q3 2024.  Trade-offs: Operational footprint is larger than gateway-only options like Kong. Commercial subscription is the standard route to support and SLAs; smaller teams can run the open-source distribution but take on operational responsibility themselves.  Best for: Enterprises with multi-cloud or multi-gateway requirements, teams running meaningful AI/agent traffic alongside traditional REST, and organizations that want one integrated platform rather than assembling gateway + governance + analytics + monetization from separate vendors.Side-by-side comparison table            Platform      Open-source?      Deployment      Lifecycle depth      AI/MCP support                  Kong      Yes (core)      Cloud, self-hosted, K8s      Medium      Plugin-based              Apigee      No      Google Cloud      High      Gemini Code Assist; no native MCP              Tyk      Yes (core)      Self-hosted, hybrid      Medium      Plugin-based              AWS API Gateway      No      AWS only      Medium      Bedrock integration              Azure APIM      No      Azure only      Medium-High      Azure OpenAI integration              IBM API Connect      No      Cloud, on-prem      High      Limited              Gravitee      Yes (core)      Cloud, self-hosted      Medium      Limited              WSO2 API Manager      Yes (core)      Cloud, on-prem, multi-cloud      High      Native (AI Gateway)      API management in 2026: AI gateway, MCP, and agent trafficTwo changes have meaningfully shifted what API management platforms need to do.AI gateway as a first-class capability. The 2025-2026 generation of API management platforms either includes an AI gateway natively (WSO2 AI Gateway, Kong AI Gateway) or integrates with one (Apigee + Vertex AI, AWS + Bedrock). The AI gateway handles inbound agent traffic, outbound LLM calls, and the cost-attribution work that comes with both.MCP server generation from OpenAPI specs. As agent runtimes adopted the Model Context Protocol, the question became how to expose existing APIs in MCP-compatible form without maintaining a separate codebase. Some platforms (WSO2) generate MCP servers from the OpenAPI spec automatically; others require manual wrappers. This is one of the few capabilities that meaningfully differentiates the 2026 generation.If your roadmap includes meaningful AI or agent traffic, the question to ask each vendor is “how does your platform handle MCP-mediated traffic and per-agent observability?” The answers vary widely.How to pick the right platform for your stackThe fastest decision shortcut:  Already on AWS? AWS API Gateway is the lowest-friction default; consider Kong or WSO2 if you need more lifecycle depth.  Already on Google Cloud? Apigee is the natural fit; consider WSO2 multi-gateway runtime if multi-cloud is also a requirement.  Already on Azure? Azure API Management is the natural fit.  Multi-cloud or on-prem? WSO2 or Tyk are the strongest options.  Need AI gateway capabilities? WSO2 has the most mature native offering; Kong and Apigee have integrations.  Need event/async support alongside REST? Gravitee is differentiated here.  Regulated industry with existing IBM relationship? IBM API Connect is the natural fit.The right platform depends on existing infrastructure choices more than on absolute feature ranking.How Moesif complements an API management platformMost API management platforms ship with operational analytics: per-endpoint latency, error rates, throughput. The gap is per-customer behavioral analytics and per-customer revenue attribution.Moesif sits behind any gateway (Kong, Apigee, Tyk, AWS, Azure, IBM, Gravitee, WSO2) and captures payload-level analytics per customer. The combination answers the questions an API platform alone cannot: which customers are growing, which are churning, which integrations are stalling, what behavior predicts conversion.For teams running an API platform plus Moesif, the typical pattern is: platform handles the gateway and lifecycle, Moesif handles the analytics and monetization layer. For teams on WSO2 specifically, the two are integrated as the analytics layer of the WSO2 API Platform.For the developer-portal half of the API management story, see our developer portal guide.Next stepsAPI management platforms are mature enough that fit-for-purpose almost always beats absolute feature ranking. Pick the one that fits your existing infrastructure and team capacity, layer per-customer analytics on top, and revisit the choice annually.To see what per-customer analytics on top of your API platform looks like, start a 14-day Moesif free trial. No credit card required.Frequently asked questionsWhat is API management? The practice of running APIs as products: gateway runtime, developer portal, lifecycle management, analytics, and monetization. The category covers the platforms that handle these functions together.Which API management tool is best? The fit-for-purpose answer depends on your existing infrastructure. AWS API Gateway for AWS-native stacks, Apigee for Google Cloud, Azure APIM for Azure, WSO2 for multi-cloud and AI-heavy workloads, Kong for cloud-native gateway-only needs.Is Kong better than Apigee? They solve different problems. Kong is gateway-focused with a lightweight footprint; Apigee is a full-lifecycle platform with broader scope (and higher cost). Neither is universally better (the right choice depends on whether you want gateway control or a full platform, and on which cloud you are committed to). WSO2 covers the same scope as Apigee with Apache 2.0 licensing and multi-cloud deployment.Do I need an API management platform at all? At a single API or a small handful of internal APIs, no. At a dozen APIs and up, yes, because the alternative is each team inventing its own governance and observability.What is an AI gateway? A 2025-2026 addition to API management platforms that handles inbound agent traffic and outbound LLM calls. Includes auth, rate limiting, cost attribution, and (in some platforms) MCP server generation.How does API management relate to API observability? API management platforms include operational analytics out of the box. Per-customer behavioral analytics and revenue attribution typically require a separate observability tool like Moesif. Most teams use both.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /technical/api-development/Top-API-Managers/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-understanding-api-codes": {
          "title": "Understanding API Codes: A Comprehensive Guide to Efficient REST API Integration",
          "content"	 : "If you’re tackling REST APIs, decoding API codes is essential. Think of HTTP status codes as the vital signs for API requests. Our guide strips away the complexities, offering a clear explanation of each code from ‘200 OK’ to ‘500 Internal Server Error’, and how to use them efficiently. Whether you’re a developer or a system architect, you’ll learn to interpret these codes swiftly and apply best practices for worry-free API communications.This comprehensive guide serves as an indispensable tool for anyone involved in API development or integration. By demystifying the nuances of each status code, we empower you to handle API responses with confidence. You’ll gain insights into the subtleties of client-server interactions, and how to gracefully handle everything from successful requests to unexpected errors.Understanding the full spectrum of HTTP status codes is akin to having a detailed roadmap when navigating the complex highways of web development. With this knowledge, you’ll be equipped to troubleshoot with precision, optimize your API’s user experience, and ensure seamless communication between services.Join us on this journey through the world of HTTP status codes, where you’ll not only decode the numerical shorthand but also master the art of API conversation. Our guide is designed to be your companion, providing you with the clarity needed to make informed decisions and maintain robust API connections.                Monitor and Analyze API Traffic with Moesif              14 day free trial. No credit card required.              Try for Free        Key TakeawaysHTTP status codes are crucial for efficient REST API communication, indicating the success or failure of a request, thereby enabling clients to understand and respond to outcomes appropriately.There are five classes of HTTP status codes (1xx, 2xx, 3xx, 4xx, 5xx) representing informational responses, successful outcomes, redirections needed, client errors, and server errors respectively, each with their specific use cases and implications.Adhering to best practices when implementing HTTP status codes—including the consistent use of standard codes, providing clear error messages, and documenting API behavior—enhances the reliability and usability of REST APIs.The Essence of HTTP Status Codes in REST APIsPicture a world devoid of these three-digit HTTP status codes. It would be akin to reading a book without punctuation - confusing, wouldn’t it? HTTP status codes play the vital role of punctuation in the prose of API communication, adding clarity and meaning to the narrative. They ensure that REST APIs are reliable and offer a consistent user experience by communicating the success or failure of an API request.Employing standardized HTTP status codes resembles the utilization of a mutually agreed vocabulary in API communication. Think of them as a universal language that allows developers to create more robust and scalable APIs. This leads to an improved user experience as clients receive clear messages and can make informed decisions based on the response status code.The Role of HTTP Status Codes in API CommunicationWe shall further explore the significance of HTTP status codes in API communication. HTTP status codes are three-digit codes returned by the server. They act as a response code to inform the client about the outcome of an HTTP request. Like the referee’s whistle in a football match, they signal the outcome of each play, or in this case, each request.However, it’s not merely about being aware of the outcome but comprehending it. HTTP status codes enable clients to easily filter requests and pinpoint problems. Just as a doctor uses symptoms to diagnose a disease, HTTP status codes help in identifying the issue at hand. Consistency and clarity in HTTP status codes is like having a predictable weather forecast, enabling clients to quickly understand the result of their API request and take appropriate action.Enhancing User Experience with HTTP Status CodesHTTP status codes go beyond the realm of effective communication to enrich the user experience. They allow clients to quickly understand the result of their API requests, much like road signs help drivers navigate their journey. A status code indicates clear messages and predictable responses from REST API response codes, such as HTTP status codes, promoting a more intuitive user experience.An unambiguous and uniform implementation of HTTP status codes aids in standardizing the results of API requests. It’s like speaking the same language or following the same rules in a game. This makes it easier for users to know what to expect and how to troubleshoot. Consistency in HTTP status codes for error responses enables API consumers to handle errors uniformly, streamlining error resolution, and enhancing user experience.Decoding HTTP Status Codes: Classes and MeaningsDeciphering HTTP status codes is akin to solving a puzzle. Each digit in the three-digit code has a specific meaning, categorized into five classes:  1xx: Informational  2xx: Successful  3xx: Redirection  4xx: Client Errors  5xx: Server ErrorsResponse codes are the keys to understanding the outcome of an HTTP request message, focusing on only the header data, specifically the request header fields.Merely possessing the key isn’t sufficient, one should also know its application. For instance, the 2xx class indicates successful API request handling, while 4xx codes signify client errors with the request itself. So, the next time you see a status code, you’ll know what it’s trying to tell you. It’s as if you have a secret decoder ring to the API’s responses.Accurate understanding and implementation of HTTP status codes can be guided by referring to original specification documents. This practice is akin to consulting a dictionary while crafting a literary masterpiece; it ensures that the language used is precise and the communication is clear. Just as literature has various genres, HTTP status codes cater to a range of scenarios, each with its own set of rules and meanings to convey the narrative of the API’s operation. By delving into the specifications, one can fully grasp the subtleties of these codes and employ them to their full potential, creating a seamless and effective dialogue between the client and server.Informational (1xx)HTTP status codes in the 1xx class act as couriers conveying crucial updates. They communicate transfer protocol-level information, providing a provisional response indicating that the request was received, and the process is continuing. It’s like getting a text saying your food delivery is on its way - the process isn’t complete, but you’re kept in the loop.Particular 1xx status codes encompass:  100 Continue: This code is like a nod from the server, indicating that the initial part of a request has been received and the client should continue with the rest of the request.  101 Switching Protocols: Sent in response to an upgrade request header from the client, and indicates the protocol the server is switching to.  102 Processing: This code communicates to the client that the server has acknowledged the request and is in the midst of processing it, although there is not yet a response ready to be delivered.  103 Early Hints: This code is like a sneak peek, offering a preview of response headers before the final HTTP message is completely ready.These codes provide updates on the request’s current status, like a progress bar on a software update. HTTP status codes in the 1xx category, such as 100 Continue, signify the progress of a long-running operation like a file upload. These codes provide feedback to the client about the status of their request. It’s like having a personal assistant keeping you informed before the final response is available.Successful (2xx)The 2xx class of HTTP status codes can be thought of as the digital thumbs up in the world of APIs, signaling that your request has been successfully received, understood, and processed. It’s like receiving a pat on the back for a job well done.Within the 2xx series, each code has its unique way of delivering good news. For example:  200 OK: This is the go-to code for a general success message when everything has gone just right.  201 Created: Used when a new resource has been successfully brought into existence as a result of the request.  204 No Content: This is like a silent nod of approval—your request was successful, but there’s no need for further conversation.Selecting the right 2xx status code is crucial—it’s like choosing the right reaction for a particular situation. Employing 200 OK for successful operations that return data, and opting for 204 No Content when the silence is golden, ensures that the communication between client and server remains crystal clear. This careful selection not only enhances the predictability of the API but also enriches the overall user experience by ensuring that the feedback is as accurate and helpful as possible.Redirection (3xx)HTTP status codes in the 3xx class function as the traffic regulators of the API universe, directing agents towards the accurate course. These codes signal that additional steps must be taken by the client to complete the request. It’s like being redirected to a new route due to roadwork on your usual path.Specific 3xx codes serve different purposes. For instance:  303 See Other: Directs the client to retrieve the requested resource at a different URI.  301 Moved Permanently: Informs the client that the resource has moved permanently.  307 Temporary Redirect: Suggests the client to repeat the request with an alternative URI.  308 Permanent Redirect: Indicates that the client should repeat the request using another URI and that this change is permanent.Each of these redirection codes serve as a guiding light, leading the client to the right path to fulfill their request.Client Errors (4xx)HTTP status codes in the 4xx class serve as warning signs, pointing out problems presumably caused by the client. They highlight errors with the request, like a referee pointing out a foul in a game, indicating that the request failed.Examples of 4xx status codes include:  400 Bad Request: This is like getting a blank puzzle piece—it doesn’t fit anywhere because the request is malformed or missing something.  401 Unauthorized: It’s as if the door you’re trying to open says “Access Denied” because proper authentication credentials were not provided.  403 Forbidden: Imagine a bouncer denying you entry to a club; you have identification but still can’t get in because you’re not allowed access to the resource.  404 Not Found: Like reaching into a cookie jar and finding it empty, the resource you’re looking for just isn’t there.  408 Request Timeout: This is akin to someone zoning out in the middle of a conversation; the server timed out waiting for the request.  429 Too Many Requests: You’re like a fan asking for too many autographs, and now you have to wait before you can get another.Each 4xx status code functions as a crucial navigational beacon, guiding clients through the error resolution process. They are a fundamental component of the dialogue between client and server, indicating that an issue originated from the client side rather than the server. These codes are invaluable for developers and system administrators as they provide immediate feedback on what went awry with the request, whether it’s due to a malformed syntax, authentication problems, lack of permissions, or non-existent resources. By understanding the nuances of these codes, clients can swiftly determine if the error stems from sending incorrect data, requesting an unauthorized action, or attempting to interact with a resource that’s simply not available. Prompt attention to these error messages allows for quick corrective measures, ensuring that the API interactions remain smooth and user-centric.Server Errors (5xx)Finally, we discuss the 5xx class of HTTP status codes, the sirens of the API domain. They indicate server-side issues, like a smoke detector warning of a possible fire. These are the distress signals that tell the client something has gone wrong on the server’s end, and it’s not the client’s fault.There’s a range of 5xx status codes, each indicating a different server-side issue. For example:  500 Internal Server Error: The catch-all error when the server is aware it has encountered a problem or is otherwise incapable of performing the request.  501 Not Implemented: The server either does not recognize the request method, or it lacks the ability to fulfill the request.  502 Bad Gateway: The server acts as a gateway or proxy and receives an invalid response from an upstream server.  503 Service Unavailable: This status code is the server’s equivalent of a ‘Gone Fishing’ sign. It indicates that, for the moment, the server is too swamped or busy getting a tune-up to handle your request.  504 Gateway Timeout: Similar to a 408 error but it indicates that a gateway or proxy server timed out waiting for a response from an upstream server.  505 HTTP Version Not Supported: The server does not support, or refuses to support, the HTTP protocol version that was used in the request message.Each 5xx status code serves as a vital warning, enabling timely identification and resolution of server-side issues. They act as an essential part of the communication between client and server, signaling that the problem lies with the server and not the request itself. These codes are crucial for developers and system administrators as they provide the first clues in the troubleshooting process, indicating that the server is experiencing difficulties that could range from temporary overloads to more serious application errors or infrastructure issues. Understanding these codes helps in quickly pinpointing whether the fault might be due to a problematic server script, a failed service, a backend connectivity problem, or even a more systemic issue within the server’s infrastructure. By effectively responding to these alerts, service providers can initiate the appropriate fixes or maintenance procedures, thereby mitigating downtimes and ensuring a smoother user experience.Best Practices for Implementing HTTP Status Codes in REST APIsIncorporating HTTP status codes in REST APIs resembles erecting a building. There are best practices that you need to follow, like:  Maintaining the consistency of status codes  Using standard codes  Selecting the appropriate HTTP status code  Documenting codes  Providing clear error messagesThese practices are the blueprints for building a robust and reliable REST API.These best practices are not set in stone; they need to be adapted based on the specific needs and context of the API. However, they serve as a guiding principle, much like a compass guiding a traveler. By adhering to these best practices, one can avoid common pitfalls and ensure that the API is easy to use, understand, and troubleshoot.Selecting the Right Status CodeChoosing the appropriate status code equates to selecting the correct words for a sentence. The choice depends on the type of HTTP request, the nature of the response, and the level of detail required to convey the correct information. It’s like being a chef, choosing the right ingredients for a dish to bring out its best flavors.For instance, you can use a decision flow chart to pinpoint the most suitable HTTP status code. It’s like a choose-your-own-adventure book, where your choices lead you to the most accurate status code. For example, using the 201 status code to indicate successful creation of a resource provides more precise information than the general 200 OK status code. Similarly, don’t overlook the 429 Too Many Requests status code as it provides essential feedback when imposing rate limits on client requests.Consistency in Status Code UsageUniformity is paramount in the application of HTTP status codes. It’s like maintaining a consistent tone of voice in a conversation. A consistent approach to using status codes ensures a clear and predictable user experience.For example, creating a standard format for error responses across all API endpoints can ensure uniformity in error responses. It’s like having a style guide for a writing project. Moreover, documenting error handling mechanisms, expected error codes, and messages in the API documentation assists developers in managing errors effectively.Clear and Meaningful Error MessagesDelivering unambiguous and significant error messages holds the same importance as employing the right status codes. It’s like a doctor explaining a diagnosis to a patient; clarity is crucial. Error messages should align with the corresponding HTTP status code to facilitate client understanding and problem resolution.Detailed error messages in the response body, including a human-readable error message and relevant details, can make a world of difference. It’s like getting a detailed map instead of vague directions. Not documenting custom error codes and their meanings in the API documentation can lead to misunderstandings, thereby reducing the API’s usability.Testing and Debugging REST API Status CodesExamining and rectifying REST API status codes equate to scrutinizing a document. It ensures that everything is in the right place and functioning as expected. Both manual and automated testing tools play a crucial role in this process, ensuring the correct implementation and behavior of HTTP status codes.Testing and debugging are not just about identifying issues; they’re about understanding them. HTTP status codes serve as a valuable debugging and monitoring tool for developers and operations teams during API integration. It’s like using a magnifying glass to examine a piece of art, providing insights into the finer details.Test Scenarios and CasesFormulating test scenarios and cases mirrors the creation of a blueprint for testing. These scenarios and cases verify that the API operates according to its requirements specification. It’s like checking off items on a to-do list, ensuring that everything is as it should be.Happy path tests for APIs should cover:  Validation of the correct HTTP status codes  Response payloads  Response headers  Application state  Basic performance criteria to meet acceptance requirementsIt’s like conducting a thorough inspection of a vehicle before a long journey, ensuring a smooth ride.Automated Testing ToolsAutomated testing tools function akin to the autopilot feature of API testing. They run test cases and validate the correct implementation of HTTP status codes, helping to identify issues or errors. It’s like having a personal assistant handling tasks on your behalf.Selecting the right testing tool is crucial for effectively testing and validating HTTP status codes. Different tools like:  Postman: which focuses on executing API requests  REST Assured: which provides a Java-based framework for simplified API testing  Apache JMeter: which focuses on load testingIt’s like choosing the right tool for the job, enhancing the efficiency and effectiveness of testing efforts.Debugging TechniquesDebugging techniques resemble investigative skills employed to detect and rectify problems. Techniques such as error reporting, notifications, and log analysis can help to identify and resolve issues. It’s like using a flashlight in the dark, illuminating the path to problem resolution.For example, to handle a 400 Bad Request error, ensure the request has no typos or syntax errors, all parameters are correct and present, and the request body matches the expected format. For a 401 Unauthorized error, verify the accuracy and permissions of authentication credentials and the correct authentication method. Each debugging technique serves as a clue, leading to the root cause of the issue. Additionally, double-check the expect request header field for any discrepancies.Common Pitfalls to Avoid When Working with HTTP Status CodesDealing with HTTP status codes is accompanied by its own set of challenges. There are common pitfalls that developers often fall into, like misusing codes, overlooking standard codes, and ignoring custom error codes. It’s like navigating a minefield; you need to know where the mines are to avoid them.These pitfalls can be avoided by adhering to best practices, staying updated with current standards, and implementing custom error codes when necessary. It’s like learning from past mistakes to avoid future ones. By avoiding these pitfalls, one can ensure that the API is easy to use, understand, and troubleshoot.Misusing Status CodesOne common error often committed is the misuse of status codes. It’s like using a hammer when you need a screwdriver; using the wrong tool for the job can lead to confusion and hamper troubleshooting. For instance, using 5xx Server Error codes for client-related errors that should be represented by 4xx Client Error codes can mislead clients.Remember, every status code has a specific purpose. For instance, using 404 Not Found for errors beyond missing resources is incorrect. Similarly, returning a 200 OK status code with an error message instead of the appropriate 4xx or 5xx can confuse API consumers. Avoiding these mistakes can ensure a smooth and efficient API communication.Overlooking Standard HTTP Status CodesAnother frequent mistake is the disregard of standard HTTP status codes. It’s like ignoring the rules of a game; you can’t play effectively if you don’t know the rules. Adhering to standard HTTP status codes is essential for consistent API communication.Stay updated with current standards and avoid implementing deprecated or unused codes. For instance, the 306 status code is reserved and no longer used. Similarly, 402 Payment Required is a status code that is currently reserved for future use. Remember, playing by the rules is the key to success.Ignoring Custom Error CodesThe final common error is the neglect of custom error codes. It’s like ignoring the special features of a gadget; you might be missing out on some useful functionality. Custom error codes can provide more granular error information, addressing situations where standard HTTP status codes might be too broad or generic, such as an internal configuration error.Implementing custom error codes facilitates quicker debugging and issue resolution by offering more detailed context about the errors encountered. Also, custom error codes can augment API interoperability, as long as they are carefully designed and integrated to complement standard HTTP status codes. It’s like adding custom features to a car, enhancing its functionality and usability.SummaryIn summary, HTTP status codes are the unsung heroes of the REST API world. Understanding and implementing them correctly can enhance API communication, streamline operations, and improve user experience. Avoiding common pitfalls and adhering to best practices can ensure a smooth and efficient API operation. So, the next time you see an HTTP status code, remember, it’s not just a number; it’s a message.If you’re looking for an API monitoring, analytics, or monetization platform to enhance your API operations, look no further than Moesif. With easy integrations with most popular frameworks and API gateways, Moesif users can quickly monitor APIs to uncover insights for improving their API operations. To get started today, sign up for a 14-day free trial with no credit card required.                Monitor and Analyze API Traffic with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Do you know what status codes your users are seeing?            Use Moesif's API analytics to discover and report status codes users see and proactively help customers with errors.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Understanding-API-Codes/",
          "author": "Dylan",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-lifecycle": {
          "title": "API Lifecycle: The 7 Stages from Design to Retirement",
          "content"	 : "The API lifecycle is the set of stages an API goes through, from the moment a designer sketches an endpoint on a whiteboard to the day that endpoint is finally retired and a 410 Gone response replaces it. Most articles describe five or six stages. We describe seven, because the last stage (the deliberate retirement of an API version) is the one teams skip most often and pay for most painfully.                Monitor REST APIs With Moesif              14 day free trial. No credit card required.              Try for Free        This post walks through each stage, what actually happens inside it, and the tools that span them. By the end you will have a working framework for thinking about where your API is in its life and what needs to happen next.What is the API lifecycle?The API lifecycle is the structured set of stages an API passes through from initial design to eventual retirement. Each stage has its own activities, decisions, and risks. Treating the lifecycle as a single repeating cycle (instead of a one-shot project) is how mature API teams keep multiple versions running without breaking customers.For organizations that operate dozens or hundreds of APIs, lifecycle management is one of the core jobs of an API platform. Postman, Akamai, IBM, MuleSoft, and WSO2 all sell tools around it. The seven stages below are vendor-agnostic; the platform you pick determines how integrated those stages feel in practice.The seven stages of the API lifecycle            #      Stage      Primary owner      Output                  1      Plan and design      Product + Architecture      OpenAPI spec, requirements doc              2      Build      Engineering      Working API server              3      Test      QA + Engineering      Passing test suite, contract tests              4      Govern and secure      Platform team      Approved spec, security review sign-off              5      Deploy and publish      Platform + DevOps      Live endpoint, developer portal listing              6      Observe and analyze      Platform + Product      Usage dashboards, alerts, customer insight              7      Iterate, deprecate, retire      Product + Engineering      New version, deprecation calendar, retired endpoints      The stages overlap in practice. A v2 endpoint can be in build at the same time as a v1 endpoint is in observe-and-analyze. The framework helps a team know what they are responsible for at each moment.Stage 1: Plan and designThe lifecycle starts before any code. The plan stage answers three questions:  Who is the consumer? External developers integrating commercially, internal teams, AI agents, or some combination?  What problem does this API solve? The mistake most new APIs make is exposing a database when they should expose a workflow.  What does success look like? A measurable target: integrations shipped per quarter, agent calls per month, revenue from API consumption, ticket reduction.The design stage turns the plan into an artifact. In 2026 that artifact is almost always an OpenAPI specification, written first, before any server code. An OpenAPI-first workflow gives you a single source of truth that generates server stubs, client SDKs, and documentation by construction. Our API design principles guide covers the design decisions that compound across the rest of the lifecycle.Stage 2: BuildThe build stage implements the OpenAPI spec. The work splits into three layers:  Routing and request handling: turning HTTP requests into function calls in your language of choice.  Business logic: the actual work the API does. Talking to databases, calling other services, computing the answer.  Response shaping: serializing results into the response shape your spec promises.The framework you pick (Express in Node, FastAPI in Python, Spring in Java, etc.) handles the first and third layers; the second is yours. The discipline that matters at this stage is not letting the implementation drift from the spec. Generate stubs from the spec; do not hand-write routes.Stage 3: TestTesting an API runs across three levels:  Unit tests for individual functions and route handlers.  Integration tests that hit the API end-to-end with realistic payloads.  Contract tests that verify the API’s actual behavior matches the OpenAPI spec. Tools like Dredd or Schemathesis read your spec and generate requests that exercise every endpoint.Status codes are where most contract violations show up. If your spec says POST /orders returns 201 Created and your implementation returns 200 OK, every consumer that branches on status code is in trouble. Our HTTP status codes reference covers the right code for every common scenario.Idempotency testing is the underrated discipline in 2026. AI agents retry requests. If your POST endpoints are not safe to retry, agent traffic will create duplicate resources in production. Test with explicit Idempotency-Key headers and verify retries return the same response.Stage 4: Govern and secureGovernance is the stage that separates a hobby API from a production one. The platform team enforces:  Naming conventions: plural nouns for collections, predictable nesting, no /createUser endpoints.  Security minimums: TLS 1.2 or higher, OAuth or mTLS authentication, no PII in URLs.  Schema compliance: every endpoint must be documented in the OpenAPI spec; specs must pass policy checks.  Rate limiting and quotas: every public endpoint has a default rate limit, with overrides for enterprise customers.  Audit logging: every call attributable to a customer, retained long enough to support investigations.This is where API platforms earn their keep at scale. Manual review on every spec change does not survive past about a dozen APIs. Automated governance against the OpenAPI spec at merge time does.Stage 5: Deploy and publishTwo distinct activities happen in this stage.Deploy is moving the code to a runtime environment. Modern paths in 2026 are managed platforms (Vercel, Railway, Render, Fly), container runtimes (AWS ECS, Google Cloud Run, Azure Container Apps), or Kubernetes if your team operates a cluster. The API gateway sits in front, handling auth, rate limiting, and routing.Publish is making the API discoverable to its consumers. For an external open API, that means a developer portal: docs generated from the OpenAPI spec, a self-service signup flow for API keys, code examples, an interactive explorer, and a changelog. For an internal API, the equivalent is an internal developer portal entry.A common mistake here is treating publish as an afterthought. The developer portal is the surface where adoption happens. Time-to-first-call (the minutes between signup and a successful API call) is the single best leading indicator of integration success.Stage 6: Observe and analyzeOnce the API is live, the question shifts. It is no longer “does it work?” but “does it work for the right customers, at the right latency, with the right error rates?”The observability discipline at this stage answers three questions:  Is it fast? P50 and P95 latency, broken down per endpoint, per region, per customer.  Is it correct? Error rate distribution, by status code, by customer, by endpoint.  Who is calling it? Usage by customer cohort, endpoint adoption curves, agent vs. human traffic split.These three questions are why API observability exists as a discipline separate from infrastructure monitoring. Server-level metrics tell you CPU and memory; they do not tell you that customer X is getting 500s on /v1/orders while everyone else is fine.Moesif API monitoring covers the API-specific layer: per-endpoint, per-customer, payload-level visibility across any gateway, with usage events that flow into billing and product dashboards. The integration from gateway to observability layer is what closes the loop between the API as a piece of infrastructure and the API as a product.Stage 7: Iterate, deprecate, retireThis is the stage teams underinvest in.Iterate is the easy part. Usage data from stage 6 feeds back into stage 1, and the cycle continues for new endpoints and new versions. The new versions ship.Deprecate is harder. When an old version is replaced, customers who built on it need warning. The standard discipline: announce the deprecation publicly, set a sunset date (twelve months for paid public APIs, six months for free public APIs, ninety days for internal APIs), send reminders, and use the Sunset and Deprecation HTTP response headers so SDKs can warn developers automatically.Retire is the part that does not feel productive but is the part that defines a mature platform. Old versions cost money to run, fragment your support load, and slow every future change. When a deprecation period ends, the old version actually has to go offline. Many teams keep “deprecated” versions running for years out of risk aversion, which is how three-version platforms become eight-version platforms and engineering velocity collapses.The retirement decision should be made on data: how many customers, how much revenue, how often called. Stage 6’s analytics are exactly the inputs to stage 7’s retirement decision.Who owns the API lifecycle: team patterns that workThe seven-stage lifecycle assumes someone is accountable for it. In small organizations that is one engineer wearing multiple hats. In large organizations the lifecycle spans product, platform engineering, security, and developer-experience teams, and the failure mode is that no single owner has end-to-end visibility.The patterns that work in practice:Dedicated platform team owns the lifecycle framework. A small platform engineering or developer-experience team (3-10 people in most enterprises) owns the lifecycle definition itself: which tools each stage uses, what the governance rules are, how new APIs get proposed. They do not build every API; they build the paved road that other teams use to build APIs.Per-API product owners drive individual lifecycles. Each API has a product owner (or technical product manager) who is accountable for its position in the lifecycle. They decide when an API moves from design to build, when it ships to production, when it is deprecated. The platform team provides the runway; the product owner steers the plane.Engineering teams build, but to the framework. Engineering teams ship the API itself, following the design rules, security baseline, and observability requirements the platform team published. The discipline is that engineering does not invent new patterns per-API; they apply the framework the platform team maintains.Security and compliance teams provide gates, not bottlenecks. Security review at design time (spec linting, threat modeling for high-risk APIs) is faster and cheaper than security review at production time. The mature pattern is automated security checks integrated into CI/CD with periodic deep-review for high-risk cases, not manual review of every API.Developer-experience and documentation are a function, not a side job. Maintaining the developer portal, writing SDKs, running developer support, and tracking time-to-first-call as a metric is a full-time function in any organization that runs a meaningful API platform. Teams that assign developer-experience as a 20% project to engineers consistently underinvest in it.Cross-functional working group reviews the portfolio. A monthly or quarterly forum where product, platform engineering, security, and (increasingly) the AI platform team review the full API portfolio together. Decisions made here are deprecation timelines, new API proposals, and lifecycle exceptions. Without this forum, APIs accumulate in the portfolio with no one explicitly authorized to retire them.Put differently: lifecycle ownership is rarely a single role; it is a coordination function across roles that the platform team facilitates. The platform team’s success metric is not “how many APIs we built” but “how easily other teams can move APIs through the lifecycle.”API lifecycle in 2026: agents, MCP, and the AI surfaceThe lifecycle framework above is mostly unchanged from five years ago, but two things did change.AI agents are a first-class consumer. When an MCP server fronts your API, the consumer of stages 1-7 is now an agent, not just a human developer. Two implications: design (stage 1) needs to think about agent-friendly operation IDs and descriptions in the OpenAPI spec, and observability (stage 6) needs to attribute traffic to specific agents and workflows, not just customers.The AI surface needs its own governance. Outbound LLM calls (your application calling OpenAI, Anthropic, etc.) have a lifecycle too: prompt design, token budgeting, model selection, evaluation, and retirement of old prompts. The WSO2 AI Gateway treats outbound LLM traffic and inbound MCP traffic under the same governance model as REST APIs, which is the simplest way to extend lifecycle thinking to AI workloads.How the WSO2 API Platform and Moesif cover the full lifecycleMost lifecycle articles stop at the framework. The harder question is which tools span which stages without making your platform team integrate five vendors.The integrated WSO2 + Moesif stack covers:  Stage 1 (design): WSO2 API Manager supports OpenAPI-first design with policy enforcement at the spec level.  Stages 2 + 3 (build, test): Your language and framework choice; WSO2 publishes governance checks that run against the spec.  Stage 4 (govern, secure): WSO2 API Manager handles multi-gateway governance (WSO2, Kong, AWS, Azure, Envoy) and applies security policies automatically.  Stage 5 (deploy, publish): WSO2 generates the developer portal from the spec; the AI Gateway exposes the same API as an MCP server for agent consumption.  Stage 6 (observe, analyze): Moesif sits behind the gateway and provides per-endpoint, per-customer, payload-level analytics.  Stage 7 (iterate, retire): Moesif analytics feed the retirement decision; WSO2 publishes deprecation policies and enforces sunset headers.The tools you can swap in for individual stages: Postman or Stoplight cover design with lighter governance and analytics; Apigee, Kong, or Tyk cover the gateway with different lifecycle depth; Datadog or New Relic cover some operational-monitoring questions but not the per-customer business analytics. The platform decision is whether you want one stack that spans the full lifecycle (WSO2 + Moesif) or to integrate multiple best-of-breed tools yourself and accept the integration cost.Next stepsThe lifecycle framework is most useful when you know which stage your API is in and what needs to happen next. If you are in stage 6 and your observability tooling is the bottleneck, start a 14-day Moesif free trial and get per-endpoint, per-customer analytics in production within an hour of integrating. No credit card required.Frequently asked questionsWhat are the stages of the API lifecycle? Plan and design, build, test, govern and secure, deploy and publish, observe and analyze, and iterate-deprecate-retire. Most frameworks call it five or six stages by combining the planning and design steps, or by dropping retirement.What is API lifecycle management? API lifecycle management is the practice of running and governing APIs across all stages above, usually with a platform that integrates the tooling. Postman, IBM API Connect, MuleSoft, Apigee, Kong, and the WSO2 API Platform are common choices.Who manages the API lifecycle? Typically the platform team or API platform organization. For smaller companies, it may be the same engineering team that builds the API. For enterprises, design and governance are platform team responsibilities, build is the API team, and operations sit with both.What is the difference between API lifecycle and API management? The lifecycle is the conceptual framework; API management is the tooling and platform that implements it. You can do lifecycle management with spreadsheets and discipline; an API management platform automates governance, deployment, and observability across many APIs.How long should an API stay deprecated before retirement? Twelve months is the standard for paid public APIs, six months for free public APIs, and ninety days for internal APIs. Publish a sunset date on day one of the deprecation, use the Sunset and Deprecation HTTP headers, and stick to the date.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /technical/api-lifecycle/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-mastering-api-gatekeeping": {
          "title": "Mastering API Gatekeeping: A Guide to Efficiently Managing API Operations",
          "content"	 : "Seeking to enhance your API operations? Delve into the world of API management, where securing, optimizing, and maximizing the potential of your API inventory becomes clear. Uncover the significance of API gateways, grasp the nuances of lifecycle management, and learn strategies to maintain uptime and performance. Ideal for those looking to tighten security, increase efficiency, or generate revenue through API management, these insights are designed to be practical and straightforward.Key Takeaways      API management is key for ensuring secure and efficient operations within organizations and involves tasks like managing API lifecycles, enforcing security protocols through API gateways, and providing developer resources through portals.        Strategically managing APIs boosts business operations by enhancing security, optimizing server performance, and creating monetization opportunities, while also facilitating deployment and maintenance through tools like automated monitoring.        Integrating API management with cloud services contributes to scalability, flexibility, and cost efficiency, supporting hybrid architectures that connect legacy systems with modern APIs, and ensuring security and compliance through proper access control, authentication, and regulatory adherence.                  Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        Exploring the Essentials of API ManagementAPI management serves as the orchestrator of the digital symphony, harmonizing various components to create a unified whole. It encompasses the principles, procedures, and technologies that ensure the secure and efficient operation of APIs within an organization. Whether you’re a burgeoning startup or a well-established enterprise, effective API management is the linchpin that holds your digital strategy together.The Role of API Gateways in Managing Traffic FlowEnvision an API gateway as the digital world’s traffic controller. It helps manage the influx of API requests, distributing them evenly across backend services to ensure smooth and efficient operation. But it doesn’t stop there. API gateways also play a significant role in routing API requests, processing them according to specified policies, and directing them to the appropriate services.Regarding security, API gateways function as vigilant gatekeepers, enforcing strong security protocols to safeguard your API traffic.Understanding API Lifecycle ManagementAn API, similar to a living organism, experiences different stages in its lifecycle, spanning from initiation to retirement. Managing this lifecycle effectively is crucial for enhancing API performance and driving productivity. This involves everything from:  Designing and developing the API  Deploying and monitoring its performance  Maintaining and updating the API  Retiring or deprecating the API when necessaryBy effectively managing each stage of the API lifecycle, you can ensure that your API remains efficient, reliable, and meets the needs of your users. In this process, API management is crucial for maintaining a seamless user experience.With the right tools at your disposal, such as specialized API lifecycle management platforms, including a reliable API management platform, you can ensure the smooth operation of your APIs, paving the way for a seamless user experience on various API management platforms.Importance of Developer Portals in API EcosystemsWithin the expansive API ecosystem, developer portals emerge as the nucleus, providing developers with the necessary resources. From comprehensive documentation to support and assistance, these portals act as a one-stop-shop for everything API-related. Developer portals offer:  Comprehensive documentation  Support and assistance  A collaborative environment  Facilitation of API discovery and exploration  Promotion of innovation  Enhancement of the overall API program.With renowned examples like the Visa Developer Portal and Spotify for Developers Portal leading the way, developer portals have become an integral part of the API landscape.Unlocking the Benefits of API Management for Your BusinessAdopting efficient API management strategies can yield a multitude of benefits for your enterprise. From bolstering security to optimizing performance and opening up new monetization avenues, the advantages are manifold.Let’s delve deeper and uncover how an API management solution can transform your business operations through effective API management.Enhancing Security with Robust PoliciesIn the contemporary digital landscape, security holds supreme importance. Tightening the security of your APIs is like installing a state-of-the-art alarm system for your digital assets. Robust security policies and access control mechanisms ensure that your APIs are protected from unauthorized access and malicious use.Implementing such comprehensive security measures not only safeguards your data but also fortifies your reputation in the market.Optimizing Performance Across Multiple ServersEfficient API management across multiple servers can boost performance, akin to a well-maintained machine operating at its peak. Load balancing, which involves evenly distributing network traffic, plays a crucial role in this. It ensures scalability and reliability, thereby enhancing user experience.High availability, another key aspect, can be achieved through multi-region deployment, providing uninterrupted service even if one region goes down. However, managing API performance across multiple servers does come with its own set of challenges, such as maintaining consistent performance and ensuring a positive user experience.Monetizing APIs Through Effective ManagementAPIs have transcended their role as mere technical tools and now serve as powerful instruments for revenue generation. Effective API management opens up a world of monetization opportunities, from selling API calls directly to developer teams to offering unique datasets or insights for a price. With proper handling of api keys, businesses can ensure secure and controlled access to their valuable resources.Usage-based pricing, where users pay according to their actual usage of the API, can offer a more flexible and equitable payment model. Premium access, on the other hand, provides access to high-value services, allowing you to maximize your revenue potential.Streamlining API Deployment and MaintenanceAPI management goes beyond handling existing APIs; it includes the optimization of deployment and maintenance processes for new APIs. From simplified API creation to automated monitoring tools, an effective Azure API management system can significantly reduce development time and effort.Simplified API Creation and ConfigurationThe process of API creation need not be intricate. With the right API management tools, you can easily create and configure APIs, reducing development time and fostering productivity. Here are some effective strategies to follow when creating APIs:  Follow web standards and conventions  Test thoroughly to ensure functionality and reliability  Create effective error codes to provide meaningful feedback to clientsBy following these strategies, you can ensure that your APIs are designed for optimal client interaction.Automated Monitoring Tools for API HealthMaintaining your APIs’ health is vital to ensure top-notch performance and a smooth user experience. Automated monitoring tools can play the role of a personal health assistant for your APIs, detecting issues and outages in real-time. These tools can provide valuable insights into:  Payloads  Customer health  API correctness  PerformanceThis helps you to maintain a healthy API ecosystem.Furthermore, they can identify a variety of issues such as technical system health problems, errors, warnings, and performance anomalies, ensuring that any issues are promptly addressed.Continuous API Iteration and Version ControlIn the ever-evolving realm of APIs, the only constant is change. Continuous API iteration and version control allow businesses to adapt and improve their APIs over time, responding to evolving market trends and customer requirements. API management tools provide the necessary support for this process, managing various versions of APIs, monitoring changes, and implementing new versions without disrupting current clients.By adhering to best practices such as understanding the API prior to integration and supporting API versioning, businesses can ensure that their APIs remain relevant and effective.Integrating API Management with Cloud ServicesThe advent of cloud technology has transformed business operations, including API management. As more and more businesses move their operations to the cloud, integrating API management with cloud services can bring about enhanced scalability, flexibility, and cost-efficiency.Leveraging Cloud-Based API GatewaysCloud-based API gateways offer a scalable and cost-effective solution for managing API traffic and security. From handling API traffic to enhancing security, these gateways offer a myriad of benefits. With reputable providers like Amazon API Gateway, Kong Gateway, and Apigee leading the way, leveraging cloud-based API gateways can significantly enhance your API management strategy.Hybrid Architectures: Bridging Legacy Systems and Modern APIsFor businesses with legacy systems, hybrid architectures offer a bridge to the modern world of APIs. By integrating on-premise and cloud-based components, businesses can effectively manage APIs across diverse environments. This approach not only enhances scalability and flexibility, but also enables businesses to securely manage and integrate with legacy systems and sensitive data.Navigating API Security and Compliance ChallengesWithin the API domain, security and compliance hold the utmost importance. From implementing robust access control and authentication mechanisms to adhering to regulatory standards, navigating these challenges can be a daunting task. But with the right API management tools, it’s a task that can be successfully tackled.Implementing Access Control and AuthenticationImplementing access control and authentication mechanisms is like building a fortified castle around your APIs. It ensures that only authorized users can access and interact with your APIs, keeping malicious entities at bay. With the right tools and best practices, you can create a robust security framework for your APIs.Adhering to Regulatory Standards with API Policy ManagerLike a seasoned navigator, an API policy manager can guide your business through the complex terrain of regulatory standards and industry-specific requirements. From ensuring compliance with security controls to maintaining data integrity, an API policy manager can keep your business on the right track.Enhancing User Experience Through API ManagementAPI management extends beyond mere management of APIs; it also focuses on enriching the user experience. From designing APIs for optimal client interaction to ensuring consistent API responses, effective API management can significantly enhance the user experience.Designing APIs for Optimal Client InteractionA well-designed API can markedly augment client interaction, much like a well-structured building enhances the living experience. By following best practices and paying attention to details like intuitive interfaces, flexible designs, and comprehensive api documentation, you can ensure that your APIs are user-friendly and easy to use.Balancing Load and Ensuring Consistent API ResponsesBalancing load and ensuring consistent API responses is like conducting a symphony. Each component must work in harmony to deliver a flawless performance. By implementing strategies like load balancing and performance optimization, you can ensure consistent API responses, even during periods of high traffic.Why Use Moesif with Your API Management Tool?While API management tools fuel your API strategy, Moesif acts as the turbocharger, amplifying their performance. By providing advanced analytics and monetization capabilities, Moesif enhances the capabilities of your API management tools, offering invaluable insights and enabling effective monetization strategies.Advanced Analytics Dashboards with MoesifMoesif’s advanced API analytics dashboards function as a control center for your APIs. They offer real-time insights into API usage, performance, and user behavior, helping you to understand how your APIs are being used and where improvements can be made. These dashboards provide a granular view of API metrics, enabling you to drill down into the specifics of API calls, error rates, endpoint performance, and even the geographical distribution of your users.The dashboards are designed with customization in mind, allowing you to tailor them to your specific needs and focus areas. Whether you’re interested in tracking the adoption rate of new API features, understanding the impact of API changes on user experience, or identifying the most engaged user segments, Moesif’s dashboards can be configured to highlight the data that matters most to your business.Moreover, these analytics dashboards are not just for viewing static data; they empower teams to interact with the data, set up alerts for anomalies, and even predict trends. By leveraging machine learning algorithms, Moesif can help anticipate user needs and identify potential issues before they escalate, enabling proactive API management.In summary, Moesif’s advanced analytics dashboards are more than just a reporting tool—they are a comprehensive solution for API intelligence that supports data-driven decision-making and strategic planning for API initiatives.Monetization Strategies Enabled by MoesifMonetization forms a pivotal element of a potent API strategy, and Moesif can be your trump card in achieving it. By offering tools for usage-based pricing and premium access, Moesif enables businesses to implement effective monetization strategies, turning their APIs into revenue-generating assets.But Moesif doesn’t just stop at enabling monetization; it provides the analytics and insights necessary to optimize these strategies. By analyzing API usage patterns and customer behavior, businesses can tailor their pricing models to better match user needs and market demands. This can lead to more nuanced monetization strategies such as tiered pricing plans, which allow businesses to cater to a wider range of customers, from small startups to large enterprises.Furthermore, Moesif facilitates the tracking of API metrics that are critical for billing, such as the number of API calls or data transferred, ensuring accurate and transparent billing processes. With Moesif, you can also experiment with different monetization strategies and immediately gauge their success through the analytics dashboard, allowing for rapid iteration and refinement.In essence, Moesif not only empowers businesses to monetize their APIs but also equips them with the data-driven insights needed to sustain and grow their API revenue streams over time.SummaryAs we’ve journeyed through the world of API management, we’ve explored the essentials of effective API management, the benefits it can bring to your business, and the challenges you might face along the way. From enhancing security and optimizing performance to enabling monetization and enhancing user experience, there’s no denying the transformative power of effective API management.If you’re looking for an API analytics or monetization platform to enhance your API operations, look no further than Moesif. With easy integrations with most popular frameworks and API gateways, Moesif users can quickly monitor APIs to uncover insights for improving their API operations. To get started today, sign up for a 14-day free trial with no credit card required.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Unlock the full potential of your APIs with Moesif's cutting-edge observability            Start optimizing your API performance today - Dive deeper with Moesif!            Try for Free!            No credit card required            ",
          "url": " /technical/api-development/Mastering-API-Gatekeeping/",
          "author": "Dylan",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-essential-strategies-for-optimizing-your-api-architecture": {
          "title": "Essential Strategies for Optimizing Your API Architecture",
          "content"	 : "Why does API architecture matter, and how can you excel at it? It’s an important question to ask as you are trying to improve existing APIs or write new ones. In this article, we will create a high-level roadmap to understanding the foundations of API architecture, from API Gateways and data management to architectural styles like RESTful and GraphQL. We will look at best practices to enhance scalability and security and look closer at tools that support robust API development. Whether you’re a novice or a seasoned developer, this guide is tailor-made to help you navigate the complexities of creating and improving your API architecture. Let’s begin by looking at why API architecture decisions are important.                Monitor and Analyze API Traffic with Moesif              14 day free trial. No credit card required.              Try for Free        The Importance of API ArchitectureModern software ecosystems heavily rely on APIs and their underlying API architecture. An efficiently designed API architecture promotes smooth integration between various software systems, guarantees scalability and performance, and fosters modular and reusable application development. It’s like a blueprint that guides the construction of a software system, laying out the structure and interactions of the API components and software components.API diagrams serve as visual representations of this architecture. By being able to visualize what your current-state or future-state API architecture looks like, you’ll have an easier path to understanding and optimizing it. An API diagram, in particular, can provide a focused view of a specific aspect of the system, while a component diagram offers a broader perspective on the overall structure. In addition, a sequence diagram can help visualize the interactions between components in a time-ordered manner. These various types of visualizations can help design and build APIs more effectively.Role of APIs in Modern ApplicationsAPIs, including web APIs, act as connectors, uniting various systems and services online and offline. APIs have become ubiquitous with the software development world’s push towards building distributed systems. APIs power the transmission of data between systems, enabling the seamless exchange of data among applications, exposing functionalities of other applications, and establishing connections with data sources. In short, APIs power everything in the modern digital space.The reach of APIs also heavily impacts one of the most used resources in the world: mobile devices. In mobile applications, application programming interfaces (APIs) enable seamless integration with other applications and services, enhancing app functionality and efficiency. As a subset of these interfaces, event-driven APIs further improve the applications’ responsiveness. Modern web and mobile app development would not be possible without APIs.Impact on Business SuccessAsserting that a well-designed API architecture can revolutionize businesses is no exaggeration. With the right API architecture in place, it can drive many new opportunities, such as:  Open up new revenue channels  Expand the brand’s reach  Enable data monetization  Foster profitable partnerships  Enhance customer experiences  Reduce development costsA well-designed API architecture also reduces development time by facilitating code reusability across multiple projects, minimizing redundancy, and conserving time and resources. The faster you can develop APIs while easily maintaining them can be advantageous when pursuing new API-related business use cases.Key Components of API ArchitectureLike all complex systems, API architecture consists of key components forming a robust and efficient system collaboratively. These components include API Gateways, API Design and Contract, and Data Management and Integration. Each of these components plays a specific role in the overall functionality of the API system, contributing to its efficiency, scalability, and security.API GatewayConsider the API Gateway as the gatekeeper of the API ecosystem. It performs the following functions:  Routes requests to the appropriate microservice based on URL and content  Enables clients to retrieve data from multiple services with a single round-trip  Reduces overhead by minimizing the number of requestsIt also facilitates caching by storing and serving endpoint responses for a defined time-to-live (TTL) duration, enhancing response speed and reducing network overhead. API Gateways are generally available through API management platforms that allow users to easily augment their APIs through enhanced security or enabling scale through automatic load balancing. API gateways are necessary for organizations looking to implement a robust API architecture.API Design and ContractThink of API design and contract as the blueprints for the API. They are essential for defining the API’s structure, behavior, and interactions. A well-thought-out design and API contract ensures that developers have consistency and ease of use when consuming the API. When designing the API, developers should consider users’ expectations, using careful planning and collaboration with potential API consumers.Data Management and IntegrationData management and integration serve as the pivotal cogs ensuring the smooth operation of the API machine. They involve:  Handling data flow, storage, and processing within the API architecture  Managing data access, security, and governance  Maintaining data integrity and consistency across various systems and applications.For APIs that handle any type of data reading or writing, you’ll want to ensure that the underlying infrastructure does not run into concurrency issues. You should also ensure that the authentication and authorization used in the API layer can be leveraged when it comes to controlling and governing data access. These factors are key to maintaining data integrity and security.Popular API Architectural StylesWhen it comes to API architectural styles, there is no one-size-fits-all. Different architectural styles offer different advantages and are suited for different use cases. Many folks automatically jump to thinking about a REST API when it comes to a web API. Even though they are the most popular type, some other popular API architectural styles include:  GraphQL  SOAP  gRPCEach style has unique characteristics and advantages, lending to particular use cases. Some styles are more widely applicable, while others are more specialized. Let’s take a look at each in more detail.RESTful APIA REST API can be compared to the Swiss Army Knife amongst API architectural styles. REST APIs are simple, stateless, and scalable, making them suitable for many web and mobile applications. A key characteristic of RESTful APIs is their reliance on the HTTP standard, which allows them to be format-agnostic and enables the exchange of data using XML, JSON, or HTML. Overall, RESTful APIs are the overarching favorite for most developers building APIs due to their widespread support and the massive amount of technologies that support building them.GraphQL APIGraphQL, a relative newcomer to the API realm, is gaining popularity due to its flexibility and efficiency in fetching data. It allows clients to request only the needed data, reducing over-fetching and under-fetching of the requested data. This makes it an excellent choice for applications where efficiency and performance are key considerations. As this technology is still relatively new, its adoption has been slowly increasing, as are the frameworks and technologies that support it.SOAP APIFor organizations that existed before the popularity of RESTful APIs, SOAP APIs are a tried and tested architectural style that was widely adopted. One of the most popular styles before REST became king, SOAP provided a standardized protocol for exchanging structured information in web service applications. It is particularly beneficial for applications requiring structured communication and managing complex data structures and transactions. Although it has slowly become less common, SOAP services are still built and maintained by many legacy companies.gRPC APIgRPC API is a modern, high-performance, open-source framework developed by Google, hence the acronym “gRPC,” which stands for Google remote procedure call. It enables efficient communication between microservices using remote procedure calls, with protocol buffers for serialization and supporting multiple programming languages. It’s particularly beneficial for applications that require efficient, scalable, and reliable communication between services.Best Practices for Designing API ArchitectureThe task of designing a competent and efficient API architecture is quite complex. It requires careful planning, thoughtful design, and adherence to various best practices. These best practices include focusing on simplicity and flexibility, prioritizing security and authentication, and ensuring scalability and performance. Let’s take a deeper look at some of these best practices.Simplicity and FlexibilitySimplicity and flexibility stand as key factors in API design. A simple API is easy for developers to understand, adopt, and maintain. At the same time, a flexible API can adapt to changing requirements, making it scalable and future-proof. Making sure that an API is a balance of both of these factors is the best approach to making sure API consumers can understand the API easily while making sure that it can handle a wide array of use cases.Security and AuthenticationAny discussion on API design remains incomplete without addressing security and authentication. Protecting sensitive data and ensuring only authorized users can access the API is crucial. This involves implementing robust authentication mechanisms and ensuring secure data transfer and protection against attacks. Much of the time, this is easily and uniformly implemented at the API gateway level. Developers could use an established identity provider, such as Okta or Auth0, to protect the API and employ other security measures to ensure data governance is adhered to.Scalability and PerformanceA well-designed API architecture is distinguished by its ability to scale and process requests efficiently. This can be boiled down to a few factors: code, infrastructure, and caching. Firstly, code should be written to be performant. This could mean leveraging various threading strategies and other tools available in the language/framework being used to develop the APIs. Next, ensure that infrastructure is provisioned correctly to ensure that servers can handle the load, preferably with automated scaling to accommodate any spikes in traffic that could impact usability. Lastly, use caching techniques to enhance the performance of your APIs. Many tools, including API gateways, have mechanisms that can help implement this efficiently and easily.Tools and Frameworks for API ArchitectureA suite of tools and frameworks is necessary for the complex task of designing, testing, and documenting API architecture. Luckily, these tools are abundant and are generally easy to set up and use. Let’s take a closer look!Unified Modeling Language (UML)Unified Modeling Language (UML) is a standardized visual language for modeling software systems. It provides a common vocabulary and a set of diagramming techniques, including UML diagrams, for visualizing and understanding complex systems, such as API architectures. Some may also use these tools to build sequence diagrams when planning their APIs.Swagger/OpenAPISwagger/OpenAPI provides a specification for describing, producing, and consuming RESTful APIs. It simplifies the API design and documentation by establishing a unified framework for various stakeholders to develop, manage, and use APIs.PostmanPostman is a popular tool for API development, testing, and collaboration. It supports various API protocols and formats, making it a versatile tool for API developers and testers.SoapUISoapUI is a testing tool specifically designed for SOAP and RESTful APIs. It offers functional, security, and performance testing capabilities, making it a comprehensive tool for API testing.StoplightStoplight is a platform for API design, documentation, and testing. It focuses on collaboration and standardization, making it a powerful tool for teams working on API development.InsomniaOwned by Kong, Insomnia is a versatile API client for testing and debugging APIs. It supports multiple protocols and authentication methods, making it a versatile tool for API testing.Using Moesif to Improve Your API ArchitectureMoesif provides features that enhance a company’s API architecture at various stages. Once an API is deployed and in use, Moesif can help users with:  Insights, analytics, and monitoring to optimize performance, security, and user experience  Deriving insights from API traffic like how your APIs are being used, which are most popular, and which are experiencing the most errors/issues  Enabling developers to make data-driven decisions to improve their API architecture and then being able to track any improvements (or the opposite) that users are experiencingAPI architecture is constantly in flux, with new APIs being added and others being deprecated regularly. A platform like Moesif can help ensure that the API user experience remains optimal and that your APIs are monitored for any conditions, pointing out that your API architecture could use improvement.SummaryIn conclusion, designing and implementing an effective API architecture is a complex but critical task. It requires understanding the importance of API architecture, recognizing its key components, adhering to best practices, and leveraging the right tools and frameworks. With a well-designed API architecture, businesses can enhance their software systems’ efficiency, scalability, and security, ultimately driving business success and growth.If you’re looking for API analytics to help you create a more robust and efficient API architecture, look no further than Moesif. With easy integrations with the most popular frameworks and API gateways, Moesif users can quickly build reports and monitor APIs to uncover insights for improving their API architecture. To get started today, sign up for a 14-day free trial with no credit card required.                Monitor and Analyze API Traffic with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Architect, develop, and optimize your APIs with Moesif            Use Moesif's API analytics to discover and report status codes users see and proactively help customers with errors.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Essential-Strategies-For-Optimizing-Your-API-Architecture/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-essential-api-design-patterns": {
          "title": "Essential API Design Patterns: A Guide to Crafting Superior Web Services",
          "content"	 : "When building APIs, developers face a crucial challenge: how to ensure they are structured for both ease of use and long-term scalability. API design patterns offer solutions to this challenge, serving as a roadmap for creating efficient, reliable, and adaptable web services. This guide unpacks these patterns, equipping you with the approaches needed to turn your API into an exemplary model of good design.Key Takeaways      RESTful API design patterns serve as a fundamental blueprint for creating scalable and stateless web services, emphasizing the use of standard HTTP methods and status codes for clarity and intuitive resource interaction.        Performance optimization in APIs, which includes pagination, filtering, partial responses, and data caching techniques, is critical for reducing server load and improving response times, contributing to an API’s usability and efficiency.        Security, thorough documentation, and strategic version control are paramount for API reliability and longevity, with practices like robust authentication mechanisms, precise error code documentation, and clear handling of API versioning and deprecation.                  Monitor and Analyze API Traffic with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding RESTful API Design PatternsRESTful API design patterns provide the architectural blueprint for creating APIs that are highly scalable and stateless. The utilization of standard HTTP methods in RESTful services makes it a preferred choice among API designers, especially when considering the benefits of a well-implemented API design pattern.The Role of HTTP Verbs in RESTful ServicesInteraction with resources in RESTful services is standardized and intuitive, thanks to the employment of standard HTTP methods like GET, POST, PUT, and DELETE, which are essential components of http requests.The artistic weaving of these methods with HTTP status codes enhances clarity and accuracy in API design.Crafting Resource URIs with PrecisionThe meticulous crafting of Resource URIs in RESTful APIs, based on nouns representing resources, enhances clarity and effectiveness. But remember, while nested endpoints clarify relationships between resources, don’t nest deeper than three levels to maintain elegance and readability.Leveraging HTTP Status Codes for ClarityIn client-server communication, HTTP status codes offer clear information on the outcome of requests, playing a pivotal role. Used judiciously, they can make your API responses self-sufficient in conveying server outcomes.Essential Patterns for REST API EndpointsCreating robust APIs requires a firm grasp of essential patterns for REST API endpoints. The use of nouns in API endpoint signifies the existing resource being addressed, and collections should be named with plural nouns to indicate the possibility of multiple resources.To enhance the clarity and efficiency of the API structure, APIs include nested resources that reflect hierarchical objects.Pagination and Filtering TechniquesPagination and filtering techniques are vital for API performance as they limit the data returned in a response, thereby reducing the server’s resource load. From cursor-based pagination to keyset pagination and Seek Paging, there are multiple techniques to allow efficient fetching of items in large datasets.Handling Partial Responses and RangesThe technique of partial response enables clients to request only the fields they wish to receive in a response. This reduces the amount of data transferred, increasing API performance. Implementing support for partial responses and range requests can reduce bandwidth and improve response times.Streamlining Client-Server InteractionsIt requires a deep understanding of content negotiation and strategic use of query parameters to balance and streamline client-server interactions. In the world of RESTful APIs, the Accept header in an HTTP GET request allows the client to specify the format of the requested data that it can handle.Effective Use of Query ParametersWithout the need for creating additional endpoints, query parameters support operations like filtering, sorting, and pagination, providing flexibility in data retrieval. They grant users the ability to customize API requests, thereby enabling them to control the granularity and specificity of the data retrieved.Strategies for Data CachingCaching acts like a magic wand, reducing server load and enhancing API performance. HTTP caching mechanisms can be leveraged by REST APIs to reduce server load and improve response times.Different caching techniques, such as client-side caching with Cache-Control headers and server-side caching, can be deployed to maximize performance.Security and Error Management in API DesignWhen designing REST APIs, as expected, security is of paramount concern. From authentication mechanisms to error management techniques, every aspect plays a pivotal role in ensuring the safety and reliability of your API. Let’s dive deeper into these critical aspects.Implement Authentication with ConfidenceTo protect web services and ensure access to sensitive data for only authorized clients, API authentication is non-negotiable. From API keys to OAuth 2.0 and JSON Web Tokens (JWT), there are numerous authentication mechanisms that provide robust authentication and information integrity.Defining and Documenting Error CodesIn API design, error codes are the unsung heroes. They provide clear and concise information about any errors that might occur during the API’s operation. Uniform exception handling across the API allows for predictable error management and streamlines both API and client interactions.Version Control and Evolution of REST APIsOver time, APIs, being living entities, evolve. Versioning in REST APIs allows the introduction of new features, bug fixes, and updates while ensuring that existing client applications remain functional.Let’s examine effective management of this evolution.Approaches to API VersioningVarious strategies, from URI versioning to content negotiation, can be employed to implement versioning in REST APIs. Each method comes with its own merits and challenges, and the choice depends on the API’s architecture and consumer preferences.Managing Deprecated EndpointsWith evolution comes deprecation. Managing deprecated API endpoints is an art that requires clear communication, providing a clear deprecation timeframe, and offering a long enough sunset period.Enhancing Discoverability and DocumentationIn API design, discoverability and documentation are the unsung heroes. They play a crucial role in simplifying the use of API endpoints and ensuring that developers have the necessary guidance for quick implementation.Creating Self-Descriptive MessagesSelf-descriptive messages in REST APIs enhance the clarity and understanding for the client. As a part of the uniform interface constraint of REST, these messages contribute to consistency and understandability in the interaction between client and server.The Benefits of OpenAPI and Other SpecificationsAdopting specifications like OpenAPI for API design brings a host of benefits. From supporting a design-first approach to ensuring comprehensive and accurate documentation, OpenAPI takes the guesswork out of API design, and implementation.Performance Optimization in RESTful APIsThe secret sauce that elevates your RESTful API from good to great is performance optimization. From investing in reliable and fast network infrastructure to tracking various aspects of an API, every detail contributes to the performance of your API.Rate Limiting for Resource ManagementActing as a gatekeeper, rate limiting protects your API resources. It sets constraints on the number of requests a user can make in a given timeframe, preventing API abuse and reducing the chances of denial-of-service attacks.Efficient Request Body and Response Message HandlingThe performance of your RESTful API can be significantly enhanced through the efficient handling of request body and response messages. Techniques like implementing PATCH for partial updates, compressing response payloads, and utilizing GraphQL enable clients to specify the data they need, reducing unnecessary load and request/response sizes.SummaryIn the journey of mastering API design, we’ve covered a gamut of topics from understanding RESTful API design patterns, essential patterns for REST API endpoints, streamlining client-server interactions, to security and error management in API design, and much more. It’s now time to put these principles into action and create high-quality APIs that stand the test of time.Start enhancing your API journey today by exploring Moesif’s extensive guides on building APIs. For a hands-on experience with Moesif’s analytics and monetization tools, sign up for a free trial or chat with our team of API experts to learn how Moesif can supercharge your API projects.Frequently Asked QuestionsWhat are the 6 design patterns of REST API?REST API design patterns include resources as collections or items, and the HTTP methods used are GET, POST, PUT, and DELETE. Other patterns like filters, pagination, search, and sorting can also be applied to resources.What is the best design pattern for Web API?The best design pattern for Web API is the RESTful (Representational State Transfer) API, which is widely adopted and based on architectural principles promoting simplicity, scalability, and interoperability.What are API designs?API design is the intentional process of making decisions about how an API exposes data and functionality to its users. It includes defining endpoints, methods, and resources in a standardized specification format.What are RESTful API design patterns?RESTful API design patterns provide the architectural blueprint for creating highly scalable and stateless APIs.How does rate limiting improve API performance?Rate limiting improves API performance by preventing abuse and denial-of-service attacks through setting constraints on the number of requests a user can make in a specific timeframe. This helps ensure stable and efficient API operation.                Monitor and Analyze API Traffic with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Do you know what status codes your users are seeing?            Use Moesif’s API analytics to discover and report status codes users see and proactively help customers with errors.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Essential-API-Design-Patterns/",
          "author": "Dylan",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-how-to-use-an-api": {
          "title": "How to Use an API: A Step-by-Step Tutorial for Beginners",
          "content"	 : "Understanding how to use an API is essential for modern software development, from simple data retrieval to complex integrations. This guide demystifies the process, providing you with the insights you need to make API requests, decode responses, and securely implement these powerful connectors in your code. Step by step, you’ll learn the key concepts that will empower you to leverage APIs effectively in your own projects.Key Takeaways  APIs (Application Programming Interfaces) are crucial for enabling communication between software applications, defining how requests are made, data formats used, and protocols to follow.  There are various types of APIs such as REST, SOAP, GraphQL, and WebSocket, each with unique capabilities; choosing the right type depends on the specific needs of the application.  Securing API access with API keys and authorization methods is essential, and developers must create, send, and handle API requests correctly while ensuring proper API integration and performance monitoring in their applications.                Monitor and Analyze API Traffic with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding APIs: The BasicsEssentially, an API, short for Application Programming Interface, serves as a software intermediary, facilitating communication between two applications. Think of it as a messenger, transporting requests from one system to another, and ensuring the correct response is delivered. APIs have become a fundamental component of modern software development, powering everything from social media platforms to e-commerce transactions, all thanks to the power of application programming interfaces.Essentially, APIs act as intermediaries to facilitate mutual understanding and cooperation between various software applications. They:  Define the kinds of requests that can be made  Specify how to make the requests  Determine the data formats that should be used  Establish the conventions to follow.Ever wondered how your weather app updates with real-time data? Or how you can share a YouTube video on your Facebook page with just a click? That’s APIs at work, silently and efficiently connecting different software systems and enabling them to share data and functionalities.Types of APIs: Exploring the LandscapeThe API landscape is diverse, comprising several types, each boasting unique benefits and applications. Among them, REST (Representational State Transfer) APIs, also known as rest api, are one of the most popular. REST APIs operate based on the HTTP standard and can handle different data formats like XML, JSON, and HTML, making them a flexible and interoperable choice for developers.On the other hand, there are different types of web APIs that serve different purposes:SOAP APIsSOAP (Simple Object Access Protocol) APIs are typically used in scenarios where formal contracts and long-running processes are involved. They are used for operations such as creating, retrieving, updating, or deleting records from a server.GraphQL APIsGraphQL APIs offer clients the ability to retrieve precise data tailored to their requirements, often using JSON as the data format.WebSocket APIsWebSocket APIs enable real-time communication and updates, proving indispensable in applications that require live interactions.Whether it’s a REST, SOAP, GraphQL, or WebSocket API, each type brings unique capabilities to the table. The key is to understand your application’s needs and choose an API that best aligns with those requirements.Acquiring API Keys and AuthorizationAPI keys and authorization methods are indispensable in securing access to APIs and managing data usage. An API key is a unique sequence of characters used to authenticate and authorize users accessing an API, serving as a security measure to regulate API access and protect sensitive information. Obtaining an API key typically involves logging into a developer account on the API provider’s website, accessing the section for API keys, and requesting a new key.API authorization, on the other hand, verifies the identity of the user or application initiating the API request, ensuring they have the necessary permissions to interact with the API and its resources. This is often accomplished using standards like OAuth, which allows a website or application to access resources from other web applications without revealing user credentials, providing a secure and regulated method for granting permissions and accessing protected resources.API Documentation: A Developer’s GuideAPI documentation serves as a comprehensive guide for developers, outlining essential information for effective API usage. It includes:  A comprehensive description of the API, detailing its functionalities, constraints, and prerequisites  Examples of each call, parameter, and response  Code samples, references, tutorials, and descriptive explanationsTo make the most of API documentation, it’s advisable to start by familiarizing oneself with API terminology, then delving into the API overview to understand its objectives and capabilities. The API reference provides detailed information about accessible resources and their interaction methods. Lastly, reviewing available tutorials can help gain hands-on knowledge on API utilization.Creating Your First API RequestPrepared for your first API request? The procedure entails endpoint selection, parameter and header configuration, along with API response management. Let’s break it down into three steps.Choosing an EndpointAn API endpoint is essentially a specific URL that provides access to a resource on the server. It’s the point of communication between an API client and an API server, where requests are received and responses are sent. Each endpoint is comprised of:  An HTTP method  An endpoint URL  Headers  A bodyThis API provider offers an organized way to manage private APIs functionalities.When selecting an endpoint, it’s important to consider factors such as:  Documentation  Libraries  Consistency  Support  Reputation  Pricing  Data privacyAdditionally, understanding the difference between HTTP methods like POST and GET is crucial. While GET API endpoints are used for data retrieval, POST API endpoints are employed for data creation. GET requests are visible in the URL and are thus considered less secure, while POST requests are more secure as the data is transmitted in the request body and hidden from the URL.Setting Parameters and HeadersParameters and headers are key components of API requests, allowing you to customize your requests and provide additional information to the server. API request parameters are configurable options that you can include with the endpoint to affect the response, acting as filters for your search. You can set these parameters as key-value pairs appended to the URL or as header parameters included in the request headers.Headers, on the other hand, provide supplementary meta-information to the server. This can include data about the request, authentication details, and more. To set headers in languages like JavaScript, methods like ‘setRequestHeader’ of an XMLHttpRequest object are used.Understanding the function and implementation of both parameters and headers is essential for effective API usage.Handling API ResponsesOnce you’ve made an API request, you’ll receive a response. Managing these responses is a crucial part of working with APIs. A key aspect of this is understanding status codes. HTTP response status codes indicate whether a specific HTTP request has been successfully completed, providing valuable information about the outcome of the API request.API responses can be returned in various formats, including:  JSON  XML  Plain Text  BinaryDepending on your programming language, different methods can be used to parse this data, such as javascript object notation (JSON). For example, in Python, you can use the json module to parse JSON data from an API response. In Java, libraries like DOM or SAX parsers can be used to parse XML data.Understanding how to handle and interpret API responses is key to successfully interacting with APIs.Integrating APIs into Your ApplicationNow, equipped with knowledge of making API requests and managing responses, it’s time to explore choosing an appropriate API for your requirements and incorporating API calls into your application code.Selecting the Right API for Your NeedsChoosing the right API for your application is a crucial decision that can impact your application’s functionality and performance. Your business requirements, budget, and the compatibility of the API with your existing application are all important factors to consider in this process. There are also cost-effective APIs suitable for small businesses such as Postman, Amazon API Gateway, and Stoplight, among others.Testing and automation scripts can be used to evaluate the compatibility of an API with your existing application. When selecting an API for business purposes, it is also important to consider factors such as:  Ease of use  Scalability  Security  Flexibility  ComprehensivenessImplementing API Calls in Your CodeOnce you’ve chosen the right API, the next step is to integrate API calls into your code. This involves:  Selecting the right API  Acquiring an API key if necessary  Making an HTTP request to the API endpoint  Receiving the response  Processing the response within your code.Depending on your application’s technology stack, you may use different libraries or tools to make these API calls. For example:  In a Python application, you can use the requests library.  In a Node.js application, libraries like Axios, node-fetch, or SuperAgent can be useful.  Even in languages like PHP, making API calls is feasible using libraries like cURL or by developing a utility function.It’s also important to follow best practices when implementing API calls in your code, such as using nouns for resource identification, ensuring proper HTTP headers, and implementing thorough error handling.Securing and Monitoring Your API UsageAPIs, while powerful, necessitate responsible usage. The implementation of security measures is vital to safeguard your API usage, alongside performance monitoring to maximize efficiency and ward off unauthorized access.Implementing Security MeasuresSecurity is of utmost importance when using APIs. API keys and authentication tokens are key to protecting your API usage, and it’s crucial to store and manage these securely. OAuth2 is a widely used standard for API authorization, providing a secure method to grant permissions and access protected resources.In addition to authentication and authorization, securing your API endpoints from abuse is also vital. This can be achieved by:  Designing the API with a minimal exposure surface  Following best practices for authentication and authorization  Implementing rate limiting  Regularly updating and patching the API to address security vulnerabilities.JWT authentication is another method for securing API usage, where a token is generated upon user sign-in and used in subsequent API requests to authenticate and gain access to protected endpoints. This process ensures smooth API integration for users.Monitoring API PerformanceIn addition to securing your API usage, monitoring API performance is critical to maintaining a high-quality user experience and identifying issues early. This can be done by:  Tracking API availability  Measuring response time  Monitoring error rates  Validating functional uptime  Tracking API dependenciesThere are a variety of tools available to monitor API performance across different technology stacks. Some of these tools include:  Moesif: an analytics and billing platform designed to help businesses understand and monetize their API usage. It offers features for tracking customer interactions, setting up usage-based billing, and providing real-time alerts, aiming to support product-led enterprises and startups in growing their API products.  AppMetrics: used for real-time monitoring and analysis in a Node.js application  Opbeat: provides automated performance tracking for Django  Sematext, Prometheus, Uptrends, AppDynamics, SigNoz, Datadog, and New Relic: effective tools that can help monitor API performance across different platforms.Continuous monitoring of API performance is crucial to maintaining optimal API performance and providing the best possible user experience.Real-Life API Examples and Use CasesChances are, you’ve engaged with APIs more frequently than you’re aware of. From checking the weather on your phone to streaming your favorite song on Spotify, APIs are everywhere. In fact, when you use an API like the Google Maps API, it allows you to access real-time experiences like plane tracking and displaying weather conditions on maps. Composite APIs can further enhance these experiences by combining multiple data sources and services.Businesses also leverage APIs to enhance their services. The X (Twitter) API, for example, is used to organize work, manage API access, monitor data, interact with tweets and profiles, analyze social media information, and engage with an X conversation. The Spotify API, on the other hand, allows developers to build applications that interact with Spotify’s music streaming service, offering functionalities like accessing metadata for music content and developing music visualizers.These real-life examples illustrate the power of APIs and their wide-ranging applications across different sectors and platforms.Troubleshooting Common API IssuesRegardless of their strength and adaptability, APIs may occasionally present obstacles. Some common issues include:  Server-level issues  Incorrect API requests  Authentication errors  Data format mismatches  Rate limiting  Versioning  Caching errors  Error handlingBut fear not, there are effective ways to troubleshoot and debug API calls, starting with a single api call.Tools like Moesif can be extremely helpful in this process. Used in conjunction with Postman, Moesif can provide valuable insights into API usage, helping developers identify and resolve issues more effectively.Ultimately, addressing API issues often involves:  Using HTTPS for secure communication  Ensuring the correct usage of HTTP methods  Gaining a deeper understanding of tips and techniques for troubleshooting API issues within your application.SummaryWe’ve journeyed through the world of APIs, exploring their basics, types, use cases, and potential challenges. APIs are the invisible enablers of our digital world, powering interactions, facilitating data sharing, and driving innovation. Whether you’re a developer, a business owner, or simply a curious learner, understanding APIs is a valuable skill in today’s interconnected world. As you embark on your own API journey, remember to select the right API for your needs, implement security measures, monitor your API usage, and never stop learning.Start enhancing your API journey today by exploring Moesif’s extensive guides on building APIs. For a hands-on experience with Moesif’s analytics and monetization tools, sign up for a free trial or chat with our team of API experts to learn how Moesif can supercharge your API projects.                Monitor and Analyze API Traffic with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Do you know what status codes your users are seeing?            Use Moesif’s API analytics to discover and report status codes users see and proactively help customers with errors.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/How-to-Use-an-API/",
          "author": "Dylan",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-essential-rest-api-best-practices": {
          "title": "12 REST API Best Practices That Hold Up in 2026",
          "content"	 : "REST API best practices come up in every API review, but the list that actually matters is shorter than most “essential best practices” articles suggest. Twelve patterns hold up across language, scale, and use case, and the rest are usually variations or implementation details. This guide walks through each, with the 2026 additions that account for AI agent traffic and MCP-mediated consumption.                Tier-Based Pricing with Moesif              14 day free trial. No credit card required.              Try for Free        For the broader strategic decisions that shape an API’s design, see our API design principles guide. This post focuses on the concrete patterns developers apply at the design and implementation level.1. Use nouns, not verbs, in endpoint paths/orders, not /getOrders. The HTTP method is the verb; the URL identifies the resource. Stripe’s /v1/charges, GitHub’s /repos, and OpenAI’s /v1/chat/completions all follow this. Putting the verb in the URL (POST /createUser) duplicates what the method already says and breaks the consistency every REST consumer expects.2. Pluralize collections, singularize items/orders for the list, /orders/42 for one order. The exception is singletons (/me, /health) where there is only ever one instance. Mixing /order/42 with /orders is the most common naming inconsistency and the first thing developers complain about in API reviews.3. Nest resources only when relationships are containment/users/123/orders reads cleanly when an order belongs to a single user. /companies/1/users/2/orders/3/items/4 does not. Two levels of nesting is the practical maximum; beyond that, switch to a top-level resource with filter parameters (/items?orderId=3).4. Map HTTP methods to CRUD cleanlyFive methods cover almost every REST endpoint:            Method      Action      Idempotent?                  GET      Retrieve      Yes              POST      Create      No              PUT      Replace      Yes              PATCH      Partial update      Sometimes              DELETE      Remove      Yes      The discipline that compounds: use the verb that matches the action, and do not invent endpoints like POST /createUser. The HTTP method says “create”; the URL identifies the resource.5. Return the right HTTP status code200 OK for success on GET/PUT/PATCH, 201 Created (with a Location header) for POST that creates a resource, 204 No Content for DELETE. 400 for malformed requests, 401 for missing or invalid auth, 403 for permission denied, 404 for missing resources, 429 for rate limit, 5xx for server-side errors. The full reference lives in our HTTP status codes guide.The mistake to avoid: returning 200 OK with {"error": "not found"} instead of a real 404. The status code is the contract; do not overload the body to compensate for the wrong code.6. Return a structured error envelopeEvery error response should include three things: a machine-readable code, a human-readable message, and (for validation errors) the field that failed. A pattern most production APIs converge on:{  "error": {    "code": "invalid_email",    "message": "Email address is not valid.",    "field": "email"  }}Consumers parse the code to handle errors programmatically; they do not string-match the message, which is fragile across translations and minor wording changes.7. Use query parameters for filtering, sorting, and paginationPath parameters identify resources (/orders/42). Query parameters modify the request (/orders?status=paid&limit=20). The conventional shape:  Pagination: ?limit=50&offset=100 or ?cursor=abc123 for cursor-based.  Filtering: named query parameters per filterable field: ?status=paid&customer_id=42.  Sorting: ?sort=created_at or ?sort=-created_at (the minus sign for descending).  Field selection: ?fields=id,name,email lets clients trim payload.The deeper rules and tradeoffs are in our query parameters guide.8. Version deliberately and publish a deprecation policyURI versioning (/v1/orders) is the most common and easiest to debug. Header versioning is cleaner but less consumer-friendly. Whichever you pick, the part that compounds is the deprecation policy: how much notice you give before removing a version. Standard is 12 months for paid public APIs, 6 months for free public APIs, 90 days for internal APIs. State it on day one.Use the IETF-standard Sunset (RFC 8594) and Deprecation (RFC 9745) HTTP response headers to communicate deprecation programmatically. Well-behaved SDKs warn developers at build time when they see them.9. Authenticate every endpoint, encrypt every transportTwo non-negotiables for any production API:  TLS 1.2 or higher on every endpoint, including ones marked “internal”. HTTP traffic leaks credentials and payloads to anyone on the network path.  Authenticate every endpoint. API keys for server-to-server, OAuth 2.0 / OIDC for user-acting flows, mTLS for high-security partner integrations. The choice depends on audience; the rule is that no endpoint should accept unauthenticated production traffic.Never put credentials in URL query strings. They leak to server logs, Referer headers, browser history, and CDN logs. Use Authorization headers.10. Rate limit at the gatewayEvery public endpoint needs a rate limit. When the limit is hit, return 429 Too Many Requests with a Retry-After header telling the client how long to wait. Pair this with X-RateLimit-Remaining and X-RateLimit-Reset headers on every response so well-behaved clients can self-throttle.The algorithm choice (token bucket, sliding window, fixed window) matters less than picking one and applying it consistently. Token bucket is one of the most common choices for public APIs.11. Cache where the response is cacheableCache-Control and ETag headers let HTTP caches (CDNs, reverse proxies, browser caches) reuse responses across calls. For read-heavy endpoints with cacheable responses, this can meaningfully reduce backend load without any client-side change. For mutation endpoints, set Cache-Control: no-store explicitly to prevent accidental caching.For conditional requests, support If-None-Match (with ETags) and return 304 Not Modified when the resource hasn’t changed since the client’s cached copy. The bandwidth savings show up immediately.12. Document with OpenAPI and keep documentation in syncA REST API without documentation is a guessing game. The discipline that compounds is generating documentation from the same source-of-truth artifact your code uses, so docs cannot drift from the implementation.The OpenAPI specification (formerly Swagger) is the de facto standard for REST API documentation in 2026. Almost every API platform reads it: code generators produce SDKs from it, gateway plugins enforce policies against it, MCP server generators expose it to agents, and developer portals render it as interactive docs. If your API does not have an OpenAPI spec, you are duplicating work every time you ship.The practical patterns:  Generate the spec from code, not vice versa. FastAPI, NestJS, and Spring with springdoc-openapi all generate the OpenAPI spec from controller annotations and type hints. This keeps the spec accurate by construction; if the code changes, the spec changes.  Write descriptions for every endpoint, parameter, and response. The description and summary fields in OpenAPI are not optional. Agents read them literally. Humans read them in the developer portal. Empty descriptions mean both audiences are guessing.  Publish example requests and responses. OpenAPI’s examples field powers the “try it” experience in modern documentation portals and gives developers a starting point. Examples are also the fastest way for a reviewer to spot a wrong response shape.  Version your spec alongside your code. Check the OpenAPI YAML/JSON into git in the same repo as the API. Tag spec versions with the API version. Treat spec changes the way you treat code changes: PR-reviewed, tested, and deployed together.  Lint the spec. Spectral (Stoplight) is the standard OpenAPI linter; it catches missing descriptions, inconsistent naming, and other issues at PR time. Pair it with a governance pre-commit hook so spec quality cannot regress.The downstream investment that pays off: an OpenAPI spec that is genuinely accurate makes SDK generation, developer-portal hosting, contract testing, and MCP exposure essentially free. The teams that treat the spec as a chore end up writing the same documentation three times (in code, in markdown docs, and in PDF reference material) and keeping none of them in sync.For the developer-experience layer that sits on top of the spec, see our developer portal guide.13. REST API best practices in 2026: agent-readinessThis is the part of the list that did not exist five years ago and that matters for any API expecting AI agent traffic.Three additions to the standard list:  Idempotency keys on POST and PATCH. Accept an Idempotency-Key request header and cache the response keyed on it. Agents retry aggressively; without idempotency, retries create duplicate orders and duplicate charges. The convention was popularized by Stripe’s API and is now widely used across payments and infrastructure APIs. The IETF httpapi working group has been progressing an Internet-Draft (“The Idempotency-Key HTTP Header Field”) aimed at standardizing the convention.  Agent-readable OpenAPI specs. Treat operationId, summary, and description as user-facing copy. Agents read these literally when deciding which endpoint to call from your spec. Vague descriptions cause wrong tool selection.  MCP exposure for agent consumers. The Model Context Protocol exposes existing REST APIs to agent runtimes in a richer form. Platforms like the WSO2 AI Gateway auto-generate an MCP server from your OpenAPI spec, so the agent surface stays in sync with the REST surface without a separate build.Most production teams in 2026 have at least the first of these three. The third is increasingly common among teams shipping AI-facing products.14. Monitor what you ship (the bonus practice)Best practices are design-time decisions. Whether they hold up in production is an observability question. Track per-endpoint and per-customer:  Status code distribution (a spike in 4xx for one customer is an integration problem; a spike in 5xx for one endpoint is a server problem)  Latency percentiles (p50, p95, p99) by endpoint and region  Per-customer call volume (capacity planning, churn signals, abuse detection)  Auth failure rate (security signal)  Error catalog frequency (which validation errors customers hit the most)Moesif instruments these out of the box across any gateway (WSO2, Kong, AWS, Azure, Envoy). Pair the design-time best practices in this guide with runtime observability and the loop closes: pick the right pattern at design time, return the right behavior at runtime, observe it actually working in production.Client-side considerations: designing for the consumerThe best-practice list above is mostly server-side. The other half of the contract is how clients consume the API; the patterns that make integration painless and the ones that turn it into a maintenance project.Provide SDKs in the languages your consumers actually use. A REST API with no SDKs forces every consumer to write HTTP-call boilerplate, handle auth manually, parse errors by hand, and retry on their own. SDKs in 3-5 dominant languages (TypeScript, Python, Go, Java, Ruby) compress integration time meaningfully. OpenAPI Generator, Stainless, and Speakeasy generate SDKs from your OpenAPI spec; for high-volume APIs the hand-tuned SDK is worth it, but for everything else a generated SDK beats no SDK by a wide margin.Support pagination cursors that survive across page loads. A cursor-based pagination scheme is the only one that survives concurrent writes. If a client retrieves page 1 (offset 0, limit 50), and the underlying collection gains 5 new records before they fetch page 2 (offset 50, limit 50), 5 records will appear on both pages. Cursor-based pagination ties the next-page token to the position of the last item, so concurrent inserts do not cause duplicates.Document retry behavior explicitly. Clients need to know which errors are retryable and which are not. The convention: 4xx errors (except 408 and 429) are not retryable because the request is wrong; 429 and 5xx errors are retryable with exponential backoff. State this in the SDK and in the API docs.Expose webhook delivery guarantees clearly. If your API delivers events via webhooks, document the retry schedule, the signing scheme, and what consumers should do on duplicate deliveries (idempotency keys on the webhook payload, just like the request side).Provide a sandbox environment. Developers should be able to try the API against a test environment without affecting production data or being billed. Stripe’s test mode, Twilio’s trial accounts, and OpenAI’s free credits are all variations on this. APIs without a sandbox lose developers at the “try it before integrating” step.Handle CORS correctly for browser-based consumers. If any of your consumers will call the API from a browser, the Access-Control-Allow-Origin configuration is part of the API contract, not an afterthought. Set the allowlist explicitly, handle preflights at the gateway, and cache preflights with Access-Control-Max-Age to reduce round-trips.Surface request IDs in every response. Include a X-Request-Id (or similar) header on every response. When a customer reports an issue, the request ID is what your support team uses to find the call in logs. Without it, every support ticket starts with “can you reproduce it?”Common REST API mistakes to avoidA short catalog of the design patterns that consistently cause pain in production.Mixing CRUD and RPC styles. A REST API that has POST /createUser next to POST /users is inconsistent. Pick one style and apply it across every endpoint. RPC-style APIs are legitimate (gRPC is RPC) but should not be half-mixed with REST.Returning 200 OK for errors. A response that returns 200 OK with {"status": "error", "code": "not_found"} forces every consumer to parse the body to know if the call succeeded. Use the HTTP status code as the primary success/failure signal; the body is for detail.Inconsistent ID formats. Some endpoints return integer IDs ({"id": 42}), others return UUIDs ({"id": "abc-123-..."}), others return prefixed strings ({"id": "user_abc123"}). Pick a convention (the Stripe pattern of typed prefixed IDs scales well) and apply it everywhere. Mixed ID formats are a permanent paper cut for SDK authors.Breaking changes without a version bump. Adding a new required field to a request, removing a response field, or changing a response type is a breaking change. Bump the version, run both versions in parallel, and follow your deprecation policy. Silent breaking changes are how trust evaporates.Endpoints that return inconsistent shapes for the same resource. GET /orders/{id} returns one shape; GET /orders returns each item in a slightly different shape. Pick one canonical shape and use it everywhere. Use OpenAPI’s $ref to enforce it.Stateless that is not really stateless. Pagination cursors that encode database offsets work until you change the database. Tokens that work only on the server that issued them break when you scale horizontally. State that lives anywhere except the request itself is leaky and surfaces as flaky tests and weird production bugs.Authentication that varies across endpoints. Some endpoints accept API keys in headers; others want them in query strings; others want OAuth. Pick one pattern (API key in Authorization: Bearer ... is the modern default) and apply it uniformly. Mixed auth is a security review nightmare.Returning huge responses without pagination. GET /transactions that returns 10,000 rows on every call ends in tears. Paginate every list endpoint from day one, and document the default and maximum page size.Ignoring rate limits in your own client code. If your own SDK retries aggressively without checking Retry-After headers, you accelerate the rate-limit problem the gateway is supposed to solve. Make every client (SDK, internal service-to-service, agent-mediated) respect the same backoff signals.Treating the OpenAPI spec as an afterthought. A spec written by hand after the implementation, and never updated, is worse than no spec because consumers trust it. Generate the spec from code, validate it on every commit, and treat regressions to the spec as production bugs.Next stepsThe twelve practices above are the parts of REST API design that consistently pay off. They cost very little to apply at design time and a lot to retrofit later.If you want per-endpoint, per-customer visibility into whether your API is actually following these practices in production, start a 14-day Moesif free trial. No credit card required.Frequently asked questionsWhat are the most important REST API best practices? Use nouns not verbs in URLs, pluralize collections, map HTTP methods to CRUD cleanly, return correct status codes, return structured error envelopes, authenticate every endpoint, rate limit at the gateway, version deliberately. Everything else builds on these.Should I use camelCase or snake_case in JSON? Pick one and use it across the entire API. Both are widely used across modern public APIs (Stripe and OpenAI use snake_case; many JavaScript-default APIs use camelCase). The discipline matters more than the choice.Is REST still the best choice in 2026? REST is still the default for public APIs because clients in every language understand it. GraphQL is better for client-driven data fetching; gRPC is better for service-to-service inside a mesh. For external developer APIs, REST is still the safe choice.How do I version a REST API? URI versioning (/v1/orders) is the most common and easiest for consumers to debug. Header versioning is cleaner but harder to use. Publish a deprecation policy on day one.What HTTP status code should I return on success? 200 OK for GET/PUT/PATCH, 201 Created (with Location header) for POST, 204 No Content for DELETE.Do these best practices apply to AI agent traffic? Yes, with three additions: idempotency keys on writes, agent-readable OpenAPI fields, and MCP exposure for agent runtimes. The fundamentals stay the same.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /technical/api-development/essential-REST-API-best-practices/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "api-strategy-top-application-insights-alternatives": {
          "title": "Top Application Insights Alternatives for Enhanced App Monitoring",
          "content"	 : "If you feel constrained by Azure Application Insights’ limitations or seek functionalities it doesn’t offer, you’ve likely begun exploring Application Insights alternatives that can better align with your application performance monitoring needs. In other cases, you may have read about the retirement of Classic Application Insights and are looking for a new platform to leverage in the same way. Regardless of why you are searching for an Application Insights alternative, this blog will help you compare top contenders like Dynatrace, Datadog, and AppDynamics, as well as dive into essential considerations for selection and open-source options that might suit your stack. Of course, this is all done while not losing sight of crucial features such as telemetry, user experience, and community support that come with Application Insights.                Monitor and Analyze API Traffic with Moesif              14 day free trial. No credit card required.              Try for Free        Key TakeawaysFor those who are looking for a quick summary, here are the highlights from the post below:  Azure Application Insights, part of Azure Monitor, is retiring its classic version, and users must configure it for workspace-based Application Insights and Visual Studio Application Insights to stay current.  Although Azure Application Insights is comprehensive, limitations exist, leading users to consider alternatives like Dynatrace, Datadog, and AppDynamics for enhanced capabilities in AI monitoring, unified cloud service monitoring, and in-depth performance analysis, respectively.  When transitioning from Azure Application Insights, it’s critical to ensure no data loss, adapt to new interfaces and features, and leverage community resources and documentation for a successful migration.To dig further into each of the above points, let’s begin by looking at what Microsoft Application Insights is.What is Application Insights?Azure Application Insights, a part of Azure Monitor, is an extensible analytics service designed for performance management and usage tracking of live web applications. Comprehensive analysis of performance and usage is possible by transmitting telemetry data from your application to the Azure portal using the Application Insights SDK. With application insights telemetry, Azure Application Insights also integrates seamlessly with development tools such as Visual Studio, supporting a range of DevOps scenarios.However, it’s important to take note that the classic version of Application Insights is being retired in February of 2024, making way for workspace-based Application Insights and Visual Studio Application Insights. With this in mind, it’s essential to configure Application Insights accordingly and to use the latest versions, if you don’t decide to go to an alternative (of which there are many).Why use an alternative to Azure Application Insights?Despite its advantages, Azure Application Insights may not be the perfect fit for every use case. For instance, the platform imposes certain limitations, such as a maximum of 100 Application Insights resources and Log Analytics workspaces that can be included in a single query. Additionally, there are restrictions on the data retention period and data gathering capacity of an Application Insights classic resource.When it comes to Classic Application Insights, it’s not much of a choice but a necessity to move off of the platform to either another Azure Application Insights technology or to search for a completely different alternative altogether.In some cases, there may be specific use cases that Application Insights is not able to handle very well. For instance, in the case of API analytics, although Application Insights does log HTTP requests out-of-the-box, it doesn’t log the content of a request and response body. For certain scenarios, these contain crucial information and insights that generic “API request counting” does not provide when trying to build reports or attempting to debug API errors.Overall, these constraints could potentially hinder your monitoring efforts and force some users to search for alternatives that are more well-suited.Exploring Top Alternatives to Azure Application InsightsWhen it comes to alternatives to Azure Application Insights, multiple platforms come to mind, including Moesif, Dynatrace, Datadog, and AppDynamics. Each of these tools brings unique strengths to the table. Let’s examine these options and compare them with Azure Application Insights.Moesif: API Monitoring and AnalyticsWhen it comes to monitoring API traffic and garnering insights, Moesif is the go-to solution for startups and enterprises alike. Moesif allows for easy integration with various API gateways and offers SDKs for many of the most popular languages and frameworks. Once integrated, Moesif can track APIs by users and company as well as derive insights from the request and response body and headers.By offering a comprehensive solution for both API monitoring (and monetization), Moesif is a strong alternative for those looking for API-specific insights.Dynatrace: Advanced AI for Smarter MonitoringDynatrace distinguishes itself with its AI-powered monitoring solution. It goes beyond tracking application performance and availability, using AI and machine learning to proactively identify and address performance issues. Its advanced AI capabilities enable teams to adhere to observability and security best practices.From real user tracking to AIOps automation, Dynatrace provides a comprehensive approach to application performance monitoring that is superior to Azure Monitor for web apps.Datadog: Unified Monitoring Across Cloud ServicesDatadog is another strong contender, offering enhanced visibility, integration, and support compared to Azure Application Insights. It’s unified monitoring across cloud services provides comprehensive visibility across all Azure services within a single platform, facilitating real-time correlation of metrics, traces, logs, and more.By providing a clear and complete view of monitoring efforts, Datadog emerges as a compelling alternative.AppDynamics: Deep-Dive into Application PerformanceAppDynamics enters the fray with an enterprise-focused suite of monitoring tools that provide deep insights into application ecosystems. It offers in-depth code-level diagnostics to swiftly identify and address issues, comprehensive application performance monitoring to guarantee smooth operations, and user journey tracking to gain a clear understanding of customer interactions. Additionally, AppDynamics measures business performance metrics, allowing IT goals to align closely with business objectives.With its broad compatibility across various platforms and major technologies, AppDynamics stands as a substantial alternative to Azure Application Insights for businesses in search of detailed and actionable application analytics.Essential Considerations When Choosing a Monitoring ToolWhile exploring alternatives, it’s crucial to consider key aspects such as integration with existing systems, scalability, and real-time analytics. Each of these factors can significantly impact the effectiveness of your chosen monitoring tool.Let’s further explore these considerations.Integration with Existing SystemsThe integration capability of the monitoring tool with your existing systems is an essential factor to consider when making a choice. This allows for smooth data exchange and automation of monitoring tasks, enhancing the overall effectiveness of the monitoring solution.Tools like Moesif, Dynatrace, Datadog, and AppDynamics offer robust integration capabilities, enabling you to leverage your existing infrastructure and workflows with external services.Scalability for Growing ApplicationsAnother factor to keep in mind is scalability. A scalable tool can efficiently handle increasing workloads, maintain performance and user experience, and readily adapt to changing requirements and technological advancements.Tools like Moesif, Dynatrace, Datadog, and AppDynamics are designed with scalability in mind, ensuring they can accommodate growth in user base, data volume, and transaction volume.Real-Time Analytics and Custom QueriesFor effective application monitoring, real-time analytics and custom queries are indispensable. Real-time analytics enable businesses to promptly make well-informed decisions and respond to the available information. On the other hand, custom queries can help users create target lists, generate reports, explore data, improve accuracy, execute tasks faster, and achieve better ROI.Incorporating Open Source SolutionsOpen-source solutions like Prometheus, Grafana, and Elastic APM provide an alternative avenue for application performance monitoring. These tools are widely used in the industry and offer a variety of features that can complement or even replace proprietary solutions.Let’s examine these open-source alternatives in more detail.Prometheus and Grafana: A Powerful DuoWhen used together, Prometheus and Grafana create a powerful monitoring solution. Prometheus handles the collection and storage of time-series data metrics, while Grafana creates visually appealing representations and interactive dashboards for that data.The combination of these two tools provides support for numerous data sources, making them a popular choice for DevOps teams seeking efficient application monitoring.Elastic APM: Searchable Data and Extensive Language SupportElastic APM offers a different approach to application performance monitoring. It provides comprehensive data on the duration of responses to incoming requests in real time. Some key features of Elastic APM include:  Observing software services and applications in real-time  Supporting a wide range of programming languages  Providing comprehensive data on response durationThis makes Elastic APM a versatile tool for various tech stacks and, as a bonus, also enhances data privacy by enabling secure searchability and anonymization of data.The Role of Telemetry in Modern APM ToolsAnother key aspect of modern APM tools is telemetry. Telemetry involves the collection of:  Traces  Logs  Events  MetricsAcross all applications within a stack that is connected to Application Insights SDKs play a crucial role in providing critical insights into system behavior. This helps to enhance the effectiveness and reliability of applications running on various platforms.When it comes to telemetry, it may be important to ensure that any alternative that you adopt also supports various telemetry features. Let’s further explore telemetry collection and analysis, as well as strategies for sensitive data protection.Understanding Telemetry Collection and AnalysisAPM tools utilize various methods to collect a variety of telemetry data, including:  Metrics related to performance  Log entries  Trace data  Connections between entities  Data reflecting user interaction patternsThis assortment of data is crucial for accurate and ongoing surveillance of application health and performance. Solutions such as Dynatrace and Datadog deploy sophisticated techniques for the aggregation and interpretation of telemetry data, transforming it into insightful and practical analytics.Ensuring Sensitive Data ProtectionProtecting sensitive telemetry data is vital. Given its susceptibility to security breaches and potential compromise of sensitive information, certain measures need to be taken to secure this data. When it comes to adopting a platform that deals with telemetry data, you’ll want to ensure that the platform does the following:  Uses TLS to protect data in transit  Identifies and classifies data  Implements encryption  Is hosted on secure infrastructureYou’ll also want to ensure anyone who is using the platform is properly trained to handle telemetry data carefully in case it contains confidential or private details.Transitioning from Azure Application InsightsIf you’ve decided to switch from Azure Application Insights to an alternative, there are certain steps to follow. The transition process involves migrating telemetry data, adapting to a new interface and features, and leveraging community resources and documentation.Migrating Telemetry DataEnsuring no loss of data is crucial when migrating telemetry data. Telemetry stored in both locations will be merged during the migration process. However, if a classic Application Insights resource is deleted, all historical data will be lost.Therefore, it’s crucial to follow a detailed guide to ensure a seamless transition of telemetry data.Adapting to a New Interface and FeaturesWhile it can be challenging to adapt to a new interface and features, with the right approach, it can be a smooth process. It’s advisable to explore documentation around the setup and management of the new platform as well as deployment models that the platform supports.Additionally, comparing features and functionality can help you identify the tool that best suits your organization’s needs and help you understand how to do tasks you’d perform in a previous platform on the new one.User Experience and Community SupportThe effectiveness of any APM tool greatly relies on user interaction, user experience, and community support. An intuitive user interface coupled with robust community support can significantly enhance the usability and effectiveness of the tool.Evaluating User Interface and Ease of UseA user-friendly interface is a key aspect of any APM tool. It should offer:  Intuitive navigation  Customization options  Clear visualizations of performance data  Prioritize ease of useAdditionally, the simplicity of setup and management, and available deployment models are aspects to consider when evaluating the user-friendliness of an APM tool.Leveraging Community Resources and DocumentationCommunity resources and documentation are invaluable when it comes to getting the most out of your APM tool. They provide a wealth of information, insights, and assistance, which can be particularly useful for troubleshooting and learning best practices. Leveraging these resources can significantly enhance your experience with the tool and enable you to use it to its full potential.Why Use Moesif?As we discussed earlier, Moesif is a compelling option for users who require deep insights into their APIs. Particularly effective for API analytics, Moesif offers features such as:  API Product Analytics  API Logs and Metrics  API Monitoring and Alerts  Custom DashboardsIts advanced features and user-friendly nature make it a viable alternative or complementary solution to other APM platforms.SummaryChoosing the right application monitoring tool is crucial for maintaining the performance and reliability of your applications. While Azure Application Insights is undoubtedly a strong contender, it’s important to consider alternatives like Moesif, Dynatrace, Datadog, AppDynamics, and open-source solutions like Prometheus, Grafana, and Elastic APM. Each of these tools offers unique strengths and can be a better fit depending on your specific needs. And of course, tools like Moesif can provide additional insights, particularly for API analytics. The key is to understand your specific requirements and choose a tool that best caters to them.Want to give Moesif a try? Sign up for a 14-day free trial, no credit card is required. Explore API analytics, monitoring, and monetization all in one place.                Monitor and Analyze API Traffic with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Looking for an App Insights alternative?            Use Moesif’s API analytics to discover and report status codes users see and proactively help customers with errors.            Try for Free            No credit card required            ",
          "url": " /api-strategy/Top-Application-Insights-Alternatives/",
          "author": "Matthew",
          "categories": "API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-tiered-pricing": {
          "title": "What Is Tiered Pricing? The Ultimate Guide",
          "content"	 : "In today’s dynamic marketplace, pricing strategies play a pivotal role in the success of businesses across various sectors. One such strategy that has garnered significant attention is Tiered Pricing – a nuanced and effective approach to pricing products and services. But what exactly is Tiered Pricing, and how can it benefit your business?This comprehensive guide delves into the intricacies of Tiered Pricing, offering insights and detailed analyses to help you understand and implement this strategy effectively. Whether you’re a startup founder, a seasoned business owner, or simply curious about pricing strategies, this guide is tailored for you.We begin by exploring the fundamentals of Tiered Pricing, unraveling its definition, and how it differs from other pricing models. Understanding the mechanics of Tiered Pricing paves the way for deeper insights into its various models and strategies. We’ll discuss four cutting-edge Tiered Pricing strategies shaping the market right now, offering you a lens through which to view potential paths for your business.A critical comparison between Tiered Pricing and Volume Pricing will highlight the unique advantages and considerations of each, aiding you in making an informed decision about which suits your business needs best. The benefits of Tier-based Pricing for businesses are numerous, and we’ll dissect these advantages to understand the tangible impacts on revenue, customer acquisition, and retention. Lastly, we’ll wrap up with key takeaways and actionable insights, equipping you with the knowledge to harness the power of Tiered Pricing in your business endeavors. Let’s begin by looking at the most fundamental of questions: What is tiered pricing?                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        What Is Tiered Pricing?Tiered pricing is a pricing strategy businesses use to offer their products or services at different price points based on predefined tiers. Each tier corresponds to a specific quantity or range of product or service use. This allows businesses to cater to diverse customers with varying needs and budgets.The essence of tiered pricing lies in its structure. Unlike a flat-rate pricing model, where a single price is applied regardless of consumption, tiered pricing divides the offering into different levels. Each level or tier offers a specific amount of products or services, often with an incremental benefit such as a lower per-unit cost at higher tiers.Advantages of Tiered PricingOf course, using a tiered pricing model uncovers benefits for both customers and the business itself. Let’s look at some of the advantages consumers and businesses will see when adopting tiered pricing.Flexibility for CustomersTiered pricing offers unmatched flexibility, enabling customers to select a tier that aligns with their needs and budget. This flexibility is particularly beneficial in markets where customer requirements vary significantly. For instance, in software services, small businesses may opt for a basic tier with essential features. On the other hand, large corporations may choose a premium tier with a broader range of functionalities that comes at a higher cost. This flexibility ensures customer satisfaction and empowers them to make choices that align with their circumstances. Overall, this gives customers the feeling of a more personalized experience.Increased Sales OpportunitiesBusinesses employing tiered pricing can capture a broader market segment. By offering multiple tiers, companies can appeal to budget-conscious customers with basic needs and those seeking premium features at a higher cost. This flexibility allows businesses to tap into customer segments that might find a single flat rate too restrictive or misaligned with their requirements. The tiered model can also be a compelling market penetration and expansion strategy, providing options for every customer category.Encourages UpgradesAs customers’ needs evolve, they often move to higher tiers, creating a natural progression path and revenue expansion. This aspect of tiered pricing is particularly advantageous for businesses as it fosters customer growth alongside their own. For example, a startup using a basic tier may upgrade to a more feature-rich tier as it gains traction and expands. This organic growth trajectory boosts the business’s revenue and solidifies customer loyalty as a service that can grow with them at their own pace.Disadvantages of Tiered PricingOn the flip side, as with any pricing strategy, there are some downsides to implementing a tier-based pricing model. Let’s take a look at a few of the low-lights below.Complexity in PricingTiered pricing can introduce complexity regarding the management and communication of available tiers and the benefits each offers. Establishing and maintaining multiple tiers requires careful consideration of pricing structures, feature allocations, and regular updates based on market dynamics. This means investing more time and resources in pricing strategy development and implementation for businesses, plus ensuring that customers can easily understand what each tier offers and which is most suitable for them.Potential for ConfusionAs alluded to in the previous point, another significant challenge of tiered pricing is the potential for customer confusion. With multiple tiers available, customers might struggle to identify which tier suits their needs best. This confusion can lead to decision paralysis, and since customers can’t decide what plan is most suited for them, they may delay or forego a purchase. On the opposite end of the spectrum, this pricing model can also result in a mismatch where customers choose a tier that doesn’t align well with their needs. Not selecting the right tier could lead to dissatisfaction in terms of pricing, features, or both. Businesses must mitigate this by providing clear guidance and support in helping customers understand and select the appropriate tier. This might involve customer education through detailed FAQs, comparison charts, and even high-touch customer support help.How Does Tiered Pricing Work?Understanding the inner workings of tiered pricing is critical in choosing this pricing model. A business must identify different customer segments and their respective needs to implement a tiered pricing model. For example, a software company might identify three key segments: individual users, small businesses, and large enterprises. Each of these segments has different usage levels and requirements and may require a specific type of tiered offering for the product to make sense.Once segments are defined, the business can then establish the applicable tiers. Each tier created will include a set quantity or range of the product or service. As the tier level increases, the price per unit often decreases, and certain features may be unlocked or improved. This helps to provide an incentive for customers to upgrade to a higher tier.Key Elements in Tiered PricingAs alluded to above, vital elements need to be established to create a solid tiered pricing implementation. Based on these critical elements, let’s look at the practical approach to implementing tiered pricing.Defining TiersThe process of defining tiers is critical in a tiered pricing model. It involves determining the number of tiers and the specific offerings of each tier. This step requires a deep understanding of your product or service and how different features or quantities can be bundled together to create compelling options for various customer groups. Here are a few things to consider at this stage:  Feature Differentiation: Each tier should offer a distinct value proposition. For example, this might mean offering basic functionalities in the lower tier and more advanced features in higher tiers for a SaaS company.  Scalability: Tiers should be designed to cater to different stages of customer growth. A start-up might begin with a basic tier, but it may require the features or volume included in higher tiers as it grows.  Clarity and Simplicity: Tiers should be easy to understand. Overly complex tiers with minor differences can confuse customers and hinder decision-making, potentially confusing the customer out of signing up.Price SettingAnother key factor is ensuring prices for each tier offer a good value balance. Setting the right price for each tier is a delicate balance that can significantly impact the success of your tiered pricing strategy. Of course, this may be an ongoing effort, but getting as close as possible in your pricing off the start is optimal. Here are a few considerations to think about when it comes to setting your prices.  Cost-Based Pricing: Making sure that you fully understand the cost required to deliver a product or service is crucial. For each tier, you’ll want to understand the cost involved to ensure that you are charging enough to cover the costs while remaining competitive.  Market Research: You should also research to understand what prices are currently being charged. Analyzing competitor pricing and understanding what customers are willing to pay for each tier can help to refine your pricing. You could conduct surveys, focus groups, or utilize various market analysis tools to get the best idea of what the market is willing to pay.  Value Perception: For the most impact and conversions, pricing should reflect the perceived value of each tier. Higher tiers should offer more value, justifying their higher price points. You’ll want to ensure that every user perceives that the value they receive from the product matches or exceeds the amount they pay.SegmentationLastly, you’ll want to consider the market segment interested in a specific tier. Segmentation can help understand and target different customer groups by helping to tailor prices, features, and even marketing to specific customer segments. Let’s take a look at a few particulars to be mindful of.  Customer Needs Analysis: Identify customer needs, pain points, and usage patterns to create tiers that directly address these segments.  Behavioral Insights: Using data analytics to understand customer behavior can help design tiers that align with how different segments use your product or service.  Feedback and Adaptation: Continuously gathering customer feedback to refine and adapt tiers. This may involve adding new tiers or adjusting existing ones to meet customer needs better.In conclusion, the critical elements of tiered pricing - defining tiers, price setting, and segmentation - require a strategic approach. The resulting tiers should be underpinned by thorough market research and a deep understanding of customer needs. Successfully implementing these elements can lead to a tiered pricing model that appeals to a broad range of customers, maximizes revenue, and balances cost and benefit to the customer.Models of Tiered PricingDepending on what you want to include in each tier, there are different models within tiered pricing that you could use. Below, we will look at the three most common, which could be used individually or even combined together in a more hybrid manner. Let’s take a look.Volume-Based Tiered PricingVolume-based tiered pricing is ideal for products or services where the usage cost decreases with increased volume. This model encourages customers to purchase more by offering lower prices at higher volumes. This approach is particularly effective for bulk goods or services that scale easily. Here are a few considerations if you are considering this model:Implementation: Businesses establish price breaks at specific quantity thresholds. For instance, purchasing 100 units may cost $10 per unit, but buying 500 units may reduce the price to $8 per unit.Suitability: This model works well for industries like manufacturing or wholesale, where selling in larger volumes significantly reduces per-unit costs.Feature-Based Tiered PricingAnother approach for creating tiers is to define them based on features. This is extremely common in the software and SaaS industries. With this model, feature-based tiered pricing offers different sets of features or service levels at different price points. Each higher-priced tier includes the features of all lower tiers plus additional functionalities. Here are a few considerations if you are considering this model:Customization: Allows customers to pay for only the features they need, making it highly customer-centric.Upgrades: Encourages users to upgrade as their needs grow, increasing customer lifetime value.User-Based Tiered PricingLastly, you may see some tiers priced based on how many users access a platform. In user-based tiered pricing, the cost depends on how many users access the service. This model is prevalent in B2B services, especially software and cloud services. Here are a few considerations for potentially going this route:Scalability: Allows businesses to start small and expand their user base as they grow.Control: Provides a straightforward way for businesses to control costs based on their size and usage needs.What Are the Four Tiered Pricing Strategies To Use Right Now?Once you have determined how you’d like to tier your product, you need to decide what to charge. Once you’ve reached this phase, there are multiple ways that you can calculate where to start your pricing. Let’s look at four of the most popular methodologies for calculating the price of each tier.Cost-Plus TieringCost-plus tiering involves setting prices by adding a standard markup to the cost of production or acquisition. It ensures profitability but also needs to be competitive within the market. The benefits of this approach include:Transparency: Customers often perceive cost-plus pricing as fair, as it directly relates to production costs.Simplicity: Easier to calculate and justify to customers.Competitor-Based TieringThis strategy involves setting prices in relation to competitors’ pricing structures. It’s vital in highly competitive markets where price plays a significant role in customer decision-making. The benefits of this approach include:Market Positioning: Helps position the product in the market as a cost-effective or premium alternative.Adaptability: Requires continuous market analysis to stay competitive.Value-Based TieringValue-based tiering sets prices based on the perceived value of the product or service to the customer rather than on the cost of production. The benefits of this approach include:Customer Focus: Focuses on customer satisfaction and perceived worth.Flexibility: Allows higher profit margins, especially if customers perceive high value in the offering.Dynamic TieringDynamic tiering involves adjusting tiers and pricing to market demand, competition, and other external factors. It’s highly adaptive and data-driven. The benefits of this approach include:Responsiveness: Quickly adapts to market changes.Optimization: Uses data analytics for price optimization.Tiered Pricing vs Volume PricingSome confusion sometimes arises when tiered and volume-based pricing terms are used interchangeably. Both of these pricing models are distinct. While tiered and volume pricing aim to incentivize higher revenue, they differ significantly in approach and impact. Let’s look at two significant factors in seeing the difference between the two pricing types: first, at pricing structure and then customer segmentation.Pricing StructureTiered Pricing: Offers different prices per unit within each tier. For example, 1-100 units at $10 each, 101-200 units at $9 each.Volume Pricing: Applies a uniform discount to the entire order based on total volume. For instance, a 10% discount on orders over 100 units.Customer SegmentationTiered Pricing: Caters to specific customer needs and usage patterns, providing tailored options for different segments.Volume Pricing: More generalized, offering the same pricing structure to all customers based on quantity alone.As you can see, although somewhat similar, both types have a distinct approach to pricing and value advantage. From an implementation perspective, volume pricing is relatively easy, while tiered pricing can be more complex to define and implement.Benefits of Tiered Pricing for BusinessesMany benefits can be seen for businesses that choose to go with a tiered pricing strategy. Although figuring out the particulars of what to offer in each tier and what to charge can be more difficult than other pricing models, the benefits can heavily outweigh any challenges. Let’s look at three of the most talked about benefits of using the tiered pricing strategy.Increased RevenueTiered pricing strategies can significantly boost a business’s revenue. By structuring prices across different tiers, businesses encourage customers to purchase more or opt for higher tiers, increasing sales volumes. For example, a customer may initially opt for a basic tier, but as they become more familiar with the product or service, they might be inclined to upgrade to a higher tier for better features or services. This incremental upgrade path drives up average sale values and nurtures long-term customer relationships, contributing to sustained revenue growth and expansion.Customer Acquisition and RetentionTiered pricing is an excellent tool for acquiring new customers and retaining existing ones. By offering a range of price points and feature sets, businesses can make their products and services appeal to a broader audience – from cost-sensitive customers to those seeking premium features or higher volumes. This inclusivity makes it easier to attract a diverse customer base and meet their varying needs effectively.Better Inventory ManagementAlthough this benefit mainly applies to businesses that sell physical products, tiered pricing can significantly enhance inventory management. By analyzing purchasing patterns across different tiers, companies can forecast demand more accurately, ensuring optimal inventory levels are maintained. This reduces the risks of overstocking or running out of stock, leading to more efficient operations and reduced costs.In summary, tiered pricing offers multifaceted benefits to businesses. It serves as a lever to increase revenue through strategic up-selling and plays a crucial role in market expansion and customer satisfaction. For businesses that focus more on physical products, it aids in operational efficiencies, particularly in inventory management, making it a versatile strategy for various business models.Tiered Pricing ExamplesExamples of tiered pricing can be seen throughout our day-to-day lives. Many of the products and services we use already implement tiered pricing. Let’s look at a few areas where tiered pricing is used.Subscription ServicesSubscription-based businesses often employ tiered pricing to cater to a diverse customer base. They can address different user needs and budgets by offering basic, standard, and premium subscriptions. For example, a streaming service might offer a basic plan with standard definition streaming, a standard plan with high definition and additional screens, and a premium plan with ultra-high definition and multiple screens. This strategy broadens the customer base and allows for up-selling as customer needs evolve.Bulk PurchasesRetailers often use tiered pricing to encourage bulk purchases, offering discounts as the quantity purchased increases. This model is prevalent in B2B and B2C contexts, such as wholesale businesses or bulk-buying clubs. For instance, purchasing a single unit might cost $10, but buying ten units may reduce the price to $8 per unit, to incentivize larger purchases.Software LicensesSoftware companies commonly implement tiered pricing based on license types. Different pricing tiers for individual, small business, and enterprise licenses allow these companies to serve a broad range of customers effectively. An individual license may offer basic functionalities suitable for a single user, while enterprise licenses might include full features, multi-user access, and enhanced support.Of course, these examples only touch the tip of the iceberg regarding how prevalent tier-based pricing is today. These examples show a few areas where this pricing model can be used in B2B and B2C contexts.Factors to Consider in Tiered PricingLastly, as you implement tier-based pricing models in your products, we should review some factors to consider. Let’s look at a few of the most critical factors, including some reiterations of important points we’ve already discussed.Market ResearchUnderstanding customer needs and market trends is crucial in designing effective tiered pricing structures. Market research helps identify what customer segments value and are willing to pay for. It involves analyzing customer preferences, industry trends, and purchasing behaviors, which inform the creation of tiers that are attractive to customers and aligned with market demands. When looking at this factor, consider the following:Customer Feedback: Regularly gather and analyze customer feedback to refine pricing tiers.Trend Analysis: Keeping abreast of market and industry trends to stay relevant and competitive.Cost AnalysisEnsuring each tier is profitable involves a thorough cost analysis. This includes assessing the cost of goods sold, operational expenses, and additional costs associated with each tier. Businesses must ensure that the pricing of each tier not only covers these costs but also contributes to the overall profitability of the business. When looking at this factor, consider the following:Profit Margins: Calculate and maintain healthy profit margins for each tier.Cost Efficiency: Identifying ways to reduce costs and increase efficiency, particularly in higher tiers.Competitor PricingStaying competitive while ensuring the product is not undervalued is a delicate balance. Analyzing competitor pricing helps in positioning your tiers appropriately in the market. Understanding how competitors structure their pricing and what value they offer at each price point is essential.Market Positioning: Determining where your product fits in the market compared to competitors.Value Proposition: Ensuring your pricing reflects the unique value proposition of your product or service.This covers some critical factors businesses must consider when developing and implementing a tiered pricing strategy. These insights can guide businesses in effectively leveraging tiered pricing to enhance customer satisfaction, market reach, and, most importantly, revenue.ConclusionTiered pricing is a versatile and effective strategy for catering to diverse customer needs while maximizing revenue. By carefully considering factors like market demand, cost, and customer segmentation, businesses can successfully implement tiered pricing models that benefit both the company and its customers. The framework and methods we discussed in this blog should provide a comprehensive understanding of tiered pricing and its various aspects.If you are monetizing your APIs, consider implementing a tiered pricing strategy. In that case, using Ship better API products with a powerful API analytics and monetization platform. When it comes to determining value and price, Moesif’s API analytics and reporting can help you to see what features and volumes customers are currently using to help you more easily determine your pricing tier definitions. Want to get started today? Sign up for a free trial or contact our team of API monetization experts today!                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Tiered-Pricing/",
          "author": "Matthew",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-strategy-how-to-make-money-with-ai": {
          "title": "Best Practices for Usage-based Billing to Monetize Gen AI",
          "content"	 : "Why monetize APIs?Artificial intelligence based APIs are reshaping traditional subscription models thanks to their unique monetization frameworks. These API products enable companies seeking tailored solutions in automation and AI workflows, departing from one-size-fits-all UI approaches and embracing a highly customizable experience.Originally designed for internal platforms, APIs built with AI are now evolving into revenue gateways, transforming them into strategic assets contributing directly to company revenue. These APIs serve as the enablers of integration within diverse systems, fostering interoperability, and enhancing overall operational efficiency. How to make money with AI starts in understanding your AI application’s value in the market.Repurposing internal functionality to serve external users enhances the value proposition of AI APIs, positioning them as drivers of innovation and profitability in today’s highly competitive SaaS landscape. Monetizing API products can seem like a complex nut to crack, but simply requires the right tools for the right product and a willingness to adapt based on user data.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Challenge of selling AISo you’re ready to learn how to make money with AI? Selling an artificial intelligence based product is a company-wide endeavor. As organizations shift from traditional on-premises solutions to SaaS and third party solutions, decision-making power moves away from the Chief Information Officer (CIO) to team managers. This trend has only intensified with the transition from SaaS to AI products, empowering individual contributing developers with significant purchasing authority and decision-making power for their machine learning.While a single developer can research and select an AI product that fits their use case or product need, deploying an app or integration to production involves multiple stakeholder parties. Various teams play a crucial role in the procurement and integration processes for AI products. Even if the intended buyer is not from an engineering department, the technical nature of AI products necessitates communication and input from diverse teams, adding layers of collaboration to the integration process of AI-based APIs.Land developers firstPursuing an intricate top-down sales approach to win over every stakeholder is an unnecessary and overly complicated way to foster interest in your AI system. Initiate new business by capturing developers’ interest through a self-serve onboarding experience. By focusing on the intended product users, you can build a more solid case for procurement with developer advocacy.This developer-first approach doesn’t negate the role of your internal sales team; rather, it focuses on getting developers to recognize the value of an AI powered tool and advocate for it within their leadership team before involving your sales team. Known as developer-first adoption or product-led growth (PLG), the objective is to encourage developers to make a swift and independent decision to test your product, such as $50/month on their credit card. This initial stage doesn’t involve procurement, allowing the subscription to be easily placed on a manager’s credit card. As a result, by the time a developer decides to request a higher seat count or usage limit, your sales team has enough data on the intended company’s use case to build a meaningful Proof of Concept (PoC), strengthening the chance at closing a deal.Sell through developersAfter your customers are actively subscribing to a self-service plan and fulfill specific usage criteria, your sales team can step in. Leveraging the recognized value of your generative AI tool, this sales approach shifts towards consultation, focusing on an “upsell” discussion. After all, the conversation is happening because the potential customer recognized a need for more use of your product. There is less of a focus on “why use our APIs” and more a focus on “leverage our products”,  allowing your sales team to guide the customer on optimizing API usage and addressing specific business requirements to explore avenues for increased value. Potential reasons for an upgrade may include heightened usage, the emergence of new use cases, or the presence of advanced requirements that require more API access or different, robust features that are more costly to your own infrastructure.In the realm of Artificial intelligence, it is crucial for your sales team to be fully aware of a customer’s usage patterns to pinpoint the right moment for engagement in the sales process. To facilitate this, implementation of an API analytics tool is essential. A good API analytics program tracks API usage for each individual customer through an account-based dashboard, offering a comprehensive view of both customer and pilot usage for sales and customer success teams.Beyond just the analytics side, a good API analytics program will allow you to Implement automatic notifications to offer the best prompt engineering experience. Setting up these alerts through your internal communication platform, such as through Slack, is one advisable way to alert teams of changes in a customer’s usage of your AI powered tool. For instance, a sales discussion might be warranted when an account’s API usage surges by more than 10% week-over-week, signaling a plan with a higher ceiling may be necessary.Choosing the consumption metricIn the context of AI products, selecting the right metric for usage-based pricing is crucial to align your monetization model with the derived business value for your users. Monetizable AI calls can include those involving predictions, model training, machine learning, data processing, customization, and use of premium features or models. On the other hand, calls for status checks, debugging, test environments, and activities with minimal user value might be less suitable for monetization as there is little incentive for your users to use them. Therefore, billing based on the number of calls sent resulting in a response from your AI technology’s framework proves to be a more effective metric than charging just per API call.As a secondary example, perhaps you provide a sentiment analysis AI chatbot or AI writing tool running off an internal artificial intelligence framework. You could charge based on the number of text sentiment analysis calls made by users who have purchased your AI integration. But, this could be potentially a bad metric to monetize, especially if these calls are very short or involve minimal machine learning processing. Charging solely based on the quantity of calls might not align with the actual value derived by your API users, as it doesn’t consider the depth or complexity of the sentiment analysis provided or the strain on your internal systems and frameworks. Instead, metrics like the volume of data processed or the level of detail in the analysis can better capture your product’s true value, as it accounts for the complexity of the query/queries in question.Common value metrics            Name      Example      When to Use      Example Company                                                  Transaction volume      # of Predictions, # of Recommendations      APIs and recommendation systems      Netflix              Revenue/cost share      % of Revenue, Transaction Fee      Platforms focused on financial transactions      Stripe              Data volume      Gigabytes Processed, Records Analyzed      Data-centric platforms such as analytics or processing      IBM Watson              User-centric      Monthly Active Users      Products charging per user engagement or interaction      Facebook              Resource      Compute Units, Active Hours      Compute infrastructure like cloud services      AWS (Amazon Web Services)      Ensure the API creates businessEven after successfully onboarding developers to use your AI algorithms, it doesn’t guarantee that your product has inherent business value. Developers might adopt the API for experimental purposes, hobbyist projects, or skill acquisition, and stop there. For AI companies, a basic “Hello World” API is likely not of significance. The primary reason external organizations procure an AI integration service is the perceived value it adds to their current tech stack. In terms of business value, three distinct types are important factors in product acquisition:  Reduced cost or time (vs a homegrown solution).  Unlocking of additional revenue for the organization.  Reduces risk for the organization.Pricing and packagingIf you’re selling your product, there are many ways to package it depending on your organization’s goals and business ideas. Due to their transactional nature, many APIs thrive under a usage-based billing model (also called Pay As You Go, or consumption-based billing) to kickstart revenue growth. Areas to consider in your pricing strategy include:  Billing  Packaging  Invoicing1. Billing StrategyBilling strategy significantly influences adoption and customer activation rates. This planning stage is often guided by product development, particularly in the context of AI. Prepaid billing for an AI platform entails customers paying for a service upfront before utilization, while postpaid billing involves customers settling payment after the services have been consumed.Prepaid billingPrepaid billing is a common payment model in the software industry. It involves customers paying for their subscription in advance of utilizing an AI application. In this approach, users commit to a predetermined plan or subscription level (usage limit), making an upfront payment, valid for a specified billing period. This model offers customers cost predictability, as they know the exact amount they will be charged, offering transparency and enabling better financial planning for customers, creating a more content userbase. Software providers, in turn, benefit from improved and consistent cash flow, receiving payments upfront. This process allows businesses to reinvest in their API products and enhance their ability to manage operational expenses efficiently.Postpaid billing​​Postpaid billing offers an alternative payment model, allowing customers to use SaaS services first and pay for them later, typically at the end of a billing cycle. In this arrangement, users are billed based on their actual usage of a product or service during a designated period. Postpaid billing offers flexibility to customers, as they are charged retroactively for the services consumed, making it suitable for those who may prefer assessing their needs before committing to a payment.However, from the API provider’s perspective, managing cash flow may present challenges, as revenues are collected after services have been rendered. Beyond this, users may be surprised at their actual bill once invoicing is completed, as postpaid billing requires customers to manage their own usage. This means it is advisable to build a dashboard or alerting system of some sort to keep users informed of their usage before a given billing cycle ends. Because you’re extending credit and API use, it can be abused by customers depending on your offering (like a dine and dash scenario). This means it’s critical to have internal usage limits to ensure a customer doesn’t accumulate “too much credit”, before they spend money.            Feature      Postpaid Billing      Prepaid Billing                  Billing Structure      Fixed charges on a schedule      Upfront payment for usage              Credit Check      Requires credit check      No credit check              Flexibility      Less flexibility      More control              Overage Charges      May incur overage charges      No overage charges              Contract Commitment      Contract commitment      No contract commitment      2. Packaging StrategyIn the world of AI, packaging strategy holds significant influence over both the initial contract value and any subsequent expansion of revenue. Given the diverse needs of customers, packaging of AI software can involve presenting various SKUs and offerings that cater to individual requirements based on a PoC. This segmentation may incorporate distinct features or usage components tailored to specific customer needs.A common packaging technique in the generative AI field is tiered pricing. Akin to SaaS models, organizations offer plans categorized as “good,” “better,” and “best,” each with predefined features and quotas. As price increases, so does feature complexity and quota cap. Another effective approach is the Pay As You Go (PAYG) model, which is really just synonymous with usage-based or consumption-based pricing. In this framework, customers purchase a specific quantity or volume, such as the number of queries made, providing flexibility and serving as a revenue accelerator, particularly for developer-first or product-first organizations.Tiered pricingFor an AI tool, a traditional tiered pricing model can offer simplicity for customers, enhancing cost understanding and predictability. Widely adopted in the SaaS industry, tiered pricing minimizes billing complexities and requires minimal implementation efforts, often relying on subscription billing software.However, a notable drawback lies in the potential disparity between price and perceived value. As customers approach plan limits, the necessity to upgrade arises, but the significant price jump to the next tier may lead to hesitancy and a refusal to upgrade. Striking a balance is crucial to prevent “analysis paralysis” and ensure a smooth customer experience. All of this must be weighed against the costs of maintaining your AI model data and frameworks effectively.Pay As You Go (PAYG) pricingPay As You Go (PAYG) pricing offers unparalleled flexibility and cost-efficiency. With PAYG, users pay only for the services they consume, aligning costs directly with actual usage, and therefore with actual value. Additionally, PAYG attracts new customers by lowering entry barriers and encourages exploration without a significant upfront commitmentA benefit of usage-based pricing is that it aligns costs directly with the value derived from a service. Since users pay for the actual consumption of resources or features, there is a guarantee that customers only pay for what they use.                   Tiered      Pay As You Go (PAYG)                  Description      Traditional SaaS tiers with predefined set of features and quota.      Usage-based or consumption-based pricing based on a unit price.              Pros      - Enforces a min spend      - Predictable for customer                     - Predictable for customer      - Easy to implement                     - Easy to implement      - More efficient for customer                     - More efficient for customer      - Less friction in expansion                     - Less friction in expansion      - Can “appear” cheaper                     - Can “appear” cheaper                     Cons      - Friction in expansion      - Friction in expansion                     - Rigid, not aligned to value      - Rigid, not aligned to value                     - Can upset customers with billing surprises      - Can upset customers with billing surprises                     - Complex to implement      - Complex to implement      3. Invoicing StrategyInvoicing strategy plays a crucial role in influencing cash flow and unit economics for an AI tool. The choice between recurring invoicing, with its predictable schedules, and threshold-based invoicing, triggered by predefined limits, shapes how revenue is generated and recognized. You can also invoice customers once customers reach a threshold such as when they reach a certain quota or outstanding spend. This type of invoicing is called threshold-based invoicing.Recurring invoicingRecurring invoicing involves a systematic approach in which customers are billed on a regular schedule. This predictable billing model provides customers with a clear understanding of their financial commitment and the time constraints on their API usage. From a business perspective, recurring invoicing contributes heavily to stable cash flow and simplifies financial forecasting thanks to guaranteed revenue on a guaranteed timeline. While this model is widely adopted in the world of SaaS and is appreciated for its predictability, it can pose challenges in scenarios where extreme usage patterns lead to low-cost transactions.Threshold-based invoicingThreshold invoicing operates on a different paradigm compared to recurring invoicing. In this monetization framework, invoices are triggered only after a customer surpasses a predefined threshold, such as a specified volume of usage. This approach offers flexibility, allowing customers to pay for services only when they have reached a certain level of consumption. However, threshold invoicing can introduce complexities in accounting and revenue recognition. The time frame between invoicing events becomes less predictable, making it challenging for financial planning and positioning your AI product as a negative opportunity that could potentially result in diminished revenue compared to the potential earnings achievable with a recurring model.                   Recurring      Threshold                  Description      Customer invoiced on a schedule like each month, quarter, or year      Customer invoiced only after a credit threshold is met (can be prepaid also)              Pros      - Easier for revenue recognition/GAAP      - More predictable                     - More predictable      - Reduced transaction cost                     - Reduced transaction cost      - Makes prepaid easier                     - Makes prepaid easier                     Cons      - Bad unit economics for low-cost SaaS      - Complex if prepaid                     - Complex if prepaid      - Harder for finance to recognize revenue                     - Harder for finance to recognize revenue      - Company liability      Implementing usage-based pricingImplementing usage-based pricing for an AI tool requires planning and adaptability. Establishing a robust tracking mechanism, integrating analytics tools, and automating notifications are key steps to success. Finding a value metric closely tied to your user’s needs can ensure that the invoicing of your API product aligns with the perceived value for your users, fostering fairness and trust within your customers. Success lies in creating a user-centric experience that reflects your product’s value based on your user data and analytics.You can build your own data warehousing on a platform like Snowflake or you can use a purpose-built usage-based billing product for APIs like Moesif. With an external solution, connect to your API gateway of choice like Kong or Tyk and then integrate it with a billing provider like Stripe to easily set up metering rules for the best AI monetization solution. For AI startups who need to invest directly in their product, outsourcing analytics is a natural fit for AI technology. For a detailed tutorial on how to do this with Kong and Stripe, check out the End-to-end API Monetization with Kong, Stripe, and Moesif.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-strategy/how-to-make-money-with-ai/",
          "author": "Rachael",
          "categories": "API-Strategy"
        }
      
    ,
  
    
        "api-strategy-api-development-what-is-apigee-api-observability-how-does-it-work": {
          "title": "What is Apigee API Observability: How Does It Work?",
          "content"	 : "Introduction to Apigee APIAPIs play a crucial role in a SaaS product’s ability to communicate with internal and external applications. API’s facilitate communication between applications and external services, revolutionizing the ways in which applications exchange data and how software developers and providers structure their systems and products. But managing the large flow of data and information while ensuring data protection and minimal downtime is no easy task.Apigee ensures seamless communication between applications, servers, and users, making it a critical tool in the modern tech landscape.Enter Apigee – a robust platform for API management that facilitates efficient design, deployment, and optimization of API products. Apigee stands out among the array of API management platforms, offering a potent and all-encompassing solution that enables enterprises to fully leverage and distribute the capabilities of their APIs.Definition of ApigeeApigee, a part of the Google Cloud Platform, is a comprehensive API management platform that enables SaaS organizations to design, secure, deploy, monitor, and scale their API products. APIs foster interoperability, accelerating development and reducing costs thanks to integrations. Apigee Edge’s toolbox includes an intuitive design interface and robust security protocols, offering developers and businesses a customizable feature catalog for building, securing, and scaling API products effortlessly.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        Overview of API ManagementAPI management is one of the most important aspects of software development. A great API management solution facilitates the efficient design, deployment, and optimization of Application Programming Interfaces (APIs). These APIs serve as communication bridges between different software applications (internal and external), allowing them to interact and share data seamlessly. API management includes everything from designing APIs for usability to security protocols for protection.Effective API management ensures streamlined processes and workflows, accelerates development, and enhances the scalability and security of software products. It also highlights the value of API services in providing critical products for both end users and developers. Platforms like Apigee, Apigee X, and Apigee Edge from Google Cloud exemplify how a comprehensive solution for organizations can optimize API management. An effective API management strategy benefits the API provider by enabling predictable revenue streams and clear definitions of value through various pricing models.Importance of API Management in Modern Software DevelopmentAPI management has grown significantly in importance over the past decade or so due to transformative shifts in technology and business practices, namely the outsourcing of integrable features and solutions. With the advent of cloud computing and IoT devices and products, the rise of microservices architecture have made communication between diverse systems a business necessity. The demand for connected systems, coupled with the increasing importance of data integration and analysis, has elevated the role of restful API management from a want to a need.Understanding the API business model is crucial in modern software development, as it highlights how APIs have transitioned from mere technological tools to valuable products that can drive business growth.Moreover, modern security concerns in the digital landscape require robust authentication and authorization protocols and mechanisms, which an API management service can provide. Good API management is indispensable for fostering agility, enabling rapid development and optimization. The evolution of technology and the changing nature of business demands and use cases have made API management more crucial now and in the future than it ever has been. Businesses can leverage various API monetization models to generate revenue from their APIs, transforming them into valuable products for developers and end users.API Management SolutionsAPI management solutions are crucial for businesses that want to monetize their APIs. These solutions provide a comprehensive platform for creating, managing, and securing APIs. With API management solutions, businesses can expose their backend services as APIs, manage API traffic, and analyze API performance. These platforms offer tools for building API proxies, securing access to APIs, and creating developer portals, ensuring that APIs are both functional and secure.By leveraging API management solutions, businesses can generate revenue from their APIs, improve customer engagement, and create new revenue streams. These solutions enable organizations to optimize their API usage, ensuring that APIs are scalable, reliable, and secure. Additionally, API management solutions provide valuable insights into API performance and usage patterns, helping businesses to make data-driven decisions and enhance their API offerings.Key Features of Apigee APIThe Apigee API platform offers a comprehensive suite of features for effective API service management:      API Design and Modeling: Developers can design APIs visually, from defining structures to configuring authentication methods.        API Security: Robust security features and protocols, like customizable authentication and encryption to protect APIs against unauthorized access.        API Deployment: Apigee facilitates real-world deployment, making APIs accessible to external developers and partners.        Traffic Management: Businesses can control and optimize API traffic to prevent overload and ensure continued performance.        Analytics and Monitoring: Apigee provides analytics tools for monitoring usage, tracking performance, and gaining insights.        Policy Enforcement: Apigee enables the enforcement of usage policies and access management through governance, ensuring adherence to custom guidelines and preventing unauthorized usage.        Integration with Backend Systems: Apigee integrates with backend systems, databases, and services, enhancing API efficiency no matter the level of API traffic.        Billing Systems: Apigee includes robust billing systems to manage API usage and costs, enabling precise charging based on actual usage and ensuring accurate metering and cost management.        Key Metrics: Apigee tracks key metrics such as adoption, engagement, and retention to monitor API performance and usage, driving the success of API programs and meeting customer needs.  Apigee API Lifecycle Management and API ProxiesApigee API Lifecycle Management guides software providers through every stage of their API lifecycle, from design to deployment. With an interface for API design, API analytics, customizable security features, and deployment capabilities, Apigee offers a solid management solution for building and scaling API solutions. Distributed tracing helps in monitoring API transactions and performance by enabling users to investigate detailed transaction information and identify anomalous traffic patterns, ultimately enhancing API monitoring capabilities. Apigee, when leveraged correctly, can enhance overall API efficiency in a way that sets your APIs up for the future. By keeping your API product agile and malleable, you can more easily make data-driven optimizations based on your usage data to your endpoint offerings, API Gateway integrations, even feature deployment. Additionally, Apigee’s lifecycle management ensures predictable revenue through tiered subscription plans that offer stability in income and costs for both the API provider and the customer.Security and ComplianceApigee prioritizes the safeguarding of API products through advanced security measures, providing customizable authentication, authorization, and encryption features to prevent misuse and abuse. This ensures protection against unauthorized access and potential threats, bolstering the integrity of sensitive data. Secure access mechanisms are vital in protecting backend services and streamlining the developer experience by using API proxies, which facilitate safe and controlled interactions with APIs while ensuring that sensitive implementations remain hidden from app developers.Beyond this, Apigee can be used for API governance, keeping your products safe from API overage. Overage occurs when a customer surpasses the agreed-upon usage thresholds or quotas associated with their API subscription or access authority and fails to comply with the terms, payment for additional usage. Unauthorized or excessive API consumption can strain your infrastructure and resources, affecting performance. Apigee allows organizations to implement effective rate limiting with usage monitoring to help mitigate the risk of API abuse. Additionally, Apigee helps expose services by making backend services accessible over the web through secure HTTP endpoints, ensuring that client apps can retrieve essential data like pricing and order tracking while maintaining security and consistency.API Proxies and SecurityAPI proxies are a critical component of API management solutions. They provide a secure interface between the API and the backend service, allowing businesses to control access to their APIs and protect their backend services from external threats. API proxies can be configured to add security features such as authentication, authorization, and encryption, ensuring that only authorized users can access the APIs.In addition to security, API proxies offer functionalities like rate limiting API calls, managing API usage quotas, and mediating API requests. These features help businesses to prevent API abuse, manage API traffic efficiently, and ensure that their APIs are reliable and scalable. API proxies also allow for the creation of custom scripts, enabling businesses to extend the functionality of their APIs and tailor them to specific needs.Developer Portals and API ProductsDeveloper portals are essential for businesses that want to create a developer community around their APIs. These portals provide a central location for developers to access API documentation, API keys, and API products. API products are bundles of API proxies that provide access to specific backend services, and they can be configured to offer different levels of access, security, and analytics.By creating API products and making them available through developer portals, businesses can incentivize developers to use their APIs, generate revenue from API usage, and create new revenue streams. Developer portals also provide valuable insights into API usage patterns, helping businesses to improve their API products and services. By fostering a developer community, businesses can enhance their API program, drive innovation, and increase the overall value of their APIs.Use Cases and API Monetization of Apigee APISome possible use cases for Apigee API management that are broad for any industry:      Digital Transformation: Apigee enhances digital transformation initiatives by modernizing systems through API management.        E-commerce: Apigee can be used to integrate various e-commerce components, like payment gateways or inventory management solutions.        IoT Integration: Apigee connects and manages APIs to enable communication between devices, sensors, and applications.        Legacy System Modernization: Apigee helps modernize legacy systems by enabling API creation to bridge the gap between old and new infrastructure.        API Monetization: Organizations can use Apigee to monetize APIs, limiting access to premium features and generating revenue.        Mobile Apps: Apigee enhances the functionality of mobile apps by embedding essential APIs, enabling companies to monetize through various models and connect with developers building applications.        E-commerce Platform: Apigee benefits e-commerce platforms by focusing on metrics specific to customer engagement and improving checkout conversion rates, aiding in understanding customer interactions with the API.  How Moesif helps with API ManagementMoesif complements Apigee’s API Management with advanced analytics, error tracking, and performance monitoring. It provides insights into API usage patterns, user behavior, and potential security threats in real time. Moesif can also seamlessly integrate with Apigee’s policies, offering customizable dashboards and alerts for swift, proactive issue resolution. With detailed API documentation and a robust developer portal, Moesif’s API analytics get to the heart of every API call. Moesif enhances API observability with advanced analytics and monitoring, providing a comprehensive view of API performance and user interactions.      Advanced Analytics: Moesif boasts advanced analytics capabilities, enabling organizations to gain deep insights into API usage patterns, traffic trends, and user behavior down to the individual user seat.        Error Tracking: Moesif tracks errors and anomalies in API requests and responses, providing detailed information based on your apps and custom specifications.        Performance Monitoring: Moesif monitors API performance metrics, including response times, latency, and throughput. This information can be set up to automatically notify your internal teams to prevent performance bottlenecks or backend service issues.        User Behavior Analysis: Moesif analyzes user behavior by tracking how users interact with your API products. This helps organizations tailor their API strategies to meet user needs and build better apps.        Integration with Apigee Policies: Moesif seamlessly integrates with Apigee API Management, allowing organizations to incorporate Moesif’s analytics into their existing Apigee workflow and improve the developer experience. Moesif’s advanced features, such as customizable dashboards and detailed API documentation, provide additional capabilities that enhance the overall user experience.  ConclusionThe Apigee platform is a robust API management solution with extensive features that make it an indispensable tool for enterprise level organizations undergoing digital transformation. Whether optimizing app development, integrating legacy infrastructure, or monetizing APIs, Apigee empowers organizations to build better data-driven products.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Looking for an alternative to Apigee?            Unlock the full potential of your APIs with Moesif's cutting-edge observability. Start optimizing your API performance today - Dive deeper with Moesif!            Try for Free            No credit card required            ",
          "url": " /api-strategy/api-development/What-is-Apigee-API-Observability-How-Does-It-Work/",
          "author": "Rachael",
          "categories": "API-Strategy, api-development"
        }
      
    ,
  
    
        "api-strategy-what-is-api-monitoring": {
          "title": "What is API Monitoring? Everything You Need to Know",
          "content"	 : "IntroductionApplication Programming Interfaces (APIs) are the glue powering SaaS application connectivity. However, ensuring smooth operation of APIs requires more than just implementation; vigilant oversight and alerting can make or break your API products.Welcome to the realm of API monitoring, where the data transferred during digital interactions is meticulously measured and cataloged. From optimizing product and user performance to safeguarding against potential pitfalls or misuse, API monitoring empowers API-first organizations to deliver reliable, scalable, and high-performing digital experiences. We’ve written extensively about the costs associated with building internal API monitoring solutions and will be focusing on external integrations for this post.                Monitor and Analyze API Traffic with Moesif              14 day free trial. No credit card required.              Try for Free        What is API monitoring?API monitoring is the practice of constantly observing and analyzing the performance, availability, and functionality of your APIs. API monitoring involves tracking key metrics - which can vary depending on your API product-  such as response times, error rates or status codes, and overall health, ensuring that APIs operate as intended and without interruption. By continuously monitoring APIs in real-time, organizations can proactively take charge of their product health, identify issues, and take prompt corrective action. A proactive approach helps maintain the reliability, efficiency, and security of the interconnected digital systems that rely on APIs, not just internally but externally for your customers, as well. Ultimately, keeping track of your API health will contribute heavily to a positive user experience.  Happy users equal lower churn, keeping your APIs in use and valuable to external ecosystems. API monitoring is a crucial aspect of modern IoT management, as a seamless operation of interconnected applications is the best way to retain market value.Monitoring plays a pivotal role in addressing essential questions for internal developer teams: Are your API products accessible? How do they perform? Are the APIs operating as intended? The failure of APIs translates directly into application failures, which can cause your paying customers to lose faith in your abilities as an API provider. In the context of digital transformation initiatives, APIs are fundamental components propelling organizations into the contemporary digital era, but only if they perform. As a result of the interconnected nature of technological stacks, a majority of applications rely on APIs for critical business transactions. However, without a truly comprehensive understanding of the inner workings of each API endpoint or the sequence and contents of API calls, organizations introduce blind spots in their performance evaluations.In scenarios where APIs that are critical to the functioning of your (or your user’s) application face issues such as unavailability, malfunctioning, or unresponsiveness, the repercussions extend to the overall performance of your application, adversely affecting the end-user experience and introducing reasons for your paying customers to consider alternatives.How does API monitoring workAPI monitoring operates by giving developers the tools needed to ensure functionality and performance of APIs. API monitoring can be done manually, but it is unreasonable to expect that manual monitoring is a scalable solution. As such, an API monitoring tool or platform is the best solution, as these programs do not require consistent human oversight, but rather work automatically.These API monitoring  tools can automatically check API performance and availability at specified intervals of time to ensure that your APIs run appropriately. The specific timing of those intervals is unique to your API product.  Endpoint Surveillance:  API monitoring begins by closely tracking the various endpoints of an API system. These endpoints represent specific functionalities or resources that the API provides.  Request-Response Analysis:  Monitoring tools can simulate API requests by sending predefined inputs and parameters to specific endpoints, then analyzing the responses for relevant factors such as response time or data accuracy.  Performance Metrics Measurement:  Key performance metrics, such as response time, latency, and error rates, are continuously measured. These metrics offer insights into the overall health and efficiency of an API.  Error Detection and Logging:  Actively identify and log any errors or anomalies in API responses with an API monitoring program. This includes capturing HTTP error codes, unexpected data/file formats, and alerting of any deviations from expected behavior.  Security Checks:  Assess your API transactions for unauthorized access attempts, potential vulnerabilities, and adherence to security protocols.  Alerting and Notification System:  Automated alerting systems can be configured with high levels of customization to notify relevant stakeholders when predefined thresholds are exceeded or when abnormal behavior is detected.  Usage Analytics Gathering:  Collect usage analytics for insights into how a given API is being utilized. This data helps software organizations plan for scalability, optimize resource allocation, and understand user behavior.  Logging for Auditing:  Detailed logs of API interactions are maintained for auditing purposes. These API logs are valuable resources for post-incident analysis and tracking historical performance and behavioral trends.  Continuous Monitoring:  API monitoring is a constantly ongoing, real-time process. It ensures that issues of any scope are identified and addressed promptly.Importance of API monitoringAPI monitoring has the ability to ensure the consistent availability, optimal performance, and secure functionality of your API products, keeping your customers happy and your product running smoothly. By continuously, automatically tracking key metrics (such as response times, error rates, and security vulnerabilities), organizations can take a proactive approach to identify and address issues before they impact end-users, instilling confidence in your paying customers that your product works. The specific metrics that are valuable to your API product will depend on your offering, your use cases, and your real user data, but a good API monitoring program will allow you to capture any main metric. With a proactive approach to safeguarding the reliability of your technological services, API monitoring contributes heavily to a positive user experience. API monitoring should not merely be a reactive measure; it can serve as a guiding tool for maintaining business continuity.What are the primary use cases for API monitoring?API monitoring serves various crucial use cases in ensuring the reliability, performance, and security of APIs. From availability monitoring to usage analytics, API monitoring can be relevant to technical and non-technical teams. Some of the most broad use cases for API monitoring that are relevant to any SaaS team include:Performance Optimization:  Track key performance metrics to identify bottlenecks in onboarding or API usage and optimize the overall efficiency of API interactions.Usage Analytics and User Analysis:  Gather insights into how APIs are being utilized, including usage patterns, popular endpoints, and user behaviors. This information aids in capacity planning and resource optimization but also shapes Developer Relations and Customer Success workflows.Compliance Adherence and Security Monitoring:  Monitor APIs to ensure they comply with industry standards and regulations regarding data security and privacy, reducing the risk of legal and regulatory complications. Understanding your API’s security and threat potential of unauthorized use or abuse keeps your API and user data safe.Running security checks from development to production means front and backend analysis.There are of course more technical reasons to monitor your API usage, such as error detection or log analysis, but the above use cases were selected to exemplify the wide range of developer and non-developer users alike who can benefit from becoming acquainted with their organization’s API monitoring tools.Factors to consider when choosing an API monitoring toolSelecting the right API monitoring tool for your API product(s) requires careful consideration of several factors to ensure it aligns with your specific use case.Usability:When selecting an API monitoring tool, always prioritize usability. Select a tool with an intuitive and easy-to-use interface. Ideally, your selected monitoring tool allows for customizable reports and a streamlined setup process, but the setup being simple is more of a financially driven choice rather than strictly matching the tool to your APIs. Real-time alerts are invaluable, as nobody, not even your most nocturnal engineers, should be on call for 404s. A good monitoring tool offers instant notifications via channels like email, Slack, or SMS for timely actions.Integrations:Seamless operations and effective error detection rely on robust integrations. Choose a tool with strong integration capabilities that match the frameworks you currently already use. Additionally, a RESTful API enables automation and integration into existing workflows, making REST APIs a natural fit for external API monitoring tools.Value Metrics:Metrics play a vital role in evaluating API performance, but not every metric is important or relevant to your API product. Selecting an API monitoring tool that allows you to customize your monitoring to surface your most valuable metrics is huge. If you’re not sure what matters to your API, check out our blog post on Vanity vs Value metrics for API products. By focusing on the analytics that matter to your product, your management solution can facilitate real-time data collection and visualization, supporting data-driven decisions, and enhance your overall API performance.Resources:Support and documentation is a crucial aspect of a smooth monitoring experience. Look for tools offering well-documented instructions for setup and troubleshooting. Some tools offer additional training resources, such as webinars or tutorials, to enhance users’ understanding of features and capabilities. Making sure that the management solution you select has a viable developer portal can save you from future headaches and blocks.Best API monitoring toolsFind the right monitoring fit for your API analytics use case.MoesifMoesif takes a user-centric approach to API observability and monitoring. It tracks how organizations interact with your APIs and applications down to the individual user seat. In case of any alert, you can count on real-time alerting, capable of integrating with any messaging platform. Alerts are validated, at which point Moesif uses your custom settings to filter alerts that are not relevant and surface important notices. Moesif supports any kind of product surfaced to a user base, not strictly API ones. With its comprehensive monitoring solutions, Moesif ensures issue resolution and provides “Full Stack Visibility” for generating valuable insights. Moesif’s PAYG monetization model lends flexibility, making it a natural choice for small and growing enterprises as well as ones who are already at scale.DataDogDatadog’s proactive API monitoring platform automates your technical system health, reducing identification time for issues.Datadog compares application performance internally and externally, with an emphasis on efficient time management through maintenance and test automation tools. Datadog is, at its core, a highly versatile and comprehensive monitoring and analytics platform that is particularly good for enterprise  level organizations with multi-stack applications.PostmanPostman’s platform gives you the power you to monitor a broad range of API products on any kind of framework. From REST through GraphQL, Postman is a versatile and user-friendly tool. Postman allows users to monitor and test APIs in a collaborative but organized manner. Its user-friendly, intuitive interface allows users to create and execute automated scripts, ensuring the reliability and performance of API products. Whether you are a developer, tester, or a member of a cross-functional team like developer relations, Postman can streamline the API monitoring process.RapidAPIWith RapidAPI, users gain access to a vast marketplace of APIs, enabling monitoring of various services and tools from a single interface. Its extensive API catalog facilitates easy integration and testing, allowing users to monitor diverse endpoints effortlessly. RapidAPI’s user-friendly dashboard simplifies the monitoring and testing processes, making it accessible to both developers and non-technical users. By leveraging RapidAPI for API monitoring, organizations can optimize their workflows.SematextSematext is an ideal choice for API monitoring, with a robust suite of tools for real-time insights and proactive issue resolution. It boasts a simple interface and customizable dashboards, Sematext enables easy visualization and analysis of key metrics. Its emphasis on anomaly detection and alerting ensures quick response to deviations, while support for distributed tracing provides detailed transaction views for optimization. Scalable and cloud-native, Sematext caters to the monitoring needs of businesses, making it a powerful tool for ensuring the reliability and performance of APIs in the digital landscape.API monitoring best practices      Define Clear Objectives: Define specific objectives and key performance indicators aligned with business goals, and update these objectives as business goals change.        Regular Endpoint Monitoring: Monitor API endpoints regularly for availability and expected functionality.        Key Performance Metrics: Track metrics like response time, latency, throughput, and error rates for insights into API health. Find the metrics that matter to your API product and build your monitoring framework around them.        Real-Time Monitoring: Implement real-time monitoring for quick issue detection and response to take a proactive approach to you API health.        Security Monitoring: Monitor for unauthorized access, data breaches, and adherence to security protocols as well as test your architecture regularly.        Scalability Planning: Assess API usage patterns to plan for scalability and handle increased traffic loads.        User Experience Focus: Consider metrics impacting user experience, such as page load times or transaction success rates.        Dependency Tracking: Monitor dependencies between APIs to address issues from interconnected systems.        Logging and Auditing: Maintain detailed logs for auditing, compliance, and post-incident analysis.        Integration with DevOps: Integrate API monitoring into DevOps processes for continuous testing and early issue detection.  Why use Moesif for API monitoring?Moesif is a standout choice for API monitoring thanks to its comprehensive and user-driven approach. Offering unparalleled visibility into API transactions and user actions, Moesif goes beyond conventional monitoring tools by providing detailed analytics on user behavior, and how it impacts performance metrics. Moesif’s ability to generate insights from behavioral analytics adds another layer of sophistication, allowing for the analysis of user journeys for fine tuning of onboarding, marketing, customer success, and beyond. It excels in error detection and troubleshooting, providing in-depth information on errors and status codes. With its robust features and emphasis on comprehensive analytics, Moesif is a powerful tool for organizations in need of informed API management.ConclusionIn summary, API monitoring ensures the reliability, performance, and security of API products and the interconnected systems they support. Beyond its role in swiftly addressing issues, API monitoring is key for delivering stellar user experiences and maintaining organizational agility for scaling growth.                Monitor and Analyze API Traffic with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Do you know what status codes your users are seeing?            Use Moesif's API monitoring and analytics to discover and report status codes users see and proactively help customers with errors.            Try for Free            No credit card required            ",
          "url": " /api-strategy/What-is-API-Monitoring/",
          "author": "Rachael",
          "categories": "API-Strategy"
        }
      
    ,
  
    
        "api-strategy-what-is-a-developer-portal": {
          "title": "What Is a Developer Portal? Definition & Examples",
          "content"	 : "A developer portal is the public front door of an API. It is the website where outside developers go to sign up, read the docs, get an API key, find code examples, and start integrating. Stripe has one. Twilio has one. So do GitHub, AWS, OpenAI, and most companies that have ever turned an API into a business.                Open-Source DevPortal from Moesif              14 day free trial. No credit card required.              Try for Free        A good developer portal compresses what used to be a six-week sales-and-onboarding cycle into one afternoon of self-service. A bad one is the reason a developer abandons your API for a competitor’s before they ever talk to your team. This guide covers what a developer portal actually is, what jobs it does, what the best ones include, and how to know whether yours is working.What is a developer portal?A developer portal is a website built specifically for developers who want to use a company’s API. It typically includes:  API documentation with request and response examples.  Self-service account creation so a developer can sign up without talking to sales.  API key or OAuth credential management so the developer can authenticate.  SDKs and code samples for the languages developers actually use.  A changelog or release notes page explaining what has shipped recently and what is deprecated.  A support path when something does not work.You will sometimes see developer portals called “developer hubs” or “API portals.” They are the same thing.What a developer portal is for (the four jobs it does)A developer portal does four jobs at once.Discovery. A developer who has never heard of your API needs to find out what it does and whether it solves their problem. That happens in the first thirty seconds on your portal homepage. If they cannot tell within thirty seconds, they leave.Education. Once a developer is interested, they need to learn how the API works. Conceptual docs and runnable code examples do that work. The faster a developer goes from “I want to try this” to a working request, the higher the conversion to a paid customer.Onboarding. This is the moment of truth. The developer signs up, gets credentials, and makes their first call. The standard metric here is Time to First Hello World (TTFHW). Stripe and Twilio target single-digit minutes; most APIs struggle to get under an hour.Long-term support. A developer who integrated last year still needs the portal: to find the spec for a new endpoint, to read the deprecation notice for an old version, to look up an error code they have never seen before. The portal is not a launch artifact. It is a permanent surface.Anatomy of a developer portalA modern developer portal usually has these components.API reference docs. Generated from your OpenAPI specification so the docs cannot drift from the API. Every endpoint, every parameter, every response shape, with copyable example requests.Getting-started guide. A short, opinionated walkthrough that takes a developer from “I have an account” to “my first request succeeded.” Twilio’s “Send your first SMS” is a frequently cited reference example. The shorter, the better.Authentication walkthrough. A clear, runnable example showing how to authenticate. API key, OAuth flow, JWT, whatever your API uses. Most onboarding problems are auth problems.SDKs and code samples. SDKs for the languages your customers use (typically JavaScript, Python, Ruby, PHP, Go, Java, .NET). Inline code samples in every doc page so developers can copy and paste.Interactive API explorer. Tools like the Swagger UI or Postman embedded directly in the docs so developers can make real calls from the browser.Dashboard. Where developers manage API keys, view usage, see rate limits, and access billing.Changelog. A dated list of every shipped change, deprecation, and breaking change. Subscribed-to via RSS or email by your most engaged customers.Status page and support links. When something is broken, the developer wants to know whether it is them or you.Real-world developer portalsA few worth studying as references.  Stripe (stripe.com/docs). The canonical example. Three-column layout, examples in seven languages, every endpoint documented with realistic payloads, a live API explorer in the docs.  Twilio (twilio.com/docs). Quickstarts that get you sending a real SMS in under five minutes. Strong product onboarding integrated with the docs.  GitHub (docs.github.com). Massive scope but well-organized. Conceptual docs separated cleanly from API reference.  OpenAI (platform.openai.com/docs). New entrant that set a high bar for AI API docs. Token economics, model selection, and code recipes all in one place.  AWS (docs.aws.amazon.com). The opposite extreme: huge surface area, dense reference docs, less hand-holding. Works because the audience is sophisticated and the API is too large for hand-curated quickstarts.The pattern across the strong ones: they treat the portal as a product, not as documentation. There is a product manager who owns it, a backlog of improvements, and metrics tied to the developer journey.Internal developer portals vs. external developer portalsTwo related concepts share the name and create confusion.External (public) developer portals are what we have been describing: the public front door of a commercial API, meant for outside developers.Internal developer portals (sometimes called IDPs or platform engineering portals) are a separate concept. They are internal tooling that helps engineers inside a company discover services, run deployments, and access platform infrastructure. Backstage (the Spotify-built open-source project) and Cortex are the well-known examples.This article is about the external kind. If you came here looking for internal developer platforms, the term you want is “internal developer portal” or “internal developer platform,” and the reference companies are Backstage, Cortex, and Port.What makes a great developer portal: a checklistA working developer portal has the same handful of things across every successful API platform we have seen. None are optional, and underinvesting in any one of them depresses adoption across all of them.A first-call experience that takes under five minutes. Sign up, get an API key, see a working request in three terminal commands or one curl. Stripe, Twilio, and OpenAI all built their reputations partly on this. APIs that require account approval, multi-step onboarding, or “contact us to provision” before a developer can call an endpoint lose a large share of casual evaluators in the first session; the exact drop-off varies, but it is consistently the biggest leakage point teams find when they measure it.Reference docs generated from the OpenAPI spec. Hand-maintained reference docs go stale. Generated docs do not. The reference section should be auto-updated on every API release and include: every endpoint, every parameter, every response shape, every status code, with example requests in 3-5 dominant languages (curl, JavaScript, Python, Java, Go is the common set).An interactive API explorer (a “try-it” console). A developer should be able to enter their API key, fill in parameters, and execute a real call against the API without leaving the portal. The explorer is what converts a docs page into an integration moment. Most modern docs tools (Mintlify, ReadMe, Stoplight, Redocly) include this out of the box if you give them an OpenAPI spec.Code samples for the dominant integration patterns. Reference docs document endpoints; code samples document workflows. A “create a payment” example should walk through auth, request shape, error handling, and the follow-up calls a real integration needs. Stripe’s recipe-style samples are often cited as a reference example for this format.SDKs in the languages your audience uses. Even with great docs, raw HTTP calls force every consumer to handle auth, retries, and parsing manually. SDKs in 3-5 dominant languages compress integration time meaningfully and create a single place to fix integration bugs.Self-serve credentials and dashboards. Developers should be able to issue API keys, rotate them, view usage, see error logs, and adjust rate limits without filing a ticket. The portal is the operational interface for everything they need to do with the API.Authentication and authorization that are visible. The auth model (API key, OAuth, mTLS) is the first thing developers need to understand. A dedicated “Authentication” section explaining the flow, the credential lifetimes, and how to scope tokens should be one of the first pages a new developer sees.A community or support channel. Slack, Discord, a public forum, or a clear ticket queue. Developers will get stuck, and the route from stuck to unstuck has to be obvious from the portal.A changelog and deprecation notice page. New endpoints, breaking changes, deprecations, sunsets. Developers maintaining production integrations need a single place to monitor for changes that affect them. RSS feed support is a small thing that earns a lot of trust.Status and incident transparency. A status page (showing uptime per region or per endpoint) and a published incident history. APIs without this look unreliable even when they are not.Portals that hit all ten items above tend to convert signups into integrations and integrations into long-running customers. Portals that ship five or six of them and skip the rest leave adoption on the table.How to measure whether your developer portal is workingThis is where most “what is a developer portal” articles stop, but a portal that does the four jobs above can also be measured against specific outcome metrics rather than just vibes. The numbers that tell you whether the portal is doing its job:  Time to First Hello World (TTFHW). From the moment a developer signs up to the moment they make their first successful API call. Target single-digit minutes for a self-service API; under one hour for a more complex one.  Signup-to-first-call conversion. The percentage of developers who sign up and actually make a successful call. The healthy benchmark varies by audience and complexity, but a meaningful gap between signups and first-call completions is one of the clearest signals the onboarding flow is failing.  First-call-to-production conversion. The percentage of developers who make a first call and eventually graduate to production traffic. The drop-off here is usually a docs or onboarding problem.  Docs page exit rate by topic. Pages where developers leave the portal entirely are usually pages where the docs are wrong or confusing.  Support ticket volume by topic. A spike of tickets on “how do I authenticate” means the auth docs are unclear, not that customers are dumb.These metrics come from instrumenting both the portal (web analytics) and the API itself (API observability). Moesif API monitoring gives you the per-developer, per-endpoint side of the equation, so you can see exactly which customer signed up, made their first call after how many minutes, and which endpoints they explored before going to production.Building blocks: docs platforms, dev portals, and observabilityYou do not have to build a developer portal from scratch. The market has matured.For the docs layer, you have several options: Mintlify, ReadMe, Docusaurus, Redocly, and Stoplight all generate developer-facing docs from OpenAPI specs. They handle the three-column layout, code samples, and API explorer for you. Pick based on price, hosting model, and how much customization you need.For the gateway and credentials layer, an enterprise platform like the WSO2 API Platform covers API design, governance, gateway routing, and the developer portal surface itself (the portal is one of the published components alongside API Manager). At smaller scale, Kong’s developer portal, Apigee’s portal, and AWS’s API Gateway custom portals fill the same role with less governance machinery.For the observability layer, Moesif sits behind the gateway and shows you what every developer is actually doing in real time. Per-customer, per-endpoint, payload-level. That is the data that tells you whether your portal is doing its job.The integrated stack at Moesif and WSO2 is: WSO2 manages the API design, gateway, and portal surface; Moesif observes the production traffic and feeds those metrics back into the portal team.Where to take this nextA developer portal is a product, and like any product it lives or dies by whether you measure it. Start a 14-day Moesif free trial to see per-developer TTFHW and first-call conversion on your own API. No credit card required.Frequently asked questionsWhat is the difference between a developer portal and API documentation? Documentation is one component of a developer portal. The portal also includes account management, credentials, SDKs, a dashboard, support paths, and a changelog. Docs are necessary but not sufficient.Is a developer portal the same as a developer experience platform? No. A developer portal is the surface where developers interact with your API. A developer experience (DX) platform is broader: it covers the portal plus the surrounding tooling and metrics that make developers productive overall. Internal developer portals are often part of a broader DX platform.How long does it take to build a developer portal? Using off-the-shelf tools like Mintlify or ReadMe and a generated OpenAPI spec, you can ship a credible portal in two to four weeks. Building one from scratch with custom branding, an interactive explorer, and SDK generation takes a quarter or more.Do I need a developer portal if my API has only a few customers? If they all came through sales, probably not. If you want self-service signups and any kind of inbound developer traffic, you do. The portal is the difference between an API that needs a sales call to adopt and one that grows on its own.What metrics define a successful developer portal? Time to First Hello World, signup-to-first-call conversion, and first-call-to-production conversion. If those three move in the right direction, the portal is working.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /api-strategy/what-is-a-developer-portal/",
          "author": "Matthew",
          "categories": "API-Strategy, API-Development"
        }
      
    ,
  
    
        "api-analytics-api-strategy-graphql-vs-rest-a-comprehensive-comparison": {
          "title": "Graphql vs Rest: A Comprehensive Comparison",
          "content"	 : "The conversation around data fetching and API management is increasingly dominated by two major players: GraphQL and REST. These technologies, while serving the same purpose of facilitating communication between client and server, offer markedly different approaches and philosophies. GraphQL, emerging as a powerful alternative to the traditional RESTful architecture, brings a new dimension to data retrieval efficiency and flexibility. REST, with its simplicity and widespread adoption, continues to be a reliable standard in the design of web services. This comprehensive comparison aims to dissect the intricacies of both GraphQL and REST, providing a deep dive into their features, differences, and ideal use cases. It’s designed to guide developers, both seasoned and new, through the nuances of each, enabling informed decisions in the rapidly evolving landscape of web development.The debate between GraphQL and REST is more than a mere comparison of technical specifications; it’s a reflection of the evolving needs of modern web applications and the challenges developers face in meeting these demands. As client applications become increasingly complex, the limitations of traditional API approaches become more apparent. GraphQL, with its innovative approach to data querying and retrieval, offers a solution tailored to this new era of web development. It allows for precise and efficient data fetching, a critical aspect in the age of mobile and real-time applications. On the other hand, REST’s adherence to the principles of simplicity and statelessness has made it a cornerstone of web API design for over two decades. This section will explore how these technologies came to be, their core principles, and the unique advantages they bring to the table in different scenarios. By understanding the strengths and limitations of GraphQL and REST, developers can better navigate the choices that shape the backbone of modern web applications.                Monitor and Analyze API Traffic with Moesif              14 day free trial. No credit card required.              Try for Free        What is GraphQLGraphQL, conceived by Facebook engineers in 2015, marked a transformative change in API design and interaction. Fundamentally, GraphQL is both a query language for APIs and a runtime environment that executes these queries, utilizing a type system tailored to your data. This technology was developed in response to the need for more efficient and flexible data fetching capabilities, particularly in complex, data-driven applications. In contrast to traditional REST APIs, which have the server determine the structure and quantity of returned data, GraphQL enables clients to accurately define their data needs. This approach not only reduces bandwidth consumption but also tackles prevalent challenges found in REST APIs, like the over-fetching and under-fetching of data. The ability to request exactly what is needed and nothing more is particularly advantageous in mobile applications, where conserving network usage and reducing latency are paramount.The architecture of GraphQL is fundamentally different from that of traditional REST APIs. It uses a single endpoint to handle all queries, which contrasts sharply with the multiple endpoints typical in RESTful services. This single-endpoint approach simplifies the overall structure of the API and reduces the need for extensive versioning often encountered in REST APIs. Furthermore, GraphQL APIs are strongly typed, relying on a schema defined using the GraphQL Type System. This schema acts as an agreement between the client and server, guaranteeing that the exchanged data adheres to a defined structure and type. The strong typing system not only enhances the predictability of API responses but also facilitates tooling and automation, such as automatic code generation and API documentation. Additionally, GraphQL supports real-time data updates through subscriptions, making it an ideal choice for applications requiring real-time functionality, such as chat applications or live data feeds. The combination of these features makes GraphQL a powerful tool for developers, offering greater control, efficiency, and flexibility in managing data exchanges between clients and servers.Key FeaturesGraphQL’s architecture introduces a paradigm shift in API design, marked by several key features that distinguish it from traditional API approaches like REST. From a broader perspective, the primary advantage of GraphQL lies in its capability to allow clients to request precisely what they require, nothing more, nothing less. This flexibility is a game-changer, particularly for complex applications where efficiency and precision in data retrieval are crucial. The structure of GraphQL queries is defined by the client, meaning that the shape of the response is dictated by the request, leading to more efficient data loading. This sharply contrasts with the rigid data structures commonly returned by REST APIs. Another notable feature is GraphQL’s single endpoint usage, which simplifies the overall API structure and makes it easier to manage. This single endpoint acts as a one-stop-shop for all data requests, streamlining the communication between client and server.The key features of GraphQL include:  Efficient Data Retrieval: This feature allows clients to tailor their queries to their specific needs, effectively eliminating the typical over-fetching and under-fetching issues seen in REST APIs.  Strongly Typed Schema: GraphQL APIs are defined by a clear and strong type system, ensuring that the data conforms to a specific structure and type. This enhances predictability and reliability in API responses.  Single Endpoint for Queries: Unlike REST, which uses multiple endpoints, GraphQL uses a single endpoint, simplifying the API structure and reducing the overhead associated with managing multiple endpoints.  Real-Time Data with Subscriptions: GraphQL’s subscription mechanism enables up-to-the-minute data updates, ideal for scenarios requiring instant data flow, such as messaging platforms or financial tickers.  Introspective: GraphQL APIs are self-documenting. Clients can query the API for details about its schema, facilitating easier exploration and understanding of the API structure.  Rapid Development and Iteration: Frontend developers can make changes and request new data without needing backend adjustments, accelerating the development process and reducing dependencies between frontend and backend teams.  Error Handling: GraphQL’s error handling is more granular and informative, providing detailed error messages in the response payload, which can include multiple error messages for different parts of the query.What is RESTRepresentational State Transfer (REST) is an architectural style that has defined the foundation of web services for over two decades. Introduced by Roy Fielding in his 2000 doctoral dissertation, REST is built upon the principles of the existing web, leveraging standard HTTP methods to create, read, update, and delete resources. This simplicity and alignment with the foundational technologies of the web have made RESTful APIs a popular choice for developers. RESTful services are characterized by their stateless nature and their ability to leverage standard web protocols and data formats, such as HTTP, JSON, and XML. This universality has led to widespread adoption and ease of integration across various platforms and technologies. REST APIs function based on the principle of resources, with each resource uniquely identified by a URL. The interactions with these resources are stateless, implying that every client-to-server request must encompass all the information required to comprehend and execute the request..The design of RESTful APIs is guided by a set of constraints which, when adhered to, yield systems that are performant, scalable, and easy to maintain. These constraints include statelessness, client-server architecture, cacheability, a uniform interface, and a layered system. The stateless characteristic of REST guarantees the independence of each client-to-server request, thereby boosting both reliability and scalability. Additionally, its client-server architecture divides responsibilities, permitting the independent evolution of client and server components. Cacheability of responses reduces the load on the server and improves performance, while the uniform interface simplifies and decouples the architecture. The layered system allows for intermediaries like proxies and gateways, facilitating scalability and security. These principles have cemented REST as a robust and versatile choice in the design of web APIs, suitable for a wide range of applications.Key FeaturesREST, an acronym for Representational State Transfer, has evolved as a key architectural style, crucial in shaping the design and development of APIs within the realm of web services. Its key features are deeply rooted in the principles of the web, making it an intuitive and straightforward choice for API development. Broadly speaking, RESTful APIs employ universally recognized and accepted standard HTTP methods, including GET, POST, PUT, and DELETE. This universality allows REST APIs to be easily used and understood by developers, fostering a wide range of applications. The stateless design of REST, in which every request carries all the essential information for its processing, eliminates the need for the server to maintain any session state. This statelessness simplifies the server design and enhances scalability and reliability. Additionally, RESTful APIs are resource-oriented, meaning they are designed around the concept of resources (data or services), each identified by URLs. This resource-based approach, combined with the use of standard HTTP methods, makes REST APIs particularly flexible and easy to work with.The key features of REST include:  Stateless Interactions: Every client request is self-contained with all necessary information for processing, enabling the server to operate statelessly, thereby enhancing its scalability and robustness.  Cacheable Responses: RESTful APIs can define responses as cacheable, reducing the need for repeated data retrieval and improving performance.  Uniform Interface: The uniform interface constraint simplifies the architecture, making it easier for different components of the system to interact and evolve independently.  Client-Server Architecture: This separation of concerns supports the independence of the client and server, allowing each to evolve separately without affecting the other.  Layered System: REST allows for a layered system architecture, enabling the use of intermediaries like proxies and gateways to enhance scalability and security.  Code on Demand (Optional): REST has the capability to augment client functionalities by allowing the download and execution of code, such as applets or scripts, offering enhanced flexibility and extensibility.  Use of Standard HTTP Methods: REST APIs leverage standard HTTP methods (GET, POST, PUT, DELETE, etc.), making them easily understandable and implementable.  Resource-Based URLs: Each resource is identified by a specific URL, providing a clear and accessible way to access the resource.GraphQL vs Rest: How GraphQL is Different from RestThe distinction between GraphQL and REST is rooted in their fundamental approach to handling data and client-server interactions. While REST, an architectural style, operates through a set of predefined endpoints each returning a fixed structure of data, GraphQL introduces a more dynamic and flexible approach. In REST, the server defines the structure of the data, and clients are limited to the endpoints provided, often leading to either over-fetching (receiving more data than needed) or under-fetching (requiring additional requests for more data). Conversely, GraphQL enables clients to specify the data structure they need in a single query, capable of encompassing a range of different resources. This shift not only reduces the number of network requests but also allows for more precise and efficient data retrieval, tailored to the client’s current needs. This difference is particularly significant in complex applications where the client might need various types of data simultaneously. In contrast to REST, which necessitates several trips to various endpoints, GraphQL has the capability to retrieve all the necessary data in just one request.Another key difference lies in how both technologies handle versioning and schema changes. In REST, changes to the data structure often lead to the creation of new endpoints or versioning of the API, potentially resulting in endpoint proliferation and backward compatibility challenges. GraphQL, with its flexible query language, allows for continuous schema evolution without the need for versioning. Clients can request new fields without impacting existing queries, and deprecated fields can be phased out gradually. This flexibility in handling schema changes makes GraphQL particularly suitable for rapidly evolving applications. Additionally, GraphQL’s error handling provides a more nuanced approach compared to REST. While REST uses standard HTTP status codes to indicate errors, GraphQL delivers errors in the response payload alongside the correct data. This approach offers more detailed insights into what part of a query failed and why, allowing for more precise debugging and error resolution. These fundamental differences highlight the contrasting philosophies of GraphQL and REST, each catering to different needs and scenarios in the world of web development.GraphQL vs Rest: Side by Side ComparisonSelecting between GraphQL and REST requires a clear grasp of their unique features and the potential effects they have on web application development and performance. Although both are designed to enable client-server communication, they significantly differ in their methodologies and capabilities. This side-by-side comparison aims to highlight these differences, providing a clear perspective on what each technology offers and how they contrast with each other.            Feature      GraphQL      REST                  Data Retrieval      Single query for multiple resources      Multiple requests to different endpoints              Over-fetching/Under-fetching      Avoids both by allowing specific queries      Common, due to fixed data structures              Performance      Can be more efficient with fewer requests      Potentially less efficient with multiple requests              Error Handling      Errors are part of the response payload      Uses HTTP status codes              Caching      More complex due to dynamic queries      Easier due to HTTP-based methods              Real-time Data      Supports real-time data with subscriptions      Typically requires additional technologies              Learning Curve      Steeper, due to unique query language      Generally simpler, using standard HTTP methods              Flexibility      Highly flexible in data retrieval      Less flexible, requires predefined endpoints      GraphQL offers a more flexible and efficient approach for complex, data-driven applications, allowing for tailored data retrieval and real-time updates. REST, with its simplicity and adherence to the principles of the web, remains a robust choice for applications with straightforward data retrieval needs and where caching is a priority. Ultimately, choosing between GraphQL and REST depends on the particular needs of the project, weighing aspects such as flexibility, performance, and user-friendliness.A Brief History of GraphQL and RestGraphQLThe inception of GraphQL can be traced back to 2012 when Facebook’s internal requirements for high-performance mobile applications necessitated a more efficient and flexible data-fetching API. Traditional REST APIs were proving inadequate in handling the complex, nested data structures required by modern web and mobile applications. GraphQL was developed as a solution to these challenges, allowing for precise data fetching with reduced network overhead. It was officially released to the public in 2015 and quickly gained popularity in the developer community for its innovative approach to API design. GraphQL’s ability to fetch multiple resources in a single request, its efficient handling of real-time data, and its flexibility in accommodating evolving data schemas marked a significant departure from traditional API design philosophies. This new technology not only addressed the performance issues associated with REST APIs but also introduced a more developer-friendly approach to data retrieval, reshaping the landscape of web development.RESTREST, standing for Representational State Transfer, originated from Roy Fielding’s 2000 doctoral dissertation. It emerged as a response to the necessity for a standardized, efficient, and scalable method in the design of web services. RESTful principles quickly gained traction as they aligned seamlessly with the architecture of the web. The simplicity and statelessness of REST made it an ideal architectural style for the burgeoning internet and its growing number of web services. Over the years, REST has become the de facto standard for API design, praised for its scalability, performance, and the ease with which it can be understood and implemented. The principles of REST, such as stateless servers and structured access to resources, have influenced the development of numerous web APIs, shaping the way data is transmitted over the internet. Despite the advent of newer technologies like GraphQL, REST remains a fundamental part of web architecture, testament to its enduring relevance and utility.Which is Better: GraphQL or RestThe choice between GraphQL and REST largely hinges on the particular needs and context of the project in question. GraphQL offers a high degree of flexibility and efficiency, particularly beneficial for applications with complex data structures and evolving schemas. Its ability to fetch exactly what is needed in a single request can significantly reduce network overhead and improve performance, especially in mobile applications where bandwidth and latency are concerns. REST, on the other hand, shines in its simplicity and statelessness, making it a strong candidate for applications where a straightforward, cacheable, and easy-to-implement API is required. It’s well-suited for public APIs and for projects where developers are already familiar with HTTP and its methods. Ultimately, the choice between GraphQL and REST is not about which is better in absolute terms, but which is more appropriate for the specific needs, goals, and constraints of your project.When to Choose GraphQLChoose GraphQL when your application requires a flexible, efficient way to handle complex, interrelated data. It’s particularly well-suited for applications with rapidly changing data models or when the frontend requires significant control over the data it receives. GraphQL’s ability to aggregate data from multiple sources into a single query is invaluable for modern web applications, where reducing the number of network requests is crucial for performance. It’s also a strong choice for real-time applications, like collaborative tools and live data dashboards, where its subscription feature enables real-time data updates. Additionally, if your project involves a rapidly evolving product or a microservices architecture, GraphQL’s flexibility in accommodating these changes without the need for versioning makes it an attractive option.When to Choose RestREST is the go-to choice when your project demands a simple, cache-friendly, and easy-to-implement API. It’s ideal for applications where the data structure is relatively stable and not overly complex. REST’s stateless nature and reliance on standard HTTP methods make it a robust choice for public-facing APIs, where ease of understanding and implementation are key. It’s also well-suited for projects where bandwidth is not a significant constraint and where the overhead of additional network requests is not a critical concern. Additionally, REST can be more appropriate in scenarios where extensive caching is required to improve performance, as its stateless interactions lend themselves well to effective caching strategies.ConclusionIn the realm of web development, the choice between GraphQL and REST is a significant one, shaping the way data is exchanged and managed in applications. Both technologies have their unique strengths and are suited to different scenarios. GraphQL, with its flexible query language and efficiency in handling complex data structures, is ideal for applications where the frontend needs significant control over the data it receives. REST, celebrated for its simplicity and alignment with the foundational principles of the web, continues to be a robust choice for more straightforward, cache-driven environments. As technology continues to evolve, the decision between GraphQL and REST will hinge on a balance of factors like flexibility, performance, and ease of implementation. Developers must weigh these considerations carefully, choosing the technology that aligns best with the specific needs and goals of their project.For teams looking to implement or optimize their use of GraphQL or REST, Moesif offers powerful tools to monitor, debug, and gain insights into your APIs. Whether you’re transitioning from REST to GraphQL, managing a complex API ecosystem, or simply looking to enhance the performance and reliability of your APIs, Moesif provides the analytics and monitoring capabilities you need. Sign up for Moesif today and understand your API usage, track errors, and make data-driven decisions to improve your API’s performance and your users’ experience.                Monitor and Analyze API Traffic with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Unlock the full potential of your GraphQL and REST APIs with Moesif's cutting-edge observability            Start optimizing your API performance today - Dive deeper with Moesif!            Try for Free            No credit card required            ",
          "url": " /api-analytics/api-strategy/Graphql-vs-Rest-A-Comprehensive-Comparison/",
          "author": "Dylan",
          "categories": "API-Analytics, API-Strategy"
        }
      
    ,
  
    
        "api-strategy-webhooks-vs-apis": {
          "title": "Webhooks vs APIs: The Difference and When to Use Each (2026)",
          "content"	 : "Webhooks and APIs are both ways for software systems to communicate. The simplest difference: an API responds when you ask, a webhook fires when something happens. If you need data on demand, you call an API. If you need to know when something changes without polling for it, you use a webhook.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        This guide covers what each is, when to use which, and how to operate both in production. Most modern integrations use both, with each handling the part of the workflow it is best at.What is an API?An API (Application Programming Interface) is a contract that lets one piece of software ask another for data or for an action. The caller initiates the request, the server processes it and returns a response, and the caller does something with the response. This is the request-response pattern that powers most modern software integration.For a deeper take on what an API actually is, our API acronym guide walks through the term in detail. For this post, the relevant property is that APIs are pull-based: the caller drives the timing.What is a webhook?A webhook is an HTTP callback. The webhook provider lets you register a URL with them; when an event happens on the provider’s side, the provider sends an HTTP POST to your URL with a payload describing the event. You receive the data without having asked for it.Common patterns: Stripe sends webhooks when a payment succeeds or fails. GitHub sends webhooks when a push, pull request, or issue occurs. Slack sends webhooks when a user interacts with your app. The receiving application reacts to the event by updating its own state.A webhook is sometimes called a “reverse API.” That framing is intuitive: with an API, you call the provider; with a webhook, the provider calls you.Webhooks vs APIs: the core difference            Property      API      Webhook                  Direction      Client calls server      Server calls client              Trigger      Client-initiated      Event-driven              Timing      When the client decides      When the event happens              Use case      On-demand data retrieval      Real-time event notification              Server-to-server side      Server hosts the endpoint      Client hosts the endpoint      The terminology gets confusing because both sides are “servers” in some sense. The cleanest way to think about it: with an API, you initiate. With a webhook, the other side initiates.When to use a webhookWebhooks are the right choice when you need to react to events as they happen, without polling.  Payment events. Stripe’s charge.succeeded, charge.failed, invoice.payment_failed. You want to know immediately when money state changes.  CRM and lead capture. Form submission triggers a webhook to your CRM, which creates the lead record. Real-time beats batch import.  Content updates. A document is edited in one system; downstream systems receive a webhook and re-render or re-index.  CI/CD events. GitHub pushes trigger CI runs via webhook; CI completion triggers deployment via another webhook.  Authentication events. A user logs in; a webhook fires to your security log or fraud detection system.  Inventory changes. Stock levels update in your warehouse system; webhooks notify your storefront so the front page reflects availability.The common pattern: a state change on one side that something on the other side needs to know about immediately.When to use an APIAPIs are the right choice when you need data or want to take an action, on demand.  Fetching current state. “What is this customer’s current balance?”, API call.  Creating or updating records. “Create a new charge for this customer”, API call.  Listing or searching. “Give me all orders from this customer in the last 30 days”, API call.  Triggering computation. “Generate a PDF invoice for this order”, API call.  Bulk operations. “Update the price on 10,000 products”, API call, possibly using a bulk endpoint.The common pattern: client decides when, what, and how much, and the API responds with whatever the question or action requires.Side-by-side comparison            Feature      Webhooks      APIs                  Initiation      Provider (event-driven)      Client (request-response)              Real-time vs polling      Real-time delivery      Polling, unless paired with webhook              Direction      One-way (provider → consumer)      Two-way (client ↔ server)              Setup      Consumer hosts an endpoint and registers it      Client knows the API URL and credentials              Protocols      HTTP POST with JSON      HTTP with REST, GraphQL, gRPC, or SOAP              Best for      Event notification, async workflows      On-demand data, mutations, queries              Failure handling      Provider retries delivery      Client retries the request              Authentication      HMAC signing in the request      Bearer token, mTLS, etc.      Webhook delivery best practicesThis is the part of webhooks most teams underestimate when they first ship them. A few patterns that hold up in production:  Respond quickly. Acknowledge the webhook within a few seconds (2xx response) and process the payload asynchronously. If you do heavy work synchronously, the provider will time out and retry, and you will have processed the same event twice.  Use HMAC signing. Providers sign the webhook payload with a shared secret; you verify the signature before processing. Without this, anyone who can reach your endpoint can forge events. Stripe, GitHub, Shopify, and most modern providers ship signed webhooks.  Make handlers idempotent. Webhook providers retry on failure, which means you might receive the same event twice. Use a unique event ID to deduplicate. Without this, a transient failure becomes a duplicate charge or a duplicate email.  Respect retry headers. When you cannot process a webhook successfully, return a clear status code (typically 5xx) so the provider retries. The retry schedule is provider-specific but usually follows exponential backoff over several hours or days.  Expose delivery status to consumers. If you are the webhook publisher, give consumers a dashboard showing what was delivered, what failed, and why. Homemade webhook implementations often skip this and the customer-support load grows accordingly.The HTTP status code conventions we cover separately apply here: 2xx for acknowledged, 4xx if the payload is invalid (no retry), 5xx for transient failures (retry).Webhooks in 2026: agents and event-driven AI workflowsTwo changes are worth flagging.AI agents trigger webhooks too. Workflows that fire when an event happens are increasingly mediated by AI agents. The pattern: a webhook arrives at an agent endpoint, the agent decides what to do with the event (call an API, send a message, update a record), and a downstream system reflects the action. Designing webhook payloads with enough context for an agent to make the right decision matters in a way it did not three years ago.Event-driven AI workflows are scaling. Beyond traditional webhook patterns, async event streams (Kafka, AWS EventBridge, Google Pub/Sub) feed AI workflows that process events in near-real-time. The webhook is often the trigger that injects events into the stream. The principles (signing, idempotency, retry) carry over.How Moesif uses webhooks for monetizationMoesif ships webhooks for monetization use cases. When a customer’s usage crosses a billing threshold, Moesif sends a webhook to your billing platform (Stripe, Recurly, Chargebee) with the usage data attached. The billing platform creates the invoice line item; your customer sees the charge on their next bill.This is the pattern most usage-based billing setups converge on: a metering platform that counts API consumption, a billing platform that turns metered usage into invoices, and webhooks that connect the two. Moesif’s usage-based billing handles the metering and webhook sync; your billing platform handles the financial side.Next stepsWebhooks and APIs are complementary patterns. Pick the one that matches the direction of initiation: APIs when you decide when to ask, webhooks when you want the other side to push events to you. In production, the two together handle most integration use cases.If you are building a product that meters API consumption and bills back via webhooks, start a 14-day Moesif free trial to see webhook-driven billing sync in action. No credit card required.Frequently asked questionsAre webhooks APIs? Webhooks use HTTP and have endpoints, so technically they are an API pattern. The terminology distinguishes them from request-response APIs because the direction of initiation is reversed.Which is better, webhooks or APIs? Different tools for different jobs. Use APIs for on-demand data retrieval and actions; use webhooks for real-time event notifications. Most production integrations use both.How do webhooks work? The provider lets you register a URL. When an event happens on the provider’s side, they send an HTTP POST to your URL with a payload describing the event. Your endpoint processes the event and acknowledges with a 2xx response.Are webhooks secure? They can be, if implemented correctly. The standard pattern is HMAC signing: the provider signs the payload with a shared secret, and the consumer verifies the signature before processing. Always validate signatures; never trust webhook payloads without verification.How do I handle webhook retries? Acknowledge webhooks quickly (2xx response) and process asynchronously. Return 5xx on transient failures so the provider retries. Make handlers idempotent by deduplicating on event ID.Can webhooks replace APIs? No. Webhooks notify of events; they do not replace on-demand data retrieval. Most integrations use both: APIs for queries and mutations, webhooks for event notifications.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /api-strategy/webhooks-vs-apis/",
          "author": "Matthew",
          "categories": "API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-rest-api-with-node-js": {
          "title": "REST API with Node.js",
          "content"	 : "REST is a powerful and ubiquitous paradigm for web application development. It offers huge benefits that can help any service be more efficient, more extensible, and more scalable.Node.js is a popular solution for building fast, efficient, and scalable APIs. It is highly extensible, offering an ecosystem of packages that deliver fast and unified development.Combining REST with Node.js can result in a powerful product that is stable while being efficient, scalable, and extensible. What is very important, however, is making a truly RESTful output – adopting REST is only the first step in becoming RESTful, and there are as many roads to RESTful design as there are pitfalls on the way there. Today, we’re going to look at what REST is, what makes something RESTful, and how you can get started in RESTful development with Node.js.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        What is REST?REST, or Representational State Transfer, is an approach to developing systems. In essence, it allows systems to communicate with one another through a stateless modality wherein the representation of the resource is shared rather than the resource itself. Every interaction in a RESTful implementation expresses the state representation alone, which allows for efficient and seamless interactions across a wide variety of implementations.Basics of RESTREST, or Representational State Transfer, was introduced in the famous seminal REST dissertation by Roy Fielding. It outlined a new approach to networked communication centered around a state transfer paradigm. Although REST can encompass various attributes, its essence can be summarized in key principles:      REST utilizes a ’’Client-Server relationship’’. In this model, the client and server operate in separate domains, each managing its own state. Interaction occurs through a simple exchange: the client requests, and the server responds. This singular mode of communication underpins the REST architecture.        REST is ’’’stateless’’’. Here, “state” refers to the current conditions or the data of a resource. In a stateless framework, the client handles state management. Thus, each server request must carry all necessary information to be processed and responded to independently, ensuring that requests are self-sufficient.        REST has a ‘’’uniform interface’’’. Consistency is key in RESTful interfaces - all components adhere to a standard interface, ensuring uniformity in interactions regardless of the specific service or resource. This includes adhering to a single URI for resources and, where applicable, incorporating additional references or data. The addition of references or data in context is a concept known as HATEOAS (Hypermedia as the Engine of Application State), and is considered a requirement of RESTful design.        REST must be ‘’’cacheable’’’. Effective cache management is crucial in REST. Servers designate data as cacheable or non-cacheable, significantly impacting performance and user experience.        REST is layered. RESTful components enable distributed architecture, allowing for a setup where clients may not directly interact with the end server, but instead interact through various intermediary services and microservices.  What Makes a RESTful API RESTful?What makes something truly RESTful? One good approach is to use the Richardson Maturity Model. This model from Leonard Richardson allows us to use a standard rubric for considering the stage of development for a project, bucketing services within a handful of categories:Level 0: The Swamp of POXThis is the lowest rung of the model, and represents an API with a URI which accepts all inputs. It is considered an API in name only without anything that sets it aside as RESTful. For this reason, ‘’’Level 0 is Non-RESTful’’’.Level 1: ResourcesAt this level, resources are defined, and users can make requests of the resource URI. Instead of asking a remote resource or function to do something, you ask the method of the resource to do it. ‘’’Level 1 is Approaching REST’’’.Level 2: HTTP verbsLevel 2 sees the introduction of HTTP verbs, the underlying verbiage for the web and RESTful APIs. In Level 2, HTTP verbs have a specific meaning, form, and function, whereas in lower levels, these verbs are often used for a variety of functions. At level 2, however, GET means GET, POST means POST, etc., and they are not conflated. Since REST requires caching, and HTTP only does caching with proper verbiage use ‘’’Level 2 is Approaching REST with Caching’’’.Level 3: Hypermedia controlsAt level 3, we see the addition of HATEOAS, or Hypertext as the Engine of Application State. HATEOAS allows for relational links to additional context or resources, and is a requirement of REST. ‘’’Level 3 is Likely RESTful’’’.What is “Likely RESTful”?Why is level 3 only “likely RESTful”? There are some requirements in the original dissertation that are a bit more variable in application. For this reason, something can be in level 3 but still be missing some very specific functions. In order to be truly RESTful, you need to meet all the requirements of the original dissertation – accordingly, being RESTful should include reaching Level 3 of the Richardson Maturity Model while also properly implementing the other characteristics of the original dissertation!What is Node.js?Node.js is an open-source and cross-platform JavaScript implementation specifically designed for server-side scripting, allowing code to execute outside of a browser. It supports a wide variety of platforms, and is designed with the concept of “JavaScript Everywhere”, unlocking a synchronicity between client and server-side development through the adoption of a universal language for web applications and those clients who consume those applications.Notably, Node.js is event-driven, and supports both real-time and synchronous communication as well as asynchronous communication. This allows Node.js to support a huge range of potential development options, and presents a unified approach for blended environments utilizing both synchronous and asynchronous modalities.Node.js has some key features that make it a strong choice:  Node.js uses non-blocking event-driven architectural design, but has libraries that enable blocking functions. In essence, this provides a unified option for both synchronous and asynchronous development utilizing JavaScript.  Because the underlying technology is event-driven and non-blocking, Node.js can handle a lot of connections concurrently, providing for extreme efficiency in network and application communication.  Since it’s built on Chrome’s V8 JavaScript Engine, JavaScript can be directly compiled to machine code, unlocking quite high performance, pretty incredible speed, and high efficiency.  Node.js comes with npm (Node Package Manager), a large ecosystem of open-source libraries and extensions. This manager also manages the various dependencies, installation processes, updates, etc. that come with utilizing this ecosystem, simplifying the development lifecycle.  Full-lifecycle integration is achieved readily. Because Node.js unifies the process of developing client and server-side code with JavaScript, development and maintenance can be unified into the same lifecycle process.These benefits have made Node.js a popular choice for fast, scalable, and efficient network applications with a simplified lifecycle.How to Create a RESTful API with Node.jsTo get started, we’re going to utilize the Express framework. Express is a Node.js framework that is minimalist, flexible, and efficient. By combining Node.js and Express together, you can unlock a lot of additional design modalities and functions with very little overhead or impact on speed.Setting Up Your Development EnvironmentThis guide assumes that you have already installed Node.js. If you have not, navigate to the Node.js website and ensure that you have installed it. From here, you’ll want to install Express.Installing ExpressTo install express, you’ll need to create a director to hold your Node.js application. Use the mkrdir to do so as follows:mkdir restnodeNext, use the cd command to change the working directory to this new location:cd restnodeFrom here, you’re going to use npm, the built-in package manager for Node.js, to create the pack.json file for your application.npm initWhile the default settings can be used for this process, Express notes in its documentation that you need to set the entry point variable to change the main file name. We’re going to use the standard default – this will generate index.js, which is where we’re going to build our service. If you wanted to change this, you would edit the following code:entry point: (yourname.js)Finally, we install express:npm install expressCreate the API SkeletonTo get started, we must first set up the basic skeleton of our API. To do this, we must edit our javascript as follows:const express = require(‘express’);const app = express ();This will create the base of our service, pointing towards Express as our framework. Next, we need to tell the application that we are using a few critical pieces:app.use(express.json());app.use(express.urlencoded({ extended: false }));app.use("/api/users", require("./routes/api/users"));This code does a few things, but the most important thing it does is enable POST and PUT requests by giving a method for data storage. “app.use(express.json());” allows our service to process JSON objects, and “app.use(express.urlencoded({ extended: false }));” allows us to process data as strings or arrays.Now that we have created the skeleton, we need to actually set a listening port. To do this, use this code:app.listen(3000, () => console.log('Ready'));This code instructs our service to listen to port 3000, and to log to the console a “Ready” state, which lets us know it is ready to process requests.Create the User ModelWhile we could connect to a database (for more information on this, check the Express documentation, we’re going to keep this simple by using a local file for storing user data. To do this, we must first create the Users.js file.We can do this again by using the mkdir command. Use the following code to create this file:touch Users.jsThis will create the file for us to edit. Now that we have the file, we need to create the actual data structure which can be used by other systems. To do this, set the data structure using the following code:const users = [  {    id: 1,    name: "ExampleUser",    email: "exampleuser@website.com"  }];With this structure in place, we can add a module export at the end to allow other project files to use this structure. To do this, append the following code to users.js:module.exports = users;Create the Route StructureNext, we need to create our routes and endpoints. To do this, we’re going to create a new directory folder and call it “routes”, and then a subfolder called “api”. This will allow us to store any and all routing data in a separate directory, cleaning up our structure a bit and clarifying exactly where points of failure may be occurring in the future during troubleshooting. You can use the “mkdir” operation here to do so.In this folder, we will create a new .js file called users.js. Note that we have used a lowercase “u” here – it’s important to remember nomenclature and standards, and as we already used the “Users” namesake for the storage solution, we’re using “users” to define this a subordinate file that contains our routes.Create the EndpointsIn this file, we need to create some logical routes. To start, again state that we require Express:const express = require(‘express’)Next, we need to define the router by using this code:const router = express.Router();In order to make sure our data routing is clear and pulls in data in a structured way, we’re going to need a way to generate unique IDs for each user entity. To do this, first use npm to install the “uuid” package in our core directory:npm install uuidBack in our Users.js file, we can call this “uuid package” using the following:const uuid = require("uuid");Next, we need to set the path for the API so that our user system can handle this data correctly. We can use the following code to do so:let users = require("../../Users");Now we can finally create our first endpoint. We’ll create a GET function to retrieve all user data. We can do this using this code:router.get("/", (req, res) => {  res.json(users);});This code uses route.get to handle the request, and passes all of the current user data stored in the system. To get a specific ID, we need to provide a way for the client to pass a user ID and check against the internal data store. We can use the following code to do so:router.get("/:id", (req, res) => {  const found = users.some(user => user.id === parseInt(req.params.id));  if (found) {    res.json(users.filter(user => user.id === parseInt(req.params.id)));  } else {    res.sendStatus(400);  }});Note that there is an “if” function here which provides a generic error code when the request is malformed or is referencing non-existent data.With this route created, we can now export this for use by the API using the following code:module.exports = router;Now we have a functional API which will present user data on request. Neat!Implement CRUD FunctionsFrom here, we can expand these endpoints to provide a variety of new functions. CRUD, or Create, Read, Update, Delete, is a critical concept for RESTful APIs, and should be the core functions in your application. We can enable each using the following code.For creating user data, we can use POST:router.post("/", (req, res) => {  const newUser = {    id: uuid.v4(),    name: req.body.name,    email: req.body.email  };  if (!newUser.name || !newUser.email) {    return res.sendStatus(400);  }  users.push(newUser);  res.json(users);});For updating, we can use PUT:router.put("/:id", (req, res) => {  const found = users.some(user => user.id === parseInt(req.params.id));  if (found) {    const updateUser = req.body;    users.forEach(user => {      if (user.id === parseInt(req.params.id)) {        user.name = updateUser.name ? updateUser.name : user.name;        user.email = updateUser.email ? updateUser.email : user.email;        res.json({ msg: "User updated", user });      }    });  } else {    res.sendStatus(400);  }});For deleting, we can use DELETE:router.delete("/:id", (req, res) => {  const found = users.some(user => user.id === parseInt(req.params.id))  if (found) {    users = users.filter(user => user.id !== parseInt(req.params.id))    res.json({      msg: "User deleted",      users    });  } else {    res.sendStatus(400);  }});Make it RESTfulOne of the big things that is lacking here is HATEOAS. HATEOAS, or Hypermedia at the Engine of Application State, provides relational links within our user data and is a sub-constraint of RESTful design. To accomplish this, we can use a wide variety of options – for our case, we’re going to use a very simple Express extension called “express-hateoas-links”.To get started, first install express-hateoas-links:npm install express-hateoas-linksWith this installed, we need to start adding in additional context to our “routes” logic. As an example, for our GET logic, we would edit the following:router.get("/:id", (req, res) => {  const found = users.some(user => user.id === parseInt(req.params.id));  if (found) {    res.json(users.filter(user => user.id === parseInt(req.params.id)));  } else {    res.sendStatus(400);  }});The edit would look like this:router.get("/:id", (req, res) => {  const foundUser = users.find(user => user.id === parseInt(req.params.id));  if (foundUser) {    const userWithLinks = {      ...foundUser,      links: [        { rel: "self", method: "GET", href: `/users/${foundUser.id}` },        { rel: "all-users", method: "GET", href: "/users" },        { rel: "update-user", method: "PUT", href: `/users/${foundUser.id}` },        { rel: "delete-user", method: "DELETE", href: `/users/${foundUser.id}` }      ]    };    res.json(userWithLinks);  } else {    res.sendStatus(400);  }});This update would add some relational links. “rel” would establish the relations of each contextual link, and then would provide additional contextual links based upon the particular method used to interact with the original node. While these relational links are simply used for function extension, additional relational links – such as context about the particular person, links to their organizations, etc. – could also be provided through this method.Best PracticesAuthentication and AuthorizationIt’s crucial to implement authentication and authorization measures in your service based on the least privilege principle. This approach involves restricting access rights for users to the bare minimum necessary to perform their tasks. It’s essential to carefully manage access to your resources, ensuring they are as secure as possible.Be judicious in your use of hypermedia; while it can be beneficial, indiscriminately exposing all resources isn’t advisable. Strive to share data that is both useful and secure, minimizing the risk of misuse or compromise.API StructureWhen designing RESTful APIs, it’s important to be resource-oriented. Each resource should be associated with a specific HTTP verb that performs a distinct function, and these interactions should be idempotent (and safe, although that’s a much broader topic than the scope of this piece). Idempotency means that each request should consistently produce the same type of response, even though the actual content may vary.Offering caching capabilities to clients is a key consideration, but make sure your cache is not caching information that may be protected or utilized for attacks, such as API Keys, user data, login methods, etc.Additional ReadingReference documentation as much as you can – this has been done before, and well, so learn from others! We have used the following pieces throughout this article, and we recommend the following as excellent follow-up documentation and content:  Roy Fielding’s original REST dissertation  Express’ documentation  Database integration options  Ravikiran’s wonderful Simplilearn walkthrough  Toptal’s secure development guide by Marcos Henrique da SilaConclusionIt’s easy to make something REST, but to make it truly RESTful can be a bit more complicated. With some planning and an approach based in context and relational linking, it can be incredibly easy to get started making a Node.js application that is powerful, extensible, and stable.Let us know what you think about this piece by leaving a comment in the section below. Thank you for reading!                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            You’ve built the API. Now let's generate revenue!            Monetize your Node.js APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/REST-API-with-node-.-js/",
          "author": "Kristopher",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "technical-api-development-python-api-tutorial": {
          "title": "Python API Tutorial (Beginner's Guide)",
          "content"	 : "IntroductionWhen it comes to using and building APIs, Python applications are one of the most popular choices. Within the Python ecosystem, many different frameworks, such as Django and Flask, allow users to build out extensive API suites. Many people turn to staples such as Python Requests to use APIs within Python applications.If you’ve landed on this blog, chances are you need to use APIs within your application and want to learn how to do so. Well, you’re in luck! In this blog, we will go over how to consume APIs within your Python application, status codes, and how to set query parameters, as well as how to work with JSON data that is getting sent to or received by an API call. Let’s begin with the basics by looking at exactly what an API is.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        Introduction to APIsAs you are likely aware, API stands for Application Programming Interface. At its core, an API is a set of rules that allows different software applications to communicate with each other. It’s a bridge between different software systems, enabling them to interact and exchange data in a structured and secure manner. Users can send a request to the API, and the API will return some type of response.To go a bit higher level, you can think of an API as a postal service for data. Similar to sending and receiving letters and packages through a postal service, software applications use APIs to send and receive data. The API defines the correct address format (the endpoint), the type of mail that’s allowed (the request type), and what you can expect to receive back (the response).APIs power almost every facet of our digital lives in the real world. APIs have a wide range of uses that you are likely experiencing multiple, likely hundreds or thousands, times per day. Here are a few common examples:      Data Integration: APIs allow different databases or software services to share data. For instance, a weather application might use an API to gather weather data from a central database.        E-commerce: Online stores use APIs to connect with payment processors, ensuring smooth transactions.        Social Media Integration: Many websites and apps use social media APIs to allow users to sign in with their social media accounts, share content, or import their profile data.        IoT Devices: In the Internet of Things (IoT), smart thermostats and fitness trackers use APIs to communicate data to servers or other devices.        Cloud Computing: APIs are crucial in cloud services, allowing applications to access cloud resources and perform tasks like storing data or running computations.  APIs are critical in modern software development because they allow for the creation of flexible, modular software systems. By using APIs, developers can build upon the capabilities of existing services, speeding up the development process and enabling more complex functionalities. For example, a payment processing API, such as Stripe, could allow multiple companies to process payments without having to build such a service for themselves.Getting Started with API Requests in PythonWhen using APIs within your Python code, you first need to figure out what type of API request you will be making. API requests can be of different types, commonly called methods. These methods each have a unique HTTP verb associated with them and a specific functionality based on that. Below are the four most common types.  GET: To retrieve information.  POST: To send new data.  PUT: To update existing data.  DELETE: To remove data.You’ll want to select the correct endpoint and verb based on what type of action you are trying to complete through the API. For instance, we may have an endpoint called /users that performs different functions depending on the type of request. If a GET request is made, maybe a list of users will be returned, whereas if a POST request is made, maybe a new user will be created based on the data received by the API.Libraries for Making API RequestsFor actually making API requests, Python has several libraries that can be used. Some of the most popular ones include:  Requests: Known for its simplicity and ease of use. It’s an excellent choice for beginners and is widely used in the industry.  HTTPx: An async-capable HTTP client similar to Requests but with async support.  Urllib: A module available in Python’s standard library, useful for basic operations but less user-friendly compared to Requests.When it comes to comparing these three libraries and deciding which one you may want to use, the choice is relatively easy. Essentially, all three of them will work for the basic operations we are talking about in this blog. However, as stuff becomes a little bit more complex, you might want to take a deeper look at the differentiators. Let’s look at those quickly below.  Requests is often preferred for its simplicity and the readability of its code. It supports synchronous requests and is ideal for blocking I/O operations.  HTTPx, on the other hand, is a more recent library that supports both synchronous and asynchronous requests, making it a good choice for high-performance applications.  Urllib is part of the standard library but requires more code for everyday tasks and is less intuitive than Requests, making it less popular for new projects.Adding Dependencies to Your Python ProjectAfter you decide which library you want to use, you’ll need to include the dependency in your Python code. To use these libraries, you first need to add them to your Python project.If you’re using Requests, you can install it using pip, Python’s package manager. To do so, open your command line or terminal and run:pip install requestsFor HTTPx, the installation command for pip is:pip install httpxSince urllib is part of the standard library, there’s no need to install it separately.Then, in your code, you can import and use any of these libraries that you’ve added to your project. As an example, if you use Requests, you can use it like so:import requestsresponse = requests.get(‘https://api.com/test-service’)Of course, the rest of this blog will focus specifically on using the Request library. Next, let’s take a look at how to make a basic request to an API in more detail.Initiating Our Initial API RequestSeeing the code in action when it comes to making a request to an API is one of the easiest ways to understand the concepts covered above. Let’s demonstrate a basic GET request using the Requests library. In the example below, the code queries a public API and then processes the API’s response.import requests# Replace with the desired API endpointurl = 'https://api.example.com/data'response = requests.get(url)# Checking if the request was successfulif response.status_code == 200:    # Printing the retrieved data    print(response.json())else:    print(f"Failed to retrieve data: {response.status_code}")In this example, we send a GET request to an API endpoint, check if the request was successful by looking at the status code (which should be 200 if the request was successful), and then print the JSON response. Next, we will look at a slightly more advanced example where we can supply some data to the API through query parameters.Integration of APIs with Query ParametersWhen it comes to a developer’s skill-set, query parameters are a fundamental aspect of building API requests. Query parameters are appended to the URL of an API request and are used to modify the behavior of the request. Essentially, query parameters act as filters or additional instructions/data that the API can use to customize the response.To add query parameters to an API request, you’ll need to use query parameter syntax, which requires a question mark (?) followed by the query parameters you want to add. Multiple parameters are separated by an ampersand (&). Below is an example of a URL with 2 query parameters.https://api.example.com/data?parameter1=value1&parameter2=value2Use Cases for Query ParametersThere are many scenarios where query parameters can be useful for API consumers and developers. Common uses of query parameters include:  Filtering Data: Retrieve a subset of data based on given criteria.  Sorting Data: Sort the returned data in a specified order.  Pagination: Limit the number of results returned in a single request, usually for large datasets.  Search: Query a dataset for specific terms.  Configuring Responses: Customize responses, such as specifying a format.Implementing Query Parameters in PythonLet’s consider a practical example where we use the Requests library in Python to make a GET request with query parameters. Suppose we are accessing an API that provides information about books, and we want to filter results based on the author and publish year. We could use the code below to filter out books by J.K. Rowling written in 1997.import requestsurl = 'https://api.example.com/books'params = {    'author': 'J.K. Rowling',    'year': 1997}response = requests.get(url, params=params)if response.status_code == 200:    books = response.json()    for book in books:        print(book)else:    print(f"Error: {response.status_code}")Line-by-line, the code above accomplishes the following:  Imports the requests library.  Defines the base URL (https://api.example.com/books).  Create a dictionary called params with the required query parameters for author and year.  Passes the URL and dictionary to the requests.get() function.  The Requests library then automatically constructs the correct URL with query parameters and sends the request.  Processes the response based on the returned status and data.This should give you a good idea of how query parameters can be used with Requests in a very simple example. Of course, query parameters can be more or less complex depending on the use case.Tips for Using Query ParametersNow that we have covered the basics of using query parameters, we can chat about some tips and tricks to cover some nuances. Let’s review some of the most important ones below.  Understanding the API: When using any API, especially those with query parameters, always refer to the API documentation. By referring to the docs, you’ll know which query parameters are supported, their format, and how they are expected to be used.  Testing: When building requests with multiple query parameters, test them to ensure they work as expected and return the correct data. As more lengthy and complex query parameters are used in a request, there is a higher chance of issues with formatting and processing. Always ensure that requests work as expected, preferably with every possible query parameter combination (if there are multiple).  Handling Special Characters: If you’re unaware, one of the most frustrating things is that certain characters may need to be encoded when used in query parameters. For instance, spaces are often encoded as %20 or replaced with +. Depending on the type of encoding the API is expecting, these values might be different. The easiest way to confirm is to look at an example in the API docs to see which characters should be encoded.  Default Values: Some APIs may have default values for specific query parameters. Understanding these defaults is crucial for interpreting the response correctly.  Security: While query parameters are convenient for simple filters and configuration, sensitive information should never be sent through query parameters, especially in unencrypted HTTP requests. When creating your APIs or using third-party APIs, you should always be conscious of the data you send in a query param and if it is considered sensitive.Real-World ExampleBefore moving on, let’s cover one last example that is more advanced. Consider an API where you must send a list of items as a query parameter. The API might expect the format to be comma-separated like this:https://api.example.com/items?ids=123,456,789In Python, you might construct this API request as follows:item_ids = ['123', '456', '789']params = {    'ids': ','.join(item_ids)}response = requests.get('https://api.example.com/items', params=params)# Further processing of the response...Here, ','.join(item_ids) creates a single string from the list of item IDs, separated by commas, suitable for the query parameter. Many ways exist within Python and other languages to easily create requests with more sophisticated values than a single-value query string.Decoding API Status CodesAs we have seen in some of the code above, when you make a request to an API, it responds with a status code. These status codes are part of the HTTP protocol and are standardized across the web, providing a quick way of understanding the result of your request. Essentially, they tell you if your request was successful or not. Specific status codes also denote an error; if an error occurs, the type of error will be reflected in the returned status code.Status codes serve several purposes:  Feedback: They inform the client (your program) about the result of its request.  Troubleshooting: They help identify issues when a request doesn’t go as planned.  Control Flow: In programming, developers can handle different scenarios based on the response status code.The 5 Most Common HTTP Status CodesAlthough many different HTTP status codes exist between 100 and 599, some are used much more frequently. Let’s look at a brief overview of five of the most common HTTP status codes you’ll likely see.  200 OK: This is the code you want to see. It means your request was successful, and the server is sending the requested data.  404 Not Found: The resource you tried to access doesn’t exist. This can happen if you mistype a URL or if the resource has been removed.  500 Internal Server Error: A generic error message indicating something has gone wrong on the website’s server. It’s not your fault but an issue on the server side.  401 Unauthorized: This status code appears when required authentication has failed or not been provided yet.  403 Forbidden: The server understands your request but refuses to authorize it. This could be due to a lack of permission to access the resource.Handling Status Codes in CodeAs we mentioned earlier, an advantage to having status codes is that they can be used to inform our applications about how a response should be handled. To demonstrate how this can be done in Python, below is a basic example using the Requests library to handle different status codes and print out a message accordingly.import requestsresponse = requests.get('https://api.example.com/data')if response.status_code == 200:    print('Success!')elif response.status_code == 404:    print('Resource not found.')elif response.status_code == 500:    print('Server error.')elif response.status_code == 401:    print('Unauthorized. Authentication required.')elif response.status_code == 403:    print('Forbidden. Access denied.')else:    print(f'Error: {response.status_code}')Of course, the example above is extremely simple; it’s just printing some stuff to the console. However, this paves the way for more advanced logic where the application may retry the API call after 5 minutes if a 500 status code is returned or prompt the user for some credentials if a 401 error is brought back by a service.Learning More About Status CodesYou can refer to detailed resources and posts for a deeper dive into the world of status codes and their meanings. At Moesif, we’ve created comprehensive guides and explanations that can help and other resources with great information on status codes. Here are links to articles from Moesif or other reliable sources to check out:  Moesif’s Guide on HTTP Status Codes  MDN Web Docs on HTTP Response Status Codes  REST API Tutorial on HTTP Status CodesThese resources will provide more information on all the possible HTTP status codes, helping you understand and handle them more effectively as you come across them as you use APIs.Navigating API DocumentationAPI documentation is essential for developers since it outlines how to use and integrate with an API effectively. Good documentation should include detailed information about the API’s endpoints, request methods, necessary parameters, and expected response formats.There are typically two types of API documentation: docs generated through an OpenAPI spec and more typical API docs that are hand-written.For OpenAPI-Generated documentation, these docs leverage OpenAPI (formerly Swagger), a specification for machine-readable API files. It allows for the generation of interactive documentation developers can use to understand and test an API directly from the browser. OpenAPI-generated docs often include an easy-to-navigate interface with expandable sections for each endpoint, showing required parameters, request examples, and response models.More typical API documentation may vary in format but generally includes a comprehensive guide detailing aspects of the API, such as authentication, endpoints, parameters, and sample requests and responses.Understanding API DocumentationLet’s consider a hypothetical example to understand how to navigate API documentation. Assume we are looking at documentation for a weather API. A section for the /weather endpoint might look like this:  Endpoint: /weather  Method: GET  Query Parameters:          city (required): Name of the city      units: Measurement units (metric or imperial)        Response: JSON object containing weather detailsBased on the above information, we can construct an API request. If we want to get weather data for London in metric units, our request will look like this in Python using the Requests library:import requestsbase_url = "https://api.exampleweather.com"endpoint = "/weather"parameters = {    'city': 'London',    'units': 'metric'}response = requests.get(base_url + endpoint, params=parameters)if response.status_code == 200:    print(response.json())else:    print(f"Error: {response.status_code}")This request would then either bring back a response that contains the weather details for London, which would be printed out, or the call may return an error, in which case, we would log the error to the screen.For a more advanced example, let’s consider a POST request example where we must set a body field. Suppose we have an API for a task manager. The documentation for creating a new task is as follows:  Endpoint: /tasks  Method: POST  Body:          title (required): Title of the task      description: Detailed description of the task        Response: JSON object with the details of the created taskTranslating this into a request:import requestsimport jsonbase_url = "https://api.exampletaskmanager.com"endpoint = "/tasks"task_data = {    'title': 'Grocery Shopping',    'description': 'Buy milk, eggs, and bread'}response = requests.post(base_url + endpoint, data=json.dumps(task_data))if response.status_code == 201:    print("Task created successfully:", response.json())else:    print(f"Error: {response.status_code}")In this POST request:  We use json.dumps to convert the task_data dictionary into a JSON-formatted string.  The requests.post method sends the request, including the task data in the request body.  A status code of 201 typically indicates successful resource creation.Tips for Navigating API DocumentationAlthough API documentation comes in all shapes and sizes, there are a few critical pieces to remember when using API docs to navigate onboarding a new API.  Start with Authentication: Check how the API handles authentication (API keys, OAuth, etc.). If you do not use the correct authentication mechanism expected by the API, you’re guaranteed an unsuccessful API call and likely a 401 response code.  Understand Rate Limits: Look for any mention of rate limits to understand how many requests you can make in a given time-frame. Not all APIs have rate limits, but almost every public API will to prevent abuse and malicious behaviors such as a denial of service attack.  Check Endpoints and Methods: Identify the available endpoints and what HTTP methods (GET, POST, etc.) they support. Some endpoints may support multiple methods, so use the proper method for your expected outcome.  Review Request and Response Formats: Understand the format of requests you need to send and the responses you will receive.  Look for Examples: Good documentation often includes sample requests and responses, which are very helpful. Most of the time, you can use these examples to build out your request without having to parse through many docs to find the same information.Handling JSON Data in PythonIf you work with RESTful APIs, you will work heavily with JSON. JSON (JavaScript Object Notation) is a lightweight data-interchange format that’s easy to read and write for humans and easy to parse and generate for machines. JSON is popular because of its simplicity and flexibility. It’s language-independent, with parsers available for every programming language, including Python. Because of this, it has become a universal data format for APIs.A JSON object is a collection of key-value pairs, where the key is a string, and the value can be a string, number, boolean, array, or even another JSON object. Below is an example of what JSON looks like.{  "name": "John Doe",  "age": 30,  "isEmployed": true,  "skills": ["Python", "JavaScript", "SQL"]}Python has a built-in json module that can be used for encoding and decoding JSON data. Using the json library, let’s look at some examples of how you can work with JSON in PythonParsing JSONKnown as deserialization, below is an example of how to convert a JSON string to a Python object.import jsonjson_string = '{"name": "John Doe", "age": 30, "isEmployed": true}'python_dict = json.loads(json_string)print(python_dict)Generating JSONKnown as serialization, below is an example of how to convert a Python object to a JSON string.python_dict = {'name': 'Jane Doe', 'age': 25, 'isEmployed': False}json_string = json.dumps(python_dict)print(json_string)Reading JSON from a FileSometimes, you’ll want to read JSON from a file into your Python program. Below is an example of how to open a file and load JSON data using the json.load function.with open('data.json', 'r') as file:      data = json.load(file)Writing JSON to a FileIn the inverse, you may also want to write JSON data to a file. Below is an example of how you can do that by using the json.dump function.data = {'name': 'Jane Doe', 'age': 25, 'isEmployed': False}with open('data.json', 'w') as file:      json.dump(data, file)Working with JSON from an APIOften, you’ll receive JSON data as a response from an API. In this case, you can use the methods above to read and write JSON (to an API request or file). Below is an example of how JSON data from an API can be read using response.json and then printed to the console.import requestsimport jsonresponse = requests.get('https://api.example.com/data')# Assuming the response contains JSON dataif response.status_code == 200:    data = response.json()  # Converts JSON to a Python dictionary    print(data)else:    print(f"Failed to retrieve data: {response.status_code}")Python’s json module, which we covered earlier, offers extensive support and functionality for more complex JSON operations, like parsing nested JSON data or handling exceptions. The examples above should cover many use cases you’ll encounter when working with APIs and JSON data in Python.Python API Tutorial: Moving ForwardNow that we’ve covered all the bases on consuming APIs with Python, you may also be interested in building your own. For this, Moesif has created some extensive guides that show you how to build APIs with Flask and Django, two extremely popular Python frameworks developers use to build APIs.In these guides, we walk through step-by-step how to build your own APIs and show you how you can leverage Moesif to track API usage, errors, and dig into user behavior. Check them out to get started on creating your own APIs with Python.ConclusionWe’ve covered a lot of ground in this blog, starting from the basics of what an API is, diving into making API requests with Python, handling JSON data, understanding query parameters, and decoding status codes. By now, you should have a solid grasp of how to consume APIs within your Python application, an essential skill in today’s interconnected digital world.While consuming APIs is crucial, creating and managing your own APIs can take your projects to the next level. Whether you’re looking to build APIs for internal use or as products for your customers, understanding the lifecycle of an API - from creation to management - is key.To aid in this journey, Moesif offers a suite of tools that can enhance your experience with APIs. With Moesif, you can gain insights into how your APIs are used, monitor for errors, and understand user behavior. This is invaluable for making data-driven decisions to improve your APIs. If you’re considering turning your APIs into a product, Moesif provides features to help you monetize them effectively. By tracking API usage, you can integrate with billing providers and set up a pricing model that aligns with your business strategy.Start enhancing your API journey today by exploring Moesif’s extensive guides on building APIs with popular Python frameworks like Flask and Django. For a hands-on experience with Moesif’s analytics and monetization tools, sign up for a free trial or chat with our team of API experts to learn how Moesif can supercharge your API projects.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Unlock the full potential of your APIs with Moesif's cutting-edge observability            Start optimizing your API performance today - Dive deeper with Moesif!            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Python-API-Tutorial/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "api-monetization-moesif-zuplo-api-observability-and-monetization-at-the-edge": {
          "title": "Moesif <> Zuplo: API Observability and Monetization At The Edge",
          "content"	 : "When it comes to supporting the latest tech, here at Moesif, we always aim to integrate with partners that offer something new and exciting. In the API gateway space, Zuplo has been quickly changing the game regarding platforms focused on developers and performance.Similar to the work we’ve focused on for the last few years, Zuplo customers have also been focused on understanding their APIs better and learning how to monetize them efficiently. Because of this, it was only a matter of time before the two platforms came together to offer a single solution for our customers. This is why we are excited to announce our partnership and integration with Zuplo! Offering new capabilities to customers on both sides.If you’re unfamiliar with either of the platforms, we will do a quick walkthrough of the highlights and then get into why combining both together unlocks a massive amount of new use cases. Let’s dive in!                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        What is Moesif?When looking for the most versatile and powerful API analytics and monetization solution on the market, most folks bump into Moesif. At Moesif, we enable analytics and monetization for some of the largest companies in the world across every vertical. By having direct integrations with the most popular languages and API gateways, users can quickly analyze and monetize their customer’s API usage. Let’s look a little deeper at some highlights of the platform. sEffortless API MonetizationFlexible Metering Rules: Moesif enables the creation of complex metering rules for API usage, supporting various billing models like prepaid, postpaid, and pay-as-you-go (PAYG).Invoicing Platform Integration: The platform can be connected to billing providers like Stripe, Recurly, Chargebee, and Zuora for automatic invoicing. Moesif can also support custom billing solutions by using Moesif’s custom webhook integrations for billing providers.Customer Experience Management: Moesif’s open-source developer portal facilitates the rapid setup of subscription portals, providing metrics to customers on their usage and quota.Deep Insights into API UsageFunnel and Retention Charts: The platform goes beyond surface-level metrics, offering funnel analytics to pinpoint where users drop off and retention charts to gauge long-term engagement with your APIs and platform.Heat Maps and Segmentation: Moesif provides a detailed view of API usage with tools like heat maps and segmentation. These features enable businesses to track and understand how customers interact with their APIs, segmenting data by various factors like URI, HTTP headers, body fields, and customer demographics.Powerful Analytics on API PayloadsField and Value Tracking: Moesif allows tracking of the specific fields and values that customers send in API payloads, offering a granular view of data flows and exactly how your APIs are being used.Privacy and Collaboration: Moesif efficiently ensures data privacy for sensitive data through multiple mechanisms. Moesif keeps the data private to your company only visible to people who should have access while facilitating collaboration through shareable dashboards.Comprehensive Understanding of Customer HealthEnd-to-End Customer Journey Mapping: The platform provides an in-depth understanding of the customer journey, integrating web activity with API usage. Stitching together the end-to-end journey of your customers from when they first land on your website or developer portal to ongoing API usage is easy.Demographic Insights and Integration Support: With integrations like Salesforce and Segment, Moesif helps understand user demographics and identify customers struggling with API integration or understanding how to use your APIs correctly.Overall, Moesif augments the experience that users already have with Zuplo, allowing for easy monetization and deep insights when it comes to APIs.What is Zuplo?Zuplo is a modern and transformative API management platform that uniquely combines speed, security, and scalability. It’s designed for businesses aiming to be API-first, providing a multi-cloud-ready, edge API Gateway. Some key features of Zuplo include:  API Security and Rate-Limiting: Easy implementation of over 50 security plugins, including authentication.  OpenAPI Runtime: Directly runs OpenAPI specs, simplifying API development and deployment.  Automatic, Stripe-Quality Documentation: Generates and updates API documentation automatically, enhancing developer experience.Zuplo’s global deployment strategy includes coverage across 200+ data centers, ensuring minimal latency and optimal performance for all API users regardless of geography. Its serverless API Gateway supports effortless scaling, handling billions of monthly requests with zero-millisecond startup time. With seamless integrations, including GitHub for source-controlled configurations and rapid API iterations, Zuplo is optimized for both speed and efficiency, making it a go-to solution for modern API management needs.What are the benefits of using the platforms together?To extend Zuplo’s capabilities, Moesif’s powerful analytics and monetization features can be accessed directly through the platform in a few clicks. Moesif and Zuplo can be connected by adding a Moesif policy entry in Zuplo’s policies.json file. The only thing you’ll require to enable Moesif is to paste your Moesif Application ID into the policy configuration. That’s it! Once integrated, the full capabilities of Moesif’s API analytics and billing features are available. Let’s look at some advantages of pairing Moesif and Zuplo in more detail.Enhanced API Analytics for ZuploHaving API observability at your fingertips allows for a massive amount of insight into the health of your business so you can be more proactive in supporting customers. Moesif, the leader in API analytics, is known for allowing you to easily create charts and dashboards to see how customers use your APIs and the value they get. API analytics from Zuplo flow directly into Moesif, allowing technical and non-technical users to get real-time insights into API usage and better support users. Moesif goes much further than other analytics solutions with its user-centric focus and deep insights into API payloads. With funnel reports, understand the complete journey of how users convert to active or paying customers. With retention reports and cohort analysis, understand signals that may lead to churn. Level up your applications and insights with Moesif and Zuplo together.Easily Monetize APIs with Moesif and ZuploAs more companies look to generate revenue from their APIs, they face the same challenge: API monetization is not easy. Handling simple and complex usage-based billing cases takes a lot of custom code and generates overhead for developers. With Zuplo and Moesif, you can monetize APIs in a few clicks using Moesif’s Metered Billing features. No coding is required. Create plans for prepaid, postpaid, PAYG, and other usage-based billing models with low effort. From here, Moesif can aggregate usage and send the correct usage to Stripe, Recurly, Chargebee, or a custom billing solution for users to be charged. No matter how complex or straightforward your API billing criteria are, Moesif and Zuplo can easily handle it.New Levels of AutomationWant to automate customer success outreach or block user access based on specific business criteria? With Moesif, you can use behavioral emails to automatically send customized emails to users based on specific events. For example, if a user is having difficulty integrating with your APIs, Moesif can detect this and email the most relevant guide to help them.Another form of automation is using Moesif’s Governance Rules feature to block users, such as those with overdue unpaid invoices, from accessing certain APIs or features within your application. New levels of automation are possible when pairing Moesif with Zuplo.Try it out!Want to experience modern API management and next-level API monetization and analytics? To get started, sign up for Zuplo and Moesif, then use your Moesif Application ID in a Zuplo policy to get data flowing. Once integrated, you can use both platforms to deliver a next-gen approach to your internal developers, those using Moesif and Zuplo, and your customers. Driving new revenue streams and increasing customer adoption and retention is now just a few clicks away with Moesif and Zuplo.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Unlock the full potential of your APIs with Moesif's cutting-edge observability            Start optimizing your API performance today - Dive deeper with Moesif!            Try for Free            No credit card required            ",
          "url": " /api-monetization/Moesif-Zuplo-API-Observability-and-Monetization-At-The-Edge/",
          "author": "Matthew",
          "categories": "API-Monetization"
        }
      
    ,
  
    
        "api-monetization-moesif-krakend-performant-api-analytics-and-monetization": {
          "title": "Moesif <> KrakenD: Performant API Analytics and Monetization",
          "content"	 : "When it comes to performant API gateways, KrakenD tends to be a name that many come across in their search. With some pretty impressive benchmarks, the team at KrakenD has put performance at the forefront of their efforts.In the latest release of KrakenD Enterprise, there’s also been another focus: analytics and monetization. Because of this focus, we are excited to announce our partnership to bring these capabilities via Moesif to the KrakenD Enterprise platform with their v2.5 release.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        How The Integration WorksWorking with the folks at KrakenD, the integration between the two platforms is effortless. To send data to Moesif from your KrakenD instance, add your Moesif Application ID to your KrakenD configuration. Of course, to fully experience what Moesif offers, you’ll also want to implement user and company tracking. Luckily, this only requires a few additional settings to map your users and companies correctly. Below is an example configuration that includes an example of how a user and company can be mapped from the JWT claim.{  "version": 3,  "extra_config": {    "telemetry/moesif": {      "@comment": "Push user activity to Moesif based on the information contained in JWT, every second, in batches of 1000 events",      "application_id": "yourapplicationid",      "user_id_headers": [        "Authorization"      ],      "user_id_jwt_claim": "sub",      "identify_company": {        "jwt_claim": "company_id"      },      "debug": false,      "log_body": false,      "event_queue_size": 1000000,      "batch_size": 1000,      "timer_wake_up_seconds": 1    }  }}Once this config is updated and applied, you’ll have full access to your KrakenD API data in Moesif. To learn more about the configuration options available, check out the KrakenD docs.Try It Out!To try this out, create a Moesif account and have KrakenD Enterprise running. If you need to get started with KrakenD Enterprise, you can do so by contacting the KrakenD team. If you want to know more about the joint solution between KrakenD and Moesif, contact our team of monetization experts today to get started!                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            You’ve built the API. Now let's generate revenue!            Monetize your KrakenD based APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /api-monetization/Moesif-KrakenD-Performant-API-Analytics-and-Monetization/",
          "author": "Matthew",
          "categories": "API-Monetization"
        }
      
    ,
  
    
        "api-monetization-api-strategy-creating-python-apis": {
          "title": "Creating Python APIs",
          "content"	 : "REST is an incredibly powerful solution for web APIs in the modern space. It offers a wide array of benefits that can help any service be more efficient, faster to iterate, and more stable.Python is a strong, high-level language that unlocks a high level of functionality across broad categories of systems and devices. It is human-readable, highly efficient, and widely adopted.These two technologies, when combined, can deliver an incredible product in the API space. In this guide, we’re going to talk through what REST is, how to make something truly RESTful, and how Python supports this effort. We’ll look at some best practices.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        What is REST?REST, or Representational State Transfer, is a paradigm for systems communication. The concept is to make systems communicate with one another through a stateless modality that utilizes representations of resource states rather than the resources themselves. In essence, every interaction is with a stateful representation utilizing a specific relationship between the resource requester and resource holder, which allows for seamless interaction across a wide variety of use cases.Basics of RESTREST as a concept was first shared in a now-famous dissertation from Roy Fielding. In this dissertation, Fielding defined a new modality of communication that was based on representational state transfer. While there’s a variety of optional attributes that make something “RESTful” in practice, the core nature of REST is best defined as follows:      REST utilizes a ’’Client-Server relationship’’. This means that the client and server have their own domains and states and that the server interacts with the client (and vice-versa) to provide functionality. To put it another way, in REST, there is only one modality of communication – ‘’’the client requests from the server, and the server responds to the client’’’’.        REST is ‘’’stateless’’’. “State” is the current attributes and nature of a given resource. In a stateless environment, the state is managed entirely on the client side, and a request to a server should contain all of the information that is needed to process and return a response. In essence, ‘’’REST utilizes statelessness to make every request self-contained’’’.        REST has a ‘’’uniform interface’’’. Every component has a pre-defined and consistent interface, meaning that interaction with components ‘’’must utilize the same modality and construct’’’ regardless of the form or function within a service. Resources must have a single URI, and should, where possible, reference additional data or information – this is HATEOAS/hypermedia, and is a major part of making something RESTful.        REST must be ‘’’cacheable’’’. In REST, servers mark data as either being something that can be cached or something that cannot be cached, which has a significant impact on performance, server demands, and user experience. ‘’’REST must support cache choice’’’. Data that should be cacheable must be cacheable and must be declared as such.        REST is layered. RESTful components allow for distributed architecture, and a client typically won’t know whether it is directly connected to the server or to one of the microservices and middleware systems in the architecture.  What Makes a RESTful API RESTful?There are a lot of opinions about what makes something truly RESTful, but one good metric for this consideration is the Richardson Maturity Model. This model, developed by Leonard Richardson, allows us to establish a rubric for judging when something is truly RESTful or only partially so.The maturity model breaks things into four levels.Level 0: The Swamp of POXThis is the lowest level of the model, and indicates an API with a URI which accepts all inputs. Level 0 suggests a system that is an API in name without any of the mechanisms or verbiage for requests and responses that make a system RESTful – for this reason, ‘’’Level 0 is Non-RESTful’’’.Level 1: ResourcesAt Level 1, resources are defined, allowing users to make requests of a resource URI rather than a service endpoint. In essence, instead of asking a function to do something, Level 1 APIs call a method of a particular resource to do something. For this reason, ‘’’Level 1 is Approaching REST’’’.Level 2: HTTP verbsLevel 2 sees an incredibly important addition in the form of HTTP verbs. In REST, HTTP verbs have a specific form and function, but in lower levels they can be used more generally. For instance, in Level 1, it’s not uncommon for both POST and GET to function identically to one another, even though they have very different purposes by definition. Because REST requires caching, and HTTP only supports caching with proper implementation of its verbiage, ‘’’Level 2 is Approaching REST with Caching’’’.Level 3: Hypermedia controlsFinally, at Level 3, we see the introduction of hypermedia with HATEOAS, or Hypertext as the Engine of Application State. HATEOAS allows for contextual additions to resources, providing additional information, linked resources, etc. This is a huge element of RESTful design that is often the missing link in services that declare themselves as RESTful without being truly RESTful. ‘’’Level 3 is Likely RESTful’’’.What is “Likely RESTful”?An important note here is that our definitions above of “Approaching REST with Caching” and so forth is an approximation of what each level means, and is not itself a definition from the maturity model. So with that said, why is the final level, Level 3, only “Likely RESTful”?Simply put, RESTful has a few guidelines that are absolutely necessary, but it also has a few needs that can be a bit more flexible. Roy Fielding, the inventor of REST, has detailed at length just what is required to be RESTful, and he rightfully pointed out that many APIs who proclaim themselves to be RESTful are not actually so.For this reason, it’s possible to be in Level 3 with some idiosyncrasies that undermine the definition of being RESTful. In such a case, your service is likely RESTful, but may not meet all the requirements in full detail. This is why it is so important to read through the entirety of the REST dissertation and its resultant qualities if you wish to be truly RESTful.What is Python?With a solid understanding of REST, let’s look at Python. Python is a programming language that was first released in 1991 as a successor language to ABC. The intention of the language was to be human-readable and efficient, with an emphasis on garbage collection, dynamic typing, and multi-paradigm support. It packages together a wide range of libraries to enable a huge amount of functions and purposes, and for this reason, it’s a very popular choice for web APIs.Python is popular because it’s quite easy to get started. It utilizes simple syntax and is highly readable, leveraging normal human speech and whitespace to make things clearer and more approachable. It abstracts a lot of complex underlying functions into simple structures, allowing you to get started very quickly with low friction.Notably, Python has high cross-platform support across a variety of systems and environments. The community is exceptionally strong, meaning that in those environments without native Python support, there’s likely a community extension or system that allows for this functionality and integration.How to Create a RESTful API with PythonFor the purposes of this piece, we’re going to use Python with Flask. There are a variety of excellent tools for Python API development, but we are using Flask as it does not require any tools or libraries to start building your web API. Flask does have extensions to add additional features, but for this API, we’re going to use the toolset straight out of the box.Setting Up Your Development EnvironmentFirst things first – this tutorial assumes you already have Python installed. If you do not, navigate to the Python webpage and install via the method that suits your environment‘’’cacheable’’’.Installing FlaskWith Python installed, you must first install Flask. To do so, issue the following command:pip install flaskThis will install Flask from the Python Package Index.Creating the EnvironmentNext, you need to create a directory for your Python API. We will call this directory “Moesif_tutorial:mkdir moesif_tutorial && cd moesif_tutorialThis code will make the directory (mkdir) and change the working directory (cd) to the newly created location. From here, you need to create your new project file. We will call this “tutorial_app”.touch tutorial_app.pyBy “touching” the file, we are creating our build file and getting it ready to hold our project. “touch” will create the file, but we’ll need to launch a virtual environment to work on it. To do this, use the following command:python3 -m venv <venv>/bin/activateThis command will depend on your environment directory – for more information on how to properly start this directory, refer to the Python documentation.Create the API SkeletonWith our environment created, we’re going to craft our API. This API is quite simple – it’s going to contain details about three Python frameworks and allow users to retrieve information about these frameworks.To get started building the API, we will directly edit tutorial_app.py. Within the file, add the following code:from flask import Flaskapp = Flask(__name__)What this does is instantiate the Flask class, in effect “creating” the application. With this, we next created an instance of the class with the name value, which is where we’ll point Flask at various files and elements.This is the most basic version of a Python application – but to make it a RESTful version, we need to query an endpoint, and in our case, this means we need some data for it to reference. Since we don’t have a database connected, we can store this data directly as a data store. In Python, a data store is a piece of arbitrary code that we can store within the app itself, which allows us to host data without a database or external data storage solution.To do this, we can append the data store to our file:from flask import Flaskapp = Flask(__name__)in_memory_datastore = {"Flask" : {"name": "Flask", "created": 2010, "parent": "Python"},"Django" : {"name": "Django", "created": 2005, "parent": "Python"},"Bottle" : {"name": "Bottle", "created": 2009, "parent": "Python"},"Tornado" : {"name": "Tornado", "created": 2010, "parent": "Python"},}Create the EndpointsNow you have the skeleton of an API – it has an instantiated application and a resource. Next, we need to create the endpoint which can be queried. To do this, we will use the HTTP verb GET to retrieve data from tutorial_app.py:@app.get('/frameworks')def list_frameworks():   return {"frameworks":list(in_memory_datastore.values())}There are a few things here to mention. Firstly, the area ‘/frameworks’ defines our endpoint. Requests using the GET HTTP verb to that endpoint will trigger this functionality. For other endpoints, you can simply change this name and generate a new endpoint. Secondly, @app.get is a shorthand for another way of doing this. Some Python developers may be more familiar with “@app.route” – in this case, app.get and app.route are one in the same, just built slightly different.Our method looks like this:@app.get('/frameworks')Another way to write this would be as follows:@app.route('/frameworks', methods=["GET"])Note that both pieces of code do the same thing – our version is just shorter. Back to the code as written:@app.get('/frameworks')def list_frameworks():   return {"frameworks":list(in_memory_datastore.values())}The rest of the code defines a JSON object with the label “frameworks” and points it to the values in our data store. This will allow us to access the /frameworks endpoint and see a list of all resources. To do so, you can navigate to:http://127.0.0.1:5000/frameworksImplement CRUD FunctionsSo far, we have an API that allows us to retrieve a list of data. What we need to do is start adding additional CRUD functions. For instance, we can create an update function utilizing PUT. To do this, we can update our code to add the POST verb to create new entries to our data store. First, we will need to make the create function:def create_framework(new_framework):framework_name = new_framework['name']in_memory_datastore[framework_name] = new_frameworkreturn new_frameworkThis defines a new way to enter a framework into the system. Next, we need to add a route method to support CLI commands to interact with the endpoint:@app.route('/frameworks'’, methods=['GET', 'POST'])def frameworks_route():   if request.method == 'GET':       return list_frameworks()   elif request.method == "POST":       return create_framework(request.get_json(force=True))This can be repeated for all of the routes needed for CRUD functionality.Make it RESTfulNow that you have an API service that provides basic functionality, you’re going to need to start adding some systems to truly make it RESTful. The first step here is to utilize an effective HATEOAS contextual system.There are a variety of options currently on the market. One good solution is JSON-LD, which allows you to provide linked data as part of a resource.An example of how this might look in our instance is as follows:{  "@context": "https://example.com/frameworks.jsonld",  "@id": "https://example.com/frameworks/Flask",  "relatedName": "Flask-RESTPlus",  "relatedDocs": "https://flask-restplus.readthedocs.io/",}This is also the point you may want to start considering additional frameworks for caching, optimization, etc. Once you have a steady API from which to work off, you can start adding additional secure and stable features to make your application truly RESTful.Best PracticesAuthentication and AuthorizationEnsure that you are deploying your service with authentication and authorization that is based on the principle of least privilege. Keep access to resources as locked down as possible. Ensure that your contextual references are valid and that they are worth making – hypermedia is great, but exposing anything and everything is not the right response to a more closed off, non-contextual world. Make sure you are providing useful data that can’t be used against you, and ensure that data is provided in a secure way.API StructureRemember that RESTful APIs are principally resource-oriented. Resources should have a single HTTP verb that has a specific function, and should be idempotent (e.g., the response is the same type each time it’s invoked, even if the contents are different). Ensure you are adequately linking to contextual information and are providing caching to clients. Ensure you are aligned against the REST considerations at the start of this piece.Additional ReadingReference documentation as much as you can – this has been done before, and well, so learn from others! Some good links include:  Roy Fielding’s original dissertation  Red Hat’s reference page for REST  Browser Stack’s evaluation of RESTful Python frameworks for 2023  Chelsea Troy’s Python walkthrough on Linode  Roy Fielding’s website of papersConclusionRESTful design is not hard to do, but it’s often hard to do right. With some forward thinking and planning, you can use Python to launch an incredibly powerful RESTful API. Even just the code in this piece can be the central bones of the next big API – it just takes some imagination and planning.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Unlock the full potential of your Python based APIs with Moesif's cutting-edge observability            Start optimizing your API performance today - Dive deeper with Moesif!            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Creating-Python-APIs/",
          "author": "Kristopher",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-product-management-api-strategy-what-is-apigee": {
          "title": "What Is Apigee? A 2026 Practitioner's Guide to Google's API Platform",
          "content"	 : "Apigee is Google Cloud’s API management platform. It sits between your APIs and the people (and agents) calling them, handling traffic management, authentication, rate limiting, analytics, developer portal hosting, and the lifecycle work of getting an API from design to deprecation. In 2026 it is one of the four or five names that consistently show up on enterprise API platform shortlists alongside WSO2, Kong, MuleSoft, AWS API Gateway, and IBM API Connect.                Tier-Based Pricing with Moesif              14 day free trial. No credit card required.              Try for Free        This guide walks through what Apigee actually does, where it fits in a modern API stack, the practitioner realities (cost, complexity, AI-era updates), and how it compares with the alternatives. For a deeper teardown of Apigee specifically as a gateway, see our Apigee API Gateway guide.What is Apigee?Apigee is an API management platform that runs in Google Cloud. The company was founded in 2004 as Sonoa Systems, rebranded to Apigee around 2010, and was acquired by Google in 2016; it has since been folded into the Google Cloud Platform as a first-class product line. Google now markets the platform simply as Apigee on its product pages, though “Apigee X” still appears in Google Cloud documentation and release notes, and “Apigee Edge” remains a distinct legacy product line in long-term support.Apigee gives an organization a single place to:  Design APIs from an OpenAPI spec  Publish them through a developer portal  Enforce security, rate limits, and quotas at the gateway  Monitor traffic across every customer integration  Monetize APIs with usage-based pricing tied to call volumeIt is positioned as a full-lifecycle platform, which means most of the work of running an external API can happen inside Apigee rather than across a half-dozen separate tools. That positioning is a real differentiator at enterprise scale and a real overkill at smaller scales.How Apigee worksThe runtime sits in front of your APIs as a proxy. When a customer or partner calls your endpoint, the request hits Apigee first. Apigee authenticates the caller, applies any traffic policies (rate limits, quotas, request shaping), routes the call to your backend, captures the response, applies any response policies (filtering, transformation), and returns the result to the caller. Every step is logged for analytics.The flow is what you would expect of any modern API gateway. Apigee’s differentiation is in the management layer that sits above the runtime:  API proxies as a first-class abstraction. Each external endpoint maps to an Apigee API proxy that decouples the public-facing contract from the backend implementation. Backend changes do not break the public API as long as the proxy mediates them.  Policy attachment as configuration. Authentication, rate limits, transformations, fault handling, and similar concerns attach to the proxy as declarative policies, not custom code. Most policies are XML configuration.  A developer portal generated from the spec. Apigee Developer Portal (built on Drupal in older versions, with a newer “Integrated Portal” option in current Apigee) renders documentation and self-service signup from the OpenAPI spec attached to the proxy.The architecture has two deployment modes worth knowing. VPC peering connects your Google Cloud VPC directly to Apigee’s runtime VPC for private routing to internal backends. Private Service Connect (PSC) is the alternative when VPC peering is not available; it routes traffic through an encrypted attachment point. The choice affects latency, security posture, and what kind of cross-cloud workloads you can sit behind Apigee.Apigee’s core capabilities at a glance            Capability      What Apigee provides                  API design      OpenAPI-spec-driven design, with a visual proxy editor              Security      OAuth 2.0, JWT, API keys, mTLS, threat protection policies              Traffic management      Rate limits, quotas, spike arrest, response caching              Analytics      Usage, performance, and error analytics in the Apigee UI              Developer portal      Auto-generated docs, self-service key issuance, app management              Monetization      Usage-based pricing tied to call volume, with rate plans              Multi-cloud      Hybrid deployment options across Google Cloud, on-prem, and other clouds      The capability list is roughly the same across all major API management platforms in 2026. The differences are in pricing, deployment flexibility, depth of analytics, and ecosystem.Where Apigee fits in the API platform landscapeApigee competes most directly with five other platforms:  WSO2 API Platform. Apache 2.0 open-source core, multi-gateway runtime that deploys across WSO2, Kong, AWS, Azure, and Envoy from a single control plane, full-lifecycle coverage including AI Gateway with native MCP exposure. Paired with Moesif for analytics and monetization. Named a Leader in The Forrester Wave: API Management Software, Q3 2024.  MuleSoft Anypoint Platform. Integration-platform-heavy positioning, common at enterprises with heavy SOAP-to-REST migration needs. Enterprise pricing.  Kong Konnect. Lightweight gateway with a plugin ecosystem, common in Kubernetes-native deployments. Lighter on lifecycle and monetization than full platforms.  AWS API Gateway. Tightly bound to the AWS stack. Fits AWS-native workloads; not designed for multi-cloud.  IBM API Connect. Enterprise-fit for organizations already in the IBM estate; release cadence and developer experience are slower than cloud-native platforms.Apigee fits when an organization is already committed to Google Cloud at meaningful scale and wants a single integrated platform. The same commitment is also its constraint: pricing and operational model assume Google Cloud is the long-term primary cloud.Apigee in 2026: Gemini, agent traffic, and MCPThree changes are worth flagging if your last read on Apigee is more than a year old.Gemini Code Assist in Apigee. Google added Gemini-powered design assistance to Apigee for tasks like generating OpenAPI specs from natural language, suggesting policy attachments, and explaining proxy behavior. The feature is part of the broader Google Cloud “AI everywhere” push and overlaps with similar additions across competitor platforms.Agent traffic patterns. A growing share of API calls in 2026 comes from AI agents calling APIs as part of multi-step tasks. Apigee handles this traffic at the protocol level (same as any other authenticated REST traffic), but the analytics and the per-customer attribution become more complex when “the customer” is a model running inside an agent runtime. Most API platforms are catching up here; Apigee is not behind, but it is not visibly leading either.MCP and the agent surface. The Model Context Protocol lets agents call APIs through a different runtime interface. Apigee does not have a dedicated MCP exposure feature in 2026 (other platforms, including WSO2’s AI Gateway, auto-generate MCP servers from OpenAPI specs). If serving agents is a major part of your roadmap, this is one of the few areas where Apigee is currently behind.Apigee best practices and tipsA short list of patterns that compound when teams adopt Apigee, and one or two that consistently cause pain.Treat the OpenAPI spec as the source of truth. Apigee can be operated as a configure-the-policy-and-deploy tool, but the teams that get the most out of it design APIs spec-first and import the spec into Apigee for proxy generation. The same spec drives downstream tools (SDK generators, MCP servers, contract tests) and keeps the proxy in sync with the underlying service.Use shared flows for cross-cutting concerns. Apigee proxies tend to accumulate copy-pasted policy snippets (auth checks, logging, transformation) across every endpoint. Extracting these into shared flows keeps the policy logic in one place and prevents drift when the auth rule changes.Cache aggressively at the proxy. The ResponseCache policy in Apigee can cut backend load on read-heavy GET endpoints meaningfully. The discipline is being explicit about which endpoints are safe to cache and for how long; the default of “do not cache” is the right starting point, but most APIs leave cacheable traffic on the table.Lean on the analytics out of the box, but layer on what’s missing. Apigee Analytics covers operational metrics well (call volume, latency, status distribution per proxy and per environment). Per-customer behavior, revenue attribution, and AI/agent traffic attribution are not part of the default offering; this is where teams add Moesif or build a custom analytics layer on top.Watch the Apigee-specific quirks. Quotas and rate-limit counters reset on quota windows rather than rolling windows by default; the difference matters for high-volume APIs. Policy execution order is left-to-right within a flow but proxy endpoint vs. target endpoint flows execute differently; the diagram in Google’s docs is worth keeping open during initial setup.Plan the Edge → Apigee migration if you are still on Edge. Google has been moving customers off the older Apigee Edge runtime onto the cloud-native Apigee runtime for several years. If your organization is still on Edge, factor migration timing into platform planning; the runtime changes are not always transparent to consumer integrations.Common Apigee challengesThe honest practitioner view on Apigee includes a few persistent issues:  Setup cost is high. A minimal Apigee deployment requires meaningful Google Cloud spend before you have processed your first request. The “Hello World” install is expensive enough that small teams should think twice.  Documentation breadth, depth, and clarity have always been uneven. The feature surface is large and the official docs do not always keep up. Teams typically rely on Apigee community Slack channels and Stack Overflow to fill gaps.  Complex pricing. Apigee’s pricing model is structured across an Evaluation tier, Pay-as-you-go, and Subscription with separate line items for traffic, environments, and add-ons. Estimating annual cost requires careful capacity planning.  Gateway-style learning curve. Apigee’s XML-based policy configuration is powerful but not the easiest model to learn for teams used to declarative YAML or code-first frameworks like FastAPI or Express.These are not deal-breakers for organizations that need Apigee’s depth. They are the friction points smaller teams should weigh before committing.How Apigee, WSO2, and Moesif compare for analytics and monetizationApigee’s built-in analytics cover usage, performance, and errors at the platform level. That is enough for many operational questions but it gets thin when the question is about a specific customer’s behavior or about turning API consumption into revenue.This is where stack composition matters. The WSO2 API Platform paired with Moesif covers the same surface as Apigee plus per-customer payload-level analytics, behavioral cohort analysis, and direct billing-provider sync (Stripe, Recurly, Chargebee, Salesforce). Apigee customers often add a separate observability and monetization tool to fill those gaps; teams that want a single integrated story for analytics and monetization typically end up with either Apigee plus Moesif, or with the WSO2+Moesif stack as a replacement.For an enterprise already on Google Cloud and Apigee, adding Moesif behind the gateway is the most common pattern we see in 2026. For an enterprise evaluating from scratch, the integrated WSO2+Moesif stack is the comparison Apigee is being measured against.Next stepsApigee is a credible choice for organizations on Google Cloud that want a full API management platform without assembling one from parts. It is expensive and complex enough that smaller teams should weigh alternatives carefully.If you are evaluating Apigee against alternatives that include per-customer analytics and direct billing-provider sync, start a 14-day Moesif free trial to see the analytics layer you would otherwise need to add behind Apigee. No credit card required.Frequently asked questionsWhat is Apigee used for? API management: traffic routing, authentication, rate limiting, analytics, developer portal hosting, and monetization for APIs at enterprise scale. It is Google Cloud’s full-lifecycle API platform.Is Apigee free? No. Apigee is a paid Google Cloud service with multiple tiers. There is an evaluation option, but production usage is not free.Who owns Apigee? Google. Apigee was acquired by Google in 2016 and is now a Google Cloud product line.What is the difference between Apigee Edge and Apigee? Apigee Edge is the older product line; Apigee is the cloud-native successor that runs natively on Google Cloud infrastructure. New deployments default to Apigee.Is Apigee an API gateway? Apigee includes an API gateway as one component. It is broader than a gateway, including developer portal, analytics, monetization, and lifecycle management as integrated capabilities.How does Apigee compare to Kong or WSO2? Apigee is heavier and more lifecycle-complete; Kong is lighter and more gateway-focused; WSO2 is open-source-led with comparable depth and paired with Moesif for analytics and monetization. The right choice depends on which cloud you run on and how much of the API platform you want pre-built.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /api-product-management/api-strategy/What-Is-Apigee/",
          "author": "Matthew",
          "categories": "API-Product-Management, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-implementing-api-analytics-with-python": {
          "title": "Implementing API Analytics with Python",
          "content"	 : "APIs serve as the connecting bridge between different software systems, establishing a common language that enables significant portability, scalability, and extensibility. Understanding these systems and gaining insights into their usage is vitally important, and proper analytics creation and usage can drive significant business success.API analytics are central in providing this understanding, offering valuable context about the performance, usage patterns, and general health of APIs. This article will delve into the creation of a strong API Analytics solution in Python, highlighting the tools and methodologies needed to build a comprehensive solution at scale.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding API AnalyticsBefore exploring Python’s capabilities for API analytics, it’s essential to grasp the fundamentals of what API analytics involves. At its heart, API Analytics comprises two main processes: establishing the metric and monitoring it. Efficient monitoring includes several stages: data collection, data analysis, and data reporting. Each of these stages necessitates careful consideration of both the form and function of the systems you’re using.Setting the MetricSelecting the right metrics to track requires determining their relevance to your business logic and specific use cases. While having comprehensive insight into all possible metrics at all times might seem ideal, this can lead to information overload. An excess of data can transform useful information into mere noise, diminishing the value and context provided by the analytics process. It’s important to focus on metrics that are meaningful and directly impactful to your business objectives.Accordingly, there are a few categories to consider when deciding on the metrics you track.  Volumetric Data – this data involves information about the number of requests made, the total data transferred, etc. This information can help you understand how the API is used and to what extent, and can help identify high-traffic systems and endpoints.  Service Data – this information can include data around how long it takes for an API to respond, how efficient these responses are, and so forth. This is especially useful to track over time to see what the incidence of failure or inefficiency across the ecosystem of offerings.  User Data – this data includes user behavior and sentiment, and can be tracked through ongoing API data collection and then enriched with consumer support systems.  Error Data – this data is relevant to the errors generated by the API, and can help set a standard for the health of the overall systems.Tracking Metrics with MoesifOnce you have established what metrics you wish to track, you must actually start to track them. There are a variety of amazing tools in the Python ecosystem that can deliver powerful API analytics. Let’s take a look at one of the more popular ones.Moesif is a top-tier insights and analytics platform designed to facilitate the deployment of an efficient metrics system at scale with minimal overhead. It functions in real-time, delivering analytics that are driven by context and observation, rather than solely relying on basic network information or raw data.This approach can provide substantial value to most users. Additionally, Moesif offers support for monetization and revenue generation solutions, which can lead to significant financial advantages. For Python-based implementations, Moesif supports numerous server integration options, simplifying and speeding up the implementation process.Getting Started with MoesifMoesif is incredibly easy to get started with. Utilizing Django, you can simply install the SDK using the following code:pip install moesifdjangoFrom here, you just need to initialize the code with your APplication ID. Add “moesifdjango” to your middleware in settings.py as follows:# add to the middleware list.MIDDLEWARE = [    ...    'django.contrib.sessions.middleware.SessionMiddleware',    'django.middleware.common.CommonMiddleware',    'django.contrib.auth.middleware.AuthenticationMiddleware',    'moesifdjango.middleware.moesif_middleware'    ...]Then add your configuration settings:def identifyUser(req, res):    if req.user and req.user.is_authenticated:        return req.user.username    else:        return NoneMOESIF_MIDDLEWARE = {    'APPLICATION_ID': 'Sign in to get your Moesif Application Id',    'CAPTURE_OUTGOING_REQUESTS': False, # Set to True to also capture outgoing calls to 3rd parties.    'IDENTIFY_USER': identifyUser # Optional hook to link API calls to users}And that’s it – you’re ready to go!Implementing API Analytics in PythonWhen implementing Python analytics, regardless of the specific solution you choose, there’s a general process that should be followed, especially when deploying at scale. This process typically includes the following steps.Step 1: Set Up Python for AnalyticsFirst, you must make sure that your Python project is connected and ready for analytics. Make sure you have identified the proper points of monitoring and that you are ready to push the data flowing in and out of these points to the correct analytics service.Step 2: Configuring the Analytics ToolsMake sure your tools are correctly configured. This not only means checking your properties files but also involves a thorough examination of your data handling processes. Proper configuration is vital for security, as many data breaches are caused by misconfiguration. Therefore, configuring your tools correctly is not just sound business practice, but also an ethical responsibility. Always perform your due diligence to maintain data security.Step 3: Collecting MetricsAt this juncture, it’s crucial to evaluate your approach to metrics collection. The choice of database solution, along with the methods of data storage and encryption at rest, will significantly influence the complexity of subsequent processes. Treat this as a potential critical failure point. It’s essential to ensure that your metrics collection is efficient to avoid any negative impact on the production service through the API.Step 4: Analyzing the DataSelect a monitoring solution that effectively visualizes and analyzes your data. This choice should primarily be guided by your business requirements. Ensure that your business strategy is robust and your needs are precisely outlined. Data analysis can incur significant costs, and these costs escalate if you’re examining aspects irrelevant to your business objectives.Step 5: Make Data-Driven DecisionsWith your metrics in hand, it’s time to leverage them for informed decision-making. This involves consistently bringing as much data to the forefront as possible while ensuring its easy accessibility. Avoid compartmentalizing your data; instead, make it openly available for comprehensive analysis.Challenges and Best PracticesChallengesAnalytics is going to largely be as valuable as the data is quality and consistent. Poor data quality can lead to inaccurate data findings, so ensure that your data methods are regularly reviewed and vetted. Similarly, you must ensure that you are collecting the same types of data if you are comparing data – collecting one category of data one month and switching to another category results in findings that are 100% inaccurate and out of line.Ensure that you are protecting sensitive data and complying with protection regulations. Analytics is important, but never forget that data is not just data – it’s information sourced from actual organizations and people, and it should be treated with the respect that it deserves.Finally, avoid trying to reinvent the wheel. There are multiple libraries and platforms that can be deployed with very little difficulty, and while many organizations may be tempted to make their own solutions, the reality is that there are already proven solutions in the market that can be used to great effect.Best PracticesGenerally speaking, the following are some best practices for API analytics in Python:  Validate and Clean Data – regularly validate and clean your data. Ensure that you are collecting the right thing, and make sure that the data means what you think it means before making any major decisions.  Optimize Your Code – Python is very efficient in the general view, but inefficiencies from poorly optimized code can reduce this benefit. Make sure your analytics is implemented in an optimized fashion, and collect only that which is necessary to get the core work done.  Design for Scalability – building for today is not good enough – build for the long-term and document heavily to ensure that you are creating a scalable solution in the long run.  Ensure Data Security – implement robust security measures. Encrypt sensitive data, manage API keys securely, and follow best practices for data privacy.Why Moesif?While there are multiple options in the market for implementing analytics in Python, Moesif is by far and away your best option for ease of integration and quality of analytics.Many developers would look at external partners within the context of their in-house capability – but developing in-house solutions often introduces a substantial process of reinventing the wheel. At this point, with some very specific exceptions, developing an analytics engine in-house introduces extensive cost with very little upside.Within this context, it’s incredibly important to find a provider who provides the most amount of benefit with the least amount of friction. Moesif unlocks an incredible amount of possibility in your implementation, and this possibility comes with very low friction. Beyond the high value and powerful analytics engine, Moesif can unlock additional benefits in terms of monetization, visibility, and billing, allowing you to not only understand your platform, but extract value from it in a very effective way.Moesif is also extremely easy to implement – with several options for integration, getting started with Moesif is very easy.            Moesif      Other Solutions                  Easy to deploy with wide integration support      Often limited to specific languages or frameworks              World-class analytics and metrics for high visibility and business metrics      Typically limited to surface-level network traffic              Powerful monetization with deep billing provider integration      Decoupled monetization and billing – often one or the other              Flexible pricing without surprises      Pricing can be highly variable              Extensions gallery offers new features with low overhead      Extensibility often requires more complexity and third party solutions              Complete all-in-one package for business success      Only handles one or two parts of your business success plan      ConclusionPython is a powerful tool which, when properly configured, can deliver unprecedented insight and analytics for the end user. Keep in mind these best practices and ensure that you are using vetted, trusted solutions, and you can reap these benefits at scale for any business implementation.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            You’ve built the API. Now let's generate revenue!            Monetize your Python based APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Implementing-API-analytics-with-Python/",
          "author": "Kristopher",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-implementing-api-analytics-with-java": {
          "title": "Implementing API Analytics with Java",
          "content"	 : "There are few technologies as ubiquitous – and crucial for business success – as APIs. APIs connect different software systems together, forming a common language that allows for substantial portability, scalability, and extensibility.What is just as important as the systems themselves is understanding the systems and discovering insights about their usage. API analytics play a vital role in delivering this insight, providing context around the performance, usage patterns, and overall health of these APIs. Java, with its robust ecosystem, offers various tools and frameworks that can be leveraged to implement effective API analytics. This article aims to explore these tools and methodologies in detail.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding API AnalyticsBefore delving into the specifics of Java, it’s crucial to understand what API analytics actually entails. API Analytics has two core processes – setting the metric and tracking the metric. Effective tracking of these metrics includes collecting the data, analyzing the data, and reporting the data, and each stage of this process requires some thought into the form and function of the systems involved.Setting the MetricWhen choosing what metrics to track, it’s important to figure out the relevancy of the data to your business logic and use case. While it would be great to have total insight into every possible metric at all times, this can introduce too much information, resulting in the conversion of data and information into pure noise. This noise can result in a reduction of value in the analytics and context generated by this process.Accordingly, there are a few categories to consider when deciding on the metrics you track.  Volumetric Data – this data involves information about the number of requests made, the total data transferred, etc. This information can help you understand how the API is used and to what extent, and can help identify high-traffic systems and endpoints.  Service Data – this information can include data around how long it takes for an API to respond, how efficient these responses are, and so forth. This is especially useful to track over time to see what the incidence of failure or inefficiency across the ecosystem of offerings.  User Data – this data includes user behavior and sentiment, and can be tracked through ongoing API data collection and then enriched with consumer support systems.  Error Data – this data is relevant to the errors generated by the API, and can help set a standard for the health of the overall systems.Each of these categories include metrics that could be argued into other categories, but understanding them in this format is a great start. There are as many metrics you can track as there are systems to track them!Tracking the MetricOnce you have established what metrics you wish to track, you must actually start to track them. There are a variety of amazing tools in the Java ecosystem that can deliver powerful API analytics. Let’s take a look at some of the most popular ones.MicrometerSpring Boot Actuator is a part of the Spring Boot framework. It provides built-in endpoints for monitoring and interacting with your application, and can be easily integrated into your Java application to provide valuable insights into the runtime behavior of your application. Actuator connects to a collection facade called Micrometer, which provides real-time monitoring and analytics. Micrometer directly connects to the following monitoring systems:* Netflix Atlas* CloudWatch* Datadog* Ganglia* Graphite* InfluxDB* JMX* New Relic* Prometheus* SignalFx* StatsD* WavefrontGetting Started with MicrometerGetting started with Micrometer is quite easy – a more in-depth guide is located on the Springboot website, but is adapted here for brevity.In order to use Micrometer, you’re going to need to create an application pair with a service application and a client application. The service application will allow you to utilize a properties file to set some variables – notably, you’ll be able to use it to set a port and to set an integration for your analytics engine of choice. In the tutorial from Springboot, they target Wavefront, an analytics dashboard for data insights. This properties file looks roughly like this:spring.application.name=service server.port=8083 wavefront.application.name=console-availability management.metrics.export.wavefront.source=my-cloud-serverFrom here, you will create your client application. This application will allow the service application to be exercised, calling the data in question and pushing it to Wavefront.Some key details of the Spring Boot approach should be considered. Firstly, Micrometer, though billed as an analytics system, is more a facade over the instrumentation part of Spring Boot – for this reason, you can use Micrometer to push to any solution (including something like Moesif). This gives you a ton of flexibility in finding the right combinatory stack of offerings. While you certainly can utilize Micrometer as a very basic analytics engine in and of itself, you’re going to need a database solution like Prometheus or Datadog to store this data, and it’s advisable to use a visualization solution to get the most benefit out of this stored data.MoesifMoesif is a world-class insights and analytics platform that can help you deploy an effective metrics system at scale with very little overhead. Moesif operates in real-time, providing analytics powered by context and observation rather than just network information or base data.This can unlock an incredible amount of value for most users in and of itself, but additional support for monetization and revenue generation solutions can deliver significant monetary benefits as well. For Java implementations specifically, Moesif supports many Java server solutions, making implementation quite quick and easy!Getting Started with MoesifConnecting a Java development to Moesif is quite easy. By using Spring Boot, we can utilize a very simple deployment system.First, you need to install the SDK. This is quite easily done, with support via Maven:<dependency>    <groupId>com.moesif.servlet</groupId>    <artifactId>moesif-servlet</artifactId>    <version>1.7.5</version></dependency>And support via Gradle:dependencies {    compile 'com.moesif.servlet:moesif-servlet:1.7.4'}With these dependencies in place, all you have to do is install the Moesif Filter object, using your Moesif Application Id to connect to the service:import com.moesif.servlet.MoesifFilter;import javax.servlet.Filter;import org.springframework.web.servlet.config.annotation.*;import org.springframework.context.annotation.*;import org.springframework.http.converter.*;@Configurationpublic class MyConfig extends WebMvcConfigurerAdapter {  @Bean  public Filter moesifFilter() {    return new MoesifFilter("Sign in to get your Moesif Application Id");  }}That’s it! You are now connected to Moesif in Java!ELK Stack (Elasticsearch, Logstash, and Kibana)The ELK Stack is another very popular solution for API analytics. Named after its components – Elasticsearch, Logstash, and Kibana – the ELK Stack is a combinatory solution that leverages each component for a specific function:  Elasticsearch – A search and analytics engine commonly used for log data storage, search, and analysis.  Logstash – Used for log collection, enrichment, and transportation.  Kibana – Provides visualization capabilities for data stored in Elasticsearch, making it easier to create dashboards and visual reports.Getting Started with the ELK StackGetting started with the ELK Stack is going to vary depending on your specific language, your tech stack, and your operating environment. Generally speaking, the process will require you to install each component individually, and then configure them to speak with each other by using the service as a common endpoint. There are many services which bundle the ELK Stack together as a single offering – for instance, logz.io provides this service, allowing you to get started pretty quickly compared to manually building. The drawback to this approach is you are buying into a product and ecosystem, and jumping out of this can be difficult.Implementing API Analytics in JavaRegardless of your chosen solution, there’s a general process to implementing Java that should be kept in mind as you deploy at scale. This process generally looks as follows.Step 1: Setting Up a Java Project for AnalyticsTo begin building API analytics, you must first set up your Java project to use a good analytics solution. Many solutions, such as Micrometer, require a set of dependencies to integrate properly. During this stage, reviewing the dependencies to ensure proper configuration is vital. Ensure that you have a long-term view of what this support looks like. Solutions like Micrometer are quite low-effort, but the trade off is generally for power and ease. The ELK Stack is much more powerful, but also requires a lot of overhead and time with more complex environments.Step 2: Configuring the Analytics ToolsEnsure you have properly configured your tools. This involves reviewing your properties files, yes, but it also requires a deep introspection as to how you handle data. This is critical to ensuring security – misconfiguration drives many data exposures, so properly configuring is good business and good ethics. Do your due diligence!Step 3: Collecting MetricsAt this stage, you’ll need to consider how you’re collecting metrics. What database solution you use, and how this data is stored and encrypted at rest, is going to determine how complex the rest of this process is, so consider this a critical point of failure. Make sure your metrics collection is efficient to prevent impact on production service from the API.Step 4: Analyzing the DataChoose your monitoring solution to visualize and analyze data. This is going to be a decision largely driven by business needs, so make sure that your business logic is sound and that your needs are clearly-defined – analyzing data can be expensive, but it’s even more so when you’re analyzing things your business doesn’t even care about!Step 5: Make Data-Driven DecisionsNow that you have your metrics, use it to make data-driven decisions! This requires surfacing as much as you can as often as you can, but it also involves making this data readily accessible. Make sure you are not siloing your data.Challenges and Best PracticesChallengesThe biggest challenge you are likely to run into with Java API analytics is in data overhead. For a long time, Java had a reputation of being a slow language, and while this is not really true in 2023, it still utilizes quite a bit of memory in delivering speed. Accordingly, API analytics can introduce a memory drain that could degrade API performance if not properly implemented.Avoiding this issue is going to require creating your solution with an eye for efficiency in design. Metrics should only be collected once, and should be transformed separately from the collection stage. They can also play double duty – some metrics can represent multiple things, allowing you to get insights by collecting only a single data point.Additionally, there are some privacy and security concerns that come with any metric approach. Depending on what data is being sent, it’s possible that PII or other data could be logged as part of a Java API analytics deployment. Ensure that you are sanitizing data, or, if such data cannot be sanitized, ensure that you are logging to a secure resource that is only accessible by trusted teams.Best PracticesEfficiency is a big part of this equation. Efficiency will determine the value of the context you generate – after all, analytics is worth very little when it costs you excessively in terms of resources to get it.Developers should consider scalability. Logging and analytics are easy to do when you’re handling ten or fifteen requests a day, but when you are handling hundreds of thousands of requests a minute, this can change dramatically. Ensure that your analytics system is built for efficiency and that you are logging the correct amount of information. If your approach is not verbose enough, you run the risk of doing analytics work that does not actually provide context or information. If it is too verbose, you run the very serious risk of running into resource crunch that occurs exponentially with little warning.Most importantly, ensure that you are utilizing a trusted system for logging and analytics. Moesif is a powerful solution that is built on efficiency and security, and as such, stands above the crowd as a feature-complete toolset that can easily be integrated into your Java stack. If you are using another solution, you must audit the solution to ensure that it complies with your security posture, that it does not introduce inefficiency, and that the libraries and frameworks it depends on are secure and efficient in their own right.Why Moesif?There are quite a few third party solutions that you can use, but Moesif stands above the rest. Moesif delivers the highest potential for success with the lowest amount of friction, and can see adoptees get started within a matter of minutes.It should be noted that the use of a third party solution begs the question – why not do it in-house? Doing in-house analytics, while possible for most developers, introduces a lot of long-term management in addition to immediate upfront cost. This also requires reinventing a solution that has already been effectively developed elsewhere – you’re doubling the cost for very little potential.Adopting a trusted third-party solution can short circuit this issue. Moesif is a highly powerful solution that can provide an incredible amount of benefit with very little downside. In addition to the powerful suite of analytics on offer, Moesif also provides systems for monetization, observability, and effective billing, which unlocks huge potential revenue sources for APIs of any size.Moesif is very easy to get started with – there are many options for integration for a wide array of languages and frameworks, and most new users could get started in minutes.            Moesif      Other Solutions                  Easy to deploy with wide integration support      Often limited to specific languages or frameworks              World-class analytics and metrics for high visibility and business metrics      Typically limited to surface-level network traffic              Powerful monetization with deep billing provider integration      Decoupled monetization and billing – often one or the other              Flexible pricing without surprises      Pricing can be highly variable              Extensions gallery offers new features with low overhead      Extensibility often requires more complexity and third party solutions              Complete all-in-one package for business success      Only handles one or two parts of your business success plan      ConclusionThe implementation of API analytics is one of the best returns on investment the average developer can deploy. Using some common sense strategies and some trusted systems, developers can unlock logging and monitoring at scale, delivering effective analytics that can play a crucial role in the scalability and extensibility of their service.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            You’ve built the API. Now let's generate revenue!            Monetize your Java based APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Implementing-API-analytics-with-Java/",
          "author": "Kristopher",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-analytics-api-strategy-api-logs-everything-you-need-to-know": {
          "title": "API Logs: What They Are and How to Use Them in 2026",
          "content"	 : "An API log is a record of one API interaction: the request that came in, the response that went out, the metadata around both. Multiply that across every call your API handles and you have the raw material for debugging, performance work, security investigations, capacity planning, and customer support. A well-instrumented API turns its logs into the most useful telemetry an engineering team has.                Monitor REST APIs With Moesif              14 day free trial. No credit card required.              Try for Free        This guide covers what API logs actually contain, the four types worth distinguishing, how to read them when something goes wrong, and the 2026 patterns around logging AI agent traffic. Most of this generalizes across any API stack; the specifics that matter for Moesif customers are called out where relevant.What API logs areAn API log entry captures one request-response pair plus the context around it. At minimum: timestamp, HTTP method, endpoint, status code, latency. At useful: request headers, query parameters, request body, response headers, response body, client identity, error details when present.Logs differ from metrics in granularity. A metric tells you “the p95 latency on /orders was 240ms last hour.” A log tells you “customer X called POST /orders at 14:23:07, the request body was Y, the response was a 422 with this validation error, and the call took 312ms.” Both are necessary; metrics tell you something is wrong, logs tell you exactly what happened to whom.The four types of API logsPractitioners typically distinguish four categories. The same log entry can fall into more than one (a 500 error during a payment call is both an error log and a security-relevant audit entry); the categories are about which question you are answering when you read the logs.  Access logs. The full record of every request: client IP, URL, method, status code, timestamp, latency. Useful for traffic patterns, top endpoints, geographic distribution, and bot detection.  Error logs. Subset of access logs filtered to non-2xx responses, plus internal exceptions logged from inside the API. Useful for debugging and incident response.  Performance logs. Per-request latency, throughput, and server resource usage. Useful for finding slow endpoints and capacity planning.  Security logs. Authentication attempts (successful and failed), authorization checks, and any access to sensitive endpoints. Useful for incident investigation and compliance audits in regulated industries.Most teams produce one log stream and tag entries with the appropriate category at query time, rather than maintaining four separate log files. The category is a view, not a storage format.What a complete API log entry containsA well-structured API log entry has six fields at minimum:  Timestamp. The exact moment the request was received, in ISO 8601 format with UTC offset. Used to reconstruct the sequence of events across distributed systems.  HTTP method and endpoint. GET /users/42, POST /orders. Tells you what the client was trying to do.  Request details. Headers, query parameters, and body. Headers carry auth tokens and content negotiation; the body carries the actual payload. Sensitive fields (auth tokens, PII) should be masked at logging time.  Response details. Status code, headers, body. Status code carries the outcome; body carries the result (success or error detail).  Client identity. The customer or user the request belongs to. Without this, logs are anonymous and you cannot answer customer-specific questions.  Latency. Time between receiving the request and sending the response, in milliseconds. Pair with status code to spot slow successes and fast failures.Two optional fields that are increasingly standard: a request ID (returned to the client so they can quote it in support tickets) and a trace ID (for joining logs across services).A request body that gets logged should be the post-validation body, not the raw bytes. Logging raw bytes risks logging malformed JSON and incomplete payloads. Logging the parsed object preserves what the API actually understood.What an API logger actually does (libraries, middleware, gateways)People say “API logging” to mean four different implementations. They are not interchangeable, and the choice between them shapes which questions you can answer from your logs.Application-level library. A logging library inside your application code (Python’s standard logging, Winston for Node.js, Logback for Java) writes structured entries when application code chooses to log. Full context (you know which user, which function, which line of code triggered the log), but every service has to opt in, log levels and formats drift across teams, and you can only log what the application explicitly chooses to.HTTP middleware. A framework-level interceptor (FastAPI middleware, Express middleware, Django middleware) that runs on every request and response and writes a log entry without the route handler having to do anything. Complete request/response coverage without per-route code, but still scoped to one service; consistency across services requires every service to install the same middleware.API gateway logging. The API gateway (WSO2, Kong, AWS API Gateway, Apigee, Envoy) sits in front of every backend service and logs every request as it passes through. Zero application code; coverage is complete by definition; format is consistent across services because the gateway controls it. The gateway sees what crossed the wire but not what happened inside the service, so deep application-level events still need application logs.Dedicated API analytics platform. A purpose-built layer (Moesif, in this case) that integrates with the gateway or middleware, captures the full request/response with customer identity attached, and stores the events in a query model optimized for per-customer, per-endpoint analytics rather than raw search. Business-level questions (which customers are growing, which endpoints are most-used by paying customers, where AI agents are retrying) are answerable from the same dataset as engineering-level questions. The trade-off is paid SaaS and one more vendor relationship.The right answer for most production setups is two of these stacked: gateway logging for the wire-level record of every call, plus a dedicated analytics platform for the business-level views. Application logs and middleware logs handle the deeper engineering debugging cases where you need code-level context.A note on the term “API logger.” You will see it used three ways in 2026: as a synonym for an HTTP-middleware logger (the most common informal usage), as a product name for SDKs that wrap a logging service, and occasionally as a category description for any tool that records API traffic. The category is broader than any one tool; the practical question is which of the four implementations above fits your use case.Tools for collecting and analyzing API logsThe tool choices fall into two categories: general-purpose log aggregators, and API-specific observability platforms.General-purpose log aggregators. These collect any text-formatted log and provide search and visualization over it. They work for API logs but you build the API-specific views yourself, and the per-customer business analytics that drive API product decisions are not part of what they ship.  ELK Stack (Elasticsearch, Logstash, Kibana). Open-source. Elasticsearch indexes the logs, Logstash ingests them, Kibana visualizes. Common starting point for self-hosted log infrastructure.  Splunk. Commercial. Established query language and dashboarding, often deployed at enterprise scale. Pricing scales aggressively with data volume; the business-level views (per-customer behavior, revenue attribution) are not part of the default offering.  Graylog. Open-source. Lighter than ELK, simpler to operate at small scale.  Datadog. Commercial. Combines logs with metrics and traces; common where the team already uses Datadog for infrastructure monitoring. Built around system health rather than per-customer API analytics.API-specific observability platforms. These understand the request-response structure of an API call natively, which means the views and alerts are tuned to API-specific questions (per-customer behavior, per-endpoint error rates, payload-aware analytics) rather than generic log search.  Moesif is purpose-built for this approach. It instruments API gateways (WSO2, Kong, AWS, Azure, Envoy) and application SDKs, ingests the full request/response per call, and presents the data in views built around the customer journey: who is calling what, where they are stalling, which endpoints are erroring, what behavior predicts churn.The right tool depends on the question you are answering. If your dominant use case is engineering-facing debugging and infrastructure log search, an ELK or Splunk install is appropriate. If your use case includes product analytics, customer behavior, monetization, or per-customer error attribution, an API-specific platform pays for itself quickly by answering those questions out of the box.Reading API logs for troubleshooting and performanceThe skill that matters most is reading logs in the moment, not reading them in aggregate.When an issue is reported, the first questions are:  Which request? Find the exact log entry by timestamp, customer, endpoint, or request ID. The faster this is, the cheaper the incident.  What happened to it? Look at the status code first, then the error detail in the response body, then the latency.  Is it isolated or systemic? Filter to the same endpoint or the same customer for the past hour. A single error is a customer support ticket; a pattern of errors is an outage.  What changed? Cross-reference the log spike with deploys, configuration changes, dependency outages, or upstream API changes.The pattern most production teams converge on is “alert on aggregates, investigate on individual logs.” Alerts fire when error rates spike or latency degrades; engineers then drop into the log stream to find specific examples and read what actually happened.Performance investigations follow the same pattern. A p95 latency spike on /orders shows up as a metric; the logs tell you whether the spike is a slow database query, an upstream API timing out, or one large customer hammering the endpoint with payloads ten times the normal size.For the underlying status code semantics that error logs depend on, see our HTTP status codes guide.API logging best practicesFive patterns that hold up across stacks:  Be selective. Over-logging is the most common mistake. Log everything you might need to debug an incident; do not log everything you can capture. Noise drowns signal, and at any meaningful traffic volume, storage cost is real.  Use structured logging. Logs in JSON (or another structured format) are queryable; unstructured text logs are not. Every field that matters for filtering should be a top-level key.  Mask sensitive data at logging time. Authorization tokens, credit card numbers, social security numbers, and healthcare identifiers do not belong in logs. Masking has to happen before the log is written, not after.  Standardize the schema across services. If three services log the same thing three different ways, joining them is painful. Use a shared schema (timestamp, method, endpoint, status, latency, customer ID) across the entire API surface.  Set retention by category. Access logs need 30-90 days for operational use. Security logs need years for compliance. Performance logs need enough history to compare across release cycles. Pick a retention per category; do not retain everything forever or you will pay for it.These are the design-time decisions that compound. They cost very little to set up correctly on day one and a lot to retrofit later.API logs in 2026: agent traffic and AI observabilityTwo changes are worth flagging if your API logging strategy was last updated before 2024.Agent traffic needs per-agent attribution. A growing share of API calls in 2026 comes from AI agents acting on behalf of users. Per-customer attribution is no longer enough; you also need to know which agent runtime, which agent session, and which workflow originated the call. Without this, “customer X is hitting our API ten times per second” could mean ten things, only some of which are actionable.Two practical changes: include an agent identifier in your logging schema (a header like X-Agent-Id or a field derived from auth scope), and instrument the log view to surface agent-vs-human breakdowns alongside the existing per-customer cuts.LLM inputs and outputs deserve their own log category. When your API wraps an LLM call (chat completion, RAG over your data, classification), the log entry should include the prompt sent to the model and the response received. This is for the same reasons regular API logs exist: debugging when the model returns wrong output, tracking which prompts cost the most, and meeting compliance requirements around what was sent to a third-party model.Most teams that hit this need build a parallel log stream for LLM-mediated calls. Platforms like Moesif handle this in the same observability layer as regular API logs, which keeps the analysis path consistent.Common API logging mistakesPatterns that show up across customer reviews:  Logging everything to a single file with no schema. Searchable in the worst possible way. Fixed by switching to structured logging early.  Logging sensitive data in request bodies. Auth tokens and PII end up in logs, then in log backups, then in incident-response timelines. Mask at logging time, not after.  No customer attribution. Logs that do not identify the customer making each call cannot answer customer-specific questions. Tag every log entry with a customer ID.  Inconsistent retention. Logs you kept for ninety days are missing the audit trail you need for the SOC 2 review. Define retention per category, write it down, automate the deletion.  Logging at the application without logging at the gateway (or vice versa). Both layers see different things. Application logs miss requests blocked at the gateway; gateway logs miss requests that fail inside business logic. You want both.The cost of getting this right is mostly upfront design work. The cost of getting it wrong shows up later as long incident investigations, failed audits, and customer-support tickets that take hours instead of minutes.Next stepsAPI logs are one of the highest-leverage pieces of telemetry an API team can produce. The design decisions are mostly upfront: schema, retention, sensitive-data masking, customer attribution, agent tagging. Once those are in place, the logs become the substrate for everything from debugging to product analytics to compliance audits.To see what live API logs look like with per-customer, per-endpoint, payload-level visibility across any gateway, start a 14-day Moesif free trial. No credit card required.Frequently asked questionsWhat is the difference between an API log and an audit log? An API log records every request-response pair; an audit log is a subset focused on security-relevant events (authentication attempts, permission changes, access to sensitive resources). Audit logs are usually subject to stricter retention and access controls.Should I log request and response bodies? Yes for debugging, with sensitive fields masked. The bodies are where the actual information lives; status codes alone do not tell you what the customer sent or what you returned. Mask auth tokens, PII, and any field your compliance posture requires.How long should I retain API logs? Access logs typically 30-90 days for operational use. Performance logs 90-180 days to track release-over-release changes. Security logs longer (1-7 years depending on your compliance requirements). Match retention to what each category is used for.Where should I send API logs? A central platform that supports search, alerting, and structured queries. ELK, Splunk, Graylog, or Datadog are general-purpose options; Moesif is the API-specific option. The right answer depends on whether your dominant use case is engineering-facing debugging (general-purpose works) or customer-facing analytics and monetization (API-specific is faster).Are API logs subject to GDPR or HIPAA? Yes, if they contain personally identifiable information. Mask PII at logging time, enforce access controls on the log store, and align retention with the relevant regulation. For HIPAA specifically, logs touching protected health information need encryption at rest and tightly controlled access.How do I log AI agent traffic differently from human traffic? Include an agent identifier in your logging schema, and instrument your analytics to surface agent-versus-human breakdowns. The interesting questions about agent traffic (which workflow, what retry pattern, which model) are different from the interesting questions about human traffic.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /api-analytics/api-strategy/API-Logs-Everything-You-Need-to-Know/",
          "author": "Matthew",
          "categories": "API-Analytics, API-Strategy"
        }
      
    ,
  
    
        "api-strategy-apigee-api-gateway": {
          "title": "Apigee API Gateway: Features, Pricing, and Alternatives (2026)",
          "content"	 : "Apigee is Google Cloud’s enterprise API management platform. It is one of three or four products in the category that consistently shows up on shortlists when an enterprise team is choosing how to expose, govern, and monetize APIs at scale, alongside MuleSoft, Kong, IBM API Connect, and the WSO2 API Platform. If you have arrived here, you are probably evaluating Apigee against one of those alternatives, looking up a specific Apigee feature, or trying to understand what changed since the “Apigee X” and “Apigee Edge” naming days.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        This guide covers what Apigee is in 2026, what it does well, where it can be a tough fit, what it costs, and how it compares to the other main choices. Most of the facts below come straight from Google Cloud’s own Apigee product page; we have cited the pricing and capability claims so you can verify them.What is Apigee API Gateway?Apigee is a fully managed API management platform from Google Cloud. It sits in front of your backend services as an API proxy layer, enforcing security, rate limiting, transformation, and analytics. Per Google Cloud’s product page, Apigee supports REST, SOAP, GraphQL, and gRPC architectural styles, and ships in three deployment shapes: fully managed (the default), Apigee hybrid (your own Kubernetes cluster), and Cloud Endpoints / API Gateway (lighter Google Cloud-native gateways for simpler use cases).People sometimes use “Apigee API Gateway” to mean only the proxy layer, but in practice Apigee is the broader platform: gateway, developer portal, analytics, monetization, security, and (newer in 2026) AI-assisted spec generation through Gemini Code Assist.Apigee in 2026 vs. legacy Apigee Edge: which one are people using nowIf you read older articles about Apigee, you will see two product names that no longer separate the way they used to.  Apigee Edge was the legacy on-prem-friendly version. It is in long-term support; no new deployments default to it.  Apigee X was the cloud-native version that runs alongside Edge. Google’s marketing pages now refer to the platform simply as “Apigee,” although “Apigee X” still appears in Google Cloud documentation and release notes as the canonical product name.In 2026, the practical lineup is:  Apigee (the managed Google Cloud service) for most enterprise use cases  Apigee hybrid for teams that need to run the gateway in their own Kubernetes cluster  Cloud Endpoints for gRPC and private-network use cases  API Gateway (Google Cloud’s lighter product) for packaging serverless functions as REST APIsIf you read a blog post that talks about “Apigee X” or compares “Apigee Edge vs Apigee X,” it is describing the same transition that is still being worked through across documentation and the customer base; the current product on Google Cloud is just labeled Apigee in marketing material, but the underlying runtime is still the one historically called Apigee X.Core features of ApigeePer Google Cloud’s documentation, the platform includes:  API proxies that front backend services with policies for security, rate limiting, quotas, and transformation.  Multi-protocol support: REST, SOAP, GraphQL, gRPC.  Advanced API Security with ML-powered abuse detection, automated configuration checks, and integration with Google Cloud’s WAAP (Web App and API Protection, combining Apigee, Cloud Armor, and reCAPTCHA Enterprise).  Developer portal for publishing APIs, managing API keys, and onboarding external developers.  Monetization with rate plans, billing models, and revenue tracking.  Analytics dashboards showing traffic, latency, and error patterns.  Gemini Code Assist in Apigee for AI-assisted OpenAPI spec generation and proactive duplicate-API detection.  API hub, a universal catalog of APIs (built or deployed anywhere) that consolidates discovery and governance.  Apigee hybrid for deploying the runtime in your own Kubernetes cluster while keeping a managed control plane.This is a broad surface. Apigee covers the full API lifecycle from design through observability, which is part of why it appears on enterprise shortlists.What Apigee is good atA few capabilities Apigee covers, with the caveat that several alternatives (notably WSO2) cover the same ground.Google Cloud-native integration. If your platform team already runs on Google Cloud, Apigee plugs into Cloud Armor, Identity-Aware Proxy, BigQuery, and the rest of GCP’s services without much glue work. The WAAP combination (Apigee + Cloud Armor + reCAPTCHA) is specific to Google Cloud and not portable to other clouds.Scale on Google’s infrastructure. Apigee is used by large enterprises on Google Cloud. The fully managed offering inherits GCP’s infrastructure characteristics. WSO2 and the major alternatives also operate at enterprise scale; the difference is which cloud the runtime lives on.Multi-protocol support. Apigee handles REST, SOAP, GraphQL, and gRPC, which matters in legacy-heavy environments running SOAP next to modern REST. WSO2 and several other full-platform competitors also cover the same protocol set; Apigee is not unique on this dimension.Developer portal and monetization. The portal and monetization features support rate plans, quotas, and revenue tracking out of the box. Apigee is one of several platforms that covers the publishing-to-revenue loop; WSO2 + Moesif covers the same loop with a more open-source-leaning stack.Recent AI integrations. Gemini Code Assist in Apigee generates OpenAPI specs from natural language and flags duplicate APIs. The competitive landscape on AI features is moving quickly: WSO2’s AI Gateway auto-generates MCP servers from any OpenAPI spec and governs both inbound agent and outbound LLM traffic natively; Kong has an AI Gateway plugin set; Apigee’s strength is the integration with Google’s Gemini stack specifically.Where Apigee can be a tough fitWhere to be careful when shortlisting Apigee.Pricing complexity. Apigee’s pricing has multiple axes (per-call, per-environment, per-deployment, plus add-ons) and the pay-as-you-go bill at scale can surprise teams used to flat-rate pricing. The Standard / Enterprise / Enterprise Plus subscription tiers exist for predictability, but they require a sales conversation.Google Cloud gravity. Apigee runs on Google Cloud. Apigee hybrid mitigates this by letting you run the data plane elsewhere, but the control plane and most integrations still assume GCP. If you are committed to AWS or Azure as your primary cloud, the integration depth is asymmetric.Migration cost from Apigee Edge. Teams still on the legacy Apigee Edge are looking at a non-trivial migration to current Apigee. Google has documented the path, but it is work.Operational learning curve. Apigee is comprehensive, which means a lot to learn. Smaller teams that want a simpler API gateway sometimes find Apigee heavier than the use case requires.Apigee pricing in 2026Per Google Cloud’s pricing page (current as of May 2026), the headline structure:  Evaluation: 60-day free sandbox.  Pay-as-you-go:          API calls starting at $20 per 1M API calls (up to 50M calls per month).      $365 per month per deployment environment, per region (entry tier).      Per-hour charges for additional proxy deployments in comprehensive environments (rates vary by tier and region).      Add-ons: API Analytics and Advanced API Security at starting prices of $20 per 1M API calls.        Subscription tiers: Standard, Enterprise, and Enterprise Plus, sold via Google Cloud sales.These are list prices; enterprise customers usually negotiate. Verify the latest figures from Google Cloud’s pricing page before budgeting, because the pricing model has shifted twice in the last three years.How Apigee compares to other API gatewaysThe realistic shortlist when evaluating Apigee in 2026:  WSO2 API Platform. Apache 2.0 open-source core, multi-cloud and on-prem deployment, full-lifecycle coverage across gateway, governance, portal, analytics, and monetization. Multi-gateway runtime (deploys across WSO2, Kong, AWS, Azure, and Envoy from one control plane). Native AI Gateway that auto-generates MCP servers from any OpenAPI spec and governs inbound agent + outbound LLM traffic. Named a Leader in The Forrester Wave: API Management Software, Q3 2024.  MuleSoft Anypoint Platform (Salesforce). Closest to Apigee in scope. Positioned more around iPaaS/integration than pure API monetization. Enterprise pricing model.  Kong (Kong Gateway / Kong Konnect). Lightweight, plugin-driven gateway. Common in Kubernetes-native environments. Lighter on lifecycle management, governance, and monetization than full platforms.  AWS API Gateway. AWS-native. Fits when the team is fully on AWS. Limited developer portal and monetization features; multi-cloud not supported.  Azure API Management (APIM). Microsoft-native equivalent. Fits when the platform lives on Azure. Pricing tiers are layered and analytics depth is lighter than the larger platforms.  IBM API Connect. Vendor-fit for enterprises already in the IBM software estate. Slower release cadence than cloud-native platforms.There is no universal winner. The right choice depends on which cloud you are already on, whether you need multi-cloud or on-prem deployment, and how much of the platform you want pre-built versus assembled.When to choose Apigee, and when to look at alternativesApigee is a reasonable fit when:  You are already committed to Google Cloud at meaningful scale.  You need multi-protocol support (REST + SOAP + GraphQL + gRPC) under one platform.  You want AI-assisted spec generation specifically tied to Google’s Gemini stack.  Your monetization needs include rate plans and developer portals out of the box.  You can absorb the operational and pricing commitment.Look harder at alternatives when:  Your primary cloud is AWS or Azure (look at API Gateway/APIM or a multi-cloud platform).  You need on-prem or air-gapped deployment with full open-source license access (look at WSO2 or Kong).  You want a simpler, more developer-centric gateway (look at Kong or Tyk).  Pricing predictability matters more than feature breadth (compare Apigee subscription tiers against alternatives’ flat-rate offerings carefully).The WSO2 API Platform is the alternative we know best because Moesif is part of it. WSO2 covers the same lifecycle stages as Apigee with a different deployment model (fully open-source-rooted, multi-cloud, with the AI Gateway covering inbound MCP and outbound LLM traffic). Whether it is the right answer for you depends on the same criteria above; we recommend evaluating both honestly if you are deciding between them.For the observability slice, regardless of which gateway you choose, Moesif API monitoring works behind any of the gateways above (Apigee, WSO2, Kong, AWS, Azure, Envoy) and provides per-customer, per-endpoint, payload-level analytics that the gateway’s built-in dashboards typically do not.Where to take this nextApigee is a credible enterprise choice when the Google Cloud fit is right. If you are still evaluating, the practical next step is to read each shortlist platform’s own product page, run a proof of concept on a small API, and watch what your actual customers do once you deploy.Once your gateway is in place, observability is the layer that closes the loop between platform decisions and customer outcomes. Start a 14-day Moesif free trial to add per-endpoint, per-customer analytics behind Apigee or any other gateway. No credit card required.Frequently asked questionsWhat is the difference between Apigee and an API gateway? Apigee is a platform that includes an API gateway alongside a developer portal, analytics, monetization, and lifecycle management. A bare API gateway is just the runtime that proxies and enforces policies on API calls. Apigee is the larger platform.Is Apigee free? Apigee offers a 60-day evaluation sandbox at no cost. Production usage starts at $20 per 1M API calls and $365 per month per environment, plus add-ons.Is “Apigee X” still a current product name? Mixed. Google’s marketing pages refer to the platform simply as “Apigee,” but “Apigee X” still appears in Google Cloud documentation and release notes as the canonical product name distinguishing the cloud-native runtime from the legacy “Apigee Edge” SaaS. Both names are still in active use; just the marketing surface has been simplified.Does Apigee support GraphQL and gRPC? Yes. Apigee supports REST, SOAP, GraphQL, and gRPC per Google Cloud’s product page.Can I run Apigee on AWS or Azure? Apigee itself runs on Google Cloud. Apigee hybrid lets you run the data plane (the actual gateway proxies) in your own Kubernetes cluster on any cloud, while the control plane stays on Google Cloud.What are the main alternatives to Apigee? MuleSoft Anypoint, Kong, AWS API Gateway, Azure API Management, IBM API Connect, and the WSO2 API Platform are the platforms that show up most often on the same shortlist.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /api-strategy/apigee-api-gateway/",
          "author": "Matthew",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-logging-api-calls-in-moesif-from-logstash-using-the-http-output-plugin": {
          "title": "Logging API Calls in Moesif from Logstash Using the HTTP Output Plugin",
          "content"	 : "One of the main value propositions of the Moesif product is the fact that it works so well with a wide variety of tools. One such tool – Logstash – is an incredibly powerful solution for managing events and logs. Understanding how to pull Logstash logs into Moesif is a critical function for any business utilizing Logstash at scale.Today, we’re going to look at how to log API calls from Logstash using the HTTP Output plugin for import into Moesif. Once you implement this solution, Moesif+ Logstash will become a powerful pair and an efficient way to track and analyze API usage.                Debug and Fix API Issues Quickly with Customer-Centric API Logging              14 day free trial. No credit card required.              Try for Free        How it WorksThis method makes use of the Logstash HTTP Output Plugin. This plugin generates a generic endpoint that feeds all logged data to an external source. By defining the external source as the Moesif API Collector endpoint, you can feed the logs into Moesif as an external service. You will need to collect some basic information to get started – notably, you will need to ensure you have the authentication elements on the Moesif side (specifically the “X-Moesif-Application-Id” parameter) as well as the form and structure of the Logstash output.The quality of what you get on Moesif’s side is going to rest heavily on how well-structured your data is on the Logstash side. Accordingly, make sure your codebase is clean and well-structured before you begin.Step 1 - Create Your Moesif AccountBefore you do anything, you should make sure that you have a Moesif account. To do this, simply head to the sign up page. Here, you will register using a few details:  Organization Name  Your current role at your organization  What you want to achieveNow that you have an account, you can move to prepping Logstash.Step 2 - Prepare LogstashOn Logstash, you want to make sure that you have prepared your instance to properly push data to the HTTP Output plugin. Firstly, ensure that Logstash is accurately retrieving your API logs. This can be sourced from a variety of sources, but you must ensure that you are ultimately pushing your logs to a singular point.One element you must consider at this stage is how you want your data filtered. Moesif benefits from having a lot of data, but if you want to limit the collected logs to only specific attributes, you can do this via a variety of Filter plugins. As an example, you can use the throttle filter to limit the amount of logging that is occurring, reducing noise for overly active systems: filter {      throttle {        before_count => 3        after_count => 5        period => 3600        max_age => 7200        key => "%{host}%{message}"        add_tag => "throttled"      }      if "throttled" in [tags] {        drop { }      }    }That being said, pre-filtering before the data exists Logstash will limit benefit you can get from integrating with Moesif, so it is better to not pre-filter in most cases.Step 3 - Utilizing HTTP Output on LogstashOutput plugins are plugins that allow the logging result to be sent in a specified way. For those who are experienced with CLIs, this is essentially piping the output – in fact, stdout is one of the many outputs provided by Logstash!The plugin we are more interested in right now, however, is HTTP Output. HTTP Output will allow you to send your logged events to any generic HTTP(S) endpoint. Within the configuration for this output, you will need to set the URL to the collector API endpoint (https://api.moseif.net/v1/events) along with any authentication or application ID data. From here, the HTTP Output plugin will POST the plugin content to the endpoint defined, and the Collector API will grab that data and store it for processing.It’s important to note that you need to include some critical pieces of information on both sides of the call. On the Logstash side, your endpoint definition will need to include the application ID and other identification/authentication systems in order to properly log. You’ll also need to make sure that you have properly defined your API output, as the logs that will be grabbed by Moesif will depend heavily on the quality of the tagging and clarity on the Logstash side. In other words, if you want to understand anything, take the time to make everything well-defined, clear, and searchable.Integrating into the Logstash PipelineIn order to integrate Moesif into the Logstash pipeline, the following is required.output {  http {    url => "https://api.moesif.net/v1/batch"    http_method => "post"    headers => {      "X-Moesif-Application-Id", "MOESIF_APPLICATAION_ID"      "Content-Type" => "application/json"    }    format => "json_batch"    http_compressioned => true    message => '[      {        "request": {          "time": "%{@timestamp}",          "uri": "%{[your_field_containing_uri]}",          "verb": "%{[your_field_containing_http_verb]}",          "headers": "%{[your_field_containing_request_headers]}",          "body": "%{[your_field_containing_request_body]}"        },        "response": {          "time": "%{your_field_containing_response_time}",          "status": "%{[your_field_containing_http_status]}",          "headers": "%{[your_field_containing_response_headers]}",          "body": "%{[your_field_containing_response_body]}"        },        "user_id": "%{[your_field_containing_user_id]}",        "metadata": {          "service": "%{your_field_containing_service_name}",          "host": "%{your_field_containing_host}",          "region": "%{your_field_containing_datacenter_region}"        }      }    ]'  }}Let’s dive into the individual parts of this code. Firstly, we have the definition of the method for connecting to our endpoint as well as the specific headers needed to integrate:output {  http {    url => "https://api.moesif.net/v1/batch"    http_method => "post"    headers => {      "X-Moesif-Application-Id", "MOESIF_APPLICATAION_ID"      "Content-Type" => "application/json"    }    format => "json_batch"    http_compressioned => trueThe “url” part of this request is where we point to the API endpoint for batch logging. We define the HTTP method as “POST”, which allows us to send data to the server. By passing the MOESIF_APPLICATAION_ID through the “X-Moesif-Application-Id” parameter, we can define the specific application that is receiving the logs, and by using both the format and http_compressioned variables, we set the way these logs are sent to that application.    message => '[      {        "request": {          "time": "%{@timestamp}",          "uri": "%{[your_field_containing_uri]}",          "verb": "%{[your_field_containing_http_verb]}",          "headers": "%{[your_field_containing_request_headers]}",          "body": "%{[your_field_containing_request_body]}"        },        "response": {          "time": "%{your_field_containing_response_time}",          "status": "%{[your_field_containing_http_status]}",          "headers": "%{[your_field_containing_response_headers]}",          "body": "%{[your_field_containing_response_body]}"        },        "user_id": "%{[your_field_containing_user_id]}",        "metadata": {          "service": "%{your_field_containing_service_name}",          "host": "%{your_field_containing_host}",          "region": "%{your_field_containing_datacenter_region}"        }      }    ]'  }}With our connection established, we can begin to set up the formatting of the request flow. With the rest of the variables set, we can append a timestamp, verb, body, metadata, pretty much any variable we would want to log that is supported by Logstash. It’s important to know that not all of this is required - in some environments, it may be beneficial to only log small amounts of data, and in those cases you can trim this down.That being said, logging works best when you provide as much context as possible, and it’s advisable to be pretty liberal with what is being sent if you can manage it.Step 4 - Process Data in MoesifNow that you have your data in Moesif, you must ensure that data is being documented, tracked, and leveraged. This can take a few different forms, but you should at least cover the following areas:  Ensure you are collecting timestamps or other time-based data to ensure you have context for logs over time outside of just the time where Moesif collected the information.  Mirror your data structure on both Logstash and Moesif – if you are working on Client data, that data should be well-marked and categorized regardless of where you’re looking at it.  Ensure you are not pushing authentication to a public endpoint – because you are transferring this information from one service to another, you should ensure basic security procedures are being followed to prevent any MITM attacks.ConclusionThe method in this piece is just one potential solution for this use case. Using Logstash to push logs to Moesif can take a variety of forms – another route that may be easier depending on local setup is to simply deploy a server integration. If you are already using a compatible server and are running Logstash locally or pushing logs to a local instance, it may make sense to default to a server integration.                Debug and Fix API Issues Quickly with Customer-Centric API Logging              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Quickly pinpoint and resolve API issues with Moesif's intelligent debugging solutions            Eradicate bugs and streamline your API performance - Get started with Moesif now!            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Logging-API-Calls-in-Moesif-from-Logstash-Using-the-HTTP-Output-Plugin/",
          "author": "Kristopher",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-implementing-a-freemium-model-for-api-monetization": {
          "title": "Implementing a Freemium Model for API Monetization",
          "content"	 : "When monetizing APIs, specific ways exist to entice customers to begin using your services. Much of the time, users like to see some value before they commit to becoming a full-blown paid customer. Other times, users want to start on a free tier and, hopefully, will upgrade later.One of the most popular billing providers we see used for API monetization is Stripe, and thankfully, by using Stripe and Moesif together, you can quickly implement a Freemium model. Let’s look at the steps required to implement a Freemium model in a matter of minutes using Moesif and Stripe. First, let’s dig a bit deeper into what a Freemium model is and the benefits of it.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        What is a Freemium Model?“Freemium” has gained significant recognition and popularity in software and subscription-based services. But what exactly is a freemium model, and why is it a go-to strategy for many businesses?Freemium combines two words: “free” and “premium.” It’s a pricing strategy that offers users both free and premium (paid) versions of a product or service. At its core, a freemium model allows users to use a basic version of a product without any cost while offering a premium version with additional features or enhanced functionality for a fee.Here’s a breakdown of the critical components of a freemium model:      Free Access:The “free” part of freemium is about providing value to users at no initial cost. The free offering could be a limited version of a software application, a restricted feature set, or usage with certain limitations. The goal is to attract a broad user base by removing financial barriers.        Premium Features:In addition to the free offering, a freemium model includes a “premium” tier. Users can upgrade to this tier by paying a subscription fee or a one-time purchase. The premium tier typically unlocks advanced features, removes usage limitations, and enhances the overall user experience.        Value Proposition:To succeed with a freemium model, providing a clear and compelling value proposition for the premium tier is essential. Users must see a tangible upgrade benefit: access to advanced tools, improved functionality, or a seamless experience.        Upselling:The freemium model is not just about offering free services; it’s also a strategic approach to converting free users into paying customers. By showcasing the value of the premium tier, businesses aim to upsell and increase their revenue.        User Base Expansion:The free tier acts as a user acquisition channel, allowing businesses to reach a broader audience. Users who sign up for the free version become potential customers for the premium offering.        Flexibility:One of the strengths of the freemium model is its flexibility. Businesses can adjust the balance between free and premium features, change pricing, or introduce new offerings based on user feedback and market dynamics.  Many businesses have successfully employed Freemium models, from software companies and mobile apps to streaming services and online publications. They offer an effective way to build a user base, nurture user engagement, and drive revenue growth.Implementing a Freemium ModelWhen implementing a freemium model for API monetization, things are pretty simple. If you have a more complex freemium offering, things could be more advanced to implement. For today’s example, though, we will keep things simple. You’ll need to leverage Moesif and Stripe (or an alternative billing provider) to create a freemium model. Here’s a breakdown of implementing a straightforward Freemium model for your APIs.In Stripe/Moesif’s Product Catalog, you will need to:  Create a product.  For that product, create a price of $0, your free offering.  Add additional pricing for your premium tier(s) for the product.In Moesif, you will need to:  Create a Governance Rule to enforce the free-tier usage allowance.  Create a Governance Rule to enforce any limits on your premium tier(s).You could also set up a Moesif Billing Meter for your premium tier if specific endpoints available on the premium tier will be metered. You may also set up a meter to charge per user on the premium tier. These are just two of many possible ways to augment your premium offering from a billing perspective.To illustrate a simple example in more detail, let’s look at how we could set up a freemium plan where a certain amount of API calls per day is allowed on the free plan and premium users have unlimited access.First, we need to set up the free price. This would look like this in the Moesif Product Catalog (or in Stripe), with us setting the charged amount as $0 per unit.Next, we would do the same for the premium pricing, setting the charged amount as $10 per unit (our monthly subscription fee for premium).Lastly, we will set up a governance rule in Moesif that will limit companies/users to 1000 API calls in 24 hours. We will first create a company cohort that will look like this.Then, we will use this in the governance rule, ensuring that the rule is blocking and that our response status and body reflect an accurate message. The complete governance rule will look like this.Now, if your premium tier also had some limitations, the number of API calls a user can make, for example. You could also implement another governance rule to ensure you’ve managed that quota. In this case, we will leave this simple setup as is.With this implemented, we now have a simple freemium model where free-tier users can only use 1000 API calls in 24 hours while premium users have unlimited resource access.Try Out Moesif For YourselfWant to try this out for yourself? Implement your Freemium monetization model with Moesif and Stripe and be monetization-ready in minutes. You can try this out by signing up for a free trial of Moesif. Have deeper questions and want to know how to scale out an enterprise-grade monetization solution? Chat with our team of API monetization experts to get started today!                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Unlock revenue potential and widen user engagement with Moesif’s API Monetization features            Start your journey towards profitable API solutions - Adopt the Freemium Model with Moesif today!            Try for Free            No credit card required            ",
          "url": " /api-monetization/Implementing-a-Freemium-Model-for-API-Monetization/",
          "author": "Matthew",
          "categories": "API-Monetization"
        }
      
    ,
  
    
        "api-analytics-api-strategy-api-analytics": {
          "title": "What is API Analytics? - The Ultimate Guide",
          "content"	 : "IntroductionIn the rapidly evolving digital landscape, the role of APIs (Application Programming Interfaces) has become increasingly pivotal. APIs act as the building blocks of modern software, enabling different applications and systems to communicate and exchange data seamlessly. However, as the reliance on APIs grows, so does the complexity of managing and understanding their performance and usage. This is where API Analytics emerges as a critical tool, transforming raw data into meaningful insights.API Analytics is more than just a technical necessity; it’s a strategic asset that offers a window into the interactions between applications, services, and users. By analyzing API usage, businesses can glean insights into how their services are being consumed, which features are most popular, and where there might be issues or opportunities for optimization. This depth of insight is essential for businesses striving to deliver flawless digital experiences and sustain their competitive advantage in the marketplace.The importance of API Analytics extends beyond mere operational efficiency. In a time when making decisions based on data is crucial, API Analytics supplies the essential information and insights needed for shaping business strategies. It enables organizations to comprehend customer behaviors, recognize market trends, and adapt their services to meet changing demands. This is particularly vital in sectors like e-commerce, fintech, healthcare, and any industry where digital interaction with customers is key.Moreover, API Analytics plays a significant role in the technical health of an application or platform. It helps in identifying bottlenecks, understanding the root causes of issues, and ensuring that APIs are scalable and reliable. This is essential in maintaining high uptime and delivering a consistent user experience, which are critical factors in customer satisfaction and retention.In this detailed guide, we will dive into the many facets of API Analytics. We will explore its workings, the differences between API Analytics and simple monitoring, and why it is indispensable in the modern API ecosystem. We will also discuss the benefits of API Analytics, the types of reporting available, key metrics to track, and how tools like Moesif can revolutionize your approach to API management. By the end of this guide, you will have a thorough understanding of API Analytics and why it is an essential component of any digital strategy.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        What is API Analytics?API Analytics transcends the basic tracking of API usage, evolving into a strategic tool that offers deep insights into how APIs are utilized and their impact on business operations. This procedure entails an in-depth examination of a range of metrics, including the frequency of API calls, response durations, error frequencies, and patterns of user engagement. Unlike basic API monitoring, which primarily focuses on operational aspects like uptime and performance, API Analytics delves into the nuances of user interactions and API efficiency.This deeper analysis allows organizations to understand not just how their APIs are performing, but also how they are being used. It uncovers trends in user behavior, preferences, and areas of difficulty. For instance, by analyzing the frequency and nature of API calls, businesses can identify which features are most popular or which endpoints are prone to issues. This level of insight is invaluable for optimizing API design, improving user experience, and making informed decisions about future development.Moreover, API Analytics helps in identifying potential bottlenecks and inefficiencies in the API infrastructure. By understanding the root causes of issues such as high error rates or slow response times, organizations can take proactive measures to enhance their API performance. This not only improves the reliability and scalability of the APIs but also aligns them more closely with the overall business strategy, ensuring that they continue to meet evolving user needs and market demands.How does API Analytics work?API Analytics operates by seamlessly integrating with an organization’s API infrastructure, enabling the systematic collection of data from every API call. This integration is designed to be non-intrusive, ensuring that the normal operation of APIs is not disrupted while comprehensive data is gathered. The collected data encompasses a wide array of information, including but not limited to, request and response headers, payloads, authentication methods, and the frequency of API calls. This data forms the foundation upon which API Analytics builds its insights.After data collection, it is subjected to a thorough and detailed analytical process. Tools used in API Analytics are equipped to handle vast amounts of data, sorting and analyzing it to construct a detailed picture of the API landscape. This analysis is not just limited to basic metrics; advanced analytics solutions employ sophisticated techniques like machine learning algorithms. These algorithms have the ability to detect patterns and trends that may not be instantly recognizable. They can predict future usage trends, detect anomalies that could indicate issues like security breaches or system failures, and provide insights into user behavior. This level of analysis transforms raw data into actionable insights, enabling businesses to optimize their APIs and align them more closely with their strategic goals.What is the difference between API Analytics and monitoring?The distinction between API monitoring and API Analytics, though subtle, is significant. API monitoring is primarily concerned with the operational health of APIs. It focuses on ensuring that APIs are functional and accessible, monitoring aspects like uptime, response time, and error rates. This form of monitoring is essential for maintaining the basic reliability and availability of APIs, acting as a first line of defense against service disruptions and performance degradation.API Analytics, however, extends far beyond these operational metrics. While API monitoring tells you if your APIs are operational, API Analytics explains how they are being used and their impact on business outcomes. It delves into the nuances of API performance and usage patterns, providing a deeper understanding of the ‘why’ behind the data. For example, API Analytics can reveal why certain APIs are more frequently used, offering insights into user preferences and behavior. It can also identify why some requests result in errors, which is crucial for improving API design and user experience. This level of insight is invaluable for strategic decision-making, allowing organizations to optimize their APIs not just for performance, but also for better alignment with business goals and user needs. In essence, while API monitoring ensures that APIs are running smoothly, API Analytics provides the insights needed to make them more effective and aligned with broader business objectives.Why Use API Analytics?API Analytics is crucial for businesses that depend on APIs, as it provides deep insights into customer behavior and API performance, directly impacting the success of digital strategies. By analyzing API usage data, companies can discern trends and user preferences, enabling them to anticipate needs and refine their services for better user engagement. This level of understanding is key in optimizing API functionality and ensuring that these crucial tools are not just operational, but also effective and aligned with business objectives.Beyond performance optimization, API Analytics plays a vital role in security. It helps in identifying anomalous patterns that could signal potential breaches, allowing for timely interventions to safeguard data and maintain user trust. In an era where data security is paramount, having a robust system to monitor and analyze API usage is indispensable for any business relying on digital interactions. This makes API Analytics an essential component in both strategic planning and operational security for modern enterprises.Benefits of API AnalyticsAPI Analytics provides a wide array of advantages essential for the proficient and successful administration of digital services. One of the primary advantages is the facilitation of better decision-making. By providing a clear view of how APIs are used and their performance metrics, businesses can make informed decisions about where to allocate resources, how to improve services, and when to introduce new features. This data-driven approach leads to enhanced customer experiences, as companies can tailor their APIs to meet user needs more precisely, ensuring smoother and more satisfying interactions.Furthermore, API Analytics contributes significantly to the technical health of API infrastructures. It enables businesses to monitor and improve API performance proactively, ensuring high availability and reliability. This is vital for preserving the trust and satisfaction of users. In terms of operational management, API Analytics is invaluable for capacity planning, helping businesses to identify when to scale their API infrastructure to meet demand. Additionally, it supports compliance and governance efforts by providing detailed logs and usage patterns, which are essential for auditing and regulatory purposes. This comprehensive overview ensures that businesses can maintain high standards of operation while continuing to innovate and grow.Types of API Analytics ReportingAPI Analytics reporting is diverse, catering to various needs and objectives of an organization. One of the fundamental types is the usage report, which offers insights into how frequently and in what ways the APIs are being accessed. This type of reporting is crucial for understanding user engagement and identifying popular or underutilized APIs. Performance reports are another key type, focusing on the efficiency and speed of API responses. These reports are essential for ensuring that APIs meet the expected standards of performance and for pinpointing areas that require optimization.Error reports and user behavior reports are also integral to API Analytics. Error reports help in identifying and diagnosing issues within the API infrastructure, providing a clear picture of any faults or failures that could impact user experience. User behavior reports, on the other hand, delve into how users interact with the APIs, revealing patterns and trends in usage. This information is invaluable for tailoring APIs to better suit user needs and for making strategic decisions about API development and enhancement. Together, these varied types of reports form a comprehensive toolkit for businesses to monitor, analyze, and improve their API ecosystem.API Analytics MetricsIn the realm of API Analytics, several key metrics stand out for their importance in assessing API performance and usage. The number of API calls is a fundamental metric, offering a basic yet crucial insight into the overall usage and demand of the APIs. Tracking this metric helps in understanding the scale of API interactions and is essential for capacity planning and scalability assessments. Response times are equally important, as they directly impact user experience. Fast response times are often synonymous with efficient performance, whereas slower times can indicate underlying issues that need addressing.Error rates and user adoption rates are also critical metrics in API Analytics. Monitoring error rates is vital for maintaining the reliability and integrity of APIs, as high error rates can lead to user dissatisfaction and potential service disruptions. User adoption rates, on the other hand, provide insights into how well new or existing APIs are being received by users. This measure is especially valuable in assessing the effectiveness of newly implemented APIs or modifications to current ones. Lastly, API throughput, which measures the amount of data processed by the APIs over a given period, is crucial for understanding the load and stress on the API infrastructure. Together, these metrics provide a comprehensive overview, aiding in pinpointing strengths and areas for improvement in API management.How Moesif can help you with API AnalyticsMoesif stands out as a prominent solution in the API Analytics space, offering a suite of advanced features tailored to enhance API management and strategy. One of its key capabilities is real-time monitoring, which allows businesses to track API performance as it happens. This immediate feedback is crucial for quickly identifying and addressing issues, ensuring minimal impact on user experience. Additionally, Moesif’s user behavior tracking feature offers a deep dive into how end-users interact with APIs. This understanding is priceless for grasping user requirements and inclinations, allowing businesses to base their decisions on data to fine-tune their API approaches.Furthermore, Moesif’s customizable dashboards bring a new level of convenience and efficiency to API Analytics. These dashboards can be tailored to display the most relevant data for a business’s specific needs, allowing teams to quickly access and interpret API performance metrics and user engagement data. This customization ensures that stakeholders can focus on the insights that matter most to their roles and objectives. By simplifying the complexity of API data, Moesif empowers businesses with actionable insights, facilitating informed decisions that can spur business growth and significantly enhance the overall user experience.ConclusionAPI Analytics transcends being merely a tool; it represents a strategic asset indispensable for businesses that leverage APIs as a cornerstone of their digital infrastructure. It offers vital insights that are key to refining API performance, elevating the user experience, and ensuring that API initiatives are in harmony with overarching business objectives. In an era where APIs are fundamental to the seamless operation of digital services, the insights gleaned from API Analytics become critical in guiding strategic decisions, from technical enhancements to market positioning.As the digital landscape continues to evolve, with APIs playing a central role in this transformation, the significance of API Analytics is set to increase exponentially. It’s not just about maintaining operational efficiency; it’s about harnessing the power of data to drive innovation and competitive advantage. API Analytics, therefore, is poised to remain a crucial element in the technological toolkit of forward-thinking organizations, helping them navigate the complexities of the digital world and capitalize on the opportunities it presents.As we’ve explored the critical role of API Analytics in shaping business strategies and enhancing user experiences, it’s clear that tools like Moesif are pivotal in this journey. Moesif offers an unparalleled ability to decode the complexities of API data, providing real-time insights, user behavior analysis, and customizable dashboards tailored to your specific needs. These features empower businesses to not only understand but also act on API data effectively. To truly harness the power of API Analytics and elevate your digital strategy, consider exploring Moesif further. Start optimizing your API performance by signing up today.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Harness the power of data with Moesif’s API Analytics to drive your digital strategy and optimize user experiences            Unlock actionable insights and elevate your API performance - Start with Moesif’s API Analytics today!            Try for Free            No credit card required            ",
          "url": " /api-analytics/api-strategy/API-Analytics/",
          "author": "Dylan",
          "categories": "API-Analytics, API-Strategy"
        }
      
    ,
  
    
        "api-analytics-api-strategy-8-best-api-observability-tools-in-2024": {
          "title": "8 Best API Observability Tools in 2024",
          "content"	 : "IntroductionWith our ever-increasing reliance on APIs, the need for effective management and monitoring of their performance has become crucial. This is where the concept of API observability comes into play. For anyone using APIs, this has become a topic that’s gaining significant traction.In this blog, we will explore the world of API observability. Whether you’re a developer deeply entrenched in APIs, a project manager overseeing digital products, or someone curious about the behind-the-scenes of digital services, API observability can bring many benefits, ranging from simple to more advanced. We’ll start by breaking down the basics: what exactly is an API, and why are they vital in the modern digital landscape? Understanding this foundation is critical to appreciating the significance of API observability. From there, we will move into the core concepts of API observability, exploring what it is, how it works, and why it’s a game-changer for businesses building and selling their APIs.As we navigate the nuances of API observability, we’ll also guide you through the factors to consider when choosing an API observability tool. You’ll see that the market is flooded with many tools, each promising to be the solution to your API monitoring needs. We’ll look at these tools, discussing their features, strengths, and how they compare against each other.By the end of this blog, you’ll have a comprehensive understanding of API observability and be equipped with the knowledge to select the right tools and practices for your needs. Whether you’re just starting with APIs or looking to refine your existing strategies, this blog will bring you closer to understanding and implementing API observability.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        What is an API?Although many readers will know what an API is, there may be some that do not. Let’s start by quickly going over what an API is from a few different angles. In the most simplistic sense, an Application Programming Interface (API) is a crucial component in software development. APIs provide a set of protocols and tools for building and integrating software applications. APIs serve as the medium through which different software components interact, allowing them to communicate and exchange data from one system to another.A More Technical PerspectiveTo understand APIs from a technical standpoint, consider them as contracts between different software systems, defining how they should interact. Each API has a set of defined rules and specifications that govern how data is exchanged and processed. For instance, when a software application needs to retrieve data from a database or another service, it calls an API with a specific request. The API then processes this request, interacts with the necessary systems, and returns the appropriate response.Types of APIsAlthough we will mainly focus on Web APIs, there are a few different types that APIs can be classified into. Generally, APIs can be categorized based on their use cases and accessibility. Here are a few of the high-level types.      Web APIs: These are designed to be accessible over the network, typically using HTTP/HTTPS protocols. They are widely used for web services and cloud-based applications, enabling them to communicate online.        Library APIs: Found in software libraries, these APIs provide a set of routines and functions that developers can call upon, streamlining the development process by reusing code.        Operating System APIs: These are provided by the operating systems to allow applications to perform operations like accessing files, handling user inputs, and managing hardware resources.  The Role of APIs in Software DevelopmentRegardless of the API type, companies use APIs for familiar reasons. APIs have become integral to modern software development, facilitating various critical functions for modern applications. These functions include:      Facilitating Integration: APIs are the key to connecting different software systems, enabling them to work together seamlessly.        Promoting Efficiency: By providing pre-built functions and routines, APIs reduce the need to write code from scratch, speeding up the development process.        Enabling Scalability: APIs offer a modular approach to building software, allowing developers to add new features or scale existing ones without extensive modifications.        Driving Innovation: APIs open up possibilities for collaboration and innovation, allowing businesses to leverage existing functionalities to create new services and solutions.  Understanding APIs is essential to understanding modern software development. This foundational knowledge sets the stage for exploring API observability, which is crucial in ensuring the reliability and performance of these vital components of modern software systems.What is API Observability?API observability is an advanced type of monitoring for APIs. Beyond just looking at the basics of API call volume and latency, observability focuses on gaining deep insights into the behavior and performance of APIs. It transcends traditional monitoring by tracking the operational status of APIs and providing a comprehensive understanding of how they interact within a system and with end-users.Beyond Simple MonitoringWhile traditional monitoring might tell you if an API is up or down, observability dives deeper. API observability platforms offer a holistic view of the API’s performance, including response times, error rates, and overall throughput. It’s about understanding the “why” behind the performance metrics. Observability helps identify patterns, detect anomalies, and understand the root causes of issues affecting API performance and, consequently, the end-user experience.Key Components of API ObservabilityAPI observability incorporates several key components to provide a thorough insight into API performance. By considering these different components, API observability tools can generate more profound insights than looking at them independently. Let’s look at these three components in more detail.      Logs: These are detailed records of events within the API. Logs can provide context around requests, responses, and any errors that may have occurred. Most API frameworks have mechanisms to write API logs, often used for debugging and understanding more about the data within an API request or response.        Metrics: Quantitative data such as response times, request counts, and error rates. These are crucial for assessing the health and performance of the API at a glance.        Traces: Traces provide a detailed journey of a request through the API, showing the path taken and where delays or errors may have occurred. This is particularly important in microservices architecture, where a single request can travel through multiple services.  By combining all of these data points in a single platform, API observability can let you dive further and search for answers to questions regarding your APis from many different angles.The Importance of Observability in Modern Software SystemsAPI observability is critical in today’s complex software environments, particularly those utilizing microservices architectures. For organizations looking to take a modern approach to optimizing and managing their APIs, API observability helps in:      Problem Detection and Diagnosis: Quickly identifying and diagnosing issues in APIs to prevent or minimize impact on the user experience.        Performance Optimization: Continuously monitoring API performance helps optimize it for better efficiency and reliability.        Data-Driven Decision Making: The insights gained from observability enable informed decisions about system improvements and resource allocation.        Enhancing User Experience: Observability contributes to a seamless and satisfying end-user experience by ensuring APIs operate efficiently and reliably.  API observability is thus an indispensable tool in the arsenal of modern software development, offering a detailed and nuanced view of API performance. This enhanced visibility is critical to managing the complexities of today’s digital services and ensuring they meet the high standards expected by users.How does API Observability work?API observability, exemplified by tools like Moesif, is about comprehensively understanding API performance and user interactions. The inner workings of an API observability tool include several key activities. Let’s take a look at each of them below.Understanding API Usage and Customer BehaviorAPI observability tools help understand how APIs are used and how users interact. This deep analysis of API usage patterns and customer behaviors is crucial for identifying usage trends and potential issues.Debugging Issues QuicklyTools like Moesif allow for the exploration and analysis of API calls, including details like headers and body fields. This capability enables quick debugging of issues, allowing teams to analyze and rectify problems efficiently.Monitoring Customer ExperienceA critical aspect of API observability is monitoring the customer experience. This includes receiving alerts when customers encounter issues with an API, ensuring that any negative impact on the user experience can be addressed quickly.High-Cardinality AnalyticsSpecific observability platforms, like Moesif, also provide high-cardinality analytics. This allows users to segment and aggregate vast amounts of API call data. This feature allows for detailed inspection of API logs and the ability to replay requests, helping to understand specific user experiences and identify abnormal patterns.Real-Time Alerts and MonitoringAPI observability tools offer real-time alerts for performance issues and anomalies. Depending on the platform, this usually includes delivering alerts via email or SMS to team members. Platforms like Moesif also support integrating with platforms like PagerDuty and Slack. This ensures that teams are promptly informed of any issues, allowing immediate response—additionally, user behavior analytics aid in monitoring customer experience and identifying security issues.As we can see, API observability provides an in-depth analysis of API usage, rapid debugging capabilities, customer experience monitoring, and real-time alerting systems. These features collectively ensure that APIs perform optimally and deliver a positive user experience to users consuming the APIs.Factors to Consider When Choosing an API Observability ToolBefore we look at some of the most popular API observability tools, it’s important to distinguish what factors and features you should be looking for. To find the right tool for your needs, certain factors must be considered when assessing various API observability tools. Below are a few key areas to consider when assessing potential tools.Integration CapabilitiesThe right API observability tool should effortlessly mesh with your existing technological ecosystem. This means it should be compatible with various programming languages and frameworks you use and offer easy plug-and-play functionality with your current cloud services, databases, and monitoring tools. Extending functionality through plugins or dedicated APIs is also crucial, as it allows for a more tailored fit to your specific needs.Data GranularityA tool that provides high data granularity is invaluable. It enables precise troubleshooting, thorough analysis of API usage patterns, and optimization of API performance. Furthermore, the capability to define and track custom metrics tailored to your business or technical requirements can provide deeper insights and more meaningful data interpretation.Real-Time AnalyticsReal-time analytics are crucial for immediate insights and swift decision-making. Tools that offer live, constantly updating dashboards enable you to keep a pulse on your API’s health and performance, making it easier to spot and rectify issues as they arise. This also plays a factor in our next point, supporting real-time alerting.Alerting and Notification SystemAn effective API observability tool should have a robust alerting system for creating customizable alerts based on specific conditions. Optimally, these alerts could monitor and detect certain conditions in real-time. The ability to send notifications through various channels, such as email, SMS, or platforms like Slack, ensures that critical alerts reach the right people quickly.ScalabilityIt’s essential to choose a tool that can grow with your business. The tool should handle increased API traffic and data volume effectively, ensuring consistent performance. It’s also important to consider how adaptable the tool is to future API strategy or infrastructure changes.User Experience MonitoringUnderstanding the impact of API performance on the end-user experience is critical. The tool should provide insights into how users interact with your APIs, including usage patterns and behaviors, to help identify potential improvements or optimizations. Tools like Moesif take this a step further by allowing for interactions outside of the API (such as frontend UI interactions) to be factored into user experience monitoring.Security FeaturesSecurity is a non-negotiable aspect of API management. The tool you chose should include features to monitor for potential security threats and ensure compliance with relevant standards and regulations. Features like Moesif’s Governance Rules can help with these requirements and augment security beyond what’s available at the API gateway level.Customization and FlexibilityThe ability to customize dashboards and reports is vital. A tool that allows you to tailor monitoring and analytics to your specific needs can provide more relevant and actionable insights. This customization should extend to creating reports and alerts that meet the varied needs of different stakeholders within your organization. Another factor is to ensure that customization and flexibility are included by default, not only on certain pricing tiers.Cost-EffectivenessAssessing the cost-effectiveness of a tool involves evaluating the return on investment, considering both the direct and indirect benefits it offers. Understanding the tool’s pricing model and how it scales with your usage can help you make a more informed decision. You don’t want to have a tool where the introductory price is enticing, but cost scales poorly as usage is ramped up.Support and DocumentationLastly, consider the level of support and documentation provided. Responsive customer support can be invaluable for troubleshooting, while comprehensive documentation is crucial for effective onboarding and ongoing tool use. The difference between a good and a great tool is often defined by the support the tool offers and other resources that can help utilize the tool to its full potential.Best API Monitoring and Observability ToolsNow that we have covered all of the basics and critical factors of API observability, we can dive into the potential solutions available. In the dynamic world of building, managing, and selling APIs, selecting the right observability tool is critical for ensuring optimal performance and user satisfaction. These tools offer a variety of features ranging from real-time monitoring to deep analytics, each tailored to different aspects of API observability. Here’s an extensive overview of some of the top tools in the market, considering their features, advantages, and potential drawbacks.MoesifMoesif is a powerful tool for user-centric API observability, providing in-depth insights into API usage and customer interactions. Some highlights of Moesif include its advanced user behavior analytics, high-cardinality data analysis, real-time event monitoring and alerting systems, and the ability to monetize APIs through its analytics engine.Pros:  Detailed analysis of user data for targeted insights  Effective debugging with granular data examination  Customizable alerts for various operational metricsCons:  Complexity of features can be daunting for beginners  The extensive data provided may be overwhelming for some usersPostmanPostman, a tool many developers will be familiar with for API testing, also offers robust monitoring capabilities. Highlights of Postman include its intuitive user interface, a comprehensive set of testing and monitoring tools, and collaborative features for team development.Pros:  User-friendly for both beginners and advanced users  Extensive testing and monitoring functionalities  Supports collaborative workflows, enhancing team productivityCons:  Large data sets can affect performance  Advanced features may require a subscriptionSaucelabsSaucelabs focuses on automated testing and monitoring, including API observability. The platform highlights include integration with various Continuous Integration/Continuous Deployment (CI/CD) tools, automated and live testing capabilities, and multi-language support for various development environments.Pros:  Emphasis on automation for efficient workflows  Wide range of integration options with other tools  Comprehensive testing and monitoring solutionsCons:  Higher cost, which may be prohibitive for smaller teams  Some features may have a steep learning curveBetter UptimeBetter Uptime combines API monitoring with robust incident management features. The platform highlights include integrated incident management, real-time alerts, customizable status pages, and detailed escalation policies.Pros:  Strong focus on incident response and management  Intuitive user interface for ease of use  Flexible notification and alerting optionsCons:  More focused on uptime monitoring, may lack some deep analytics features  Incident management features may be excessive for users only seeking basic API monitoringAPI ToolkitAPI Toolkit specializes in API traffic management and observability. Although it is a higher-level tool compared to other API observability platforms, it does have a lot of features outside of observability that can also be helpful. The platform highlights include traffic management and analysis, detailed API usage insights, and performance and reliability monitoring for your APIs.Pros:  Tailored for API traffic management and analysis  Provides essential insights into API usage patterns  Focuses on performance and reliabilityCons:  May not offer as extensive analytics as some other tools  Limited in terms of integration with external platformsRapidAPIMore popular as an API marketplace, RapidAPI can also offer certain observability features for APIs running through the platform. At its core, RapidAPI is a platform for exposing and connecting users to various APIs offered through the marketplace. The platform highlights include access to a vast array of APIs, monitoring features for API performance, and a developer-friendly interface and community.Pros:  Wide range of accessible APIs  Useful for both consuming and monitoring APIs  Strong developer community for support and resourcesCons:  Primarily a marketplace for APIs, with monitoring as a secondary feature  Some features might require a subscriptionDatadogOne of the most well-known application performance monitoring tools available, Datadog offers a comprehensive monitoring platform that includes extensive features for API observability. The platform highlights include detailed performance tracking and analytics, integration with various platforms and services, and advanced alerting and dashboard capabilities.Pros:  In-depth performance analysis tools  Highly customizable dashboards and alerts  Broad integration capabilitiesCons:  Can be complex and overwhelming for beginners  Higher price point compared to some other optionsTreblleTreblle focuses on simplifying API observability, offering easy-to-use monitoring and debugging features. A comprehensive platform, Treblle offers a lot of proprietary tech to allow developers and companies to create better APIs. The platform highlights include simplified monitoring and debugging, real-time tracking and analytics, and a user-friendly interface.Pros:  Streamlines the API monitoring process  Great for quick setup and ease of use  Useful for real-time problem detection and resolutionCons:  May lack some advanced analytics and customization options  Best suited for smaller to medium-sized projectsEach tool above offers unique features and benefits for different API observability needs. Your choice will depend on factors like the complexity of your APIs, the level of detail required, integration needs, and budget considerations. Considering the points we spoke on earlier around what to look for in a solution, you should be able to quickly narrow down the best platform for your needs.ConclusionAPI observability is vital in the modern digital landscape, ensuring that APIs function efficiently and effectively. In this blog, we’ve explored the essence of APIs, delved into the intricacies of API observability, and discussed how it works, highlighting its significance in monitoring and improving API performance. We also covered essential factors when choosing an API observability tool, emphasizing integration, data granularity, real-time analytics, and more. Finally, we provided an extensive overview of some of the best API monitoring and observability tools available, like Moesif, Postman, and Saucelabs, each with unique strengths and limitations. With this information, you’re well on your way to finding the best API observability tool for your use case.Want to explore the most comprehensive observability and monetization platform for your APIs? Give Moesif a try to explore features that go above and beyond basic observability and monitoring, including billing meters, behavioral emails, and governance rules. To get started today, sign up for a free trial of Moesif and begin exploring your APIs in a matter of minutes. Do you have other questions and want to chat with our team of API experts? Contact us today!                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Unlock the full potential of your APIs with Moesif's cutting-edge observability            Ensure your APIs meet modern demands - Discover and implement leading observability solutions with Moesif today!            Try for Free            No credit card required            ",
          "url": " /api-analytics/api-strategy/8-Best-API-Observability-Tools-in-2024/",
          "author": "Matthew",
          "categories": "API-Analytics, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-volume-based-pricing": {
          "title": "Apigee Versus Moesif for Volume-Based Pricing",
          "content"	 : "APIs are ubiquitous in the modern business landscape, connecting business to resources, customers, and partners. These APIs require effective management, and this often carries a cost – in such cases, these costs are often passed through to the consumer in the form of a pricing model. Volumetric pricing, that is, pricing depending on the volume of resources used in the specific API instance, has become a popular way for businesses to balance their need for growth and scalability while reducing overall overhead.In this piece, we’re going to look at two management platforms, Apigee and Moesif, and compare their business models for volume based pricing. We’ll look at how each implements this in practice and compare how they handle specific use cases differently.                Implement Volume-Based Pricing with Moesif              14 day free trial. No credit card required.              Try for Free        What is Apigee?Apigee is a comprehensive API management platform which was acquired by Google in 2016. It offers a suite of tools to help organizations build, deploy, and manage APIs at scale. Apigee’s core services range from proxy creation to traffic segmentation, with additional toolsets offering governance, security, and collaboration tools. Apigee offers monetization with tiered pricing and usage based pricing, giving users the option to decide their optimal pricing structure and pricing strategy. What is Moesif?What is Moesif?Moesif is a full-featured API analytics and monitoring platform built to unlock business success through insights and context. Moesif provides real-time analytics and insights, offering major capabilities in troubleshooting, error detection and correction, and effective monetization. Building upon its deep analytics capabilities, Moesif specializes in monetization strategies as a business model. Moesif provides substantial support across a variety of monetization models, from customized tiered pricing to value based pricing, and usage or quantity based pricing. Moesif allows organizations to price their API products according to the perceived value of their loyal customers.What is Volume-Based PricingVolume-based pricing, also called volumetric monetization, is an approach in which the customer is charged based upon their API usage metrics. The number of API calls made, the amount of data transferred resulting from these calls, and even the number of endpoints utilized can all be used as criteria for this billing. Regardless of the criteria, this chargeable element is often referred to as a “unit”, and is best thought of in the same way you utilize water or electricity – in the same way you can be charged for Watt Hours or Liters of water, you can be charged for specific units of API resources. Volume based transaction pricing can lead organizations to volume discounting, which can be optimized with user analytics. By offering a volume pricing model with a volume discounting tier, you can offer a discounted price for loyal customers, enabling brand loyalty and reducing churn.Volume-Based Pricing in ApigeeApigee utilizes a system called a Rate Plan to set a usage based billing strategy. This allows developers to specific a specific variable that can be charged based on a few customizable parameters.Creating a Rate PlanTo start, developers must create their rate plan.  Navigate to apigee.com/edge once logged into your account.  On this page, in the left navigation bar, select “Publish > Monetization > Rate Plans”.  On this page, in the upper right corner, you will see a blue bar that says “+ Rate Plan”. Click on this to enter the rate plan creation page.Define Your Rate PlanFrom here, you will define the base elements of your rate plan. You will need to configure the following fields:  ‘’’Rate plan name’’’ – this will allow you to set a custom name for your rate plan.  ‘’’Rate plan type’’’ – this type will allow you to set the monetization strategy for the rate plan. To enable volumetric monetization, select “Rate card”.  ‘’’Product bundle’’’ – select the API product bundle that you want to enable volume-based pricing for.Additional fields are option, and allow you to set a start and end date amongst other attributes.Set the Rate PlanIn order to set the plan as currently configured, you will need to click one of two options:  “Save as Draft” will save the rate plan without making it live.  “Publish New Plan” allows you to publish it as it currently is.It’s advisable to simply save as a draft at this point as you still need to configure the volume pricing – to this end, select “Save as Draft”.Define Volume PricingAt this point, you will need to configure the actual volume pricing from a few specific options.  ‘’’Flat Rate’’’ – flat rate allows you to set a fixed rate for each transaction.  ‘’’Volume Banded’’’ – volume banded allows you to charge a different price depending on the total volume of transactions.  ‘’’Bundles’’’ – bundles allow you to define batches of transactions, e.g. “1,000 transactions”, which are charged up front to the end user. This is a bulk purchase.Enable ModelNow that you have set the model, you can put it live. Return to your draft and click “Publish New Plan” to set it live.ConsiderationsA few considerations should be noted for this model. Firstly, this assumes that you are utilizing the Enterprise Edge solution for Apigee – the private cloud model does provide for volume-based pricing, but the implementation is quite a bit more complicated as it requires setting some specific values on the internal services to manage payment. Speaking of payment, this process also assumes you have a vendor configured such as Stripe – if you need to configure your payment vendor, you will need to directly work with Apigee to enable this, and on the Private Cloud product, you will need to internally connect your billing process to a third-party billing API.Volume-Based Pricing in MoesifMoesif makes it super easy to implement volume-based pricing.Create the PlanAfter logging into Moesif, users should navigate to “Product Catalog” in the left navigation pane. From here, click on “Plans”, then in the upper right, click “Create New”. Here, you will fill out a Plan Name field as well as select your billing provider. To keep things as simple as possible in this article, let’s go with Stripe in this scenario as well. Now that you have created the plan basics, click “Create” in the top right to commit.Create the PriceNext, you will need to create the pricing model in Moesif. Simply click on “Prices” in the left-pane “Product Catalog” area, and click “Create New” in the upper right hand corner. From here, add a Price Name and select “Linked Plan”. Select the plan you created in the first step, and toggle the Pricing Model field to “Volume Tiers”.From here, you can create volume bands utilizing the “Add Tier” button, setting specific volumetric triggers for monetization. You can additionally set different time frames or values for this volumetric billing – for instance, you can charge per month of use or per variable pay periods, which is especially useful when you are providing introductory rates or other variable volume pricing for a specific time period.Deploy the Billing MeterWith our pricing model set up, we now need a way to actually record usage and bill users. To do this, click the “Billing Meters” menu in the left pane, and click “Add Billing Meter” in the top right hand of the screen. In this view, you will be able to create a billing meter.Select a name for your meter, and then link it ot the plan and price under the section labeled “Link to”. After you have done this, select “Create” in the top right corner to activate the meter.That’s it! You are now running a volume-based pricing model!ComparisonIn a lot of ways, these implementations are very similar. Where they differ is in the friction to create each offering. Apigee provides a substantial amount of control, but in some cases this control comes with a lot of complexity – this is the case with volume-based pricing. Apigee might deliver greater control, but it takes more steps to get there, and for many people just trying to deploy a simple volumetric solution, this may be too convoluted.Moesif, on the other hand, makes this process incredibly simple and quick, and most users – even those who may not have the technical knowledge required to get into the details with Apigee – will be able to create volumetric bands and pricing models. Beyond an ease of use argument, this also means that users who are not necessarily technical, such as financial or business staff, can help in the maintenance and development of pricing strategies. The easy to use system unlocks greater collaboration and reduced friction in deployment, making Moesif a very strong offering for most use cases.                Implement Volume-Based Pricing with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Implement Volume-Based Pricing with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Volume-Based-Pricing/",
          "author": "Kristopher",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-tier-based-pricing": {
          "title": "Apigee Versus Moesif for Tier-Based Pricing",
          "content"	 : "Tier-based pricing is an effective strategy to monetize APIs based upon form and function. Let’s look at two methods for deploying tiered pricing.What is Apigee?Apigee is an API management platform that has been part of the Google ecosystem since it was acquired in 2016. It offers a range of tools for the creation, deployment, and managing of APIs, including tools focused on API proxies, traffic segmentation, governance, security, and monetization. Apigee can often be quite a complex system to utilize and deploy, but it is nonetheless quite powerful and feature-rich.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        What is Moesif?Moesif is an advanced API monitoring and insights solution. It provides real-time analytics and insights, and helps businesses unlock monetization at scale, identity and resolve issues across APIs, and improve ongoing operations. Moesif offers extensive support for a variety of monetization strategies, allowing businesses to develop their own business logic and approach to revenue generation. The product is known for being very user-friendly and simple to use, even with complex monetization strategies.What is Tier-Based PricingTier-based pricing  is a pricing strategy that allows businesses to set tiers of available resources, and then to charge based on the use of those tiers. For example, a product may offer multiple tier price points with different features associated. A “free” tier that provides base level functionality such as API logging in JSON, but support for custom data models or transformation may be locked behind a different tier, one that has more premium benefits with a higher price.Tier pricing allows businesses to provide ample systems for low or no cost to their customer base, which keeping more resource-intensive functions or specific business value behind a revenue generating tier barrier. By having different price point offerings, a tiered pricing structure can capture a larger audience than a traditional flat rate pricing model or subscription pricing strategy. A tiered pricing method can deliver unprecedented value at scale, especially when trying to balance costs with availability and extensibility.Tier-Based Pricing in ApigeeApigee allows you to implement Tier-Based plans through the use of their Product Bundles feature.In Apigee, each collection of APIs that is monetized as a whole is considered a single monetized container, and this container is referred to as an “API Product Bundle”. In essence, if you move behind an API Gateway and into a collection of microservices, you can start to see those services bundled into specific types – on the Apigee side, you can bundle these projects into groupings. For example, all of your User APIs could be bundled into a single collection, all of the Transformation APIs could similarly have their own bundle, and so forth.Navigate to the Bundles PageAssuming you have your APIs segmented into these groupings, you can enable Tier-Based pricing by utilizing the Product Bundles page. Navigate to the API Product Bundles page, and select “Publisj > Monetization > Product Bundles” in the left-hand navigation pane.Create Your BundleFrom here, click “+ API Product Bundle”. At this point, you will be asked to provide the name of the API product in the Add a Product field. You will want to select one of the API Products that you have internally added to Apigee. Click your product – if you have multiple API products that represent a collection, e.g. multiple microservices that, when bundled together, represent the “User” or “Product” segment of your offering, then you will want to select additional products at this time.Once you have selected your products, you will have to configure the transaction recording policy. This policy allows you to configure the various attributes for each product – while the Apigee documentation goes into this in greater detail, for now, you’ll just want to set the following fields:  ‘’’Transaction Success Criteria’’’ – the specific criteria that marks an event as monetizable.  ‘’’Status’’’ – the value that is used by the Transaction Success Criteria field.  ‘’’Use Optional Attributes’’’ – a toggle and optional field that allows you to set custom values in the monetization flow, such as reporting an Error Code or setting a Currency type.From here, select “Save Product Bundle” to enable.Tier-Based Pricing in MoesifMoesif makes it much easier to get started with a tiered pricing structure - just one of the many value-based pricing options available to deploy. Part of the reason the Apigee approach is so complex is that it bundles things together, which can make the approach quite unwieldy. Moesif, on the other hand, breaks things into a much cleaner set of operations. We’re going to use Stripe for this tiered pricing example.Define the Tiers in StripeFirst make sure to meet these prerequisites.Then follow these steps:  Go to your Stripe Dashboard.  Go to Product Catalog and then select Create product.  Select Recurring and then select More pricing options.  Select Recurring and choose Usage-based as the pricing model and choosethe usage amount as unit.  Define your price amounts.      In the Meter section, select the plus icon + to create a billing meter to associate the price with.    a. Enter the meter name.    b. Enter the event name for which the billing meter reports usage.    c. Select the aggregation method for meter events. Moesif supports the Sum and Last aggregation methods.    d. Expand Advanced Options and make sure that Event Time Window is set to Raw.    In the Advanced section, enter a description for the price.  Select Next.  Select Add another price and repeat the same steps to add more tiers.  After you finish defining your tiers, select Add product.After you’ve created your tiers, you should see that the Stripe Product catalog now contains your product and prices.Create the Billing Meter in MoesifNext, we will want to create a Billing Meter to track each tier. Log in to Moesif, and select “Billing Meters”. Here, click “Add Billing Meter”, and give the Billeting Meter a name for the tier you are creating. Under “Link to > Billing Provider”, select “Stripe”, then click the Product and Price connected to your tier.Next, create a “Filter” to set up an event trigger that will detect when a tier is utilized. Finally, create a “Metric” as “Event Count” to count each utilization and bill it as a single instance. Repeat these steps for each tier.Establish GovernanceIn theory, you’re done – that being said, you should also set up governance rules to ensure that your tiers are not broken out of.  By enforcing usage based pricing with your subscription pricing model, you can stop overuse of your API product and create up sell opportunities to your existing customers, giving them reason to convert to higher price tiers. To do this, you will first need to create a group of customer users that fall into a specific tier so that Moesif knows what to measure.To do this, head to the “Quotas & Governance” screen, and click “Add New”. Select “Start”. Now, click “Create New Company Cohort”, and enter the filter value under “Companies Where”. Set the Who Performed > Event Where parameters to match the filter in the Billing Meter, and set the Period to “Current Monthly Billing Period”. Click “Create Cohort”.Now that you have your cohort, you’ll need to make a Governance Rule to use it. Add a name for the Governance Rule in the name field, and Override Response Status and Override Response Body to what you want the user to see when they go beyond their tier. Once you have set this, click “Create/Save Cohort”. Set the rule to “ON”, and you’re good to go!ComparisonThe big gain in Moesif is in the way it handles the different pricing tiers and different levels of usage. With Apigee, you need to adopt a design-centric way of handling this tier – and in a lot of situations, that may result in a sprawl of microservices that requires more gateway implementations and complications in logging and monitoring. Moesif allows you instead to filter by specific functions and oversee the process that way, which is more in line with smaller or less complex API environments for a more targeted tiered pricing strategy. This also gives you an easier method to deploy your tiered pricing model rapidly without having to change the underlying API structures to get simple tier based pricing in the wild.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Tier-Based-Pricing/",
          "author": "Kristopher",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "technical-rest-api-tutorial": {
          "title": "REST API Tutorial: Build Your First API with Node.js (2026)",
          "content"	 : "REST is still the default way to build an API in 2026. GraphQL has a foothold for complex client-side data fetching, gRPC is common inside service meshes, and MCP is emerging for AI agent traffic, but if you are exposing a public API for outside developers to integrate with, a REST API over HTTP is what they expect.This tutorial walks through what REST actually means, the six constraints that define it, the HTTP methods you will use every day, and how to build a working REST API from scratch with Node.js and Express. By the end you will have an API running locally with GET, POST, PUT, and DELETE endpoints that you can extend into a real product.                Monitor REST APIs With Moesif              14 day free trial. No credit card required.              Try for Free        What is a REST API?A REST (Representational State Transfer) API is a mechanism that allows different software applications to communicate with each other over the internet or local network. REST APIs follow specific rules and standards that enable applications and users to use HTTP requests to access and use data. The data sent and received by a REST API is generally in the JSON (JavaScript Object Notation) format. Data sent is a request, and the data received from the API call is called the response. Two main characteristics of RESTful APIs:  Stateless interactions: Each request from a client to a server must contain all the information needed to understand and process the request. The server does not store any session information about the client.  Uniform interface: A REST API is designed to use standard HTTP methods and should be easy for any developer familiar with HTTP.Why use a REST API?Many different types of APIs are available; however, RESTful APIs have become the go-to technology for almost every API in production today. This is not by chance but due to the simplicity and scalability of implementing and supporting REST technologies. The sheer amount of frameworks and technologies available to develop and support REST APIs alone is an excellent advantage over other popular API technologies. The benefits REST APIs bring to developers:  Scalability: Due to their stateless nature, REST APIs can handle multiple types of calls, return different data formats, and even change structurally with the correct implementation of hypermedia.  Flexibility and portability: Data is not tied to resources or methods, allowing more flexibility in its representation. This makes REST APIs suitable for different types of applications.  Independence: The separation between client and server allows for development across various parts of an application to coincide with less dependency on each other.Key features of REST APIsREST APIs are built around six fundamental principles, sometimes called Roy Fielding’s REST constraints.  Uniform interface: This principle simplifies the architecture by establishing a standard communication method for clients and servers. This uniformity decouples clients from the server, allowing them to evolve independently as long as the interface remains consistent.  Client-server separation: The separation of concerns enhances the portability of the user interface across multiple platforms and improves scalability by simplifying the server components. The client and the server operate independently, and changes on one side do not directly impact the other.  Statelessness: In a stateless architecture, each client request must contain all the necessary information for the server to process it. The server does not store any state about the client session between requests, which simplifies design and increases reliability.  Cacheable: Making responses cacheable can significantly improve performance by reducing the need for clients to fetch the same data repeatedly. Proper caching management helps ensure clients have up-to-date information and reduces the load on the server.  Layered system: This principle allows an architecture to consist of hierarchical layers by restricting the behavior of components in each layer. Clients interact with a layer without knowing whether it is the end server or an intermediary. This adds flexibility and scalability to the system.  Code on demand (optional): This constraint allows servers to extend or customize the functionality of a client by sending executable code (such as JavaScript). It can reduce the client’s complexity by offloading some functionality to the server, but it is less common than the other constraints.REST API methodsMost developers are familiar with HTTP verbs. Because of this, understanding REST API methods is quite simple. REST APIs use the standard HTTP methods that developers (and most technical people) already know. Each method is intended to perform specific functions:  GET: Requests data from a specified resource. It should only retrieve data and should have no other effect.  POST: Sends data to the server for creation. It is often used when uploading a file or submitting a completed web form.  PUT: Updates all current representations of the target resource with the uploaded content.  DELETE: Removes the specified resource.  HEAD: Similar to GET, but only transfers the status line and the header section.  PATCH: Applies partial modifications to a resource.Each method corresponds to the CRUD (Create, Read, Update, Delete) operations in database management. For a full explainer on the codes the server returns alongside these methods, see our HTTP status codes guide.Advantages of REST APIsREST APIs are known for their simplicity and flexibility. They use standard HTTP methods that are universally understood and easy to work with. This universal approach to web communication allows for platform independence, making REST APIs compatible across different systems and languages.One of their key strengths is scalability, thanks to their stateless nature, which simplifies server architecture by not requiring the server to maintain a session state. REST APIs are also performant and efficient because responses can be cached to minimize data transfers. The portability and ease of integration of REST APIs facilitate the development of distributed systems and microservices, aided by massive communities and tooling support due to the widespread adoption of REST technologies.Challenges when using REST APIsREST APIs come with specific challenges. The statelessness of REST can lead to larger requests because all necessary data must be included in each one. This also creates issues like data overfetching and underfetching, which can impact performance: multiple requests may be needed to gather all data, or excessive data may be returned unnecessarily.Security is a potential challenge, requiring proper implementation of authentication and data transfer security practices to keep data secure. Versioning of REST APIs can be complex, with changes potentially breaking backward compatibility. At scale, REST APIs might experience performance bottlenecks due to HTTP/HTTPS overhead. The lack of a strict standard in REST implementation can also lead to inconsistencies across different APIs, which is why following API design principles matters.How to build a REST APINow that we have reviewed the basics, it is time to build one. The example below shows a straightforward implementation of a REST API with Node.js and Express. No actual CRUD operations are being done within the endpoints, but you can use this code as the starting point for those functionalities. Let’s start by setting up the environment and then build out a few endpoints.Step 1: Setting up the environment  Install Node.js: Ensure you have Node.js installed. You can download it from nodejs.org.  Initialize your project:          Create a directory for your project.      Navigate to this directory in your command line and run npm init to create a package.json file.      Step 2: Installing ExpressSince this API project will use Express, we must install it within our project using npm. To do this, run npm install express in your project directory. Once the command is complete, Express will be installed and ready for us to use.Step 3: Creating your server (server.js)Now, we will begin to implement the actual API code. First, we need to set up the basic infrastructure for our app. Create a file named server.js and add the following code:const express = require('express');const app = express();app.use(express.json()); // This middleware is used to parse JSON bodies.app.listen(3000, () => console.log('Server running on port 3000'));In the code above:  require('express') imports the Express module.  express() initializes a new Express application.  app.use(express.json()) is middleware to parse JSON request bodies.  app.listen(...) starts the server on port 3000.If we were to run the app right now, our service would be able to start, but we would have no API endpoints for users to use. Next, we will implement some endpoints.Step 4: Implementing RESTful endpointsWhen creating APIs, having multiple endpoints that achieve various tasks makes sense. We need to use the HTTP methods we covered earlier for CRUD APIs. The methods include GET, POST, PUT, and DELETE; each corresponds with a different CRUD operation. Below is an example of each type of endpoint. This code can be added inside our Express project after the app.use() statement.GET endpointFirst, we will implement the GET endpoint that fetches data from the server. The basic structure:app.get('/api/items', (req, res) => {  res.send('List of items');});In the app.get(...) statement, we define our GET route. When a GET request is made to /api/items, the callback function is executed. In the case above, we return a string using res.send(). In a more functional endpoint, you would go off to some resource (such as a database) and return real data.POST endpointNext, the POST endpoint, which contains the logic to add new data to the server:app.post('/api/items', (req, res) => {  const newItem = req.body; // Data sent in the request body.  res.send(`Item added: ${newItem.name}`);});The app.post(...) function handles POST requests. The req.body contains the data sent in the request and is sent back in the response. In a more functional endpoint, you would write some data to a database and return a confirmation (such as created: true or the ID of the newly created entity).PUT endpointNow, a PUT endpoint that will update existing data:app.put('/api/items/:id', (req, res) => {  const itemId = req.params.id; // Access URL parameter.  res.send(`Item with ID ${itemId} updated`);});The app.put(...) handles PUT requests. The ID of the resource to update is often passed as a query parameter or URI parameter. We use req.params.id to fetch the id parameter from the URL. In a real-life implementation, you would make a database call to update the resource, with data usually found in the request body, and then return a boolean stating whether the update was processed.DELETE endpointFinally, a DELETE endpoint that removes data from the server:app.delete('/api/items/:id', (req, res) => {  const itemId = req.params.id;  res.send(`Item with ID ${itemId} deleted`);});The app.delete(...) method handles DELETE requests. Like PUT, it uses req.params.id to determine which item to delete. In a more functional application this would also call out to a database to delete the specified resource.Step 5: Testing your APIWith our API built, the next step is to get it running on our local system and test it with Postman. Let’s look at the individual steps.Set up and run your APIYou must ensure your Node.js server is running to access your API endpoints. From the root of your project, run node server.js in a terminal.Configure PostmanNext, download and install Postman (or Insomnia, if you prefer) to issue requests to our endpoints. Once installed, create a request by clicking “New”, then “Request”, and saving it to a new or existing collection.Test your API endpointsIssue a request for each endpoint with the appropriate method selected:  To test GET: Select GET, input your endpoint (for example, http://localhost:3000/api/items), and click Send to view the response.  To test POST: Switch to POST, add your data in the request body in JSON format, and send the request to check the response.  To test PUT and DELETE: Repeat similar steps for PUT (to update data) and DELETE (to remove data), ensuring you include the item ID in the endpoint URL.Analyze responsesAs you test each endpoint, check that the responses for each request in Postman are correct. Successful operations typically return status codes like 200 OK or 201 Created. If there are errors, use the response details and your server console logs to debug. If your endpoints are changing resources in a database, check that the correct actions have occurred there too.Where REST still wins in 2026A few words on REST in the current API landscape, because 2026 introduced real alternatives.REST is still the best default for public APIs. Nearly every web developer has worked with REST at some point. The tooling (Postman, OpenAPI, every HTTP client in every language) targets REST first. If your buyer is a developer integrating your API for the first time, REST is the lowest-friction choice.GraphQL still wins for complex client-side data fetching. When the client needs to pull deeply nested, customer-specific shapes of data in a single round trip, GraphQL beats REST. For most B2B APIs, the round-trip count never matters enough to justify the operational overhead.gRPC wins inside service meshes. If both ends of the call are services you control and performance matters, gRPC’s binary protocol and streaming support beat REST/JSON. It is rarely the right choice for a public API.MCP is the new piece in the puzzle. When AI agents consume your API, exposing the same endpoints through an MCP server lets agents discover and call them using the descriptions in the spec. The WSO2 AI Gateway can auto-generate an MCP server from any OpenAPI spec, so you do not have to build a second API to support agent traffic. The REST API you build in this tutorial is the foundation; the MCP exposure is a layer on top.Adding observability and monetizationBuilding an API is only the start. Once your API endpoint is created, you will want to monitor and analyze incoming traffic in addition to using an API testing tool. Doing this lets you identify potential issues and security flaws and understand how your API design is used in practice. These are crucial aspects of growing and supporting your APIs.As your API platform grows, you may begin to focus on creating API products. By concentrating on API products, you shift from simply building APIs to using them as a business tool and revenue stream. Much like a more formal product, an API product needs to be managed and likely monetized.Moesif integrates through an SDK or plugin and is up and running in minutes. Once Moesif is integrated with your APIs, you can explore charting and reporting to look at:  Live API traffic  Time-series reports on usage  Conversion funnels  Retention reportsMoesif also enables API monetization by tracking usage and syncing it to a billing provider like Stripe, Recurly, or Chargebee.ConclusionThat covers the basics of building a REST API. In this post, we used Node.js and Express to build endpoints that you can expand into a real product. The code above is a good place to start when building APIs for your applications.Once you have built your API, you will want to start analyzing and monetizing usage. To try Moesif on your own API, start a 14-day free trial. No credit card required.Frequently asked questionsWhat is a REST API in simple terms? A REST API is a way for one piece of software to ask another for data or to make a change. It uses standard HTTP methods (GET, POST, PUT, DELETE) and usually exchanges data as JSON over the internet.What is the difference between REST and RESTful? “REST” is the architectural style; “RESTful” is the adjective for an API that follows that style. In practice the terms are used interchangeably.Is Node.js the best choice for a REST API? It is a popular choice, especially for teams already using JavaScript. Python (FastAPI, Flask), Go, and Java (Spring) are all common alternatives. Pick the stack your team already knows.Do I have to use Express to build a REST API in Node.js? No. Express is the most common choice, but Fastify, Koa, NestJS, and Node’s built-in http module are all options. Express is the default because of its tooling ecosystem.How do I deploy a REST API once it works locally? The common paths in 2026 are a managed platform (Vercel, Railway, Render, Fly), a container running on a cloud provider (AWS ECS, Google Cloud Run, Azure Container Apps), or a Kubernetes cluster if your team operates one.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /technical/rest-api-tutorial/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "api-monetization-api-strategy-api-billing": {
          "title": "Choosing Billing Metrics in Apigee vs. Moesif",
          "content"	 : "Perhaps the most important part of any monetization strategy is the effective selection of API billing metrics. Billing metrics form the backbone of your monetization strategy, and giving ample thought to what – and how – to monetize will pay long-term dividends both in economic terms as well as management ones.Today, we’re going to look at what API billing metrics actually are, and how they related to your long-term monetization strategy. We’ll look at two very different approaches – one from Apigee, and the other from Moesif – and see how each handles a variety of monetization metrics.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        What is Apigee?Apigee, acquired by Google in 2016, is a full-service API management platform. It encompasses a broad array of functionalities for API creation, deployment, and management. While Apigee’s comprehensive and robust features make it a great tool for a lot of environments, its complexity and idiosyncrasies can pose significant challenges in practice, impeeding customer success.What is Moesif?Moesif is a sophisticated solution for API monitoring and analytics that specializes in real-time analytics and insights. This enables businesses to effectively scale their data-driven monetization efforts, swiftly address issues in API operations, and unlock new revenue modalities. Known for its user-friendly interface, Moesif facilitates the implementation of complex monetization strategies, offering extensive support for a variety of environments and paradigms. Moesif excels at usage based billing, enabling businesses to deploy customized pricing and billing meters to charge users based on their API access and API usage.What is a Billing Metric?A billing metric is, put simply, the thing that you charge for. Billing API usage can have a multitude of potential points where monetization can kick in, and these points are often variable from product to product, even within industries. At its most basic, a billing metric is the unit of usage or service that can be measured and charged for via invoice, and is the basis of all monetization strategies, regardless of the particular billing system implementation at hand.As an example, a provider can bill for something very simple, such as API call volume. In this billing metric, the sheer number of calls is what we base the billing on, but this can have serious drawbacks – if a single call only takes 4kb of data to do what it needs to do, should that really cost the same as a request that takes multiple megabytes and touches more than one API endpoint, as would be the case with AI photo generation or audio manipulation? When building your subscription model, understanding the current endpoint and API usage and fluctuating load on your internal infrastructure can help guide your payment structure.Data transfer could also easily be the billable metric, but in that case, what happens when the complexity of the request is variable? Two users might get the same end product – say a 1mb image – but one user may have requested something that was easily processed with existing cached resources while the other might have requested something altogether new and complex requiring more API access or additional resources.For this reason, selecting your billing metrics is vital. The following is a brief overview of some common billing metrics:      ‘’’Call Volume’’’ – the total number of calls made in a period of time.        ‘’Data Transfer’’’ – the total amount of transferred data between server and client.        ‘’Compute Requirements’’’ – how much computational resources were utilized.        ‘’Transaction Volume’’’ – monetized based upon the number of transactions, even when those transactions are part of the same batch of calls.        ‘’Function Tier’’’ – bills based upon the kind of functions requested, e.g. how complex the transformation or service is compared to relatively simpler tasks.  Apigee Billing MetricsApigee allows for a variety of billing metrics, but these metrics are roughly divided into two categories.First, there are “default” metrics. These are found as part of the core product offering – for example, via the Rate Card configuration system, you can set billing metrics by flare rate per transaction, you can charge via bundled Volume Bands, etc. Via the API Product Bundles offering, you can charge for access to specific tiers of functions, limiting users to specific function groupings.Next, there are the “custom” metrics. Custom metrics in Apigee allow you to set billable metrics outside of the default offerings. Because the default offerings are largely based around volumetric measurements, there are times where you will want to enable something else – in this case, Apigee provides some custom subscription solutions that you can utilize based on your user’s historical usage data.The biggest drawback with Apigee’s approach is that everything is largely separated into their product category, which makes managing combinatory metrics – for instance, a tier system with a combinatory volumetric billing process – pretty cumbersome.Moesif Billing MetricsConversely, Moesif is very straight-forward for billing and governance. Moesif utilizes the Billing Meter product offering to present a few pre-set metrics for pre-configured monetization within a billing account. These include:      ‘’’Event Count’’’ – the total number of requests that are made to the API and any API endpoint.        ‘’’Unique Users/Keys’’’ – the total number of unique users or sessions utilizing the API.        ‘’’Average Latency’’’ – an SLA-specific monetization strategy that allows you to set a latency metric for API usage.        ‘’’Request Body Count’’’ – a metric based around the total number of elements in an API request body.        ‘’’Response Body Count’’’ – a metric based around the total number of elements in an API response body.  Moesif also provides a “Custom Metric” option, which allows you to create or calculate a metric based on any available data from the request or response flow within your app.The huge benefit here is that these metrics are bundled under a single product offering, and as such, there’s very little complexity across systems. If your API has a Billing Meter, you can create a variety of monetization strategies without making any changes to the underlying API or managing any gateways or routers. This also makes combinatory metrics much, much easier – simply create the proper Billing Meter collection and you’re good to go!ComparisonUltimately, the main difference here is in how deep you’re integrated with Apigee and the Google Cloud Platform. Apigee is quite powerful, but on the road to that power, it has become a very complex cloud billing API and app development option. This complexity passes through to the customer, and while simple monetization strategies won’t notice this at first, more complex ones will quickly find that the added complexity only gets in the way of monetization and revenue generation.Moesif, on the other hand, has taken a monetization-driven approach from the beginning, meaning that everything is simplified and streamlined to get you monetized quickly and with little overhead. When implementing simple monetization strategies, you can spend five minutes in the backend and walk away with an effective strategy. When doing something more complex, this doesn’t grow substantially with each additional step – you can simple create a new Billing Meter and have your system ready to go. Enacting a usage based billing system shouldn’t be a headache; with Moesif, billing API products based on real-time user data is a breeze. Just because an app is complex doesn’t mean billing information has to be, too.Much of this will obviously depend on how complex your billing situation is – ultimately, if ease of implementation is your priority, it’s hard to argue against Moesif.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            You’ve built the API. Now let's generate revenue!            Monetize your APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/API-Billing/",
          "author": "Kristopher",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-api-lifecycle-podcast": {
          "title": "Podcast: API Lifecycle",
          "content"	 : "Moesif’s Podcast Network: Providing actionable insights for API product managers and other API professionals.Joining Mike Amundsen is Derric Gilling, currently the CEO of Moesif, the leading API analytics and monetization platform. Gilling is also a regular speaker at developer conferences, including API World, Developer Week, and APIDays.As a CEO, Derric shares his experience and thoughts on how to build API products to be more beneficial to your business. Table of Contents   0:00Introduction    0:50The Aha Moment    2:12Pricing API Products    4:03Developer Driven Sales    5:07Communication and Deprecation    6:39Helping Developers  IntroductionMike: Hi, Mike Amundsen here again with API Strategy for Decision Makers. As you know, I recently got a chance to sit down with Derek Gilling, CEO of Moesif, the leading API analytics and monetization platform. We were talking about the whole process of the API lifecycle; this notion of discovery, adoption, designing for that first hello and for strategic applications. Then figuring out the pricing strategy, how you sell to developers, and then working through the full cycle of eventually deprecating old APIs for new. Let’s pick up when we were talking about this notion of the developer moment, the “aha moment”. Let’s listen in.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        The Aha MomentMike: Yeah, you know, I think when we were talking about the API, the continuous API management book with the API Academy, we talked about this “aha” moment. I forget who taught it to us, whether it was Twilio or somebody else, they called it the “Neo moment”, when somebody goes, “whoa”. I’s like they suddenly get that sort of rush. You’re exactly right in the sense that that very first “aha” moment app is often not native to whatever your environment is. It’s not integrated into your own platform yet. It’s not solving a direct problem, but you kind of get this buzz of, “hey, this thing works!”.And I know Twilio does this, and we talk about it a little bit in the report, and I know you do this in your product space, really monitoring that whole experience along, like finding out who signs up, you know, you’re working that funnel. That funnel is really important all through the developer experience of using that API until you finally can get them to some kind of integration step. So I think that’s really incredible.Now, assuming that you’ve got their interest, you can monetize this experience in some kind of way. That leads you to pricing, and I think that was actually one of the, another one of the really enlightening sections in the report where you talk a lot about, there’s a lot to think about in the pricing space, isn’t there?Pricing API ProductsDerric: There is, and so this goes back to what you mentioned around landing developers first. So you’re not trying to sell to the developer itself. What you’re trying to do is get them to pay a token amount, maybe it’s $50 a month, maybe it’s $100 a month, because at that point, they already see value in the platform, and they had the audacity to go ask their manager for the credit card.So by the time that sales is getting engaged, a lot of times the developer’s already paid  for a couple months worth of service. They’re paying that 50 bucks a month, they already have a working app they can show to the management team. So what we like to call this is “selling through developers to the decision makers”. In this case, the decision maker is looking for, maybe buying discounts, maybe it’s a specific integration, certain compliance requirements that they might need to meet. If you look at this process, that also means you have some type of uses-based pricing model, right?So going from $100 a month to suddenly $10,000 a month, that’s of course what we want to do as a product, but it’s also a very delicate balance because you don’t want to piss off developers. No one wants to be blamed for that $10,000 a month bill. So how do you make sure you have the right process to identify these different developers where their usage is growing over time, week over week or month over month? That’s when sales would get engaged saying, “Hey, you know, you’re going to have this very large bill. Let me work with you on some discounts. Maybe it’s through volume discounts or something else to make sure we’re really meeting your needs and you’re able to get the most value out of what you’re paying here.” And so that’s really what sales is.Developer Driven SalesMike:  Yeah, and again, to me, that goes right to the consultative selling idea. What you really just kind of described is, “Look, we’ve been seeing you’re using the API and that’s really great. This is what, you know, our experience tells us, let’s get together and talk about setting up a package,” right, that’s one of the things we talk about in the report as well is getting a package that, like you say, meets your needs and stays focused so you don’t get surprises and you don’t tick off somebody along the way.I think that’s another thing that’s unique in the API space. Because you’re focused on the time to first working app, you’ve already got them kind of hooked, right? And now what you want to do is you literally kind of want to land the fish in the boat. You want to get them so that they can do this consistently and comfortably and be a great advocate for your API.Nothing lasts forever, everything changes over time. And you mentioned deprecation as well, which is another part of that cycle. And again, that’s a huge topic, there’s a lot to go on, but it’s important to think about deprecation pretty early in the process, isn’t it?Communication and  DeprecationDerric: It is, and so one of the worst things you can do when you have some type of developer ecosystem is to piss off developers. And a fast way you do that is if you don’t communicate changes or impact to either themselves or their integrations or their applications.When we talk about deprecation, it’s really important to have a process around how do you identify the impact, whether it’s to your own revenue or to your developers. So, how many different developers are impacted, what do they have to do in order to migrate, and then let them know. It’s much better to over-communicate than to under-communicate and give them plenty of lead up time before that API is actually shut off. So it might mean you have to notify developers six months ahead of time saying, this and this day is when it will be shut off.Lastly, continue to remind them, “Hey, you’re using this API that’s soon to be deprecated, it looked like you had 20,000 API calls in the last seven days, this is the geolocation or the user agent that we’re seeing the traffic from.” So they know exactly which integrations might be causing that traffic, ‘cause a lot of times you don’t know. If I’m a developer, I’m using all these different APIs, using all these different endpoints, how do I know if I’m using this deprecated endpoint or not?So providing that capability and that visibility is really important, whether it’s through email, through embedded dashboard, something else inside your developer portal.Helping DevelopersMike:  Yeah, and that’s another thing that I think was so valuable when we were going through the report. This notion of helping the developers out because they might have a team in another location, another part of the world, another part of the country, just another floor in the building that we didn’t know who are also using the API as well. So like you said, geolocating, helping them figure out what the details are, so they can work through exactly who it is that they need to help upgrade.And I think that’s a really, really important part of it. That’s another example of really living the developer life and helping developers work through that process. So that’s API Strategy for Decision Makers, and I hope we’ll see you next time in another episode.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            You’ve built the API. Now let's generate revenue!            Monetize your APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/API-Lifecycle-Podcast/",
          "author": "Rachael",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-api-strategy-podcast": {
          "title": "Podcast: API Strategy",
          "content"	 : "Moesif’s Podcast Network: Providing actionable insights for API product managers and other API professionals.Joining Mike Amundsen is Derric Gilling, currently the CEO of Moesif, the leading API analytics and monetization platform. Gilling is also a regular speaker at developer conferences, including API World, Developer Week, and APIDays.As a CEO, Derric shares his experience and thoughts on how to build API products to be more beneficial to your business. Table of Contents   0:00Introduction    1:23APIs as Products    2:26API Product Lifecycle    3:34API Discovery and API Adoption    4:46Developer Evangelists    6:00Monetizing APIs: Developer First Growth  IntroductionMike: Hey, Mike Amundsen here and I’m with Derric Gilling. We’re talking about API strategy for decision makers. Derric and I worked on an O’Reilly report that was recently released and we thought we would bring this to some videos and talk a little bit more about it. I’ve known Derric for a couple of years. Through his company, Moesif. And it was a real pleasure to work with Derric on this project.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        APIs as ProductsMike: Why is it that products are important now? What’s changed? What’s different? Why is it important that everybody sort of think about their APIs, even their internal APIs as a product?Derric: Well, Mike, the way that we’re treating APIs is drastically different today than it was 10 years ago. If we think even as recently as 2015, Twilio was valued around half a billion dollars and look at it as where it is now. The way that we’re selling software is fundamentally different.You know, 10 years ago, you might have had an enterprise sales team and they’d do sales demos, pitch decks and actually sign on the dotted line before anyone is ever using the product. These days, a lot of times, by the time that sales is getting engaged, you already have developers using the API. They might be testing it in a sandbox or some type of local environment. And so it’s a much larger engagement process. And at that time, it’s mostly like a upsell, right? And so how do you make sure you can actually cover the rest of that journey before sales is engaged?API Product LifecycleMike: Yeah, I think that’s a really important element that we talked about in the report and that I’ve seen you talk about in articles on your website as well. And that is this idea that it’s a very different kind of cycle. It’s a very different kind of selling experience now that we’re dealing with APIs. Often, it’s developers that are driving the bus. It’s developers that make it happen in one form or another.And I think the other thing that’s really interesting is you at Mozav have developed a really, I think a really clear cycle, sort of a product lifecycle. Every product has a lifecycle of something, but they’re unique for APIs. And I think there are lots of things that we need to cover, right? When we think about a product lifecycle for API.Derric: Indeed, and so in the book itself, we talk about five different steps around that API strategy. You start off with discovery and adoption, and then you move into the monetization part. And of course that includes pricing and packaging. But last but not least is how do you actually handle deprecating an API? What does that end of the lifecycle look like when we start treating these as products?API Discovery and API AdoptionJust touching on the first two, discovery and adoption. One of the biggest differences today is that developers have a lot more autonomy and they’re actually involved in that decision-making process. They get to pick and choose which APIs, which tools they want to use. And that means by the time that, again, sales is getting engaged, they’re actually leaning on these developers, who is already been playing with the API. And they’re actually excited to bring this up to their manager to say, “Hey, this is what I built. I think this could be important at the organization.”So when we think about adoption, that means it’s really important for these API-first companies to actually pull developers into their ecosystem, whether that’s through inbound marketing, through partner channels, through really anything that you’re doing around the marketing side should be pulling people into your ecosystem versus trying to push the product onto developers.The worst thing you could do is have a developer see these random emails from a salesperson trying to push that product and they’re not even excited yet. They’re not going to champion or even spend the time to look at this API if they’re not excited.Developer EvangelistsMike: Yeah, I think that was a real learning moment, this notion of pull rather than push. And I think it really explains the rise of developer evangelists in the API space as well, because that’s one of the key ways that you can really pull developers onto your platform or into your product world; by showing them what’s possible and evangelizing the notion of solving problems.So I think that’s a really, really interesting way to think about it. I agree with your view on adoption too. You’re trying to get to that “aha” moment, right? That time when developers get that passion, get excited about something.I think Twilio knows this really well because I remember years ago, Twilio had a campaign to decision makers, which basically said, talk to your developers. So it was really keying right into that notion about how important developers are in this life cycle. And I think you talk about that in another aspect as well, and that is this sort of idea of monetization, right?So when you talk about monetizing, you talk about landing developers first. What is that really? What’s that about?Monetizing APIs: Developer First GrowthDerric: Sure, and so when we talk about this activation or adoption process, there’s two key metrics we like to speak on. The first is time to first hello world, and the second is time to first work out. And it’s really important, especially when we look at the first metric, that encompasses your entire developer experience, and it can be a Northstar metric for the entire product team or even the company.When we think about building out a great developer experience, what time to first hello world is, is the time it takes a developer to sign up and make that very first API call, whether that’s through a tool like Postman or curl command or something like that, they’re at least investing enough time to test out the API.Now, the tricky thing with “time to first hello world” is they haven’t actually received any value yet, other than just a very, very little taste of what the API could do. And that’s where we get to this next notion of ‘time to first working app’. Some people might call this’ time to a revenue app’ or “revenue generating app”. And what that means is a developer got to that “aha” moment, which might mean sending over text messages, 100 text messages, I visit telecommunications API, it might mean a number of transactions sent through the API. And that’s what we really mean by time to “first working app”.Mike: Derric, you’re so right. We’ve really covered a lot here in this episode, talking about adoption, discovery, the time to first hello, the actual working strategic application, and there’s so much more.Now, I’ve got lots of questions about pricing and planning your modifications and eventually your deprecation of the old APIs, but we’ve kind of run out of time here. So we’ll cover those details in our next episode of API Strategy for Decision  Makers.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            You’ve built the API. Now let's generate revenue!            Monetize your APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/API-Strategy-Podcast/",
          "author": "Rachael",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-best-practices-maximizing-ai-revenue-growth": {
          "title": "Best Practices: Maximizing AI Revenue Growth with Customer Success",
          "content"	 : "In the world of artificial intelligence, maximizing revenue growth is of great importance for businesses seeking to capitalize on their AI products. Like all SaaS tools, the defining linchpin in widespread product adoption is the end user experience. There is an intricate relationship between customer success and how to make money from AI products, as the successful integration of AI products into a given market relies heavily on not only the technological capabilities of the solution but also on how effectively businesses cater to their customers.The intersection of customer success with how to make money from AI offerings is a strategic balance where technology meets user satisfaction and drives business growth. As businesses introduce AI products to the market, an emphasis on customer success becomes a differentiator. Customer success practices including personalized onboarding, continuous support, and proactive engagement are not just ancillary components of CS success. They’re integral elements that ensure users derive optimal value from AI solutions, encouraging customer satisfaction and lowering churn.The commercial viability of an AI tool is not solely contingent on its technical capabilities; if that were the case, there would be a greater monopoly in the market. Rather, success hinges on how effectively businesses can align their AI technology with the unique needs of their customers, fostering loyalty, and driving sustained revenue growth.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Commercializing AI-Based Offerings: An OverviewGenerating revenue from AI-based offerings is a multifaceted process to turn innovative (or more user friendly than competition) AI technology into a marketable and profitable product. As mentioned before, sometimes a product doesn’t have to be groundbreaking to be successful. Rather, a focus on a cohesive and enjoyable user experience can be the deciding factor in a company’s choice of machine learning solution.Value PropositionThe journey begins with identifying opportunities in a given market where AI solutions can address specific needs or challenges. This phase includes market research to understand customer demands, competition, and potential niches for AI software. Challenges arise in navigating the complexity of AI development, from the initial investment in cutting-edge technology and talent acquisition to research and development. The commercialization process also entails creating a compelling value proposition, highlighting how the AI application addresses pain points and delivers unique benefits to potential users.AI in HealthcareOne example of an industry that could significantly benefit from the adoption of AI-based software is the healthcare sector. Generative AI tools and AI algorithms have the potential to revolutionize various aspects of healthcare, ranging from diagnostic tools and personalized treatment plans to administrative processes assisted by AI model technology. For this example, AI startups for healthcare would need to build a HIPAA compliant AI powered tool, and likely trained on unique, complex language models that may not be easy for the AI product provider to set up. As such, AI companies would need to satisfy a need that could be monetized effectively to offset the costs associated with creation and maintenance of a medical LLM, or some kind of integration accessing a current provider like Med-PaLM.Pricing ModelOpportunities emerge as businesses position their generative AI solutions as efficient and capable of driving significant value for users. Once developed, deciding on a pricing model strategy becomes a business priority. Having effective marketing strategies is one thing, but pricing your AI based product is a can of worms on its own. Because of the expensive nature of offering an AI system, the upstart and maintenance costs associated with artificial intelligence  and big data models can’t be overlooked and should be accounted for in the pricing structure of your AI API. By employing a usage based pricing plan, businesses can directly offset the actual usage of their AI products. Usage based pricing also enables organizations to tailor sales approaches to appeal to a diverse customer base, including self service data scientist or data analyst users.Customer SuccessEnsuring user-friendly interfaces, seamless integration into existing workflows, and comprehensive customer service further enhance the customer experience and, by result, improve customer success. Throughout this journey, the iterative nature of AI development enables continuous improvements and adaptations based on user feedback and analytics, presenting ongoing opportunities for refinement and feature expansion. Ultimately, successful commercialization via the AI revolution requires transformative capabilities to not only meet user needs but to create new opportunities.Cost Considerations in AI CommercializationBuilding AI products comes with unique challenges and expenses that reflect the complex nature of artificial intelligence development.Substantial computational power is necessary to train and run AI models effectively, often requiring specialized hardware which can be expensive to acquire and maintain. Another challenge lies in procuring and managing large volumes of high-quality big data for training AI models. Acquiring datasets for large language models can be time-consuming and costly. Additionally, ensuring data privacy and compliance with industry or governmental regulations adds another layer of complexity and expense.Beyond initial model costs, research and development can be substantial financial strains, given the iterative nature of AI development and prompt engineering. Continuous testing, algorithm refinement, and research based on the latest natural language processing advancements require ongoing investment, which can be costly in terms of hours and money.Customer Success Best Practices for AI Revenue GrowthThe role of customer success holds pivotal importance as businesses strive to not only deliver cutting-edge technology but also ensure that customers derive maximum value from their AI investments. Customer service for generative AI tools extends beyond the initial purchase, as lasting partnerships are cultivated through personalized experiences and ongoing support. This approach can maximize customer satisfaction, retention, and, ultimately, foster long-term revenue growth.Strategies for Maximizing Customer Success  Personalized Onboarding: Tailor the onboarding process to individual customer use cases to ensure a smooth introduction to your generative AI solutions.  Ongoing Support: Continuous support is essential for addressing evolving customer requirements. Responsive and knowledgeable dedicated customer success teams and intuitive, full-scope documentation reinforces customer confidence and satisfaction.  Proactive Engagement: Anticipating customer needs and engaging proactively based on user insights can cultivate a stronger partnership. Regular check-ins, support around bottlenecks, and timely content updates or enhancements demonstrate a commitment to customer success.Customer Success through MoesifMoesif can play a pivotal role in elevating customer success for any SaaS product, but particularly within the AI landscape. By providing powerful, real-time insights into how customers interact with machine learning products, Moesif empowers AI companies to understand user behavior, optimize product usage, and intervene proactively when necessary.For example, monitoring API usage as it relates to free trial milestones and notifying sales teams of MQLs at critical junctures enables timely engagement, enabling faster conversion of trial users into paying customers. By analyzing user behavior, customer support teams can offer personalized, targeted content and support to help users maximize their usage of your AI product, streamlining their workflows and reducing your churn rate. Additionally, Moesif’s ability to detect changes in API usage patterns offers valuable signals for potential upsells for non-technical teams, ensuring that AI startups can align their pricing and offerings with customer growth.Moesif’s ability to manage customer success metrics extends beyond mere analytics; it can be a strategic enabler for AI-based organizations to nurture strong, value-driven relationships with their customers. Enhances your overall customer experience and position your AI products for sustained success and revenue growth in an ever-evolving landscape with Moesif.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Best-Practices-Maximizing-AI-Revenue-Growth/",
          "author": "Rachael",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-what-is-plg-for-ai": {
          "title": "What Is Product-Led Growth and Why Is It Critical for AI Dev Tool Companies?",
          "content"	 : "The misconception that product led growth implies a business neglects sales couldn’t be further from the truth. In reality, product led growth (PLG) involves integrating sales later in the customer journey, placing the purchasing power back in the hands of your users. This self-service approach is highly effective, especially when paired with insightful user data to streamline and target growth. For AI-centric companies, this product-led strategy serves as a fundamental pillar for success.What Is Product Led Growth?Product-led growth (PLG) is a business model that places a product itself at the center of the growth engine. Instead of relying solely on traditional sales and marketing approaches, product led companies leverages the perceived value of a product to drive customer acquisition, retention, and expansion. PLG is particularly effective for AI companies thanks to the inherent iterative and user-driven nature of AI development. PLG champions a user-centric approach, which enables customers to experiment with the practical applications and advantages of a given product. This approach resonates strongly with AI companies, as it not only enhances user comprehension but also amplifies engagement levels.A Different Approach to SalesA PLG company leverages their sales pipeline with  the product experience at the forefront of the customer acquisition and retention strategy. In a PLG model, a product itself becomes the catalyst for growth by leveraging the value customers believe it has through sandbox trials and Proof of Concepts. By influencing users to adopt, engage, and eventually convert into paying customers, PLG allows businesses to capture a wider range of potential developer users who typically are unreachable through traditional sales channels. After all, there’s a reason many developers aren’t converted by customer success professionals - they want to test the product under their terms, for their use cases, in their environments. Giving these self-service users the potential to become customers means frictionless onboarding for organizations already used to your product through a trial.The key elements of a PLG approach to sales include:  Self-Service Onboarding: Your product should be designed to be intuitive, allowing users to easily onboard themselves without extensive assistance. This self-service onboarding approach empowers users to explore your product independently.  Free Trials or Freemium Models: Offering free trials or freemium versions of the product enables users to experience the core functionalities before committing to a purchase.  Growth Loops and Referrals: Satisfied users become word-of-mouth advocates of your product. Through features like referrals, existing users play a role in bringing in new, organic users.  Data-Driven Iteration: Continuous analysis of user behavior guides relevant improvements to your product, solving user frustrations and reducing churn with a great user experieince.  Trial-Driven Conversion: Give your users a chance to experience your most marketable features during a trial period. When upselling features to enterprise level users, consider giving them the chance to test those features as well.A product led growth model can lead to rapid growth through new users, expanding revenue growth and freeing up your internal marketing team and product team professionals to focus on growth. Product led companies sales model is rooted in the philosophy that a great product, if designed to be user-friendly and value-driven, can attract, retain, and convert users independently based on its inherent value to the market. By focusing on delivering an exceptional product experience to improve your user experience, companies adopting a PLG approach can build a user base that sustains itself and contributes to product recognition and growth.Why Is Product-Led Growth So Important to AI-Based Companies?Product-Led Growth can be a significant growth strategy for AI-based companies. Product led sales emphasizes a offering a user-centric experience is particularly complimentary to AI solutions, which often involve complex product development and intricate technologies shaped or built on by the end user. The user-friendly approach of PLG allows users to engage with AI products independently, giving them a chance at hands-on exploration of your product for their use case.Moreover, the complexity of AI solutions poses a significant challenge in user adoption. PLG addresses this by enabling a product qualified lead to trial the capabilities of AI products through trials , demystifying the complexities associated with integrating your generative AI tool into their stack and encourages technology adoption.PLG integrates with customer success models, as well. Providing users with personalized onboarding, ongoing support, and proactive engagement aligns with the long-term goals of AI-based companies as well as can be done entirely without the direct involvement of internal sales representatives, fostering sales led growth independently.Selling to DevelopersSelling an AI product through a developer-centric product led strategy approach offers distinct advantages over C-suite targeting. By empowering developers with hands-on experiences, companies accelerate adoption timelines and allow for quicker evaluations thanks to support from the bottom up. Reduced sales friction, a developer advocacy model, and alignment with the use cases of intended developers further benefit AI companies employing a PLG strategy. By turning developers into your stakeholders, you turn them into a key decision-making audience. Ultimately, PLG leverages the influential role of developers in product adoption.Getting Billing RightProduct-led growth requires some thought around your billing model. This is where usage-based billing comes into play. A developer can pay a token amount to get started with your artificial intelligence product or large language model to trial it in their test account while they complete their proof of concept. After that, when the developer takes the product into production, your usage-based billing model needs to scale alongside the customer’s growing usage.AI based products are a natural fit for this pay-as-you-go style of billing, as there are various ways that product usage can be tracked. Just be sure that you’re using the right metrics! Broadly speaking, value metrics can be focused around:  Transaction volume – number of API calls, tokens used  Revenue/cost share – percentage of revenue, transaction fee  Data volume – gigabytes sent, custom model training, processed data volume  Users – monthly active unique users  Compute Resources Utilization – compute units, active hoursThese examples hint at the huge variety that can be built into usage-based billing, and the complexity of getting your invoicing right.Consider an AI-based language processing API that assists developers in analyzing and extracting insights from large volumes of text data. Developers could pay based on the volume of text data their application sends to the API for analysis. This aligns directly with the value they receive from your product – the more documents processed, the more context for analysis gained. A usage-based approach ensures that developers are billed proportionally to the actual utility and value derived from the AI service without causing strain on the side of the API provider.Data-Driven SalesUsing data to drive your sales is a major success converter. By focusing on the actual usage of your AI product, your customer success teams can more accurately manage and unpack the flow of conversions. As with any SaaS company, real-time data on your active users can be the true lens into their needs.With tracking in place for account health and usage growth, sales teams can open discussions with customers at just the right moment to ensure that the customer gets more value out of their product without jumping the gun around usage milestones. This is where a product analytics  tool like Moesif can help, providing a scalable solution with automated resources that can notice and alert customer success teams around increases in usage, giving your organization a chance to intervene and ensure the customer is still on the best value plan to meet their needs.Tracking usage data isn’t just about sniffing out new sales opportunities on your end. It’s also for ensuring that your users understand the cost of your product and the scale at which they actually use it. This way, customers don’t get surprise charges on their invoices following an increase in usage. By giving users (and your sales team) the chance to discuss plan limitations and subscription value, you can minimize the chance of them canceling due to the unexpected jump in cost. Lower churn and more confident users can keep your product afloat.How to Engage Developers with Your ProductFor AI-based companies, engaging with your product should be a seamless journey tailored to enhance their AI endeavors. By proactively engaging with a self-service crowd through user-friendly interfaces and intuitive documentation, you can provide a comprehensive understanding of how your AI product fits into a customer’s tech stack. Giving developers an interactive sandbox environment and allowing hands-on experimentation and testing of robust features will allow them to understand the value of your product, increasing the likelihood of conversion. With transparent pricing models and scalable options, integrating your product into any workflow can be done with confidence on both ends.    ",
          "url": " /api-monetization/api-strategy/What-Is-PLG-For-AI/",
          "author": "Rachael",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-analytics-api-strategy-top-10-apigee-alternatives": {
          "title": "Top 10 Apigee Alternatives",
          "content"	 : "In the dynamic world of API management, Apigee has emerged as a prominent player. However, with diverse needs and evolving technology landscapes, seeking alternatives that align better with specific requirements is a common pursuit. In this comprehensive guide, we’ll explore the top 10 alternatives to Apigee, delving into their key features, pros, and cons.What is Apigee?Apigee, now part of Google Cloud, is a platform for developing and managing API proxies. It offers tools for API security, analytics, developer portals, and more. Apigee’s strength lies in its ability to enable enterprises to design, secure, analyze, and scale APIs.Why Do You Need Alternatives To ApigeeWhile Apigee is robust, it may not suit every organization’s needs. Factors like pricing, specific feature sets, ease of use, or integration capabilities can drive businesses to explore alternatives. Each alternative offers unique features and benefits that might be more aligned with different business requirements.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Top 10 Alternatives of ApigeeMoesifMoesif positions itself as a powerful platform for growing and monetizing API products. It is recognized for its capabilities in API observability and monitoring, catering to both enterprises and startups. Moesif’s platform is designed to provide deep insights into API usage, enabling businesses to understand and optimize their API strategies effectively.Key Features of Moesif      API Analytics and Real-time Event Logs: Moesif offers detailed analytics on API usage, allowing businesses to track and understand how customers interact with their APIs and applications. This feature is crucial for identifying usage patterns and making data-driven decisions.        Metered Billing and Quotas & Governance: Moesif enables the setup of usage-based billing meters on API calls and other metrics. This functionality is essential for businesses looking to monetize their APIs effectively. Additionally, it provides tools for managing quotas and governance, ensuring that API usage stays within desired limits.        Developer Portal and Embedded API Metrics: The platform includes a developer portal that can be customized and used to embed API usage metrics. This feature is particularly useful for enhancing the developer experience and providing transparency.        Behavioral Cohorts and Retention Analysis: Moesif allows the segmentation of users into behavioral cohorts, offering insights into different user groups’ behaviors. This is complemented by retention analysis tools, which help in understanding and improving customer retention.        Conversion Funnels and Behavioral Emails: The platform provides tools for analyzing the user journey through conversion funnels and sending behavioral emails. These features are vital for iterating on strategies that drive customer conversion and engagement.  Pros of Moesif  Advanced Analytics: Moesif’s strong suit is its advanced analytics capabilities, which offer deep insights into API usage and user behavior.  Monetization Tools: The platform provides robust tools for API monetization, including metered billing, which is a significant advantage for businesses looking to generate revenue through their APIs.  Developer-Friendly: With its developer portal and embedded metrics, Moesif is highly developer-friendly, making it easier for teams to collaborate and optimize their API strategies.  User Behavior Insights: The ability to analyze user behavior through cohorts and retention tools offers businesses a unique perspective on their customer base.Cons of Moesif  Limited Free Tier: Moesif’s free tier is limited to 30,000 events per month. While this may be sufficient for very small projects or initial testing phases, businesses with higher API traffic will need to move to a paid plan to accommodate their needs.  Advanced Features on Higher Tiers: Governance and sampling tools, crucial for managing API usage efficiently, are only available on the enterprise tier of Moesif. This may put these advanced features out of reach for smaller startups or businesses with limited budgets, potentially limiting their ability to fully leverage Moesif’s capabilities.WSO2 API ManagerWSO2 API Manager is a comprehensive open source platform designed for the creation, deployment, and management of APIs. It is recognized for its robust feature set and flexibility, making it a popular choice for organizations looking to build and manage complete API ecosystems. The platform is designed to cater to a wide range of business needs, from simple API management scenarios to complex integrations, offering extensive protocol support and customization options.Key Features of WSO2 API Manager      Interoperability with Open Standards: WSO2 API Manager supports modern API approaches like REST, GraphQL, and AsyncAPIs, ensuring seamless interoperability between diverse systems. This feature is crucial for businesses looking to avoid vendor lock-in and adopt collaborative innovation.        Advanced Integration Support: The platform offers an integration runtime that supports the creation of composite microservices, message routing, transformation, mediation, and service orchestration. It is also capable of consuming and processing streaming data, making it a comprehensive solution for complex integration needs.        Extensibility and Customizability: WSO2 API Manager is highly versatile and adaptable, allowing businesses to tailor various aspects such as authenticators, policies, mediations, API lifecycles, workflows, portals, and login pages to their specific requirements.        Robust Security and Compliance Support: The platform includes essential security features like OAuth access control, fine-grained security policies, and threat protection mechanisms. It also offers pre-built open source extensions and connectors for compliance in regulated environments like healthcare and finance.        Flexible Deployment Models: WSO2 API Manager can be deployed in the cloud, on-premises, or in a hybrid environment, providing businesses with the flexibility to choose the deployment option that best suits their needs.  Pros of WSO2 API Manager  Open Source Flexibility: As an open source solution, WSO2 API Manager offers a high degree of flexibility and customization, allowing businesses to adapt the platform to their specific needs.  Comprehensive Integration Capabilities: The platform’s advanced integration support makes it ideal for businesses with complex integration requirements.  Strong Security and Compliance Features: WSO2 API Manager’s focus on security and compliance is a significant advantage for organizations operating in regulated industries.  Support for Modern API Standards: The platform’s support for modern API standards like GraphQL and AsyncAPIs makes it a future-proof choice for businesses looking to stay ahead in the API management space.Cons of WSO2 API Manager  Learning Curve: The extensive features and capabilities of WSO2 API Manager may present a steeper learning curve, especially for teams new to API management.  Resource Intensiveness: For some deployments, the platform can be resource-intensive, requiring adequate infrastructure and expertise for optimal performance.MulesoftMulesoft, a Salesforce company, is renowned for its comprehensive integration platform, Anypoint Platform™, which is widely recognized as a leading solution in the realm of API management. Mulesoft’s platform is designed to facilitate the connection of applications, data, and devices across both on-premises and cloud environments. It stands out for its API-led approach, which enables businesses to achieve exceptional agility by re-architecting their integration strategies.Key Features of Mulesoft      Unified Integration Platform: Mulesoft’s Anypoint Platform™ offers a single solution for SOA, SaaS, and API integrations. This unified approach simplifies the process of connecting various systems, whether they are on-premises or in the cloud.        API-Led Connectivity: The platform emphasizes an API-led connectivity approach, allowing businesses to create reusable building blocks (APIs) that can be leveraged across the entire organization. This approach enhances agility and speeds up the delivery of new services.        Hybrid Deployment Capabilities: Mulesoft supports hybrid deployment, enabling seamless integration of both SaaS applications and on-premises systems. This flexibility is crucial for businesses operating in diverse IT environments.        Comprehensive Toolset: The platform includes tools like Mule ESB, CloudHub iPaaS, and API Manager, along with hundreds of connectors and templates. These tools provide the necessary components for building a robust and scalable integration architecture.        Automation and Efficiency: Mulesoft claims a 27% faster automation of business processes, highlighting its focus on increasing efficiency and productivity in the integration and API management space.  Pros of Mulesoft  Extensive Integration Capabilities: Mulesoft excels in its ability to integrate a wide range of systems, making it an ideal choice for complex enterprise environments.  API-Led Approach: The API-led connectivity model promotes reusability and agility, enabling businesses to adapt quickly to changing needs.  Hybrid Deployment Flexibility: The platform’s support for hybrid deployment models offers significant advantages for businesses with diverse IT landscapes.  Comprehensive Toolset: Mulesoft’s extensive range of tools and connectors ensures that businesses have everything they need for effective integration and API management.Cons of Mulesoft  Complexity and Learning Curve: The breadth of features and capabilities offered by Mulesoft can be overwhelming, particularly for teams new to integration and API management.  Cost Considerations: As a comprehensive enterprise solution, Mulesoft may come with a higher cost, which could be a consideration for smaller businesses or those with limited budgets.PostmanPostman has emerged as a highly influential platform in the API development world. It is not just an API client but a comprehensive API platform that simplifies each step of the API lifecycle. This platform streamlines collaboration, enabling teams to create better APIs faster. With over 25 million developers using Postman, it has become a staple in the API development process for many organizations.Key Features of Postman      Comprehensive API Lifecycle Tools: Postman offers a full suite of tools that accelerate the API lifecycle, including design, testing, documentation, and mocking. This comprehensive approach ensures that all aspects of API development are covered.        API Repository: The platform provides a centralized repository for storing, iterating, and collaborating on API artifacts. This feature is crucial for maintaining consistency and efficiency across teams.        Collaborative Workspaces: Postman’s workspaces enable organization and collaboration among team members and stakeholders worldwide. This fosters a more cohesive and productive API development environment.        API Governance: Postman includes governance tools that help ensure APIs are designed, built, tested, and distributed according to organizational standards. This feature is vital for maintaining quality and compliance.        Public API Network: The platform hosts the largest network of APIs, allowing developers to explore and share APIs with a global community. This feature encourages innovation and collaboration on a broader scale.  Pros of Postman  User-Friendly Interface: Postman is known for its intuitive and user-friendly interface, making it accessible to developers of all skill levels.  Strong Collaboration Features: The platform’s emphasis on collaboration, including shared workspaces and repositories, makes it ideal for team-based API development.  Comprehensive API Lifecycle Management: Postman’s extensive range of tools covers every aspect of the API lifecycle, from design to deployment.  Community and Network: The vast network of APIs and the large community of users provide a rich environment for learning, sharing, and collaboration.Cons of Postman  Focus on API Development and Testing: While Postman excels in API development and testing, it may not offer the same level of management features as some other platforms, such as lifecycle management or analytics.  Limited Advanced Analytics: The platform’s analytics capabilities, while sufficient for many use cases, may not be as advanced as those offered by more specialized API analytics platforms.IBM API ConnectIBM API Connect is a comprehensive API management solution that facilitates the creation, securement, and management of APIs. It is designed to support digital transformation initiatives both on-premises and across various cloud environments. IBM API Connect is recognized for its intuitive user experience and robust capabilities, making it a leader in the API management space.Key Features of IBM API Connect      Full Lifecycle API Management: IBM API Connect offers a complete suite of tools for the entire API lifecycle, including creation, management, security, and monetization. This ensures a consistent and efficient approach to API management.        Flexible Deployment Options: The platform can be deployed in any Red Hat OpenShift environment, offering flexibility for cloud, on-premises, or hybrid deployments. This adaptability is crucial for businesses with diverse IT infrastructures.        Enterprise-Grade API Gateway: A powerful, enterprise-grade API gateway is a key feature of IBM API Connect, providing robust security and the ability to manage cybersecurity risks effectively across multicloud environments.        Microservices-Based Architecture: The platform’s architecture is based on microservices, allowing for scalability and secure management of services across various endpoints.        Award-Winning User Experience and Developer Portal: IBM API Connect boasts an award-winning user experience and a developer portal with robust self-service features, enhancing the ease of API strategy implementation across the full lifecycle.  Pros of IBM API Connect  Comprehensive API Lifecycle Management: The platform’s full lifecycle management capabilities make it a one-stop solution for all API-related needs.  Flexibility in Deployment: The ability to deploy in various environments, including cloud and on-premises, offers significant versatility.  Strong Security Features: The enterprise-grade API gateway ensures robust security, which is essential for businesses handling sensitive data.  Microservices Architecture: This modern architecture approach facilitates scalability and efficient management of APIs.Cons of IBM API Connect  Complexity for Smaller Teams: The extensive features and capabilities might be overwhelming for smaller teams or businesses with simpler API management needs.  Resource Intensiveness: Depending on the deployment, IBM API Connect can be resource-intensive, requiring adequate infrastructure and expertise for optimal performance.Kong API GatewayKong API Gateway is a leading open source API gateway known for its high performance and scalability. It is built on a lightweight, fast, and flexible architecture, making it a popular choice for modern, cloud-native applications. Kong Gateway is designed to handle massive scale and complexity, supporting over 50,000 transactions per second per node.Key Features of Kong API Gateway      High Performance and Scalability: Kong Gateway boasts an ultra-lightweight, infinitely scalable NGINX engine, capable of handling a vast number of transactions efficiently.        Architectural Flexibility: The gateway offers exceptional flexibility, allowing operation across any cloud, platform, or protocol. This adaptability is crucial for businesses operating in hybrid or multi-cloud environments.        Easy Deployment and Configuration: Kong Gateway can be configured natively using an API, web UI, or declarative configuration, facilitating easy management and updates via CI/CD pipelines.        Extensibility with Plugins: The platform is highly extensible, offering granular control over API traffic with a range of security, authentication, transformation, and analytics plugins. It also supports custom plugins via Kong’s Plugin Development Kit.        Kubernetes Native Integration: Kong Gateway integrates seamlessly with Kubernetes, allowing for efficient traffic management, transformations, and observability across Kubernetes clusters with zero downtime.  Pros of Kong API Gateway  Exceptional Performance: Kong Gateway’s high-performance capabilities make it suitable for handling large-scale API traffic.  Flexibility and Adaptability: The gateway’s architectural flexibility ensures it can be deployed in various environments, catering to diverse business needs.  Ease of Deployment and Management: The platform’s straightforward deployment and configuration processes enhance operational efficiency.  Extensive Plugin Ecosystem: Kong’s rich plugin ecosystem allows for extensive customization and control over API traffic.Cons of Kong API Gateway  Complexity for Beginners: The extensive features and capabilities of Kong Gateway may present a learning curve for beginners or smaller teams.  Resource Requirements: For optimal performance, Kong Gateway may require significant infrastructure resources, especially in large-scale deployments.Axway APIAxway Amplify Platform stands out in the API management landscape with its universal approach. It offers full lifecycle API management and is distinguished by its ability to automate discovery, reuse, and governance of multi-vendor APIs. Axway positions itself as a unique provider in this space, focusing on open, secure, and revolutionary API management practices.Key Features of Axway Amplify Platform      Universal API Management: Axway Amplify Platform extends API management capabilities beyond traditional boundaries, accommodating various API patterns like SOAP, REST, GraphQL, Events, Service Mesh, gRPC, and Async API.        Open Platform: It allows for the publication, validation, and governance of APIs across multiple clouds, on-premises data centers, and vendors, emphasizing an open approach to API management.        Secure APIs: The platform enforces security policies at the gateway level and provides tools to find and secure unmanaged APIs, ensuring robust API security.        Business-Driven Approach: Axway focuses on API adoption by managing, marketing, and monetizing APIs, aligning API strategies with business objectives.        Amplify Enterprise Marketplace: This feature accelerates digital initiatives by making API products more accessible and easier to use, encouraging adoption across various integration patterns and deployments.  Pros of Axway Amplify Platform  Universal API Management: The platform’s ability to handle a wide range of API patterns makes it versatile and future-proof.  Open and Flexible: Axway’s open approach to API management ensures compatibility and integration with a variety of environments and vendors.  Strong Focus on Security: The emphasis on securing APIs, both managed and unmanaged, is a significant advantage for organizations concerned with data security.  Alignment with Business Goals: The business-driven approach of the platform ensures that API strategies contribute directly to achieving business objectives.Cons of Axway Amplify Platform  Complexity for Smaller Organizations: The broad scope and capabilities of the platform might be overwhelming for smaller organizations or those with simpler API management needs.  Resource Intensiveness: To fully leverage the platform’s capabilities, organizations may need to invest in additional resources and infrastructure.RedhatRed Hat 3scale API Management is a versatile and powerful API management platform that supports agile integration and drives business value in the digital world. It is designed to make it easy to manage APIs by sharing, securing, distributing, controlling, and monetizing them on an infrastructure platform built for performance, customer control, and future growth. Red Hat 3scale API Management can be deployed on-premise, in the cloud, or in any combination of the two, offering flexibility for various business needs.Key Features of Red Hat 3scale API Management      API Traffic Control: The platform provides self-managed or cloud components for traffic control, security, and access policy enforcement, ensuring efficient management of API traffic.        API Program Management: It centralizes control of the API program, including analytics, access control, monetization, developer workflows, and more, offering a comprehensive management solution.        OpenShift Integration: Red Hat 3scale API Management integrates with OpenShift for building and running high-performance applications in a contained and automated way.        Hybrid Cloud Support: The platform supports hybrid cloud deployments across all components, allowing businesses to design their API management system in the cloud, on-premise, or a combination of both.        Comprehensive Security: It supports a wide range of encryption, authentication, and authorization protocols, ensuring robust security for APIs.  Pros of Red Hat 3scale API Management  Flexible Deployment Options: The ability to deploy in various environments, including hybrid cloud setups, offers significant versatility for different business models.  Comprehensive API Management: The platform’s extensive features for API traffic and program management make it a robust solution for managing APIs at scale.  Integration with OpenShift: The seamless integration with OpenShift enhances the platform’s capabilities for building and running applications efficiently.  Strong Security Protocols: The focus on comprehensive security protocols is a major advantage for organizations prioritizing API security.Cons of Red Hat 3scale API Management  Complexity for Smaller Teams: The platform’s extensive features might be overwhelming for smaller teams or businesses with simpler API management needs.  Resource Requirements: Optimal performance may require significant infrastructure resources, especially in large-scale deployments.SmartBearSmartBear ReadyAPI is a powerful, low-code API testing platform designed for development teams focused on creating comprehensive test automation across various workflows. It is part of SmartBear’s suite of software quality tools and is recognized for its ability to ensure end-to-end quality for all types of APIs and web services. ReadyAPI is particularly suited for Agile and DevOps environments, offering tools for automated functional, security, and performance testing in a centralized interface.Key Features of SmartBear ReadyAPI      Comprehensive API Testing: ReadyAPI allows for the creation, management, and execution of automated functional, security, and performance tests for APIs and web services.        Support for Various API Types: The platform can handle legacy SOAP to REST services, microservices, and more, making it versatile for different API testing needs.        Flexible API Testing Options: ReadyAPI supports continuous integration and deployment with native support for Git, Docker, Jenkins, Azure DevOps, TeamCity, and more.        Comprehensive Reporting and Analytics: The platform provides insightful dashboards and customizable reporting formats, including JUnit, HTML, CSV, for up-to-date testing metrics.        Data-Driven Testing: ReadyAPI supports importing data from external files or databases and creating synthetic data, enabling realistic, dynamic data usage in API tests.  Pros of SmartBear ReadyAPI  Versatility in API Testing: ReadyAPI’s ability to handle a wide range of API types and protocols makes it a comprehensive tool for diverse testing scenarios.  Integration with CI/CD Tools: The platform’s integration capabilities with various CI/CD tools enhance its utility in Agile and DevOps workflows.  Robust Reporting Features: The extensive reporting and analytics capabilities provide valuable insights into the API testing process.  Data-Driven Approach: The focus on using realistic data in testing ensures thorough and effective API test coverage.Cons of SmartBear ReadyAPI  Learning Curve: The wide array of features and capabilities might present a learning curve for new users or smaller teams.  Resource Intensiveness: Optimal use of the platform’s features may require significant resources, particularly in large-scale testing environments.Software AGSoftware AG’s API Management platform is a comprehensive solution designed to connect data, applications, devices, and more, accelerating innovation with APIs and microservices. The platform is known for its ability to manage the full lifecycle of APIs, from creation to retirement, ensuring they support business strategies effectively. It stands out for its universal approach to API management, accommodating a wide range of API patterns and integration scenarios.Key Features of Software AG API Management      Full Lifecycle API Management: Software AG’s platform manages the entire process of designing, developing, deploying, versioning, and retiring APIs, ensuring adherence to standards and practices throughout the API lifecycle.        Support for Every API Standard: The platform offers comprehensive support for all API standards, including REST, SOAP, GraphQL, and more, staying ahead of the curve in API technology.        Centralized Management, Governance, and Visibility: It provides a centralized view of the entire API landscape, enabling effective management, monitoring of activity, and informed decision-making.        API Marketplace Builder: Software AG allows the construction of a personalized API marketplace, facilitating API discovery, collaboration, and transactions, unlocking new revenue streams.        Easy API Monetization: The platform offers intuitive tools and flexible pricing models for efficient API monetization, turning APIs into profitable assets.        Policy Management: It enables precise control over the API ecosystem with policy management, setting usage limits, access permissions, and other criteria for consistent and compliant API interactions.        API Gateway: A robust API Gateway is included to optimize traffic, ensure security, and enhance overall API performance.  Pros of Software AG API Management  Comprehensive API Lifecycle Management: The platform’s ability to manage the entire API lifecycle makes it a robust solution for API management.  Versatility in API Standards Support: Its support for various API standards ensures flexibility and future-proofing.  Centralized Management and Visibility: The centralized approach to management and governance provides clear visibility and control over the API ecosystem.  Monetization and Marketplace Features: The platform’s focus on monetization and marketplace building offers significant business value.Cons of Software AG API Management  Complexity for Smaller Teams: The extensive features and capabilities might be overwhelming for smaller teams or businesses with simpler API management needs.  Resource Intensiveness: To fully leverage the platform’s capabilities, organizations may need to invest in additional resources and infrastructure.ConclusionChoosing an Apigee alternative depends on your specific needs, whether it’s advanced analytics, ease of use, integration capabilities, or cost considerations. Each of these platforms offers unique strengths and weaknesses, making it essential to evaluate them based on your organizational requirements. With the right tool, you can effectively manage your APIs, leading to more efficient, secure, and successful digital operations.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            You’ve built the API. Now let's generate revenue!            Monetize your APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /api-analytics/api-strategy/Top-10-Apigee-Alternatives/",
          "author": "Dylan",
          "categories": "API-Analytics, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-ready-set-ai": {
          "title": "Cultivate Success: The Ultimate Guide to Growing AI Apps",
          "content"	 : "Commercializing an AI-based product requires turning technology into a marketable product, navigating challenges from development to market entry. Appealing to enterprise buyers is crucial for sustainable, continuous growth, as their interest not only validates your product’s value but also lays the foundation for long-term success and scalability. More specifically, the larger the businesses of your potential customers are, the more of a monopoly your product likely has in the market. By strategically aligning your product offering with enterprise buyers’ needs, you can ensure a robust market presence and pave the way for impactful industry adoption of your AI solutions. How to make money from AI products has everything to with how AI companies or AI startups position their generative AI tools to the greater public.When looking at the initial investment and cost of building an AI solution, it’s important to assess not only the financial feasibility but also the strategic allocation of resources and costs associated with AI model maintenance. By considering factors such as data acquisition, algorithm development, machine learning infrastructure, and potential unknown challenges in the AI landscape, you can better position your product as a viable, long-term solution to your user’s problems.When it comes to the cost of launching and maintaining an artificial intelligence model or product, it becomes evident that technological infrastructure and robust research can significantly contribute to the overall development and investment required to successfully develop and launch an AI tool.Enterprise customers generally require a “tried and true” solution, one that instills confidence rather than doubt. Because the costs of enterprise contracts for AI software can be extremely high due to resource investment (on both ends of the deal), a viable proof of concept (PoC) and trial can be the difference between a signed deal and no customer. This necessitates a comprehensive understanding of how each element of your big data infrastructure impacts the project’s financial scope as well as what costs can be eaten by your organization versus which can be monetized and offset by your enterprise users.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Emphasizing the importance of a realistic budget and cost-effective strategies is critical for long-term success, and longevity is paramount for ensuring enterprise interest. Building an AI system can be expensive; the complexity of AI algorithms and models often requires significant computational power, which can demand high-performance hardware, leading to substantial upfront costs. Beyond this, hiring knowledgeable people to fill data scientist or data analyst roles can be costly.Additionally, the need for continuous, uninterrupted data storage, processing, and model training, coupled with rapid advancements in AI technology all require ongoing financial and technological investments in both hardware and software components (and talent!). These financial constraints can be prohibitively expensive for newer organizations to set up, let alone maintain their newfound business idea. Taking part in the AI revolution may seem like a natural step for your organization, but the associated costs of maintaining AI companies can be prohibitive.Taking an AI technology live involves distinct challenges. Initially, ensuring the readiness of the product and any associated AI model(s) is the most crucial step, encompassing rigorous testing, addressing potential bugs, and optimizing performance to deliver a seamless and accurate user experience that will make even the most critical users content. Once a product is viable, onboarding users effectively becomes equally important, requiring intuitive user design, comprehensive but accessible documentation, and responsive customer service. Striking the right balance between accessibility and security while adhering to compliance standards can be the difference between a successful product with data security and a lawsuit in waiting.Tailoring your marketing and sales strategies to enterprise buyers is also a large part of successful adoption of any natural language processing product. For enterprise clients, the emphasis should be on scalability, security, customization, and alignment with their specific use cases. Effective marketing involves showcasing the AI powered tool’s ability to solve complex challenges, increase efficiency, and provide a significant return on investment, likely through a PoC or sandbox trial. Leveraging Moesif’s user insights during this phase adds a valuable dimension to the deal by offering real-time monitoring on user behavior and API usage, providing your business with actionable data for PoC development and refining strategies for optimal targeting and new market penetration.Moesif’s deep API analytics delve into the nuances of customer behavior and usage patterns, making it easier for you to detect subtle changes and triggers that may signal potential churn or blockages for your AI application. Proactive alerts serve as a preemptive tool for businesses to take timely action and retain valuable customers through data-driven workflows. Moesif enables non-technical business teams to intervene promptly when addressing diminishing customer value, offering insights that facilitate strategic decision-making to enhance customer satisfaction and prolong brand loyalty.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Ready-Set-AI/",
          "author": "Rachael",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "technical-api-development-django-rest-api-tutorial": {
          "title": "Django REST API Tutorial: The Ultimate Guide",
          "content"	 : "Are you ready to start exploring the world of API development with Django? In this tutorial, we will begin an exploration of how to leverage Django together with the Django REST framework, a robust and versatile framework for creating efficient and scalable APIs. This guide is designed to provide you with a clear and structured approach to Django REST API, ensuring that both beginners and experienced developers can navigate and utilize this powerful tool with ease. Join us as we delve into the intricacies of building a Django REST API with the REST API framework, transforming your understanding and skills in API development.What is Django?Django is an open-source web framework. It is written in Python and known for its simplicity, flexibility, and robust features. It follows the “batteries-included” philosophy, providing an all-encompassing set of tools and features required for web development right out of the box. This includes components for handling database operations, URL routing, HTML templating, and security features, among others. Designed to facilitate rapid development and clean, pragmatic design, Django helps developers avoid many common pitfalls of web development, thereby streamlining the process of building scalable and maintainable web applications. Its emphasis on reusability and “pluggability” of components, less code, low coupling, and the principle of Don’t Repeat Yourself (DRY) makes it a popular choice for both beginners and experienced developers in building a wide range of web applications, from small-scale projects to large-scale enterprise solutions.What is a REST API?A REST API, known formally as a  Representational State Transfer Application Programming Interface, is a software architectural style for creating and offering web services. It facilitates communication between various software applications over the internet through standard HTTP methods like GET, POST, PUT, and DELETE. In a RESTful architecture, both data and functionality are treated as resources, accessible via Uniform Resource Identifiers (URIs), commonly in the form of web links. These resources are manipulated using stateless operations, meaning the server does not need to remember any previous interactions and each request from a client contains all the information needed to process it. REST APIs are crafted to be lightweight, straightforward, and scalable, which contributes to their popularity in web services and as a foundation for the backend development of web and mobile applications. They often return data in formats like JSON or XML, allowing for easy integration with various types of clients.What makes Django REST framework a compelling choice?Django REST framework (DRF) stands out as a compelling choice for building web APIs due to its powerful and flexible toolkit that seamlessly integrates with the Django web framework. It offers a clean, simple, and rapid development approach, making it highly efficient for creating RESTful APIs. One of the key strengths of DRF is its browsable API feature, which greatly enhances developer productivity and debugging processes by allowing API endpoints to be easily explored and tested directly from the web browser. Additionally, DRF provides a comprehensive set of features including serialization for ORM and non-ORM data sources, extensive support for class-based views, and a wide range of authentication and permission policies. Its modularity and customizability enable developers to tailor the framework to fit specific project requirements, while its adherence to the DRY (Don’t Repeat Yourself) principle helps in reducing code redundancy. The framework’s robust documentation and active community support further add to its appeal, making it a top choice for developers looking to build high-quality, maintainable, and scalable RESTful web services.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        Setting Up Django REST FrameworkStep 1: Setting Up Your Python EnvironmentBefore building your API, it’s important to set up your development environment correctly. This step ensures that all the necessary tools and packages are in place, paving the way for a smooth development process. Here’s how to get started:      Install Python: Django is a Python-based framework. That means we need to make sure that Python is installed on your system. Python 3 is recommended as it offers better support and more features. You can download Python from the official Python website. During installation, make sure to select the option that adds Python to your PATH variable, which makes it accessible from the command line.        Set Up a Virtual Environment (Optional but Recommended): Virtual environments are always a good idea, and that goes for Django projects as well. This isolates your project and its dependencies from other projects, preventing any conflicts. To create a virtual environment, navigate to the directory where you want your project to be and run:    python -m venv myenv        To activate the virtual environment, use:          On Windows: myenvScriptsactivate      On macOS and Linux: source myenv/bin/activate        Your command line will show the name of the virtual environment, indicating that it’s active.        Installing Django: Once Python is ready, your next move is to install Django. This is effortlessly achieved using Python’s package manager, pip. Execute the command below:    pip install django        This will download and set up the most recent version of Django.        Adding Django REST Framework: To build Web APIs with Django, the Django REST Framework is indispensable. It provides an extensive selection of tools to reduce the friction of creating a Django REST API from scratch. Install it using pip:    pip install djangorestframework            Verify the Installation: After installing Django and Django REST Framework, it’s good to verify that everything is set up correctly. You can do this by running:    python -m django --version      This command should return the version of Django that’s installed on your system. Similarly, you can check the Django REST Framework version in your Python shell:   import rest_framework   print(rest_framework.__version__)With these steps, your environment is now ready for developing APIs using Django REST API. This setup provides a solid foundation for the rest of the tutorial, where you’ll start building your actual API.Step 2: Creating a Django ProjectOnce your environment is set up, the next step is to create a new Django project. This project will act as the home base for your API. Here’s how to get started:      Create a New Django Project: A Django project encapsulates the configurations and settings for a group of Django applications. To create a new project, run the following command in your terminal or command prompt (make sure your virtual environment is activated if you’re using one):    django-admin startproject myproject        Replace myproject with your desired project name. This command creates a new directory with your project name and sets up a basic Django project structure within it.        Understanding the Project Structure: After running the command, you’ll notice a new directory with the name of your project. Inside this directory, you’ll find several files:          manage.py: This is a command-line tool that facilitates various interactions with your Django project.     - myproject/: Named after your project, this subdirectory houses your project’s actual Python package.     - myproject/__init__.py: A blank file that signals Python to treat this directory as a Python package.     - myproject/settings.py: Contains all the settings and configurations for your Django project.     - myproject/urls.py: This file is tasked with defining the URL patterns of your project.      myproject/asgi.py and myproject/wsgi.py: These files are used for deploying your project to a web server.            Modify Settings: Open the settings.py file in your project directory. Here, you can adjust various settings like time zone, static files path, installed apps, middleware, etc. For now, you’ll need to add ‘rest_framework’ to the INSTALLED_APPS section to include Django REST Framework in your project:    INSTALLED_APPS = [...'rest_framework',]            Run the Development Server: To check if everything is set up correctly, you can run Django’s development server. Go to the command line, navigate to your project directory (where manage.py is located), and run:    python manage.py runserver        This command starts a development server on your local machine. You can visit http://127.0.0.1:8000/ in your web browser to see the default Django welcome page. This confirms that your project has been set up successfully.        Initial Migration: Before starting development, it’s a good practice to run the initial database migrations. Django uses these migrations to set up a database schema. Run the following command:    python manage.py migrate        This command applies the default migrations that come with Django, setting up your database with the necessary tables.  With these steps, you have successfully created a new Django project and are ready to proceed to the next phase of building your API with the Django REST API framework. This foundation is crucial as it sets the stage for the development of your API endpoints and data models.Step 3: Create a Django AppAfter setting up your Django project, the next step is to create a Django app. An app in Django is a web application that does something – e.g., a blog, a database of public records, or a simple poll app. A project can contain multiple apps, and an app can be in multiple projects. Here’s how to create your first app within your Django project:      Creating the App: To create a new app, you need to run a command in the terminal (ensure you are in the directory containing manage.py). Use the following command:    python manage.py startapp myapp        Replace myapp with your desired app name. This command creates a new directory inside your project with the name of your app, containing several Python files and a subdirectory.        Understanding the App Structure: The newly created app directory will have the following structure:          migrations/: This directory stores database specific information related to your models.      __init__.py: An empty file that tells Python that this directory should be considered a Python package.      admin.py: Here, you can register your models to include them in the Django admin site.      apps.py: This file is used for app-specific configurations.      models.py: This file is used to define your app’s data models.      tests.py: You can write test cases for your app here.      views.py: This file is used to handle the request/response logic for your web application.            Register the App: To include your app in the project, you need to register it in the settings.py file of your project. Open the settings.py file and add your app to the INSTALLED_APPS list:    INSTALLED_APPS = [...'myapp',]            Plan Your App: Before diving into coding, it’s a good practice to plan out what your app will do. For instance, if you’re creating an API, you should define what resources it will expose, what data models it will use, and how the API endpoints should behave.        Create Models: If your app will be using a database, this is the time to define your models in models.py. Models in Django are Python classes that define the structure of your database tables and the behavior of the data stored within them.        Initial Migration for Your App: Once you have defined your models, you should create and apply migrations for them. Run the following commands:    python manage.py makemigrations myapppython manage.py migrate        These commands create new migration files (based on the models you defined) and apply these migrations to the database, creating the necessary tables.  By following these steps, you have successfully created a Django app within your project. This app will serve as a component of your project where you can develop specific functionalities, such as the API endpoints you’ll be creating using the Django REST API framework.Now, with your app set up and registered, you can start developing its functionalities. This typically involves writing views, defining URLs, and creating templates (if your app has a frontend component).Setting up Django Rest frameworkStep 4: Model Your DataModeling your data is a crucial step in building an API with Django REST API. This involves defining the structure of the data your application will handle. In Django, models are Python classes that define the fields and behaviors of the data you’re storing. Essentially, each model maps to a single database table.Here’s how to model your data effectively:      Understanding Django Models: In Django, a model is the single, definitive source of information about your data. It contains the essential fields and behaviors of the data you’re storing. Django follows the DRY (Don’t Repeat Yourself) principle, so the goal is to define your data model in one place and then derive things from it.        Define Your Models: In your app directory, open the models.py file. This is where you will define your models. For example, if you are creating a blog API, you might have a model for blog posts:    from django.db import modelsclass BlogPost(models.Model):title = models.CharField(max_length=200)content = models.TextField()published_date = models.DateTimeField(auto_now_add=True)def __str__(self):    return self.title        In this example, BlogPost is a model with three fields - title, content, and published_date. Django provides a variety of field types to represent different types of data.        Field Types and Options: Django models offer a range of field types and options. Some common field types include:          CharField for character fields      TextField for large text fields      DateTimeField for dates and times      IntegerField, DecimalField, and FloatField for numbers        Each field type in Django comes with various options you can use to customize its behavior, such as max_length for CharField, auto_now_add for DateTimeField to automatically set the field to the current date and time when the object is created, etc.        Model Methods: You can also define methods within your model. In the example above, the __str__ method is used to return a human-readable representation of the model, which is helpful in Django’s admin interface and shell.        Making Migrations: After defining your models, you need to create migrations for them. Migrations are Django’s way of propagating changes you make to your models (adding a field, deleting a model, etc.) into the database schema. Run the following commands:    python manage.py makemigrations myapppython manage.py migrate        The makemigrations command tells Django that you’ve made some changes to your models and that you’d like to store these changes as a migration. migrate applies the migrations to the database.        Admin Interface: To add your model to the Django admin interface, you need to register the model in the admin.py file of your app:    from django.contrib import adminfrom .models import BlogPostadmin.site.register(BlogPost)        This step is optional but highly recommended, as it provides a convenient, GUI-based way to interact with the data.  By following these steps, you have successfully modeled your data in Django. This model will act as the blueprint for your database, allowing Django to create the necessary database tables and providing a structured way to interact with the data through your API.Step 5: Migrate Your ModelsAfter defining your models in Django, the next crucial step is to migrate these models to create the corresponding database schema. Migrations in Django are a way of applying changes you make to your models (like adding a field, deleting a model, etc.) into the database structure. Here’s how to effectively handle migrations:      Understanding Migrations: Migrations are Django’s way of propagating changes in your models (like adding a new field, deleting a model, etc.) into the database schema. They are designed to be mostly automatic, but you’ll need to know when to make migrations and when to run them.        Making Migrations: Once you have defined or updated your models, you need to tell Django to create migrations for these changes. Run the following command:    python manage.py makemigrations myapp        Replace myapp with the name of your app. This command creates new migration files - which are Python scripts - in the migrations folder of your app. These files are named automatically with a timestamp to help you identify the order of migrations.        Review Migration Scripts: Before applying the migrations, it’s a good practice to review the migration scripts that Django has generated. This can be particularly important in a production environment or when working on complex data models. You can find these scripts in the migrations folder of your app.        Apply Migrations: To apply the migrations to your database, run:    python manage.py migrate        This command looks at all available migrations and applies those that haven’t been applied yet to your database, synchronizing the changes you made in your models with the schema in the database.        Migration Dependencies: Sometimes, your migrations depend on each other, especially when you’re working with multiple apps. Django takes care of these dependencies, but it’s important to be aware of them, especially when rolling back migrations or when you have complex database schemas.        Rolling Back Migrations: If you need to undo a migration, you can use the migrate command with the name of the app and the migration you want to revert to. For example:    python manage.py migrate myapp 0001      This command will revert all migrations up to (and including) 0001_initial.py for myapp.      Using Migrations in a Team Environment: When working in a team, it’s important to regularly pull and push migrations. Conflicts in migrations can occur and need to be resolved carefully, usually by discussing with the team and deciding on the correct order of migrations.        Migrations and Version Control: Migrations should be included in your version control system. They represent important changes and history of your database schema and are necessary for other team members and for deployment.  By following these steps, you ensure that your database schema is always in sync with your Django models. Migrations are a powerful feature of Django that facilitate the evolution of your database schema over time without requiring you to drop your database and lose data.Step 6: Create a SerializerAfter setting up your models and applying migrations, the next step in building your API with Django REST API is to create serializers. Serializers in Django REST Framework are responsible for converting complex data types, such as querysets and model instances, to native Python datatypes that can then be easily rendered into JSON, XML, or other content types. They also provide deserialization, allowing parsed data to be converted back into complex types, after first validating the incoming data.Here’s how to create and work with serializers effectively:      Understanding Serializers: Think of serializers in Django REST Framework as similar to Django Forms. Like forms, serializers handle both the conversion of data to and from Python datatypes and validation of incoming data. They are an essential component in a Django REST API for data handling.        Define Your Serializer: In your Django app, create a new file named serializers.py. This is where you will define your serializers. For each model, you typically create a corresponding serializer. For example, if you have a BlogPost model, you can create a BlogPostSerializer:    from rest_framework import serializersfrom .models import BlogPostclass BlogPostSerializer(serializers.ModelSerializer):class Meta:    model = BlogPost    fields = ['id', 'title', 'content', 'published_date']        In this example, BlogPostSerializer is a ModelSerializer that automatically generates a set of fields for you, based on the model. The Meta class inside the serializer class specifies which model it should serialize and which fields should be included.        Field Types in Serializers: Just like models, serializers can define various field types. The Django REST Framework provides a set of field classes that map closely to Django’s model fields (like CharField, IntegerField, etc.). You can also define custom fields for more complex data handling.        Validation in Serializers: Serializers also handle validation. You can define custom validation methods for your serializer fields or override the .validate() method to add any specific validation logic for your serializer.        Nested Serializers: Sometimes, you might need to include related objects in your serialized output. Django REST Framework allows you to nest serializers within each other. For example, if your BlogPost model has a foreign key to a User model, you can create a UserSerializer and include it in your BlogPostSerializer.        Read-Only and Write-Only Fields: In some cases, you might want certain fields to be read-only or write-only. This can be specified in your serializer fields using the read_only=True or write_only=True arguments.        Using Serializers in Views: Once you have defined your serializers, you will use them in your views to handle the incoming and outgoing data. Serializers will convert querysets and model instances to JSON data that can be returned in HTTP responses, and they will also handle parsing and validating JSON data sent by clients.  By following these steps, you create a bridge between your complex data types (like model instances) and the JSON data that is sent and received in your API. Serializers are a powerful feature of Django REST Framework that streamline the process of data serialization and deserialization, making it easier to build robust and efficient APIs.Creating API Views in DjangoStep 7: Set Up ViewsIn Django REST Framework, views are where you define the logic of your API. They determine how to process incoming requests and return responses. For a RESTful API, you typically need to handle various HTTP methods like GET, POST, PUT, and DELETE. In Django, these methods are referred to as actions. The actions include list, create, retrieve, update, and destroy. Here’s how to set up your views effectively, including examples for each of these methods:      Understanding ViewSets: Django REST Framework introduces the concept of ViewSets, which are classes that provide the logic for handling requests. They are a high-level abstraction over the traditional Django views and are particularly suited for standard database operations. They reduce the amount of code you need to write to create API endpoints.        Creating a ViewSet: In your views.py file, you can create a ViewSet for your model. For example, if you have a BlogPost model and a corresponding BlogPostSerializer, your ViewSet might look like this:    from rest_framework import viewsetsfrom .models import BlogPostfrom .serializers import BlogPostSerializerclass BlogPostViewSet(viewsets.ModelViewSet):queryset = BlogPost.objects.all()serializer_class = BlogPostSerializer        This BlogPostViewSet class will automatically provide list, create, retrieve, update, and destroy actions.        Handling GET Requests (List and Retrieve): The list action in a ViewSet handles GET requests to the root of the URL, returning a list of all instances. The retrieve action handles GET requests to a specific instance, like /api/blogposts/1/, returning that particular instance.        Handling POST Requests (Create): The create action in a ViewSet handles POST requests. It allows clients to create a new instance of a model. The data sent in the request body is validated against the serializer and, if valid, a new instance is created and saved to the database.        Handling PUT Requests (Update): The update action handles PUT requests. It is used to update an existing model instance. The request URL specifies the instance to update, and the request body contains the updated data.        Handling DELETE Requests (Destroy): The destroy action handles DELETE requests. It allows clients to delete an existing instance. The request URL specifies which instance to delete.        Customizing ViewSet Behavior: While ViewSets provide a lot of functionality out of the box, you might need to customize their behavior. You can override action methods like create(), update(), or destroy() to add custom logic.        Example of a Custom Action: Suppose you want to add a custom action to your BlogPostViewSet that allows users to like a blog post. You can do this using the @action decorator:    from rest_framework.decorators import actionfrom rest_framework.response import Responseclass BlogPostViewSet(viewsets.ModelViewSet):# ... existing code ...@action(detail=True, methods=['post'])def like(self, request, pk=None):    blogpost = self.get_object()    # Add logic to like the blog post    return Response({'status': 'blog post liked'})        This custom action like will be available at a URL path like /api/blogposts/1/like/.  By setting up your views in this manner, you create a robust and flexible API that can handle various types of requests. Django REST Framework’s ViewSets and the ability to customize them provide a powerful way to build efficient and maintainable APIs.Step 8: Configure URL RoutingAfter setting up your views, the next step in building your Django REST API is to configure URL routing. This involves mapping URLs to your views so that the correct view is called for each endpoint. Django REST Framework offers a straightforward way to handle URL routing, making it easy to map resources to their corresponding views.Here’s how to configure URL routing effectively:      Understanding Django URL Dispatcher: In Django, the URL dispatcher is used to direct HTTP requests to the appropriate view based on the request URL. It uses a URLconf, which is a set of patterns that Django tries to match the requested URL to find the right view.        Setting Up URL Patterns: In your Django project’s urls.py file (located in the project’s main directory), you will include the URL patterns for your app. First, you need to import the necessary functions and include your app’s URLs. For example:    from django.urls import include, pathfrom rest_framework.routers import DefaultRouterfrom myapp.views import BlogPostViewSetrouter = DefaultRouter()router.register(r'blogposts', BlogPostViewSet)urlpatterns = [path('', include(router.urls)),]        In this example, a DefaultRouter is used for automatic URL routing to your views. The router.register method connects the URL pattern to the viewset.        URL Patterns for ViewSets: When you use a router, it automatically generates the URL patterns for the standard actions in your viewsets (like list, create, retrieve, update, and destroy). For example, the BlogPostViewSet will have URL patterns for listing all blog posts, retrieving a single blog post, creating a new blog post, etc.        Custom URL Patterns: If you have custom actions in your viewsets (like the like action in the previous example), the router will also generate appropriate URL patterns for these actions.        Namespacing URL Patterns: If your project contains multiple apps, it’s a good practice to namespace your URL patterns. This means including the app’s URLs under a specific namespace:    urlpatterns = [path('api/', include((router.urls, 'myapp'), namespace='myapp')),]        This allows you to reverse URLs unambiguously in templates and view functions.        Testing Your URLs: After setting up your URL patterns, you should test them to ensure they correctly map to your views. Django’s shell and test framework can be useful for testing URL configurations.        URL Design Considerations: When designing your URLs, consider RESTful principles. For instance, use simple, readable URLs that reflect the nature of the data being accessed or manipulated. Also, use nouns instead of verbs in your URL paths (e.g., /blogposts/ instead of /get_blogposts/).  By configuring your URL routing properly, you ensure that your Django REST API is well-structured and that each endpoint is correctly mapped to its corresponding view. This step is crucial for the functionality of your API, as it defines how clients interact with your application and access its resources.Step 9: Implement Authentication and PermissionsIn building a robust Django REST API, implementing authentication and permissions is a critical step. This ensures that only authenticated users can access certain endpoints, and users can only perform actions they’re permitted to. Django REST Framework provides a flexible authentication and permissions system that can be tailored to your needs.Here’s how to implement authentication and permissions effectively:      Understanding Authentication and Permissions: Authentication determines the identity of a user, while permissions determine if an authenticated user has access to perform a certain action. Django REST Framework supports various authentication schemes like Basic Authentication, Token Authentication, and OAuth.        Setting Up Authentication: To set up authentication, you need to choose an authentication scheme and configure it in your Django settings. For example, to set up Token Authentication, you first need to add 'rest_framework.authtoken' to your INSTALLED_APPS and run python manage.py migrate to create the necessary database tables. Then, in your settings.py, add:    REST_FRAMEWORK = {'DEFAULT_AUTHENTICATION_CLASSES': [    'rest_framework.authentication.TokenAuthentication',],}            Implementing Permissions: Permissions are used to grant or deny access to different parts of your API. In your views, you can set permission classes to control access. Django REST Framework provides a set of built-in permission classes like IsAuthenticated, IsAdminUser, and IsAuthenticatedOrReadOnly. For example:    from rest_framework.permissions import IsAuthenticatedfrom rest_framework import viewsetsclass BlogPostViewSet(viewsets.ModelViewSet):permission_classes = [IsAuthenticated]# rest of the viewset code        This will ensure that only authenticated users can access the endpoints defined in BlogPostViewSet.        Custom Permissions: If the built-in permissions don’t fit your needs, you can define custom permission classes. A custom permission class is a Python class that extends rest_framework.permissions.BasePermission and overrides the .has_permission() and/or .has_object_permission() methods.        Token Generation and Handling: For token-based authentication, when a user logs in, a token is generated and sent back to the user. This token must be included in the Authorization header of HTTP requests to access protected endpoints.        Securing Sensitive Information: Be cautious with sensitive information. Ensure that tokens and credentials are transmitted securely, preferably over HTTPS. Also, be mindful of storing sensitive data on the client side.        Testing Authentication and Permissions: It’s important to test your API’s authentication and permissions to ensure that they are working as expected. You can write tests to check if the API responds correctly to authenticated and unauthenticated requests, and if the permissions are correctly enforced.        Third-Party Packages for Extended Functionality: If you need more advanced authentication features, like OAuth or social authentication, you can use third-party packages like django-rest-auth or django-allauth.  By implementing authentication and permissions, you add a layer of security to your Django REST API, ensuring that only authorized users can access and modify data. This is a crucial aspect of any API, especially when dealing with sensitive or personal data.Step 10: Thoroughly Test Your APITesting your Django REST API is an essential step to ensure its functionality, reliability, and security. It involves a series of checks and validations to make sure that your API behaves as expected under various conditions. Here’s an expanded guide on how to thoroughly test your API:      Start Your Development Server: Before testing, you need to run your development server. Use the command python manage.py runserver in your terminal. This will start the server, typically accessible at http://localhost:8000/.        Manual Testing with Browser and Tools: Initially, you can perform manual testing by visiting endpoints in your browser. For example, navigate to http://localhost:8000/blogposts/ to test your blog posts API. For more complex requests (like POST, PUT, DELETE), or to test headers and authentication, use tools like Postman or cURL. These tools allow you to craft specific HTTP requests and inspect the responses.        Automated Testing: Automated testing is crucial for maintaining long-term quality and reliability. Django’s test framework allows you to write test cases in Python. Create a tests.py file in your app directory and write test classes that inherit from django.test.TestCase. Test different aspects of your API, including:          Testing HTTP Methods: Write tests for GET, POST, PUT, and DELETE requests. Ensure that your API responds with the correct status codes and data.      Authentication and Permissions: If your API uses authentication, write tests to verify that unauthenticated requests are properly rejected and that users have the correct permissions for different actions.      Edge Cases and Error Handling: Test how your API handles invalid inputs, unexpected request formats, and other edge cases. Your API should respond with appropriate error messages and status codes.            Writing a Sample Test Case: Here’s an example of a test case for creating a blog post:    from django.urls import reversefrom rest_framework import statusfrom rest_framework.test import APITestCasefrom .models import BlogPostclass BlogPostTests(APITestCase):def test_create_blogpost(self):    """    Ensure we can create a new blog post.    """    url = reverse('blogpost-list')    data = {'title': 'Test Post', 'content': 'Test content'}    response = self.client.post(url, data, format='json')    self.assertEqual(response.status_code, status.HTTP_201_CREATED)    self.assertEqual(BlogPost.objects.count(), 1)    self.assertEqual(BlogPost.objects.get().title, 'Test Post')            Continuous Integration (CI): Implementing CI practices, such as using GitHub Actions or Jenkins, can help automate the testing process. These tools can run your test suite on every push or pull request, ensuring that changes do not break existing functionality.        Performance Testing: If your API is expected to handle high traffic, conduct performance testing. Tools like Apache JMeter or Locust can simulate multiple users accessing your API to identify performance bottlenecks.        Security Testing: Ensure your API is secure against common vulnerabilities. This includes testing for SQL injection, data leaks, and proper authentication and authorization checks.        Documentation and Test Cases: Keep your test cases well-documented. This documentation serves as a valuable resource for understanding the expected behavior of your API and can be particularly helpful for new developers joining the project.  By following these steps and regularly running tests, you can catch bugs early, prevent regressions, and maintain the overall health of your Django REST API. Remember, a well-tested API is a cornerstone of a reliable and trustworthy application.ConclusionDjango REST API framework is a powerful tool for your web development projects, whether small or large. To elevate your API’s performance and gain valuable insights, integrate Moesif with your Django projects. Moesif offers real-time analytics, event logging, and user behavior analysis, essential for optimizing your API and enhancing user experiences.For a seamless integration process, follow our Django integration guide. And don’t stop there – sign up for Moesif today and unlock the full potential of your Django APIs.                Monitor and Secure your APIs with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            You’ve built the API. Now let's generate revenue!            Monetize your APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Django-REST-API-Tutorial/",
          "author": "Dylan",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "api-monetization-adding-a-free-trial-to-your-api-monetization-model": {
          "title": "Adding a Free Trial to Your API Monetization Model",
          "content"	 : "When monetizing APIs, specific ways exist to entice customers to begin using your services. Much of the time, users like to see some value before they commit to becoming a full-blown paid customer.One of the most popular billing providers we see used for API monetization is Stripe, and thankfully, Stripe supports free trial models. Paired with Moesif, you can easily adhere to what model you require to support your API monetization needs. Let’s take a look at a few ways that you can implement a free trial to give some incentive to your newest customers.What is a Free Trial?When monetizing APIs, attracting and retaining customers is crucial for the success of your API-based business. Users often want to experience the value of your services before committing to a paid subscription. That’s where free trials come into play.Understanding Free TrialsA free trial is a marketing strategy that allows potential customers to access your API’s premium features or services for a limited period without any upfront payment. It’s like offering a sneak peek into the world of your API, allowing users to explore its capabilities and assess its suitability for their needs.Key characteristics of a free trial include:Limited DurationFree trials have a set time-frame for users to access premium features for free. Depending on your business model, this duration can vary but is typically 7, 14, or 30 days.Access to Premium FeaturesDuring the trial period, users get access to the full range of premium features, enabling them to experience your API’s value to the fullest.No Financial CommitmentUsers don’t need to enter their payment details, although you may still collect them for an easy transition to a paid plan when the trial expires. Regardless, for the length of the trial, users don’t need to make any financial commitments upfront. Having no financial commitment lowers the barrier to entry and encourages more users to try your API.Conversion OpportunityFree trials are not just about providing free access; they serve as a pathway to converting trial users into paying customers. By showcasing the benefits of your premium offering, you aim to persuade users to upgrade once the trial ends.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        When Should You Use a Free Trial?Free trials are a valuable tool in your API monetization toolbox, but using them strategically is essential. Here are some scenarios where implementing a free trial makes sense:1 - New API LaunchWhen introducing a new API or a significant update, offering a free trial can generate buzz and encourage early adoption. It allows users to experience the improvements firsthand.2 - Product ValidationIf you want to test the market and validate your API’s value proposition, a free trial can attract initial users and gather feedback to refine your offering.3 - Converting Free UsersIf your API already offers a free tier with limited features, a free trial can serve as a bridge to convert free users into paying customers by letting them explore the full capabilities.4 - Competitive AdvantageIn competitive markets, a free trial can differentiate your API from competitors and entice potential customers to choose your services.5 - Feature ShowcaseUse free trials to showcase specific premium features or functionality that sets your API apart. Highlight what users can achieve with the premium version.By offering a free trial, you provide potential customers with a risk-free way to evaluate your API’s benefits. It’s a win-win situation, as users gain hands-on experience, and you can turn them into loyal, paying customers.In the upcoming sections, we’ll delve into the practical aspects of implementing free trials for API monetization using Stripe and Moesif, exploring various methods to provide incentives to your newest customers.Creating a Free Trial Manually in StripeIf you want to give users free trials manually, you can use the Stripe Dashboard to do this. The benefit to doing things this way is that you can pick and choose who you give a trial to and for how long.To add a free trial to a subscription, you’ll need to do the following:  Log into the Stripe Dashboard and navigate to the Subscriptions screen.  Click on the subscription to which you’d like to add the free trial.  On the Subscription Details screen, click the Actions button in the top-right and select Update Subscription.  In the modal that appears, under Subscription Details >  Free trial days, click the Add trial days button and input the number of free trial days you’d like to grant.  Once complete, click the Update Subscription button in the lower right of the modal.Here is an example of what the screen will look like for a subscription with a 7-day trial period.After this, the free trial days will be added to the subscription, and the regular billing will start/resume when the trial ends.Creating a Free Trial via the Stripe APIIf you want something more automated and already use the Stripe API to register customers and subscriptions, adding a free trial through the Stripe API is easy. Below is a short snippet of how to do so via the NodeJS API and cURL.Using the NodeJS Stripe SDK, you can add a free trial to a subscription by adding the trial_period_days flag to your call to the subscriptions.create function from the StripeJS library.const subscription = await stripe.subscriptions.create({  customer: customer.id,  items: [    { price: “STRIPE_PRICE_KEY” },  ],  trial_period_days: 7,});With the code above, when a new subscription is created, it will start with a 7-day trial period. During this time, the customer won’t be charged. After the trial period ends, the subscription will automatically begin billing according to the specified price.Ensure that your Stripe environment is properly set up and that STRIPE_PRICE_KEY contains the correct price ID from your Stripe dashboard. Additionally, always test your implementation in Stripe’s test environment before deploying it in a production setting.Alternatively, you can also do this directly through cURL as well. The same command in cURL format will look as shown below.curl https://api.stripe.com/v1/subscriptions   -u sk_test_YourSecretKey:   -d customer=YourCustomerID   -d "items[0][price]=YourPriceID"   -d trial_period_days=7Replace the following placeholders with your actual data:      sk_test_YourSecretKey: Your Stripe secret key (prefixed with sk_test_ for test mode).        YourCustomerID: The ID of the customer for whom you’re creating the subscription.        YourPriceID: The ID of the Stripe price object you want to use.  This command sends a POST request to Stripe’s subscriptions API endpoint, creating a new subscription with a 7-day trial period.Of course, Stripe offers many other ways to access this functionality. Here is a link to the docs, which describe how to do the same with other languages Stripe supports, including Java and Python.Creating a Free Trial in a Stripe Pricing TableThe last way we will cover will be how to add a free trial through a Stripe Pricing Table. For those using the Moesif Developer Portal, this is how you would also do it there since the portal leverages Stripe Pricing Tables for the checkout experience.To add a free trial to a Stripe Pricing Table, you must do the following:  Log into Stripe and go to the Product Catalog page.  Select the Pricing Tables tab and then select the pricing table to which you’d like to add the free trial.  On the Pricing Table Details page, click the Edit pricing table button in the top-right of the screen.  Under Products, select Include a free trial and specify how many days the trial should last.  Once complete, click Continue and Save on the next screen.Below is an example of a Product Catalog with a free trial enabled.After savings, customers will now be granted a free trial as part of the checkout experience. If you want to remove a free trial, you can simply go back through the same steps and uncheck the Include a free trial checkbox.Try Out Moesif For YourselfWant to monetize your APIs and offer your customers a free trial experience? Implement your Free Trial monetization model with Moesif and Stripe and be monetization-ready in minutes. You can try this out by signing up for a free trial of Moesif. Have deeper questions and want to know how to scale out an enterprise-grade monetization solution? Chat with our team of API monetization experts to get started today!                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            You’ve built the API. Now let's generate revenue!            Monetize your APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /api-monetization/Adding-a-Free-Trial-to-Your-API-Monetization-Model/",
          "author": "Matthew",
          "categories": "API-Monetization"
        }
      
    ,
  
    
        "podcasts-developers-podcast-from-vision-to-venture-james-hirst-tyk": {
          "title": "From Vision to Venture Ep. 02: James Hirst, Co-Founder and COO at Tyk",
          "content"	 : "Joining us is James Hirst, Co-Founder and COO at Tyk, an API Management Platform and Gateway. In today’s episode, we’re going to chat with James about some of the challenges that he’s faced as well as some of the big wins they’ve had over at Tyk in the last few years.Matt Tanner, Head of Developer Relations at Moesif, is your host today.Moesif · From Vision to Venture E02: James Hirst - Co-Founder and COO at TykListen to the episode on SoundCloud, Apple Podcasts, YouTube Music, or wherever you listen to podcasts. You can also watch the video on our YouTube Channel. Table of Contents   00:00 Introduction    06:30Significant Challenges as a Founder    14:08Tips for Founders - Funding    17:51Recommendations                  Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        IntroductionMatt - Hey everyone, welcome to the Vision to Venture podcast. Today, we’ve got James Hurst from Tyk joining us and he’s gonna give us a bit of background on Tyk and himself and then we’re gonna dig into some of those great founder questions that we’ve been looking forward to, such as some significant problems that you faced or challenges and kinda how you solved those. And then we’re also gonna dig into a couple of tips and tricks for other founders or aspiring founders that you’ve learned that you’d like to pass out to others that are listening into this podcast. So James, welcome, thanks so much for joining us. There’s a little bit of a time difference between the two of us, but I’m really happy that you’re able to jump on.James - Thank you, Matt, it’s great to be here.Matt - Awesome, so in this first two minutes here, since we’re gonna try and limit everything to about 10 minutes, in the first two minutes, let’s cover a few things. So the first thing that I wanna chat about is who you are, a little bit of background on yourself, and then I’d love to hear a little bit of an intro on Tyk and some of the problems that you’re tackling over at Tyk.James - Sure, okay, thanks, Matt. Well, I’m James Hurst, I’m co-founder of Tyk. I’m based here in London in the UK. Background, I’m a law school dropout. I manage less than a year at law school. I found it a little bit restrictive, a little bit boring. And in parallel to that, I’d learned how to use a dial-up modem, ’cause it was the 1990s, and I found a dial-up internet company who would take me on as a junior. Well, I figured out what I wanted to do with my life, and pretty quickly realized what I wanted to do with my life was work with the internet, ’cause it was pretty exciting. So I launched a web design agency in the UK, ran that for a few years, doing music festivals and online video, and eventually went on to be the director of a digital consultancy in London, which is where I met my co-founder, Martin Buur, and we later founded Tyk together. So that’s how I ended up chief operating officer of Tyk.Matt - Awesome, awesome, yeah. And what we’ll look at next is, what exactly is Tyk tackling? So I mean, being familiar with Tyk, I know lots about it, but for those who are unfamiliar with Tyk, what kind of space are you working in right now, and what are the main challenges that you feel like Tyk is tackling?James - Well, we’ve been trading for about seven years now, but I think it’s really useful to understand why we got started, ’cause that really tells the story of what Tyk is trying to tackle. My co-founder, Martin, at the time, was trying to build a load testing platform, and his idea was to build something that was multi-cloud, that was containerized, that was auto scaling, that was very resilient, and his hypothesis was that by building a load testing platform of that type, he’d have a technical advantage, which would give him a commercial advantage, and he could take a bit of that market share. And this was, I guess, 2013, 2014, when all of those terms, containerization, and Docker, and multi-cloud, was brand new, cutting edge. I mean, that was just a roster of buzz terms. And to do that, he realized he needed to build everything API first. And at the time, there was no platform that helped him do that. Everything that had been built to deal with APIs had been built for telcos, and banks, and these big organizations dealing with really big regulation. Nothing that really respected the world of cloud, of containers, of developing fast, and scaling quickly. So he built his own API management layer. He built his own API gateway, which he made open source, and then built some code around it, so to manage it, to monetize it, to monitor it. Anyway, long story short, the load testing business didn’t go anywhere. It was a very stocky business, because it’s all about very tight margins, and no matter what technical advantage you have, there are some behemoths in that space, and he shuttered that business. But the open source gateway hit at the right time, because he wasn’t the only person who was looking to build products, businesses, services that were API first, were built for this cloud native world, as it’s now called, where everything is containerized, everything can move seamlessly, and scale seamlessly across clouds. And so it just drove adoption, and pretty soon, we saw banks, and big retailers, and big media companies, telcos, the people who were using the old legacy system moving onto ours, and that’s when we launched Tyk. And so Tyk has continued to do the same thing. Tyk has continued to be a platform that enables engineers, developers, architects to build resilient, performant, secure API infrastructure, and allows API product owners to take an API first product, and take it to market, get it out there, get it consumed, get it managed, and make money off it. And so that’s where Tyk came from, and it’s a great place to be, because APIs really are just eating the internet, everything is API driven, and we get to work on some really interesting use cases.Significant Challenges as a FounderMatt - Awesome, that’s a great background for us. So based on that, you talked about how Tyk has scaled up. In that scaling, I’m sure you’ve come across a few more than significant challenges. Many of us in the startup space know it’s not without its pitfalls. What would you say, whether it’s from the inception of Tyk through to now, what would you say is one of the more significant challenges that you faced as a founder? This could be a personal challenge that you faced in that business, or it actually could be something that you faced as a business as well.James - Okay, well, I guess as a founder, every day is just challenge after challenge after challenge, and that in itself is a big thing to get to grips with. You’re constantly being challenged, and that takes a degree of mental resilience, and a support network around you to navigate that over time. I think something that’s probably applicable to anyone who’s looking at scaling a business right now is it’s very difficult to instill, develop, and maintain a culture within a business, and culture’s a misused word sometimes, I think, within businesses, but it’s so important. I think the culture of a business dictates how people respond in the face of adversity. It dictates how people progress, and innovate, and collaborate. The culture’s central to all of that, and yet it’s a very intangible thing, and I think it’s often, culture’s often seen as, I don’t know, the relaxed dress code in a business, or the fact that the business goes out for a few beers after work, or maybe they have a ping pong table in their basement, or whatever, but actually, we’re a remote first business, so I’m in London, and my co-founder is in Auckland, New Zealand. We are antipodes. We couldn’t be further apart on the globe if we tried, and our team is spread across 24 different countries. We’re on every continent in every time zone, so we rely heavily on culture to enable people to get things done, to know how to collaborate, to know how to deliver results, because we don’t have someone sat next to all of our teams in all of these places telling them how to do it. They have to make those decisions themselves, and culture’s what guides it. Now, our definition of culture we took from some smart people at Harvard Business Review, I think, is it’s how people respond when the manager isn’t in the room, and if you dig into that, you realize that a lot of it comes down to access to information, and what level of comfort people have with taking autonomous decisions. Some companies restrict a lot of information, very command and control, everything’s by rote, everything’s by checklist, everything is on rails, and that’s the culture of the business, and it delivers results. Other cultures, such as ourselves, because we’re asynchronously working, because we’re default remote, we rely actually on a lot of transparency, a lot of visibility of all the business metrics, because someone could be waking up in the morning in Colombia, or Nigeria, or Singapore, and have a customer question. We don’t want them to have to wait until someone else in their team wakes up in eight hours time, we want them to take a decision, and take a good decision. So, building a culture like that, making sure it’s understood by people, and delivering it through tangible policies, and information sharing, and backing people when they act the way you want them to act, is really important, and I think it gets overlooked. I think it gets seen as, oh, it’s a people thing, it’s an HR thing, the really important thing is the spreadsheet, but actually, culture’s central to all of this. So, that’s been a real challenge for us, ’cause we are default remote, and our culture’s evolved over time, but it’s something we spend a lot of time thinking about, and is probably the thing that occupies most of the founder’s time, is the culture developing well, is the culture developing results, and if not, how do we adjust it, how do we tweak it?Matt - Gotcha, no, that’s great. Now, one thing that I really like about Tyk is this idea of, I believe, if I recall correctly, radical responsibility, where everyone really is responsible for their output, and I think that’s a great response to this type of challenge, where making sure that things, that everyone is working in the way that they want to work, but also making sure that the way that they’re working is delivering the results that they’d like.James - Yeah, I think you have to be very intentional in the way that you describe these things, and you set these things out, because that isn’t for everybody. Not everybody wants radical responsibility in their role. Some people actually prefer to have a little less radical responsibility, ’cause it can be quite confronting, it can be quite stressful, it puts a lot of onus on yourself, but for some people, it’s ideal, and it helps them flourish, and so right the way from the recruitment process, if you’re clear on the kind of culture you’re trying to build, and the kind of working practices that will support that culture, then it helps you hire the right people, so that you don’t have people causing friction against that culture, and radical responsibility is a term we use, that we try to call that out, we try to make it very clear to people that in the same way that we don’t measure the hours you work, we don’t measure where you work, we completely trust you to be responsible to deliver the results that we need to deliver, in the same way that there’s that total trust in that, what comes with that is total responsibility as well. If you’re gonna work a certain number of hours, a certain working pattern on certain days, then it’s on you to make sure the results come out, it’s not on someone else, and I think examples like that, that you can put out, you can explain to people clearly when they join, means that the culture can kind of flourish, and it’s not just a, what you sometimes see, which is a motivational poster on a wall, or an onboarding pack that people read on day one, and then never go back to, instead saying to people, “Oh no, no, you can work whatever days you want,”but you are responsible for the results, “so make the choice wisely.” That says a lot more, I think, than telling people that we are a family, or so on.Matt - Yes, yeah. (laughs) No, that’s cool, I mean, not just putting out the statement, but also living by that statement, it sounds like that, and you know, you and I have probably both worked at a number of places where that isn’t, there’s a statement on the wall, and the reality of it is completely different, and it’s really great to see that Tyk is living to the words that they put on the wall.James - Well, we try to, and I think it’s important, because it’s, this is something that founders have to do, because there is no correct culture, there is no one culture that is better than all the others. It’s situational, it’s personal, it’s something you create, and you develop with your team, and so it should be a primary focus for a founder, is what kind of culture do we want, what kind of business, do I always want to be signing everything off, because it gives me total control, or do I never wanna sign anything off, and do I wanna have that level of trust in my people, and you know, you gotta pick your own way through this.Tips for Founders - FundingMatt - Right, awesome, well let’s move on from that, and head over to some tips and tricks that you can give to other founders. I would love to hear something around fundraising, ’cause I know that, I believe it was last year, was it last year that you guys raised to Series B?James - Just before that, it was the year before, I think, about 18 months ago, we raised a growth round, a significant growth round, actually.Matt - Right.James - But initially, we started out as a bootstrap, the first three years of Tyk’s existence, we were a complete bootstrap. We started an open-source project, which is where the traction came from, then we started as a side project, so my co-founder and I would work evenings and weekends, pitching to our first customers, securing our first contracts, getting a few customers on-boarded and running, and starting to generate some cash, and at that point, we realized, actually, we can quit our jobs and make this a full-time job as long as we continue to deliver cash. And so, first tip on fundraising is, if you can be in a position where you don’t need to fundraise, in other words, if you can generate cash, no matter how much cash, just some cash, that’s a really powerful statement, because when all’s said and done, the most critical factor in any business is, do you have any cash? And telling people that you will have cash in the future if I can have some investment is a different pitch to, oh, we have cash, but we’d like your investment to get more cash. So I would often encourage folks to maybe think of the fastest way to deliver cash and see if you can do that without raising funding, and then look at the funding round afterwards. It’s easier said than done, but it’s worth exploring, because it fundamentally changes the relationship to fundraising, if fundraising is an accelerator rather than a gate to reaching cash flow.Matt - And when you were looking for those initial customers, so obviously early adopters can sometimes be hard to come by, what kind of pattern were you looking for? ’Cause obviously you wanted to get some cash coming in, you wanted to leave your current full-time jobs, so were you looking for larger contracts and fewer of them or lots of smaller contracts?James - Honestly, we didn’t care. The key thing at the start is you’re just trying to find that product market fit, and you may have an idea that our ideal customer is X, but until you’ve secured a few and worked with them and developed the relationship over time, you can’t be sure. So actually our starting point was, we went to the community of users that we built and were supporting on the open source, who were paying nothing, but were using it, and we just scouted them out. We just contacted them, found out what they were doing, identified areas where actually our paid offering, which sat on top of the open source, might offer some value and set up some meetings. And in fact, our first few customers, I think our first few customers were a bank and a very well-known networking company who paid significant sums. In parallel to that, we then signed up a few people who were paying 50 bucks a month. And over the course of Tyk, those different profiles of customers have both been valuable to us and continue to be part of the mix. So I think in the early days, you want to go broad, you want to go wide, you want to build as many relationships as possible, learn as much as you can, and then figure out which are the ideal customers, ’cause day one, any customer is ideal.RecommendationsMatt - Awesome, now that’s great insights. One last thing I want to ask you is, if you had to recommend a book or a podcast or something to a founder or aspiring founder, which would it be? Maybe one that you’re reading right now or one that kind of started you off? It’s usually a tough question, right?James - Yeah, I occasionally read kind of business-focused books and strategy-focused books, and I think I hesitate to recommend any, ’cause there are so many out there and there are better people to recommend that than me. Actually, what I’d recommend is it’s useful as a founder to get some escapism. I’d recommend “Exhalation” by Ted Chiang. It’s an amazing book, a collection of short stories, thought-provoking, gives you a mental workout that isn’t related to sales or product development. And of course, keeping diverse interests and keeping a good, broad view of life is just as important as doubling down on the unit economics of a business. So I’d recommend that as a broad-term thing. The only business book that permanently sits by my computer is “Good Strategy, Bad Strategy,” which is a great primer for what is strategy and how is it misused and abused and have some really great case studies. So if someone’s looking for a business-focused book, good strategy, bad strategy is a great starting point. Otherwise, go to Ted Chiang. He can change your perspective on life in many ways. It’s great.Matt - Awesome, thank you so much for your recommendation. And James, thank you so much for joining us on our podcast and look forward to chatting with you in the future. If anyone wants to get ahold of you to chat more, where’s the best place for them to find you?James - They’ll find me everywhere. Find me at tyk.io and use the contact form there. You’ll find me on LinkedIn as James Hurst. You’ll find me on, I may still be on Twitter. Are people still on Twitter? I don’t know, @hirstys. You’ll find me there.Matt - Awesome, thank you so much for joining and we’ll chat with you soon.James - Thanks a lot, Matt, I appreciate it.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /podcasts/developers/Podcast-From-Vision-To-Venture-James-Hirst-Tyk/",
          "author": "Dylan",
          "categories": "Podcasts, Developers"
        }
      
    ,
  
    
        "api-monetization-api-strategy-charging-for-api-usage-by-unique-user": {
          "title": "Charging For API Usage By Unique User",
          "content"	 : "When it comes to monetizing APIs, there are a lot of ways you can do it. One popular way to do so is to charge based on how many unique users are utilizing your API. A good example would be charging $7 per month for each user using your API. For a company with five users accessing the API in a given month, their monthly statement would show a charge of $35 (plus any applicable taxes).Implementing this can be challenging; however, a solution like Moesif can help you build and deploy a monetization model in a matter of minutes. Let’s look at how it can be done..  To follow the steps below, you must integrate your APIs with Moesif. Check out our integration documentation for more details on integrating your APIs with Moesif.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Create the Plan and PriceThe first thing we must do is create a plan and price in Moesif so that usage can be accurately tracked against it. Set up a plan in Moesif:  Log into Moesif and navigate to the Product Catalog from the left-side menu.  On the Plans screen, select Create New in the top-right corner.  On the Create New Plan screen, fill in the following details:          Plan name: the name of your plan, such as “My API Plan”.      Billing Provider: select Stripe from the dropdown menu        Select Create in the top-right corner.An example of what the plan would look like in Moesif is shown below.With your plan created, we can move on to creating a price for the plan. To create a price, do the following.  Under the Product Catalog menu item on the left side of the screen, select Prices.  On the Prices screen, select the Create New button in the top-right corner.  On the Create New Price screen, fill in the following details:          Price name: This is the name of our price, such as “Per User Price”      Price ID: Leave this blank, as it will be auto-generated by Stripe.      Linked Plan: Select the plan we created in the previous step.      Pricing Model: Set this to Per Unit (Package)”      Pricing Structure: Set this as “$7.00” per “1” unit(s).      Usage Measurement Method:                  Select Stripe Price Meter.          Select an existing Stripe meter if you have any. Otherwise, select Create New Meter from the dropdown menu and select Month as the period of time to aggregate usage over.          Specify a name for your Stripe meter in Display Name.          In Event Name, specify the name of the meter event that you want to record usage for with the meter.          Set Sum as the aggregation method.                    Set tax structure to Auto.        Select Create in the top-right corner.An example of what the price would look like in Moesif can be seen below.Now that we have our plan and price in Moesif, we can create our Billing Meter to track usage and attribute it to the plan.Create the Billing MeterBy creating a Billing Meter in Moesif, we will create a mechanism to meter API usage and derive how many unique users are accessing the APIs. To make a Billing Meter in Moesif to track unique users, we will do the following:  Go to the Billing Meters screen by selecting the corresponding menu item in the left-side menu.  On the Billing Meters screen, select the Add Billing Meter button in the top-right.  Once on the New Billing Meter screen, fill in the following details:          Give the meter a name in the text input at the top of the screen      In the Link To > Billing Provider dropdown, select Stripe, then select the plan and price created in the previous step.      Under Filters, we will add two criteria:                  Events where user ID exists          Events where event type is API call                    In the Metrics dropdown, select Uniques > Unique Users.        Select Create in the top-right corner to create the Billing Meter.An example of how this would look in Moesif can be seen below.With the billing meter created and active, unique users will now be metered and reported to the subscription in Stripe so usage can be charged accurately.Try It OutAs you can see from our above demonstration, this billing model is easy to implement with Moesif and Stripe. In a matter of minutes, you can begin to meter usage and report it to Stripe (or another billing provider of your choice) to accelerate your API monetization journey. Want to monetize your APIs quickly and effectively? Give Moesif a try by signing up or connect with our team of API monetization experts.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Charging-For-API-Usage-By-Unique-User/",
          "author": "Matthew",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-chatgpt-api-pricing": {
          "title": "ChatGPT API Pricing: How OpenAI Bills in 2026 (Tokens, Batch, Caching)",
          "content"	 : "OpenAI’s ChatGPT API is billed by tokens: every input you send and every response the model generates counts toward your usage, and you are charged a per-token rate that varies by model. In 2026 the pricing structure also includes batch processing discounts, prompt caching discounts, and tiered enterprise rates that did not exist a year or two ago. The mechanics matter for any team building on the API, because token usage at scale becomes a meaningful line item quickly.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        This guide walks through how OpenAI prices the API in 2026, the model tiers worth knowing about, how to estimate cost ahead of time, the discount mechanisms that materially affect the bill, and how teams typically re-bill or charge back LLM consumption to their own customers. Specific prices change frequently; this post focuses on the structure rather than dollar amounts (check openai.com/api/pricing for the current numbers).What is the ChatGPT API?The ChatGPT API is OpenAI’s developer-facing interface to the GPT family of language models. Where the consumer ChatGPT product is a finished application, the API exposes the underlying models for direct integration: you send a prompt, the model returns a response, your application does whatever it needs to with the output.Most developer use cases fall into a few patterns: chat-style applications, retrieval-augmented generation (RAG) over private data, classification and extraction over structured documents, agent workflows that call APIs as part of a multi-step task. The underlying API behavior is the same across patterns; the integration shape and the token budget vary.How OpenAI prices the ChatGPT APIThe fundamental unit is the token. A token is roughly a word or sub-word piece of text; “tokenization” is the process by which text is split into the units the model actually processes. As a rough rule of thumb, 750 words is around 1,000 tokens in English. The exact ratio depends on the language and content (code, structured data, and non-English text tokenize differently).OpenAI charges per million tokens, with separate prices for input tokens (the prompt you send) and output tokens (what the model generates back). Output tokens are typically priced higher than input tokens, because generation is more compute-intensive than reading the prompt.The bill at the end of the month is roughly: sum across calls of (input tokens × input rate) + (output tokens × output rate), with adjustments for any discounts that applied.The current model tiersOpenAI’s 2026 lineup includes several model families, each with its own pricing tier. The pattern: larger, more capable models cost more per token; smaller, faster models cost less. The structure (rather than exact numbers, since prices move):  Flagship general-purpose models sit at the top of the pricing table. Used when output quality is critical.  Mid-tier models trade a small amount of quality for substantially lower per-token cost. The right choice for most production traffic that does not need the absolute highest quality.  Small/fast models are aggressively priced for high-volume, low-complexity tasks: classification, simple extraction, lightweight chat. Some are an order of magnitude cheaper than the flagship.  Specialized models. OpenAI ships dedicated models for chat-style consumer workloads and code, priced separately.  Realtime, image, video, and transcription models. Each has its own rate card. Realtime audio is priced per token by modality; image generation is priced per token with separate input/cached/output rates; video generation (Sora-family) is priced per second; transcription is priced per token plus a per-minute equivalent.  Embedding models are priced separately, on a much smaller per-million-token scale. Used for semantic search and RAG.The right model for a given task is rarely the most expensive one. Production teams that haven’t reviewed their model selection in six months are almost always over-paying.Fine-tuning availability shifts with each model generation. OpenAI continues to support fine-tuning on its current GPT family, but availability and pricing vary by model and have changed multiple times since 2023. If your roadmap depends on a specific fine-tuning capability, verify the current support status on OpenAI’s documentation before committing.Input tokens vs output tokensA practical detail that catches teams by surprise: the input side of a chat call is usually larger than people expect. In a multi-turn conversation, the API is stateless. Every call you make has to include the full conversation history in the prompt, and that history is billed as input tokens on every turn.By turn 20 of a conversation with 100-token user messages and 200-token assistant responses, each new call carries roughly 6,000 input tokens of accumulated history, since the entire prior conversation is re-sent with every request. Production chat applications spend a significant share of their budget on input tokens for this reason.The implications:  Trim conversation history aggressively. Drop or summarize old turns rather than carrying them forever.  System prompts add up. A 500-token system prompt sent on every call is 500 input tokens × every call.  Few-shot examples in the prompt are not free. They are billed as input tokens on every call.The model’s output side is typically capped by the max_tokens parameter, so output cost has a natural ceiling per call. Input cost has no ceiling unless you impose one in your client.2026 discount mechanisms: Batch API and prompt cachingTwo cost-reduction features OpenAI introduced over the last 18 months meaningfully change the cost equation for many production workloads.Batch API. For workloads that do not need real-time responses (overnight enrichment jobs, async classification, large-scale extraction), the Batch API accepts a file of requests and returns results within a stated SLA (typically up to 24 hours). The trade-off is a substantial per-token discount: Anthropic publishes the Batch API as a flat 50% off both input and output tokens, and OpenAI’s pricing page exposes a comparable Batch column on every flagship model. If your workload is mostly batch-shaped, this is the largest single cost reduction available without changing models.Prompt caching. When the same prefix appears in many of your prompts (a long system prompt, a stable instruction header, a RAG context that doesn’t change between turns), the provider caches the prefix server-side and charges a discounted rate for cached input tokens on subsequent calls. Cache hits run at a small fraction of the standard input rate: Anthropic publishes a 0.1x multiplier (90% off) for cache reads, and OpenAI’s pricing table shows a similar order-of-magnitude discount for “cached input” across OpenAI’s current model family. Cache TTLs are short (Anthropic offers 5-minute and 1-hour write durations; OpenAI does not publish a single TTL), so the discount is most useful for high-frequency calls.The combination matters: a RAG application with a stable instruction prefix and overnight batch jobs can routinely cut its bill by 50-70% versus a naive implementation that ignores both features.How to estimate your cost: a worked exampleSuppose you are building a customer-support assistant that handles 10,000 conversations per day, with each conversation averaging 6 turns. The system prompt is 400 tokens, user messages average 80 tokens per turn, and assistant responses average 250 tokens per turn.Per conversation, the input bills compound across turns because the prior conversation history is re-sent on every call:  System prompt re-sent on each turn: 400 × 6 = 2,400 input tokens  User messages, accumulating across turns: 80 × (1+2+3+4+5+6) = 1,680 input tokens  Assistant responses, re-sent as input on subsequent turns: 250 × (0+1+2+3+4+5) = 3,750 input tokens  Assistant responses generated this conversation: 250 × 6 = 1,500 output tokensTotal per conversation: roughly 7,800 input tokens and 1,500 output tokens.Across 10,000 conversations/day: about 78M input tokens and 15M output tokens per day, or roughly 2.3B input and 450M output per month.At current flagship-model rates that runs into thousands of dollars per month. Switching to a mid-tier model or applying prompt caching to the stable system prompt can cut that by half or more without changing the integration shape. Switching to the Batch API does not apply here because the use case is real-time, but a parallel batch job for nightly conversation summarization or analytics would qualify.The shape of this exercise is what matters. Estimate before you ship, instrument once you ship, and revisit when usage doubles.ChatGPT API vs ChatGPT Plus subscriptionA frequent confusion: ChatGPT Plus is OpenAI’s consumer subscription (around $20/month at launch pricing) that gives priority access and feature unlocks for the chat.openai.com web product. It is not API access. API access is billed separately at the per-token rates above.Teams that need both (developers using the consumer ChatGPT product for their own work plus production API integration) end up paying for both separately. Some enterprise tiers bundle, but the default assumption is that the two products are independent SKUs.How ChatGPT API pricing compares to Anthropic Claude and Google GeminiOpenAI is not the only frontier-model API in 2026. The competitive landscape worth knowing:  Anthropic Claude API. Per-token pricing structurally similar to OpenAI, with a flat 50% Batch API discount and explicit prompt-caching multipliers (5-minute or 1-hour write windows, 0.1x cache-read multiplier). The current lineup at the time of writing spans Opus, Sonnet, and Haiku tiers with several recent versions of each in active service alongside Anthropic’s published deprecation schedule. Verify the latest model names and rates on Anthropic’s pricing page before committing to a specific model.  Google Gemini API. Per-token pricing structurally similar, with the Gemini family of models. Cost-competitive at the lower end; bundled deals available for organizations on Google Cloud.The frontier-model market in 2026 is competitive enough that all three providers offer broadly similar pricing structures, with the specific dollar rates moving frequently as model generations roll out. Multi-provider deployment (routing different workloads to different providers based on price-performance) is increasingly common at meaningful scale, and this is where a dedicated AI gateway becomes important: the WSO2 AI Gateway routes outbound calls across providers, applies guardrails, and reports token cost per customer through Moesif’s metering layer.For an evaluation of provider choice that goes beyond pricing, see our API pricing strategy guide. The decision is rarely just about per-token rates.Rebilling and chargeback for LLM consumptionA pattern that has become standard at companies building products on top of the ChatGPT API: re-billing or charging back LLM consumption to the end customer. The customer’s usage of your product drives LLM API calls, and you pass through (often with a margin) the cost of those calls.This is where Moesif shows up in the conversation. Moesif’s usage-based billing records every customer’s API consumption, attributes the underlying LLM costs back to that customer, and syncs the usage to billing providers (Stripe, Recurly, Chargebee). The work of measuring “customer X consumed Y tokens this month” becomes operational telemetry rather than a quarterly finance project.Two common patterns:  Pass-through pricing with a markup. Charge customers a fixed multiplier (1.5x-3x) of the underlying LLM cost. Works when the value to the customer is proportional to the LLM consumption.  Tiered subscriptions with token allowances. Bundle a monthly token allowance into each subscription tier, charge overage at the per-token rate. Works when usage is reasonably predictable and customers value cost certainty.Whichever pattern fits your product, the prerequisite is per-customer LLM cost attribution. Without it, neither pricing model can be executed cleanly.Next stepsThe ChatGPT API pricing model is straightforward in structure (per token, per model) but operationally complex once you account for Batch, caching, multi-turn conversation cost accumulation, and per-customer attribution. The teams that handle it cleanly are the teams that instrument early and revisit model selection every six months.If you are building a product that meters or re-bills LLM consumption, start a 14-day Moesif free trial to see per-customer attribution across your ChatGPT API usage. No credit card required.Frequently asked questionsHow does ChatGPT API pricing work? Per token. You pay separately for input tokens (the prompt) and output tokens (the response), at a per-million-token rate that varies by model. Discounts apply for the Batch API and prompt caching.What is a token in the ChatGPT API? A unit of text the model processes. Roughly a word or sub-word piece. For English text, ~750 words is around 1,000 tokens.Is the ChatGPT API expensive? Cost depends on volume and model choice. A handful of calls per day costs cents; production workloads at meaningful scale run into thousands of dollars per month. The cost equation depends heavily on model selection, Batch API usage, and prompt caching.How is the ChatGPT API different from ChatGPT Plus? ChatGPT Plus is OpenAI’s consumer subscription for the chat.openai.com product (around $20/month at launch pricing). The API is billed separately by token. Different products, different SKUs.Does OpenAI offer volume discounts? Effectively yes through the Batch API (lower rates for async workloads) and prompt caching (lower rates for repeated prompt prefixes). Direct enterprise-tier pricing is available for high-volume customers via OpenAI’s sales team.Can I track per-customer LLM costs in my product? Yes, by attributing the API calls your application makes to the customer that triggered them and accumulating the token counts per customer. Moesif’s usage-based billing layer automates this for teams building products on top of the ChatGPT API.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /api-monetization/api-strategy/ChatGPT-API-Pricing/",
          "author": "Matthew",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-analytics-api-strategy-11-most-popular-tools-for-logging-and-monitoring": {
          "title": "11 Most Popular Tools for Logging and Monitoring API Calls",
          "content"	 : "Monitoring and API logging, facilitated by various API monitoring tools, is no longer a nice-to-have – for many API providers, it has become a key part of ensuring growth and success in the ever-evolving API landscape. There are as many API monitoring tool providers as there are approaches to this topic, but we’ve gathered together a list of the top 11 in the market today. But first, let’s take a look at a few things to cosider.What is API Monitoring?API monitoring is the process of continuously tracking and analyzing the performance, functionality, and reliability of Application Programming Interfaces (APIs). It involves monitoring API calls, requests, and responses to ensure they are functioning correctly and efficiently. A well-structured API log includes timestamps, HTTP methods, endpoints accessed, request and response details, client information, and latency. By keeping a close eye on these interactions, developers can proactively identify and address issues, minimizing downtime and optimizing overall efficiency. Real-time insights into API performance and reliability enable quick troubleshooting and data-driven decision-making, ensuring that APIs deliver a seamless user experience.Key API Metrics to MonitorMonitoring key API metrics is crucial for ensuring the performance, reliability, and security of APIs. Some of the essential metrics to keep an eye on include:      Response Time: The time taken by an API to process a request and return a response. Lower response times indicate better performance.        Error Rate: The number of API calls resulting in non-200 status codes. A high error rate can signal underlying issues that need immediate attention. Error logs provide detailed information about exceptions and errors encountered during API operations, including error messages and stack traces.        Uptime: The percentage of time a service is available. High uptime is critical for maintaining user trust and satisfaction.        CPU Usage: The percentage of CPU resources used by an application. Monitoring CPU usage helps in identifying performance bottlenecks.        Memory Usage: The amount of memory used by an application. Efficient memory usage is vital for optimal performance.        Endpoint Performance: The performance of specific API endpoints. Monitoring individual endpoints helps in pinpointing issues.        API Calls per Minute: The number of API calls made per minute. This metric helps in understanding the load on the API.        Log Data: The data generated by API logs, including request and response data. Analyzing log data provides deep insights into API interactions.  API Logging and Monitoring Best PracticesEffective API logging and monitoring are critical for maintaining robust API performance. Here are some best practices to follow:      Log All API Requests and Responses: Ensure that every API request and response is logged for comprehensive tracking.        Use a Centralized Logging System: Collect and manage log data in a centralized system for easier analysis and troubleshooting.        Monitor API Performance and Reliability in Real-Time: Real-time monitoring helps in quickly identifying and addressing issues.        Set Up Alerts and Notifications for Critical Issues: Configure alerts for critical issues to ensure prompt response and resolution.        Use Log Data to Troubleshoot Issues and Optimize Performance: Analyze log data to identify and fix performance bottlenecks.        Implement Secure Logging Practices: Protect sensitive data by following secure logging practices.        Regularly Review and Refine Logging and Monitoring Practices: Continuously improve your logging and monitoring strategies to keep up with evolving needs.        Use Structured Logs: Structured logs use a uniform format across all entries, facilitating easier querying and analysis.  Real-Time Monitoring and API ObservabilityReal-time monitoring is essential for achieving API observability, providing instant insights into API performance and reliability. By analyzing log data and metrics in real-time, developers can quickly detect and respond to issues, optimizing API performance and reducing downtime. Real-time monitoring enables:      Quick Issue Detection and Response: Immediate identification of problems allows for prompt resolution.        Optimized API Performance and Reliability: Continuous monitoring helps in maintaining high performance and reliability.        Improved User Experience: Minimizing downtime and performance issues leads to a better user experience.        Data-Driven Decision Making: Real-time insights support informed decision-making.        Integration with Other Observability Tools: Seamless integration with other tools enhances overall observability.  Choosing the Right API Monitoring ToolSelecting the right API monitoring tool is crucial for effective API monitoring. Consider the following factors:      Comprehensive Visibility: The tool should offer complete visibility into API performance and reliability.        Real-Time Monitoring: Ensure the tool provides real-time monitoring and analysis of log data and metrics.        Customizable Alerts: Look for tools that offer customizable alerts and notifications for critical issues.        Integration: The tool should integrate well with other observability tools and processes.        Scalability: Choose a tool that can scale to meet growing API demands.        Security: The tool should implement secure logging practices to protect sensitive data.        User Experience: A user-friendly interface is essential for easy monitoring and analysis.  By considering these factors, developers can choose the right API monitoring tool for their needs, ensuring effective API monitoring and observability.1 - MoesifMoesif is a powerful, feature-rich solution for API monitoring and tracking. Moesif also provides detailed insights into API transactions, capturing information about API requests and responses to ensure quality and performance. Rather than synthetic monitoring, Moesif provides real user monitoring, giving businesses the ability to dive into their API performance and API call data. By combining real-time event logging with product analytics (including API endpoint analysis, API testing, and API gateway integration), monetization systems (including quotas, governance, and metered billing), and alerts (both real-time and behavioral, as well as API security), Moesif promises to unlock better revenue performance, improve user retention, and generate unprecedented insight and awareness across your API.Benefits      Provides powerful context and deep introspection through unlocked application-layer visibility        Easy to set up across a variety of systems and implementations  Drawbacks  Governance Rules only available on enterprise tier                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        2 - PrometheusPrometheus is a widely-adopted open source monitoring solution. Prometheus also excels in infrastructure monitoring, helping to ensure system reliability and performance. It has generated quite a following due to its developer outreach backing, with ample documentation, blogs, video content, etc. That being said, Prometheus was designed to generate insights based on metrics, and thus is often considered more an “insights” solution than a “logging” one. Logs are stored locally and are then pointed to by Prometheus when an issue is detected per the metrics – for this reason, users typically use Prometheus in addition to an API logging solution for remote hosting, which makes Prometheus – at least for a subset of users – a half solution for monitoring API products.Benefits      Well-documented and easy to implement        Ample insights generated from small amounts of data  Drawbacks      Lack of native cloud logging support        May not be a complete solve for many users  3 - SematextSematext offers a very popular solution in Sematext Logs. Sematext Logs supports the creation of detailed log entries, capturing essential data for troubleshooting and performance monitoring. Sematext positions their offering as a Log Management-as-a-Service model, supporting the exfiltration of API logs from containers, infrastructure, applications, etc. It facilitates this by supporting Syslog or the Elasticsearch API, which opens it to a wide range of implementations. Because it supports these solutions, you can use Sematext natively, bypassing more expensive options which require third party solutions for performance monitoring.Benefits      Wide support for systems and applications        Cheaper to use than many other solutions  Drawbacks      Requires using either Syslog or Elasticsearch, which can be limiting in some situations        Requires buying into the “as-a-Service” dynamic  4 - PapertrailPapertrail is a logging solution from SolarWinds Cloud, a Software-as-a-Service solution. Each log entry in Papertrail captures critical elements like timestamps and request details, aiding in diagnosing issues. Papertrail allows for a large amount of customizability, and because it’s part of what is typically a holistic solution, it offers substantial cross-service and cross-database API logging that is often much slower and more expensive in other solutions. The drawback, of course, is that Papertrail works best as part of the SolarWinds Cloud service – and if you’re not using that service, your experience may not be as good as it could be with other open-source or free software solutions. If you currently are a SolarWinds customer, the best solution for your API monitor may be Papertrail.Benefits      Highly customizable        Cross-service and cross-database  Drawbacks      Part of a suite of offerings that do better as part of SolarWinds Cloud        Not robustly open-source  5 - ChecklyCheckly is a very new player in the logging software scene, but it brings a lot to the table. Checkly provides insights into key metrics such as response times and error rates, crucial for evaluating API performance. Beyond its powerful synthetic monitoring and API logging, it also allows for deep contextual inspection, diving into payload, responses, headers, and more. Latency, which refers to the time delay between API request and response time, is another critical metric Checkly helps to monitor. This amount of logging and API analytics provide a great amount of context and can improve your visibility quite significantly through application performance monitoring. Pricing is relatively simple and follows a freemium model – that being said, more complex environments might quickly run above the relatively modest 10,000 API calls in the free plan, and the custom plan may have higher costs compared to something that is self-hosted.Benefits      Relatively inexpensive        Provides high context  Drawbacks      Very new player, so not as proven        Limited freemium offering  6 - DatadogDatadog bills itself as an observability platform, offering a relatively feature-complete solution for logging, website monitoring, application monitoring, and reporting for REST API-based products and beyond. Datadog’s suite of monitoring tools provides real-time insights into API performance and reliability. It allows users to set specific metric-based rules to generate alerts across channels like Slack, SMS, email, etc. Datadog is relatively cheap, which is a big reason it has seen good adoption across the board – that being said, it doesn’t provide as much detail as some other solutions in the marketplace, making it a good generalist middle-of-the-road API management solution.Benefits      Feature-complete compared to others on this list        Relatively cheap to deploy  Drawbacks  Limited detail compared to other solutions7 - Sauce LabsSauce Labs used to be called API Fortress, and under that name, it generated a bit of a reputation as a cloud-based REST API monitoring solution. Setting up Sauce Labs for monitoring involves establishing secure connections to ensure data integrity and security. Sauce Labs continues this success by providing testing, monitoring, and reporting, but for those looking principally for API log tooling, Sauce Labs can seem a bit too feature-complete. Ultimately the use case for Sauce Labs is a bit of column A and column B – if you want to collect API logs for the purpose of generating reports and insights, Sauce Labs is a great solution. If you want to log to just log, this might be another in the column of “too good for what you need”.Benefits      Very mature in the market        Widely adopted giving a huge user community  Drawbacks      Perhaps too mature for those looking only for logging        A bit too general for specific use cases  8 - Amazon CloudWatchAmazon CloudWatch brings the proven logging and monitoring approaches on offer from Amazon. Amazon CloudWatch is one of the most flexible API monitoring tools available, offering robust logging and monitoring capabilities. This solution is relatively robust, but the main selling point is in the pricing model – CloudWatch is very flexible, offering pay-as-you-go, which allows for easy scaling. That being said, the solution is tooled specifically for the AWS API gateway, which makes it a hard sell to anyone not utilizing AWS systems. Because of the usage based nature of CloudWatch’s payment structure understanding the performance metric that eat your budget can help guide the decision to use CloudWatch for uptime monitoring or synthetic API monitoring.Benefits      Relatively efficient and feature rich        Pay-as-you-go pricing makes it flexible  Drawbacks  Tooled for AWS specifically, which may exclude it from some stacks9 - AppDynamicsAppDynamics is particularly powerful and is backed by the Cisco product offering. It offers real-time data visualization and analysis, and provides relatively robust solutions like resource waterfalls to show the impact of each element of the API service in the overall efficiency and user experience. AppDynamics provides comprehensive monitoring for any application programming interface, ensuring optimal performance and reliability. That being said, this is another offering that is run by a corporate entity, and its quite expensive – for many developers, this might be a huge negative that is difficult to overcome no matter how beautiful the visualizations are in practice.Benefits      Backed by Cisco and its mature product offering        Real-time data visualization  Drawbacks      Not open source and corporate in nature        Quite expensive  10 - UptrendsUptrends offers a robust multi-step logging and API monitoring tool, selling itself as a logging and alerting system first and foremost. Uptrends offers “Private Checkpoints”, a solution that unlocks testing behind private networks and firewalls. Uptrends is very clearly leaning on its solution as a “problem resolution” one, with one area of the site boasting “Yesterday we detected 332k errors. How’s your site doing?”. The main drawback here is that Uptrends is very clearly selling itself as an analysis tool rather than just an API log tool. For developers looking for logging – and just logging – Uptrends is too far down the monitoring path.Benefits      Robust logging and monitoring        Works on private systems and networks  Drawbacks      Monitoring-heavy makes logging a second thought        Provides ample analysis which may just be a cost increase vs. a utility increase for some stacks  11 - GraphiteGraphite is an open source monitoring and logging system that utilizes a push-based design architecture. What this means is that Graphite allows services to push their API logs into a component called Graphite Carbon, which is then stored in a database for later deep introspection and transformation. Prometheus, another open-source monitoring toolkit designed for cloud-native applications, is often used alongside Graphite for enhanced monitoring capabilities. Graphite’s main selling point is its easy deployment through its native Synthesize product, an automated installation and configuration system which promises to get users up and running with minimal headache.Benefits      Open source and feature-rich        Easy deployment  Drawbacks  Depends on its own solutions - may be stack limitingWe hope this list has helped you see the offerings in the current marketplace. Please note that this list is not exhaustive – nonetheless, it provides a good overview of the most common solutions available today! Feel free to let us know if there are more solutions you’d love included in this list.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-analytics/api-strategy/11-Most-Popular-Tools-for-Logging-and-Monitoring/",
          "author": "Kristopher",
          "categories": "API-Analytics, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-guide-to-monetization-models": {
          "title": "A Beginners Guide to Monetization Models",
          "content"	 : "IntroductionThe art of turning a product or service into profit is not always a straightforward path. ‘Monetization models’ serve as a crucial strategy for businesses to generate revenue. As simple as it sounds, choosing the right monetization model can be a game changer, a “make-or-break” piece that impacts the sustainability and growth of a business.In this blog, we will look at the intricacies of monetization models. We start by covering a monetization model and why choosing the right one is pivotal in today’s business environment. From there, we’ll explore the various monetization models with unique characteristics and applications. Understanding these differences is critical, especially when distinguishing between monetization and revenue models – terms often used interchangeably but with distinct meanings.Lastly, we’ll also dive into real-world examples, showcasing how different business models are applied in practice and how Moesif can help to make this possible. With that, let’s embark on this journey to understand how effective monetization models drive revenue and shape the future of businesses in diverse industries.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        What is a Monetization Model?A monetization model is a blueprint that outlines the methods and processes a business will use to make money from its assets. Monetization models are a core concept in business, defining the strategy a company uses to convert its offerings and products into financial returns.Monetization models can take various forms depending on the nature of the business and the market in which it operates. For instance, a software company might opt for a subscription-based model, where customers pay a recurring fee for continued access to a product. In contrast, an e-commerce platform might implement a transaction fee model, earning a commission on each sale made through its site.The choice of monetization model is strategic and impacts every aspect of a business. It influences product development, marketing strategies, customer relations, and the company’s sustainability. A well-thought-out monetization model aligns with the company’s overall goals, target audience preferences, and the industry’s competitive landscape.In today’s digital age, where consumer preferences and market dynamics constantly evolve, selecting the right monetization model is critical. Even after implementing a monetization model, businesses must be agile, often iterating their monetization strategies to adapt to change. These changes could come from changing market conditions, technological advancements, or consumer behavior patterns.A monetization model is not just a revenue generation mechanism; it’s more comprehensive than that.  The best monetization models consider the various aspects of a business, ensuring that all efforts contribute towards the ultimate goal of sustained profitability and growth.What are the Different Types of Monetization Models?As mentioned earlier, there are plenty of monetization models that might apply to a business. Understanding the various monetization models available is crucial for businesses to select the one that best aligns with their products, services, and market demands. Let’s take a look at some of the most common types of monetization models.Subscription ModelIn a subscription model, customers pay a recurring fee to access a product or service, usually monthly or annually. The benefits of this model include predictable revenue, customer loyalty, and scalability. The downside of this model is that it requires continuous value delivery to retain customers. This approach best suits services that provide ongoing value, like software-as-a-service (SaaS), streaming platforms, or subscription boxes.Freemium ModelAnother common model is the Freemium Model. In the freemium model, basic services are offered for free, while premium features are available at a cost. This makes for easy user acquisition and the potential for a high-volume user base. On the downside, converting free to paid users can be challenging. This approach can work well for digital products like apps, software, or online platforms.Advertising-Based ModelThe Advertising-Based Model is a more traditional model where revenue is generated by displaying ads to users. This approach allows monetization, whilst still making the service free for users. Advertising-based models are scalable and suitable for platforms with large user bases. The downside is that the advertisements can affect user experience and revenue is dependent on high traffic. This model is best suited for media sites, social networks, or any platform with high user engagement and traffic.Transaction Fee ModelA transaction fee model charges a commission, or fee, for each transaction processed through the platform. Customers like this, since they are charged directly for their transaction volume. But, on the downside, this requires high volume transactions to be profitable. Traditional uses of this model include E-commerce platforms, payment gateways, or marketplaces.Licensing ModelWith a licensing model, users pay for permission to use intellectual property or software, similar to a subscription model. This model provides a consistent revenue stream and high profit margins. However, to be successful, it requires strong Intellectual Property (IP) or unique software, and does leave open the potential for piracy. This model is used frequently by software providers, media companies, or any businesses with proprietary technologies.Affiliate ModelIn the affiliate model, commissions are earned by referring customers to other businesses’ products or services, similar to an advertisement model. This approach boasts low overhead costs and is entirely scalable. The downside is that revenue depends on affiliate partners’ success, which you have limited control over. This approach best suits blogs, review sites, or influencers with niche audiences.Each of these models has its unique strengths and challenges, and the effectiveness of each can vary based on industry, market conditions, and customer preferences. When choosing which route to go with, businesses must carefully consider the factors that will best support the growth and sustainability of the monetization model selected.What is the Difference Between a Monetization Model and a Revenue Model?While often used interchangeably, the concepts of a monetization model and a revenue model are distinct. Each concept uniquely influences the business strategy you plan to implement. Understanding the differences between these two concepts is crucial for effectively designing and implementing the mechanisms you will use to generate revenue.Monetization ModelAs we covered earlier, a monetization model is a business’s overarching strategy to generate money from its products or services. It encompasses the broader approach to creating, delivering, and capturing value. Your monetization model is concerned with turning a product or service into revenue. Components of the monetization model include pricing strategies, target audiences, value propositions, and methods of delivering the product or service to customers. For example, A software company might use a subscription-based monetization model where customers pay a recurring fee for continuous access to the software.Revenue ModelOn the other hand, a revenue model is more specific and focuses on the practical aspects of generating income. It details the specific streams of revenue within the broader monetization strategy. The revenue model breaks down the specific ways in which money comes into the business. It includes various revenue streams like sales, advertising, data monetization, and other applicable revenue sources. For example, revenue streams within the same software company might include monthly subscriptions, pay-per-use fees, and revenue from in-app purchases.Key DifferencesA monetization model is broader and more strategic, encompassing the entire money-making approach. A revenue model is more tactical, detailing specific sources of income. The monetization model is a strategic choice about how the business will operate and create value, while the revenue model is more operational, focusing on how that strategy will directly generate income and play into the overall operations of the company. Monetization models may evolve as the business grows and market conditions change, but the revenue model can be more dynamic, adapting to immediate market opportunities and challenges.Understanding the distinction between these two concepts, even though minute, is critical for business leaders. While the monetization model sets the stage for how a business plans to make money, the revenue model dives into the specifics of where that money will come from, thus allowing for a more nuanced and effective financial strategy. The success of a business is heavily dependent on dialing in both of these concepts.Monetization Model Examples and Use CasesTo better understand how monetization models work in practice, let’s explore some examples and use cases across various industries. Below are a few real-world scenarios that illustrate how businesses effectively implement different monetization strategies. Let’s take a deeper look!Subscription Model: NetflixNetflix, a streaming service many of us use daily, employs a subscription model. Customers pay a monthly fee for unlimited access to a wide range of TV shows, movies, and documentaries. By using this model, Netflix can invest in original content and improve platform features, enhancing customer value and retention. As user acquisition goes us, so does the budget to create new content and features to further drive adoption.Freemium Model: SpotifyOne of the most successful freemium monetization models ever, Spotify offers a free, ad-supported version of its music streaming service and a premium, ad-free subscription. This freemium model enables user growth while providing an upgrade path for enhanced features.With this approach, the free version attracts a large user base, some converting to the paid version for an improved listening experience.Advertising-Based Model: GoogleAs we know, Google provides numerous free services (like Search, Gmail, and Maps) but earns most of its revenue through advertising. Businesses pay to display their ads in Google’s search results and partner websites. This model capitalizes on the high traffic across Google’s services, offering targeted advertising opportunities to businesses.Transaction Fee Model: eBayA great example of a transaction fee model is eBay, an online marketplace that charges sellers a fee for each sale made on its platform. This fee is a percentage of the sale price. This model aligns eBay’s revenue with the activity on its platform, to incentivize it to provide a seamless buying and selling experience. As more sales happen on the platform, revenue increases.Licensing Model: Microsoft OfficeOne of the most used software products ever, Microsoft licenses its Office software suite to users and organizations. Customers pay for the software license, granting them the right to use Office applications. It should be noted that Microsoft has also recently adopted a subscription-based model for Office365. The license approach ensures a steady revenue stream for Microsoft while continuously updating and improving the software suite.Affiliate Model: Amazon AssociatesA great example of an affiliate model is Amazon Associates. In this affiliate marketing program, website owners and bloggers earn commissions by referring visitors to Amazon products. This allows Amazon to extend its market reach through various external content creators who earn revenue from successful referrals.These examples highlight the diversity and adaptability of monetization models. By aligning their monetization strategy with their business goals, customer needs, and market dynamics, these companies have managed to not only sustain but also grow their businesses significantly.Monetizing with MoesifFor businesses looking to monetize their products effectively, Moesif offers a comprehensive suite of tools designed to streamline this process. Here’s how Moesif can help in monetizing your products, especially when it comes to API monetization:Understanding API UsageMoesif enables businesses to track and understand customer usage of APIs and apps. This understanding is crucial for developing effective monetization strategies.Driving Usage-Based RevenueWith Moesif, companies can set up billing meters to calculate usage-based metrics, such as API calls or transaction percentages. This helps create a billing model that aligns with how customers use the services.Ensuring Customer SuccessMoesif provides real-time alerts for changes in customer usage patterns. This feature allows businesses to engage proactively with customers, potentially leading to upselling opportunities and enhanced customer success.Empowering External DevelopersMoesif’s solutions include embedding usage metrics in developer portals and offering an open-source portal. This empowers external developers by providing valuable insights into their API usage and the ability to manage their accounts and API keys.Understanding the User JourneyMoesif offers tools to measure user journeys and iterate based on what drives customer conversion. This deep understanding of the user journey is critical to optimizing monetization strategies.Moesif’s platform is tailored for product-led enterprises and startups, providing them with the tools they need to understand their API usage and monetize it effectively. By leveraging these features, businesses can develop more nuanced and successful monetization strategies for their API products.ConclusionIn conclusion, understanding and implementing a suitable monetization model is critical for businesses seeking sustained growth and profitability. As we’ve explored, there are several models to consider, each with its advantages and challenges. From subscription to freemium, advertising-based to transaction fee models, the key lies in selecting a strategy that aligns with your business objectives and customer needs.Moreover, the distinction between monetization and revenue models is crucial for a comprehensive financial strategy. Tools like Moesif provide invaluable assistance in monetizing API products, offering insights into customer usage, and enabling tailored and effective monetization strategies.As the digital landscape continues to evolve, staying adaptable and informed about monetization strategies will be essential for businesses aiming to thrive. Remember, the right monetization model not only drives revenue but also shapes your business’s overall customer experience and long-term success. To try Moesif today, sign up and try out our extensive monetization platform to help you successfully implement many of the monetization models covered above.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/guide-to-monetization-models/",
          "author": "Matthew",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-introducing-the-moesif-developer-portal": {
          "title": "Introducing The Moesif Developer Portal",
          "content"	 : "When exposing your APIs to the outside world, many companies do so through a developer portal. Developer portals have become a staple in the API community, allowing developers to create accounts, check out available APIs, and generate access tokens.Of course, developer portals have many uses, and their look and feel are highly custom. When it comes to actually implementing a developer portal, engineers usually have two approaches: build or buy.When developers decide to build a developer portal, they are essentially creating an entirely new application. Building your own developer portal comes with the work of developing, testing, and maintaining the solution. These initiatives become extensive once you add a checkout, authentication, and other essential aspects of the developer-portal experience.On the other hand, developers may leverage a solution they can buy. A developer portal within an API management solution is the most common. As many have found out, this usually still requires writing code to customize the platform, sometimes leading to a very brittle solution. Are you looking to use these built-in developer portals to monetize your APIs? That’s going to be a stretch.Because of these reasons, we created the Moesif developer portal that meets users halfway. Our open-source developer portal marries the flexibility of a custom solution, with the framework of a pre-built/bought solution. Let’s dig a bit further into the details.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        What Is The Moesif Developer Portal?Aimed at helping developers monetize their APIs, the Moesif Developer Portal is an open-source project that gives developers a framework (and working application) that they can extend to fit their needs. Built with React and NodeJS, the solution presents developers with a mid-way solution for creating their developer portal experience, all with the benefits of both build and buy options.The Developer Portal can connect to your favorite API gateway, and easily integrates with Stripe for a seamless subscriber and checkout flow. For security, the Developer Portal supports Okta, Auth0, and others right out of the box. So, you get an end-to-end developer-portal solution that is scalable and that uses the technologies you’re already leveraging.Our developer portal can be stood up by simply selecting a few environment variables. Then, users can extend its features just like they would with any other React or Node app, but without having to build any of the basics.Why Did We Create It?We found that many developers looking to implement API monetization, and expose those APIs through a developer portal, had trouble finding any viable options. Since gateway-based developer portals are complex to extend for monetization, developers are left with the option to build their own. The challenge with building your own from the ground up is where to start.The Moesif Developer Portal allowed us to provide a springboard to assist users in building a monetization-ready solution that is scalable and highly customizable. By providing the basics, users can get up and running in minutes, and all with scalability and customization inherently built in. The result is the Moesif Developer Portal.How Does It Compare To Other Developer Portals?As already alluded, the Moesif Developer Portal gives developers flexibility in which gateway they want to use for monetizing their APIs. With gateway-based developer portals, users are locked into their chosen platform. If they decide to move to another API management solution, they can’t migrate their developer portal and must create it again from scratch—vendor lock-in at its finest.  With Moesif’s Developer Portal, users can easily flip out their gateway while still maintaining their developer portal functionality, without needing to re-platform it just to switch API gateways.When using a gateway’s developer portal, developers can also be limited in their portal customization. Adding or changing pages and screens within the portal, if even possible, can be highly complex. Some developer portals require users to edit the portal code in a browser or management console and don’t allow for easy source control management. With Moesif’s Developer Portal, users can leverage React and CSS to build out whatever functionality they want and easily keep track of changes in the source control system of their choice.Moesif’s Developer Portal gives developers another option in their “build vs buy” decision, a decision that most companies face when starting out on the road of developer portals.How To Use The Moesif Developer PortalWhen it comes to deploying the Moesif Developer Portal, this can be done in a few easy steps:  Download the Developer Portal source code.  Populate the .env file by following the readme.  Optionally, customize your screens and CSS.  Build and deploy!Once the Developer Portal is deployed, users can easily create accounts, log in, subscribe, and access APIs within the portal.To see a more in-depth overview, check out our latest video detailing how to use the Moesif Developer Portal.Try It Out!To try out the Moesif Developer Portal and to get started with Moesif, you can sign up for a free account today! Are you interested in learning more about how our Developer Portal can help you with your API monetization efforts? Talk to our team of API monetization experts.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Introducing-the-Moesif-Developer-Portal/",
          "author": "Matthew",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-introducing-product-catalog": {
          "title": "Introducing The Moesif Product Catalog",
          "content"	 : "When monetizing APIs, a crucial part of the implementation is setting up the plans and prices that correspond to the different API products and/or usage tiers. For Moesif users, this generally required them to set up their plans and prices in their billing provider, such as Stripe. And then correspondingly configure Billing Meters within Moesif to report usage.With Moesif’s latest feature, Product Catalog, you no longer need to do that. Managing your plans and prices can be done right within the Moesif platform. Let’s take a closer look at this feature.What is the Moesif Product Catalog?When monetizing your APIs with Moesif, many of our users tend to use one or more billing systems, such as Stripe,  Chargebee, Recurly, etc. Because of this, managing prices and billing meters in Moesif required users to bounce between the billing providers’ platforms to configure their products and the Moesif platform to set up their metered billing logic. With the addition of the Product Catalog within Moesif, we’ve introduced a one-stop shop for managing all billing plans and prices.In the Product Catalog, you can easily create, manage, view, and archive plans across your billing providers. Using a simple workflow you can now easily create products within the billing provider that are compatible with Moesif’s API metering and billing solution.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Benefits of Product CatalogA few of the biggest benefits of Moesif’s Product Catalog include:  Business owners can create Plans and Prices right in the Moesif UI, so no access to billing systems is needed - no need to juggle two different systems.  Your billing system is still the system of record (which Moesif pushes to), so no impact to your accounting flows.  You have a single set of APIs to query your product catalog, which provides a layer of abstraction.  This makes it easy to use multiple billing systems in parallel, for example Zuora for enterprise customers, whilst Stripe for self-service.How do you use it?To use the Moesif Product Catalog, you can log into Moesif and select Product Catalog in the right-side menu.From the Plans page, you are able to easily view, add, update, and archive plans across billing providers. On the main page, you’ll be able to see a list of all of your available prices.Below is what a Plan looks like when you create a new one or make edits:For more details on how to add a Plan, you can check out our docs.Once Plans are added, you can also add Prices to them. By clicking on the Prices screen, you’ll first see a list of all the prices that are available within your configured billing providers.By clicking on a Price or clicking the Create New button, you’ll be able to update or add a new price. Below is an example of the add/edit Price screen.To see a more in-depth overview, check out our latest video detailing how to use the Product Catalog feature.By using the Product Catalog, you can easily create and manage the plans and prices you are planning to use to monetize your APIs in a single place.Try it out!Want to monetize your APIs easily and effectively? Give Moesif a try by signing up or connecting with our team of API monetization experts.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Introducing-Product-Catalog/",
          "author": "Matthew",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-implementing-volume-based-pricing": {
          "title": "Implementing Volume-Based Pricing",
          "content"	 : "When monetizing APIs, a popular approach is volume-based pricing. Of all the monetization models you can apply to your APIs, volume-based pricing is one of the easiest to implement. This blog will cover the basics of applying volume-based pricing to your APIs so your customers can be billed accordingly. Let’s start by looking at the finer details of volume-based pricing regarding monetized APIs.What is volume-based pricing?Volume-based pricing is a billing strategy commonly used to monetize APIs. This approach adjusts the cost based on the volume of usage. Typically, usage is measured by the number of API calls or data transferred. This pricing model is designed to cater to diverse user bases, from startups to large enterprises, by offering flexible pricing that aligns with their usage levels. With volume-based pricing, the unit price traditionally goes down as usage increases.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Why Implement Volume-Based Pricing?As mentioned, in most volume-based pricing models for APIs, the price per API call typically decreases as the volume of usage increases. This pricing structure incentivizes higher usage while making the service more cost-effective for large-volume users. Here are a few facets and benefits of volume-based pricing.Tiered Pricing StructureAPI providers often use a tiered pricing model. Each tier has a set range of API calls, and the cost per call decreases as you move to higher tiers. For example, the first 1,000 calls might cost 10 cents each, but calls 1,001 to 10,000 might cost only 8 cents each, and so on.Encouraging Higher UsageVolume-based pricing encourages users to increase API usage since the unit cost becomes more economical at higher volumes. This can be particularly appealing for growing businesses anticipating increased API usage.Cost PredictabilityWhile the per-call cost decreases, customers can also predict their expenses more accurately as they understand how much the cost will reduce as they scale up. This makes cost a very linear calculation, one that is easy to forecast.Balancing Accessibility and RevenueFor API providers, this model helps balance making their services accessible to smaller users (who might be sensitive to high costs at low volumes) while still generating significant revenue from larger customers.Custom Agreements for Very High VolumesSome API providers might negotiate custom pricing agreements for high-volume users, which could deviate from the standard tiered model to better suit large clients’ specific needs. These agreements can still keep the essence of volume-based pricing but on a more customizable level.A massive amount of benefits can be derived from a relatively simple implementation using volume-based pricing models. It’s easy for customers to understand and a great revenue driver at scale.What is Moesif?Regarding API monetization and implementing volume-based pricing, Moesif is a go-to solution for making implementation easy. At its core, Moesif is an advanced API analytics and monetization platform. Within the platform, users can uncover critical insights into how their APIs are being used and efficiently monetize their existing APIs. It’s designed to help companies optimize, troubleshoot, and secure their API infrastructures, with a strong emphasis on aiding in effective API monetization strategies. Let’s look at some functionality in greater detail:API Usage AnalyticsMoesif allows businesses to track and analyze how their APIs are used. This includes detailed data on API calls, error rates, endpoint performance, and user behavior patterns.API Monetization CapabilitiesA standout feature of Moesif is its robust support for API monetization, allowing users to track and charge users for API usage. API monetization with Moesif enables businesses to:  Track Billing Metrics: Understand and monitor API usage in the context of billing and revenue generation.  Identify Revenue Opportunities: Discover which features or endpoints are most popular and generate the most revenue.  Implement Volume-Based Pricing: Easily track and manage usage-based billing, such as volume-based pricing models.Customer InsightsMoesif’s analytics capabilities provide deep insights into customer usage patterns, helping businesses understand which customers are the most active or potentially at risk of churning. This also helps companies determine which APIs they should charge for and how much they can charge for usage.Real-Time Monitoring and AlertsThe platform offers real-time monitoring of API performance, alerting businesses to issues as they arise, such as an increase in latency, enabling quick response and resolution.What is Moesif’s Product Catalog?Moesif’s Product Catalog is a comprehensive tool designed to manage billing plans and prices for APIs. It serves as a centralized platform where business owners can create, manage, view, and archive various billing plans and prices, streamlining the product creation process within Moesif. This integration ensures API product compatibility with Moesif’s API metering and billing solution. The key benefits of using Moesif’s Product Catalog include:  Ease of Use: Business owners can create plans and prices directly within the Moesif UI, eliminating the need to access multiple systems.  Integration with Billing Systems: Moesif integrates with your billing system, which remains the system of record. Any plan or price created in Moesif will also appear in your billing provider, ensuring seamless accounting and financial management.  Support for Multiple Billing Systems: The Product Catalog is designed to work with multiple billing systems simultaneously, such as Zuora for enterprise customers and Stripe for self-service options.How to implement volume-based API pricingTo implement volume-based pricing, we will use Stripe and Moesif. With Moesif’s Product Catalog and Billing Meter functionality, we can do all this right within the Moesif platform!  NOTE: You need to enable and configure the Stripe plugin in Moesif for the steps below to work. The simple steps to set that up are available here.Create the PlanAfter logging into Moesif, navigate to the Product Catalog by clicking the corresponding menu item in the left-side navigation. On the Plans page, click the Create New button in the top-right.On the Create Plan screen, you’ll fill out the plan name and select the billing provider. In this case, we will select Stripe. Once done, click on the Create button in the top-right.Create the PriceNext, we will create the Price in Moesif by clicking on the Price menu item under the Product Catalog on the left-side menu. On the Price screen, click the Create New button in the top-right.On the next screen, you’ll add the price name and select the Linked Plan from the dropdown. In this case, we will select “My API Plan,” the plan we made in the previous step above.Under Pricing, we will select the volume Volume Tiers pricing model. You can add each of your tiers under the Price Structure section. You can click the Add Tier button at the bottom if you need another tier. You’ll see we’ve added four tiers in the example below.Then specify the usage calculation and aggregation method for your pricing model:  The Stripe Price Meter option uses Stripe Meters for usage-based billing. We recommend choosing this option. For this option, you must:          Select the Stripe Meter to attach the price to. You can also create a new   Stripe meter by selecting Create New Meter and then attach the new meter.      Specify the period of time to aggregate the usage over.        The Legacy Usage Aggregation uses Stripe’s legacy usage-records billing.Create the Billing MeterNow that the plan and price have been created, we will create the Billing Meter to record the usage and send it to the correct plan in Stripe. To do that, we will click on the Billing Meters menu item in the left-side menu. On the Billing Meters screen, click the Add Billing Meter button in the top-right of the screen.This will bring you to the Create Billing Meter screen. Here, you will give the meter a name, link it to our plan and price under the Link to section, and create our filter and metrics. Below is an example of a meter for a specific API endpoint (“/test-service”) that will count each API call and attribute it to the “Volume Pricing” price that we created earlier. Once you’ve created your meter, click Create in the top-right to create and activate the meter.ProfitWith this in place, API usage will be billed based on your volume-based pricing model. This example has shown you how easy it is to create a volume-based pricing model and track usage towards it with Moesif and Stripe. Of course, this could also be done with other billing providers, including Chargebee, Zuora, and many others.Try it outWant to try this out for yourself? Be monetization-ready in minutes with Moesif by signing up for a free trial. Want to know how to scale out an enterprise-grade monetization solution? Fill out the form below and an API monetization expert will get you started today.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/Implementing-Volume-Based-Pricing/",
          "author": "Matthew",
          "categories": "API-Monetization"
        }
      
    ,
  
    
        "api-monetization-api-strategy-nordic-apis-creating-great-developer-experiences-with-metrics-and-automation": {
          "title": "Creating Great Developer Experiences with Metrics and Automation",
          "content"	 : "In API development, the discourse often gravitates towards technical specifics and functionality. While attending the 2023 Platform Summit hosted by Nordic APIs in Stockholm, our head of Developer Relations, Matt Tanner, shifts the spotlight to the critical yet underappreciated facet of developer experience. His insights underscore its centrality in API adoption and effectiveness.Here’s some highlights:  Shifting Metrics Focus: Advocating for a transition from general web analytics, like page views, to API-centric metrics that gauge adoption, engagement, usage, and retention.  Essential Developer Experience Components:          Practical, Problem-solving API Design: APIs should be solutions-oriented, addressing tangible problems.      Thorough, Intuitive Documentation and Tutorials: Clear, comprehensive documentation is vital for seamless integration and a positive developer experience.      Streamlined Integration and Onboarding: Simplified onboarding processes enhance initial user experiences.        API Reliability and Scalability: Ensuring API stability and capacity for growth is crucial.  Experience Optimization via Metrics: Providing a detailed analysis of the API usage lifecycle, from site visits to regular API interactions. Identifying and addressing user hurdles, such as integration challenges or errors, is key to enhancing the experience.  Improving Experience with Automation: Automated responses to common issues and proactive user guidance via emails and in-app alerts can markedly better the developer experience. It’s also essential for internal teams to be quickly informed about API issues for swift resolution.  Fostering Engagement and Retention: Use communication tools not just for issue resolution but also for showcasing API features and guiding users towards more efficient utilization. Such proactive engagement can boost retention.  API Governance and Misuse Prevention: Establishing regulations to deter abuse and directing users towards appropriate API practices are vital for a robust ecosystem.Moesif’s vision for developer experience in the API sector goes beyond technical prowess. It involves a comprehensive grasp of developers’ needs and interactions with APIs. Emphasizing practicality, superior documentation, ease of integration, and active engagement, organizations can notably uplift developers’ experiences, thereby enhancing API adoption and long-term utilization.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Nordic-APIs-Creating-Great-Developer-Experiences-with-Metrics-and-Automation/",
          "author": "Dylan",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-deep-dive-building-an-api-monetization-stack": {
          "title": "Deep Dive: Building an API Monetization Stack",
          "content"	 : "API monetization is a nuanced and complex process that demands both strategic business understanding and technical expertise. While at  the 2023 Platform Summit by Nordic APIs, our Head of Developer Relations, Matt Tanner, recently shed light on this topic, emphasizing the bespoke nature of API monetization strategies and the importance of aligning business needs with technical capabilities.Some key takeaways:  The Uniqueness of Each Implementation: API monetization isn’t a one-size-fits-all solution. It requires custom coding and implementation, heavily influenced by specific business needs and available technology.  Business Needs Drive Technology: The process isn’t linear but synergistic. The technology used for monetization must align with business objectives, and sometimes compromises or adjustments are necessary.  Critical Questions for Determining Business Needs: Consider the importance of understanding which APIs to monetize, the monetization approach (subscription or meter-based), the timeline for deployment, and any specific technology requirements.  User Access and Billing Strategies: It’s crucial to decide how users will access APIs (self-service or manual) and how they will be billed (prepaid or postpaid). Each choice has its pros and cons, impacting scalability, customer relationships, and billing complexity.  Pricing and Overages: Determining pricing strategy and handling subscription overages are critical. Options include blocking access upon reaching quotas, charging for overages, or adjustable API tiers.  Technology Stack for Monetization: The stack includes the APIs themselves, a sign-up and enrollment process, an access control mechanism, a system for recording usage, and a billing provider. Each component requires careful consideration to ensure seamless integration and functionality.  Developer Experience and Customer Communication: Maintaining clear communication with users, particularly developers, is essential. This includes providing usage reports, enabling easy integration, and delivering value through the APIs.  Challenges and Solutions: Learn about the challenges in implementing API monetization, such as legal and security delays, and the importance of opting for self-service sign up and credit card payments to streamline the process.API monetization is not just about the technology but also about asking the right questions and understanding the unique needs of your business and customers. As APIs become increasingly integral to business strategies, mastering their monetization will be crucial for sustained growth and success.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Deep-Dive-Building-an-API-Monetization-Stack/",
          "author": "Dylan",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-tracking-product-qualified-leads-with-moesif": {
          "title": "Tracking Product Qualified Leads with Moesif",
          "content"	 : "In the business world, the best case scenario is a client who wants what you can provide. Having a highly engaged, informed, and active consumer means having a partner who can help make your product and market performance that much better. The gold standard in this category is a PQL, or a Product Qualified Lead.What exactly is a PQL? And why is it so beneficial to target? In this piece, we’ll dive into PQLs, what they mean for the business use case, and how Moesif can unlock this huge benefit for your organization.What is a PQL?Product Qualified Leads (PQLs) represent a paradigm shift in how companies approach sales and customer engagement. Unlike traditional leads, which are often gauged based on generalized or demographic-driven data, PQLs are assessed based on their interactions with a product and their proximity to completion in the engagement flywheel.These interactions could be in the form of feature usage, engagement metrics, or other behavioral data that signify a high likelihood of purchasing or upgrading beyond the free or freemium tiers. In the era of SaaS and cloud services, PQLs have become increasingly relevant, offering a more nuanced and actionable lead qualification criterion compared to standard marketing qualified leads (MQLs).Let’s imagine you are running an expensing service that utilizes an API to collect receipts and forward them to relevant partners. If you were to simply target all users – ranging from mega-corporation utilizing a free trial to test the solution to single users sending a single receipt to their friends – you may have a significant cost to securing even a single new customer. This would be highly inefficient, and would be akin to shouting in a storm – after all, without knowing how likely a consumer is to buy into the ecosystem, acting upon every consumer is taking a massive risk.PQLs, however, are potential consumers who have hit a threshold or committed an action that signifies a strong likelihood of becoming a paying customer. In our example, this threshold could be how many receipts are sent, whether the email in question is a corporate address or not, or any number of usage-based tripwires. When one of these triggers are hit, this signals to your sales team that there is a client who obviously wants to use this product that could be engaged with in a meaningful way.The best part about PQLs is that the triggers can be varied for a variety of different PQL categories. Single consumers, corporate consumers, even seasonal consumers can be effectively separated into different buckets, with their own sales processes and funnels.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        How Can PQLs Benefit Organizations?The adoption of PQLs as a key metric can significantly impact an organization’s sales and marketing strategies. PQLs can improve sales efficiency by allowing sales teams to focus specifically on those most engaged with the product, and thus most likely to convert. This increased efficiency and higher conversion rate likelihood can convert an organization into a product-led one, allowing the product to guide sales rather than the inverse.On the customer side, PQL-driven organizations can improve the Customer Experience. By ensuring that the sales team is driven by the number of convertible users, you can put the product experience front and center, creating a flywheel in which only those elements which improve the user experience are prioritized at the top. The consumers can rest assured that they will only be contacted when it is relevant, eliminating a lot of the anxiety that cold-calling or high-pressure sales tactics can deliver.Finally, PQLs can make your sales funnel more data-driven, resulting a healthier customer lifecycle. By basing PQLs on concrete data points derived from actual product usage, the sales process is made more transparent and grounded in measurable customer behavior. Additionally, tracking PQLs helps in understanding how customers interact with a product throughout their lifecycle, enabling more targeted and relevant marketing strategies.How Moesif Helps Track PQLsMoesif can help you leverage and understand your PQLs at scale:  API Usage Monitoring – one of Moesif’s core value propositions is API usage monitoring. This monitoring delivers incredible visibility into the consumer use of the product, allowing developers to identify usage patterns that signify (or suggest) a high potential for conversion.  Segmentation and Analytics – Moesif allows segmentation of users based on their interactions, enabling businesses to identify those who are most engaged with the product. By leveraging Moesif’s detailed analytics, companies can gain insights into which features or aspects of their product are driving the most value for users.  Integration with Billing Systems – integration with business billing systems allows developers to have the lowest possible friction when acting upon PQL opportunities. The higher a process friction is, the more likely it will fail, meaning that Moesif allows you to act upon high value potential and deliver results.  Scalable and Secure Architecture – Moesif’s scalable infrastructure ensures that it can handle vast amounts of data without impacting the performance or reliability of the application. This allows for a wider range of introspection, allowing you to dig into a wider variety of use cases, user types, and potential PQL action points.  Real-World Applications – companies using Moesif have successfully monetized their API usage at scale, and Moesif remains a trusted partner for companies across the web. This means that developers can trust Moesif and its systems for all of its PQL operations.ConclusionSimply put, PQLs has incredible potential upside for most developers. The idea of having a lead who is engaged is amazing, and being able to automatically track this and act upon it at scale is a dream come true for many organizations.Moesif offers a comprehensive solution for businesses looking to leverage the concept of PQLs. By providing in-depth insights into how users interact with a product, Moesif enables organizations to make more informed decisions about their sales and marketing strategies, ultimately driving growth and enhancing customer satisfaction. As companies continue to evolve in the digital landscape, tools like Moesif will become indispensable in harnessing the full potential of data-driven sales and marketing approaches, and those who leverage PQLs now will see compounding success.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Tracking-Product-Qualified-Leads-with-Moesif/",
          "author": "Kristopher",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-performance-issues-with-apigee-private-edge": {
          "title": "Performance Issues with Apigee Private Edge: A Comprehensive Guide",
          "content"	 : "Apigee Private Edge is a natural solution for organizations in need of reliable API management for their private infrastructure. Apigee Private Edge is a comprehensive suite of features designed to facilitate seamless API development, deployment, and monitoring in a secure and controlled environment. As organizations increasingly use APIs to power their products and digital strategies, the performance of these interfaces becomes a major factor in ensuring a seamless user experience. However, when delving into the intricacies of Apigee Edge performance, common performance issues faced by users begin to surface.Understanding Apigee Private EdgeApigee Private Edge sets itself apart with a large set of features intended for the complex, modern API management demands organizations have for their private infrastructure. The comprehensive Apigee API lifecycle management suite facilitates seamless design, development, and deployment for API products.Key architectural components of Apigee Private Edge are designed for scalability and flexibility, which ensures adaptability no matter the workload or business needs. One of the major benefits of Apigee Private Edge is its ability to enhance API security through features like access controls, encryption, authentication, and threat protection for safeguarding sensitive data. Additionally, Apigee’s platform empowers organizations with monitoring, troubleshooting, and performance optimization for API products.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Common Performance IssuesSome of the most common issues that API products experience are centered around latency and response time. These API performance challenges are major considerations around API performance, as users increasingly demand uninterrupted interactions with digital interfaces.Apigee Private Edge users often encounter issues related to latency due to delays in data transmission that can undermine user experience and operational efficiency. Scalability concerns during high traffic times compound these challenges, requiring a robust infrastructure that can handle increased request loads without compromising performance. Security and compliance, while paramount, almost always introduce complexities in a product’s workflow that impact performance, requiring a balance between safeguarding data and ensuring API transactions happen swiftly.Diagnosing Performance ProblemsUnderstanding performance issues within Apigee Private Edge requires an analytical approach that leverages monitoring tools, logging, error tracking, and performance testing. Apigee Edge analytics serve this purpose, offering insight into API health. This internal analytics feature provides a comprehensive view of the platform’s performance, aiding in the identification of potential performance impactors that users or their APIs could encounter.Logging and error tracking aid in pinpointing issues, allowing API developers to understand the flow of requests and identify anomalies or address bottlenecks more effectively. Performance testing strategies such as load and stress testing, are invaluable for proactively identifying system limits and optimizing resource allocation.By taking proactive measures to secure their API uptime, organizations can better understand the scalability of their deployments through the lens of Apigee Private Edge and ensure optimal performance during challenging conditions. By combining monitoring, logging, and performance testing practices, API providers can establish a reliable framework for diagnosing and addressing performance issues in their Apigee environments to create a more resilient product.Apigee Edge Latency IssuesApigee Private Edge performance often suffers from challenges related to caching. Improper configuration or inadequate utilization can lead to lengthy response times, impacting API performance and disturbing the user experience for app developers. Load balancing and traffic distribution, essential components for distributing workloads evenly, can also encounter issues if they are not configured to dynamically adapt to fluctuations in traffic.API design best practices play a key role in performance as a poorly designed API can increase latency and decrease overall efficiency. While this may be suitable for internal APIs on a small scale, more users encountering latency issues can decrease workflow efficiency dramatically. Addressing these issues requires conscious effort, as incorporating best practices in workflows often requires new API documentation and frameworks based around the metrics that matter to your shared flow.A Costly API Management SolutionApigee Private Edge is, at its core, a reasonable option for medium-sized enterprises. But as API volume scales, the Apigee platform can become prohibitively expensive. Beyond this, Apigee is a complex tool that requires a fundamental level of familiarity with API management tools. With a steep learning curve and complex functionality, Apigee analytics can take a while to become familiar with, let alone master. As with any subscription service initial setup costs must take into account the engineering hours required to make the integration viable. Apigee offers a wide range of features with customization options, but a mastery of the product is necessary to utilize them. Beyond this, the Apigee developer portal is fairly limited and lacks deep customization, resulting in a less tailored developer portal and experience.Future-proofing Your Apigee Private Edge Deployment with MoesifMoesif can enhance your Apigee Private Edge experience in several ways:  Cost-Effective Scaling: Moesif’S pricing model aligns with the scaling needs of business owners, no matter the size of their enterprise. This ensures that as organizations grow and their API call volume increases, the cost of using Moesif remains reasonable and scalable.  Granular Analytics: Moesif provides granular insights into API traffic, API request load, performance, error patterns, and more. These detailed analytics help identify specific endpoints, methods, and payloads causing performance bottlenecks, allowing for pointed and targeted optimizations.  Integration Capabilities: Moesif can streamline data flows and enhance the synchronization of analytics data with its native integration to Apigee and other API gateways. This creates a more seamless and real-time monitoring experience for users and organizations alike.  Error Tracking and Latency Detection: Moesif can enhance Apigee’s error tracking capabilities by providing more context around API errors to facilitate more accurate issue resolution within the Apigee Edge Platform. Beyond this, Moesif can measure latency in real-time, creating a proactive notification system that enables rapidly response to fix the issue.  User-Friendly Customized Dashboards: Offering customizable dashboards, Moesif empowers users to tailor an API analytics dashboard according to their specific monitoring needs based on their specific API product or use case. This flexibility allows for custom report building, creating a more personalized and efficient monitoring experience.Moesif has the potential to augment monitoring and analytics capabilities, providing users with a more comprehensive view of their user behavior and API usage across any API gateway. This ultimately empowers businesses to make informed decisions for optimization and performance enhancement of their API products.ConclusionAddressing common performance issues with Apigee Private Edge is paramount for a seamless API management solution. From latency challenges to scalability concerns, a proactive approach to monitoring and optimization is essential. By understanding the significance of staying ahead of potential bottlenecks, organizations can mitigate risks and ensure consistency. With Moesif, gain deeper insights into your API product and efficiently identify, analyze, and address performance issues.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Performance-Issues-with-Apigee-Private-Edge/",
          "author": "Rachael",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-how-to-connect-cloudflare-with-moesif": {
          "title": "How to Connect Cloudflare with Moesif",
          "content"	 : "Integrating Cloudflare with Moesif allows businesses to leverage Cloudflare’s robust network services along with Moesif’s sophisticated API analytics and monitoring. This integration can be particularly beneficial for understanding user interactions with your APIs and improving API performance and reliability, and is quite easy to accomplish.There are two core paths to using Moesif with Cloudflare. The first, the “Simple” method, takes only a few seconds to do, but is somewhat more limited than the “Custom” method. Both will be described in this piece.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Simple Method – How it WorksThis method makes use of the Cloudflare Apps marketplace to integrate Moesif with your Cloudflare services. By navigating to the marketplace and updating your application ID, you can deploy a Moesif integration with very little friction. While this is an easy solution, it does lack some flexibility such as custom hooks which may make it not an appropriate solution for some enterprise or complex environments.Step 1 - Create your Moesif AccountBefore you do anything, you should make sure that you have a Moesif account. To do this, simply head to the sign up page. Here, you will register using a few details:  Organization Name  Your current role at your organization  What you want to achieveStep 2 - Install via the MarketplaceNow that you have your basic Moesif account data, you can navigate to the Moesif App on the Cloudflare Marketplace. From here, simply select the “Preview” button. This will allow you to update your Moesif Application Id, which will authenticate your Moesif integration. Simply click “Finish installing onto your site” to activate.This is by far the simplest way to do this, and is appropriate for most users. Complex hooks or data flows will not be able to use the simple install due to limitations in the default worker deployment, and as such, the custom install route will have to be used.Custom Method – How it WorksThis method will use the Cloudflare Workers Dashboard to create a worker using code provided by Moesif. This worker will allow for more complex flows using custom hooks, and is more appropriate for complex data flows and enterprise consumers.Step 1 - Create your Moesif AccountBefore you do anything, you should make sure that you have a Moesif account. To do this, simply head to the sign up page. Here, you will register using a few details:  Organization Name  Your current role at your organization  What you want to achieveStep 2 - Create the Moesif WorkerTo begin this process, navigate to the Cloudflare Workers Dashboard. On this dashboard, you will be creating a Moesif worker using some custom code.Select “Manage Workers”, and then click the “Create a Worker” option. Here, you will be given a script window, which will allow you to set the behavior and constraints of the worker. Copy the code from the Moesif Cloudflare GiHub repo and paste it into the script window. Please note – you will need to add the Moesif Application Id by updating “INSTALL_OPTIONS.applicationId”. You may also update “INSTALL_OPTIONS.urlPatterns”, but for most users, simply updating “INSTALL_OPTIONS.applicationId” is sufficient.Finally, you might want to consider renaming your worker. While Cloudflare auto-generates a name, it is typically machine-friendly, not user-friendly, and as such can be complicated to work with. Naming it something like “moesif-logger” can go a long way towards cleaning up your code.Step 3 – Set the Worker RouteNow that you have your worker, you will need to set its route. Navigate back to the Cloudflare Workers Dashboard, and select the “Add Route” option. From here, you should utilize the route for your domain – in other words, the route pattern for Moesif.com would be moesif.com/. Select the worker you created in order to apply the route.To finalize this process, you should ensure that your worker and route are configured correctly. To do this, issue a request via the shell with something like the following:curl https://my-cloudflare-domain/my-pathThis request should show up in your Moesif event stream. Do note that if you are testing in the Cloudflare Playground, the response may be empty as there is no origin server. When you release to production, you should set  logIncomingRequestsback to false to prevent duplicate events.ConclusionCloudflare is incredibly powerful, and it becomes even more so with the inclusion of Moesif. By combining the logging and filtering powers of Cloudflare with the analytics and insights of Moesif, you are unlocking a powerhouse of information and data that can drive more effective business decisions and resource allocations.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/How-to-Connect-Cloudflare-with-Moesif/",
          "author": "Kristopher",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-apigee-pricing": {
          "title": "Choosing the Right API Monetization Solution: Apigee vs. Moesif for Different Customer Sizes",
          "content"	 : "As companies increasingly rely on API based systems and processes, API management becomes more and more important, serving as the linchpin for facilitating communication between internal and external applications or services. Deciding which API management solution best fits your business’ use cases is no easy feat. Picking a tool that caters to the complex needs of your organization’s digital transformation starts with understanding the market and your products’ needs.Apigee, widely known for its robust scalability and powerful capabilities, has often been associated with large enterprises and API security, while Moesif boasts a versatile approach, successfully navigating large-scale deployments with industry giants like UPS and Deloitte and mid-sized to small enterprises thanks to its Product-Led Growth (PLG) strategy.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Apigee: Tailored for Large EnterprisesApigee API management is an industry leader APIM space, particularly when it comes to the demands of large enterprises. Its largest strength lies in its scalability, providing a powerful infrastructure capable of handling high volumes of API traffic. Apigee Edge has the ability to scale resources means that even in the face of increasing workloads or requests, performance remains consistent and reliable.Apigee also prioritizes security and compliance, recognizing the importance of protecting user’s sensitive data and adhering to industry regulations. The API management platform balances advanced security protocols and compliance measures to instill confidence in enterprise customers that their API ecosystems and products are not only functional but also well-guarded against potential threats. The emphasis on scalability and security makes the Apigee API platform a go-to solution for large enterprises seeking a dependable and secure foundation for their extensive API management needs.When it comes to deployments, the Apigee pricing structure fits the needs of expansive enterprises. Or, rather, of enterprises that are currently or will be able to financially afford scaling. Because Apigee is designed for handling large amounts of API traffic, the initial costs of standing up the management tool may not cover the total integration cost. Apigee is a complex tool for developers to adjust to, and is often perceived as expensive due to its enterprise-grade capabilities that may exceed the requirements of smaller businesses. Apigee understands the complexities inherent in managing large-scale API cloud ecosystems, offering a pricing model to accommodate the scalability needs of large businesses. While specific pricing plans vary, Apigee generally provides flexible options through traditional subscriptions and Pay-As-You-Go (PAYG) options, allowing enterprises the flexibility to scale their API management solutions according to the magnitude of their operations.This flexibility around pricing and the ability to customize plans to match the size and scope of a large deployment make Apigee an attractive choice for enterprises in need of not only robust API management capabilities but also a pricing structure that scales with their evolving needs. This ensures that as enterprises grow, their API management solution remains both powerful and cost-effective.Moesif: Versatility Across All Customer SizesMoesif sets itself apart with remarkable flexibility by catering to a diverse range of customer sizes and apps. Moesif’s capacity to handle the API analytics needs of sizable organizations and small enterprises alike proves its capabilities extend to enterprises of any size. Moesif employs a strategic approach to cater specifically to mid-sized and small enterprises through its innovative Product-Led Growth (PLG) strategy. This approach empowers developers with a self-service model, allowing businesses of any size to leverage Moesif’s capabilities, enabling them to scale their API product or solution seamlessly.  Notably, Moesif also successfully supports large enterprises, exemplified by industry leaders like UPS and Deloitte -the adaptability of Moesif for various customer scales is attributed to key features embedded in the platform.Moesif’s analytics and API monitoring capabilities, for instance, offer a highly detailed level of insight that can benefit both large enterprises managing complex products and smaller organizations seeking to streamline solutions. Additionally, Moesif emphasizes user-friendly API documentation, a customizable developer portal, and easy integration options for low and no-code users, ensuring accessibility for businesses at different stages of development and across functional teams to facilitate meaningful API testing. As part of its commitment to adaptability, Moesif’s pricing structure accommodates the diverse needs of businesses, offering plans that scale with specific requirements for large enterprises and smaller organizations alike. This flexibility in pricing ensures that Moesif remains an inclusive choice, providing robust API management solutions regardless of a customer’s size or scale of operations.Feature ComparisonScalability and Performance Benchmarks  Apigee: Renowned for its scalability, Apigee can handle high volumes of API traffic. Its architecture ensures that as the API demands increase or business scales, Apigee distributes the load across multiple instances seamlessly. Apigee’s ability to maintain low latency even during usage spikes makes it an ideal choice for enterprises with extensive or fluctuating API workloads.  Moesif: Moesif emphasizes ease of scalability, evidenced by its successful partnerships with large and small enterprises alike. Moesif offers flexibility and confidence to the API management needs of corporations of any size. Moesif’s performance benchmarks can handle substantial data while providing deep insights, making it suitable for businesses with varying levels of API traffic or API call volume.Customization and Flexibility  Apigee: Apigee meets various needs of large enterprises through extensive customization options. Organizations can create custom policies to fine-tune security, access management, and analytics.  Moesif: Moesif’s flexibility makes it attractive to mid-sized and small enterprises. Moesif offers user-friendly interfaces and easy, low, and no-code configuration. This makes it ideal for businesses needing a powerful yet straightforward API analytics solution.Integration Options and Ease of Use  Apigee: Apigee offers various integration options and connectors for compatibility with diverse API systems. However, these API management tool features can pose a steep learning curve for users, especially in larger enterprises with complex tech stacks or products.  Moesif: Moesif prioritizes ease of use through its PLG strategy. The platform provides simple integration options and intuitive interfaces, making it suitable for users with varying levels of technical knowledge. Moesif’s user-friendly design suits organizations seeking an approachable but granular API program. Unlike Apigee, Moesif interfaces with various API gateway partners and SDKs, so users are not locked into a single vendor or cloud endpoint.Total Cost of Ownership (TCO)Cost Implications with ApigeeApigee offers a pricing structure designed to accommodate the diverse scales of businesses. While Apigee may be the best fit for large enterprises with expansive budgets, mid-sized and small businesses should assess whether Apigee’s advanced features align with their use case. This cost consideration should extend beyond initial investment, to account for potential scaling of API request volume or ongoing maintenance. While Apigee’s comprehensive features are valuable for large enterprises, small businesses may not require the same level of scalability, making the cost of Apigee seem disproportionately high.Cost-Effectiveness and Value with MoesifMoesif’s pricing structure was designed to provide scalable solutions for different customer sizes. Moesif’s pricing structure reflects a balance between affordability and functionality. It’s important for businesses to understand not just the upfront costs of integration but also the long-term benefits and potential savings when opting for Moesif. Thanks to Moesif’s flexible pricing tiers, organizations can easily scale their plans as feature needs become necessary or irrelevant to their API program.Decision-Making ConsiderationsThe choice between Apigee and Moesif hinges on factors such as scalability, customization needs, and financial constraints. For large enterprises with complex API ecosystems, Apigee’s robust features and inherent scalability may be apt. Conversely, mid-sized and small enterprises may find Moesif appealing for its user-friendly but accurate reporting, ease of use, and cost-effectiveness. Businesses should align API management choices with their size and product requirements. Carefully consider factors like API traffic, customization, forecasted growth, and budget.In the end, organizations can find the perfect API management solution for their digital goals by exploring their unique deployment needs. Plus, they can easily test Moesif’s solution with a free trial.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Apigee-Pricing/",
          "author": "Rachael",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "podcasts-developers-podcast-from-vision-to-venture-josh-twist-zuplo": {
          "title": "From Vision to Venture Ep. 01: Josh Twist, Co-Founder and CEO at Zuplo",
          "content"	 : "Joining us is Josh Twist, Co-Founder and CEO at Zuplo, one of the most cutting-edge gateways that are out there today. In today’s episode, we’re going to chat with Josh about some of the challenges that he’s faced as well as some of the big wins they’ve had over at Zuplo in the last few years.Matt Tanner, Head of Developer Relations at Moesif, is your host today.Moesif · From Vision to Venture E01: Josh Twist - Co-Founder and CEO at ZuploListen to the episode on SoundCloud, Apple Podcasts, YouTube Music, or wherever you listen to podcasts. You can also watch the video on our YouTube Channel. Table of Contents   00:00 Introduction    05:03Significant Challenges as a Founder    08:46Trailblazing vs Chasing the Competition    11:03Lessons From Working With a Co-Founder    13:35Stress Management and Self-Care    19:02Tips for Founders  IntroductionMatthew: Hello everyone and welcome back to the Vision to Venture podcast.Today I have with me Josh Twist from Zuplo, which is an API management platform that we’regoing to hear lots about today.Then we’re also going to chat about some stuff that Josh has experienced as a founder ofan early stage company.With that being said, Josh, we’re going to hand it over to you to do a nice little introfor yourself and then give us a bit of background on Zuplo and some of the stuff you’re tacklingthere.Josh: Awesome.Hey Matt.Yeah, so Josh Twist, co-founder and CEO of Zuplo.Some background, I’m British if you can still hear the remnants in my accent, but live in the West Coast ofthe US.Used to be an engineer with Microsoft.I moved over with them in 2010 and live in Redmond just outside Seattle.I was there for five years, founded several services in Microsoft Azure, Azure Logic Apps,Power Automate Pro, mobile services, and most notably Azure API Management, which is a competitorto the product I’m shipping today.Then I was at Facebook for five years.Led product for Facebook Analytics and for an org called New Experiences, which was reallyfun.I was one of the first teams to try and take on TikTok.Lots of good stories there, but not super relative to DevTools and so on.And then I was at Stripe for a little while where I was head of product for payment methods,so I led the payment methods org, including all the APIs.So I’ve been a customer and a vendor and I’ve been around APIs my whole career and verypassionate about developer tools and developer experiences, which you asked for some backgroundon Zuplo.I’ll sort of start with the notion to me that API management as a category is kind of brokentoday.And I saw this as a vendor at Microsoft when we launched a product, Azure API Management,through an acquisition, we acquired a company.It was around the time Apigee was coming to fame long before they were acquired by Google.And instantly it struck me just how almost hostile these tools are to developers in termsof how alien they feel.You know, your configuration is stored in databases, they don’t work naturally withGit.You have to write weird scripts to deploy them.They’re heinously unprogrammable.Yes, most of them have some kind of extensibility model with plugins, but it’s really hard andit should be so easy for a developer to deploy their superpowers and just extend the gatewayand it should feel like writing code anywhere else.And so we’ve designed Zuplo to solve all of those problems.It’s extremely collaborative and one of the most important things we think about is shorteningthe feedback cycle.So you can deploy a new environment in Zuplo in under 20 seconds, you can have as manyenvironments as you like, speed is everything to us.And then another thing that’s unique about us, you know, when we started this journey,I thought, well, what does the future of a gateway look like?If you’re starting from scratch, how would you design the future gateway?And instantly we knew we had to build something from the ground up because we knew that edgecompute is the ideal place to run and host and operate a gateway.And so we built Zuplo from the ground up.We run at the edge, we deploy you to 300 data centers around the world, so we’re the bestsolution for multi-cloud.We’re the only solution really if you’re doing any edge compute and you want to have a gatewayin front of it.So that’s a little intro to Zuplo.                Grow Your APIs With Moesif              14 day free trial. No credit card required.              Learn More        Significant Challenges as a FounderMatthew: Awesome.Thank you so much.I’m sure many of the listeners will go and check that out, especially if they’re lookingfor a new API solution or if you’re looking to replace the one that you already have withsomething just a little more performant and edge-based.Now let’s move on to some founder-specific questions.So you and I have chatted lots over the last few years and there’s been some really, reallygood insights that you’ve given me.One thing that I really want to touch on is, and every founder is going to have tons ofstories, but maybe you can pick one where you’ve come across a significant challengeas a founder and then you’ve had to solve that challenge for the better, hopefully,or maybe it’s something that you’re still dealing with.But yeah, I would love to hear just a little bit about some of the challenges that you’vefaced as a founder and how you solved those?Josh: Yeah, I think this is a good one to talk about because I don’t think it’s talked aboutenough and that is just how hard it is to get started, to get traction going.You only hear really when you look at TechCrunch and YC or wherever you source your information,you only hear about all these rocket ships where it looks like overnight success or lightningin a bottle.And let’s be brutally honest, most businesses are not like that.I had the benefit of working at these large companies before where I started lots of newservices and just had access to such incredible distribution that I’m used to getting like100 customers on day one or if it’s in the consumer space, millions when I was at Facebook.That’s really hard when you don’t have that distribution when you’re not at BigCo.And at first it’s disheartening.I just chatted with a fellow founder, friends with a guy called Vijay Raju who said he wentthrough the same thing where we kind of built the product, we had a hypothesis and we knewwe’d build this extremely differentiated experience so people just had to taste it and they wouldknow it was better and they’d come and use it.But actually getting people to just come and try it out and motivating them and understandinghow to pitch the value of the product was so hard.It took us nearly a year really before we started to see an uptick and things are goingvery well now but just having the tenacity to stick with it.And it’s interesting.I wonder if you think about YC companies, they don’t get a lot of time to prove they’regoing to have growth and I don’t want to say you shouldn’t pivot but I would say just beaware there’s lots of startups out there where it is not an overnight success.I think another example that’s not from the dev tool space is Figma which is an enormouslysuccessful design tool.I believe their first few years they weren’t doing much at all, they were like in the wildernessfor a long time.So having the stomach to sit that out, the tenacity to believe in your vision and keeppushing but I mean maybe this is something we’ll talk about, it’s like finding ways tolearn as fast as possible, learn discrete things that are going to improve your gameand how you go to customers and shorten that feedback cycle and have a lot of disciplinearound it.That was how we solved the problem, constantly launching, constantly trying new things.I think the lesson learned for us was we thought we could build a slither of a product, anAPI management product that would say focus on things like rate limiting but the realityis anyone deploying a gateway is aware that these things can do lots of things and doesn’twant to take the effort of deploying something that only does one thing even if it does itreally well.And so it just took us a while to hit table stakes until we had enough and now I thinkwe’re really quite competitive, enough features, enough policies, enough of a thing to earna seat at the table when people are sending out RFPs against our competitors like theusual names in the Magic Quadrant.So that was I think the biggest challenge I think of when I look back over the justunder two years we’ve been doing this.                Easily Gain API Product Insights              14 day free trial. No credit card required.              Learn More        Trailblazing vs Chasing the CompetitionMatthew: So with that being said, one thing I’d like to hear more on is, okay, so you’ve builta product, you know you have competitors, do you chase what they are doing or do youtry and blaze a new trail, not necessarily on their coattails?What’s kind of your thoughts there because obviously if one product, maybe a competitoris getting traction because they have a certain feature, do you think it’s better to kindof replicate that or something similar or do you think it’s better to go, okay, we’regoing to completely forget about that, let’s actually do something different and better?Josh: One of the things, the best product engineering culture I ever worked in was Facebook.It’s just a mind-blowing place and one of the mantras is don’t worry about the competition.And so you would build your product and try and not spend too much time worrying aboutthe competition.You’re focused on delivering user value and that opens your mind to a different approach.For us that was very much the take.You know we sort of started with a fresh sheet of paper, what does API management reallymean?And you know we actually have some different answers to that, you know some stuff in thepipeline we’re working on that is not what the competitors are doing.But our technical approach was vastly different.Having said that, so to me clearly like think out of the box, think different, don’t justbuild a better mousetrap I think.You know try and come at it with a very fresh set of eyes and not just incremental improvementson the approach.But it’s very important to have an eye on your competitors, understand what they’redoing and honestly there’s lots that can be learned as a startup in terms of how are theyselling?How are they positioning in the market?How are they convinced, you know if you’re selling tools that can be sold to enterpriselike we are, how do they think about the buyer versus the practitioner and prioritize acrossthat?I’m not saying you would copy them, but you should learn about them and have that andlearn about all of those approaches and all of those as inputs to your model for decidingyour approach.I mean that’s my take.Matthew: Okay, awesome.Yeah, no I think that’s a really good way to look at it.                Manage Your APIs With Moesif              14 day free trial. No credit card required.              Learn More        Lessons From Working With a Co-FounderMatthew: One thing that we chatted about with another guest was about, I know you have a co-founder,one thing that we chatted about was he was a solo founder who said, hey, I really kindof, if I had to do it again, I would try and bring on a co-founder who complimented kindof the gaps that I have.Would love to hear just maybe a little bit about, maybe not necessarily the challengeof finding a co-founder, but how did that kind of come to be and what are some of thelessons you’ve learned running a business as two founders or more as opposed to a solofounder?Josh: Yeah, so we’re two founders.We’ve been very close friends, like best friends for 10 years and had always talked about doinga business together.So it sort of was easy.We were just chatting to some investors the other day and they remarked like what a strongpartnership we had and as we sort of tag teamed the conversation.So I was very lucky in the finding a co-founder.I do think it would be very hard to do this alone.Doing a startup is very much a rollercoaster.If it’s a good day today, brace yourself, it’s going to be a day tomorrow and vice versa.I now know I’m very level as a person.I know if some bad news comes in, I’m like, well, I know something awesome will happentomorrow.It doesn’t go that way.So it’s a real rollercoaster.So having someone else to talk to and open your heart to is important.In terms of founder dynamics, I think one thing we’ve found, especially if it’s a closefriend like my co-founder Nate is, is quickly breaking the taboo of giving each other feedbackand being very direct and honest and doing it quickly and in real time.That’s not an easy thing to do with your, we’d never worked together before, he’s mybuddy.And then suddenly we’re on a call, I’m like, look, I don’t think this was the right wayto do this.Or I think there’s some learnings here.Run into the spike though.I mean, in my experience, it’s always been easier than I thought it would be.And I think it’s critically important to be transparent.Actually maybe, I don’t know if you’re going to share links with this, but one of our investorsposted an article about this topic recently that I think is a good read about how to managethat relationship.So that’s my tip, is be honest, be transparent, give feedback frequently and transparently.Matthew: Awesome. Yeah, no, that’s good.That kind of supplements some of the stuff that we’ve heard before.                Debug Your APIs With High Cardinality              14 day free trial. No credit card required.              Learn More        Stress Management and Self-CareMatthew: Now, you did mention that startups are kind of this up and down kind of roller coasterthat anyone who’s worked for a startup or worked with a startup sees.It’s even more amplified as a founder, I’m sure.So what kind of stuff can you do from a personal perspective?What kind of stuff can you do to kind of ease that?You said you’re pretty level-headed, and knowing you for the last few years, I absolutely agree.What are some, like, I mean, for me, I know meditation is one of those things that’s helpedme kind of grow and deal with the ups and downs of not just regular life, but beingpart of a relatively high velocity startup.Do you have some tips there for folks?Maybe some stuff that you’ve tried and tested and liked?Josh: Well, I wouldn’t say I’ve always been so level.I think that’s definitely something that’s newer.I think that I’ve always improved since doing a startup because it’s been tested.Right.Look, I don’t have any magic pill here, but I think meditation is one.I personally struggle with meditation.I’m sure I can do it.I’ve tried many times, but my head is like a box of frogs.So probably would be a good thing for me to do.What is critical to me, and actually I broke my rib recently, and I’d say five weeks out,is exercise.So, you know, I live in two places right now.I live in this little room here, and then I live in the gym.That’s my only two places I exist mostly.But the gym is so important to me, working out, getting a variety of exercises.I play tennis and so on.And this isn’t novel.If you look at really smart, big companies, they very much encourage, they push theirleadership and talk about how important exercise is for like stress reduction, getting outsideof the workspace, doing something different.But overall, I sense my anxiety is higher.So exercise, like do it, find the time for it, really prioritize it.I have blocks in my calendar where I’m going to go and exercise, and they’re pretty muchnon-negotiable.Matthew: Right.Josh: There’s exceptions, but they’re rare, honestly.They’re rare.Matthew: Do you see exercise as kind, that major kind, of foundation to making sure that you’re successfulwithin the business and personally?Josh: Yeah.It’s the most important thing for me.It really helps.And I know one thing that we could, we won’t discuss in detail, but you know, you and Iare big proponents of plant-based eating as well, which is, so are you still on the?I still am plant-based, I’m still vegan.Yeah, I don’t, I mean, so is Nate actually.So it’s working for us.It’s certainly, I mean, the other, I mean, the other thing that I would say is sleepand focusing on sleep.Actually, this is a good point.I’m glad you made me think of this.I used to work with folks like execs at Microsoft and so on, and they would be sending emailsat 2 a.m. and then again at 5 a.m. and I’m like, are you getting three hours sleep?I used to think, cool, some people are wired like that.I think about the world very differently now.I think that’s not healthy and I don’t think it’s productive.I don’t think you’re getting the best out of yourself if you’re only giving yourselfthree hours sleep on a regular basis.I avoid that.I try and get seven.I usually don’t, I just struggle past the six hour mark, but that is something I’m willingto prioritize.I’ve also learned, because my next day is just so much less productive and less impactful,I’m less good at decision making, I’m more anxious, all of these things.I even have learned, because I track everything, I have a whoop and an eight-sleep bed andso I very much prioritize it.I’ve learned that if I work too late and I’m very actively working very late at night,it then messes my sleep up.So whenever possible, I try and stop working by like 8.30, which I know a lot of startupfounders might think that’s crazy.But I’ve just found the balance for me is that overall I’m going to get more productivityif I find that balance, particularly around sleep and exercise.My overall productivity is going to be way higher.I think even Elon says less than six hours sleep is counterproductive and he’s prettyintense.Matthew: Yeah.I mean, there’s a book, I forget, his name is Matthew, I can’t remember, but it’s a bookcalled Why We Sleep.It goes into all of this stuff too and there’s another book called Rest.I forget exactly who the author is, but they speak about the exact same thing where productivityis about health and part of that cornerstone of that is making sure that you’re restingand sleeping correctly.When you talk about exercise, it sounds like you talked about playing tennis and stufflike that.It’s exercise, but it’s also restful in a sense that you’re doing something you enjoy.A lot of people think rest means sitting on the couch, watching Netflix, and that’swhat you should be doing with your nights as a founder.But maybe it’s about spending time with your family.It’s about going out and playing tennis or doing stuff with friends.You need a second place, right?Josh: It’s a place where I’m not thinking.You do.I can’t think about work whilst I’m playing tennis.I just can’t do it.Matthew: Exactly.That’s a good thing to have occasionally is to be forced to think about something else.The last piece here, we’ve got a couple of minutes left.                Faster API Integration With Moesif              14 day free trial. No credit card required.              Learn More        Tips for FoundersMatthew: You’ve given us a ton of insight and we’ve kind of already covered some of this, butwhat are the one to two tips that you would give to other founders or aspiring founders,whether it’s block out time on your calendar for exercise or anything like that, what wouldyou say if someone came to you, you’re in an elevator going down, you’ve got a few minutesto chat with them, what kind of advice would you give them?Josh: Tricky.Look after your health.That’s going to be a superpower for you.I think another one is around how you work a problem and give yourself time.This is, you know, I’ll do a corollary.First of all, decide what your organization really cares about.We are an organization that cares desperately about developer experience.It is the very core of our DNA is like, how good is the developer experience?And so when it comes to designing solutions for that core number one priority, it’s sometimestempting to say, hey, we want to ship X and we’re going to just move really fast and getsomething out and we’re just going to work the problem.I don’t always think that’s the right call now.I have found if I give things a little bit of time to marinate, and so when I’m drivingaround in my car, driving to the gym, that I’m still thinking about work all the timewhen I’m doing that.If I give myself, even if it’s just a day or two to like sleep on a problem I’m workingon versus like rush it out, if it’s an important problem, it’s worth giving yourself that time.It is worth giving you that time.I’m all about pace and execution speed.That is, you know, my ultimate manager is like, we’ve got to go fast.Learned a lot about Facebook, but I have seen this countenance to just forcing a designthrough a hole.Like this is, I’m going to get this important thing out through a hole.So give yourself time and honestly the solutions and the realizations that I and Nate and theteam come to when we do give ourselves a little bit of soak time on a problem, it really allowsus to come up with an optimum design.I keep quoting this, but you know, love Elon or hate him, whatever you think about him,he’s a pretty smart guy.And he said recently about how engineers, if you present them with something, they willlike optimize that thing and not question its existence.You know, it’s just the way we’re designed to think is we don’t challenge the processenough.The best design for these things, often you’re questioning, why do we even need this thingand can we remove it?And it takes time to really swim in the waters of what a solution looks like and what weend up coming up with when we do that is so much more elegant.The best thing about it is when someone doesn’t notice the design work that’s gone into it,like, you know, they’re like, oh, well, that’s obvious.That’s obvious how this should work.And you’re like, you have no idea how not obvious that particular solution is, but wemarinated on it.We thought on it and we swam on it and yeah, we came out with what we think is a greatdesign.Matthew: Awesome.Yeah, that’s, that is honestly some really great feedback, I think, especially for folksthat are kind of in the weeds and want to push stuff out quick.It’s good to be able to sit back, make, like you said, let things marinate and get a littlecloser to perfection versus, you know, decide what you’re going to do that on.Josh: Don’t do it on everything.Don’t do it on your, which insurance company you’re going to use or, you know, like garbagethings really like what is your core thing?That’s the one that’s, that’s the core sort of brand value or whatever it is you wantto say.That’s probably the thing where it’s worth giving yourself some marination, right?So if it’s important enough to think about in a really deep level, give yourself thetime to make sure that you’ve, you know, seen it more completely.Matthew: Awesome.I think that’s some great advice.And with that, we’re going to sign off for today.Josh, thanks so much for joining.If folks want to get a hold of you, see what you’re up to, where’s the best place for themto find you?Josh: I’m on Twitter, x, joshtwist, Zuplo.com, josh at Zuplo.com email.I’m open and happy to chat with anyone.Check us out.Matthew: Awesome.Thanks so much for joining and we’ll chat again soon.Josh: Thanks, man.                Collaborate On Your APIs              14 day free trial. No credit card required.              Learn More        ",
          "url": " /podcasts/developers/Podcast-From-Vision-To-Venture-Josh-Twist-Zuplo/",
          "author": "Dylan",
          "categories": "Podcasts, Developers"
        }
      
    ,
  
    
        "api-monetization-zuora-and-moesif-drive-success": {
          "title": "Maximize API Revenue: How Zuora & Moesif Drive Subscription Billing Success",
          "content"	 : "The concept of API-first companies relies on prioritization of building and maintaining robust APIs as a fundamental part of their business strategy. However, for API companies, effective and accurate subscription billing is a crucial element for success. To address this critical need, API management must be combined with accurate API analytics for the most precise billing.Zuora, a prominent name in the subscription management space, offers a comprehensive platform that empowers API-centric companies to handle invoicing for subscriptions easily. What sets Zuora apart is its flexibility and scalability, allowing businesses to customize their subscription models to their customer’s use cases. Zuora offers a subscription economy hub with features like dynamic pricing, revenue recognition, and a highly customizable billing engine, all designed to streamline the billing process.all designed to streamline the billing process.Moesif, on the other hand, specializes in API analytics and monitoring. It plays a significant role in ensuring subscription billing accuracy when connected to a billing solution. Moesif’s advanced API analytics capabilities provide valuable insights into how customers interact with APIs with real-time data, enabling businesses to optimize pricing models and prevent revenue leakage. This proactive approach ensures that API subscription billing models remain efficient and profitable. As an API monetization platform, Moesif allows any API provider to meter the usage of API services. Use Moesif’s real-time API consumer data to shape direct and indirect API monetization strategies.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        The Rise of API-First Companies and Subscription ModelsUnderstanding API-First Business ModelsAPI-first companies prioritize developing and utilizing Application Programming Interfaces (APIs) as a core component of their operations and a significant part of their business model. These companies rely on their APIs to connect with external products, customers, API gateway partners, and systems, enabling seamless data exchange and interaction. As API-first companies often offer subscription-based services or products, their billing needs stem from their offerings’ dynamic and constantly evolving nature. Traditional billing systems may need help to adapt to the flexible pricing models, real-time data tracking, and complex subscription structures that API-first companies often employ. APIs require billing solutions that can handle recurring payments, tiered pricing, usage-based billing, and dynamic subscription changes while ensuring accuracy, scalability, and a seamless customer experience.The Basics of API MonetizationAPI monetization is the process of generating revenue by allowing external users to purchase use of your APIs. In a nutshell, an API monetization strategy involves the planning and execution of charging users, developers, or businesses for access to and usage of APIs. API monetization can take various forms, such as subscription-based pricing, pay-per-use models, one-time fees, or revenue-sharing agreements. We’ve written extensively on our blog about selecting the correct monetization framework for your API product. The business benefits of API monetization are significant:  Revenue Generation:  API monetization creates a new source of income for businesses. By offering valuable APIs to external developers, companies can charge for access, data, API call volume, etc. This, in turn, improves their revenue streams.  Increased Market Reach: API monetization allows businesses to expand their market reach by offering their API initiatives to third-party applications, partners, and customers. This can lead to increased visibility and customer acquisition.  Ecosystem Growth: Encouraging third-party developers to create applications built using your APIs allows companies to foster growth around their products. This can lead to an ecosystem effect, where more applications and services are built on top of their offerings, attracting a larger user base from the API economy.  Data Insights: Monetizing APIs requires granular usage data. This data provides valuable insights into customer behavior, helping businesses make informed decisions about product development and marketing strategies. These insights improve customer engagement and drive business growth.  Competitive Advantage: Implementing an effective API monetization model can give a business a competitive edge by creating a barrier to entry for competitors.  Flexibility and Scalability: API monetization models should be tailored to suit different business needs and can scale as your business grows. This flexibility allows companies to adapt to changing market conditions and customer use cases.Monetization ModelsAPI monetization models come in various forms, each suited to different business needs and strategies. Some of the most popular models include:Usage Based PricingUsage based pricing, or Pay-Per-Call, is a billing model in which customers are charged for the actual consumption or use of an API’s resources or services. Instead of paying a fixed, upfront fee, users are billed based on the volume of API calls, data transfer, or any other quantifiable metric relevant to the API within a given billing period. This model ensures that customers pay in direct proportion to their usage of a monetized API, making it a flexible and cost-effective option for businesses and users alike. Organizations can scale their API consumption up or down based on their needs. Usage-based pricing is commonly seen in cloud services, data storage, and other services where resource demand can vary significantly over time.Subscription PricingSubscription pricing is a billing model where customers are charged a recurring fee at regular intervals (e.g., monthly or annually) in exchange for continued access to an API resource or service. This model is based on ongoing, subscription-based usage, providing customers predictable costs and uninterrupted access to the API product. Subscription pricing is suitable for APIs that offer continuous or ongoing services and is a direct monetization pricing model.Freemium PricingFreemium pricing is a business model offering a two-tiered API access approach. A freemium model offers a basic version of the API at no cost (“free”) along with a “premium” tier that offers enhanced API resources or higher usage limits for a fee. This approach is prevalent for attracting developers and encouraging them to explore and integrate the API. This API business model puts the power in the hands of the end user, giving them the chance to learn more about a product before committing to payment.Tiered PricingTiered pricing offers multiple commitment tiers with different service levels, features, and usage limits. This approach allows API providers to cater to diverse customers with varying user requirements, from small developers and startups to large enterprises, by offering pricing plans that align with various needs and budgets. This revenue model also empowers the API user, allowing them to decide what commitment level best suits their requirements.The Subscription EconomySaaS businesses are increasingly embracing subscription models for software products. Subscriptions offer companies a steady and predictable revenue stream, which bolsters financial stability. Subscriptions align the interests of SaaS providers with those of their customers, resting on a customer-centric approach and encouraging, if not requiring, continuous value delivery. With lower upfront costs, subscriptions can make software more accessible, particularly to startups and small businesses who may have trouble justifying the price of enterprise contracts. A subscription model also helps mitigate software piracy with rate and user limiting and allows for scalability to accommodate a diverse range of customers. The shift to subscription models reflects a strategic response to the new market dynamic of attracting and supporting self-service users.Zuora’s Subscription Billing FeaturesFlexible Pricing Plans with ZuoraZuora empowers businesses to create highly customized pricing tiers and add-ons, catering to the diverse needs of their customer base. With Zuora, companies can design pricing structures that align precisely with their product or service. With Zuora’s flexibility, businesses can create tiered pricing models that reflect different service levels, features, and usage limits to accommodate a wide range of customer segments. With Zuora’s add-on feature, businesses can offer supplementary services or features that seamlessly integrate into existing subscription plans, enhancing the overall value proposition for customers.Automated Billing CyclesZuora’s automated billing, invoicing, and payment processes are the key to maximizing its subscription management platform. By streamlining financial operations for businesses that rely on recurring revenue models, Zuora enables direct monetization for services and products with customization.Zuora’s automated billing features allow businesses to set up various billing models to accommodate business needs. The platform automatically generates invoices repeatedly based on the imputed subscription terms and usage. This helps ensure timely and accurate billing for customers. Zuora also enables organizations to prorate charges for customers who change their subscription plans mid-cycle, calculating charges or credits accordingly without forcing internal finance teams to crunch numbers.Businesses can create customized invoices directly within Zuora’s management platform that reflect specific details, terms, and payment methods. Invoices then are automatically delivered to users depending on customer preferences or internal business requirements. Zuora allows for multi-currency and multi-language invoicing for businesses operating globally, facilitating international transactions without stalling business activity.Zuora integrates with various payment gateways and processors, enabling automated payment collection. With dunning management for failed payments, Zuora automates the dunning process, sending out reminders and escalating communications to ensure on-time payments. This can free up finance and customer support teams to focus on tasks other than chasing down payments. The platform automatically matches payments to invoices and provides clear visibility into financial transactions, helping your organization track revenue more effectively.Zuora helps businesses reduce manual errors, save time, improve cash flow, and enhance the overall customer experience by automating billing, invoicing, and payment processes. It provides the necessary financial infrastructure to effectively manage subscription-based revenue, allowing businesses to focus on delivering value to their customers and growing their subscription offerings.Scalability for GrowthZuora’s scalable platform empowers API-first companies by providing flexible subscription management and billing solutions, enabling API providers to adapt to new use cases or industry standards and foster sustainable growth.Moesif’s API Analytics and Monitoring FeaturesReal-Time API Usage TrackingMoesif offers insight into API usage patterns by tracking and analyzing real-time user data to understand how customers interact with APIs. This deep user monitoring allows businesses to gain a deeper understanding of user behavior and usage trends, helping to shape how businesses offer and manage their API products. This data-driven approach enables companies to optimize their API product strategy, improve user experiences, and make informed decisions to enhance their API offerings.Customer Behavior AnalysisMoesif helps API providers understand how customers interact with their APIs by providing detailed analytics and monitoring capabilities. Businesses can use Moesif to track how users engage with their APIs in real time to identify usage patterns, detect issues, and optimize APIs, ultimately improving customer experiences.Data-Driven Decision MakingMoesif’s analytics capabilities empower businesses to make data-driven decisions about their API product offerings by providing critical insights into API usage, enabling them to refine and enhance their APIs to meet the needs of their current (and hypothetical future) customers.Synergizing Zuora and Moesif for Enhanced Billing SolutionsIntegrating Zuora and MoesifIntegrating Zuora’s billing system with Moesif’s API analytics involves setting up data collection and synchronizing usage data from Zuora with Moesif’s platform. This combination enables businesses to gain insights into their customers’ API interactions and automate billing-related services.Feature Highlight: Unified Billing and AnalyticsHaving billing and analytics in one place streamlines operations and empowers API-first companies with comprehensive insights to optimize pricing strategies and enhance the customer experience without latency.Advanced Features for API MonetizationUsage-Based Billing with Zuora and MoesifUsage-based billing models are increasingly popular among API-first companies, as they allow for billing based on the actual consumption of services. Zuora provides the billing platform, while Moesif offers the analytics to track API usage. Integrating these two services enables businesses to bill their customers accurately and fairly based on their API usage.Step 1: Integrate your APIs with MoesifUsing a Moesif SDK or Gateway plugin, get your API usage data flowing into Moesif.Step 2: Configure the Zuora extension in MoesifOnce you have usage data in Moesif, you can send it to Zuora to handle the billing. To do this, follow our guide on setting up the extension.Step 3: Create a Billing MeterIn Moesif, create a new Billing Meter to meter your API usage and report it to Zuora.Step 4: Profit!Once your APIs are plugged in, and Zuora is configured within a Billing Meter, your customer’s API usage will automatically be synced to Zuora to charge for use accordingly. With this, you’ve successfully monetized your APIs with Moesif and Zuora in a few easy steps.Optimizing Revenue with Zuora and MoesifReducing Churn with Predictive AnalyticsMoesif’s predictive analytics reduce churn by identifying at-risk customers and providing actionable insights through the lens of API usage. With Zuora, automate retention strategies and proactively engage with customers to increase retention using Moesif’s predictive data.Maximizing Lifetime ValueLeverage Moesif for in-depth, precise insights into customer behavior based on the metrics that matter to your API product and identify behavioral trends to build products that catalyze innovation in other technologies. Utilize Zuora’s flexible billing and subscription management tools to personalize pricing models and customer programs that foster long-term customer engagement, increasing customer lifetime value opportunities.Combining analytics and billing for API products is the only way to ensure strategy-driven growth. Having insight into how your users interact with your API product can shape the subscription models your organization currently offers to meet the different use cases of your customers. Elevate your API strategy with Moesif’s powerful analytics—unlock insights, optimize performance, and accelerate business growth.To try Moesif out today for your API monetization needs, sign up for a free trial or contact our team of monetization experts.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/Zuora-and-Moesif-Drive-Success/",
          "author": "Rachael",
          "categories": "API-Monetization"
        }
      
    ,
  
    
        "api-monetization-monetizing-apis-with-stripe": {
          "title": "Monetizing APIs with Stripe",
          "content"	 : "In the bustling digital marketplace, the art of monetizing APIs has emerged as a game-changer for businesses. Stripe, a leader in online payment processing, and Moesif, an expert in API analytics, are pioneering this frontier. The API economy is projected to grow significantly, reaching $72.6 billion by 2033, highlighting the immense potential of this space. This article delves into the essence of Stripe, the dynamics of API monetization, and how the synergy of Moesif and Stripe can revolutionize your API revenue streams.What is Stripe?Imagine a world where financial transactions are as seamless as sending an email. That’s the world Stripe has been building. Stripe is a comprehensive suite of APIs that powers online payment processing and commerce solutions for businesses of all sizes. Imagine having a financial guru right at your fingertips, assisting you in processing payments, overseeing transactions, and boosting your income. Stripe’s platform is designed to handle everything from billing and payments to scaling up operations for global reach.Key Features of Stripe  Global Payment Processing: Stripe makes it a breeze to accept payments from anywhere in the world.  Integrated Financial Services: It offers a one-stop solution for managing all your financial transactions.  Scalability: Stripe evolves alongside your company, fitting the needs of both startups and large corporations.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        What is API Monetization?API Monetization is like turning your API into a goldmine. It involves creating revenue streams from your APIs by charging for access or usage. APIs allow other businesses to tap into your services, enhancing their products without the effort to replicate or build the service on their own. Think of it as a toll booth on a highway; every time someone uses your API, they pay a fee. This approach is gaining traction among businesses as they begin to recognize the untapped potential within their APIs. Understanding common API monetization models is crucial, as they highlight strategies that can generate revenue and align with customer value and usage patterns.Importance of API Monetization in the API EconomyIn the thriving API economy, API monetization is a cornerstone for generating revenue and creating new revenue streams. As the demand for APIs continues to surge, businesses have a golden opportunity to monetize their APIs and deliver value to their customers. APIs can provide significant stickiness to existing services, making customers less likely to switch to competitors. By implementing a robust API monetization strategy, companies can measure the true value of their APIs, make data-driven decisions, and continuously enhance their offerings. This not only boosts revenue but also strengthens customer relationships and market positioning.Ways to Monetize APIs: API Monetization Strategy      Pay-per-use: Charge users based on how much they use your API.        Subscription Models: Introduce graduated pricing structures for varying degrees of API access. In the subscription model, users pay a flat fee for access to an API or set of APIs.        Freemium Model: Allow free access to basic API functions, while reserving advanced features for paid tiers. This model lowers barriers to entry and encourages developers to adopt your API. It requires careful management of conversion rates from free to paid tiers to ensure profitability.        Usage Based: Implement billing models such as pre-paid and post-paid that charge based on API usage. This approach encourages developers to utilize your API effectively and creates additional revenue opportunities.  Example of API MonetizationLet’s say you have an API that provides real-time weather data. You could offer basic weather information for free but charge for advanced features like historical weather data or climate trends. In doing so, you’re not just offering a sought-after service but also carving out a fresh stream of revenue. A monetized API can generate new revenue streams, enhance competitive advantage, and foster developer ecosystems, positioning your business to thrive in a competitive landscape.Using Moesif to Monetize APIs with StripeMoesif is the secret sauce to supercharge your API monetization strategy. Moesif is an API analytics platform that helps you understand and monetize your API usage. Effective API monetization strategies can lead to increased engagement and feedback from users, enabling businesses to refine their offerings. By integrating Moesif with Stripe, you can set up sophisticated billing models based on API usage, track customer interactions, and optimize your API for maximum revenue generation.How Moesif Works with Stripe      Track API Usage: Moesif provides detailed insights into how your customers are using your APIs.        Set Up Billing Models: Easily create billing models based on API usage with Stripe integration.        Optimize Revenue Streams: Analyze data to understand customer behavior and adjust your strategy for better monetization.  Deep Dive into Stripe API MonetizationStripe API monetization is not just about charging for API usage; it’s about creating a seamless and efficient payment experience that adds value to both the provider and the user. Stripe’s robust infrastructure ensures secure, reliable, and scalable payment solutions, making it an ideal choice for businesses looking to monetize their APIs.Benefits of Using Stripe for API Monetization      Security and Compliance: Stripe’s adherence to the highest security standards ensures safe transactions.        Customizable Pricing Models: Flexibility to design pricing models that suit your business needs.        Global Reach: Accept payments in various currencies and expand your market worldwide.  The Role of Moesif in Enhancing API MonetizationMoesif serves as a conduit linking your APIs to Stripe’s payment processing prowess, offering the necessary analytics and insights for informed API monetization decision-making.Moesif’s Contribution to API Monetization      User Behavior Analysis: Understand how your APIs are being used and identify potential revenue opportunities.        Real-Time Data: Get up-to-date information to quickly adapt to market changes.        Customizable Reports: Tailor your analytics to focus on what matters most to your business.  Integrating Stripe and Moesif for Optimal MonetizationIntegrating Stripe with Moesif for API monetization involves several key steps, each contributing to a seamless and efficient billing process. Here’s an expanded look into this integration, drawing from Moesif’s documentation.1. Setting Up the Integration      Prerequisites: Before integrating, make sure you meet these requirements. Then follow the instructions in Creating a Product and Price in Stripe to create a product.        Creating Billing Meters in Moesif: This is the first step where you define the metrics and criteria for billing. You can create a new billing meter through Moesif’s UI, specifying the name, billing provider information, filters, and the metric to charge on. The metric could range from the number of API calls to unique users or custom metrics based on specific data fields.        Linking Moesif and Stripe: In Moesif, navigate to Settings > Extensions and install the Stripe extension. This setup involves two-way communication between Moesif and Stripe.  2. Adding the Moesif Webhook to Stripe  Webhook Configuration: In your Stripe account, navigate to the Developers section and add a new endpoint in the Webhooks menu. The endpoint URL from Moesif, which contains your Moesif Application ID, should be pasted here. Ensure the endpoint is configured to listen to all events for comprehensive data capture.3. Configuring Stripe in Moesif  API Details: Enter your Stripe API details in Moesif. This encompasses the Stripe API Version and the Secret API Key, accessible in the Developers area of your Stripe account. Moesif currently supports Stripe API Versions 2020-08-27 or higher.4. Setting Up ID Mapping  Linking Subscriptions and Companies: In Moesif, configure the ID Mapping to link Stripe Customers with Moesif Companies. Correct linking is crucial for the functionality of metered billing.5. Linking Plans and Billing Meters  Finalizing the Setup: In Moesif, when creating a new billing meter, specify Stripe as the Billing Provider and select the appropriate Product and Price that the usage data should be linked to. This step ensures that the usage data captured by Moesif is accurately reflected in Stripe for billing purposes.6. Additional Configurations      Usage Multiplier/Tiering: If needed, you can set up a usage multiplier or tiered pricing in your billing provider. This allows for more flexible and tailored billing models, like charging a set rate per certain number of API calls.        Adding Billing Criteria: Define the specific criteria for billing, such as billing all API calls returning an “HTTP 200 - OK” response. This level of customization ensures that you’re billing precisely for the services used.  7. Managing Billing Meters  Editing and Archiving: Moesif allows you to edit the name and status of existing billing meters. For audit purposes, other changes are limited. You can also archive billing plans that are no longer needed. Integrating Stripe with Moesif for API monetization is a multi-step process that involves careful setup and configuration. By following these steps, businesses can create a robust and flexible billing system that accurately reflects API usage and ensures a smooth billing experience for their customers.FAQs      Can small businesses benefit from Stripe API monetization? Absolutely! Stripe’s scalable solutions are perfect for businesses of all sizes.        Is it complicated to set up API monetization with Stripe and Moesif? Not at all. Both platforms are intuitively designed for user-friendliness, backed by a wealth of supportive resources.        Can I offer different pricing models for my API? Yes, Stripe and Moesif allow you to create various pricing models, including subscriptions and pay-per-use.  Stripe API monetization, especially when combined with Moesif’s analytics, opens up a new world of opportunities for businesses.By grasping and utilizing these tools, you have the power to elevate your APIs from basic functions to major sources of revenue. So why wait? Dive into the world of API monetization, sign up for Moesif, and watch your business soar to new heights!                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/Monetizing-APIs-with-Stripe/",
          "author": "Dylan",
          "categories": "API-Monetization"
        }
      
    ,
  
    
        "api-monetization-api-strategy-implementing-subscription-tiers-with-moesif-and-stripe": {
          "title": "Implementing Subscription Tiers with Moesif and Stripe",
          "content"	 : "As we have seen in the last few years, subscription-based models align customer needs with business growth. Whether you’re a startup or an established enterprise, mastering the art of subscription tiers and learning how to apply them to your API monetization strategy is crucial. In this blog post, we will look at navigating through the implementation of subscription tiers for monetized APIs with the aid of two vital tools: Moesif and Stripe. By the end of this blog, you’ll be equipped to implement a subscription model that caters to your customer’s needs and scales with your business. Let’s begin by looking at what subscription tiers are.What are subscription tiers?Subscription tiers are the bread and butter of SaaS business models, especially regarding API monetization. They offer customers a choice of service levels, each with features and price points that suit their needs. This monetization model allows businesses to cater to a wide range of customers. Regarding subscription tiers within the API space, you’ll generally see companies offer a range of options that cover each user’s needs. For individuals or small businesses that may only need access to specific endpoints and low usage quotas, there may be an entry-level or free tier, up to large enterprises requiring full access and larger usage quotas. Once defined, tiers are strategically priced and packaged to encourage upgrades as customer needs evolve. By properly planning your tiers and pricing, you can quickly expand revenue as companies using your service also expand and require more from your service.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        An example of API monetization tiers might look something like this:            Tier Name      Endpoints Available      Quota Limit      Price                  Bronze      Basic Endpoints      1000 calls/month      $100              Silver      Basic + Intermediate Endpoints      100,000 calls/month      $250/month              Gold      All Endpoints      1,000,000 calls/month      $1000/month      The table above showcases a tiered strategy where each ascending tier offers more endpoints and a higher quota limit, with the price reflecting the increased value. The ‘Starter’ tier serves as an entry point for users to test the API, while the ‘Enterprise’ tier is tailored for large-scale users who may need customized solutions and dedicated support.What is Moesif?Moesif is a platform designed to help businesses grow and monetize their API products. Moesif contains many key features that are relevant to API monetization, including:  Billing Meters: Set up usage-based Billing Meters on API calls and other metrics.  Billing Provider Integrations: Facilitate easy billing by directly integrating with leading providers or custom providers.  API Analytics: Real-time event logs to track and understand customer usage of APIs and apps.  Quotas & Governance: Manage API usage limits and enforce governance policies.  Developer Portal: An open-source portal for developers to subscribe and access monetized APIS.  Behavioral Cohorts and Emails: Understand user behaviors and send targeted communications based on subscription status, quota limits, etc.  Conversion Funnels: Optimize user onboarding and journeys to drive revenue and customer adoption.Moesif’s monetization capabilities allow for the setup of metered billing, creating billing rules, and automating invoicing by integrating with billing providers. Overall, Moesif aims to help API providers effectively generate revenue from their APIs with an easy-to-use interface and features.Implementing the subscription tiersImplementing subscription tiers for API monetization is a multi-stage process that weaves together the functionalities of Stripe and Moesif. Below, we will set up an implementation that uses the prices defined in Stripe to enforce usage quotas in Moesif. This will require using Moesif’s Billing Meters and Governance Rules features. Let’s get started!  Note that the setup below will only work if you use a Moesif SDK or Plugin that supports governance. Please check the documentation to ensure that governance is supported in your setup.#1 - Define the tiers in StripeFirst, we will create our subscription tiers in Stripe. To do so, perform the following steps in Stripe.  Go to your Stripe Dashboard.  Go to Product Catalog and then select Create product.  Select Recurring and then select More pricing options.  Select Recurring and choose Usage-based as the pricing model and choosethe usage amount as unit.  Define your price amounts.      In the Meter section, select the plus icon + to create a billing meter to associate the price with.    a. Enter the meter name.    b. Enter the event name for which the billing meter reports usage.    c. Select the aggregation method for meter events. Moesif supports the Sum and Last aggregation methods.    d. Expand Advanced Options and make sure that Event Time Window is set to Raw.    In the Advanced section, enter a description for the price.  Select Next.  Select Add another price and repeat the same steps to add more tiers.  After you finish defining your tiers, select Add product.After you’ve created your tiers, you should see that the Stripe Product catalog now contains your product and prices.#2 - Define the Billing Meter in MoesifNow that we have our product and prices defined in Stripe, we must set up a Billing Meter for each tier we have created. To do so, we will perform the following steps:  Log into Moesif.  In the left-side menu, select Billing Meters.  On the Billing Meters screen, click Add Billing Meter in the top-right corner.  On the Add Billing Meter screen, give your Billing Meter a name to match the tier you are creating it for.  Under Link to > Billing Provider, select “Stripe” from the dropdowns and the Product and Price corresponding to your tier.  Then, set up a Filter that will outline which API calls you’d like to count towards the user’s usage ( See example below for specifics).  Lastly, select the Metric that you would like to use, like “Event Count”, which will count every API call as a unit of usage.  Repeat the steps above for each tier you’d like to implement.Here’s an example of what this might look like.  Note that the filter also includes the Stripe Price ID that corresponds to the tier the meter is for. For this, you’ll need to add a filter on the Company metadata like so: company.metadata.stripe.subscription.items.data.price.nickname. If the name of your price does not show up, carefully type it in to match the name you input in Stripe, as this will be case and whitespace-sensitive.Once this is done, we will enforce the limits based on the tier the user has subscribed to with Moesif’s Governance Rule feature.#3 - Define the Governance Rules in MoesifTo ensure customers stay within their subscription bounds, we will set up Governance Rules within Moesif. This requires two steps:  Create the appropriate cohort for the users within a specific tier.  Create the governance rule in Moesif to apply quota management to users within that cohort/tier.To do these two things, we will need to do the following in Moesif:  Navigate to the Quotas & Governance screen by clicking on the corresponding menu item in the left-side menu.  On the Quotas & Governance screen, click the Add New button in the top-right corner of the screen and select Start from blank in the modal that appears.  On the Add Governance Rule screen, we will select Create New Company Cohort in the Applicable Cohorts dropdown.  For the company cohort input under Companies Where we will once again use the price identifier we used above (company.metadata.stripe.subscription.items.data.price.nickname) as part of the filter. We will set this to match the tier that the cohort should be applied to.  Next, we will set the Who Performed> Event Where criteria to match the filter we added in the Billing Meter (minus the Stripe Price filter since it was already added above in this case). We will also set the filter to At least “1000” times, or whatever the quota limit is for your tier.  Lastly, we will set the Period selector to “Current Monthly Billing Period” and click Create Cohort in the bottom right.An example of this can be seen below:After the cohort is created, you’ll be brought back to the Create Governance Rule screen. Here, we will do the following:First, add a name for the Governance Rule in the Name field.Next, ensure that the cohort for the tier is selected in the Applicable Company Cohorts dropdown. Also, ensure that the Apply to Companies in above Cohorts option is selected.Ensure that Blocking is set to true/checked off.Then, set the Override Response Status and Override Response Body to the values you would like to return when the user has reached their usage limit/quota.Click Create/Save Cohort to save the cohort.Below is an example of what this might look like in practice.  Ensure the Governance Rule is set to ON, or the quota will not be enforced.Once you have implemented the above steps for each tier in your pricing scheme, you’ll be ready to launch your APIs. As each tier quota is met, Moesif will inform the SDK or Gateway (via the applicable Moesif plugin) to block traffic and return a custom response to the user. This ensures that users stay within their quota and protects upstream APIs from abuse.ConclusionIntegrating Stripe and Moesif to manage subscription tiers is a strategic move that can significantly enhance your offering to your customers. By defining subscription tiers in Stripe and setting up Billing Meters and Governance Rules in Moesif, you can create a robust API monetization ecosystem that scales and maximizes revenue.If the above setup is the missing piece you require for your API monetization strategy, contact the Moesif team today to discuss how our team of API monetization experts can help—looking to take Moesif for a spin first? You can also sign up for an account and get started in minutes. Let us help you get started with API monetization using Moesif and Stripe.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Implementing-Subscription-Tiers-with-Moesif-and-Stripe/",
          "author": "Matthew",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-apigee-monetization-the-ultimate-guide": {
          "title": "Apigee Monetization: The Ultimate Guide",
          "content"	 : "In today’s digital age, APIs (Application Programming Interfaces) are pivotal in modern businesses, connecting them with customers, partners, and the broader technical ecosystem. Growing far beyond just the backbone of an application, APIs have now caught the attention of businesses looking to create a new revenue stream. As organizations recognize the revenue potential of their APIs, effective API monetization strategies and tools have grown in popularity. One tool that gets talked about a lot is Google’s Apigee Monetization platform.In this guide, we will dive deeply into Apigee Monetization, covering everything from the fundamentals to implementation best practices. The particulars of what we will cover include:  What is Apigee Monetization?  Key Features of Apigee Monetization  How Does Apigee Monetization Work?  Steps for Setting up Apigee Monetization  Best Practices for Implementing Apigee Monetization  How Moesif Helps with API MonetizationBy the end of this guide, you’ll have a comprehensive understanding of how to use Apigee for API monetization and strategies to monetize your APIs effectively. Whether you’re new to API monetization or seeking to enhance your approach, this guide will equip you with the knowledge to succeed in the journey of API monetization. We will also show you a more flexible and scalable alternative to Apigee monetization by looking at Moesif. First, let’s take a look at the details surrounding Apigee monetization.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        What is Apigee Monetization?API monetization has become a key component in modern API management, transforming APIs from passive tools into active revenue generators. Monetization is a crucial feature of Google’s Apigee API platform, designed to help businesses begin to generate revenue through their APIs.Apigee, founded as Apigee Corporation in 2004, quickly gained recognition in the tech industry as a robust API management solution. This popularity of the platform led to Google’s acquisition of Apigee in 2016. Later, Apigee became the standard issue APIM for companies working within Google Cloud.The evolution of APIs from simple connectors to strategic assets led to the development of Apigee’s monetization capabilities. As the creation of APIs increased, businesses realized that their APIs held untapped revenue potential. The Apigee monetization features emerged as the solution to this challenge, providing one of the most comprehensive platforms for API monetization available at the time.Apigee’s monetization features offer vital pieces that make monetization possible within the Apigee platform. These include:Revenue Generation: Apigee Monetization empowers businesses to create new revenue streams by monetizing API usage effectively. It enables organizations to define flexible pricing models, accurately meter API usage, and generate revenue reports.Seamless Integration: The solution seamlessly integrates with your existing API management infrastructure, making it easy to implement and manage.Market Agility: In today’s competitive digital landscape, agility is paramount. Apigee Monetization allows you to adapt and change your pricing and billing strategies quickly, keeping you ahead of the curve.Comprehensive Analytics: Gain insights into your API monetization efforts through detailed analytics and reports, allowing you to refine your monetization strategy for better results.Apigee Monetization is a suite of features within the Apigee platform that allows companies to monetize their APIs. While not the only way to monetize APIs, it has become a popular method, especially among legacy companies or enterprises with large enough budgets to support the cost of Apigee.Of course, implementing API monetization is not strictly a capability of Apigee. For instance, alternatives such as Moesif provide a more modern and flexible approach to API monetization. Moesif allows flexible billing parameters beyond just counting API calls and will enable users not currently within the Apigee ecosystem to implement API monetization with their existing infrastructure. We will dig into this in more detail later in the guide.Key features of Apigee MonetizationApigee Monetization features expose a versatile solution that empowers organizations to monetize their APIs effectively. Understanding some of the key features within the platform is crucial to harnessing its full potential. Let’s look at a few of the key elements that users get when they use Apigee for monetization.Flexible Pricing ModelsApigee Monetization allows businesses to define pricing models based on their needs and use cases. Popular pricing models that Apigee supports include pay-per-call, tiered pricing, subscription-based billing, or custom pricing. By offering different methods for charging users for API usage, you can flexibly align your monetization strategy with your business objectives.Usage MeteringApigee Monetization provides robust usage metering capabilities, ensuring you can monitor API consumption to a relatively granular level. The ability to precisely monitor API usage is an essential piece of billing API consumers accurately.Revenue ReportingBeing able to trace where revenue comes from is essential to scale operations. With Apigee, users can gain insights into your API monetization efforts with comprehensive revenue reports and analytics. The reports within Apigee can help users understand which APIs are performing well, which pricing models are most effective, and how revenue is trending over time.Developer Portal IntegrationSince developers access APIs, a developer portal can help with the adoption of your monetized APIs. Apigees developer portal can help to create a smooth user experience. By combining Apigee’s monetization features and developer portal, API consumers can easily access pricing information, subscribe to APIs, and view their usage and billing details directly in the Apigee developer portal.Automated InvoicingGenerating invoices and handling billing processes can be time-consuming. Apigee’s monetization features can automate this process, making it effortless to create and distribute invoices to your API consumers. Automated invoicing helps to enhance the efficiency of monetization operations and gives a consistent experience to API consumers.Payment Gateway IntegrationIntegrating Apigee with your preferred payment gateway ensures you can collect payments seamlessly, reducing friction for your API consumers. Apigee supports credit card payment processing through Worldpay and can integrate with other payment gateways via custom development for pre- and post-paid customers.These features help to implement a robust API monetization strategy. Combined, these features create the framework to create a seamless and lucrative revenue generation model.How does Apigee Monetization work?Regarding API monetization with Apigee, a few steps are required to make it work. Let’s look at these in detail below.Adding Apigee MonetizationApigee Monetization needs to be added as an add-on to the existing Apigee API management platform. This step provides the foundation for enabling monetization features.API IntegrationNext, APIs must be integrated with Apigee to be used within Apigee’s monetization features. This integration ensures that your APIs are ready to be monetized effectively.User AccessUsers of the monetized APIs need to be provided with access to the APIs. Apigee can assist with user management, making it easy to grant access to the API resources that users require.Payment CollectionApigee can also integrate with a payment gateway, such as WorldPay, for smooth payment collection. Other payment methods can also be added with custom code to cater to the preferences of your API consumers.By simplifying these critical steps in implementing monetization, Apigee Monetization helps organizations transform APIs into revenue sources seamlessly.Steps for Setting up Apigee MonetizationAs with any monetization implementation, setting up monetization within Apigee requires meticulous planning and configuration. By following the steps needed, users can ensure a successful API monetization process. Here are the essential steps users must follow to monetize their APIs within Apigee.Step 1: Add Monetization to Your Base Apigee SubscriptionTo start, you must ensure you have a base Apigee subscription. Apigee’s monetization features can be unlocked as an add-on to the core Apigee API management platform. This add-on, which comes at an additional cost, is required to leverage the monetization features within Apigeee.Step 2: Integrate Your APIs With ApigeeAfter monetization has been added to your subscription and enabled in Apigee, you will integrate your APIs with Apigee. This step ensures that your APIs are integrated and ready to be monetized effectively.Step 3: Configure User Access Controls for API UsersWith the APIs added to Apigee, the next step is to grant users access to the monetized APIs. For this, you will configure user management within Apigee, providing users access to the API resources they require via API key or other means.Step 4: Add Payment Provider IntegrationAs a last step in the monetization flow, you must integrate Apigee with your chosen payment gateway. By doing this, users can then initiate seamless payment collection for their API usage. Apigee supports various payment methods, most requiring custom work, to cater to the preferences of your API consumers and how they want to pay.Step 5: Test and Validate Your Monetization SetupOnce the monetization flow works, you’ll want to conduct thorough testing and validation before fully deploying it into the wild. This will allow you to verify that usage is being accurately tracked and charge calculations are being produced as expected. This step helps to prevent billing errors and ensures consumer satisfaction with the service out of the gate.Step 6: Monitor and Optimize Your Monetization SetupAs time progresses, monitoring the API monetization setup is crucial to ensure ongoing accuracy. Apigee users can do this by using the analytics and reporting features within Apigee. From these insights, you can gain insights into consumer behavior and pricing model performance and then make adjustments and optimizations that maximize revenue.With these streamlined steps, you can establish API monetization within the Apigee platform effectively, creating a seamless experience for your organization and API consumers. The system automates many aspects of the monetization process, allowing you to focus on growing your revenue streams.Best Practices for Implementing Apigee MonetizationWhen implementing a monetization strategy successfully, certain best practices can help. Using Apigee to monetize APIs successfully is no different and requires a strategic approach. Below are some best practices to ensure your API monetization efforts are efficient and yield the results your business aims for.Clearly Define Your Monetization StrategyFirst, the key to effective API monetization is to define clear and well-thought-out monetization goals. This includes understanding the value your APIs provide and determining the most effective way to generate revenue from them. Ensure your pricing models and revenue expectations align with the business objectives outlined in your monetization initiative.Choose the Right Pricing ModelsWhen choosing how to charge for usage, you’ll want to select pricing models that resonate with your target audience and the nature of your APIs. Consider factors like usage patterns, customer preferences, and industry standards when deciding between pay-per-call, subscription-based, or tiered pricing.Accurate Usage TrackingYou’ll also need to ensure that you have mechanisms to support precise usage tracking. As part of this, ensure that your Apigee instance is configured to capture API consumption data accurately. This accuracy is crucial for fair, transparent billing that will keep users happy.Transparency and CommunicationTransparent communication is another critical factor in building trust with users of your monetized API. Be sure to communicate your pricing structure, billing cycles, and any changes to your monetization strategy that will affect your customer’s bill. This information should be readily available on your website and developer portal.Optimize Your Developer PortalSince API consumers are likely developers, enhance your developer portal to provide a seamless experience for them. This includes making it easy for users to understand pricing details, subscribe to APIs, and access their usage and billing information. Improving this experience can be one of the most crucial areas for getting developers onto your API platform and getting them to continue to use it.ScalabilityYou’ll also want to plan for scalability as your API offering and traffic grow. Here, you can use Apigee to seamlessly adapt to increased API traffic and monetization transactions, ensuring your monetization strategy remains robust and efficient.By implementing these best practices, you can maximize the effectiveness of your monetization strategy and create a win-win situation for your organization and API consumers. Of course, these best practices can also be applied to other platforms that enable monetization. One such platform is Moesif, which we will look at next.How Moesif Enhances API MonetizationRegarding API monetization, both Moesif and Apigee offer robust solutions with different focuses and advantages. Moesif is a complete solution that is flexible and well-suited for API monetization with a strong emphasis on analytics, flexibility, and ease of implementation.Moesif uses the power of API analytics to help monetize APIs through usage-based billing. The platform also facilitates the enforcement of quotas and governance of contract terms, which is crucial for API monetization. Moesif’s scalable infrastructure allows it to handle billions of events while reducing cost, a significant advantage for businesses looking to monetize their APIs efficiently and at scale. Within Moesif, users can implement usage-based pricing for their APIs and integrate with billing platforms such as Stripe, Chargebee, or custom providers with ease. Moesif offers additional tools to enhance the API user journey and drive API adoption, including an open-source developer portal that can be customized to the extremes.On the other hand, Apigee provides a comprehensive API management platform that includes features for designing, securing, analyzing, and scaling APIs. It is recognized for its capabilities in API design, security, and scalability through its Apigee Edge API management platform. However, the deployment of Apigee is noted to have a higher level of complexity due to its multi-node architecture, requiring multiple nodes to be able to run on-premises. This adds to the operational complexity and requires a centralized infrastructure team for planning and deployment, which could potentially slow down the process of pushing monetized APIs live and into the wild.When comparing Moesif to Apigee for API monetization, Moesif stands out as the more accessible and flexible solution, particularly for businesses looking to implement API monetization strategies and go to market quickly. This helps to alleviate the operational overhead of complex deployment architectures like that seen when using Apigee for API monetization. The analytics and billing platform provided by Moesif is mainly geared toward understanding and monetizing API usage, making it a preferred solution in this context. It also offers much more flexibility since it supports many different API management solutions, has available SDKs, and supports all possible billing providers without customization.ConclusionAs we’ve seen, Apigee has become a game-changer for organizations seeking to monetize their APIs effectively. With the ability to define flexible pricing models, track API usage, and generate revenue reports, it empowers businesses to turn their APIs into profitable assets. Of course, more modern solutions, like Moesif, can give potential Apigee users a more flexible and scalable way to implement API monetization.By following best practices and utilizing tools like Moesif, you can maximize the potential of API monetization and drive revenue growth within the growing API economy. To implement API monetization with ease, sign up for Moesif and start your journey towards API monetization success today!                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Apigee-Monetization-The-Ultimate-Guide/",
          "author": "Matthew",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-bill-on-query-content": {
          "title": "Not Just Cracking API Calls, but Digging into Payloads: How Queries Get to the Heart of Your Data",
          "content"	 : "Monetizing an AI-based API can be a strategic decision that holds the potential to drive growth and financial stability for artificial intelligence companies. An API that doubles as an AI tool offers valuable services and capabilities that can be utilized by a diverse range of users and industries. From generating income to fueling research and development, monetization can play a pivotal role in shaping the present and future roadmaps for your product, machine learning functions, and feature offerings.Query content plays an important role for AI applications, capturing user interaction and intent. The specificity and context provided by AI query content is essential in extracting meaningful insights to ensure that AI models remain accurate. By analyzing the content of query metadata, AI systems can extract relevant insights about user needs and preferences, ultimately leading to more personalized and efficient prompts and responses. Optimizing AI applications rests on a fundamental understanding of AI query content in order to tailor responses, improve user experiences, and enhance the value of a given solution.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        The Value of Query ContentThe role of queries in AI interactions is to unveil the communication between humans and machines. Queries serve as the conduits through which a user expresses their information needs, turning abstract thoughts into actionable commands for any AI tool.For example, a user could ask an AI application what the weather is like in a given location, which expresses interest but not much detail about intent. However, if a user were to ask for something more complex, their intent can be used to shape their answer. So if the user instead prompts for a detailed weather report for Seattle, including precipitation probability, for a specific week, it could be inferred that they have an interest in that particular location at that particular time. Whether they’re going for a party, a business trip, or possibly just researching for a school project cannot be determined from this single query. But a better picture of that user is painted with each query they ask.Understanding “why”, or the context of an AI query, is crucial for extracting relevant insight for context-aware AI. Queries enable AI engines to decipher not just the literal contents of a request, but also the user’s underlying intent, making interactions more targeted and accurate. This deeper level of understanding can pose solutions for usual challenges when it comes to billing for AI app usage. Determining the value of AI services based on the nuances of queries and the complexity of user needs requires precision and adaptability, factors that are integral to sustainable pricing models in any SaaS technology, but particularly important for AI based applications. Leveraging your API product as a data source will allow you to make informed decisions around your APIs. Valuable insight is a result of running analytics on your current dataset as well as monitoring how developer users interact with your product.How Moesif Can HelpMoesif adeptly handle query payloads, enabling users to ask nuanced questions and extract invaluable insights from their data. By delving into API payloads, Moesif offers a thorough understanding of your user behavior. It empowers users to query and interpret the complex information within payloads, providing a comprehensive view that goes far beyond API call tracking.Understanding Query ComplexityCustomers interact with AI-powered APIs by making requests to the API provider’s servers to leverage an AI model’s capabilities. But billing users of your API product based solely on the number of times a given AI model is accessed will likely not provide an accurate representation of the actual processing costs incurred. This is because there is a huge correlation between query complexity and the associated costs of processing within AI-based systems.Simple QuerySimple queries, whether in an SQL database or when interacting with large datasets, usually involve a straightforward and basic process of data retrieval. These queries often involve minimal computation power and can be executed quickly, making them ideal if a developer group is working with large language models. Typically, simple queries employ basic algorithms characterized by low time and space complexity, ensuring efficient execution. As a result, developers can interact with large datasets seamlessly, benefiting from low latency and quick response times, even when dealing with information within the database or their development projects.Complex QueryOn the other hand, complex queries require extensive computational resources or involve intricate data transformations. Generating these responses can put more strain on an AI infrastructure compared to simple, straightforward requests. Complex queries usually involve multi-step operations, requiring extensive computational resources for the tasks involved. Analyzing, transforming, or modeling large bodies of data demands significant computational power. Additionally, complex queries may utilize advanced algorithms with higher latency. These algorithms demand more resources to process the data effectively, making them more costly to offer.When dealing with complex queries, the costs associated with maintenance for an API provider can skyrocket. Features such as parallel processing require additional computational power. Complex queries may require specialized hardware, such as GPUs, and almost certainly need a robust infrastructure to meet computational demands. When you begin to factor in computing clusters or cloud-based solutions, the prices associated with offering a generative AI-based API tool almost require some form of monetization to offset the maintenance costs.Billing Beyond Simple Access CountCharging a flat fee per access without considering query complexity can lead to an unbalanced distribution of costs, as it fails to reflect the varied resource requirements for different queries. To ensure sustainable billing practices for AI services, it is important to take into account both the frequency of model access and the complexity or intricacy of the queries made. By valuing the query over flat access, you can ultimately provide a more accurate pricing model that will allow your business to maintain an AI-based API without cannibalizing resources.The resources required for processing various query types in AI APIs can vary significantly. Simple queries demand relatively low processing power while complex queries require substantial computational capacity. To achieve a cost-effective billing model based on query complexity for AI-based applications, API providers should charge users based on the computational burden placed on their infrastructure rather than the amount of times a query was submitted. This usage-based approach ensures that users generating complex queries pay proportionally for your service while offering fair and competitive pricing for simpler queries. By aligning subscription costs with query complexity, AI API providers can offer equitable access to their services while maintaining a sustainable and scalable business model. To learn more about usage-based billing, we’ve written extensively about varying billing meters on our blog.ConclusionUnderstanding query payloads is of utmost importance, particularly for AI companies, where its significance cannot be overstated. To ensure that AI-enhanced API products do not become an internal resource binge, it is critical to employ a usage-based monetization model which factors in query content into the billing strategy rather than access count. AI companies must recognize that understanding the “why” behind a query, its context, is essential for refining their AI models. But more than this, the context of a query can directly impact its complexity, making query literacy a cornerstone of precise billing for AI products.Moesif offers invaluable capabilities in handling query payloads, by comprehending query sophistication and offering the tools necessary to bill users based on complex, consumption-based billing meters.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Bill-On-Query-Content/",
          "author": "Rachael",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-ai-infrastracture-costs": {
          "title": "Keeping AI Infrastructure Costs Down with API Governance",
          "content"	 : "The growing importance of AI in business is undeniable, with more than 50% of businesses employing artificial intelligence for security and combating fraud. Additionally, beyond the practical applications for businesses externally, AI can be used internally to deliver better customer experiences through competitive tools and features. As the role of AI within an API business’ operations expands, so do the associated AI infrastructure costs. These expenses can quickly become a significant financial burden if left unchecked. Like all outward facing API-based tools, the key to success is API governance.That’s where API governance steps in as a way of managing infrastructure costs and avoiding financial setbacks of monumental proportions. API governance allows an organization to regulate and optimize how AI resources and services are accessed and utilized, ensuring that businesses can offer an AI solution and features without breaking the bank. Governance serves as a strategic framework for controlling expenses in an effort to maintain the quality and reliability of offered AI implementation within services and solutions.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding AI Infrastructure CostAI infrastructure costs are a significant consideration for businesses considering offering artificial intelligence solutions. These costs typically stem from three primary factors: data storage and processing, model training and deployment, and infrastructure management.Data Storage and ProcessingData management can be a substantial financial burden, as AI models often require many datasets, requiring efficient or vast storage solutions and powerful processing capabilities. This can be a large cost investment for businesses, as non-AI based solutions pivoting towards AI likely will not inherently have the capability to manage the volume of data required for an AI project.Model Training and DeploymentModel training and deployment costs quickly add up, as they involve the computational power required to develop and deploy an AI model (or, more likely, multiple generative AI models). This process can strain a company’s finances, especially if frequent model updates are needed due to data drift, bug fixes or optimizations, or even changes in regulations.Infrastructure ManagementThe need for infrastructure management adds to overall expense, as businesses must ensure that their chosen AI system(s) can handle an increase in queries. This requires an optimization of resource allocation, and maintaining infrastructure to support AI products, which can be costly to manage.In the realm of AI technology, cost optimization is not just a financial concern; it’s a strategic imperative. As a business increasingly relies on AI features and products to drive growth, cost efficiency becomes a major business goal. Without a proper AI cost optimization strategy, AI development can quickly become unsustainable financial endeavors that can financially cripple an organization’s growth efforts.To maintain profitability, organizations must continually assess and streamline their AI infrastructure costs through budget allocation, efficient resource utilization, and a concerted focus on eliminating unnecessary expenditures. Cost optimization not only ensures that an AI application remain financially viable but also allows businesses to direct their resources towards new features/products, research, and other strategic initiatives, ultimately maximizing the value AI can bring to the organization. By understanding that computing power will add to your overall artificial intelligence cost, you can optimize your internal business processes.What is API Governance?API governance refers to a structured framework or set of practices that dictate how APIs are deployed and managed within an organization. Governance consists of policies, procedures, and standards that “govern” the use of APIs (internally and externally) to ensure consistency, security, and compliance around more than just AI initiatives. API governance allows businesses to regulate how their software components, data, and services interact with one another or with external integrations, providing a roadmap for API development and usage.API governance is of particular value in the realm of artificial intelligence. In an AI product, where data and models are often shared across applications and platforms, having a well-defined API governance strategy should be the cornerstone of any AI API product plan. Governance ensures that AI resources are used effectively and responsibly by employees internally and paying customers externally, enabling a financially-motivated approach to API development, deployment, and maintenance. By setting clear guidelines for AI APIs, businesses can foster interoperability, data security, and compliance with industry standards.Because the relationship between AI and APIs is symbiotic, robust API governance can help businesses strike a balance between offering an API product with AI capability, and controlling access to AI assets. It not only streamlines the integration of AI capabilities into external applications but also ensures that AI services are secure and align with the broader goals of your organization.The Benefits of API Governance for Cost ControlAPI governance plays a major role in controlling the costs associated with AI infrastructure by providing several essential benefits through analytics insight.Governance frameworks ensure efficient resource allocation. AI models often require significant computational resources. With proper governance, organizations can allocate these resources optimally, preventing over-provisioning or underutilization. This process can be cost saving through means of eliminating wasteful spending on unnecessary infrastructure.API governance also is used to enable monitoring and management of API usage by customers. By closely tracking how APIs are utilized, businesses can identify usage patterns, bottlenecks, and potential areas for optimization. This real-time insight into API usage helps organizations make data-driven decisions, ensuring that they are investing in the right places to achieve their data science objectives effectively.Furthermore, API governance serves as a guard against unauthorized access and misuse. It establishes access controls, authentication mechanisms, and security measures that protect sensitive AI assets from unauthorized users and potential data breaches.API governance can be used to enhance AI product management. With facilitated version control, documentation, ensure that AI models remain up-to-date, reliable, and cost-efficient over time. Effective lifecycle management and user analytics prevent the accumulation of obsolete models that drain resources without delivering value and reduce the risk of costly errors resulting from poorly managed model updates.Best Practices for Implementing API GovernanceImplementing API governance is the key to maintaining control, security, and efficiency for any API product, but particularly for  AI operations. These policies should define who has access to AI resources, what they can do with them, and under what circumstances. Some guiding principles of API governance:  Establish Clear API Usage Policies: Create transparent policies that define who can access AI resources, what they can do with them, and under what conditions.  Utilize Rate Limiting and Throttling: Set limits on API usage to prevent resource overconsumption and employ throttling to maintain consistent service quality internally and externally.  Authentication and Access Controls: Implement strong authentication mechanisms and access controls to protect AI data and resources from unauthorized access and misuse with API keys, OAuth2 tokens, etc.  Monitoring and Reporting Regularly: Continuously monitor API usage, performance, and security, and generate reports to detect anomalies, spot trends, and address issues promptly.  API Versioning and Deprecation Strategies: Develop strategies to manage API versions effectively, ensuring the orderly transition from older, potentially inefficient APIs to newer, optimized ones while maintaining compatibility.Challenges and PitfallsAPI governance, while vital for the efficient management of AI infrastructure, is not without its challenges and potential pitfalls. There are two prominent issues that organizations face: data security and compliance concerns. Beyond the delicate equilibrium of cost control and performance optimization data security and compliance concerns are hugely important. Proper API governance must prioritize data protection, privacy, and compliance with regulatory standards, like GDPR or HIPAA. Striking the right balance between enabling access for AI-driven tasks and safeguarding data integrity is a challenge that demands extensive planning.Additionally, a balance between cost control and performance can be challenging to achieve. Organizations need to allocate resources optimally and strategically to ensure AI systems operate without overspending or under delivering. However, an overly cost-centric approach can compromise performance and severely impact user experience.Treading carefully is essential to maximize the value of an AI infrastructure while maintaining financial sustainability for your organization at large. To navigate these challenges, companies must adopt an approach to API governance that integrates security, compliance, cost-efficiency, and performance optimization.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/AI-Infrastracture-Costs/",
          "author": "Rachael",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-amberflo-vs-moesif": {
          "title": "Amberflo.io vs Moesif - A Deep Dive Comparison",
          "content"	 : "When adopting new tools, it’s often easy to get overwhelmed by the sheer amount of choices. Today, we’re going to look at two common tools for monetization and analytics – Moesif and Amberflo – and take a bit of a deep dive into the two feature sets.What is Amberflo.io?Amberflo.io is a usage metering and billing solution that utilizes a decoupled cloud-based metering system to provide affordable metering at scale. Amberflo prioritizes four domains of operation – Metering, Pricing and Billing, Price Modeling and Analytics, and Sales and Customer Success.Amberflo specifically focuses on a type of pricing known as “Usage Based Pricing”, which utilizes specific measurements of usage in order to generate on-demand metered invoicing. It does this by ingesting events in the real-time through their cloud billing platform and passing it through to a processing and aggregation service, which then provides pricing attributes based upon feature definitions, price groups and volume scales, and specific pricing plans that may be in play for users.This processing and aggregating service is then used to provide a dashboard of reports for companies on costs and revenues through the Amberflo Reveue Explorer, showing the cost of service, the revenue generated by the service, etc.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        What is Moesif?Moesif is a full-featured analytics and monetization platform based around the idea of observability – it is positioned around the concept of first building understanding before effectively monetizing, and as such, it offers a variety of systems and solutions to that end. Moesif takes a five stage growth towards effective analytics-based understanding and monetization, separating its offerings and features into these categories. By focusing on actual usage and usage patterns defined by customer insights, efficient usage based pricing model is possible for product led growth.The first of these categories, Track, hosts Moesif’s analytics and event logging solutions. Features including API Product Analytics, API Logging, Body Analytics, and Billing Meters allow Moesif users to gain a firm understanding of what their system is actually doing in context with the overall whole, delivering high API usage understanding with actual product usage.Next, Moesif provides features in a Monetize categories. These features, including Automatic Invoicing, Quotas & Governance, and a well-designed Developer Portal, allow users to monetize their systems based upon the understanding gained in Track to bill customers with confidence.Third, Moesif takes these systems to the next level by offering a Retain track. This track includes real-time alerts and behavioral cohorts facilitated through Retention Analysis, Profile Dashboards, and more, allowing users to ensure customer success through identification, support, and retention planning.The fourth category of functional features is an interesting one. Delight is the idea of providing functions such as Embedded Metrics and Quota Emails to provide for opportunities to engage and delight your customer base. This high-touch communication approach has proven time and time again to be quite effective in generating goodwill and improving user experience, so it’s great to see it in a feature set like this.Finally, Guide offers a series of solutions in the Conversion Funnel category, including Funnel Analysis and Analytics, in order to guide users to specific behaviors and end states. This, in combination with the Delight functions noted above, allows users to guide the experience of the end user with customized customer success efforts.Comparing Amberflo.io and MoesifNow that we have introduced these systems, let’s compare them feature set to feature set.API MonetizationMoesifMoesif provides a feature-rich solution to monetization. No-code API metering is easy to implement, allowing for complex metering across body fields, headers, and more, with more complex functionality enabled through scripted fields and formulas. Invoices can be automatically generated through a variety of systems both in-house and third-party, including Stripe, Chargebee, Recurly, Zuora, and more. This is all provided without any transaction fee and is guided via an effective open-source developer portal.Amberflo.ioAmberflo leverages real-time ingestion and aggregation to deliver its monetization strategy, leveraging their decoupled cloud-based solution in order to allow for greater flexibility in billing strategy. Because Amberflo is much more focused on the idea of metering rather than analytics, some users may find the data provided by Amberflo to be somewhat more limited, resulting in more complex flows requiring some custom metering solutions.Quotas and GovernanceMoesifMoesif has a feature-rich set of solutions for ensuring proper quota definition and governance for policies to create a robust usage based business model. These systems can be automatically enforced, both through soft mechanisms that limit interaction and through outright API blocking. This enables monetization across freemium and trial based approaches.Amberflo.ioAmberflo is very limited in this regard. While you can in theory use the metering system to achieve similar results as with Moesif, it requires very complex implementation and still does not provide blocking outside of the use of third-party systems.API AnalyticsMoesifMoesif considers analytics as core to monetization, and as such, the analytics system underlying the product is extremely strong. Analytics across product usage, customer insights, API logs and metrics, subscriptions, funnel analysis, and retention analysis goes a long way towards ensuring that users have ample data to make decisions and leverage monetization solutions.Amberflo.ioAmberflo is in an interesting middle ground here. While Amberflo does provide very good analytics systems for very specific domains, it lacks some major tooling that Moesif provides. As such, Amberflo is a very good solution for a very small subset of needs, while Moesif provides a more complete solution for a wider range of scenarios.Customer GuidanceMoesifBehavioral analysis is a big part of what Moesif highlights in their toolset, and this is a very sensible investment – by being able to track behaviors, email according to certain event triggers, and even lump users together in specific behavioral cohorts, additional metered usage and support strategies are unlocked, allowing users to identify potential strike points. This, combined with embedded metric systems, allowing Moesif users to really gain a full understanding of the system in which they are working and ensure real time customer success.Amberflo.ioAmverflo again finds itself in a strange place – when you consider only the embedded metrics part of this, Amberflo and Moesif are neck-to-neck. In holistic consideration, though, Moesif again edges out the competition, providing an equivalent functionality with additional systems that help to make the most out of the existing system set.API Monitoring, Dashboards, and CollaborationMoesifA major strength in Moesif is the fact that it brings its real-time usage alerts, anomaly detections, usage instrumentation, and customer notification systems into a cohesive frontend through the use of dashboards and collaborative tooling. The monitoring systems are quite robust, but the provision of additional dashboards that are shareable and collaborative allow for a greater high-level view that is second to none.Amberflo.ioAmberflo does provide effective real-time usage alerts, but the lack of effective anomaly detection hurts the product as a whole. This, in addition to the lack of dashboards and collaborative toolings, make this category perhaps the most important when considering the tools in comparison to one another.ImplementationMoesifMoesif provides a variety of integrations for API frameworks to get accelerate product adoption with relatively low friction. This leads to incredible scalability, especially when one considers the amount of control the solution as a whole provides to the end user. Additional features such as a robust data retention scheme, client-side encryption systems, and synchronization plugins allow for incredible flexibility.Amberflo.ioWhile Amberflo does provide its own set of integrations and synchronization tools, it is nowhere near as robust as Moesif. This, paired with a lack of native client-side encryption, makes it a hard sell for more complex workflows dealing with personal information and critical security items.ConclusionUltimately, Amberflo is a good, if limited, choice. Where it falters in comparison to Moesif is in the provision of additional tooling. Amberflo does some very specific things very well, but it is limited to just those features.Moseif provides those features in spades while also providing additional tooling, unlocking a variety of new approaches, monetization strategies, and analytics views. For this reason, Amberflo can only be recommended in very specific use cases, and in the lion’s share of cases, Moesif is a better option if you believe you may benefit from even one of the bevvy of additional features on offer.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Amberflo-vs-Moesif/",
          "author": "Larry",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-comparing-moesif-and-apigee-a-deep-dive-into-api-monetization-features": {
          "title": "Comparing Moesif and Apigee: A Deep Dive into API Monetization Features",
          "content"	 : "In today’s digital age, APIs have become the backbone of many businesses, enabling seamless integration and communication between applications, platforms, and users. As the demand for APIs grows, so does the need for effective API management and monetization. In this post, we’ll compare two leading API platforms, Moesif and Apigee, focusing on their monetization features.What is API Monetization?API monetization is more than just charging for API access; it’s a strategic approach allowing businesses to derive value from their API assets. In today’s digital landscape, APIs act as conduits, channeling valuable data and functionalities to third-party developers, partners, or even internal teams.While the direct monetization method involves charging users based on API usage, such as pay-per-use or tiered models, there’s also significant value in indirect monetization. You can achieve this by using APIs to drive user engagement, foster partnerships, or enhance a primary product or service offering.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Moreover, APIs play a pivotal role in fostering collaborations and building digital ecosystems. For instance, a travel platform might not charge airlines for its API but benefits from the integrated services, enriching its content and making it more appealing to end-users. Such collaborations not only enhance the service offering but also open doors to new revenue streams and market opportunities.In essence, API monetization is a multifaceted strategy that extends beyond mere access charges. It encompasses understanding the intrinsic value of an API and leveraging it to create both direct and indirect revenue channels. As businesses navigate the evolving digital domain, effective API monetization emerges as a key differentiator for success.What is Moesif?Moesif is a cutting-edge API analytics platform tailored for businesses aiming to optimize and monetize their API-driven products. In an era where data-driven decisions are paramount, Moesif shines as a beacon, offering deep insights into how APIs consume, interact with, and leverage.It’s not just about tracking API calls; Moesif delves deeper, providing a holistic view of customer interactions, usage patterns, and potential bottlenecks. This granular level of detail empowers businesses to refine their offerings, ensuring they meet the evolving needs of their user base.Building on this foundation, Moesif offers a suite of features designed to drive growth and revenue. From real-time event logs that shed light on customer API usage to powerful monetization tools like billing meters and automatic invoicing, Moesif is more than just an analytics platform—it’s a comprehensive solution for businesses aiming to harness the full potential of their APIs. With its emphasis on enhancing the developer experience, Moesif ensures that both businesses and their end-users reap the maximum benefits from API integrations.With Moesif, companies can:      Track and Understand API Usage: Real-time event logs help businesses understand how customers are using their APIs and applications.        Monetize APIs: Moesif offers features like metered billing and quotas & governance, allowing businesses to set up usage-based billing meters on API calls and more.        Retain Customers: Real-time alerts and behavioral cohorts notify businesses when a customer’s usage changes, indicating potential up-sell opportunities or issues.        Delight Developers: Moesif provides embedded API metrics and an open-source developer portal, enhancing the developer experience.  What is Apigee?Apigee, part of the Google Cloud suite, stands as a testament to the evolution of API management tools. Designed with scalability, security, and performance in mind, Apigee offers businesses a robust platform to build, manage, and secure their APIs, irrespective of the use case, environment, or scale. In a world where digital transformation is no longer a luxury but a necessity, Apigee acts as a bridge, connecting disparate systems, facilitating seamless integrations, and ensuring that data flows smoothly and securely.One of Apigee’s standout features is its innovative use of AI and machine learning. With Duet AI, businesses can create API specifications using natural language, a testament to the platform’s commitment to simplifying complex processes. Furthermore, Apigee’s focus on security is evident in its offerings like “Automated API Security,” which uses machine learning to detect and protect against potential threats.Beyond just management and security, Apigee also offers flexibility in its architectural styles, supporting REST, gRPC, SOAP, and GraphQL. This ensures that businesses can choose the best approach that aligns with their goals and technical requirements. In essence, Apigee is not just an API management tool; it’s a comprehensive solution designed to empower businesses in their digital journey.Some of its features include:      Duet AI in Apigee API Management: This feature allows users to create API specifications using natural language, generating OpenAPI specifications that are consistent and compliant with enterprise policies.        Automated API Security: Apigee offers ML-based abuse detection to protect APIs from misconfigurations, malicious bot attacks, and other threats.        High-Performance API Proxies: Apigee supports various architectural styles like REST, gRPC, SOAP, and GraphQL, providing flexibility in API implementation.  How does Moesif compare to Apigee for API Monetization?When it comes to API management and monetization, both Moesif and Apigee have carved out a niche for themselves in the market. However, the nuances in their offerings and focus areas make a significant difference, especially for businesses that prioritize monetization.Moesif, with its dedicated emphasis on monetization, offers businesses a suite of tools tailored specifically for revenue generation. Its real-time event logs provide granular insights into API usage patterns, enabling businesses to identify potential revenue streams and upsell opportunities. Furthermore, Moesif designs its features like billing meters, automatic invoicing, and quotas & governance with monetization at their core. This ensures that businesses can set up and manage their monetization strategies seamlessly, without the need for extensive integrations or custom solutions.Apigee, on the other hand, while being a comprehensive API management tool, has a broader focus. Its monetization features, though robust, are part of a larger suite of offerings. This means that while Apigee provides flexibility with options like pay-as-you-go or subscription pricing, its monetization features might not be as specialized or in-depth as those of Moesif. For businesses that prioritize monetization, this distinction is crucial.While both Moesif and Apigee offer robust API observation and management features, there are some distinctions when it comes to monetization:      Monetization Focus: Moesif places a strong emphasis on monetization, offering features like billing meters, automatic invoicing, and quotas & governance. This makes it easier for businesses to set up and manage their API monetization strategies.        Developer Experience: Moesif offers an open-source developer portal and embedded API metrics, which can enhance the developer experience and drive more API usage, leading to increased revenue.        Flexibility: Apigee provides flexibility in pricing with options like pay-as-you-go or subscription pricing. However, its monetization features are more generalized as part of its broader API management offering.  In essence, while both platforms offer robust API management features, Moesif’s dedicated focus on monetization gives it an edge, especially for businesses aiming to maximize their API-driven revenue. Its specialized tools, combined with powerful analytics, make Moesif a compelling choice for businesses that view their APIs not just as integration tools, but as significant revenue drivers.Why Use Moesif?In today’s interconnected digital ecosystem, APIs serve as the linchpins that bind various services, platforms, and applications together. As businesses increasingly rely on APIs to drive growth, innovate, and deliver value, they must not overlook the importance of effective API management and monetization.Within this context, Moesif emerges as a frontrunner, offering a suite of tools and features tailored to meet the unique challenges and opportunities presented by the API economy. But what truly sets Moesif apart? Why should businesses, from startups to enterprises, consider Moesif as their go-to solution for API analytics and monetization? Let’s delve into the distinct advantages that Moesif brings to the table.      Dedicated Monetization Features: Moesif’s core design prioritizes monetization. Unlike platforms that offer monetization as an add-on or secondary feature, Moesif has built its platform around the concept. This ensures that businesses have access to specialized tools, like billing meters and automatic invoicing, tailored specifically for revenue generation.        Granular Analytics: Moesif empowers businesses to gain a deep understanding of how they are using their APIs through real-time event logs and analytics. This granular insight is invaluable, allowing businesses to identify trends, anticipate user needs, and refine their offerings accordingly.        Enhanced Developer Experience: Moesif acknowledges that the success of an API is closely tied to the developer experience. With its open-source developer portal, Moesif ensures that developers have all the tools and resources they need at their fingertips. This not only drives API adoption but also fosters a community around the product.        Proactive Customer Retention: With features like real-time alerts and behavioral cohorts, Moesif enables businesses to be proactive in their customer retention efforts. Moesif ensures that it promptly addresses potential up-sell opportunities or issues by notifying businesses of changes in customer API usage.        Scalability and Flexibility: Moesif scales to accommodate business growth, enabling it to keep pace with API management and monetization capabilities. Whether catering to a small user base or managing enterprise-level traffic, Moesif offers the flexibility and scalability businesses need.  ConclusionIn conclusion, Moesif is not just another API observation and management platform; it’s a comprehensive solution designed with the needs of modern businesses in mind. Its dedicated focus on monetization, combined with its commitment to analytics and developer experience, makes it a compelling choice for businesses looking to maximize the potential of their APIs.Both Moesif and Apigee are powerful platforms for API observation and management. However, for businesses specifically looking to monetize their APIs, Moesif’s dedicated features and focus on developer experience make it a compelling choice. As always, businesses should evaluate their specific needs and requirements before making a decision.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Comparing-Moesif-and-Apigee-A-Deep-Dive-into-API-Monetization-Features/",
          "author": "Dylan",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-self-serve-and-sales-led-api-monetization-unlocking-product-led-growth": {
          "title": "Self-Serve and Sales-Led API Monetization - Unlocking Product Led Growth",
          "content"	 : "There are as many monetization pitfalls as there are methods of monetizing APIs. As the market has shifted over the years, selling APIs and creating product userbases has become more important than ever. Today, we’re going to discuss a strategy that can lead to explosive growth – let’s talk self-service and product led growth.The Shifting Product LandscapeThe reality is that selling APIs is more difficult than ever before. The modern landscape has shifted dramatically over the years, moving both the consumption model as well as the value proposition for most adoptees of API platforms. A monolithic API provider with structured UIs and full-service integration plans used to be the gold standard, delivering a value proposition of a full-service partnership with full-service integration. As the industry and community has shifted towards automated microservices and product led growth, which deliver value through specific and targeted use cases, the idea of buying into a traditional integration approach has diminished substantially for the average API consumer.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        The Decline of Sales-Led GrowthWhat this has ultimately resulted in is a shift of power between the provider of an API product and the consumer. When integrating a service requires bringing a team on premises to install proprietary systems and launch custom server clusters, the power and cost of this API management overwhelmingly lands in the camp of the software. As this has shifted, the calculus has changed substantially, with the adoptee becoming the powerhouse authority.This has undermined the traditional pathway of sales-led API monetization and, subsequently, product growth. Sales-led is conceptually based on the idea that you have something someone else wants, and as such, you should set the price and API monetization strategy as close to the upper limit as possible to extract value and revenue. The problem with this growth strategy is that it assumes the product has a clear value proposition and is an outstanding choice. Today, the consumer has never had so much choice and flexibility, so the idea that a product can extract value through this pathway is not really true in most industries anymore.All of this adds up to a simple truth – adoptees are more empowered than ever, and they need to have a solution that meets their specific needs with as little friction in adoption as possible. Each hiccup along the way reduces the likelihood of success and long-term retention of API users. When the authority was in the product, things could afford to be complex or high-friction – after all, the product was what the product was. In today’s API marketplace, the product has to be fast to deliver and easy to integrate with as few initial blockers as possible.The Self-Serve ModelEnter the self-serve model. The basic idea of the self-serve model is to lower the friction to onboarding as much as possible by allowing the adoptee to decide on their API consumption and API usage: what they need, how they need it, and how they will pay for it. This can take several forms, but the core concept remains the same – low friction leads to high adoption. Self-serve delivers some major benefits – let’s take a look at a few of them.Reduced Onboarding FrictionSelf-service delivers reduced initial friction through a variety of mechanisms. Using click-through terms of service can help reduce initial legal paperwork and hurdles for an API business and the API developers using a given service alike. Automated integration of an API gateway with a simple step-by-step process can reduce the time to market for integration of the product. Simple payment options can reduce the need for client approvals and expenses. Even something as simple as allowing the user to decide between pre-pay and post-pay agreements can be provided far more simply and effectively through a self-serve business model.Enhanced User ExperienceThe self-serve API monetization model is all about prioritizing the experience of the user, and as such, it delivers the best user experience for both adoptees and developers. Self-serve delivers ease and autonomy – not only is the product easy to use, the adoptee has autonomy to choose how they use it. This autonomy can result in long-term increases in adoption and retention, but even more importantly, it results in a product that is easy to use and easy to advocate for in a competitive API ecosystem.Scalability and ExtensibilityThe self-serve model boosts scalability and extensibility by allowing adoptees to choose exactly what services they need when they need it, increasing revenue growth without losing direct monetization upsell opportunities. Standard systems of old might require a full rollout before any benefit can be gained, but with a self-serve approach, this barrier is lowered substantially. If a user only needs a low tier of functionality now, but is likely to use a higher tier later, they will prioritize a solution that allows them to increase their services at will over a system that requires manual deployment or support. With tiered pricing options, a flexible usage based pricing model is attractive to users who may not know their true product usage requirements, where they are granted API access, and which then results in the SaaS company to ensuring API revenue generation.Cost EffectivenessSelf-serve systems are cost effective for all parties involved. Because you can tie business logic to the services being rendered at the developer level, costs can be controlled by scaling only to the demand your clients have placed on your self-serve product. For the adoptee, costs can be controlled by using only what API call volume (or whatever monetizable metric you’ve chosen) is needed at the moment and scaling over time. When an organization decides to open APIs up to the public, they not only join the API economy, but diversify their revenue recognition. This increased cost effectiveness positions your API to be a powerhouse option, as flexibility is almost always going to be valued quite highly on a ranked list of attributes for services.Accelerating the Flywheel - How Self-Serve Feeds Product Led GrowthSelf-serve models unlock a massive potential benefit in the form of product led growth acceleration. product led growth is a simple concept – the product should sit at the center of your growth, driving user acquisition, conversion, and retention. A self-serve model is the first big step towards accelerating this process.Consider what a typical self-serve model looks like in practical terms. First, your user onboards and decides to check out the product. From that moment, everything rides on the product – all the user needs to experience is their “Aha” moment, the moment where they realize the product is a good fit for their use case. Flexible adoption through a self-service API business model increases the chances of this happening, and a good product should provide ample opportunity for multiple “Aha” moments.Once the user has had that moment, however, the self-service models kicks the product led growth model into high gear. As the adoptee accelerates their use, the sales team should engage their efforts, identifying new use cases and leading the charge on new product iteration. As the adoptee becomes an evangelist for the product, pushing for adoption and further iteration, the product itself becomes better and more attractive, bringing additional users into the fray, while accelerating the use of the existing client base.This strategy has proven itself time and time again for exceptional growth – and all it takes is the creation of a strong product and a low-friction self-serve approach.Creating EvangelistsOne big benefit of this model is the creation of product evangelists. A product evangelist is a dream come true for many developers – an adoptee who falls in love with the product to such an extent that they evangelize its use and accelerate user awareness and acquisition. In order to effectively creative product evangelists, you have to think like a product evangelist – so what specifically does this type of developer want?Product evangelists need:  a product they trust,  a product they can share widely and  a product others can validate their experience with.Effective self-serve models and product led growth strategies should meet these demands. A product that is low-friction and transparent in its pricing and functionality is a product that can be trusted – this can be bolstered even further through adequate and effective documentation. A product that is shareable is one that has clarity as to the value proposition – a product should have clear benefits and should document these benefits through blog posts, analytics, customer case studies, white papers, etc. Finally, a product that can validate experience is one where a new user can be influenced into adoption and immediately see the value in the product – in other words, a validating product is a low-friction product.ConclusionUltimately, a self-service model is an incredibly powerful way to enable product led growth in API monetization, and should be considered within the full context of the form and functionality of the API. With proper attention, developers can create an incredibly powerful product led growth flywheel that is powered by a self-serve model which compounds evangelists into new evangelists day by day – and all it takes is a great product and the right intent.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Self-Serve-and-Sales-Led-API-Monetization-Unlocking-Product-Led-Growth/",
          "author": "Kristopher",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-product-management-api-strategy-5-popular-developer-portals-and-the-business-features-they-might-be-missing": {
          "title": "5 Popular Developer Portals and the Business Features They Might be Missing",
          "content"	 : "As APIs have become the centerpiece of online data exchange in the modern era, the need for documentation and communication between developers of these APIs and their users has become incredibly important. Effective developer communication can unlock massive potential, create new iterative success, and serve as a compounding conduit that improves the industry at large. Introducing new tools can significantly enhance developer communication and experience, making it easier for developers to discover, learn, and adopt innovative solutions. Accordingly, developer portals have become exceptionally useful and important ways to share API documentation with API consumers.Unfortunately, developer portals do by-and-large suffer from a over-specificity issue. Developer portals too often focus on connecting the API developer to information about the product without connecting them to adequate business logic and systems designed to support said logic. In essence, there still exists a gap between the product and the business application of that product.The Moesif developer portal addresses this gap by blending traditional documentation with rich, user-centric insights. Beyond just reference materials, it empowers API providers to expose usage metrics, consumption analytics, and monetization features directly to their API consumers. This turns the portal into not just a technical resource, but a business tool that helps developers understand how their integration drives value.Today, we’ll look at some clear examples. These developer portals are all very popular and much beloved, and for good reason – they are great examples of what they do. Nonetheless, they still largely miss the mark when it comes to providing ample business logic to the end user.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding Developer ExperienceDeveloper experience (DevEx) is a critical aspect of software development that encompasses the systems, technology, processes, and culture that influence the effectiveness of developers. DevEx is about empowering developers to write high-quality code efficiently and effectively, resulting in a great outcome. A positive developer experience is essential for achieving business goals, as it enables developers to build with more confidence, drive greater impact, and feel satisfied.DevEx is not just about the tools and technology used, but also about the processes and culture that support developers in their work. A well-conceived DevEx provides greater consistency across environments, processes, and workflows, leading to improved productivity and reduced errors. Automating tedious and manual processes is essential for a well-conceived DevEx, enabling companies to outperform their competitors. Research shows that a better DevEx can lead to extensive benefits for organizations, including improved employee attraction and retention.StripeStripe’s developer portal is a very interesting example of this problem. On the one hand, it is a well-made portal that does what it does very well. With ease of access, it is use-centric, clear, and thorough, and expresses a variety of payment processing systems clearly through its resources, guides, and reference material. An API developer adopting Stripe will find the developer portal to be highly effective for their given use case.However, for those using Stripe to provide more complex payment services as part of their own service, it does have some issues. The developer portal provides ways to show a customer their subscriptions and to generate their invoices, but doesn’t support more complex capabilities such as usage-based and usage-limited billing. Furthermore, there is no capability to provide reporting on these types of payment processes.For this reason, Stripe is a wonderful solution for a specific set of use cases – given how many services are pivoting towards usage-based monetization, however, Stripe does lack some additional tooling that would be helpful. The lack of developer resources inherently built into the Stripe portal means that its functionality is best for users who are not building custom billing meters.TwilioTwilio’s developer portal is a combinatory portal and suite that leverages SMS, voice, and communication systems to great heights of commerce and engagement. The tutorials and SDKs that are provided by Twilio are in-depth and actively maintained, representing a very strong example of constant iteration and updates for developer-to-user communication.That being said, while Twilio provides robust communication systems, it is missing a product which facilitates direct business logic implementation manipulation, a key to a successful developer experience. Revenue management and revenue to source tagging is missing, making it more difficult to pass through cost or monetize blended systems. For this reason, monetization using Twilio would often come down to a bulk cost rate or direct bill rate, making usage-based or other more specific billing systems something requiring internal code, or partnering with an expert in usage-based billing.FastlyThe Fastly Developer Hub is a great resource for edge development. Fastly is known for its effective edge cloud systems, and their hub is a wonderful entry point to the vast and varied offerings they provide to users who want to build apps. This hub includes a testing sandbox, code snippets, ample documentation, API reference materials, and more. For anyone getting started with Fastly, this is a super effective developer portal.Like most on this list, however, it is missing a good amount of business features. Developers creating content in the cloud will at some point want to monetize their features, and Fastly does not provide complex monetization systems based around usage, paradigm, etc. This is a big gap for many adopters, as a huge chunk of business logic is sidestepped.MailchimpMailchimp is an email marketing platform, and their developer portal provides ample documentation around this topic. User guides are effective and complete, and automation guidance is effective, especially in the context of campaign management. For most users, this developer portal is complete and stands as a great example of what a comprehensive overview can look like without being overwhelming.Where it falls short, however, is in the more specific monetization systems and business-logic systems that may arise in complex systems. A big missing feature with Mailchimp is cross-list management, and when utilizing lists of users who may have differential billing policies, complex utilization enforcement with business logic, etc., this is a big miss and requires a lot of hands-on business logic to application management that negates the automation it boasts so heavily.SlackSlack is incredibly well-known and highly utilized, and for good reason – it is a wonderful workspace management tool for communication, file sharing, and collaboration. Its developer portal connects developers to a wide variety of APIs and backend systems, allowing for the creation of various applications to boost productivity and deliver exceptional extensibility.Where it struggles, however, is in its monetization systems for applications developed using this portal. When an app is created, it can be monetized, but in a relatively limited way. There are the typical subscription models and use-based models, sure, but more complex business logic such as the amount of data processed or the complexity of said request is bundled under the same category as anything else. When you use an app, your complexity is largely ignored outside of the fact that it is a request. This is problematic for a variety of reasons, but for more complex applications that border on being a standalone application or a Slack application, it will tilt the calculus hard in one direction.Measuring Developer Experience SuccessMeasuring developer experience success is crucial for identifying areas for improvement and optimizing DevEx efforts. Key metrics for measuring DevEx success include time-to-value, customer satisfaction, and retention. The DevOps Research and Assessment (DORA) framework can be helpful in measuring DevOps performance. Lead time, deployment frequency, mean time to recovery, and change lead time are essential metrics for measuring DevEx success.Time to first contribution for a new hire is a great metric for measuring DevEx success. Customer response time is another good metric, indicating that the team has what they need to move quickly. Measuring DevEx underscores the need to continually check in with developers and see how they’re feeling. Regular feedback loops and continuous monitoring are essential for maintaining a high level of developer satisfaction and productivity.Strategies for ImprovementCompanies and development teams should improve their DevEx using a strategy that includes research, discovery, user testing, and other key design components. Collaboration is essential to DevEx, especially in the age of AI. Organizations need to understand their current DevEx and the most critical friction points. Simplifying, accelerating, or optimizing existing systems and processes can improve DevEx.Cutting down on the number of meetings can seem like a great idea, but it may introduce new friction. Many organizations create DevEx teams dedicated to understanding, improving, and monitoring DevEx. Generative AI is the future of DevEx, enabling developers to write high-quality code faster and more efficiently. By adopting these strategies, companies can create a more productive and satisfying environment for their developers.ConclusionUltimately, most developer portals do what they were designed to do – they connect developers with internal systems. Where this gap comes from is the fact that developers and business logic can often be separate things – business logic more often than not arises from financial consideration, whereas developer logic arises from technical consideration. Providing a system that effectively unlocks monetization while delivering a robust feature set for security and analytics is vitally important to most API developers, necessitating something more than a “one size fits all” implementation.Moesif offers a wonderful and complementary solution to this problem. With Moesif, you can understand, grow, and monetize API usage with a comprehensive analytics and billing platform. Through API Analytics, usage-based systems (including metered billing, quotas and governance, and more), and customer success and retainment tooling, Moesif can deliver unprecedented success to your application.The best part of it all? Moesif works with your stack. Moesif connects your APIs and apps to billing platforms, customer data platforms, analytics systems, and more, serving as a node that delivers success and growth while delivering a better developer and user experience. Check out our product’s guides to find out exactly how we can help you today.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-product-management/api-strategy/5-Popular-Developer-Portals-and-the-Business-Features-They-Might-Be-Missing/",
          "author": "Kristopher",
          "categories": "API-Product-Management, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-strategy-how-to-enforce-api-usage-policies": {
          "title": "How to Enforce API Usage Policies",
          "content"	 : "One of the most difficult aspects of creating an API service for public consumption is the balance between developer control and user freedom. Ensuring that users can leverage an API to new heights requires a certain amount of freedom, both in modality of usage and in applicability of the use case. The security of underlying systems and the API itself relies on controlling this usage and ensuring a level of control for the greater good. The balance between these two is complicated, depending on a variety of API usage policy enforcement mechanisms.These policies can be tricky to implement, however, and their enforcement can present difficulties both obvious and not. Let’s dive into some of those challenges when it comes to the limits of API usage.Major Challenges for Effective Usage Policy EnforcementExternal System IntegrationThe most immediate problem for most APIs in terms of enforcement is where the API usage actually occurs. It’s often easier to enforce use policies when the interaction is happening natively on the API, but APIs are designed for external use and implementation. When APIs are used as part of an external system, this dynamic can result in uses that are outside of the visibility of the enforcement scheme.For instance, if your API is implementing a limit based on origination, that is relatively easy to enforce access control on a native application. What if someone is using your API in a way that funnels their own service’s calls into a single unified endpoint that then connects to your API? In such a case, origination is obfuscated, and this can be difficult to identify and mitigate with API governance.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Differentiation Between Security and Business LimitsWhile these topics are often considered one in the same, there is a very big difference between limiting based upon security reasons and limiting based upon business concerns. Rate limiting is often implemented for very clear security reasons, preventing Denial of Service attacks and alert fatigue. Business-centric quotas, however, limit API interactions based upon business needs. While an external system might handle business quotas, rate limiting might be managed by an API gateway – the reality of this implementation means that you have the same effective process split into two very different categories and two very different systems, increasing complexity of design and reducing clarity in operation at the top level. This can be further complicated when you introduce business-to-business relationships that might bypass the business logic and depend on the rate limiting logic as a mode of “API protection” implementation for the business logic itself. This can obviously get very complicated very fast.Inter-System CommunicationThe reality of the microservice paradigm and the business-to-business marketplace is that systems are almost never located in a single place. Monitoring might occur using one system while enforcement occurs with another. Rate limiting might be handled by different systems both internal and external depending on the origination of the request. Sometimes, microservices will have their own systems for monitoring that are separate from a centralized enforcement, and in some cases, the opposite may be true!Ultimately, the communication between these systems represents a major challenge, as usage policy enforcement is complicated with each additional system.Customization and FlexibilityBusiness needs change, sometimes week to week and day to day. Due to this, business needs to be flexible. In order for a business to be flexible, best practices for the systems that are in place to support the business is to also be flexible. This can drastically increase the complexity of enforcement – usage policies that are static are relatively easier to uniformly enforce across different systems, as the end result is the same regardless of the use case or origination. When you introduce differentiation based on custom requirements or changing business environments, you may very quickly enter a situation where multiple disparate systems must be updated to varied and complex needs and goals. The API manager must ensure that the acceptable use policy is up to date and that any API policy or security policy centered on mitigating vulnerabilities are adaptable to their varied user base. Real-Time Monitoring and EnforcementEffective enforcement requires effective monitoring, and this monitoring often has to be real-time to be proactive as opposed to reactive. This introduces additional layers of complexity, however, as real-time monitoring requires adequate system design and resource allocation. Discrepancies in monitoring of API traffic can lead to massive breaches in usage policies, exposing businesses to legal and economic problems should sensitive data be accessed.Unfortunately, this requirement introduces cost and complexity to the equation. There is no way to solve this problem except through proper resourcing and security testing, both of which cost time, money, and attention. This adds application security challenges to any business, but is especially problematic for startups and other businesses trying to effectively manage capital and resourcing.Transparency and ReportingBusiness and technical operation requires transparency and reporting. Businesses need clear insights into their API state, API data, and the state of its use across varied user groups to ensure compliance with policies and alignment to goals. The lack of transparent reporting can make effective enforcement difficult, and the implementation of effective transparency and reporting systems again introduces cost in terms of capital and complexity.Exceptions Without CompromiseNothing is set in stone – as a fundamental business approach, exceptions to usage policies will occur. How these exceptions are managed is vital to decreasing API vulnerability . Do you have a promotional period? Do you have a special policy for non-profit clients? How do you handle coupons, sales, an incoming request, etc. that may include usage exceptions? Every new exception introduces complexity, but every new exception could carry with it significant business upside for an API provider, both in terms of monetary goals and utilization numbers.API management and balancing these exceptions introduces more complexity into the equation, but also requires increased transparency and reporting to ensure the API security exceptions are just that – exceptions, and not the steady state or rule.ConclusionWhile usage policies are fundamental to effective management and monetization of APIs, enforcement presents several challenges. Thankfully, there’s a fully featured solution – Moesif. We have developed a core set of features designed to provide effective solutions to all the specific API issues noted above.Moesif enables deep understanding of API responses and usage to understand the intent behind the user. By surfacing signals around adoption and usage, Moesif delivers unprecedented insight and authentication into the consumer behavior, unlocking new paths of iteration. Our powerful analytics tools provide additional deep insight into the customer behavior and utilization paradigm, allowing for effective establishment and measurement of usage across the board.This enforcement is carried forward into a variety of channels, including behavioral emails and workflows to notify consumers who meet various usage criteria such as those approaching rate limits and those using deprecated APIs. A powerful enforcement system leverages quota and governance approaches designed to increase security, deliver more performant monetization, and improve consumer experience.Moesif can be an effective solution to all of the issues noted here. Ready to join a product with powerful and proven tools? Schedule a demo today.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/How-to-Enforce-API-Usage-Policies/",
          "author": "Kristopher",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-analytics-product-definitive-guide-to-reverse-trials": {
          "title": "Definitive Guide to SaaS Reverse Trials",
          "content"	 : "In the world of software-as-a-service, product-led growth (PLG) is an easy way to enable users to experience the value of a product firsthand, without restrictions. PLG can drive adoption, retention, and sustainable business growth with its robust and open framework. But what drives PLG best, a freemium model or a free trial? With freemium, your top of funnel growth can skyrocket, while free trials see a 2-3x higher conversion rate thanks to urgency (ie, users lose access immediately on trial end).While both have appealing factors, there’s a way to combine the benefits of both freemium and free trials while still maintaining your bottom line.Meet the ‘reverse trial’. In a reverse trial, new signups start with a limited time trial of your paid features. This means they have access to your entire product, giving them a true taste of your total value. At the end of their trial, they can either purchase your product, or downgrade to a free tier. By setting up a reverse trial, both acquisition and conversion goals can be met. Beyond this, your product’s true, unlimited potential can be unleashed. By allowing users to understand the premium benefits of your product, you can achieve true PLG in the self-service developer world.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Most PLG companies employ a free trial or a freemium model, but very few have both. This is because combining a free trial with a freemium framework does not necessarily mean a successful reverse trial. With a multitude of hybrid approaches, there are two main types of reverse trial that can successfully be offered: an optional reverse trial and a required reverse trial. To understand the difference between these two more clearly, let’s look at some examples.Optional Reverse Trial - KongAn optional reverse trial offers users the option of starting with a free plan or a trial of a premium tier. If you register for a premium plan demo, there is an option at a later date to drop down to a free tier. Generally, these kinds of plans do not require a credit card to sign up, but rather rely on collecting user information (work email, phone number, etc) in order to establish a relationship.One such example of an optional reverse trial is Kong.Kong offers a free, no-frills tier of Konnect for customers who know they want to use Kong’s APIs but do not have need for the offerings in Plus or Enterprise. The ‘Plus’ level tier allows for a trial run of Kong’s full product offering, before users are pushed to make a decision on payment. Finally, Kong allows potential customers to connect with their sales team for enterprise level feature demonstrations. Once inside the program, Kong Konnect’s dashboard lets you know how long is left in your trial length as well as allows you to compare features.For individuals that know they want to use a product but are unsure how much usage they will actually consume, a reverse trial is the natural path to take: by testing the premium tools and integrations a product offers, you can more clearly understand the true value the product can offer to your team, product, and users. By offering the option to drop down to a free tier, organizations can foster trust and confidence around changing usage tiers with their customers.To set up the enterprise level of Konnect, users must contact the Kong sales team directly with information about their specific use case. This helps to strengthen Kong’s understanding of a potential customer’s expectations prior to a demo call, all while maintaining an honest and trustworthy relationship given their product transparency from the Plus tier’s reverse trial.To achieve short term monetization via PLG is not easy; many users are distrustful of “free trials”, as they often seem like opportunities to collect personal information. To truly have success for a freemium experience, real value must be provided to potential customers before requesting a decision around subscription. By allowing users to engage with your product in a self-service manner, users become not only leads, but advocates. Successful, happy users are the best asset internal sales and marketing teams could ask for.Mandatory Reverse Trial - MoesifWith a mandatory reverse trial, every new user who signs up for your product gets enrolled in a free trial. This required trial has a finite cap, generally time or usage, before a downgrade to the free tier offering occurs.A great example of this is us! With a breakdown on our pricing page to show the feature differences between tiers, Moesif takes the stance of a true sandbox test. After all, how can an organization know if an API provider is a good fit without tinkering with their whole product?Moesif offers a free trial of the Growth tier immediately upon signup for two weeks which enables new signups to test all the advanced features and up to 10 million events. As users get close to the end of their trial, Moesif sends behavioral emails to let them know they will be downgraded to the free tier. This ensures customers are able to get to their “aha moment” and see the full value of Moesif before the trial expires, but the trial also helps drive a conversion to paying. New signups are more likely to convert if they know something will be taken away that they already have vs trying to subscribe to something new. Yet, new signups also gain the peace of mind that a free tier still exists. This drives a larger adoption funnel as well. Hobbyists and individual developers can still get some benefits of Moesif which helps with word of mouth, even if not paying.This requirement for potential customers results in more robust use case demonstrations for API-first companies. By enabling a direct dialogue between customer and sales, Moesif is able to get a deep understanding of a customer’s specific needs and challenges. This personalized approach not only fosters a stronger connection between the customer and Moesif, but increases the likelihood of delivering a compelling use case that resonates strongly with a customer’s business objectives.Is My Product A Good Fit For a Reverse Trial?Reverse trials have a lot of pros, but some extremely limiting cons. Reverse trials are a faster road to revenue generation, as they can speed up the sales process dramatically. With a reverse trial, users face the payment decision point quicker due to the threat of their trial ending. A committed user can convert right away when they understand the full range of how your product can improve theirs. This can have a significant impact on your recurring revenue in the moment, which can be critical for smaller organizations.However, a reverse trial may not be a good option for your product:  Your free and premium tiers solve different issues: If you require users to sign up with a trial, you may be forcing them into a more complex scenario than they require. If your users could easily be overwhelmed or confused because your Free and Premium offerings tackle different issues, you may not be able to sell your value proposition.  Your product may suffer with multi-account signups: Reverse trials are best for products that require time invested from users. If there is no impetus to pay, users can sign up over and over again with false credentials, destroying any reason to pay for your product. If users can sign up for multiple accounts and still see value from your product, a reverse trial can be more harmful than helpful.  Cost-to-Serve: A reverse trial is most successful if the cost for a freemium and trial offering can be served at scale. If the costs of running trials outweigh your current freemium offering, a reverse trial will become a cost-sink. Because the cost of servicing your product may be high, it must be economically sound to offer a reverse trial on top of your current tiers.Implementation and Best PracticesImplementing an effective reverse trial strategy involves a systematic approach that maximizes user engagement and conversion based on your product and current users.  Clear Usage Limits: Begin with transparent usage limits for the free version of your product. These limits should be carefully chosen (and backed by your user data) to provide value to users while encouraging them to upgrade. Communicate these limits during a simple but effective onboarding process to manage user expectations, as transparency fosters trust and lets users understand the benefits of upgrading.  Communication Channels: Establishing communication channels is essential during the reverse trial period. Ensure that users have easy access to support (whether through chat, email, or documentation). This proactive support demonstrates your commitment to user satisfaction while simultaneously triaging customer issues, encouraging a positive perception of your product.  Seamless Downgrade Options: It’s important to offer seamless downgrade options as well as upgrade ones. Users should have the flexibility to switch back to the free version if they find it better suits their needs. Providing intuitive downgrade buttons within the user interface can simplify this issue. By taking a user-centric approach, you can prevent frustration and maintain goodwill, even if users decide to revert to a free account.  Optimizing User Experience: Design the free version of your product to showcase the core value of your product while subtly introducing the benefits of the premium features. Prioritize user feedback and iterate on pain points to refine the experience continually.  Gradual Feature Introduction: Consider gradually introducing premium features to users during the reverse trial. This can allow users to experience the value of these features firsthand, creating a sense of anticipation and motivation to upgrade. By exposing users to features gradually, you can avoid overwhelming users with a sudden influx of functionality.At its core, the reverse trial approach revolves around a user-centric philosophy, elevating user experience and decision-making to the forefront. This concept is bolstered by commitment to transparency, clearly defined usage limits, and open channels of communication. Software providers are starting to recognize the potential of reverse trials, considering them as a means to an end for reshaping user engagement and perceptions. By incorporating a reverse trial strategy into their subscription models, API providers can move toward increased conversions and, more importantly, cultivate enduring and meaningful customer relationships.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-analytics/product/Definitive-Guide-to-Reverse-Trials/",
          "author": "Rachael",
          "categories": "API-Analytics, Product"
        }
      
    ,
  
    
        "api-analytics-product-pricing-strategies-for-apis": {
          "title": "Pricing Strategies for API Products",
          "content"	 : "APIs and their integrations are paramount for the inner and outer workings of SaaS organizations. As such, pricing strategies for API products have taken center stage for businesses as a crucial determinant of success. APIs have become the linchpin of modern software development, enabling applications to communicate, share data, and provide enhanced functionality. As organizations across industries embrace digital transformation, APIs have gained unprecedented relevance, creating value and innovation outside the world of SaaS. For many organizations, API access through third party apps is an additional cost that can quickly add up. Despite volume discounts and free trial options, finding the right API analytics product often is based around price first and foremost.Given the importance of API products, the significance of pricing strategies cannot be understated. Effective API pricing not only defines the perceived value of API offerings, but plays a pivotal role in actuating business objectives. Crafting a well-calibrated pricing strategy not only influences revenue generation, but also guides the accessibility of API services, customer adoption, and the overall market positioning of a product.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Value-Based PricingValue-based pricing stands as a cornerstone in the API context, where the intricacies of functionality, data exchange, and integration intertwine. Rooted in the principle of charging customers based on the value they derive from a product, value-based pricing is particularly apt for APIs due to their ability to directly impact a company’s efficiency, scalability, and innovation.Value-based pricing allows organizations to price their API products based on its perceived value by their customers. Rather than relying on a traditional ROI framework (innovation and production costs, industry markups, etc), value-based pricing bases the price point for your product on the value proposition it holds for potential customers. This means it’s not necessarily about the quantity of features your product offers, or even the quality.Value-based pricing is all about the use case and story. Value comes down to the longevity and worth of an integrated product; understanding customer needs is at the heart of this strategy, as it allows you as an API provider to align product pricing with the specific value propositions that resonate most with your potential customer.By tailoring API pricing plans to cater to different customer segments and their different, unique use cases, API providers can enhance the perceived value of their products with targeted messaging. Ultimately, this strategic alignment empowers API-centric companies to foster deeper customer relationships, optimize conversion rates, and secure a competitive edge in the market.One such example of value-based pricing is Algolia, an end-to-end search and discovery API-based platform. With Algolia, there are two “lower” need tiers, Build and Grow. However, Algolia’s USP hinges on their AI-enhanced API tools. To access any of the AI suite, a Premium or Elevate level subscription is required, both of which require a conversation with Algolia’s sales team.This process ensures that Algolia is able to control the narrative of the sale, tailoring the features and capabilities of the Premium tier to a company’s specific needs. This also allows for a stronger bond between Algolia and a prospective customer - because the outside organization must request pricing, Algolia is able to collect information directly around their use case. Beyond this, Algolia’s data for optimizing their sales funnel grows stronger with every contact submission.Tiered Pricing ModelsTiered pricing models offer flexible but defined buy-in levels. This dynamic approach to API pricing resonates with the varying needs of a diverse customer use cases, allowing for a broader customer base. This model involves segmenting users into different tiers, each offering a distinct set of features and resources at varying price points. By offering different features or quotas at different price points, new users can be reached who may have previously been unable to justify the costs of your product.Pros of Tiered Pricing Models for API Companies:  Diverse Customer Segmentation: Tiered pricing allows API companies to cater to a wide range of customer segments with varying needs and budgets. This approach provides customers with options that align closely with their requirements, increasing the chances of attracting a larger customer base.  Scalability: Tiered pricing enables seamless scalability for customers and API companies alike. As customers’ needs grow, they can upgrade to higher tiers to access more features, resources, and usage quotas. This scalability not only fosters customer loyalty but also supports evolving requirements with growth.  Revenue Optimization: By offering multiple pricing tiers, API companies can maximize their revenue potential. Different customer segments are willing to pay different amounts based on the value your API product provides, allowing API providers the ability to extract value from varying customer groups.Cons of Tiered Pricing Models for API Companies:  Complexity: Managing multiple tiers can introduce complexity in terms of feature value and viability. This complexity can require additional resources for customer support and internal management, potentially increasing operational overhead.  Customer Confusion: With varying tiers and associated features, customers might find it challenging to understand which tier best suits their needs. This confusion can lead to decision paralysis and potentially delay potential purchases.  Limited Customization: While tiered pricing provides options, it may not cater to every unique customer requirement. Some customers might need a combination of features that don’t currently or easily fit within existing tiers, potentially pushing them toward alternative solutions with higher levels of control.A good example of a tiered pricing model is MailChimp. With a straightforward pricing model that requires no conversation, MailChimp serves as a self-service option for email marketing campaigns. Their pricing page also offers a breakdown of features and usage limits per tier, making it simple to figure out which option best fits an organization’s needs. With the option to contact sales made available through a Chat widget and a 1-800 number rather than a required lead gen form, users can purchase the highest tier with the click of a button.Balancing the benefits and drawbacks of tiered pricing models is essential for API companies to effectively meet customer demands, maintain competitiveness, and maintain a healthy revenue stream. This monetary scalability aspect is crucial, as it ensures that API products remain aligned with an organization’s growth trajectories and goals.Freemium and Usage-Based PricingA freemium model is a strategic pricing approach that entices users by offering a basic version of a product for free, and reserving advanced features for a paid plan (likely an enterprise plan). Effectively a permanent “trial”, the freemium tier offers a truncated version of your API product. This strategy effectively lowers the barrier to entry, attracting a large user base that can test the product firsthand. However, a major shortcoming of freemium models is oversimplifying your offering. By watering down your product, you may lose your most important value propositions. A company like Slack or Canva is free to get started with, but requires financial backing to unlock features such as Slack Huddles or Canva export resizing. For most small ventures, a freemium account will suffice for their needs. But as teams grow, more advanced features become more top of mind.On the other hand, usage-based pricing involves charging customers based on their actual usage levels rather than feature accessibility, providing flexibility and closely aligning costs with value derived. Rather than a usage limit within a given billing cycle, a pay-as-you-go API plan  bills developer users for their cumulative transaction volume. Companies can benefit from a usage-based pricing plan, as they encourage transparency and ensure that customers pay directly in proportion to the value they receive, instilling trust in your business and product. However, customers may be wary of unpredictable costs or might optimize their usage to minimize expenses. Notable examples of successful usage-based pricing strategies in action include Dropbox, who offers free storage and charges for additional space, or AWS, where customers pay based on their computing and storage usage.To mitigate the challenges associated with usage-based monetization tiers, clear communication about the benefits of paid plans is crucial in the freemium model. By navigating these challenges thoughtfully, API companies can harness the power of freemium and usage-based pricing while effectively addressing potential risks.A highly successful example of a usage-based billing model is Amazon Web Services. AWS offers a large array of web based tools and services, some free and some not, but they all employ the same philosophy: pay for what you use. Each individual service has a distinct pricing structure, but customers only pay for the resources they use. By offering all their services at a PAYG rate, a highly personalizable experience has emerged for AWS customers. This also ensures that Amazon keeps their user pipeline actively pumping with new companies and ventures at any scale.Subscription Pricing Models and Add-OnsSubscription pricing models offer a traditional structured approach that benefits both customers and providers in a predictable way. Customers appreciate the ease of budgeting with regular payments, while API providers enjoy a steady revenue stream. Additionally, subscription models can often correlate to higher customer retention rates, as users have ongoing access to services they rely on.Leveraging this model, API companies can also introduce add-ons or premium features as supplementary offerings, providing tailored solutions to cater to varying customer needs. This approach not only enhances the quality of service but also opens avenues for diversified revenue streams. For users looking for a reliable service without a particularly complex use case, a subscription based service can be attractive. Subscription pricing highlights how well-thought-out add-ons can offer customers tailored solutions while securing steady revenue streams for API companies in a reliable manner, provided there’s a commitment to transparent communication and value-driven offerings.Moesif for Billing MetersFor an API pricing model to succeed, the appropriate pricing strategy for API products must be selected. Dynamic API pricing holds major weight in shaping success and market positioning. It underscores the necessity of adopting a customer-centric approach that places users’ needs, preferences, and perceived value at the forefront of pricing decisions. With the rapid pace of shifting market demands, API-first businesses are encouraged to embrace experimentation and iteration when refining their pricing models. This flexibility is critical for aligning business goals with evolving market dynamics. Such strategies not only influence revenue generation but also directly impact customer adoption, satisfaction, and the overall market perception of your API product.Using an API management solution like Moesif to automatically meter customer usage and invoice users can ease the stress of standing up a monetization framework. From no-code billing integrations with billing providers like Stripe and Chargebee, Moesif analyzes API usage down to the individual user level for precise, complex invoicing (and simple invoicing, too). By prioritizing potential and current customer needs, staying agile, and refining pricing approaches, businesses can harness the potential of well-crafted pricing strategies to not only drive success but also thrive in the ever-evolving landscape of API products.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-analytics/product/Pricing-Strategies-for-APIs/",
          "author": "Rachael",
          "categories": "API-Analytics, Product"
        }
      
    ,
  
    
        "api-analytics-product-build-vs-buy-api-management-solutions": {
          "title": "Build Vs Buy: API Management Solutions",
          "content"	 : "API-first startups must prioritize the development and utilization of their Application Programming Interfaces (APIs) as the foundation of their growth model. API-based organizations heavily rely on management and analytics to ensure the smooth functioning, monitoring, and optimization of their APIs. However, the decision to build or buy an API management tool becomes crucial as it directly impacts the SaaS provider’s resource allocation, time-to-market, and overall business strategy. Custom software, internal or external, can be the difference between a great GTM strategy and a straggling SaaS company.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Making informed decisions around your analytics stack is essential to optimize resources effectively, minimize SaaS development costs, and enhance business growth. By carefully evaluating available options, a software company can strike a balance between custom development and leveraging existing solutions, enabling efficient analytics while focusing on core business objectives. Remember, what is right for one company may not work for the other. The specifics of your API product will premise what the best choice of action is. When trying to decide on the solution to your API problem, consider what resources are needed.Building An In-House API Management SolutionAdvantages of an In-House API Management SolutionThe “build” approach refers to the strategy adopted by startups in which development of their own API management and analytics program occurs internally. This process allows an organization to tailor their solution to their specific requirements. Building a custom software solution offers several advantages over buying software:  Allows for true customization: Startups create features and functionalities that precisely align with their unique business needs, resulting in a highly personalized experience.  An in-house build provides full control over the custom software development process, enabling smaller companies to make modifications as necessary.  Complexity is no issue: When a solution is created for a complex product, the pain of fitting your API solution to a pre-built solution melts away.  Scalability: By creating a solution designed for a specific product, accommodation of  future growth and evolving demands can be built in as needed.Disadvantages of an In-House API Management SolutionHowever, there are equally significant challenges associated with a “build” approach. Developing a custom solution requires significant investment in terms of time, resources, and expertise, which all translate to higher development costs. The time to market may also be increased if a startup focuses on building their analytics solution from scratch, as being underprepared for real-time use would result in a loss of data for analysis. Ongoing maintenance and updates also become the responsibility of an organization, demanding continuous effort and resources to ensure the solution remains up-to-date and secure:  Requires time and resources: These directly come out of engineering hours spent working on building software that pads your bottom line.  Costly: Because you are paying your employees to work, you will directly eat the cost of development, pushing back your actual product timeline financially.  Additional maintenance costs: By taking on the burden of maintenance and product management, it is critical that both your API management choice and your product stay up and running.Advantages of an External API Management SolutionThe “buy” approach involves a startup purchasing a pre-existing API management or analytics solution from a third-party. Opting for a ready-made solution offers a cost-effective approach with a quicker implementation period than an in-house solution. Because the analytics solution is readily available, the prohibitive development costs associated with standing up a custom solution are eliminated. Similarly, buying or licensing a solution from an external vendor can provide access to functionalities that can further internal advancement in ways not originally anticipated, like in the case of API monetization. Discovering new use cases and improving your API product becomes easier with a robust API management platform, as does aligning your product and business goals. As always, the decision to buy software requires research into product feature capabilities and how a SaaS service can aid your own feature development.  Quick results: Ideally the API management solution you decide to use can be integrated into your tech stack by an engineering team easily and quickly, allowing you to start generating ROI immediately.  Try before you buy: Self-service friendly, most API analytics solutions offer some sort of trial to ensure it’s the right choice for your product.  Minimal dev time required: Because the solution is pre-built, your team can focus on your product, not on building an internal tool.  API-first: Kind of obvious, but an API-based product requires an API-first solution. This allows for specific use cases that are specialized to the problems you need to solve.  External innovation: Because API management platforms need to also stay market savvy, they continuously improve their products and features. This means there is to grow for them, enabling more growth opportunities for you.Disadvantages of an External API Management SolutionAt the same time, there are potential drawbacks to outsourcing your analytics solution. Because they are developed elsewhere, API management programs from external developers have to fit multiple use cases. This means that customization options may be limited compared to an in-house, targeted solution. This can lead a company, especially startups, to adjust their internal processes to fit the capabilities of their sourced solution. This can slow down what is meant to be an expedited process, as well as lead to possible data gaps. Relying on an external vendor also creates a certain level of dependency, as API management and analytics are tied to a vendor’s services. This level of uncertainty can be unattractive, so a careful evaluation of a vendor’s capabilities, reputation, reliability, and viability are necessary.  Time Intensive Decision Process: It can take time to evaluate existing solutions. If your use case is a common one, there are likely a large number of options available.  Additional Features or Integration: A solution may not be a perfect fit. If there’s a mismatch, additional integrations or features may be needed. This can lead to higher time and cost spend than anticipated.  Possible Outages: Working with an external vendor means higher chances of data gaps due to outages or issues that can be more difficult to triage than internal problems.Factors to Consider in the Decision-making ProcessDeciding between building and buying your API management solution starts with asking why you need the analytics in the first place. Why do you need new software? What is missing in your current processes or understanding of your data/users? Once you have an idea of “why”, you can lay out the necessary, pertinent goals. Having a baseline for what goals you want to achieve with an analytics solution will allow you to shape your search and usage strategy. For example, goals can include:  API Governance: An API management solution can ensure access to your API is secure and controlled with authentication, authorization, and rate limiting.  Scalability and performance: Take control around optimizing the performance and scalability of your APIs with load balancing, caching, and traffic management.  Analytics and monitoring: Gain insights via analytics on API usage, such as the number of requests, response times, and error rates to identify usage patterns, detect issues, and make data-driven decisions for product optimizations.  Monetization and billing: An API management solution can provide features for billing, usage tracking, and managing different pricing plans/tiers, as well as integrate directly with your preferred billing provider.  Expand to New Markets/Verticals: Expand use cases for your API product with deep analytics into your current users, user behavior, and usage trends.  Reduce Churn: Increase retention rates and improve overall customer satisfaction thanks to real-time insights, and accelerate onboarding with automated behavioral emails.Once you have a clear understanding of what issues your API management strategy has and the goals that need fulfillment to solve them, it will become easier to decide on an internal or external solution. In either case, your use case will be more clearly defined, accelerating your development or procurement phase.Finding the Right Balance: MoSCoWBecause the decision to build vs. buy your API management solution will directly impact your business, it’s critical that you make the most informed decision possible. One such way of doing this is to use the MoSCoW framework. The acronym translates to M (Must have), S (Should have), C (Could have), W (Won’t have). The MoSCoW consists of four main categories:This framework can be useful for both building and buying. Regardless of your choice, the MoSCoW framework can help you to align your strategy and goals using prioritization:  If evaluating third party software, the MoSCoW framework can be part of the evaluation process. It can help you to prioritize specific features that are necessary for your API product as well as to ignore features that add no value to your use.  If building a custom solution, the MoSCoW framework can help guide what features are relevant as well as which do not need building.In general, SaaS product development can be an expensive undertaking in terms of internal resources. To build applications that not only appeal to existing customers but can meet the business need of potential new developers, a SaaS business must focus on turning their existing solution into one with a stellar customer experience. Creating market demand and optimizing opportunity cost are made easier with an API oriented SaaS solution.It is crucial for API-first organizations to conduct a thorough, honest assessment of the build vs buy costs associated with implementing an API management and analytics solution. Making an informed decision that aligns with your specific needs, available resources, and growth objectives is vital. With careful evaluation of the advantages and drawbacks of each approach, organizations can determine the most suitable path (build vs buy) forward. A well developed approach to API management sets the stage for long-term growth, enabling enhanced scalability, improved software developer experiences, and maintaining a competitive edge in the market.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-analytics/product/Build-vs-Buy-API-Management-Solutions/",
          "author": "Rachael",
          "categories": "API-Analytics, Product"
        }
      
    ,
  
    
        "api-analytics-product-poc-in-a-day-vs-a-month": {
          "title": "PoC in a Day vs. PoC in a Month with Moesif",
          "content"	 : "In today’s digital landscape, APIs (Application Programming Interfaces) play a crucial role in enabling seamless integration and communication between software applications and platforms. With API technology being front and center in digital transformation, there has been a significant rise in the demand for API-first solutions that offer a wide array of services for software developers. However, this surge in demand has led to a pressing need for outsourcing the development of these API-centric tools for startups. This is where proof of concept (PoC) comes into play. A successful PoC serves as a valuable tool for SaaS companies to showcase the value and potential of their APIs to prospective customers. By demonstrating the practicality and effectiveness of their APIs through a PoC, companies can build trust, attract customers, and stand out in a crowded market. With Moesif, your engineers can complete their API analytics and monetization PoC in less than a day, freeing up their time to work on their products.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding the Significance of Proof of Concept (PoC)A SaaS proof of concept (PoC) is a framework focused on determining whether a product concept can be made into reality. A PoC is meant to focus on testing whether an idea is possible by walking through the steps and benchmark goals necessary to bring a minimum viable product to life. This process enables teams to optimize their product development process and can strengthen the viability of a given product or feature thanks to measured goals. PoC demonstrations can benefit many industries, as the PoC process can help refine ideas before presenting them to a stakeholder (internal or external) for approval. For the purposes of this post, we will examine how a PoC can accelerate development of a SaaS product and API adoption.Generally, a PoC requires time and other resources. By focusing on a project and the process of making it viable, a concept template demonstrates that a given idea can meet a customer’s needs. PoCs provide a tangible, interactive idea validation opportunity to showcase the capabilities and value of a product, feature, or service before a potential customer (and your development team!) invests in your solution. With a small-scale prototype, organizations have the power to assess the potential value and success of a SaaS application or feature without wasting large-scale resources, reducing the risk of failure and expediting the innovation process. PoCs also provide internal workflows and expectations for teams to build viable features and capabilities for their products while improving the overall application development process within an organization. With this, teams can refine and improve offerings based on insights generated from use.Because these models can offer potential customers the chance to test a given product, PoCs can be a powerful tool for marketing and sales teams. Focusing on customer experience is an important technical feasibility for any SaaS company. By showcasing a product’s capabilities, companies can directly show what differentiates their product from competitors and attract new customers or, ideally, investors. The results and success generated from a PoC can serve as persuasive evidence of a company’s value proposition, offering a chance to appeal to self-service users. Ultimately, by leveraging PoCs, your company can make informed decisions, mitigate risks, enhance your offerings, and accelerate your path to market.Planning and Preparation for a Successful API PoCAn API provider can use thorough market research and identify target customers when embarking on a proof of concept. Understanding the competitor landscape, industry trends, and customer needs can aid companies in their PoC efforts. By clearly articulating what a company aims to achieve through their PoC, like validating a specific functionality or addressing a particular pain point for a given customer or audience segment, companies can measure their PoC’s outcomes effectively. Collaboration with potential customers is equally as important, as it fosters a deeper understanding of their pain points and requirements, giving your team a clearer idea of what needs to be highlighted or added before the sales cycle.Engaging with and actively listening to customer feedback can help companies tailor PoCs to address specific pain points, increasing the chance of customer buy-in. By focusing on user experience and improving basic functionality to incorporate the core feature and product idea cornerstones that a given customer needs, the POC’s final product will be more properly suited for the specific customer’s sales process. With a detailed PoC roadmap, including timelines and milestones, actuating a structured approach to the implementation of the PoC is made quicker and easier. The PoC process helps engineering teams manage resources efficiently, sets realistic expectations for customers, and ensures that progress can be tracked and evaluated at each stage of the PoC process. When it comes to your technological product, Moesif can enable you to create PoCs in minutes, saving your team time and effort.Designing and Executing a PoC for API ProductsCustomizing an API solution based on customer requirements is the pivotal drive behind a successful proof of concept. By closely listening to the needs of potential customers and understanding the holes in their current solution, companies can tailor their API product to address their pain points directly with a PoC. The PoC framework can demonstrate commitment to delivering a solution that aligns with a customers’ objectives, inspiring confidence for buy-in. By establishing measurable success criteria, a potential customer can evaluate the PoC’s effectiveness for their business goals. With clearly defined benchmarks and metrics that reflect a customer’s desired outcomes, companies can objectively assess if a PoC has or can achieve its goals. This informed decision-making helps customers determine the potential of an API solution.Additionally, providing comprehensive documentation and technical support throughout the PoC implementation is crucial. Clear, detailed documentation allows customers to understand your API’s functionalities, integration processes, and any potential limitations or issues in a self-service manner. By allowing developers to test your API product without interference while simultaneously offering timely technical support, you can ensure that customers overcome any challenges they encounter. By gathering feedback from potential customers throughout the PoC phase, your team will be enabled to gain insights into real-time user experiences. This will allow for identifying areas for improvement and refine the API solution or PoC accordingly.Demonstrating Value and Addressing ConcernsShowcasing the benefits and advantages of your API with real-world use cases is a powerful way to validate its value during a PoC. By providing real, concrete examples of how your API product can solve problems and improve processes, you can effectively demonstrate its practicality and potential impact for customers. Sharing success stories and illustrating the positive outcomes achieved by existing customers using your API can help potential customers really visualize the benefits gained by adopting and integrating your API solution. Addressing security, scalability, and performance concerns with a PoC based is crucial for building trust and confidence. Companies need to proactively address these aspects to instill confidence in potential customers that their API is robust, reliable, and capable of handling their specific needs effectively. Monitoring customers during the PoC phase is essential for refining and improving the API.By actively seeking feedback, valuable insights into areas requiring enhancements or modifications can be made clear in a timely manner, offering an opportunity to upsell features. This approach ensures that the API solution evolves based on the needs and preferences of the customers, enhancing its overall value proposition for that particular customer through collaboration. Leveraging analytics and real-time data to showcase the impact of your API on a potential customer’s workflows provides concrete evidence of its effectiveness in a particular use case. By analyzing key metrics (such as time saved or increased efficiency), companies can quantitatively demonstrate the value and ROI that an API solution can deliver, creating a compelling case for purchase and integration.Converting PoC Success into Business OpportunitiesFor SaaS companies with API products, analyzing the outcomes of their PoCs and ensuring alignment with customer expectations is crucial for adoption and long-term success. Evaluating the results of a PoC can allow an organization to assess whether a given API solution met the outlined goals and delivered the intended value to customers. By closely examining PoCs, companies can identify areas of improvement, address gaps or shortcomings, and refine their API offering accordingly.Based on PoC results, a company can tailor their pricing models and features to align with the value determined by the PoC. This ensures that customers feel an API is a worthwhile investment, enhancing the overall value proposition of your API product and creating upsell opportunities for your sales team. Scaling up operations and expanding your customer base based on the achievements of a PoC becomes feasible; with validated results and happy customers, companies can confidently invest in scaling their infrastructure (backed by PoC data), improve support capabilities for paying subscribers, and pursue opportunities to reach a wider audience and solidify their position in the API marketplace.Scale PoCs Confidently with MoesifA POC holds immense value for SaaS companies with API products and with enterprise customer bases. It serves as a critical step in validating API functionality for an intended use case, addresses customer questions and concerns, and showcases the value of integration. Embracing the power of PoCs should be a priority for API companies, as they unlock opportunities for growth and sustainable success. By investing time and resources in executing successful PoCs, API-first companies can gain valuable insights and refine their solutions while building trust with their customers in a symbiotic relationship. PoCs act as a stepping stone towards business success in the competitive API industry, allowing companies to differentiate themselves with targeted solutions and to tailor their products based on real-time feedback. By monitoring the data captured during PoCs with Moesif, your developer team can quickly assess and triage customer issues, providing quiet support for self-service users. Have confidence in your analyses with Moesif’s granular API analytics.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-analytics/product/POC-in-a-Day-vs-a-Month/",
          "author": "Rachael",
          "categories": "API-Analytics, Product"
        }
      
    ,
  
    
        "api-analytics-product-monetizing-apis-accelerate-growth-and-relieve-strain-on-your-engineers": {
          "title": "Monetizing APIs: Accelerate Growth and Relieve Strain on Your Engineers",
          "content"	 : "APIs have become increasingly popular in the current SaaS ecosystem due to their ability to seamlessly integrate software systems. APIs provide standardized ways for applications to share data. API monetization is a powerful way for businesses to drive growth and generate revenue from existing API consumer data and usage. By offering your APIs as products or services, your company can tap into new markets, attract more developers, and create self-sustaining ecosystems around your product line. The “API as a product” approach unlocks monetization opportunities for expansion and diversification, leading to increased profits and market share.By turning APIs into revenue streams, organizations can allocate more resources to their engineering departments, empowering them to focus on core product development and innovation. At the same time, by automating the monetization process, companies can alleviate the burden of monitoring and reporting on engineering teams. According to Gartner, 75% of application providers will revise their current product pricing models to support customers’ consumption of APIs by 2025. Monetizing APIs allows you to provide your valuable APIs to external developers, creating a collaborative ecosystem that not only fuels business growth but accelerates innovation                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding the Power of Your APIsAPIs play a critical role in modern software development and have had a transformative impact on businesses across industries, not just within software. APIs serve as a sort of bridge to allow software applications to communicate and interact, enabling seamless integration and data exchanges. Organizations developing new applications and services will leverage external APIs to take advantage of existing functionality and resources without having to reinvent the wheel. This allows for accelerated development cycles and reduced costs for users, making APIs a powerful revenue opportunity. However, the costs associated with integrating 3rd party APIs can lead to trepidation during the procurement process. In 2022, virtually 100% of buyers wanted a self-serve buying experience, up from 13% in 2021. In order to satisfy the market demand for flexibility (and to free up your sales team for enterprise-level deals), it’s beneficial to offer demo or trial accounts to potential developer customers. 81% of consumers say that they want more self-service options, meaning less meaningful, personal points of contact from your sales team. While leaving the power of persuasion up to chance may feel anxiety provoking, giving sales the chance to focus on upselling features and increasing API usage will allow them to spend more time on deals that will generate ROI. By leaving the lower usage accounts alone for self-service users to play with, you will also win over developers who have no interest or buying power in talking to sales.When developing your strategy for attracting developers to your API product, it is important to understand what value and impact your API can have on their businesses. By finding your API value propositions, you will be able to position your API as a solution to an existing problem. Planning an API program starts with the users of your API product before planning for stakeholders. It may be tempting to ask, “Why worry about enablers when they aren’t the direct users of my product?” or “Why care about users when they aren’t the financial decision makers?” These perspective both have a glaring issue:  API users need support from their organizations. This means there needs to be buy-in from both management and developers.  Stakeholders want assurance that an API is worth paying for. This means developers must sandbox test APIs in order to confidently say they’re worth integrating for the entire team.The Importance of a Strong API Monetization StrategyWhen Postman surveyed over 40,000 developers in 2023, almost two-thirds of respondents said their APIs generate revenue for their organization. 43% said that their APIs generate over a quarter of company revenue. It is important to recognize your API as a valuable asset for generating income. Diversifying your revenue streams will allow your business to grow, but monetizing your APIs enables you to do so more efficiently. By leveraging the demand for API-based solutions in the marketplace, you can foster development internally and externally. Expanding your business horizons through API monetization strategies allows you to tap into new markets, attract developers, and create thriving ecosystems around your API offerings.In the 2022 Gartner March Hot Topics Survey (which favored American and EU business), 94% of respondents, SaaS leaders who are members of Gartner’s Research Circle, reported using third-party APIs, up from 52% in 2019. The need for third party API integrations is urgent, but monetization can be time consuming and complex. To accelerate your business growth through API monetization most effectively, it is vital to use an API management platform to facilitate your monetization strategy. Selecting the right management platform will allow your growth to be quick but controllable. By automating your API monitoring and reporting, engineering teams can focus their efforts on innovation rather than upkeep. Similarly, by managing your monetization strategy within an API platform, you can automatically set up billing meters and invoices. Tapping into new markets and reaching a wider customer base is the best way to create new use cases for your API products. With the right API monetization platform, you as an API provider can automatically bill users with minimal effort by integrating with a chosen payment provider.Accelerating Business Growth through API MonetizationDiversifying your revenue stream options through monetizing can enable your business to unlock additional sources of income by leveraging your existing infrastructure, data, and users. By offering APIs to external developers and partners, you can reach a wider customer base of developers who need a solution but are unable to build one internally due to financial or time constraints. This expansion allows for new revenue streams to be captured and increases market share. MuleSoft surveyed IT and business decision makers, they found that most organizations had implemented or were in the process of implementing automation processes to improve productivity (96%) and operational efficiency (93%). By relying on an API management tool, the product monetization process can be expedited while improving workflows with little effort. This leaves more time for iterating on your product to deliver a wider, more “full stack” solution, which can lead to new monetization opportunities. Freeing up engineering teams to work on your API products rather than triage customer issues and manual monitoring will strengthen the merit of your product for new and current users. By providing value-added API services alongside core offerings, businesses can enhance customer loyalty and retention while creating upsell opportunities for sales teams. Delivering additional benefits and tailored experiences will foster stronger relationships with your customers and differentiate your products from your competitors.Alleviating Strain on Engineering TeamsThe impact of monetization can be felt company-wide, but the impact of automation directly benefits your engineering staff. By using an API management platform to handle data collection and analysis, you can ensure that engineering resources are focused on innovation and customer success rather than the minutiae of reporting. API management tools are a necessary choice for any company intending to productize their API offerings. An API management platform can alleviate strain on engineering teams with comprehensive, automated solutions for managing API operations and streamlining the maintenance process. However, finding the correct monitoring solution is as important as the decision to integrate one. In 2022, companies using 3rd party monitoring tools had, on average, 16 different tools. This overload of data overwhelms teams with more information than time for processing, resulting in subpar reporting, not to mention wasted time and money. By deciding what features are necessary prior to implementation of a management system, you can ensure that the data flowing in is accurate, timely, and relevant to your business’ KPIs.API management platforms offer a range of features and functionalities designed to simplify the lives of your engineers, leaving more time to focus on feature development. By centralizing your API management solution to one place, you can reduce the strain of necessary monitoring (like authentication or rate limiting) and your margin for manual error. Focusing on core development tasks is made infinitely easier when the burden of monitoring reliability is offloaded. Utilizing AP management platforms allows your engineering teams to optimize resource allocation, improve development efficiency, and accelerate time-to-market for new products or services.Monetization Models for API ProductsWhen it comes to deciding which monetization model to implement, it helps to understand your users and how they interact with your API product. Once you understand the value metrics and use cases for your API consumers, you can roll out an appropriate monetization choice. Historically, pre-paid billing has been the standard for SaaS businesses. This means that customers purchase up to a quota ahead of time and consume usage later. This allows for better cash flow for you as money is paid upfront, but doesn’t allow for upselling or increasing usage. On the other hand, postpaid billing can simplify consumption-based business models by allowing customers to use your APIs as they see fit and deal with the invoice amount later. It’s like going out to a bar and opening a tab rather than paying for a drink and being cut-off involuntarily; the power is in your hands to decide when and what to pay for rather than being charged prematurely and attempting to hit your drink cap. Understand what value metric fits your API use case with data analysis on your current, active API users and their current API consumption patterns. Some common value metrics are transaction volume, data volume, resource usage, API call volume, and cost share. Understanding common value metrics that are repeated from user to user through log analysis and real-time usage monitoring will help you to nail and capitalize on the most monetizable metrics for your business.                   Tiers      Pay-As-You-Go (PAYG)                  Description      Traditional SaaS pricing with predefined suite of features/capacity for each tier      Usage-based or consumption-based pricing based on a unit price.              Pros      - Enforces a min spend  - Predictable for customer  - Easy to implement      - More optimal for customer  - Less friction in expansion - Can “appear” cheape              Cons      - Friction during expansion  - Rigid, not aligned to value      - Can upset customers with billing surprises  - Complex to implement      Successful Revenue Recovery with AutomationCreating and implementing a robust API management program requires investing in the right infrastructure. By continuously adapting your monetization strategies based on market need and user data, recovering revenue with your API products has never been easier. While it may seem difficult to set up, a monetization model is simpler to implement than a new product or feature. Free up your engineering team from the monotony of monitoring with automation thanks to an API management platform. With more time to spend on improving your current products, your engineering process can directly improve your customer experience and strengthen user loyalty.The API economy continues to grow at an exponential rate. Accelerate your business growth by offering a monetization structure for your API products and enable your business to generate new revenue streams by leveraging the value of your APIs. Charging for access to your API products will allow your organization to tap into API revenue and increase your bottom line without extending your product line. Additionally, automated monetization relieves engineering strain by providing a reliable workflow for developers to maintain and improve APIs without requiring manual monitoring and alerting. This enables businesses to allocate resources more efficiently, focusing on core competencies and growing feature availability. It is crucial for businesses to take advantage of the ways in which automation and monetization can drive growth and align business and customer goals.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-analytics/product/Monetizing-APIs-Accelerate-Growth-and-Relieve-Strain-on-Your-Engineers/",
          "author": "Rachael",
          "categories": "API-Analytics, Product"
        }
      
    ,
  
    
        "technical-envoy-how-to-support-api-analytics-and-monetization-with-the-moesif-plugin-for-envoy-wasm": {
          "title": "How to support API analytics and Monetization with the Moesif Plugin for Envoy WASM",
          "content"	 : "Moesif is offering a new Envoy plugin for Envoy’s latest proxy supporting WebAssembly.Envoy is an open-source edge and service L7 proxy designed for cloud-native applications. Originally built at Lyft, it’s now part of the Cloud Native Computing Foundation. It provides a universal data plane API and is commonly used as a service mesh in microservices architectures, where it provides advanced load balancing, and API observability.Web Assembly (WASM) is a binary instruction format for a stack-based virtual machine.  It’s an architecture independent compilation target for several languages, including Rust which we used for this plugin. By design each WASM plugin runs in its own sandboxed environment for security and stability. WASM plugins in Envoy have near-native execution speed, and the async design of the Moesif WASM plugin adds no latency to monitored traffic.Moesif provides deep product analytics and effortless monetization for API-enabled companies. Detailed insights on how your API product is being used span the gamut from tracking and altering on key metrics, through to identifying issues in onboarding flows. Additionally, your APIs can be easily monetized by just connecting Moesif to your billing provider and defining your usage-based meter like prepaid, postpaid or PAYG.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Moesif’s Envoy WASM plugin is designed to be simple to install and configure, and supports our advanced API analytics and monetization features out the gate. It’s available as a free trial, and it can be installed in minutes.  Sign up for a free trial at https://www.moesif.com  Checkout the Envoy WASM PluginHow to set up Envoy API monitoringThe Moesif Envoy WebAssembly (WASM) plugin enables comprehensive API logging for analytics and API monetization. It helps engineering teams understand API usage, resolve issues, and empowers product teams to understand customer journeys and optimize resource allocation. It aids in identifying customer behavior patterns and helps in understanding the usage of specific payload keys.Envoy, with its configurable APIs, permits dynamic proxy configuration. It offers robust and detailed customization of request handling to route traffic and apply the plugins to only some or all traffic as desired.The Moesif Envoy WASM plugin, as a crucial part of this architecture, captures traffic data for detailed API analytics. It identifies API users, associates API users with organizational customers, and supports the creation of complex customer funnel reports, alerts, and usage based billing calculation. Automatic identification of users and companies from events, and efficient handling of traffic even during peak loads, are standout features of this plugin. The plugin ensures optimal performance by queueing events and periodically flushing them to Moesif, thus adding no latency even under high traffic conditions.Run and Configure the Moesif Envoy WASM PluginIf you want to try this out now, you can start the ready to use docker-compose example here: Moesif Envoy WASM PluginAssuming you have an existing Envoy setup, you can add the Moesif WASM plugin in just a few steps:  Navigate to the GitHub release page, and download the moesif_envoy_wasm_plugin.wasm file from the assets section of the latest release.  Place the file in a directory accessible to Envoy, such as /etc/envoy/proxy-wasm-plugins/ and note this for the configuration below.  Add the following to your Envoy configuration:http_filters:# ... other filters ...- name: envoy.filters.http.wasm  typed_config:    "@type": type.googleapis.com/envoy.extensions.filters.http.wasm.v3.Wasm    config:      name: "moesif_api"      root_id: "moesif_api_root_id"      configuration:        "@type": "type.googleapis.com/google.protobuf.StringValue"        value: |          {            "moesif_application_id":"<YOUR APPLICATION ID HERE>",            "user_id_header":"X-User-Example-Header",            "company_id_header":"X-Company-Example-Header",            "upstream": "moesif_api"          }      vm_config:        vm_id: "moesif_api_vm"        code:          local:            # Path to the plugin file you downloaded above            filename: "/etc/envoy/proxy-wasm-plugins/moesif_envoy_wasm_plugin.wasm"# ... other filters ending with router- name: envoy.filters.http.router  typed_config:    "@type": type.googleapis.com/envoy.extensions.filters.http.router.v3.Routerclusters:# ... other clusters ...# Add the following cluster to enable Envoys WASM plugin to send data to Moesif- name: moesif_api  type: strict_dns  load_assignment:    cluster_name: moesif_api    endpoints:    - lb_endpoints:      - endpoint:          address:            socket_address:              address: api.moesif.net              port_value: 443  transport_socket:    name: envoy.transport_sockets.tls    typed_config:      "@type": type.googleapis.com/envoy.extensions.transport_sockets.tls.v3.UpstreamTlsContextNote: The moesif_application_id is required  Sign up for a free trial at https://www.moesif.com/signup  Your application id will be generated during onboarding and will look something like U3RhcnQgbm93IGF0IGh0dHBzOi8vbW9lc2lmLmNvbS9zaWdudXAK      Restart Envoy to load the new configuration.        Make a few API calls that pass through the Envoy proxy. These calls should now be logged to your Moesif account.  This blog article section describes the set up of a Lua Envoy plugin.  We have a new WebAssembly Envoy Plugin.  Given the blog section below, use the readme of the new plugin to write a similar blog section for the new WebAssembly Envoy plugin.How to use API observabilityEngineering metricsThe first thing you’ll probably be interested in is engineering metrics like your API performance. This type of metric can be pulled up by going to Events -> Time Series view within Moesif. Then you can select 90th percentile latency as the metric to plot. You can then group by the URI route to understand which endpoints have the worst performance. Here, you can filter your traffic by API attributes like route, verb, along with HTTP headers and body fields.Business metricsTo associate API calls to individual customers, configure Envoy with the user_id_header or company_id_header configuration option.Once done, you can then store additional customer attributes like Company Domain or User Email via Moesif’s user tracking SDK. This provides clarity for customer-facing teams to understand who is using your APIs along with how they are using them.Moesif supports analyzing common payload content-types including JSON and XML. As an example, you can group API calls by response.body.label and then group by Company Domain as shown in the below chart. This shows which customers received a bad experience due to running into Out of Stock issues even though no 500 error was thrown.Closing thoughtsWith the Moesif Envoy WASM plugin, your engineering and business teams are empowered with deep API observability and monetization without the high cost of building and maintaining your own homegrown tooling.To see the Envoy integration with Moesif in action, you can git clone and run this example appfrom GitHub                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/envoy/How-to-support-API-analytics-and-Monetization-with-the-Moesif-Plugin-for-Envoy-WASM/",
          "author": "Brian",
          "categories": "Technical, Envoy"
        }
      
    ,
  
    
        "technical-wso2-announcing-moesif-api-analytics-and-monetization-for-wso2-choreo": {
          "title": "Announcing Moesif API Analytics and Monetization For WSO2 Choreo",
          "content"	 : "As part of our mission to serve developers, product managers, and other Moesif users better, we’ve teamed up with the API experts over at WSO2 to connect the capabilities of Moesif and Choreo.Choreo was created by WSO2 to push forward the next generation of application development. Inside Choreo is a SaaS application development suite designed to accelerate the creation of digital experiences. Choreo allows developers to build, deploy, monitor, and manage their cloud-native applications with ease. Adopting Choreo helps organizations increase developer productivity and focus on innovation.To extend Choreo’s capabilities, Moesif’s powerful analytics and monetization features can be accessed in a few clicks directly through the platform. Moesif and WSO2’s Choreo can be connected by navigating within the Choreo console, going to Settings -> API Management -> Moesif Dashboard, and pasting in your Moesif Application Id. That’s it! Once integrated, the full capabilities of Moesif’s API analytics and billing features are available to you. Let’s take a look at some of the advantages in more detail.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Powerful API Analytics for WSO2 ChoreoHaving API observability at your fingertips allows for a massive amount of insight into the health of your business, so you can be more proactive in supporting customers. Moesif, the leader in API analytics, is known for allowing you to easily create charts and dashboards to see how customers use your APIs and the value they get. API analytics from Choreo flow directly into Moesif, allowing technical and non-technical users to get real-time insights into API usage and better support users. Moesif goes much further than other analytics solutions with its user-centric focus and deep insights into API payloads. With funnel reports, understand the full journey of how users convert to active or paying customers. With retention reports and cohort analysis, understand signals that may lead to churn. Level up your applications and insights with Moesif and Choreo together.Easily Monetize APIs with Moesif and WSO2 ChoreoAs more companies look to generate revenue from their APIs, they face the same challenge: API monetization is not easy. Being able to handle both simple and complex usage-based billing cases takes a lot of custom code and generates overhead for developers. With Choreo and Moesif, you can monetize APIs in a few clicks using Moesif’s Metered Billing features . No coding is required. Create plans for prepaid, postpaid, PAYG, and other usage-based billing models with low effort. From here, Moesif can aggregate usage and send the correct usage to Stripe, Recurly, Chargebee, or a custom billing solution, for users to be charged. No matter how complex or simple your API billing criteria, Moesif and Choreo can easily handle it.New Levels of AutomationWant to automate customer success outreach or block user access based on specific business criteria? With Moesif, you can use Behavioral Emails to automatically send customized emails to users based on specific events. For example, if a user is having difficulty integrating with your APIs, Moesif can detect this and email the most relevant guide to help them.Another form of automation is using Moesif’s Governance Rules feature to block users, such as those with overdue unpaid invoices, from accessing certain APIs or features within your application. New levels of automation are possible when pairing Moesif with WSO2’s Choreo platform.Trying It Out!Trying out the Moesif and WSO2 is as simple as creating a Choreo account, a Moesif account, and adding your Moesif Application ID to the Moesif configuration in Choreo. For more details, check out the Choreo installation guide . Get started today with a 14-day free trial and experience the next generation of application development with Choreo and Moesif.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/wso2/Announcing-Moesif-API-Analytics-and-Monetization-For-WSO2-Choreo/",
          "author": "Matthew",
          "categories": "technical, wso2"
        }
      
    ,
  
    
        "api-monetization-stripe-end-to-end-api-monetization-with-go-stripe-and-moesif": {
          "title": "End-to-End API Monetization with Go, Stripe, and Moesif",
          "content"	 : "Many API developers and companies struggle to find ways to easily set up systems to monetize their APIs. Some are simple but not customizable, some are complex and require massive engineering effort to actually get it all running.To make things easier, Moesif created a feature a few months ago called Billing Meters which gives massive customizability but with a minimal amount of code and engineering effort.For this example, which could actually be used out of the box, we will use Moesif, Go, and Stripe to charge users for API usage. For this setup there are a few assumptions:  You have Go installed on your machine  You have an active Stripe account  You have an active Moesif accountThe setup is pretty simple from the outside. We will create a /register endpoint which:  Registers a user in Stripe  Subscribes that user to a product  Registers the User and Company in Moesif  Create a JWT to authenticate/authorize calls to our monetized endpoint                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free        1 - Create Your Product and Price in StripeThe first step we will take is to create a product and price in Stripe. It’s best to do this step first because then when you integrate Stripe into Moesif you’ll already have some pricing plans for Moesif to pull in. A pricing plan can then be associated with specific billing criteria set up within a Billing Meter in Moesif.First make sure to meet these prerequisites.To create a product and price, log into Stripe and proceed to the Products page in the Stripe UI. Once there, click on the + Add Product button in the top right corner.You’ll then be able to add in the details for your product and price(s) for it. The form for your product will have a few fields to fill out.Product InformationName  This is the name of your product. In the example below, we use the name “My API”.Description  This field is optional but you could put a brief description of the product here. In the example below, we use a description of “This is a monetized API”.Image  Optionally upload an image that can help you easily recognize a item on the Products page. We’ll be using the default placeholder image in this example.Pricing InformationYou can choose between Recurring and One-off pricing for your product.Recurring PricinngIn recurring pricing, your customers pay an ongoing fee according to the pricing model you define. After selecting Recurring, you can enter the amount you want to charge and the billing period.To further configure your recurring pricing, select More pricing options. This allows you to specify the pricing model, amount, billing period, price description, and more.The following pricing models are available in Stripe for recurring pricing:  Flat rate  A fixed price for a single unit or package.  Package pricing  Charge for API usage by the package, or a group of units. For example, you could set it up to charge $10 for every 1000 API calls. Every time the user goes over the 1000 API call threshold, they are charged another $10.  Tiered pricing  Offer different price points for based on unit quantity.  Usage-based  Pay-as-you-go billing based on metered usage. You can charge per package, per unit , or per tier and define the prices and units accordingly. You can also set up a billing meter for the price to meter usage. See Creating a Product and Price in Stripe for instructions on how to set up a usage-based scheme.One-Off PricingIn one-off pricing, you charge a one-time fee rather than recurring amount in each billing period. After selecting One-off, you can enter the amount you want to charge in the Amount field.Similar to recurring pricing, you can select More pricing options and configure your pricing further by specifying the pricing model, amount, price description, and more.The following pricing models are available for one-off pricing:  Package pricing  Charge for API usage by the package, or a group of units. For example, you could set it up to charge $10 for every 1000 API calls. Every time the user goes over the 1000 API call threshold, they are charged another $10.  Flat rate  A fixed price for a single unit or package.  Customer chooses price  The customer sets a custom price. You can set a limit and define a preset amount to suggest to the customer.Billing periodThe billing period can be set as the following for recurring pricing models:  Daily  Weekly  Monthly  Every 3 months  Every 6 months  Yearly  CustomFor your configuration with Moesif, we recommend setting the billing period as Monthly.Price descriptionThis is an optional field but recommended. Here you can put a brief description of your price. This will allow you to more easily decipher which price you are selecting in the billing meter in Moesif, especially if you have multiple prices for a single product.Once you’ve input all of the details for your price, you can select Next and then select Add productAs you create products, you will be able to view and edit them on the Product Catalog screen.2 - Enable the Moesif-Stripe IntegrationOnce your products and prices are created, it’s time to begin to integrate Stripe with Moesif. To begin configuring Stripe in Moesif, go to the Billing Meters page and click the Edit Billing Provider dropdown in the top right corner of the screen.This will bring up the Stripe configuration screen walking you through the integration. From this screen, you can get all of the info needed to plug Stripe into Moesif. Each step for configuration is covered within the modal.Add the Moesif Webhook to StripeThe first step in the integration is to add the Moesif webhook into the configuration in Stripe. Adding this allows Stripe to send subscription updates to Moesif.To add the Moesif webhook to Stripe, from the upper right-hand side click on Developers, and then Webhooks in the left-side menu. This will bring you to the Webhooks page where you can view existing webhooks and add new ones. To add a new webhook we will click the Add an endpoint button at the bottom of the screen.From here, we will plug in our Moesif API endpoint URL and configure the events to listen to. You’ll want to copy your Moesif Webhook URL into the Endpoint URL field and then click the + Select Events button.  These details can all be found on the Stripe configuration page in Moesif mentioned in the previous section.You should select the option under Customer for Select all Customer events. After this, click the Add events button at the bottom of the screen.After this, you’ll be returned back to the original screen where you added the endpoint details. Scroll to the bottom of the screen and click Add endpoint to save the endpoint to Stripe.Plug the Stripe API Details into MoesifFor Moesif to add usage quantities to subscriptions in Stripe, we need to add the Stripe API details into Moesif. This is done in the Stripe configuration screen in Moesif, the same screen we’ve been working with previously.Currently, Moesif only supports version 2020-08-27 of the Stripe API so that field defaults for the Stripe API Version field.For the Stripe API Key field, you’ll need to retrieve the API key from Stripe to plug it in. From the Developers screen, the same one we used in the previous step, you’ll click on API Keys. You’ll then be able to see the private key for your API in either the Secret key or a generated Restricted keys field on the screen. Either key can be used.After copying the key from Stripe, you’ll paste this key into the Stripe API Key field back in Moesif. After doing this, back in Moesif you can scroll down to the bottom of the screen and click Save to save the configuration.Optionally, you have the ability to customize the company_id in Moesif as well. The default should work fine for most purposes. However, you can fully customize it to specify how to map Stripe Subscription and Customer objects to Subscription ID and Company ID in Moesif respectively:  A Stripe Customer maps to Moesif Company.  A Stripe Subscription maps to a Moesif Subscription.3 - Create a Billing MeterOnce you have the Stripe integration active in Moesif, you can begin to set up your billing meter. Billing meters created in Moesif do two things: track usage based on specific criteria and report that usage to the billing provider. Moesif allows you to set up very simple and very complex billing meters with relative ease.To create the Billing Meter, in Moesif you will navigate to the Billing Meter screen. You can do this from the left-side menu. On the Billing Meter’s screen, you’ll then click + Add Billing Meter in the top-right corner of the screen.The next screen is where you can actually input the criteria for your Billing Meter.Fields on this screen include:      Billing Meter Name          This is the Moesif internal name of your new Billing Meter            Billing Provider          In this dropdown you can choose the billing provider you want to send your usage metrics to.            Product (Stripe only)          Here you can choose which Product that you’ve set up in Stripe you want your usage metrics to be tied to.            Price (Stripe only)          The last field in the Billing Provider settings for the Billing Meter, here you will choose which Price you want to tie your usage metrics to.            Filters          Under the Filters configuration, you will configure your billing criteria to only include requests that fit a certain criteria.            Metrics          Here you can choose which metric you would like to bill on. Available options include:            Event Count          This will increment usage for every event that fits the criteria outlined in the Filter criteria.            Unique Users          This will increment usage whenever a unique user sends a request that fits the Filter criteria. For every unique user, the count will be incremented by 1 regardless of the event count for that user.            Unique Companies          This will increment usage whenever a unique company sends a request that fits the Filter criteria. For every unique company, the count will be incremented by 1 regardless of the event count for that company.            Unique Sessions/API Keys          This will increment usage whenever a unique session or API key is used to send a request that fits the Filter criteria. For every unique session or API key, the count will be incremented by 1 regardless of the event count for that particular session or API key.        There are other options under Metrics as well but the above 4 tend to be the most applicable to usage-based billing.As an example, for this guide we will create a Billing Meter that will filter traffic for a single endpoint, named /test-service, and where requests received a successful HTTP 200 response. We will use the Event Count metric to make sure that every request is added to the tally and sent to the billing provider.In Moesif, the billing meter will be configured as shown below.We will then click Create. This will create and activate the Billing Meter. A modal will appear notifying you that the billing meter has been created and presents a walk-through to ensure the meter is correctly configured.First, we will set up a flow to get users registered, subscribed, and create a JWT so they can use our monetized API. Once that is complete we will come back and proceed with the walk-through.4 - Create the /register endpointInstead of using a pre-built onboarding flow, such as through a Developer Portal within an API gateway, we will build our own. We will create an endpoint called /register which we can then use to onboard our users who want to use the API. The result will be that the user receives a JWT that they can use that will track their usage.Since we are using Moesif, Stripe, and Go as part of our overall solution, we need to make sure each of the components is working together properly.Here’s what the endpoint will do:  Create a customer in Stripe  Subscribe the new customer to the API subscription in Stripe  Create the CompanyID in Moesif (which will be the Stripe subscription ID)  Create the UserID in Moesif (which will be the Stripe Customer ID)  Create a JWT with an id field that contains the Stripe Customer ID  If you already have User and Company identifiers in Moesif and other systems that you want to use, instead of using Stripe’s customer and subscription as your IDs, you can do that in Moesif under the Stripe configuration settings.In this example, I will create a simple Go API to do the above.Initialize the Go ProjectFirst, we will create a folder called moesif-monetization where we will add our API code. We will then run go mod init to turn moesif-monetization into a Go project so we can use dependencies within our project. For that, you’ll run the following command in the moesif-monetization directory.go mod init moesif-monetizationNow, open this directory in your favorite IDE or text editor. I will be using VS Code for the remainder of this tutorial for all the coding.Create the main.go FileIn the root directory of our app, we will create an main.go file (if not already created). In this file we will add the following code that adds our dependencies and creates two different REST endpoints, one for /test-service/ and another for /register.package mainimport (   "encoding/json"   "log"   "math/rand"   "net/http"   "strings"   "fmt"   jwt_go "github.com/dgrijalva/jwt-go"   stripe "github.com/stripe/stripe-go/v72"   "github.com/stripe/stripe-go/v72/customer"   "github.com/stripe/stripe-go/v72/sub"   jwt "github.com/golang-jwt/jwt/v5"   moesifmiddleware "github.com/moesif/moesifmiddleware-go"   models "github.com/moesif/moesifapi-go/models")const creditScoreMin = 500const creditScoreMax = 900type credit_rating struct {  CreditRating int `json:"credit_rating"`}var (   tokenSecret    = []byte("mytokensecret")   stripePriceKey = "STRIPE_PRICE_KEY")type requestBody struct {   Email     string `json:"email"`   FirstName string `json:"firstname"`   LastName  string `json:"lastname"`}type response struct {   JWT string `json:"jwt"`}var moesifOptions = map[string]interface{}{  "Application_Id": "MOESIF_APP_ID",  "Identify_User":  identifyUser,}func generateAccessToken(id string) (string, error) {}func literalFieldValue(value string) *string {   return &value}func identifyUser(request *http.Request, response moesifmiddleware.MoesifResponseRecorder) string {hmacSecretString := "mytokensecret"hmacSecret := []byte(hmacSecretString)authHeader, ok := request.Header["Authorization"]    if ok {        if strings.Contains(authHeader[0], "Bearer ") {tokenString := strings.Replace(authHeader[0], "Bearer ", "", -1)token, err := jwt.Parse(tokenString, func(token *jwt.Token) (interface{}, error) {// check token signing method etcreturn hmacSecret, nil})if claims, ok := token.Claims.(jwt.MapClaims); ok && token.Valid {userId, ok := claims["id"].(string)if ok {return userId} else {return ""}} else {print(err.Error())return ""}} else {return ""}    } else {        return ""    }}func handleRequests() {   fs := http.FileServer(http.Dir("./static"))   http.Handle("/", fs)   http.Handle("/register", moesifmiddleware.MoesifMiddleware(http.HandlerFunc(registerHandler), moesifOptions))   http.Handle("/test-service/", moesifmiddleware.MoesifMiddleware(http.HandlerFunc(creditScoreHandler), moesifOptions))   log.Fatal(http.ListenAndServe(":8080", nil))}func main() {  handleRequests()}func creditScoreHandler(w http.ResponseWriter, r *http.Request) {  var creditRating = credit_rating{CreditRating: (rand.Intn(creditScoreMax-creditScoreMin) + creditScoreMin),}  w.WriteHeader(http.StatusOK)  json.NewEncoder(w).Encode(creditRating)}func registerHandler(w http.ResponseWriter, r *http.Request) {}In the above code we are:  Importing a few dependencies  Creating a few structs and a few default values, including moesifOptions  Creating a few helper functions to write data and create a JWT  Configuring the Moesif middleware, including implementing the identifyUser function to pull the id field from the JWT to enable user tracking in Moesif  Creating the /register endpoint (without any logic yet)  Creating a simple /test-service endpoint (this will be our monetized API)  Set our app to run on port 8080 in the handleRequests function  Our main function that will be the entry point for our applicationWith our base code in place and our dependencies added in, run the following to install the necessary dependencies:go get .In the next step, we will implement the registerHandler and generateAccessToken functions.Implement the /register EndpointOur next step is to implement the /register endpoint. This endpoint will essentially create the binding between our generated JWT, Stripe, and Moesif. The outcome will be a generated JWT which will associate usage with a user in Moesif, which will then be reported to Stripe.When the request first comes into the endpoint, we want to validate that the request body can be parsed correctly. To do this, we will use the json.NewDecoder and json.Decode functions. We will add the following code to the body of the registerHandler function.   var reqBody requestBody   if err := json.NewDecoder(r.Body).Decode(&reqBody); err != nil {       http.Error(w, err.Error(), http.StatusBadRequest)       return   }Next, we will create the customer in Stripe. We will use our Stripe JS dependency to do just that. We will use the parameters from the request body (email, first name, last name) to create the customer in Stripe. We will then store the created customer in a custom variable (cust) so we can access the customer ID generated in Stripe.   // create Stripe customer   stripe.Key = "STRIPE_KEY"   customerParams := &stripe.CustomerParams{       Email: &reqBody.Email,       Name:  stripe.String(fmt.Sprintf("%s %s", reqBody.FirstName, reqBody.LastName)),   }   cust, err := customer.New(customerParams)   if err != nil {       http.Error(w, err.Error(), http.StatusInternalServerError)       return   }Next, we will subscribe this new user to our API subscription we created in Stripe earlier. We will create the subscription in Stripe and use the generated customer ID from the previous function call to subscribe them to specific plan/price. This will return back a subscription object containing an ID we will use later.    // create Stripe subscription    subscriptionParams := &stripe.SubscriptionParams{        Customer: stripe.String(cust.ID),        Items: []*stripe.SubscriptionItemsParams{            {                Price: stripe.String(stripePriceKey),            },        },    }stripeSubscription, err := sub.New(subscriptionParams)if err != nil {http.Error(w, err.Error(), http.StatusInternalServerError)return}After our customer and subscription are created in Stripe, we will then use the Moesif middleware to create the user and add their relevant details into Moesif. To do this, we will first create a company using the models.CompanyModel struct and call the Moesif middleware’s UpdateCompany function to map the Stripe cust.ID to the companyId in Moesif.    // create company in Moesif    company := models.CompanyModel{        CompanyId: cust.ID,    }    // Update Company    moesifmiddleware.UpdateCompany(&company, moesifOptions)We will then do a similar step with models.UserModel and the UpdateUser function to map the Stripe cust.ID to the userId and companyId, as well as some other metadata we collected on the user into Moesif.user := models.UserModel{UserId:    cust.ID,CompanyId: literalFieldValue(cust.ID),Metadata: map[string]interface{}{"email":     reqBody.Email,"firstName": reqBody.FirstName,"lastName":  reqBody.LastName,},   // Update User   moesifmiddleware.UpdateUser(&user, moesifOptions)Next, we will call our generateAccessToken function and pass the Stripe Customer ID to inject it into the JWT. We will do this just below the last snippet of code we added to add the user and company to Moesif.   // generate new jwt for user   token, err := generateAccessToken(cust.ID)   if err != nil {       http.Error(w, err.Error(), http.StatusInternalServerError)       return   }Lastly, we will marshal the token in the response body and return a 200 OK response back to the caller.  responseData := response{       JWT: token,   }   jsonResponse, err := json.Marshal(responseData)   if err != nil {       http.Error(w, err.Error(), http.StatusInternalServerError)       return   }   w.Header().Set("Content-Type", "application/json")   w.WriteHeader(http.StatusOK)   w.Write(jsonResponse)The completed function, end-to-end, will look like this:func registerHandler(w http.ResponseWriter, r *http.Request) {    var reqBody requestBody    if err := json.NewDecoder(r.Body).Decode(&reqBody); err != nil {        http.Error(w, err.Error(), http.StatusBadRequest)        return    }    // create Stripe customer    stripe.Key = "STRIPE_KEY"    customerParams := &stripe.CustomerParams{        Email: &reqBody.Email,        Name:  stripe.String(fmt.Sprintf("%s %s", reqBody.FirstName, reqBody.LastName)),    }    cust, err := customer.New(customerParams)    if err != nil {        http.Error(w, err.Error(), http.StatusInternalServerError)        return    }    // create Stripe subscription    subscriptionParams := &stripe.SubscriptionParams{        Customer: stripe.String(cust.ID),        Items: []*stripe.SubscriptionItemsParams{            {                Price: stripe.String(stripePriceKey),            },        },    }            stripeSubscription, err := sub.New(subscriptionParams)if err != nil {http.Error(w, err.Error(), http.StatusInternalServerError)return}// create user and company in Moesifcompany := models.CompanyModel{CompanyId: cust.ID,}// Update Companymoesifmiddleware.UpdateCompany(&company, moesifOptions)user := models.UserModel{UserId:    cust.ID,CompanyId: literalFieldValue(cust.ID),Metadata: map[string]interface{}{"email":     reqBody.Email,"firstName": reqBody.FirstName,"lastName":  reqBody.LastName,},}// Update Usermoesifmiddleware.UpdateUser(&user, moesifOptions)    // generate new jwt for user    token, err := generateAccessToken(cust.ID)    if err != nil {        http.Error(w, err.Error(), http.StatusInternalServerError)        return    }    responseData := response{        JWT: token,    }    jsonResponse, err := json.Marshal(responseData)    if err != nil {        http.Error(w, err.Error(), http.StatusInternalServerError)        return    }    w.Header().Set("Content-Type", "application/json")    w.WriteHeader(http.StatusOK)    w.Write(jsonResponse)    }Create the GenerateAccessToken FunctionOur next step is to generate a JWT with the Stripe Customer ID attached. To do this, first, we will create a function that will call the jwt_go.NewWithClaims function to create a new JWT with our Moesif UserId (the Stripe Customer ID) in the id field. Add the following code to the function we created earlier named generateAccessToken to create a JWT for us to use with our endpoints.func generateAccessToken(id string) (string, error) {   token := jwt_go.NewWithClaims(jwt_go.SigningMethodHS256, jwt_go.MapClaims{       "id": id,   })   return token.SignedString(tokenSecret)}Add in variable values for Stripe, Moesif, and JWT generationSome of the variables in the code we wrote will need values added to them. Below are the values you’ll need to replace and where they can be found.  In a production environment, you’ll not want to have these values hardcoded. You’ll want to use a secrets manager as well as making sure that your API keys only allow for the minimum amount of priviledges.Obtaining Your Stripe API and Price KeyYou will see in the code example above that we need to provide a secret or restricted key for our createCustomer and createSubscription methods.Your Stripe API Key can be found in the same place we grabbed the key for our Stripe and Moesif integration we did earlier for the Billing Meter. You can actually use the same key for both or create a restricted key with just the scope needed for each function.While we’re at it, lets grab our Stripe product’s price key. We will need it in the next section. Your Stripe price key is an identifier for the price you created earlier in Stripe. This can be found by going to the product in Stripe and grabbing the value from the API ID column.Obtaining Your Moesif Application IDYour Moesif application ID is found in Moesif by going to the menu link in the bottom-left of the screen (which will show your name) and selecting API Keys.The key will then be on the page that appears under Collector Application Id.Creating Your TokenSecretThis will be the secret that is used as part of generating and validating your JWTs. This could be any string you’d like, however, for production purposes you are best off using something much more secure/generated.Once you’ve populated the file with the four key-value pairs, save the file. We won’t need to touch this file again for the remainder of the tutorial.With that, we can now actually try out our endpoint to make sure that each piece is working as expected. The outcome should be a registered user with an associated JWT which will record and report usage data to Stripe. Let’s move onto testing it.5 - Send a Test Request to the /register EndpointTo start the app, run the following command:go run .After running that command, the app will be up and ready to receive requests.Once your /register endpoint has been coded and deployed, it’s time to test it. For right now we will simply use Postman to send a request. Our request will contain a JSON request body that will contain a:  First name  Last name  EmailOf course, this is the minimal amount of information we would want to configure our system and profiles in Stripe and Moesif, plus, generate the JWT. You can easily add more fields as needed for your specific use case.In Postman, we will create our request with the following information:Request Type: POSTEndpoint URL: http://localhost:{port}/registerRequest Body:{  "firstname": "Userfirstname",  "lastname": "Userlastname",  "email": "test@test.com"}  Replace port in your endpoint URL with the assigned port number.Once everything is plugged into Postman, it should look like the following:Once the request is sent, the response should contain an JWT that the newly registered user can use.We will now check Stripe to ensure that the information we registered the customer with is correctly entered into Stripe.Logging back into Stripe, you’ll navigate to Customers screen. You should see your newly created user in the list.Click on the newly added customer in the list. On the next screen, you should see that the customer is also subscribed to your APIs subscription.With this check completed, we can safely assume that our /register endpoint is correctly setting up our users accounts and subscriptions in Stripe.6 - Call Your API Using the Generated JWTOur next step is to actually use our generated JWT. We will then confirm that all the correct information is added into Moesif. The data we are confirming includes:  The Stripe Customer ID is mapped to the Moesif User ID  The Stripe Subscription ID is mapped to the Moesif Company ID  Moesif contains the Stripe metadata in the users profileUse Postman to Send the RequestNext, let’s use Postman, or another platform, to send a request to the /test-service endpoint. This is the endpoint that we set up the billing meter for in Step 3, above.In Postman, we will:  Put the /test-service API endpoint as the request URL  Select the Authorization tab  Select the Type as Bearer Token  Populate the token details  Set the Token as the JWT received from our /register callBelow is an example of the populated request configuration in Postman. To send the request to our endpoint, click Send.Once sent, the API call analytics should land in Moesif.Confirm That Moesif Received the Request Info Using Profile DashboardsBack in Moesif, you’ll navigate to the Live Event Log screen. You can do this by clicking the New button and selecting Live Event Log.On this screen, you should see the request you just sent. You should see the entry has both a User ID and Company ID populated with the Stripe user and subscription ID’s. The entries should look like this:  The customer ID will look like “cus_XXXX” and the subscription ID will look like “sub_XXXX”.If you click on the User ID shown in the entries on the Live Event Log screen, you will come to the users profile page. On this page, we will confirm that the Stripe metadata is present. We will need to add a new column to our profile to display the Stripe data. To do this, from the profile page, click on the … More Actions button and click Customize Profiles’ Layout.We will then add a new column for the Stripe metadata. You will click the + button on the far right of the screen to create a new column where we will add the Stripe metadata.  You may need to scroll to the right to see it depending on your resolution and screen size to see the + button.You will then drill down to Metadata > stripe > customer > created and use this field in the new row. I’ve also changed the column image to one more fitting. You can customize this by clicking on the image and selecting whichever one fits best.You can also add other fields, but for right now just this single field is enough to tell us that Moesif is correctly receiving data from Stripe.  If you don’t see the Stripe metadata entry as an available field, wait a few minutes. If after a few minutes the Stripe metadata isn’t present, ensure that your Stripe configuration is correct in Moesif. After confirming or editing it, try creating a new user and sending a request again to confirm that the integration is working.At this point, we now have confirmed that our API call is working and is stamped with the correct user and company details in Moesif. We also confirmed that Stripe is sending data back to Moesif which is correctly being mapped to the corresponding user profile, confirmed through the Stripe metadata in Moesif.7 - Send a Request to Your Monetized APIWe will once again want to make sure that everything is working with our UI, through to our backend systems. For this, simply repeat the steps from Step 6 to confirm that the user and company IDs are populated correctly and that the Stripe metadata is returned for this user and the new JWT. We should see these calls populated within our Live Event Log as well.8 - Confirm All the Pieces are Working CorrectlyAlthough this is optional, this step may help with troubleshooting any issues that may have came from our previous steps. Here are a few things to check to make sure that all is working as it should. After creating a new user through the UI and using the generated JWT to place a call to your API, confirm the following:In Stripe  Confirm that a customer has been created in Stripe with the details you entered into the UI  Confirm that the customer has been subscribed to the correct product and priceIn Moesif  Your API call was recorded in Moesif in the Live Event Log  Your API call has the Stripe Customer ID and Subscription ID in the User and Company fields in Moesif, respectively.  Confirm that the Stripe metadata is populated in Moesif  All Billing Meter test conditions have passed9 - Check Stripe for UsageLastly, After a few hours, it’s best to go into Stripe to confirm that usage is being added to a users subscription. Be sure that you’ve sent a few requests through in order to make sure you have some data that should be sent to Stripe.  It may take a few hours for usage to make its way from Moesif to Stripe. If data still isn’t in Moesif after a few hours, ensure you’ve followed all the steps outlined within this guide. This includes making sure that your user and company ID’s from Moesif are correctly mapped to the corresponding keys in Stripe.To check the usage, in Stripe you’ll want to navigate to the Customers screen and select the customer that you made the API call with. Once selected, you should see some active subscriptions for the users that you’ve registered through the /register endpoint. The one we created earlier is called My API. Click on the subscription entry.On the next screen, click on View Usage beside the price entry.A modal should now pop up showing you the usage for the API that has been reported to Stripe from Moesif.  Remember, there is a delay in Moesif’s reporting to Stripe. If you data isn’t there yet, check back in a bit later.10 - Determining If the Billing Meter is Working CorrectlyTesting the created Billing Meter is easy with out Test Meter function. Navigate to your created Billing Meter from the left side navigation pane and selecting your Stripe Test billing meter. Select Test Meter on the top right.We will first confirm the meter that you are attempting to test. Click the Next button at the bottom of the modal.Moesif will wait for Subscriptions to created within Stripe and those subscriptions to be associated within Moesif itself. This page will update automatically, no need to refresh.Moesif will then wait for an API call to our any endpoint associated with our billing meter using our the JWT that has been created for us.Finally, Moesif will sync all usage data to Stripe every 15 minutes. This step may take a few minutes depending on when the API call was initiated but will update on the given interval.Wrapping upMonetization has always been a tough hurdle to get past. Many custom solutions offered flexibility but at a very high engineering and support cost. With Moesif, API monetization is possible in an extremely minimal amount of time. As demonstrated in this article, With a little bit of configuration and minimal amount of code we can create a production-ready, post-paid monetization scheme easily and effectively.                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue with your GO REST APIs?            Monetize your GO APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /api-monetization/stripe/End-To-End-API-Monetization-With-Go-Stripe-And-Moesif/",
          "author": "Matthew",
          "categories": "API-Monetization, Stripe"
        }
      
    ,
  
    
        "api-monetization-stripe-end-to-end-api-monetization-with-dotnet-stripe-and-moesif": {
          "title": "End-to-End API Monetization with .NET, Stripe, and Moesif",
          "content"	 : "Many API developers and companies struggle to find ways to easily set up systems to monetize their APIs. Some are simple but not customizable, some are complex and require massive engineering effort to actually get it all running.To make things easier, Moesif created a feature a few months ago called Billing Meters which gives massive customizability but with a minimal amount of code and engineering effort.For this example, which could actually be used out of the box, we will use Moesif, a .Net 7 Minimal API, and Stripe to charge users for API usage. For this setup there are a few assumptions:  A working .NET Core installation          We will be using .Net Core 7                  SDK version 7.0.302          AspNetCore 7.0.5          NETCore 7.0.5                      You have an active Stripe account  You have an active Moesif accountThe setup is pretty simple from the outside. We will create a /register endpoint which:  Registers a user in Stripe  Subscribes that user to a product  Registers the User and Company in Moesif  Create a JWT to authenticate/authorize calls to our monetized endpoint                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free        1 - Create Your Product and Price in StripeThe first step we will take is to create a product and price in Stripe. It’s best to do this step first because then when you integrate Stripe into Moesif you’ll already have some pricing plans for Moesif to pull in. A pricing plan can then be associated with specific billing criteria set up within a Billing Meter in Moesif.First make sure to meet these prerequisites.To create a product and price, log into Stripe and proceed to the Products page in the Stripe UI. Once there, click on the + Add Product button in the top right corner.You’ll then be able to add in the details for your product and price(s) for it. The form for your product will have a few fields to fill out.Product InformationName  This is the name of your product. In the example below, we use the name “My API”.Description  This field is optional but you could put a brief description of the product here. In the example below, we use a description of “This is a monetized API”.Image  Optionally upload an image that can help you easily recognize a item on the Products page. We’ll be using the default placeholder image in this example.Pricing InformationYou can choose between Recurring and One-off pricing for your product.Recurring PricinngIn recurring pricing, your customers pay an ongoing fee according to the pricing model you define. After selecting Recurring, you can enter the amount you want to charge and the billing period.To further configure your recurring pricing, select More pricing options. This allows you to specify the pricing model, amount, billing period, price description, and more.The following pricing models are available in Stripe for recurring pricing:  Flat rate  A fixed price for a single unit or package.  Package pricing  Charge for API usage by the package, or a group of units. For example, you could set it up to charge $10 for every 1000 API calls. Every time the user goes over the 1000 API call threshold, they are charged another $10.  Tiered pricing  Offer different price points for based on unit quantity.  Usage-based  Pay-as-you-go billing based on metered usage. You can charge per package, per unit , or per tier and define the prices and units accordingly. You can also set up a billing meter for the price to meter usage. See Creating a Product and Price in Stripe for instructions on how to set up a usage-based scheme.One-Off PricingIn one-off pricing, you charge a one-time fee rather than recurring amount in each billing period. After selecting One-off, you can enter the amount you want to charge in the Amount field.Similar to recurring pricing, you can select More pricing options and configure your pricing further by specifying the pricing model, amount, price description, and more.The following pricing models are available for one-off pricing:  Package pricing  Charge for API usage by the package, or a group of units. For example, you could set it up to charge $10 for every 1000 API calls. Every time the user goes over the 1000 API call threshold, they are charged another $10.  Flat rate  A fixed price for a single unit or package.  Customer chooses price  The customer sets a custom price. You can set a limit and define a preset amount to suggest to the customer.Billing periodThe billing period can be set as the following for recurring pricing models:  Daily  Weekly  Monthly  Every 3 months  Every 6 months  Yearly  CustomFor your configuration with Moesif, we recommend setting the billing period as Monthly.Price descriptionThis is an optional field but recommended. Here you can put a brief description of your price. This will allow you to more easily decipher which price you are selecting in the billing meter in Moesif, especially if you have multiple prices for a single product.Once you’ve input all of the details for your price, you can select Next and then select Add productAs you create products, you will be able to view and edit them on the Product Catalog screen.2 - Enable the Moesif-Stripe IntegrationOnce your products and prices are created, it’s time to begin to integrate Stripe with Moesif. To begin configuring Stripe in Moesif, go to the Billing Meters page and click the Edit Billing Provider dropdown in the top right corner of the screen.This will bring up the Stripe configuration screen walking you through the integration. From this screen, you can get all of the info needed to plug Stripe into Moesif. Each step for configuration is covered within the modal.Add the Moesif Webhook to StripeThe first step in the integration is to add the Moesif webhook into the configuration in Stripe. Adding this allows Stripe to send subscription updates to Moesif.To add the Moesif webhook to Stripe, from the upper right-hand side click on Developers, and then Webhooks in the left-side menu. This will bring you to the Webhooks page where you can view existing webhooks and add new ones. To add a new webhook we will click the Add an endpoint button at the bottom of the screen.From here, we will plug in our Moesif API endpoint URL and configure the events to listen to. You’ll want to copy your Moesif Webhook URL into the Endpoint URL field and then click the + Select Events button.  These details can all be found on the Stripe configuration page in Moesif mentioned in the previous section.You should select the option under Customer for Select all Customer events. After this, click the Add events button at the bottom of the screen.After this, you’ll be returned back to the original screen where you added the endpoint details. Scroll to the bottom of the screen and click Add endpoint to save the endpoint to Stripe.Plug the Stripe API Details into MoesifFor Moesif to add usage quantities to subscriptions in Stripe, we need to add the Stripe API details into Moesif. This is done in the Stripe configuration screen in Moesif, the same screen we’ve been working with previously.Currently, Moesif only supports version 2020-08-27 of the Stripe API so that field defaults for the Stripe API Version field.For the Stripe API Key field, you’ll need to retrieve the API key from Stripe to plug it in. From the Developers screen, the same one we used in the previous step, you’ll click on API Keys. You’ll then be able to see the private key for your API in either the Secret key or a generated Restricted keys field on the screen. Either key can be used.After copying the key from Stripe, you’ll paste this key into the Stripe API Key field back in Moesif. After doing this, back in Moesif you can scroll down to the bottom of the screen and click Save to save the configuration.Optionally, you have the ability to customize the company_id in Moesif as well. The default should work fine for most purposes. However, you can fully customize it to specify how to map Stripe Subscription and Customer objects to Subscription ID and Company ID in Moesif respectively:  A Stripe Customer maps to Moesif Company.  A Stripe Subscription maps to a Moesif Subscription.3 - Create a Billing MeterOnce you have the Stripe integration active in Moesif, you can begin to set up your billing meter. Billing meters created in Moesif do two things: track usage based on specific criteria and report that usage to the billing provider. Moesif allows you to set up very simple and very complex billing meters with relative ease.To create the Billing Meter, in Moesif you will navigate to the Billing Meter screen. You can do this from the left-side menu. On the Billing Meter’s screen, you’ll then click + Add Billing Meter in the top-right corner of the screen.The next screen is where you can actually input the criteria for your Billing Meter.Fields on this screen include:      Billing Meter Name          This is the Moesif internal name of your new Billing Meter            Billing Provider          In this dropdown you can choose the billing provider you want to send your usage metrics to.            Product (Stripe only)          Here you can choose which Product that you’ve set up in Stripe you want your usage metrics to be tied to.            Price (Stripe only)          The last field in the Billing Provider settings for the Billing Meter, here you will choose which Price you want to tie your usage metrics to.            Filters          Under the Filters configuration, you will configure your billing criteria to only include requests that fit a certain criteria.            Metrics          Here you can choose which metric you would like to bill on. Available options include:            Event Count          This will increment usage for every event that fits the criteria outlined in the Filter criteria.            Unique Users          This will increment usage whenever a unique user sends a request that fits the Filter criteria. For every unique user, the count will be incremented by 1 regardless of the event count for that user.            Unique Companies          This will increment usage whenever a unique company sends a request that fits the Filter criteria. For every unique company, the count will be incremented by 1 regardless of the event count for that company.            Unique Sessions/API Keys          This will increment usage whenever a unique session or API key is used to send a request that fits the Filter criteria. For every unique session or API key, the count will be incremented by 1 regardless of the event count for that particular session or API key.        There are other options under Metrics as well but the above 4 tend to be the most applicable to usage-based billing.As an example, for this guide we will create a Billing Meter that will filter traffic for a single endpoint, named /test-service, and where requests received a successful HTTP 200 response. We will use the Event Count metric to make sure that every request is added to the tally and sent to the billing provider.In Moesif, the billing meter will be configured as shown below.We will then click Create. This will create and activate the Billing Meter. A modal will appear notifying you that the billing meter has been created and presents a walk-through to ensure the meter is correctly configured.First, we will set up a flow to get users registered, subscribed, and create a JWT so they can use our monetized API. Once that is complete we will come back and proceed with the walk-through.4 - Creating the APIInstead of using a pre-built onboarding flow, such as through a Developer Portal within an API gateway, we will build our own. We will create an endpoint called /register which we can then use to onboard our users who want to use the API. The result will be that the user receives a JWT that they can use that will track their usage.Since we are using Moesif, Stripe, and .Net Core as part of our overall solution, we need to make sure each of the components is working together properly.Here’s what the endpoint will do:  Create a customer in Stripe  Subscribe the new customer to the API subscription in Stripe  Create the CompanyID in Moesif (which will be the Stripe subscription ID)  Create the UserID in Moesif (which will be the Stripe Customer ID)  Create a JWT with a sub field that contains the Stripe Customer ID  If you already have User and Company identifiers in Moesif and other systems that you want to use, instead of using Stripe’s customer and subscription as your IDs, you can do that in Moesif under the Stripe configuration settings.In this example, we will create a simple .Net 7 Minimal API to do the above.Creating the .Net 7 ProjectWe’ll be using the .NET cli tool to create our initial project. The .NET command line interface provides the ability to develop, build, run, and publish .NET applications.The .NET cli new command provides many templates to create your project. You can also add the search command to find community-developed templates from NuGet or use dotnet new list to see available templates provided by Microsoft.We’ll be creating a Minimal API and starting from as clean a slate as possible. We’ll be using the empty ASP.NET Core template. In the directory of your choosing; enter the following in the terminal:dotnet new webYou’ll notice that the directory structure will look something like this:We’ll be doing all of our work in the Program.cs file. Its starting code should look similar to the following:var builder = WebApplication.CreateBuilder(args);var app = builder.Build();app.MapGet("/", () => "Hello World!");app.Run();We can see how concise and readable our starter code is. Let’s break down the code provided by the template line by line:  The WebApplication.CreateBuilder(args) method creates a new instance of the WebApplicationBuilder class, which is used to configure and build the WebApplication instance. The args parameter is an optional array of command-line arguments that can be passed to the application at runtime.  The builder.Build() method is called to create a new instance of the WebApplication class, which represents the running application. This instance configures the application, defines routes, and handles requests.  The third line defines a route for the root path (“/”) of the application using the app.MapGet() method. This means that when the root path is requested, the application will respond with the string “Hello World!”.  We start the application by calling the app.Run() method.Using the builder pattern, we can configure and customize the WebApplication instance. This allows us to define the application’s behavior, including middleware, routes, and other settings, in a structured and extensible way. For example, the WebApplication instance created by the builder can be thought of as the “entry point” of the application, which handles requests and generates responses.Overall, this code block creates a simple Minimal API in .NET 7 that responds with a “Hello World!” message when the application’s root path is requested.Next, we’ll customize our API to provide /register and /test-service endpoints.Adding Project DependenciesWe will install the Moesif Middleware using the NuGet package manager. Enter the following in your terminal while in the correct directory:dotnet add package Moesif.Middleware Stripe.net System.IdentityModel.Tokens.JwtOur <ProjectName>.csproj will be updated to reflect the newly added NuGet package.<Project Sdk="Microsoft.NET.Sdk.Web">  <PropertyGroup>    <TargetFramework>net7.0</TargetFramework>    <Nullable>enable</Nullable>    <ImplicitUsings>enable</ImplicitUsings>  </PropertyGroup>  <ItemGroup>    <PackageReference Include="Moesif.Middleware" Version="1.3.20" />    <PackageReference Include="Stripe.net" Version="41.16.0" />    <PackageReference Include="System.IdentityModel.Tokens.Jwt" Version="6.30.1" />  </ItemGroup></Project>Run the following command to ensure your project builds correctly.dotnet buildYour dependencies will be brought into the project and compiled if necessary. The Moesif.Middleware dependency is what allows us to connect to Moesif.Adding the Moesif Integration CodeFirst, we’ll add some configuration details into our appsettings.json file. It’s here we can define various options like our application ID, whether or not to log the body of a request, or enable local debugging. Enter the following, editing as you see fit:{  "MoesifOptions": {    "ApplicationId": "YOUR_MOESIF_APPLICATION_ID",    "LocalDebug": false,    "LogBody": true,    "LogBodyOutgoing": true,    "ApiVersion": "1.1.0",    "EnableBatching": true,    "BatchSize": 25  },  ...}Obtaining Your Moesif Application IDYour Moesif application ID is found in Moesif by going to the menu link in the bottom-left of the screen (which will show your name) and selecting API Keys.The key will then be on the page that appears under Collector Application Id.Creating Moesif OptionsNext, we’ll create a file to host all of our Moesif middleware implementations. We’ll create a folder called Settings and create a file within it called MoesifOptions.cs.Don’t be intimidated by the wall of code. This file includes a fair amount of boilerplate. We don’t need to edit this at all right now, but it will be useful to have in the future if you wish to enable more features in Moesif.// MoesifOptions.csnamespace ProjectName.Settings{    public class MoesifOptions    {        private readonly IConfiguration _config;        public MoesifOptions(IConfiguration config)        {            _config = config;        }        public static Func<HttpRequest, HttpResponse, string> IdentifyUser = (HttpRequest req, HttpResponse res) => {            // Implement your custom logic to return user id            return req.HttpContext?.User?.Identity?.Name;        };        public static Func<HttpRequest, HttpResponse, string> IdentifyCompany = (HttpRequest req, HttpResponse res) => {            return req.Headers["X-Organization-Id"];        };        public static Func<HttpRequest, HttpResponse, string> GetSessionToken = (HttpRequest req, HttpResponse res) => {            return req.Headers["Authorization"];        };        public static Func<HttpRequest, HttpResponse, Dictionary<string, object>> GetMetadata = (HttpRequest req, HttpResponse res) => {            Dictionary<string, object> metadata = new Dictionary<string, object>            {                {"string_field", "value_1"},                {"number_field", 0},                {"object_field", new Dictionary<string, string> {                    {"field_a", "value_a"},                    {"field_b", "value_b"}                    }                }            };            return metadata;        };        public static Func<HttpRequestMessage, HttpResponseMessage, Dictionary<string, object>> GetMetadataOutgoing = (HttpRequestMessage req, HttpResponseMessage res) => {            Dictionary<string, object> metadata = new Dictionary<string, object>            {                {"string_field", "value_1"},                {"number_field", 0},                {"object_field", new Dictionary<string, string> {                    {"field_a", "value_a"},                    {"field_b", "value_b"}                    }                }            };            return metadata;        };        public Dictionary<string, object> getMoesifOptions()        {            Dictionary<string, object> moesifOptions = new Dictionary<string, object>            {                {MoesifOptionsParamNames.ApplicationId, getConfigString(MoesifOptionsParamNames.ApplicationId)},                {MoesifOptionsParamNames.LocalDebug, getConfigBool(MoesifOptionsParamNames.LocalDebug)},                {MoesifOptionsParamNames.LogBody, getConfigBool(MoesifOptionsParamNames.LogBody)},                {MoesifOptionsParamNames.LogBodyOutgoing, getConfigBool(MoesifOptionsParamNames.LogBodyOutgoing)},                {MoesifOptionsParamNames.ApiVersion, getConfigString(MoesifOptionsParamNames.ApiVersion)},                {MoesifOptionsParamNames.EnableBatching, getConfigBool(MoesifOptionsParamNames.EnableBatching)},                {MoesifOptionsParamNames.BatchSize, getConfigInt(MoesifOptionsParamNames.BatchSize)},                {MoesifOptionsParamNames.IdentifyUser, IdentifyUser},                {MoesifOptionsParamNames.IdentifyCompany, IdentifyCompany},                {MoesifOptionsParamNames.GetSessionToken, GetSessionToken},                {MoesifOptionsParamNames.GetMetadata, GetMetadata},                {MoesifOptionsParamNames.GetMetadataOutgoing, GetMetadataOutgoing}            };            return moesifOptions;        }        public string getConfigString(string paramName)        {            return _config.GetValue<string>(MoesifOptionsParamNames.asKey(paramName));        }        public bool getConfigBool(string paramName)        {            return _config.GetValue<bool>(MoesifOptionsParamNames.asKey(paramName));        }        public int getConfigInt(string paramName)        {            return _config.GetValue<int>(MoesifOptionsParamNames.asKey(paramName));        }        public bool isConfiguredMoesifApplicationId()        {            string appId = null;            try            {                appId = (string)getMoesifOptions().GetValueOrDefault(MoesifOptionsParamNames.ApplicationId);            }            catch (Exception ex)            {                Console.WriteLine("Error Reading Moesif Application Id in appsettings(.env).json: " + ex.Message);            }            return !string.IsNullOrWhiteSpace(appId) && !appId.StartsWith("<");        }    }    public class MoesifOptionsParamNames    {        // Read from appsettings.json        public static string Key = "MoesifOptions";        // Read from appsettings.json        public static string ApplicationId = "ApplicationId";        // Read from appsettings.json        public static string LocalDebug = "LocalDebug";        // Read from appsettings.json        public static string LogBody = "LogBody";        // Read from appsettings.json        public static string LogBodyOutgoing = "LogBodyOutgoing";        // Read from appsettings.json        public static string ApiVersion = "ApiVersion";        // Read from appsettings.json        public static string EnableBatching = "EnableBatching";        // Read from appsettings.json        public static string BatchSize = "BatchSize";        public static string IdentifyUser = "IdentifyUser";        public static string IdentifyCompany = "IdentifyCompany";        public static string GetSessionToken = "GetSessionToken";        public static string GetMetadata = "GetMetadata";        public static string GetMetadataOutgoing = "GetMetadataOutgoing";        public static string asKey(string suffix)        {            return Key + ":" + suffix;        }    }}In this file we define our configuration of the Moesif middleware. We can also define details that can enable user and company tracking, add custom metadata that will be associated with an event, and add the ability to skip certain events entirely.Configuring the Initial ProjectWe’ll jump over to our Program.cs file to enable the Moesif middleware, configure our Stripe and Moesif API clients, and define our /register and /test-service endpoints. First, we’ll import a couple namespaces into our project. Next, after our builder declaration, we will configure ASP.NET’s Kestrel server to allow synchronous operations. We configure our Stripe and Moesif API clients in order to create our products and customers within Stripe as well as our users and subscriptions within Moesif. We also configure our Moesif Middleware which forwards events to Moesif.using System.Text;using System.Text.Json;using System.Security.Claims;using System.IdentityModel.Tokens.Jwt;using Microsoft.IdentityModel.Tokens;using Moesif.Middleware;using Moesif.Api;using Moesif.Api.Models;using Stripe;var builder = WebApplication.CreateBuilder(args);builder.WebHost.ConfigureKestrel(serverOptions =>{  serverOptions.AllowSynchronousIO = true;});StripeConfiguration.ApiKey = "YOUR_STRIPE_API_KEY";var apiClient = new MoesifApiClient("YOUR_MOESIF_APPLICATION_ID").Api;var app = builder.Build();var moesifOptions = new ProjectName.Settings.MoesifOptions(app.Configuration);app.UseMiddleware<MoesifMiddleware>(moesifOptions.getMoesifOptions());Obtaining Your Stripe API and Price KeyYou will see in the code example above that we need to provide a secret or restricted key for our createCustomer and createSubscription methods.Your Stripe API Key can be found in the same place we grabbed the key for our Stripe and Moesif integration we did earlier for the Billing Meter. You can actually use the same key for both or create a restricted key with just the scope needed for each function.While we’re at it, lets grab our Stripe product’s price key. We will need it in the next section. Your Stripe price key is an identifier for the price you created earlier in Stripe. This can be found by going to the product in Stripe and grabbing the value from the API ID column.  Take note of the price key. We will be using it next.Creating the Test Service and Registration EndpointNext, we’ll define our /test-service and /register endpoints....app.MapGet("/test-service", () => "Hello World!");app.MapPost("/register", async context =>{    var json = await JsonDocument.ParseAsync(context.Request.Body);    var email = json.RootElement.GetProperty("email").GetString();    var firstName = json.RootElement.GetProperty("firstname").GetString();    var lastName = json.RootElement.GetProperty("lastname").GetString();try{    // create Stripe customer    var customerId = CreateStripeCustomer(email, firstName, lastName);    var subscriptionId = CreateStripeSubscription(customerId, "YOUR_PRICE_KEY");    // create jwt for user using customerId    var token = GenerateAccessToken(customerId);    var moesifCompanyResponse = CreateMoesifCompany(subscriptionId);    var moesifUserResponse = CreateMoesifUser(customerId, subscriptionId, email, firstName, lastName, token);    context.Response.StatusCode = 200;    await context.Response.WriteAsJsonAsync(new { jwt = token });}catch (Exception ex){    context.Response.StatusCode = 500;    await context.Response.WriteAsync($"An error occurred: {ex.Message}");}});Our /test-service endpoint returns a simple string.The /register endpoint utilizes several helper functions and where is most of the work happens. We extract our users name and email from the body of the request then creates a customer and subscription within Stripe returning the necessary identifiers as well as passing along a price key that we got in the previous step. A JWT is then created using the customer identifier and finally passes all this data along to CreateMoesifCompany and CreateMoesifUser to create a company and user within Moesif. The JWT is passed along in the response we get from the call to the /register endpoint which we will use as the bearer token in our call to the /test-service endpoint.Creating our Helper FunctionsNext’ we’ll define all the helper functions that are used in the /register call....string GenerateAccessToken(string id){    var tokenHandler = new JwtSecurityTokenHandler();    var key = Encoding.ASCII.GetBytes("RANDOM_256_BIT_STRING");    var tokenDescriptor = new SecurityTokenDescriptor    {        Subject = new ClaimsIdentity(new Claim[]        {            new Claim("sub", id)        }),        SigningCredentials = new SigningCredentials(new SymmetricSecurityKey(key), SecurityAlgorithms.HmacSha256Signature)    };    try    {        var token = tokenHandler.CreateToken(tokenDescriptor);        return tokenHandler.WriteToken(token);    }    catch (Exception ex)    {        Console.WriteLine($"An error occurred while creating the JWT: {ex.Message}");        throw;    }}GenerateAccessToken takes an id parameter, which will be our customerID, and returns a JWT token as a string. The method creates a JwtSecurityTokenHandler object, generates a key which requires a 256 bit string, creates a SecurityTokenDescriptor object, and creates a JWT token using the CreateToken method of the JwtSecurityTokenHandler class. If an exception is thrown while creating the token, the catch block will catch the exception and log an error message to the console. Finally, the method returns the JWT token as a string....string CreateStripeCustomer(string email, string firstname, string lastname){  try  {    var customerOptions = new CustomerCreateOptions    {      Email = email,      Name = $"{firstname} {lastname}",      Description = "Customer created through /register endpoint"    };    var customerService = new CustomerService();    var customer = customerService.Create(customerOptions);    return customer.Id;  }  catch (Exception ex)  {    return $"An error occurred while creating Stripe customer: {ex.Message}";  }}string CreateStripeSubscription(string customerID, string plan) {    try    {        var subscriptionOptions = new SubscriptionCreateOptions        {        Customer = customerID,        Items = new List<SubscriptionItemOptions>          {            new SubscriptionItemOptions            {                Price = plan            }          }        };        var subscriptionService = new SubscriptionService();        var subscription = subscriptionService.Create(subscriptionOptions);        return subscription.Id;    }    catch (Exception ex)    {        return $"An error occurred while creating Stripe subscription: {ex.Message}";    }}Next, we define the helper functions that create our Customer and Subscription within Stripe.The CreateStripeCustomer method takes three parameters: email, firstname, and lastname. It creates a new Stripe customer using the CustomerCreateOptions object. It then returns the ID of the newly created customer. If an exception is thrown while creating the customer, the catch block will catch the exception and return an error message that includes the exception message.The CreateStripeSubscription method takes two parameters: customerID and plan. It creates a new Stripe subscription using the SubscriptionCreateOptions object, which contains the customer ID and the ID of the price plan. It then returns the ID of the newly created subscription. Again, if an exception is thrown while creating the subscription, the catch block will catch the exception and return an error message that includes the exception message....string CreateMoesifUser(string customerId, string subscriptionId, string email, string firstname, string lastname, string jwt){  try  {    var metadata = new Dictionary<string, object>      {        {"email", $"{email}"},        {"first_name", $"{firstname}"},        {"last_name", $"{lastname}"},        {"metadata", new Dictionary<string, object>          {            {"jwt", $"{jwt}"},          }        }      };    var user = new UserModel()    {      UserId = customerId,      CompanyId = subscriptionId,      Metadata = metadata    };    apiClient.UpdateUser(user);    return "User information updated successfully.";  }  catch (Exception ex)  {    return $"An error occurred while updating user information: {ex.Message}";  }}string CreateMoesifCompany(string subscriptionId){  try  {    var company = new CompanyModel()    {      CompanyId = subscriptionId,    };    apiClient.UpdateCompany(company);    return "Company information updated successfully.";  }  catch (Exception ex)  {    return $"An error occurred while updating company information: {ex.Message}";  }}The CreateMoesifUser method takes six parameters: customerId, subscriptionId, email, firstname, lastname, and jwt. It creates a new Dictionary object that contains the user’s email, first name, last name, and JWT token. It then creates a new UserModel object that contains the user’s ID, company ID, and metadata, and updates the user’s information using the UpdateUser method of the apiClient object.The CreateMoesifCompany method takes one parameter: subscriptionId. It creates a new CompanyModel object that contains the company’s ID, and updates the company’s information using the UpdateCompany method of the apiClient object.Finally, we’ll make sure we include the method that actually runs the application:...app.Run();With our code written, run the following command to compile and run our project:dotnet runThe API is now operational and ready for testing. After running the previous command, you will see which port has been used to host your API in the console. You can define which port you would like to use by editing the Properties > launchSettings.json file or by adding editing the app.Run() command in Program.cs like so, replacing 3000 with your desired port number:app.Run("http://localhost:3000");  If you are having issues with dependencies building, try: dotnet clean && dotnet build && dotnet run5 - Send a Test Request to the /register EndpointOnce your /register endpoint has been coded and deployed, it’s time to test it. For right now we will simply use Postman to send a request. Our request will contain a JSON request body that will contain a:  First name  Last name  EmailOf course, this is the minimal amount of information we would want to configure our system and profiles in Stripe and Moesif, plus, generate the JWT. You can easily add more fields as needed for your specific use case.In Postman, we will create our request with the following information:Request Type: POSTEndpoint URL: http://localhost:{port}/registerRequest Body:{  "firstname": "Userfirstname",  "lastname": "Userlastname",  "email": "test@test.com"}  Replace port in your endpoint URL with the assigned port number.Once everything is plugged into Postman, it should look like the following:Once the request is sent, the response should contain an JWT that the newly registered user can use.We will now check Stripe to ensure that the information we registered the customer with is correctly entered into Stripe.Logging back into Stripe, you’ll navigate to Customers screen. You should see your newly created user in the list.Click on the newly added customer in the list. On the next screen, you should see that the customer is also subscribed to your APIs subscription.With this check completed, we can safely assume that our /register endpoint is correctly setting up our users accounts and subscriptions in Stripe.6 - Call Your API Using the Generated JWTOur next step is to actually use our generated JWT. We will then confirm that all the correct information is added into Moesif. The data we are confirming includes:  The Stripe Customer ID is mapped to the Moesif User ID  The Stripe Subscription ID is mapped to the Moesif Company ID  Moesif contains the Stripe metadata in the users profileUse Postman to Send the RequestNext, let’s use Postman, or another platform, to send a request to the /test-service endpoint. This is the endpoint that we set up the billing meter for in Step 3, above.In Postman, we will:  Put the /test-service API endpoint as the request URL  Select the Authorization tab  Select the Type as Bearer Token  Populate the token details  Set the Token as the JWT received from our /register callBelow is an example of the populated request configuration in Postman. To send the request to our endpoint, click Send.Once sent, the API call analytics should land in Moesif.Confirm That Moesif Received the Request Info Using Profile DashboardsBack in Moesif, you’ll navigate to the Live Event Log screen. You can do this by clicking the New button and selecting Live Event Log.On this screen, you should see the request you just sent. You should see the entry has both a User ID and Company ID populated with the Stripe user and subscription ID’s. The entries should look like this:  The customer ID will look like “cus_XXXX” and the subscription ID will look like “sub_XXXX”.If you click on the User ID shown in the entries on the Live Event Log screen, you will come to the users profile page. On this page, we will confirm that the Stripe metadata is present. We will need to add a new column to our profile to display the Stripe data. To do this, from the profile page, click on the … More Actions button and click Customize Profiles’ Layout.We will then add a new column for the Stripe metadata. You will click the + button on the far right of the screen to create a new column where we will add the Stripe metadata.  You may need to scroll to the right to see it depending on your resolution and screen size to see the + button.You will then drill down to Metadata > stripe > customer > created and use this field in the new row. I’ve also changed the column image to one more fitting. You can customize this by clicking on the image and selecting whichever one fits best.You can also add other fields, but for right now just this single field is enough to tell us that Moesif is correctly receiving data from Stripe.  If you don’t see the Stripe metadata entry as an available field, wait a few minutes. If after a few minutes the Stripe metadata isn’t present, ensure that your Stripe configuration is correct in Moesif. After confirming or editing it, try creating a new user and sending a request again to confirm that the integration is working.At this point, we now have confirmed that our API call is working and is stamped with the correct user and company details in Moesif. We also confirmed that Stripe is sending data back to Moesif which is correctly being mapped to the corresponding user profile, confirmed through the Stripe metadata in Moesif.7 - Send a Request to Your Monetized APIWe will once again want to make sure that everything is working with our UI, through to our backend systems. For this, simply repeat the steps from Step 6 to confirm that the user and company IDs are populated correctly and that the Stripe metadata is returned for this user and the new JWT. We should see these calls populated within our Live Event Log as well.8 - Confirm All the Pieces are Working CorrectlyAlthough this is optional, this step may help with troubleshooting any issues that may have came from our previous steps. Here are a few things to check to make sure that all is working as it should. After creating a new user through the UI and using the generated JWT to place a call to your API, confirm the following:In Stripe  Confirm that a customer has been created in Stripe with the details you entered into the UI  Confirm that the customer has been subscribed to the correct product and priceIn Moesif  Your API call was recorded in Moesif in the Live Event Log  Your API call has the Stripe Customer ID and Subscription ID in the User and Company fields in Moesif, respectively.  Confirm that the Stripe metadata is populated in Moesif  All Billing Meter test conditions have passed9 - Check Stripe for UsageLastly, After a few hours, it’s best to go into Stripe to confirm that usage is being added to a users subscription. Be sure that you’ve sent a few requests through in order to make sure you have some data that should be sent to Stripe.  It may take a few hours for usage to make its way from Moesif to Stripe. If data still isn’t in Moesif after a few hours, ensure you’ve followed all the steps outlined within this guide. This includes making sure that your user and company ID’s from Moesif are correctly mapped to the corresponding keys in Stripe.To check the usage, in Stripe you’ll want to navigate to the Customers screen and select the customer that you made the API call with. Once selected, you should see some active subscriptions for the users that you’ve registered through the /register endpoint. The one we created earlier is called My API. Click on the subscription entry.On the next screen, click on View Usage beside the price entry.A modal should now pop up showing you the usage for the API that has been reported to Stripe from Moesif.  Remember, there is a delay in Moesif’s reporting to Stripe. If you data isn’t there yet, check back in a bit later.10 - Determining If the Billing Meter is Working CorrectlyTesting the created Billing Meter is easy with out Test Meter function. Navigate to your created Billing Meter from the left side navigation pane and selecting your Stripe Test billing meter. Select Test Meter on the top right.We will first confirm the meter that you are attempting to test. Click the Next button at the bottom of the modal.Moesif will wait for Subscriptions to created within Stripe and those subscriptions to be associated within Moesif itself. This page will update automatically, no need to refresh.Moesif will then wait for an API call to our any endpoint associated with our billing meter using our the JWT that has been created for us.Finally, Moesif will sync all usage data to Stripe every 15 minutes. This step may take a few minutes depending on when the API call was initiated but will update on the given interval.Wrapping upMonetization has always been a tough hurdle to get past. Many custom solutions offered flexibility but at a very high engineering and support cost. With Moesif, API monetization is possible in an extremely minimal amount of time. As demonstrated in this article, With a little bit of configuration and minimal amount of code we can create a production-ready, post-paid monetization scheme in minimal time.                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue with your .NET REST APIs?            Monetize your .NET APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /api-monetization/stripe/End-To-End-API-Monetization-With-Dotnet-Stripe-And-Moesif/",
          "author": "Dylan",
          "categories": "API-Monetization, Stripe"
        }
      
    ,
  
    
        "api-monetization-api-strategy-commercializing-your-apis": {
          "title": "Commercializing your APIs",
          "content"	 : "No go-to-market strategy is complete without having a way to generate revenue from your product. APIs are no different. Indeed, in today’s flourishing API economy, you have a great opportunity to unlock revenue and really make your API work for you. All you need is a sound strategy to commercialize your API product and the right tools to support your monetary goals.Whichever vertical you’re in: biotech, telecom, healthtech, web3/blockchain, DaaS, SaaS, etc, we’ll get you the API commercialization essentials you’ll need to release revenue fast.Build and Sell Your APIIn the for-profit corporate world, there’s no point in building a product if you can’t be remunerated for it. When developing an API there’s certain core considerations that need to be taken into account if you want the end result to be sellable. Clearly, your product needs to be useful -  it needs to meet a need that your users are willing to pay for. But it also has to be easy for them to discover and interact with. An API that fulfills a particular function, but comes with poor documentation, a weak user experience or is not in the major API marketplaces will quickly lose out to competitors’ products.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        But the building side of the process is only half the battle. You also need to think about delivering revenue if you want to successfully commercialize.Did You Know You Can Make Money by Selling Your APIs?The precise way that you monetize and sell your APIs will likely depend on your industry. After all, different verticals often have distinct parameters they like to bill around. To successfully commercialize your product, you first need to understand which parameters you want to bill against, and then put a solution in place to do so.It can be particularly difficult to decide how to bill for APIs. You might have to really dig into the calls’ payloads to extract the parameters your vertical cares about. This introduces a conundrum – do you commit time to building a billing solution in-house, thus lengthening your time to market, or accept that buying an existing solution is a necessary and a worthwhile expense?For example purposes, let’s consider a business in the web3 vertical. Such an enterprise will have a whole host of KPIs that it could bill against - ones that are unique to the distributed nature of web3 - as well as parameters that businesses in other verticals might utilize. This could include metering on any relevant metric, including transactional, security, or reconciliation KPIs, with billing on: number of miners, number of transactions, hash rate, number of fraud detection/alerts, number of data breaches prevented, number of on-time reconciliations, number of aging reconciling items, percentage of automated reconciliations, and so on.Building a solution in-house to monitor all of these will take plenty of time and resource away from concentrating on core API development. This is where the flexibility of Moesif comes into play. Moesif built its monetization capability on top of an analytics tool – that means that it’s designed from the ground up to bill for any parameter. You just need to decide which parameters are most meaningful and/or profitable in your vertical, then you can use Moesif to bill your API consumers accordingly. It’s an easy solution for generating revenue.Should I List in an  API Marketplace?If you build your API product, will they come? Philosophical questions notwithstanding, as an API provider you -do- need to promote and market your product so that prospective API consumers can discover it. Unless you’re building an internal API, the first port of call should be to add your API to an API marketplace. Technical marketplaces, as popularized by the AWS marketplace, serve as central repositories where developers can discover, access, and manage software products.API marketplaces, like the one run by Rapid API, do increase your API product’s exposure and, if timing is right, can put you in front of an end user who’s ready to buy. That’s why both API developers and enterprises use API marketplaces to sell their APIs, reach their target audience, and increase their revenue.What Are the Challenges of Selling APIs?Assuming you’ve built a secure, discoverable, and easily usable API, then the real challenge you will face when it comes to commercializing and selling it will be accurately quantifying customers’ usage so that you can charge them appropriately.Business-to-business sales intelligence firm SalesIntel is one company that has embraced the potential of API commercialization. The firm’s databases consist of a wide variety of account intelligence, such as firmographics, intent, technographics, and more. This helps SalesIntel’s customers refine their ideal customer profile. In order to accurately quantify their customers’ usage and assess rates to charge them, SalesIntel bases its pricing on the characteristics of queries or data, using Moesif to provide global account visibility to enable this.What are the challenges of selling APIs, aside from quantifying usage? Well, jumping into the API economy also means providing some form of customer support for your API product. You’ll also need to think about ongoing development. Taking on an API product manager as you scale can be a smart move in this respect. Finding the right API gateway and API management solution is also important. Finding a solution that avoids vendor lock-in, doesn’t hide essential features behind a paywall, and won’t vastly ramp up your costs as you grow, may not feel like a key priority when your company is small. However, this can have a significant impact further down the line, so make sure you think long-term when it comes to bringing in such solutions.How Do You Ensure the APIs Actually Deliver Business Value and Agility?Ensuring your APIs deliver business value and agility means examining your customers’ use of them at a granular level. This is the only way that you can assess the impact of metering based on different parameters, and the value that doing so will contribute to your bottom line.Broadly speaking, metering based on number of API calls, length of VoIP calls, search criteria or accessing a unique endpoint, will meet a wide range of businesses’ needs. Implementing metering based on these metrics means measuring every count. You may also need to use data in the request and response body to calculate usage to be billed for. This is where buying in a billing solution based on in-depth analytics really comes into its own, when compared with trying to build a solution with that level of granularity yourself.Of course, once you’ve established which parameters you should be billing on to ensure your business gains maximum value from the commercialization of your API, you still need to think about things like pricing plans customer support packages, service level agreements, and more. Decisions around these factors need to build in agility and the ability to scale, just as your API itself does. This ensures that you are future-proofing your business model by being able to flex and grow in response to shifting market conditions and continually evolving technology. As such, be sure to use a billing solution that molds to your specific needs, but also provides flexibility to meet future changes.How Can Your API Drive Revenue for Your Business?We’ve looked at web3/blockchain and DaaS examples of how an API strategy can drive revenue for your business, but there are plenty more verticals where this is happening. API integration is widespread across the globe, from the logistics sector, to telecoms and to the fintech industry.If you want your API to deliver revenue for your business, you need a clear, commercialized billing and invoicing system in place. In the biotech sector, the bioinformaticians at BioLM are doing this by using Moesif with Stripe:“Moesif monitors consumption of APIs and pipelines and pro-rates them… Ultimately, that data is sent to Stripe, where billing and invoices are securely handled.”  Nikhil Haas, CEO, BioLMThe BioLM model enables drug discovery utilizing cloud-based GPUs and AI modeling, accessible through unique endpoints. As the company wanted to charge based on calls made to specific API endpoints, it needed detailed insights into which users were accessing its API and how. Using Moesif to meter and block usage provided a one-stop solution for this in terms of BioLM’s user analytics stack. This powered the transformation of BioLM’s API-based platform from a tool to a product, driving revenue for the business by giving biotech researchers the ability to drive new innovations without overloading BioLM’s GPU framework.What Is Your Business Objective in Creating an API?Key to ensuring that your API drives revenue for your business is a clear business objective behind the creation of that API and a clear API monetization strategy. In the communications vertical, conversation intelligence firm Symbl.ai exposes simple endpoints that directly screen both real-time and recorded audio, analyzing millions of conversation minutes every day. They opted to use Moesif rather than building an in-house solution, with Symbl.ai CEO Surbhi Rathore pointing out:“When you’re small as a company it’s very hard to focus 20% of your continued engineering efforts on building a dashboard for yourselves.”Symbl.ai is using Moesif’s advanced analytics to track the number of API calls and the number of conversation minutes within those calls, with both a clear business objective and a clear commercialization approach.Do You Have More Ideas or Examples of Ways an API Can Increase Revenue?There are examples of increasing revenue using APIs across every vertical. In the healthtech sector, for example, Paul Fung, GM of Developer APIs at Nexhealth, comments:“We were soon able to start billing our customers for the API usage using Moesif and it quickly became a revenue generator for us.”Nexhealth has taken a multi-faceted approach to commercializing its APIs to increase revenue. The company accesses HIPAA-compliant health records from multiple providers over a single API platform. By exploiting the extensive flexibility of Moesif’s analytics, Nexhealth has implemented both flat-rate and usage-based pricing plans. The firm is also testing billing based on additional usage metrics, such as the number of practices onboarded per customer and the number of API calls a practice uses per day.These are all examples of ways in which an API can increase revenue for your business. Obviously, the way in which you commercialize your API will depend on your particular vertical and on the needs of the API consumer within that vertical. The common factor is the need for in-depth analytics in order to monetize your API based on granular data. You can do so based around the idiosyncrasies of your business, in line with clearly defined objectives and a detailed understanding of how to realize your API’s value.Commercialize Your APIProduct builders often overlook the difficulty of how exactly to bill for their product in order to make money. But generating revenue is crucial when building a sustainable business. To commercialize and monetize your API, contact Moesif today.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Commercializing-Your-APIs/",
          "author": "Larry",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-monetization-api-product-management-moesif-new-usage-based-pricing-model": {
          "title": "Moesif’s New Usage-Based Pricing Model",
          "content"	 : "This month at Moesif, we released a new usage-based pricing model for our platform. With these changes, we have brought in a new pricing scheme that is better aligned with customer value and requirements. In this article, we will walk through the history of pricing at Moesif and how we designed our latest pricing update, which is our third iteration.History of Moesif PricingFor many startups, pricing can become an afterthought as founders and operators focus on building and selling their latest and greatest products. However, nailing the right pricing can have an outsized impact on your growth. The right pricing can bring better value to customers and also help to fuel further product development. Recently, Moesif released our third iteration of pricing. How we moved through each iteration of pricing is important to see how we have landed at our new, most-optimal pricing yet!When we first brought Moesif to market, we initially released it with fixed pricing. Then, after picking up some steam and customers, we layered in flexible quotas to those fixed pricing options. This month, we released a new pricing model designed to be even more flexible. The goal was to ensure that pricing is meeting customer needs and enabling our customers to scale their analytics in the most cost-effective manner. Let’s take a deeper dive into each of these pricing iterations.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Fixed Pricing TiersLike many SaaS startups, Moesif launched with simple, fixed pricing tiers. There was no usage-based pricing at that time, as with many very early-stage startups with limited engineering resources, the engineering investment was too high. At that time, usage-based billing platforms, like Moesif, did not exist yet to allow for easily implementing usage-based billing. This meant that usage-based pricing would require building a custom, in-house solution that could handle accurate metering of usage, a system to reconcile usage back to revenue, and auditable history of usage records. With these limitations in place, we created three self-service tiers, each with a hard quota being enforced. These plans, as some will remember, looked like this:  A free plan to pilot Moesif without requiring a credit card.  A growth plan for individual users at early-stage startups. ($100/mo)  A pro plan for teams at scaling startups. ($550/mo)As a company, we quickly realized this pricing model was too rigid to handle our customer’s requirements. In particular, customers would get locked out of their accounts even when going just slightly above their quota. Yet, the jump between tiers was harder to justify for many users due to a gap in perceived value. An individual user who barely exceeded their plan quotas had to pay over a 5x increase in their monthly price, from $100/mo to $550/mo, to jump to the next tier. These customers were typically in the discovery stage of launching a new API product and weren’t ready to upgrade. Thus, the old fixed tiers were inflexible and couldn’t support a variety of different customer sizes, each with varying maturities of their API products and varying event volume requirements. Even so, this was an important upgrade path for users since it was the critical transition point to move from an individual user to a team plan with multiple seats.Fixed Pricing Tiers With Flexible QuotasAt this point, in the second iteration of pricing we set out to solve the gap between cost and perceived value and to avoid large cost jumps between tiers. Our solution was to roll out a new Pay-As-You-Go (PAYG) pricing strategy that would be added on top of our fixed tiers. This enabled a customer who is slightly above their quota to have the ability to stay on their plan and increase usage without getting locked out of their account. Additional events beyond the plans quota were simply billed as they were being used via the new Pay-As-You-Go capabilities. A customer whose event volume was between our Grow and Pro tiers could pay a price that landed somewhere in between the two tiers without having to upgrade. Adding this ability greatly improved our pricing to match customer requirements. This iteration remained in place for a while, However, we were not done with optimizing our pricing to deliver the best value for our customers.What’s Wrong With The Old Pricing?Our fixed + Pay-As-You-Go pricing model was an interim solution since we still had the rigid tiers but could now handle overages without a forced upgrade. Even with the addition of the Pay-As-You-Go component, since it was “added on” to the existing fixed tiers, some customers were still limited in the flexibility of our pricing. Overall, customers were happy with the new pricing models and as more customers came on board we were able to gather more feedback and data to optimize pricing even further. With the data and feedback gathered over the last year, we started to recognize some gaps in our existing pricing model and began to rally together to optimize our offering even further. Below we go over some of the factors that led to our latest pricing updates.Overages Were PenalizedSince we left the pricing of the tiers the same in the last update (Free, Grow, and Pro), any additional events beyond the quota were billed as an overage. While normally this isn’t an issue if the overage volume is a fraction of the plan quota, we started seeing customers with larger projects deploying Moesif on a self-service plan. Unfortunately, the largest commitment level on self-service was still the Pro plan. If customers wanted to commit to a larger volume to gain volume discounts on anything larger than a Pro plan, they were required to sign up for an enterprise plan. This didn’t always make sense since some customers were not ready for an enterprise-level commitment. This penalized customers who got a ton of value from Moesif and wanted to use Moesif to capture and analyze events from additional APIs and apps. Because of these factors, in these cases, our pricing was not aligned with our and our customer’s goals. In addition, a customer was unable to gain any discounts (such as an annual upfront discount) on any overage since they were always billed in the following month, in arrears.Hard to Gain Value on The Smallest PlansWhile our old Pro plan had a full year of data retention, our Grow plan had only 90 days of data retention. While this might be fine for a new user testing out Moesif on a new project, it’s not enough for someone serious about leveraging Moesif for business analytics and monetization in the longer term. For instance, some finance teams required the ability to measure usage for the last quarter and product teams wanted the ability to measure month-over-month and quarter-over-quarter. In these cases, these teams were limited on data retention of the Grow plan. Thus, the smaller plans were too limiting to demonstrate real value for customers who required the ability to look back at the history of usage in their platform within Moesif.Redundancy Between TiersBesides the quota difference, the Grow and Pro plans had almost the same functionality except for a small set of features that initially had higher support costs. These more expensive features (which included retention analysis, chart downloads, and embedded dashboards) were initially launched on Pro to ensure we could support all customers with our highest level of support. Over time, we gathered enough feedback to reduce the cost of supporting those features. By improving the ability to support these features, the cost to support customers on a Grow plan became roughly similar to the cost to support customers on a Pro plan. Because of this, having two plans for roughly the same functionality introduced unnecessary pricing complexity. We believe that pricing should be easy to understand and predict. This has become a driving factor, the opportunity to remove complexity and make pricing easier to understand for new and existing customers.Moesif’s New Flexible PricingWith the latest update to pricing, there were three goals we wanted to achieve:  Reduce the complexity in pricing by removing unnecessary/overlapping plans  Ensure customers are not penalized for increased usage  Ensure value can be gained on even the smallest plansMoesif’s new pricing was launched in May 2023 to achieve these three goals. Let’s go into more detail on each of these factors which are outlined further below.Reduced Complexity With a Single Self-Service PlanThe old Grow and Pro tiers have been merged into a single tier we have now called Growth. Having a single tier helps to greatly simplify pricing. The new plan provides access to all the original features on our previous Pro plan but with lower introductory prices. Instead of trying to decipher complex plan comparisons, the only difference a user needs to decide on is how many monthly events to commit to which makes plan selection and cost much easier to plan for. This means Moesif now has only three tiers:  A Free tier for hobbyists and small projects.  A self-service Growth tier for startups and medium-sized companies.  An Enterprise tier for customers with more complex needs.Avoid Overages Via Flexible CommitmentsInstead of being hit with overage fees, the new unified Growth tier introduces the concept of “commitment levels”. This enables a customer to commit to higher event quotas and save on per-unit pricing as their needs and usage grow. For example, committing to 300k events monthly costs $1.8 per 10k events, but when committing to 5 million events per month, the unit cost drops to $1.42 per 10k events. Thus, users are encouraged to leverage Moesif more without getting penalized, a win-win solution. Previously, customers who went above the included 5 million events on Pro had two options: pay overages or upgrade to the enterprise tier. With this being the only option, there wasn’t a way to scale within the current Pro plan they were on. Now, with the latest Growth tier, you can choose whether to commit monthly or annually (which includes a discount) depending on your budget and needs. In addition, the annual discount is now expanded as it applies to the full commitment level. Previously, overage fees above the Pro plan quota were considered monthly and therefore not eligible for annual discounts.A New, Generous Introductory PriceThe smallest introductory Moesif plan now starts at $60/month while providing access to all features previously offered only on the Pro plan. Previously, if a customer required access to a feature available only on the Pro plan or wanted a year of data retention, they would have to pay $550/month even if their volume requirements were too low to justify the cost. The new Growth plan enables customers to leverage all of the features previously on Pro, but with a lower intro price of only $60/month. This helps new users get to their “aha!” moment more quickly without requiring a large commitment upfront, a hallmark of bottoms-up adoption. All Growth customers also benefit from a full year of data retention, regardless of their commitment level. This ensures you’re able to report on long-term product and business trends without fear of premature deletion of your user’s usage data.What About Existing Customers?Ensuring existing users are not impacted by pricing changes is critical for any SaaS product. When a customer signs up for a service like Moesif, they expect to pay the price at that time. Because of this, all existing customers have been grandfathered on their current plans as long as they keep their paid subscriptions active. For some customers, it may make sense to stay on their existing plan, others may want to take advantage of the new pricing model. Contact us today to chat with us about your existing plan or sign up to try out Moesif and experience our latest pricing for yourself!                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-product-management/Moesif-New-Usage-based-Pricing-Model/",
          "author": "Derric",
          "categories": "API-Monetization, api-product-management"
        }
      
    ,
  
    
        "api-monetization-api-strategy-invoicing-and-billing-software": {
          "title": "Invoicing and Billing Software",
          "content"	 : "The decisions you make around software for billing in small business operations can have major repercussions in terms of how you can track billable services and raise invoices. The right invoicing software for your API business can even help you get paid on time.Invoicing and Billing Software ExplainedThere’s plenty to think about when it comes to invoicing and billing software for API products. Billing software that’s easy to implement and use is obviously a must. But there is more to the right software than usability. For usage-based billing, invoicing software needs to help you get the most out of your APIs, enabling you to monetize them and implement a range of different billing options and tiers.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        How Will This Software Make Your Invoicing Process Easier?Automation is key when it comes to implementing a billing system that makes invoicing easier. After all, the more you can automate, the less manual time and effort your team needs to allocate to invoicing. This becomes increasingly important as you scale your API-first small business.Achieving automated billing isn’t hard; you just need the right invoicing software in place. Moesif is a case in point. You simply connect it to a billing provider such as Stripe, Chargebee, or Recurly to enable automatic invoicing in minutes. Bi-directional sync means that you can take a deep look at how your customers’ API usage translates into revenue, whilst ensuring that each of them receives accurate invoices based on their precise usage.When selecting a billing system, as well as choosing one that will make your invoicing process easier, make flexibility a priority. Doing so will enable you to experiment with different pricing models and to change these as business needs demand. Make sure you can easily include custom pricing plans too – you never know what the future holds. With a custom pricing option at your fingertips, you can be ready to bill for any eventuality.How Does Billing Software Handle Payment Processing and Invoicing for Online Sales?Different billing software handles payment processing and invoicing in different ways. If you’re making sales online and driving adoption through self-service, then you need a payment processing solution that’s an integral part of your model.Moesif handles this by providing metrics to customers on their usage and quota. The system empowers customers by providing detailed reports on what’s consuming their quota, then invoices automatically based on that consumption. It supports prepaid, postpaid, pay-as-you-go, and other pricing models, metering any usage metric – whether API transactions, compute resources, unique users, or anything else. In doing so, it handles payment processing and invoicing in one seamless service – just what small businesses need from a billing system.Best Billing Software for a Small BusinessThere are so many options out there when it comes to choosing the best API billing software for a small business. There are free and low-cost billing software options, as well as systems with a huge range of functionality, designed to meet every need of your business both now and as you scale.What is the best billing software for a small business? Answering this means breaking things down a bit and thinking about your business needs (now and in the future), your budget, your billing model, and more. It’s also worth considering what additional attributes you could benefit from when it comes to choosing the best invoicing software. For example, could you benefit from behavioral emails or embedded dashboards? The more you explore considerations like this when choosing billing software for your API business, the more likely you will be to end up with a product that’s the perfect fit for your particular needs.How Is Your Small Business Going to Benefit from This Billing Software?The first question to address is how your API small business is going to benefit from the billing software. This should be your list of non-negotiables – the essential things that you need your new billing system to deliver. Some examples of the benefits that invoicing software can deliver include increased billing accuracy and reduction of errors, easy and anytime access to your billing data, a reduction in the amount of staff time spent on invoicing, and even faster payment of your invoices.Most API businesses can benefit from all of these. Other benefits can include making it easier to release revenue by understanding customers’ usage and billing patterns and enjoying easier access to an accurate overview of your business’ financial health.Rating these factors by importance ensures you remain focused when researching invoicing system options.Should Small Businesses Look for Certain Accounting Software Attributes?There are certain attributes that small businesses should look for as standard when seeking the best way to monetize their APIs with usage-based billing. Ease of use should top the list every time. An invoicing system that’s easy to use will save your team time. It will also help ensure you’re able to use the software fully, implementing all of its features, and reaping all of its benefits. Consider ease of setup, installation and integration with the systems you are already using, as well as how easy the billing software will be to use on an ongoing basis.While most invoicing software for small businesses will include these attributes as standard, there are also additional features that may be of benefit. For example, could your API business benefit from behavioral emails that trigger when a customer reaches a certain percentage of their quota, or takes an action like adding another user? Automated email suggestions, that they move up a pricing plan tier, could be an excellent way to unlock revenue.Embedded dashboards are another example of a handy attribute that can add value as part of a billing software package. They can provide customers with clear visibility of their subscription quotas, enabling them to keep a close eye on their usage. This can be an excellent customer sat tool, as it keeps them up to speed regarding their usage and avoids surprises, like quotas running out or bills being higher than expected – both of which can contribute to customer churn.Clearly, the best invoicing software can deliver benefits on multiple fronts for your small business. It can actively increase revenue when you deploy the right features, ensuring that you don’t just have an efficient billing software solution, but one that underpins robust long-term business health and growth.Benefits of Billing and Invoicing SoftwareBenefits of billing and invoicing software summary:  Increased billing accuracy  Reduced error rate  Efficient access to your billing data whenever required  Savings on team time, both in terms of raising invoices and dealing with queries and errors  Faster payment of your invoices  The ability to realize your APIs’ value through enhanced monetization opportunitiesThese lead to headline business benefits of greater operational efficiency and, when you choose the right billing software for your invoice processing, the potential for higher customer sat rates. After all, little is likely to irritate your customers as much as issues with billing that lead to an interruption of their service.What Are the Factors to Consider When Choosing Invoicing Software?When choosing invoicing software for your API business, consider your system from your customers’ perspective. An automated email that warns a customer when they are approaching their API rate limit, for example, or the due date of their invoice payment, could head off an inadvertent missed payment and/or service interruption. This simple, automatic notification could pay big dividends in terms of avoiding awkward, stressful situations that result in customer churn.Another factor to consider is that automated notifications can help identify unusual spikes in traffic. Whether as a result of something positive, such as demand for your service snowballing, or something more sinister, such as a distributed denial of service attack, early awareness of traffic spikes can help your customers avoid unexpectedly large bills or service interruption when they hit their quota early.Does Your Company Need a Custom Invoicing Software for Smart Billing?Smart billing enables you to bill on any metric and in any format, whether prepaid, postpaid, pay-as-you-go, or whatever else you need. Invoicing software that delivers this kind of customizability can be invaluable when it comes to the way you manage revenue from your APIs, enabling a comprehensive and clear approach to billing.What Features Can You Get with Free Billing Software?There are plenty of free billing software options on the market, ranging from genuinely useful systems to those that are awkward to implement and end up being more hassle than help. What features can you get with free billing software?What Features Do You Have with the Free Version of Invoice Software?Each software offering varies in terms of its free plan features. However, broadly speaking, free versions can include anything from unlimited invoices and unlimited clients, to integration with other software, such as inventory or time-tracking systems. Some free invoicing systems may also include a client dashboard or portal.What Things Should I Consider When Choosing Monthly Billing Software?For a small business, staying within budget is hugely important. As such, if you’re looking for free API recurring billing software, you need to consider what “free” really means, as a free software for billing in small businesses could end up costing you money.That’s because some “free” software is only really offered as a trial. After a certain period, you end up having to pay to continue using the system. Other billing software uses a freemium model. That can mean the software will be free for as long as you need it, but that certain key features and functions are locked away behind a paywall. If you want to use them, you need to pay. Other freemium models let you use the billing software until you hit a certain usage quota, after which you have to move up to a paid plan.The danger here is that you implement a free piece of software, including going through integration with your current systems and the staff (and customer) learning curve, then suddenly you’re into billable territory with it. Often, it’s far easier to start paying for the software than to go through the process of finding and implementing a different free system.Given that, it may be worth accepting from the outset that there will be a cost attached to your invoicing software. Understanding this means you can look at paid versions of billing software from the outset, thus widening your options.Can It Accommodate Several Different Payment Options?Any decent invoicing system, whether a free or paid model, should be able to accommodate a range of different payment options, to enable easy and speedy payment of your invoices. Check the options each system has as part of your research.Invoicing Software SecurityOne key factor to consider with API invoicing software is how secure it is. After all, billing software has access to some of the most important and sensitive data within your business. Encryption, secure key management, firewalls, and more therefore need to be on your radar when it comes to choosing the right invoicing software for your small business.How Can You Know if Your Data Is Safe Using Invoice Software?One of the best ways to ensure your data is safe when using invoicing software is to choose software that adheres to current standards. Moesif, for example, maintains an active SOC 2 Type 2 attestation. All data is encrypted at rest and in motion with Moesif, with server-managed keys in ISO/IEC 27001 and ISO/IEC 27017 compliant data centers. Client-side encryption enables zero-knowledge security, with Bring Your Own Key functionality, meaning that not even Moesif can decrypt customer data or access it. Automatic key rotation is available with numerous prebuilt plugins.This kind of compliance means you can enjoy peace of mind when invoicing, whether you are issuing one-off bills or recurring invoices. Other features to consider include things like enterprise single sign-on, audit logs, and fine-grained access control, all of which can ensure you keep your data private and secure.How Does Invoice Management Software Handle Verifications and Approvals?Asking is invoicing software secure also involves considering how an invoice management system handles verifications and approvals. Invoice processing means not only issuing invoices but ensuring they are verified and approved for payment in good time – and that payments are recorded in order to avoid service interruption.When seeking invoicing software for your API business, be sure to look into how your potential billing software providers handle matters such as verifications and approvals, as part of researching their security standards and processes.Best Invoicing Software to Get You Paid on TimeIssuing accurate invoices at regular intervals and combining them with automated notifications such as email payment reminders, can help you get paid faster. Exploring the best invoicing software to get you paid on time is best done through an example – in this case, using Moesif and Stripe.It’s easy to implement Moesif with Stripe, Recurly or Chargebee, as no coding is required. You simply connect Moesif to your billing provider and start enjoying the ability to accurately meter usage, embed usage metrics, and automatically invoice. This ‘plug and play’ style of implementation is something to look for when choosing the best invoicing software for your API business, whether you’re opting for free invoice software or a paid product.At the heart of using Moesif and Stripe to get paid on time is the inclusion of “billing meters” in the Moesif platform. This is what enables you to customize your billing approach to suit your needs. both now and as you scale, all with a minimal amount of engineering effort.All you need is a Moesif account and a Stripe account to start enjoying unlimited invoice potential. Moesif sits between your APIs and the payment provider, enabling you to implement complex usage-based meters to suit your business.Metering is one of the most common ways of monetizing API products. If your customers are using your API, metering will allow you to charge for that usage. Building on Moesif’s powerful analytics system you monitor usage statistics and then pass those metrics to Stripe. The billing provider then facilitates the collection of the funds.Moesif’s role is to collect all the data you need in order to make this happen seamlessly. The platform can break that data down by user or by company, dependent on your needs, with billing based on a vast array of metrics. Because Moesif built its analytics capabilities first, then implemented billing meters on top, you have an outstanding level of granular detail on which to base your invoicing needs. It’s a simple solution to a complex problem, taking care of all the coding, integration, testing, and support that you need to implement a billing system.Integration is key to enabling this simplicity. It can take days to make billing software work with other systems. Yet with Moesif + Stripe, you can be ready to bill customers for using your API in a few clicks. You just need to set up your products and prices in Stripe, using the system’s intuitive interface. Prices can be set to recur, with monthly billing periods and metered usage. You can also add descriptions to your prices, for smoother ongoing usage.This rapid implementation process is important for small businesses, which often need to control costs tightly and maximize efficiency when it comes to using resources – including staff time. Instead of billing software posing a major engineering headache, it becomes a matter of routine.Implementing this kind of billing software puts your API business in a strong position to get paid on time. Customers’ bills are based on accurate usage data, which they can monitor throughout the billing period courtesy of Moesif’s embedded dashboards. This minimizes queries at invoice time, meaning fewer delays to payment. It also means less staff time spent dealing with billing queries – another win when your API-first small business is watching every cent.Automated, behavior-based emails add to the chances of you getting paid in a timely fashion. They allow you to take a proactive approach to ensuring your customers are kept up to date with their usage and reminded of when their bills are due (among other things).When we consider that 52% of small businesses experience late payments, it’s clear to see why it’s so important that the right choice of billing software makes raising an invoice simple.Choose the Right Invoicing and Billing SoftwareWhen you’ve poured your time, skill, and energy into creating API products and growing your business, the last thing you need is your invoicing software solution turning into a drain on your resources and enthusiasm. Why not reach out to Moesif to discuss how our billing solution could help?                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-monetization/api-strategy/Invoicing-and-Billing-Software/",
          "author": "Larry",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "technical-api-debugging-debugging-production-apis-with-postman-and-moesif": {
          "title": "Debugging Production APIs with Postman and Moesif",
          "content"	 : "Debugging APIs can be a challenge for any developer dealing with REST APIs. Trying to create an exact API request, especially for highly complex requests with large bodies and multiple headers, is essential but also tough to do. By using a tool like Postman to create a request for debugging and API testing purposes, you can easily replay an API request with the exact configuration of the original request. This can enable developers to reproduce the scenario when running API tests and debugging in a consistent manner.Moesif can also help to make the process of debugging and running API tests against your REST APIs easier as well. Since Moesif receives data around every aspect of a request, it makes it a great platform to export all of the details of a request so it can easily be replayed to assist with debugging. In Moesif, you can easily export all of your API call data into a Postman Collection. This helps developers to replicate the calls in Postman automatically by importing the generated Collection.Debugging best practices aim to ensure that the conditions in which you are debugging an error match exactly with the conditions that caused the error. By using Moesif’s export functionalities as part of your debugging and API testing method, you’re guaranteed that the API endpoint and debugger are receiving the same request data that caused the error.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Exporting Events in MoesifTo export calls from Moesif, first, navigate to the Live Event Log screen. Click Create New on the left navigation pane and select Live Event Log.You can choose the calls you wish to export into the Postman Collection. You can easily select the desired calls by checking the boxes next to them, and then export them by clicking on Run in Postman.  By exporting the calls to a Postman API collection, you will have all the necessary components at your disposal to recreate an API call. This includes the body, headers, parameters, and any other relevant elements.After clicking the button labeled Run in Postman, a modal window will pop up, giving you the option to download the collection. Simply click on Download Postman Collection to acquire the collection file in a easy to read json format and save it on your local machine.The Postman API Collection will be downloaded and ready to load into Postman. Let’s check out how to do that!Importing the Collection in PostmanOpen Postman and click the Collections tab on the left side of screen. Click the Import button on the top left.After that, a modal will appear where you can either select the Postman Collection file or drag-and-drop it onto the modal to import it.The collection will be displayed in the Collections pane in Postman.Replaying Requests and DebuggingChoose one of the requests to replay and all the details of the HTTP request will be shown, including the parameters, headers, and body.Click the blue Send button located beside the URL to send a Postman API call which is an exact copy of the web API request you are trying to debug to the desired endpoint. By doing this, your debugger will be loaded with the same data as the error call you are currently debugging since it has been loaded from the exported Postman API Collection.Experience It for YourselfThe next time you encounter an issue while debugging or conducting API tests with your REST API , you can make use of Moesif to easily replicate the HTTP requests that led to the error. No need to worry about missing details by manually inputting the HTTP request into Postman. Simply export the request from Moesif and replay it through Postman to debug your API in the exact same conditions that caused the issue in the first place. Just choose the requests that you need, export them, and start debugging. By doing so, you’ll be able to hit your debugger breakpoint with the same data included in the original request. Debugging REST API errors should be a breeze, and with Postman and Moesif, it definitely is!If you want to enhance your debugging and API testing capabilities, Moesif is the way to go. It lets you effortlessly replicate the HTTP request that caused an error in your REST API code, without any risk of omitting important details. You can then export the requests and replay them in Postman to debug your API under the exact same conditions that led to the issue. With Moesif, debugging REST API errors becomes a breeze. And while you’re at it, be sure to explore our other amazing features like alerts, embedded templates, and our latest billing features that can help you monetize your APIs. Sign up or log in to Moesif now and take your API game to the next level!                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-debugging/Debugging-Production-APIs-with-Postman-and-Moesif/",
          "author": "Dylan",
          "categories": "Technical, API-Debugging"
        }
      
    ,
  
    
        "api-monetization-api-product-management-saas-billing-best-practices-what-you-need-to-know": {
          "title": "SaaS Billing Best Practices: Models, Metrics, and Tools in 2026",
          "content"	 : "SaaS billing is the part of running a software business that quietly determines whether the rest of the business works. The pricing model controls who buys; the billing system controls whether they pay reliably; the revenue recognition controls how the finance team books what gets paid. All three are decisions you make early and pay the cost of changing later.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        This guide covers the four SaaS billing models in common use in 2026, the metrics that matter, what to look for in billing software, the basics of revenue recognition, and the new wrinkles that AI and API products introduced over the last 18 months.What SaaS billing actually coversSaaS billing is shorthand for a stack of related capabilities:  Pricing model. What the customer pays for and how the price scales with usage.  Subscription management. Who is on which plan, when their plan changes, how upgrades and downgrades work.  Invoicing. Generating, sending, and tracking invoices.  Payment processing. Collecting payment from the customer’s preferred method.  Dunning and retries. Handling failed payments before they become churn.  Revenue recognition. Booking the revenue in the right accounting period.  Reporting and analytics. MRR, ARR, churn, and the rest of the SaaS metric stack.Some companies build all of this themselves. Most use a combination of a billing platform (Stripe Billing, Chargebee, Recurly, Maxio) and a usage/metering layer that feeds it the right data (Moesif, Metronome, Lago).The 4 SaaS billing modelsFlat-rate subscriptionOne price for one product. Easiest to understand, easiest to forecast, and the least common at modern SaaS scale because it leaves money on the table for high-usage customers and overcharges low-usage ones.  Best for: simple products with predictable usage; early-stage businesses where pricing complexity gets in the way.  Trade-offs: does not capture additional value from heavier users, and does not give a clear upgrade path.Tiered subscriptionMultiple pre-defined plans (Starter, Pro, Enterprise) with increasing features and pricing. The most common SaaS pricing pattern of the last decade.  Best for: products with clear feature differentiation across customer segments.  Trade-offs: customers cluster at tier boundaries; expansion revenue is limited to tier upgrades rather than continuous growth.Usage-based (consumption) pricingCustomers pay for what they use. Per API call, per token, per gigabyte, per active user. Stripe, Twilio, AWS, and (increasingly) the AI model APIs use this model.  Best for: products where consumption is a strong proxy for value, and where customers vary widely in usage intensity.  Trade-offs: harder to forecast revenue; customers face bill-shock risk if they don’t have visibility into their usage; requires real metering infrastructure to bill accurately.Hybrid (subscription + overage)A monthly subscription that includes a usage allowance, with per-unit overage charges above the allowance. The dominant model for AI products in 2026 (e.g., subscription tiers with included tokens, overage at the per-token rate).  Best for: products with usage-based cost structures where customers also want predictability. The compromise that captures most of the benefit of both approaches.  Trade-offs: more complex to implement and explain than either pure model alone.Choosing the right model for your productA decision matrix that holds up across SaaS verticals:            Product shape      Recommended model                  Feature-tier-driven (e.g., project management, CRM)      Tiered subscription              Consumption-heavy (e.g., APIs, AI, storage)      Usage-based or hybrid              Seat-driven (e.g., team collaboration tools)      Per-seat subscription (tiered)              Outcome-driven (e.g., AI agents that close tickets)      Outcome-based pricing (still experimental in 2026)      The 2026 trend is toward hybrid models even for products that started flat-rate or tiered. The reason: usage-driven cost structures (AI inference, infrastructure) make pure subscriptions financially fragile when usage spikes outpace pricing.For the deeper strategic decisions around API and AI pricing specifically, see our API pricing strategy guide.SaaS billing metrics that matterA short list of metrics every SaaS billing system should expose:  MRR / ARR (Monthly / Annual Recurring Revenue). The baseline subscription run-rate.  Net Revenue Retention (NRR). Revenue retained from existing customers, including upgrades, downgrades, and churn. The leading indicator for sustainable growth.  Gross Revenue Retention (GRR). Same as NRR but excluding expansion. Tells you the churn-only picture.  Customer Acquisition Cost (CAC) and CAC payback. How long it takes a new customer to pay back the cost of acquiring them.  Churn (logo and revenue). Customers leaving vs. revenue leaving. Revenue churn is usually less than logo churn because high-revenue customers stick around longer.  ARPU / ARPA (Average Revenue Per User/Account). Customer-base average; trends in this number are early signals of pricing fit.Each of these is a derived metric: the billing system records the source events (invoice issued, payment received, subscription changed, customer churned), and the metric falls out of the math. A billing stack that does not produce these cleanly will produce them inconsistently, which is one of the more expensive mistakes a SaaS company can make.SaaS billing software: what to look forThe market has consolidated around a few patterns:  Billing platforms. Stripe Billing, Chargebee, Recurly, Maxio, Zuora. Handle subscription lifecycle, invoicing, dunning, and revenue recognition. They cover subscription mechanics but rely on a separate layer for detailed usage metering (particularly per-customer LLM-call attribution, which is not part of what these platforms ship).  Usage/metering platforms. Moesif, plus Metronome, Lago, and Orb. Handle the metering side (counting API calls, tokens, GB, active users) and feed billing platforms with the right data. Moesif is the option that pairs natively with the WSO2 API Platform and is the metering layer most enterprises evaluate when LLM and agent traffic is a meaningful share of consumption.  Integrated platforms. A few players (Stripe in particular) cover both layers, though depth on the metering side varies and per-customer LLM cost attribution typically still needs an additional tool.For most SaaS products with significant usage-based pricing, the stack is “billing platform plus metering platform.” For pure subscription products, a billing platform alone often suffices. The integration shape (real-time metering vs. batch sync at invoice time) matters; real-time metering catches problems faster and gives customers in-product usage visibility.What to look for in billing software, in order of importance:  Accurate usage metering if your model includes any usage component  Proration support for plan changes mid-cycle  Multi-currency if you sell internationally  Tax automation (Avalara, Stripe Tax, TaxJar) for global compliance  Dunning automation to recover failed payments  Revenue recognition compliance with ASC 606 / IFRS 15  API-first integration so your application controls the customer experienceRevenue recognition and ASC 606 / IFRS 15 basicsRevenue recognition is the accounting question: when do you book the revenue from a customer’s payment? For SaaS, the relevant standards are ASC 606 (US GAAP) and IFRS 15 (international). Both say roughly the same thing: recognize revenue when the performance obligation is satisfied, not when the cash arrives.For a typical annual subscription paid upfront, that means recognizing 1/12 of the contract value each month rather than the full payment at signing. For usage-based billing, that means recognizing revenue per unit of usage as the usage occurs.The practical implication: a billing system that does not separate billing from revenue recognition produces accounting that doesn’t match the books. Most modern billing platforms handle this, but it is worth verifying explicitly when evaluating tools. Public SaaS companies and any SaaS company on a path to audit need to get this right.Handling failed payments and dunningFailed payments are one of the largest drivers of involuntary churn. Cards expire, fraud rules trigger, and customer banks block recurring charges. Without a dunning process, those customers are lost; with one, most can be recovered.A complete dunning workflow:  Smart retries. Automatic re-attempts at intervals tuned to the failure reason (expired card vs. insufficient funds vs. issuer decline). Smart-retry logic from billing platforms can recover a meaningful share of failed payments without any customer interaction.  Customer outreach. Emails (and, increasingly, in-product banners) inviting the customer to update their payment method.  Grace period. Continued service for a window after the failure (typically 7-14 days) before any access is reduced.  Account state management. Clear progression from “payment failed” to “payment overdue” to “account suspended” to “account cancelled,” with reactivation paths at each stage.Dunning is one of the highest-ROI activities in SaaS billing. A 10% improvement in payment recovery shows up directly in net revenue retention.SaaS billing for AI and API products in 2026This is the part of SaaS billing that has changed the most in the last two years.Per-token re-billing. Products built on top of LLM APIs (OpenAI, Anthropic, Google) pass through their model costs to end customers, often with a margin. The metering question becomes: which customer’s usage drove which underlying token consumption, and how do we attribute and bill it back? This requires per-customer usage tracking at the LLM call level, not just at the application API level.Hybrid models for AI products. Most AI-product subscriptions in 2026 are hybrid: a monthly subscription tier with an included token allowance, plus overage at a per-token rate. The structure handles the bill-shock risk of pure usage-based pricing while still capturing additional value from heavy users.Agent attribution. As AI agents become first-class API consumers, the question “which agent (running on whose behalf) caused this call” becomes a billing question. Some products bill the underlying user; some bill the agent operator; the choice is a pricing-strategy decision that the billing system must support.The stack that handles this in 2026 typically combines a billing platform (Stripe Billing, Chargebee) with a usage/metering platform that understands LLM-call attribution. Moesif’s usage-based billing is built specifically for this pattern.How Moesif handles usage-based billing for API and AI productsMoesif records every API call (including LLM API calls made by your application) with per-customer attribution. The billing meter aggregates usage by whatever dimension matters to your pricing (calls, tokens, MB, active users, custom events) and syncs the totals to your billing provider (Stripe, Recurly, Chargebee, Salesforce) on the cadence you choose.The combination handles three problems most SaaS billing stacks struggle with:  Real-time usage visibility for customers. Customers see their current usage in your product before the invoice arrives, which prevents bill-shock support tickets.  Accurate per-customer attribution for LLM consumption. When your product calls OpenAI’s API on behalf of Customer X, Moesif tracks Customer X as the cost driver, not your aggregate company total.  Hybrid billing models out of the box. Subscription + overage models that would otherwise require custom code are configured rather than built.For the pricing-strategy decisions that drive billing-model choice, refer back to the model comparison earlier in this guide.Next stepsSaaS billing is one of the parts of building a software business where early decisions compound. Pick a pricing model that fits how your product creates value, instrument metering from day one, automate dunning, and verify your revenue recognition is audit-ready before you need it to be.If you are building a SaaS product with API or AI consumption that needs to bill back to customers, start a 14-day Moesif free trial to see per-customer attribution and billing meter sync in action. No credit card required.Frequently asked questionsWhat is SaaS billing? The combination of pricing model, subscription management, invoicing, payment processing, and revenue recognition that runs a SaaS company’s revenue operations.What is the best SaaS pricing model? Tiered subscriptions for feature-driven products, usage-based or hybrid for consumption-driven products (APIs, AI, infrastructure). The right model depends on what creates value for your customers.What is the difference between billing and revenue recognition? Billing is when you invoice and collect cash. Revenue recognition is when you book that revenue in your accounting. For most SaaS subscriptions, revenue is recognized over the subscription period even though the cash arrives upfront.How do I reduce involuntary churn? Implement dunning automation: smart payment retries, customer outreach when payments fail, grace periods, and clear account-state progression. Most billing platforms include this; using it well recovers a meaningful share of failed payments.Do I need separate billing and metering tools? For pure subscription products, a billing platform alone often suffices. For products with significant usage-based pricing (APIs, AI, infrastructure), most stacks pair a billing platform with a dedicated metering platform.How do I bill for AI and LLM consumption? Hybrid models (subscription with included token allowance plus overage) are the 2026 default. Per-customer attribution of LLM costs is a prerequisite; most general billing platforms do not handle this without an additional metering layer.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /api-monetization/api-product-management/SaaS-Billing-Best-Practices-What-You-Need-to-Know/",
          "author": "Matthew",
          "categories": "API-Monetization, api-product-management"
        }
      
    ,
  
    
        "technical-stripe-kong-end-to-end-monetization-with-kong-stripe-and-moesif": {
          "title": "End-to-end Monetization with Kong, Stripe, and Moesif",
          "content"	 : "Many API developers and companies struggle to find ways to easily set up systems to monetize their APIs. Some are simple but not customizable while others are complex and require massive engineering effort to actually get it all running.To make things easier, Moesif created the Billing Meters feature which allows for massive customization with a minimal amount of code and engineering effort.For this example, which can actually be used out-of-the-box, we will use Moesif, Kong, and Stripe to charge users for API usage. For this setup there are a few assumptions:  You have a running instance of Kong (with an endpoint and a route created)  Your Kong instance has key-auth enabled for your endpoint(s)  You have an active Stripe account  You have an active Moesif account  You have installed and configured the Moesif plugin in KongThe setup is pretty simple from the outside. We will create a /register endpoint which:  Registers a user in Stripe  Subscribes that user to a product  Creates a user/consumer in Kong  Registers the User and Company in Moesif  Uses Kong to generate an API keyWe will also create a simple frontend for it which contains a form that registers a user by calling the /register endpoint and then displays the generated API key for the newly registered user.                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free        1 - Create Your Product and Price in StripeThe first step we will take is to create a product and price in Stripe. It’s best to do this step first because then when you integrate Stripe into Moesif you’ll already have some pricing plans for Moesif to pull in. A pricing plan can then be associated with specific billing criteria set up within a Billing Meter in Moesif.First make sure to meet these prerequisites.To create a product and price, log into Stripe and proceed to the Products page in the Stripe UI. Once there, click on the + Add Product button in the top right corner.You’ll then be able to add in the details for your product and price(s) for it. The form for your product will have a few fields to fill out.Product InformationName  This is the name of your product. In the example below, we use the name “My API”.Description  This field is optional but you could put a brief description of the product here. In the example below, we use a description of “This is a monetized API”.Image  Optionally upload an image that can help you easily recognize a item on the Products page. We’ll be using the default placeholder image in this example.Pricing InformationYou can choose between Recurring and One-off pricing for your product.Recurring PricinngIn recurring pricing, your customers pay an ongoing fee according to the pricing model you define. After selecting Recurring, you can enter the amount you want to charge and the billing period.To further configure your recurring pricing, select More pricing options. This allows you to specify the pricing model, amount, billing period, price description, and more.The following pricing models are available in Stripe for recurring pricing:  Flat rate  A fixed price for a single unit or package.  Package pricing  Charge for API usage by the package, or a group of units. For example, you could set it up to charge $10 for every 1000 API calls. Every time the user goes over the 1000 API call threshold, they are charged another $10.  Tiered pricing  Offer different price points for based on unit quantity.  Usage-based  Pay-as-you-go billing based on metered usage. You can charge per package, per unit , or per tier and define the prices and units accordingly. You can also set up a billing meter for the price to meter usage. See Creating a Product and Price in Stripe for instructions on how to set up a usage-based scheme.One-Off PricingIn one-off pricing, you charge a one-time fee rather than recurring amount in each billing period. After selecting One-off, you can enter the amount you want to charge in the Amount field.Similar to recurring pricing, you can select More pricing options and configure your pricing further by specifying the pricing model, amount, price description, and more.The following pricing models are available for one-off pricing:  Package pricing  Charge for API usage by the package, or a group of units. For example, you could set it up to charge $10 for every 1000 API calls. Every time the user goes over the 1000 API call threshold, they are charged another $10.  Flat rate  A fixed price for a single unit or package.  Customer chooses price  The customer sets a custom price. You can set a limit and define a preset amount to suggest to the customer.Billing periodThe billing period can be set as the following for recurring pricing models:  Daily  Weekly  Monthly  Every 3 months  Every 6 months  Yearly  CustomFor your configuration with Moesif, we recommend setting the billing period as Monthly.Price descriptionThis is an optional field but recommended. Here you can put a brief description of your price. This will allow you to more easily decipher which price you are selecting in the billing meter in Moesif, especially if you have multiple prices for a single product.Once you’ve input all of the details for your price, you can select Next and then select Add productAs you create products, you will be able to view and edit them on the Product Catalog screen.2 - Enable the Moesif-Stripe IntegrationOnce your products and prices are created, it’s time to begin to integrate Stripe with Moesif. To begin configuring Stripe in Moesif, go to the Billing Meters page and click the Edit Billing Provider dropdown in the top right corner of the screen.This will bring up the Stripe configuration screen walking you through the integration. From this screen, you can get all of the info needed to plug Stripe into Moesif. Each step for configuration is covered within the modal.Add the Moesif Webhook to StripeThe first step in the integration is to add the Moesif webhook into the configuration in Stripe. Adding this allows Stripe to send subscription updates to Moesif.To add the Moesif webhook to Stripe, from the upper right-hand side click on Developers, and then Webhooks in the left-side menu. This will bring you to the Webhooks page where you can view existing webhooks and add new ones. To add a new webhook we will click the Add an endpoint button at the bottom of the screen.From here, we will plug in our Moesif API endpoint URL and configure the events to listen to. You’ll want to copy your Moesif Webhook URL into the Endpoint URL field and then click the + Select Events button.  These details can all be found on the Stripe configuration page in Moesif mentioned in the previous section.You should select the option under Customer for Select all Customer events. After this, click the Add events button at the bottom of the screen.After this, you’ll be returned back to the original screen where you added the endpoint details. Scroll to the bottom of the screen and click Add endpoint to save the endpoint to Stripe.Plug the Stripe API Details into MoesifFor Moesif to add usage quantities to subscriptions in Stripe, we need to add the Stripe API details into Moesif. This is done in the Stripe configuration screen in Moesif, the same screen we’ve been working with previously.Currently, Moesif only supports version 2020-08-27 of the Stripe API so that field defaults for the Stripe API Version field.For the Stripe API Key field, you’ll need to retrieve the API key from Stripe to plug it in. From the Developers screen, the same one we used in the previous step, you’ll click on API Keys. You’ll then be able to see the private key for your API in either the Secret key or a generated Restricted keys field on the screen. Either key can be used.After copying the key from Stripe, you’ll paste this key into the Stripe API Key field back in Moesif. After doing this, back in Moesif you can scroll down to the bottom of the screen and click Save to save the configuration.Optionally, you have the ability to customize the company_id in Moesif as well. The default should work fine for most purposes. However, you can fully customize it to specify how to map Stripe Subscription and Customer objects to Subscription ID and Company ID in Moesif respectively:  A Stripe Customer maps to Moesif Company.  A Stripe Subscription maps to a Moesif Subscription.3 - Create a Billing MeterOnce you have the Stripe integration active in Moesif, you can begin to set up your billing meter. Billing meters created in Moesif do two things: track usage based on specific criteria and report that usage to the billing provider. Moesif allows you to set up very simple and very complex billing meters with relative ease.To create the Billing Meter, in Moesif you will navigate to the Billing Meter screen. You can do this from the left-side menu. On the Billing Meter’s screen, you’ll then click + Add Billing Meter in the top-right corner of the screen.The next screen is where you can actually input the criteria for your Billing Meter.Fields on this screen include:      Billing Meter Name          This is the Moesif internal name of your new Billing Meter            Billing Provider          In this dropdown you can choose the billing provider you want to send your usage metrics to.            Product (Stripe only)          Here you can choose which Product that you’ve set up in Stripe you want your usage metrics to be tied to.            Price (Stripe only)          The last field in the Billing Provider settings for the Billing Meter, here you will choose which Price you want to tie your usage metrics to.            Filters          Under the Filters configuration, you will configure your billing criteria to only include requests that fit a certain criteria.            Metrics          Here you can choose which metric you would like to bill on. Available options include:            Event Count          This will increment usage for every event that fits the criteria outlined in the Filter criteria.            Unique Users          This will increment usage whenever a unique user sends a request that fits the Filter criteria. For every unique user, the count will be incremented by 1 regardless of the event count for that user.            Unique Companies          This will increment usage whenever a unique company sends a request that fits the Filter criteria. For every unique company, the count will be incremented by 1 regardless of the event count for that company.            Unique Sessions/API Keys          This will increment usage whenever a unique session or API key is used to send a request that fits the Filter criteria. For every unique session or API key, the count will be incremented by 1 regardless of the event count for that particular session or API key.        There are other options under Metrics as well but the above 4 tend to be the most applicable to usage-based billing.As an example, for this guide we will create a Billing Meter that will filter traffic for a single endpoint, named /test-service, and where requests received a successful HTTP 200 response. We will use the Event Count metric to make sure that every request is added to the tally and sent to the billing provider.In Moesif, the billing meter will be configured as shown below.We will then click Create. This will create and activate the Billing Meter. A modal will appear notifying you that the billing meter has been created and presents a walk-through to ensure the meter is correctly configured.First, we will set up a flow to get users registered, subscribed, and create a JWT so they can use our monetized API. Once that is complete we will come back and proceed with the walk-through.4 - Create the /register endpointInstead of using a pre-built onboarding flow, such as through a Developer Portal within an API gateway, we will build our own. We will create an endpoint called /register which we can then use to onboard our users who want to use the API. The result will be that the user receives an API key that they can use that will track their usage.Since we are using Moesif, Stripe, and Kong as part of our overall solution, we need to make sure each of the components is working together properly.Here’s what the endpoint will do:  Create a user in Stripe  Subscribe the new user to the API subscription in Stripe  Create the user/consumer in Kong (with the Stripe Customer ID as the custom_id field)  Create the CompanyID in Moesif (which will be the Stripe subscription ID)  Create the UserID in Moesif (which will be the Stripe Customer ID)  Create an API key  If you already have User and Company identifiers in Moesif and other systems that you want to use, instead of using Stripe’s customer and subscription as your IDs, you can do that in Moesif under the Stripe configuration settings.In this example, I will create a simple NodeJS API with Express to do the above.Create the npm projectFirst, we will create a folder called moesif-monetization where we will add our API code. We will then run npm init to turn moesif-monetization so we can use npm in our project. For that, you’ll run the following command in the moesif-monetization directory.$ npm init  You can fill out the details or use the defaults as needed when creating the npm project.You should then see a package.json file in your moesif-monetization folder.Now, open this directory in your favorite IDE or text editor. I will be using VS Code for the remainder of this tutorial for all the coding.Add in the project dependenciesWe will now edit our package.json file with our correct dependencies. In the package.json we will add the following entries under the dependencies object."dependencies": {  "@stripe/stripe-js": "^1.29.0",  "body-parser": "^1.20.0",  "dotenv": "^16.0.0",  "express": "^4.17.1",  "http": "0.0.1-security",  "moesif-nodejs": "^3.5.8",  "node-fetch": "^2.6.5",  "path": "^0.12.7",  "stripe": "^8.219.0"}Save the file, then navigate to the terminal and run:$ npm installNow, your dependencies will be brought into the project and added to the node_modules folder. These dependencies will help us to make calls to REST endpoints, connect to Stripe and Moesif, and various other capabilities we will build into our app.Create the .env fileInstead of hardcoding the Stripe keys and other static values into our app, we will abstract them into a .env file. We can do this using the dependency we added in our package.json called dotenv.In the root directory, create a file named .env. Within this file, we will add a few entries that will contain the keys and values used in our code.STRIPE_KEY="sk_test_XXX"STRIPE_PRICE_KEY="price_XXX"MOESIF_APPLICATION_ID="YOUR_MOESIF_APP__ID"KONG_URL="http://KONG_URL:8001"The values that are here can be found in the following places:Obtaining Your Stripe API and Price KeyYour Stripe API Key can be found in the same place we grabbed the key for our Stripe and Moesif integration we did earlier for the Billing Meter. You can actually use the same key for both or create a restricted key with just the scope needed for each function.While we’re at it, lets grab our Stripe product’s price key. We will need it in the next section. Your Stripe price key is an identifier for the price you created earlier in Stripe. This can be found by going to the product in Stripe and grabbing the value from the API ID column.Obtaining Your Moesif Application IDYour Moesif application ID is found in Moesif by going to the menu link in the bottom-left of the screen (which will show your name) and selecting API Keys.The key will then be on the page that appears under Collector Application Id.Your KONG_URLThis will be the URL of the Kong Management API. Usually, this will be hosted on port 8001 but it may be different if you have a custom configuration.Once you’ve populated the file with the four key-value pairs, save the file. We won’t need to touch this file again for the remainder of the tutorial.Create the app.js fileIn the root directory of our app, we will create an app.js file (if not already created). In this file we will add the following code that adds our dependencies and creates a base REST endpoint.require('dotenv').config();const express = require('express');const path = require("path");const bodyParser = require('body-parser');const moesif = require('moesif-nodejs');const Stripe = require('stripe');// npm i --save node-fetch@2.6.5const fetch = require('node-fetch');const app = express();const port = 5000;const stripe = Stripe(process.env.STRIPE_KEY);var jsonParser = bodyParser.json();const moesifMiddleware = moesif({ applicationId: process.env.MOESIF_APPLICATION_ID});app.use(moesifMiddleware);app.post('/register', jsonParser, async (req, res) => { })app.listen(port, () => { console.log(`Example app listening at http://localhost:${port}`);})In the above code we are:  Importing a few dependencies  Configuring the Stripe dependency  Configuring the Moesif middleware  Create the /register endpoint  Set our app to run on port 5000 and start our Node app  You will notice that we have process.env.STRIPE_KEY and process.env.MOESIF_APPLICATION_ID. These values will come from the .env file that we created in the last step.Implement the /register endpointOur next step is to implement the /register endpoint. This endpoint will essentially create our binding between Kong, Stripe, and Moesif. The outcome will be a generated API Key from Kong which will associate usage with a user in Moesif, which will then be reported to Stripe.Our first step in the flow is to create the customer in Stripe. We will use our Stripe JS dependency to do just that. We will use the parameters from the request body (email, first name, last name) to create the customer in Stripe using the stripe.customers.create function. We will then store the created customer in a custom variable so we can access the customer ID generate in Stripe.const customer = await stripe.customers.create({     email: req.body.email,     name: `${req.body.firstname} ${req.body.lastname}`,     description: 'Customer created through /register endpoint',   });Next, we will subscribe this new user to our API subscription we created in Stripe earlier. We will use the stripe.subscriptions.create function and use the generated customer ID from the previous function call to subscribe them. This will return back a subscription object containing an ID we will use later.   const subscription = await stripe.subscriptions.create({     customer: customer.id,     items: [       { price: process.env.STRIPE_PRICE_KEY },     ],   });Then, we will create the consumer in Kong, allowing us to generate a key for this user. We will POST to Kong’s /consumers endpoint through the Kong admin APIs. This will generate a user using the supplied email as their username and the Stripe customer.id as the custom_id field in Kong.   var body = { username: req.body.email, custom_id: customer.id };   var response = await fetch(`${process.env.KONG_URL}/consumers/`, {     method: 'post',     body: JSON.stringify(body),     headers: {'Content-Type': 'application/json'}   });   var data = await response.json();Once the user is created in Kong, we will now use the Moesif middleware to create the user and add their relevant details into Moesif. First we will call the Moesif middleware’s updateCompany function to map the Stripe customer.id to the companyId in Moesif.  var company = { companyId: customer.id };  moesifMiddleware.updateCompany(company);We will then do a similar step with the updateUser function and use it to map the Stripe customer.id to the userId and companyId and some other metadata we collected on the user into Moesif. This will link the user to the company as well.  var user = {     userId: customer.id,     companyId: customer.id,     metadata: {       email: req.body.email,       firstName: req.body.firstname,       lastName: req.body.lastname,     }   };   moesifMiddleware.updateUser(user);At this point, we now have all of our user details plugged into the necessary platforms. Our next step is to generate an API key for this user. For that, we will call the Kong Management API again using the /consumers/{user}/key-auth endpoint. This will return an API key to us for that user in the response. We access that API key through data.key.  var response = await fetch(`${process.env.KONG_URL}/consumers/${req.body.email}/key-auth`, {     method: 'post',   });   var data = await response.json();   var kongAPIKey = data.key;Optionally, we will add the users API key to our metadata in Moesif as well. For testing purposes, this makes it easy to get the key if you’ve lost track of it and don’t want to go back into Kong to retrieve it. We will use the updateUser function again in the Moesif middleware to do this, supplying the generated API key in the metadata object.   var user = {     userId: customer.id,     metadata: {       apikey: kongAPIKey,     }   };   moesifMiddleware.updateUser(user);Lastly, we will return a 200 OK response back to the caller with the API key in the response body.   res.status(200);   res.send({ apikey: kongAPIKey });The completed function, end-to-end, will look like this:app.post('/register', jsonParser, async (req, res) => {    // create Stripe customer    const customer = await stripe.customers.create({      email: req.body.email,      name: `${req.body.firstname} ${req.body.lastname}`,      description: 'Customer created through /register endpoint',    });    // create Stripe subscription    const subscription = await stripe.subscriptions.create({      customer: customer.id,      items: [        { price: process.env.STRIPE_PRICE_KEY },      ],    });    //create Kong consumer    var body = { username: req.body.email, custom_id: customer.id };    var response = await fetch(`${process.env.KONG_URL}/consumers/`, {      method: 'post',      body: JSON.stringify(body),      headers: {'Content-Type': 'application/json'}    });    var data = await response.json();    // create user, company, and subscription in Moesif    var company = { companyId: customer.id };    moesifMiddleware.updateCompany(company);    var user = {      userId: customer.id,      companyId: customer.id,      metadata: {        email: req.body.email,        firstName: req.body.firstname,        lastName: req.body.lastname,      }    };    moesifMiddleware.updateUser(user);    // send back a new API key for use    var response = await fetch(`${process.env.KONG_URL}/consumers/${req.body.email}/key-auth`, {      method: 'post',    });    var data = await response.json();    var kongAPIKey = data.key;    // add API key to users metadata in Moesif    var user = {      userId: customer.id,      metadata: {        apikey: kongAPIKey,      }    };    moesifMiddleware.updateUser(user);    res.status(200);    res.send({ apikey: kongAPIKey }); })With that, we can now actually try out our endpoint to make sure that each piece is working as expected. The outcome should be a registered user with an API key which will record and report usage data to Stripe. Let’s move onto testing it.5 - Send a test request to the /register endpointOnce your /register endpoint has been coded and deployed, it’s time to test it. For right now we will simply use Postman to send a request. Our request will contain a JSON request body that will contain a:  First name  Last name  EmailOf course, this is the minimal amount of information we would want to configure our system and profiles in Kong, Stripe, and Moesif correctly.You can easily add more fields as needed for your specific use case.In Postman, we will create our request with the following information:  Request Type: POST  Endpoint URL: http://localhost:5000/register  Request Body:    {  "firstname": "Userfirstname",  "lastname": "Userlastname",  "email": "test@test.com"}      Once everything is plugged into Postman, it should look like the following:Once the request is sent, the response should contain an API key that the newly registered user can use.We will now check Kong and Stripe to ensure that the information we registered with is correctly entered into each of the destination systems. The first one we will check is Stripe.Logging back into Stripe, you’ll navigate to Customers screen. You should see your newly created user in the list.Click on the newly added customer in the list. On the next screen, you should see that the customer is also subscribed to your APIs subscription.Once these two entries are confirmed in Stripe, you can move over to Kong to also make sure that the user was correctly set up there too.Using Kong Manager, navigate to the Consumers screen. Here you should see an entry where the Username and Custom_ID fields are set to the supplied email and the users Stripe customer ID, respectively.Clicking on the entry will bring you to the Consumer Details screen. Once here, clicking on the Credentials tab will show you the generated API keys for this user. The API key shown should match the one returned in Postman.  You can also use the Kong admin APIs to retrieve this information too if you are not using Kong’s Enterprise EditionWith these checks completed, we can safely assume that our /register endpoint is correctly setting up our users accounts and subscriptions in Kong and Stripe.6 - Call your API using the generated API keyOur next step is to actually use our generated API key. We will then confirm that all the correct information is added into Moesif. The data we are confirming includes:  The Stripe Customer ID is mapped to the Moesif User ID  The Stripe Subscription ID is mapped to the Moesif Company ID  Moesif contains the Stripe metadata in the users profileUse Postman to send the requestNext, let’s use Postman, or another platform, to send a request to the /test-service endpoint. This is the endpoint that we set up the billing meter for in Step 3, above.In Postman, we will:  Put the /test-service API endpoint as the request URL  Select the Authorization tab  Select the Type as API Key  Populate the API key details          Set the Key as “apikey”, or whatever you have the Key set to in Kong      Set the Value as the generated API key      Set the Add to field value to “Header”      Below is an example of the populated request configuration in Postman.To send the request to our endpoint, click Send.Once sent, your request should be proxied through Kong and the API call analytics should land in Moesif.Confirm that Moesif received the request infoBack in Moesif, you’ll navigate to the Events screen where you should see the request you just sent. You should see the entry has both a User ID and Company ID populated with the Stripe user and subscription ID’s. The entries should look like this:  The customer ID will look like “cus_XXXX” and the subscription ID will look like “sub_XXXX”.If you click on the User ID shown in the entries on the Live Event Log screen, you will come to the users profile page. On this page, we will confirm that the Stripe metadata is present. We will need to add a new column to our profile to display the Stripe data. To do this, from the profile page, click on the … More Actions button and click Customize Profiles’ Layout.We will then add a new column for the Stripe metadata. You will click the + button on the far right of the screen to create a new column where we will add the Stripe metadata.  You may need to scroll to the right to see it depending on your resolution and screen size to see the + button.You will then drill down to Metadata > stripe > customer > created and use this field in the new row. I’ve also changed the column image to one more fitting. You can customize this by clicking on the image and selecting whichever one fits best.You can also add other fields, but for right now just this single field is enough to tell us that Moesif is correctly receiving data from Stripe.  If you don’t see the Stripe metadata entry as an available field, wait a few minutes. If after a few minutes the Stripe metadata isnt present, ensure that your Stripe configuration is correct in Moesif. After confirming or editing it, try creating a new user and sending a request again to confirm that the integration is working.At this point, we now have confirmed that our API call, proxied through Kong, is working and is stamped with the correct user and company details in Moesif. We also confirmed that Stripe is sending data back to Moesif which is correctly being mapped to the corresponding user profile, confirmed through the Stripe metadata in Moesif.7 - Create the frontendNext, we want to add a simple little frontend so we don’t need to call for our API key through Postman. We will make a quick little registration form that will then return an API key for our newly registered user to use.Add your frontend files to the appIn the root directory of the application, we will add two files. We will add both an index.html and an index.js.Add in your route to serve the static html filesIn the app.js file, we will add in a route to serve the static HTML files. Underneath our code for the /register endpoint, we will add another endpoint. Add the following code:pp.get("/", function (_req, res) { res.sendFile(path.join(__dirname, "index.html")); res.sendFile(path.join(__dirname, "index.js"));});This code will now load the website (once we have the code plugged in) when you navigate to http://localhost:5000/ .Code the frontend form and logicFinally, let’s add the code for our frontend HTML and JavaScript functionality. In the index.html file, we will add markup that looks like this:<!DOCTYPE html><html lang="en"> <head>   <meta charset="utf-8" />   <meta name="viewport" content="width=device-width, initial-scale=1" />   <meta name="theme-color" content="#000000" />   <meta     name="description"     content="Moesif Embedded Dashboard example"   />   <title>None React App Example</title> </head> <body>   <noscript>You need to enable JavaScript to run this app.</noscript>   <h1>     Moesif Monetization Example   </h1>   <div id="form-input">     email: <input id="email-input" placeholder="email" />     first name: <input id="firstname-input" placeholder="first name" />     last name: <input id="lastname-input" placeholder="last name" />     <button onClick="submitRegistration()">Register</button>   </div>   <div id="apikey-output"></div>   <p id="error-message"></p>   <script src="index.js"></script> </body></html>his markup will display a form which allows users to input an email, first name, and last name. It also has a Register button that will call the JavaScript function for submitRegistration(). That function will be in our index.js JavaScript file.The index.js file will look like this:async function submitRegistration() {  const email = document.getElementById("email-input").value;  const firstname = document.getElementById("firstname-input").value;  const lastname = document.getElementById("lastname-input").value;  const errorElement = document.getElementById("error-message");  const apikey = document.getElementById("apikey-output");  var body = { email, firstname, lastname };  console.log(body);  var response = await fetch('http://localhost:5000/register', {    method: 'post',    body: JSON.stringify(body),    headers: {'Content-Type': 'application/json'}  });  var data = await response.json();  console.log("Kong create consumer");  console.log(data);  apikey.innerHTML = data.apikey;}This function will take the input from the form, post it to our /register endpoint, and display the returned API key on the screen.8 - Test the frontendTo test the frontend, save your code changes and restart the server. Then, in a browser, navigate to http://localhost:5000/. You will then see the form show up.Fill out the form fields and submitNow that the form is loaded, fill in the fields and click the Register button. This will take the info, post it to our /register endpoint, and give us the generated API key.  It is suggested that you use a different email than you used earlier when you created an API key directly through the /register endpoint.Confirm the API key is returnedOnce the submit button is clicked, after a few seconds, the API key should be returned back to the UI.9 - Send a request to your monetized APIWe will once again want to make sure that everything is working with our UI, through to our backend systems. For this, simply repeat the steps from Step 6 to confirm that the user and company IDs are populated correctly and that the Stripe metadata is returned for this user and the new API key.10 - Confirm all the pieces are working correctlyAlthough this is optional, this step may help with troubleshooting any issues that may have came from our previous steps. Here are a few things to check to make sure that all is working as it should. After creating a new user through the UI and using the generated API key to place a call to your API, confirm the following:  In Stripe          Confirm that a customer has been created in Stripe with the details you entered into the UI      Confirm that the customer has been subscribed to the correct product and price        In Kong          The consumer has been created      The consumers custom_id field matches the customer ID from Stripe (which begins with cus_XXXX)        In Moesif          Your API call was recorded in Moesif in the Live Event Log      Your API call has the Stripe Customer ID and Subscription ID in the User and Company fields in Moesif, respectively.      Confirm that the Stripe metadata is populated in Moesif      11 - Check Stripe for usageLastly, After a few hours, it’s best to go into Stripe to confirm that usage is being added to a users subscription. Be sure that you’ve sent a few requests through in order to make sure you have some data that should be sent to Stripe.  it may take a few hours for usage to make its way from Moesif to Stripe. If data still isn’t in Moesif after a few hours, ensure you’ve followed all the steps outlined within this guide. This includes making sure that your user and company ID’s from Moesif are correctly mapped to the corresponding keys in Stripe.To check the usage, in Stripe you’ll want to navigate to the Customers screen and select the customer that you made the API call with. Once selected, you should see some active subscriptions for the users that you’ve registered through the /register endpoint. The one we created earlier is called My API. Click on the subscription entry.On the next screen, click on View Usage beside the price entry.A modal should now pop up showing you the usage for the API that has been reported to Stripe from Moesif.  Remember, it may take up to an hour before usage is reported to Stripe. If you data isn’t there yet, check back a bit later for the updates.12 - Determining If the Billing Meter is Working CorrectlyTesting the created Billing Meter is easy with out Test Meter function. Navigate to your created Billing Meter from the left side navigation pane and selecting your Stripe Test billing meter. Select Test Meter on the top right.We will first confirm the meter that you are attempting to test. Click the Next button at the bottom of the modal.Moesif will wait for Subscriptions to created within Stripe and those subscriptions to be associated within Moesif itself. This page will update automatically, no need to refresh.Moesif will then wait for an API call to our any endpoint associated with our billing meter using our the JWT that has been created for us.Finally, Moesif will sync all usage data to Stripe every 15 minutes. This step may take a few minutes depending on when the API call was initiated but will update on the given interval.Wrapping upMonetization has always been a tough hurdle to get past. Many custom solutions offered flexibility but at a very high engineering and support cost. With Moesif, API monetization is possible in an extremely minimal amount of time. As demonstrated in this article, With a little bit of configuration and minimal amount of code we can create a production-ready, post-paid monetization scheme in minimal time.                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue with your Kong managed APIs?            Monetize your Kong managed APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/stripe/kong/End-To-End-Monetization-With-Kong-Stripe-And-Moesif/",
          "author": "Matthew",
          "categories": "technical, stripe, Kong"
        }
      
    ,
  
    
        "technical-stripe-enabling-api-monetization-with-moesif": {
          "title": "Enabling API Monetization with Moesif",
          "content"	 : "As an API provider, you probably picture  bringing in some type of revenue as part of your API business model. When you first begin the journey of monetizing your API product, you’ll likely find that the complexities of monetization run deep. Much of the time, solving this problem extends beyond the capabilities of a typical API gateway or API management platform. When thinking about how to recover revenue from API usage, questions begin to pop up, such as “will you charge per API call or per user?” or “how do I block API users from using an API if they have an overdue invoice?”. To solve these issues, a lot of customization, testing, and support is required. Luckily, with Moesif, it’s extremely simple to create a smooth journey for users with end-to-end monetization offered through the platform. Moesif can be used as an API monetization platform that offers a massive amount of flexibility. It also offers other tools that can supplement your user journey, drive API adoption, and improve your API product. Implementing usage based pricing can diversify your revenue stream and allow for new, direct monetization campaigns.Let’s take a look at how you can use Moesif to bring in revenue by implementing an API revenue model with Moesif’s API monetization capabilities.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Billing ProvidersDepending on your exact use case, you may have a preference towards which billing provider will best fit your needs. Currently, Moesif supports 3 different billing providers. The billing providers include:  Stripe  Recurly  ChargebeeEach of these platforms has its own advantages and disadvantages. To explore more about that, check out this great article which goes over all the intricacies of each platform. Depending on your API monetization model, you may find that one or more of these platforms will support your use case.With Moesif, these billing providers are connected to Moesif so that usage data can be sent to the provider and so that the provider can also send data back to Moesif, such as invoice details or subscription updates. By using Moesif to send billing data to the provider and receive data back, Moesif can play a pivotal role in the overall revenue leakage prevention of your APIs and allow for easy configuration and maintenance/troubleshooting.Billing MetersOnce you’ve connected Moesif to a billing provider, you will have the ability to create billing meters. Billing meters do two main things:  Let you create a filter that will monitor and tally up API usage  Send the usage of your API resources to the selected billing providerMoesif is the optimal spot to initiate your billing flow since it has all of the data from your API calls as well as the capabilities to create sophisticated criteria for billing.To create a billing meter, simply log into Moesif and navigate to the Billing Meter screen from the left-side navigation. From there, click on the Add Billing Meter button.On the next screen you can set up the filter you would like to use to specify which traffic you’d like to bill on. For example, we could set up a filter for a specific endpoint and status code, such as only billing for API calls to the /myservice endpoint that receive a 200 OK response status code. You’ll also select the metric to bill on at this point. We will choose Event Count for this one so that every single API call will be counted as one unit but other options are also available including billing by Unique Users, Response Body Count, or using a Custom Metric.  You’ll also need to make sure to have the billing providers configured in Moesif. This can be done in as little as 5 minutes! Curious about how easy it is? Check out our guides on how to do it with Stripe, Recurly, and Chargebee.Once the Billing Meter is created, API usage will then be automatically synced over to the Billing Provider you’ve selected for the billing meter. In a matter of minutes, you’ll have monetized APIs that leverage metered billing through Moesif.For more indepth examples, check out our end-to-end guides using Kong, Tyk, and Node.Governance RulesMonetizing APIs is one thing but controlling access based on billing status is another. In general, best practices dictate communication with your users to keep them aware of their API consumption levels. If a user has an overdue invoice or has exceeded their approved spend, you may want to suspend their access. This can be done in Moesif using Governance Rules.Governance Rules are easy to set up. Simply navigate to the Governance Rules screen and click Add New.We will then add a new User Rule.On the next screen, will configure the rule to block users who belong to the Users with Unpaid Invoices cohort. We will return a response with a 402 - Payment Required response code and a Response Body that outlines the problem.Once this Governance Rule is created, it will effectively block the users from accessing the API until their invoice is settled. For Pre-paid scenarios, you could also use a Governance Rule to block calls when a user’s credit balance reaches $0. To look at this pre-paid scenario in more detail, check out our blog on pre-paid billing with Moesif.  2 important notes when it comes to Governance Rules in Moesif. Firstly, Governance Rules are an enterprise plan feature so you will require an enterprise subscription. Secondly, not all SDKs and Plugins support governance rules. Please verify that your specific setup and plan will support governance rules before trying to implement them.Behavioral Emails and AlertsAlthough optional, using Moesif’s behavioral email and alert capabilities can augment the experience your users have with your monetized APIs. Both of these features use a customizable filter to establish the conditions in which the email or alert should be sent.Behavioral emails can be used to guide users as they sign up for your APIs, notify them of issues that may be occurring and how to fix them, or in the case of a monetized API, let a user know that they have an outstanding invoice that is blocking their API access.As an example, we can set up a behavioral email that will send when an API consumer tries to access an API with an unpaid invoice. To do this, navigate to the Behavioral Emails screen and select Create Template.From here we will fill out the details and create a simple email letting the user know that their API access has been suspended due to an outstanding invoice and how to pay it.Once the email is created, whenever a user becomes part of the Users with Unpaid Invoices cohort, they will automatically be sent an email to help them regain access to the API.Alerts can be used to alert internal teams about events such as users experiencing a high number of errors or issues with integration. In terms of API revenue though, you could go further to use alerts to let sales and customer success teams about certain conditions that might require outreach or allow for a possible upsell opportunity.Going along with the example from above, we could also alert the Customer Success team (or the accounting team, pending they are responsible for following up on overdue accounts) to let them know that a customer may need assistance settling an overdue invoice. To create an Alert, navigate to the Alert Rules screen and select + Alert Rule.From here we will configure the alert to send to our support teams email whenever someone with an unpaid invoice tries to access the API more than 5 times in a 15-minute time period.Once the alert is activated, our customer success team will then be notified. Of course, there are also ways to alert through SMS, Slack, PagerDuty, or custom Webhook configuration. More information on Alert Rules can be found in the docs.Try it out!Implementing a monetization strategy has always been a tough hurdle to get past. Many custom solutions offered flexibility but at a very high engineering and support cost. With Moesif, setting up an API monetization model to generate additional revenue is possible in an extremely minimal amount of time. As demonstrated in this article, With a little bit of configuration and a minimal amount of code we can create a revenue generating API in minimal time.On top of that, supporting customers is even easier with Governance Rules, Behavioral Emails, and Alerts to guide and give any customer a great experience. Moesif is the most flexible and easy-to-use end-to-end API monetization product on the market. Wanna give it a try? Sign up today to create a pricing model for your APIs and building your API revenue in a matter of minutes.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/stripe/Enabling-API-Monetization-With-Moesif/",
          "author": "Matthew",
          "categories": "technical, stripe"
        }
      
    ,
  
    
        "technical-api-development-building-minimal-api-with-dotnet": {
          "title": "Building a RESTful Minimal API with .NET Core 7",
          "content"	 : ".NET Core and ASP.NET Core are popular frameworks for creating powerful RESTful APIs. In this tutorial, we will use it to develop a simple Minimal API that simulates a credit score rating. Minimal APIs provide a streamlined approach to creating high-performing HTTP APIs using ASP.NET Core. They allow you to construct complete REST endpoints with minimal setup and code easily. Instead of relying on conventional scaffolding and controllers, you can fluently define API routes and actions to simplify the development process.We will create an endpoint allowing a user to retrieve a credit score rating by sending a request to the API. We can also save and retrieve credit scores using POST and GET methods. However, it is essential to note that we will not be linking up to any existing backend systems to pull a credit score; instead, we will use a random number generator to generate the score and return it to the user. Although this API is relatively simple, it will demonstrate the basics of REST API development using .NET Core and ASP.NET. This tutorial will provide a hands-on introduction to building RESTful APIs with .NET Core 7 and the Minimal API approach.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        PrerequisitesBefore we start, we must ensure that we have completed several prerequisites. To follow along and run this tutorial, you will need the following:  A working .NET Core installation  An IDE or text editor of your choice  Postman to test our endpointCreating the Initial ProjectWe’ll be using the .NET cli tool to create our initial project. The .NET command line interface provides the ability to develop, build, run, and publish .NET applications.The .NET cli new command provides many templates to create your project. You can also add the search command to find community-developed templates from NuGet or use dotnet new list to see available templates provided by Microsoft.We’ll be creating a Minimal API and starting from as clean a slate as possible. We’ll be using the empty ASP.NET Core template. In the directory of your choosing; enter the following in the terminal:dotnet new webYou’ll notice that the directory structure will look something like this:We’ll be doing all of our work in the Program.cs file. Its starting code should look similar to the following:var builder = WebApplication.CreateBuilder(args);var app = builder.Build();app.MapGet("/", () => "Hello World!");app.Run();We can see how concise and readable our starter code is. Let’s break down the code provided by the template line by line:  The WebApplication.CreateBuilder(args) method creates a new instance of the WebApplicationBuilder class, which is used to configure and build the WebApplication instance. The args parameter is an optional array of command-line arguments that can be passed to the application at runtime.  The builder.Build() method is called to create a new instance of the WebApplication class, which represents the running application. This instance configures the application, defines routes, and handles requests.  The third line defines a route for the root path (“/”) of the application using the app.MapGet() method. This means that when the root path is requested, the application will respond with the string “Hello World!”.  We start the application by calling the app.Run() method.Using the builder pattern, we can configure and customize the WebApplication instance. This allows us to define the application’s behavior, including middleware, routes, and other settings, in a structured and extensible way. For example, the WebApplication instance created by the builder can be thought of as the “entry point” of the application, which handles requests and generates responses.Overall, this code block creates a simple Minimal API in .NET 7 that responds with a “Hello World!” message when the application’s root path is requested.Next, we’ll customize our API to mimic retrieving a credit score rating.Adding in the CodeIn Program.cs, we will house our endpoints and business logic. We’ll define our creditscore endpoint to provide GET and POST operations. We’ll implement a list to store any credit score we would like. We’ll also define an endpoint to retrieve the list of saved credit scores. We’ll be utilizing a CreditScore record, a new reference type in C# 10 similar to structs. A record is a lightweight and immutable data object optimized for comparison and equality checking.Populate Program.cs with the following code:var builder = WebApplication.CreateBuilder(args);var app = builder.Build();var userAddedCreditScores = new List<CreditScore>();app.MapGet("/creditscore", () =>{    var score = new CreditScore    (        Random.Shared.Next(300, 850)    );    return score;});app.MapPost("/creditscore", (CreditScore score) => {    userAddedCreditScores.Add(score);    return score;});app.MapGet("/userAddedCreditScores", () => userAddedCreditScores);app.Run();record CreditScore(int Score){    public string? CreditRating    {        get => Score switch        {            >= 800 => "Excellent",            >= 700 => "Good",            >= 600 => "Fair",            >= 500 => "Poor",            _ => "Bad"        };    }}As mentioned, our code first creates a builder object for the web application and then uses it to build an application instance. It also defines a record type called CreditScore with a single property called Score and a read-only property called CreditRating. This may look a little strange as we define our record after using it. However, this is due to namespaces, and the record must be defined outside of the WebApplication namespace.The application exposes multiple endpoints using app.MapGet() and app.MapPost() methods. The first endpoint, /creditscore is a GET method that generates a new random CreditScore object with a score between 300 and 850. We’ll define a POST method for the same endpoint that accepts a CreditScore object in the request body, adds it to a list called userAddedCreditScores, and returns the same CreditScore object to the caller. The other endpoint /userAddedCreditScores is a GET method that returns a list of all the CreditScore objects that have been added to userAddedCreditScores.Finally, the application starts running using app.Run().Running and Testing the APIWith our code written, run the following command to compile and run our project:dotnet runThe API is now operational and ready for testing. After running the previous command, you will see which port has been used to host your API in the console. You can define which port you would like to use by editing the Properties > launchSettings.json file or by adding editing the app.Run() command in Program.cs like so, replacing 3000 with your desired port number:app.Run("http://localhost:3000");You can use a tool like Postman to send an HTTP request to the API. For me, the endpoint to get a credit score is localhost:5242/creditscore. When you send a request to this endpoint, you should receive a 200 OK status code, a credit score generated by the random number generator, and a credit rating.We can save a credit score by sending a POST request to the creditscore endpoint. We form the request’s body with a CreditScore object.Finally, we can retrieve all added scores by sending a GET request to the /userAddedCreditScores endpoint.Wrapping UpIn summary, we have developed a basic RESTful Minimal API using .NET Core 7 and ASP.NET. This code can be a foundation for creating more complex APIs for your application. As you continue to develop the API, you may want to consider implementing security measures such as an API key, integrating with an API gateway, monitoring the usage of the API, or generating revenue through API monetization. If you are interested in exploring options for API analytics and monetization check out Moesif.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Building-Minimal-API-with-Dotnet/",
          "author": "Dylan",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-building-restful-api-with-flask": {
          "title": "How to Build an API With Python Flask",
          "content"	 : "Python Flask is a popular framework for building web applications and APIs in Python. It provides developers with a quick and easy way to create RESTful APIs that can be used by other software applications. Flask is lightweight and requires minimal setup, making it a great choice for building small to medium-sized APIs. This makes Flask an ideal choice for developers looking to build robust and scalable APIs in Python. This example will review how to create a simple rest API Flask tutorial.                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free        PrerequisitesBefore we start, we must ensure that a couple of prerequisites are completed. To follow along and run this tutorial, you will need to:  Install Python  Have an IDE to edit your code  Install Postman so that you can test the endpointAfter all 3 of these prerequisites are completed, we can begin!Creating the Base ProjectTo create the base project,  the first thing we will do is create a folder named python-flask-api in your chosen directory.With the new folder created, we will open a terminal in the root of the folder so that commands can be executed to build and run our Python project. Once your terminal is pointed to the root directory of your project, run the following commands so you can initialize the Python Rest API Flask project and manage the dependencies.First, we will use pip to install Flask in the project directory. To do this, run the command below.pip install FlaskWriting the CodeIn our first line of code in the app.py file, we will import the modules for json, Flask, jsonify, and request.import jsonfrom flask import Flask, jsonify, requestNext, we will create a new Flask application by adding the following code just below our import statements.app = Flask(__name__)Next, to give our API a little bit of data to work with, we will define an array of employee objects with an ID and name.employees = [ { 'id': 1, 'name': 'Ashley' }, { 'id': 2, 'name': 'Kate' }, { 'id': 3, 'name': 'Joe' }]To define our API endpoint, we will now add code to define a route for GET requests to the ‘/employees’ endpoint. This will return all employees (from our employees array defined above) in JSON format.@app.route('/employees', methods=['GET'])def get_employees(): return jsonify(employees)On top of our GET method, we will also define a route for POST, PUT, and DELETE methods as well. These functions can be used to create a new employee and update or delete the employee based on their given ID.@app.route('/employees', methods=['POST'])def create_employee(): global nextEmployeeId employee = json.loads(request.data) if not employee_is_valid(employee):   return jsonify({ 'error': 'Invalid employee properties.' }), 400 employee['id'] = nextEmployeeId nextEmployeeId += 1 employees.append(employee) return '', 201, { 'location': f'/employees/{employee["id"]}' }@app.route('/employees/<int:id>', methods=['PUT'])def update_employee(id: int): employee = get_employee(id) if employee is None:   return jsonify({ 'error': 'Employee does not exist.' }), 404 updated_employee = json.loads(request.data) if not employee_is_valid(updated_employee):   return jsonify({ 'error': 'Invalid employee properties.' }), 400 employee.update(updated_employee) return jsonify(employee)@app.route('/employees/<int:id>', methods=['DELETE'])def delete_employee(id: int): global employees employee = get_employee(id) if employee is None:   return jsonify({ 'error': 'Employee does not exist.' }), 404 employees = [e for e in employees if e['id'] != id] return jsonify(employee), 200Once our code is complete, it should look like this:import jsonfrom flask import Flask, jsonify, requestapp = Flask(__name__)employees = [ { 'id': 1, 'name': 'Ashley' }, { 'id': 2, 'name': 'Kate' }, { 'id': 3, 'name': 'Joe' }]nextEmployeeId = 43@app.route('/employees', methods=['GET'])def get_employees(): return jsonify(employees)@app.route('/employees/<int:id>', methods=['GET'])def get_employee_by_id(id: int): employee = get_employee(id) if employee is None:   return jsonify({ 'error': 'Employee does not exist'}), 404 return jsonify(employee)def get_employee(id): return next((e for e in employees if e['id'] == id), None)def employee_is_valid(employee): for key in employee.keys():   if key != 'name': return False return True@app.route('/employees', methods=['POST'])def create_employee(): global nextEmployeeId employee = json.loads(request.data) if not employee_is_valid(employee):   return jsonify({ 'error': 'Invalid employee properties.' }), 400 employee['id'] = nextEmployeeId nextEmployeeId += 1 employees.append(employee) return '', 201, { 'location': f'/employees/{employee["id"]}' }@app.route('/employees/<int:id>', methods=['PUT'])def update_employee(id: int): employee = get_employee(id) if employee is None:   return jsonify({ 'error': 'Employee does not exist.' }), 404 updated_employee = json.loads(request.data) if not employee_is_valid(updated_employee):   return jsonify({ 'error': 'Invalid employee properties.' }), 400 employee.update(updated_employee) return jsonify(employee)@app.route('/employees/<int:id>', methods=['DELETE'])def delete_employee(id: int): global employees employee = get_employee(id) if employee is None:   return jsonify({ 'error': 'Employee does not exist.' }), 404 employees = [e for e in employees if e['id'] != id] return jsonify(employee), 200if __name__ == '__main__':   app.run(port=5000)Lastly, we will add a line of code to run our Flask app. As you can see, we call the run method and get the Flask app running on port 5000.if __name__ == '__main__':   app.run(port=5000)Running and Testing The CodeWith our code written and saved, we can start the app up. To run the app, in the terminal we will execute the follow pip command.pip api.pyNow, our API is up and running. You can send a test HTTP request through Postman. By sending a request to localhost:5000/employees. After the request is sent. you should see a 200 OK status code in the response along with an array of employees returned.For this test, no request body is needed for the incoming request.Wrapping UpWith that, we’ve created a simple RESTful API Flask using Python. This code can then be expanded on as needed to build APIs for your applications. Moving forward, you may want to secure the API with an API key, integrate the API with an API gateway, check out how your API is being consumed and used, or build revenue through API monetization? For a solution to your API analytics and monetization needs, check out Moesiftoday to explore all of this and more!                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            You’ve built the API. Now let’s generate revenue!            Monetize your Flask APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Building-RESTful-API-with-Flask/",
          "author": "Preet",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-building-a-restful-api-with-rails": {
          "title": "Building a RESTful API with Rails",
          "content"	 : "When it comes to building an API, Rails is an extremely popular programming language choice to build powerful RESTful APIs.  In this tutorial, we will build a simple REST API using Rails. The Rails REST API endpoint will allow users to retrieve a credit score rating. Of course, we won’t be linking up to any backend systems to pull a credit score but will instead use a random number generator to generate the score and return it to the user. Although simple, this tutorial will show you the basics of REST API development with Rails.PrerequisitesBefore we start, we must ensure that a couple of prerequisites are completed. To follow along and run this tutorial, you will need to:  Have Ruby installed  Install Rails - sudo gem install rails  Have an IDE available to edit your code  Install Postman so that you can test the endpointAfter all 4 of these prerequisites are completed, we can begin!                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Adding in the CodeOur first step in creating our code is to generate a Rails API project. To do this, we will run the following command to create a new Rails API application.rails new my_api --apiOptionally, we will also set up the CORS configuration to allow traffic from all origins to make things easier for testing. In the Gemfile in the root directory, uncomment the rack-cors entry. It should then look like this.gem "rack-cors"Once this is uncommented, we need to install the dependency. Then, in your terminal, run the following to install the dependency.bundle installIn the root folder of the project, in the config/initializers directory, open the cors.rb file. In the cors.rb file, uncomment the CORS config, and change the origins entry to “*”. This will allow ALL origins, essentially allowing all traffic to flow into the service. For product applications, you’ll likely want to lock things down and dial in the CORS config a bit more.The file should now look like this:Rails.application.config.middleware.insert_before 0, Rack::Cors do allow do   origins "*"   resource "*", headers: :any, methods: [:get, :post, :put, :patch, :delete, :options, :head] endendNext, we will create our GET endpoint for api/getcreditscore. Our first step is to create the route for the endpoint. To do this, open the routes.rb file in the config directory.Rails.application.routes.draw do # Define your application routes per the DSL in https://guides.rubyonrails.org/routing.html # Defines the root path route ("/") # root "articles#index" get 'api/getcreditscore', to: 'application#creditscore'endNow that the route is defined, we will create the controller function for the endpoint. The function will return a JSON payload with a random number between 500 and 900, representing a credit score. To add this logic in, navigate to the application_controller.rb in the controllers directory. The completed controller file will look like the example below.class ApplicationController < ActionController::API   def creditscore       render json: { creditscore: rand(500..900) }   endendWith that, our API is ready to be deployed.Running and Testing the CodeIn this case, we will run our Rails app locally. To run the API locally we will need to start up the Rails server. We can do this by using the rails server command in the root directory of the app.rails serverNow, our Rails application and API are up and running. You can send a test HTTP request through Postman. By sending a request to localhost:3000/api/getcreditscore, you should see a 200 OK status code and a credit score returned from the creditscore handler written in the application_controller.rb file. For this test, no request body is needed for the incoming request.  By default, rails will run on port 3000. To change this, you can open the puma.rb file in the config directory and change the following line of code to patch the port you want to have the server listen on.  port ENV.fetch("PORT") { 3000 }Wrapping UpWith that, we’ve created a simple RESTful API using Rails. This code can then be expanded on as needed to build APIs for your applications. Moving forward, you may want to secure the API with an API key, integrate the API with an API gateway, check out how your API is being consumed and used, or build revenue through API monetization. For a solution to your API analytics and monetization needs, check out Moesif today to explore all of this and more!                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Building-A-RESTful-API-With-Rails/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "api-analytics-product-analytics-platform-analytics": {
          "title": "What Is an Analytics Platform? How to Choose One for API, Product, and BI Use Cases",
          "content"	 : "Analytics platforms have changed quickly over the last two years. AI assistants now sit inside the dashboard, semantic layers have replaced one-off SQL, and real-time event streams have moved from niche infrastructure into mainstream buying criteria. The result is a market where “analytics platform” can mean a self-serve dashboard for a marketing team, a lakehouse query engine for data science, or a behavioral analytics tool wired into a product or API.This guide walks through what an analytics platform actually is, the architecture that defines a modern one, the main types you should know, and a head-to-head look at nine platforms worth evaluating. The list spans general-purpose BI, product analytics, web analytics, and API analytics, including how a specialist category like Moesif fits when your product itself is an API.What Is an Analytics Platform?An analytics platform is integrated software that ingests, processes, visualizes, and analyzes data from multiple sources so businesses can turn raw events into insights. Modern analytics platforms combine data pipelines, dashboards, machine learning, and AI assistants in one environment to support real-time decision-making.The category sits between two adjacent concepts that often get confused with it.Analytics platform vs. business intelligence toolA business intelligence (BI) tool, historically Tableau, Power BI, or Looker, is primarily a presentation and exploration layer. It connects to a warehouse, models the data, and renders dashboards. An analytics platform is broader: it includes ingestion, transformation, storage, and a query engine alongside visualization. In practice, BI tools are often the visualization layer of a larger analytics platform, and most modern BI vendors have expanded upstream to claim the full platform label.Analytics platform vs. data warehouseA data warehouse, Snowflake, BigQuery, Redshift, is the storage and compute substrate where analytical data lives. It does not, on its own, produce charts, run experiments, or surface anomalies. An analytics platform consumes data from a warehouse (or replaces parts of one with its own store) and adds the modeling, visualization, and AI layers on top. The line has blurred as warehouses add BI features and BI tools add storage, but the distinction still matters when you’re scoping a vendor decision.How Analytics Platforms Work: Core ComponentsA modern analytics platform is a stack of layers, not a single product. Understanding the layers makes it far easier to compare vendors honestly, because most “analytics platforms” excel at two or three layers and outsource the rest.Data ingestion and pipelinesThe ingestion layer collects events from sources: web SDKs, mobile SDKs, server logs, API gateways, CDC streams from production databases, third-party SaaS connectors, and increasingly LLM telemetry. Some platforms ship their own SDK and pipeline (Amplitude, Mixpanel, Heap, Moesif); others assume you’ll bring your own (Tableau, Looker). Latency at this layer determines whether your dashboards lag by seconds or hours.Storage layer (warehouse, lakehouse, real-time stores)Storage choices fall into three groups: cloud warehouses (Snowflake, BigQuery, Redshift) for batch analytical workloads, lakehouses (Databricks, Iceberg-based stores) for mixed structured and unstructured data, and purpose-built real-time event stores (ClickHouse, Pinot, Druid) for sub-second product or API analytics. The right pick depends on query latency, data volume, and how much of the cost you want to carry yourself.Modeling, transformation, and semantic layerBetween raw events and a chart sits a modeling step where business definitions are encoded, what counts as an active user, how revenue is recognized, which events are bot traffic. Modern platforms expose this as a semantic layer (LookML, dbt models, Cube, AtScale) so the same definition is reused across dashboards, embedded analytics, and AI assistants. Without a semantic layer, three teams will produce three different numbers for the same metric.Visualization and dashboardingThe layer most people picture when they hear “analytics platform.” Dashboards, charts, drill-downs, alerts, and scheduled reports. Differentiation here is increasingly cosmetic; the real divergence has moved into how dashboards integrate with the layers above and below them.AI assistants, NLQ, and embedded analyticsThe newest layer. Many platforms now market AI assistants for natural-language querying (“which cohorts converted last week?”), with varying actual quality; the real question is whether the AI assistant grounds its answers in the semantic layer or hallucinates a SQL query. Embedded analytics, surfacing dashboards inside your own product, has become a standard expectation rather than a separate product category.Types of Analytics PlatformsMost teams confuse themselves by comparing tools across categories. The five groups below are the practical buckets that matter when shortlisting.Business intelligence (BI) platformsTableau, Power BI, Looker, Qlik. Optimized for analysts and business users working off a warehouse. Heavy on visualization, governance, and self-serve reporting. Weak on event-level behavioral analysis without significant modeling work.Product analytics platformsAmplitude, Mixpanel, Heap, PostHog. Built around event streams from a product. Strong at funnels, retention, cohorts, and experimentation. Less suited to financial reporting or warehouse-native analysis.Web/digital analytics platformsGoogle Analytics 4, Matomo, Adobe Analytics. Optimized for website and marketing channel measurement, acquisition, sessions, content engagement, conversion paths. Often weak at deep product analytics beyond the page-view model.API analytics platformsMoesif, Apigee Analytics, Postman. Purpose-built for teams whose product is an API, including AI APIs, payment APIs, and developer platforms. Track user-level behavior on API traffic, anomaly detection, and monetization. Distinct from general application monitoring tools (Datadog, New Relic), which focus on infrastructure health rather than customer behavior.AI/data-science analytics platformsKNIME, Databricks, Dataiku. Designed for data scientists building models and pipelines, not for business analysts building dashboards. Visualization is a side feature; the core value is in workflows, notebooks, and model deployment.Nine Analytics Platforms Worth EvaluatingNine platforms worth evaluating, grouped by the type of analytics work each is built for. The order starts with platforms purpose-built for specific data types (API events, product events) and moves through general product analytics, web analytics, and BI.[IMAGE: Comparison table, 9 platforms × 5 attributes (Use case, Pricing model, AI features, Best for, Free tier). Insert PNG export of the HTML table that follows in production.]1. Moesif: built for API and AI product analyticsMoesif is a purpose-built analytics platform for teams whose product is an API, including AI APIs, payment APIs, and developer platforms, and who need analytics, monetization, and observability in one tool. It treats each API call as a behavioral event tied to a user, customer, or organization, then layers funnels, retention cohorts, and anomaly detection on top of that event stream.What separates us from general APM tools is the user-level dimension. Datadog tells you a route is slow; Moesif tells you which customers were affected, how their usage pattern changed before the incident, and whether the affected accounts are on the plans you most want to retain. What separates us from generic product analytics tools is that the event model is shaped for API calls, with built-in support for monetization billing, SOC 2 and HIPAA compliance, and a secure proxy ingestion path for sensitive payloads.Best for: Engineering and product teams operating API products, AI inference platforms, or developer-first SaaS who need user-level behavioral analytics, anomaly detection, and usage-based billing in a single tool.Not for: Web analytics, static-site traffic measurement, or general BI use cases on warehouse data. If your product is a marketing website, one of the other platforms below will fit better.2. Mixpanel: event-driven product analyticsMixpanel is a product analytics platform built around an event-and-properties data model, with strong funnels, retention reports, and cohort analysis. Its dashboard experience has been refined over a decade of competing with Amplitude, and its query engine answers most behavioral questions in seconds rather than minutes.Best for: Product, growth, and engineering teams that want to ship instrumentation today and start asking questions tomorrow. The free tier and developer-friendly SDKs lower the barrier for early-stage adoption.Not for: Teams whose primary need is financial or operational reporting from a warehouse, or organizations that need everything to live in their own cloud.3. Amplitude: product analytics at scaleAmplitude is the other anchor of the product analytics category. It separates itself from Mixpanel through its data governance features, behavioral cohort modeling, and the breadth of its enterprise integrations. Larger product orgs tend to land here because the platform handles many product lines and many teams without the analysis layer fragmenting.Best for: Mid-market and enterprise product teams running multiple products or running coordinated experimentation programs. Strong fit if you’ve already outgrown a single-team analytics tool.Not for: Small teams who will not use the governance and team features, and any team that wants its data to stay in its own warehouse without a sync layer.4. Google Analytics 4: free web analyticsGA4 replaced Universal Analytics with an event-based data model that brings web analytics conceptually closer to product analytics. It remains free for almost all sites, integrates natively with Google Ads, and has the largest ecosystem of integrations of any analytics tool in this list.Best for: Marketing teams measuring website traffic, acquisition channels, and conversion paths, particularly in a Google Ads-heavy stack. The free tier covers most small and mid-sized organizations.Not for: Teams that need server-side or API-level behavioral analytics, organizations with strict EU data residency requirements that find GA4’s posture uncomfortable, and product teams that need deep cohort and retention work beyond what GA4’s interface comfortably supports.5. Adobe Analytics: enterprise digital experience analyticsAdobe Analytics is the enterprise-grade counterpart to GA4, bundled into the broader Adobe Experience Cloud. It offers more depth around segmentation, attribution modeling, and integration with Adobe’s content and personalization tools, which is the main reason enterprises pick it.Best for: Large organizations already invested in the Adobe stack, Experience Manager, Target, Campaign, that want digital analytics aligned to that ecosystem.Not for: Smaller teams without an existing Adobe footprint. The cost of entry is high, and the platform’s learning curve is steeper than its alternatives.6. Heap: autocaptured product analyticsHeap’s differentiator is autocapture: instead of asking engineers to instrument every event, Heap records every interaction and lets analysts retroactively define what counts as a meaningful event. That dramatically reduces instrumentation work but introduces its own discipline problem, every team must agree on what events mean.Best for: Teams that need to start measuring product behavior quickly without a full instrumentation project, and analysts comfortable defining events after the fact.Not for: Highly regulated environments where capturing every interaction creates compliance overhead, or teams who would rather invest in explicit instrumentation upfront.7. Looker: governed, warehouse-native BILooker, now part of Google Cloud, is a well-known example of a modeled BI platform. Its LookML semantic layer forces a single source of truth for metrics across an organization, which is what makes it popular at companies that have been burned by inconsistent dashboards.Best for: Data teams who want a governed semantic layer between their warehouse and their dashboards, and organizations standardizing on Google Cloud or BigQuery.Not for: Small teams without a dedicated analytics engineer to maintain LookML, and use cases that need real-time event analytics rather than warehouse queries.8. Tableau: analyst-driven visualization and explorationTableau remains the benchmark for visual data exploration. Its drag-and-drop interface and visualization breadth make it the platform analysts reach for when the question is “what does this data actually look like?” Salesforce ownership has accelerated its AI features under the Tableau Pulse and Einstein product lines.Best for: Analyst-led teams doing exploratory analysis on warehouse data, and organizations that already have Salesforce as a core platform.Not for: Teams that need behavioral product analytics, real-time event monitoring, or API-level data. Tableau is a visualization platform first; if your bottleneck is upstream of the chart, the gain from switching is limited.9. Matomo: privacy-first web analyticsMatomo is a widely used open-source web analytics platform, available as self-hosted software or a managed cloud service. Its main appeal is data ownership: events are stored on your infrastructure, which makes EU compliance, healthcare deployments, and government use cases much simpler than they are on GA4.Best for: Organizations with strict data residency, privacy, or sovereignty requirements, and teams that want a Google Analytics-style experience without sending data to Google.Not for: Teams without the operational appetite to self-host, or product analytics use cases that need behavioral cohorts and funnels beyond what a web analytics tool naturally provides.How to Choose the Right Analytics PlatformThe biggest selection mistake is picking a tool from the wrong category and then bending it into the wrong shape. Five filters resolve most of that risk.Match the platform to your data sourceWeb events, product events, API calls, warehouse tables, and operational systems all have their own native categories of analytics tool. Picking a BI platform to do behavioral product analytics, or a product analytics tool to do API monitoring, usually leads to a year of integration work and a half-broken result. Identify which data source is the primary input, then shortlist platforms native to that source.Evaluate integration depth and total costSticker price is rarely the real cost. Ingestion volume, seats, query compute, premium connectors, and the engineering time required to instrument and maintain the platform all add up. Ask vendors for a price model based on your actual event volume and team size, not a generic per-seat number. Cross-check against pricing transparency from the tools-for-logging-and-monitoring category if you’re also evaluating observability tools.Check security, privacy, and compliance postureSOC 2 Type II is the baseline expectation for any commercial deployment. If you handle health data, HIPAA matters; if you serve EU users, GDPR data residency matters; if you handle payments, the platform should not be storing PAN data without explicit support. Get the latest report under NDA before signing, not the marketing page that says “we are compliant.”Stress-test AI and natural-language querying claimsEvery analytics platform now markets an AI assistant. Most of them are usable for a small fraction of real questions and confidently wrong for the rest. The honest test: hand the assistant five real questions from your team’s Slack channel, see which it answers correctly without invented columns, and check whether its answers are grounded in your semantic layer or generated ad hoc.Plan for the team that will actually use itA platform’s adoption rate is more predictive of its value than its feature list. A governed BI tool that nobody outside the analytics team can use will deliver less than a lighter-weight tool that product managers actually open. Match the platform’s complexity to the technical depth of the people who will operate it day-to-day.Benefits of a Modern Analytics PlatformThe case for replacing a fragmented stack with a modern analytics platform reduces to three things.Faster decisions. When ingestion is real-time, the semantic layer is consistent, and the AI assistant actually works, the cycle from “I have a question” to “I have a defensible answer” shrinks from days to minutes. That speed compounds across product, marketing, and operations decisions.Unified metrics. A platform with a shared semantic layer eliminates the three-different-numbers problem. Active users mean the same thing in the executive deck, the product roadmap, and the customer-facing report.Lower total cost than a custom stack. Building ingestion, modeling, dashboards, alerting, and AI on top of a raw warehouse takes a small data team a year and ongoing maintenance forever. A modern analytics platform absorbs most of that work and lets the team focus on the analysis itself.[IMAGE: Architecture diagram, modern analytics platform with ingestion → storage → semantic/modeling → visualization/AI layers. Insert SVG diagram in production.]ConclusionThe right analytics platform is rarely the one with the longest feature list. It’s the one that matches your primary data source, fits the team that will use it, and integrates with the rest of your stack without a year of glue code. The nine platforms above cover the main categories worth evaluating, but the categories themselves matter more than the order.If your product is an API, an AI inference service, or a developer platform, and you need user-level behavioral analytics, anomaly detection, and usage-based billing in one tool, Moesif is built for that specific shape of the problem. If your product is a website or a warehouse-backed BI use case, one of the other eight will fit better.Analytics Platform FAQWhat is an analytics platform?An analytics platform is integrated software that ingests, processes, visualizes, and analyzes data from multiple sources so businesses can turn raw events into insights. Modern analytics platforms bundle data pipelines, dashboards, machine learning, and AI assistants into one environment.What are examples of analytics platforms?Common examples include Amplitude, Mixpanel, and Heap for product analytics, Google Analytics 4 and Matomo for web analytics, Tableau, Power BI, and Looker for business intelligence, Databricks and KNIME for AI and data science workloads, and category specialists like Moesif for API and AI product analytics.What are the top 5 analytics companies?By market share, the largest general-purpose analytics vendors are Google (Google Analytics, Looker), Microsoft (Power BI), Salesforce (Tableau), Adobe (Adobe Analytics), and SAP. The most relevant platform for a given team depends on the data being analyzed. For broader product analytics, Amplitude and Mixpanel anchor the category; for API and AI products specifically, Moesif is built for that use case.What are the 4 types of data analytics?The four standard types are descriptive analytics (what happened), diagnostic analytics (why it happened), predictive analytics (what is likely to happen next), and prescriptive analytics (what action to take). Most modern analytics platforms cover the first two natively and offer predictive and prescriptive features via AI and machine learning modules.What is the difference between an analytics platform and a CRM?A CRM stores and manages customer relationship data, accounts, contacts, deals, support cases, and supports operational workflows like outreach and pipeline management. An analytics platform analyzes data, often including CRM data, to surface trends and insights. CRMs are systems of record; analytics platforms are systems of analysis.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free                        Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /api-analytics/product-analytics/Platform-Analytics/",
          "author": "Larry",
          "categories": "API-Analytics, product-analytics"
        }
      
    ,
  
    
        "api-analytics-product-management-api-business-analytics": {
          "title": "API Business Analytics: Metrics, Tools, and Patterns in 2026",
          "content"	 : "API business analytics is the practice of turning the data your API generates into decisions about revenue, growth, and product direction. It is different from operational monitoring (which asks “is the API up?”) and from product analytics on a UI (which asks “where did this user click?”). The question API business analytics answer is “which customers are growing, which are stalling, and what does that mean for the next quarter?”                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        This guide walks through what the category covers, the metrics that drive decisions, the categories of tools, and the 2026 patterns around AI consumption and agent traffic. The piece most teams get wrong is conflating API business analytics with infrastructure monitoring; they are related but distinct disciplines.What API business analytics actually meansAPI business analytics measures how your API is performing as a product. It pulls from API traffic data and combines it with customer identity, billing data, and product context to answer questions like:  Which customers are increasing their API consumption month-over-month?  Which endpoints drive the most revenue?  Which integrations stall between signup and first production call?  Which customer cohorts are growing in their use of new features?  What is the typical time from signup to first 100 calls? To first 10,000? To first paid invoice?The data flows through three layers: the gateway (raw call records), the customer mapping (which call belongs to which account), and the business mapping (which account belongs to which segment, plan, and revenue tier). The analytics happen on the joined view.API business analytics vs business analytics APIs (the naming confusion)These two phrases sound alike and mean different things:  API business analytics is the discipline above: using your own API’s data to drive business decisions.  Business analytics APIs are APIs offered by analytics providers (Google Analytics API, Salesforce Analytics API, Sendgrid Stats API, Mixpanel’s Query API) that let your application pull analytics data programmatically.This post is about the first. The second is a category of products you might use as part of an analytics workflow.Why API business analytics matterAPI-first companies generate most of their telemetry from API calls. Without analyzing that telemetry from a business angle, the API becomes an opaque revenue line: dollars come in, dollars go out, and the levers that change the trajectory are invisible.The practical wins from running this analytics layer:  Identify expansion candidates. Customers approaching plan limits or growing month-over-month are candidates for proactive upsell conversations.  Spot churn early. Customers whose usage flattens or declines for two or three months are at risk; analytics surfaces the pattern before the renewal conversation.  Validate pricing. Revenue per call (or per token, per active user) tells you whether your pricing model is capturing the value customers see.  Inform product investment. Endpoints with high usage but high error rates are priority targets for engineering work. Endpoints with low usage are candidates for deprecation.  Detect abuse and anomalies. Sudden traffic shifts (a customer spiking 10x, a new IP range hitting auth endpoints) need to be visible without waiting for an incident.Each of these is a routine question for a product or growth team. None of them is answerable from infrastructure-level metrics alone.The metrics that matterThe core API business metrics most teams converge on:  Active customers per period. Daily, weekly, monthly active customers (DAC/WAC/MAC). The leading indicator for retention.  API calls per customer per period. The unit-level measure of engagement.  Revenue per customer per period. The output side of the value loop, paired with calls per customer to compute ARPU and CAC payback.  First-call latency. Time from signup to first successful API call. The leading indicator for activation; high first-call latency predicts low conversion.  Endpoint adoption. Which endpoints are gaining adoption among customers; which are stagnant or declining.  Error rates by customer. Distinguishes “the API has bugs” from “Customer X’s integration has bugs.” Both matter, but they require different responses.  Cohort retention. Revenue-weighted retention by signup cohort. Tells you whether new customers are converting and sticking.These metrics are derivative of underlying call records. The analytics platform’s job is to produce them cleanly from raw traffic plus customer identity.Categories of toolsThe market roughly splits into three categories:  Application Performance Monitoring (APM) tools. Datadog, New Relic, Dynatrace. Built for operations and SRE teams. Strong on infrastructure metrics (CPU, memory, latency percentiles); not built for per-customer business analytics or revenue attribution. The right tool when the primary question is “is the system healthy?”  Log aggregators. Elasticsearch/Kibana, Splunk, Graylog. Built for engineering debugging. Useful for searchable raw event data but require substantial custom dashboard work to produce business-level views. The right tool when the primary question is “what happened in this specific request?”  API-specific analytics platforms. Moesif is the analytics platform paired with WSO2 in this stack and is purpose-built for API-as-a-product analytics: per-customer attribution, behavioral cohorts, revenue mapping, and per-endpoint payload-level visibility. The right tool when the primary question is “how is the API performing as a product?” (which is the question APM tools and log platforms were never designed to answer).The right choice depends on which question dominates. Many teams end up using more than one: APM for operations, log search for debugging, and API-specific analytics for product and business decisions.API business analytics in 2026: AI consumption and agent attributionTwo changes are worth flagging if your analytics layer is more than a year old.AI consumption attribution. A meaningful share of API calls in 2026 either come from AI agents (calling on behalf of users) or are themselves LLM calls (your application calling OpenAI on a customer’s behalf). Both need to be attributable to the underlying customer for analytics and billing to work. The standard pattern: include a customer ID in the auth context of every call, and propagate it down to any downstream LLM API call so the cost can be re-attributed.Agent identity as a first-class dimension. When the consumer of your API is an agent rather than a human developer, the questions you ask about that traffic are different. “Which workflows are triggering this endpoint?” matters more than “which IP is calling?” Analytics platforms are starting to expose agent identity as a built-in dimension alongside customer identity.These shifts matter because the older analytics models, built on the assumption that one human developer drives one customer’s API usage, break when an agent runtime mediates the calls. Per-customer attribution is no longer enough; you need per-agent attribution within each customer.How Moesif’s API business analytics workMoesif instruments API gateways (WSO2, Kong, AWS, Azure, Envoy) and application SDKs, captures every request and response with customer identity attached, and rolls the data into analytics views built around customer behavior rather than infrastructure health.Out of the box: active customers, calls per customer, error rates per customer, endpoint adoption per customer, time-from-signup metrics, cohort retention, and per-customer revenue when integrated with a billing provider. The data feeds both product analytics dashboards and the usage-based billing meters that re-bill consumption.The pricing-strategy decisions downstream from these analytics (per-call vs. per-token vs. tiered) are covered in our broader API pricing guidance.Common API business analytics mistakesThe patterns we see across customer reviews:  Treating it as an engineering project. API business analytics is a product and growth function. Engineering-only ownership produces dashboards no one outside engineering reads.  No per-customer attribution. Anonymous traffic data cannot answer business questions. Tag every call with customer identity at the gateway layer.  Mixing operational and business views. Dashboards that combine “p95 latency” with “weekly active customers” serve neither audience well. Build separate views for ops and for product/business teams.  Picking tools before defining questions. Buying Datadog when your dominant question is “which customers should we upsell” produces a dataset you cannot answer that question from. Pick the tool that fits the question, not the other way around.Next stepsAPI business analytics is one of the higher-leverage instrumentation decisions an API-first company makes. The metrics are operationally cheap to capture if you do it at design time, and the questions they answer drive the largest decisions about pricing, product, and customer success.If you want per-customer, per-endpoint analytics on your live API within an hour of integrating, start a 14-day Moesif free trial. No credit card required.Frequently asked questionsWhat is API business analytics? The discipline of turning API call data into business decisions: identifying expansion candidates, spotting churn early, validating pricing, informing product investment.How is API business analytics different from APM? APM (Datadog, New Relic) focuses on infrastructure and application health. API business analytics focuses on customer behavior and revenue. Different audiences, different questions, often used together.What metrics should I track? Active customers, calls per customer, revenue per customer, first-call latency, endpoint adoption, error rates per customer, cohort retention. The exact set depends on your business model.Can I use Datadog or New Relic for API business analytics? They can be configured to produce some of these views with custom dashboards, but they were not built for it. API-specific platforms like Moesif handle the per-customer behavioral cuts out of the box.How do I attribute LLM consumption to customers? Propagate the customer ID through your application down to the LLM API call, and record the token usage with the customer ID attached. Analytics platforms that handle this (including Moesif) are built specifically for the pattern.Do AI agents change my analytics needs? Yes. Per-customer attribution is no longer enough; you also need per-agent attribution within each customer. Most platforms are catching up to this in 2026.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /api-analytics/product-management/API-Business-Analytics/",
          "author": "Larry",
          "categories": "API-Analytics, product-management"
        }
      
    ,
  
    
        "api-analytics-api-product-management-5-key-considerations-for-building-defi-apis": {
          "title": "5 Key Considerations for Building DeFi APIs",
          "content"	 : "Decentralized Finance (DeFi) is a financial service based on ledgers, just like the ones used by cryptocurrencies. In the U.S., DeFi technology challenges the current centralized finance system by empowering individuals to manage their own financial exchanges via a crypto wallet. Because decentralized finance eliminates fees from banks or other financial institutions, anyone with an internet connection can use DeFi.As a developer, building APIs that can push and pull DeFi data is a vital way to impart value to your customers. When building a DeFi API, there are a few key considerations that you should pay special attention to.SecurityBecause a DeFi application deals with financial and other sensitive user data, it’s critical to build a secure API. This means protecting user data and preventing unauthorized access is incredibly important. A huge privacy concern in the world of DeFi and cryptocurrency is the privacy limitations of current public blockchain technologies; a public blockchain allows for any user to view transaction amounts and addresses involved. This is a lack of “financial privacy” that is inherent when dealing with crypto such as Ethereum or Bitcoin.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        It’s essential to follow best practices for securing your API, such as using HTTPS and implementing proper authentication and access controls to ensure that only authorized users can see data on a financial transaction or in a wallet. Services like Provable can encrypt your API calls and feed data from off the blockchain (off-chain) data sources and to your blockchain (on-chain) for smart contracts to use. Building a long lasting, risk-based framework based on user analytics will allow your API product to evolve with the DeFi market.Given the sensitive nature of financial transactions, it matters that the tools you use to analyze incoming data are built with security in mind. Because Moesif has always handled customer data, we built our solution with security in mind. Our HIPAA and SOC 2 compliant analytics platform has multiple processes for handling data securely, including: encrypting stored data at rest, internal controls for accessing production data, and strictly controlling changes to platform software.Beyond server-side encryption and SOC 2 compliance, projects with sensitive data can stay private to your organization via client-side encryption. The privacy benefits of proprietary software are gained, without the complexity of building and scaling your own data infrastructure. Moesif’s SOC 2 compliance allows our platform to enable growth in privacy-conscious markets with confidence.InteroperabilityInteroperability is when a product or system works with other products or systems. Ensuring your DeFi API is interoperable with other DeFi protocols and blockchains can increase their usefulness and chances of adoption. As an example, users of the Ethereum blockchain cannot easily interact with other blockchain technology, like Avalanche or Polkadot. This requires careful consideration of data format, APIs, and integration methods. Cross-chain interoperability gives your crypto holders the freedom to decide how to conduct DeFi transactions, without being held back by a specific blockchain network.Cross-chain interoperability can enable wider adoption of your DeFi solution. By allowing users to access DeFi services across blockchain networks, it can create incentive for them to interact with your DeFi API. This open access can allow for more users in the Web3 space, leading to greater liquidity flowing into the DeFi ecosystem. Over time this allows for larger lending, staking, and borrowing operations. In short, more users that are able to access your DeFi app means more crypto being mined.PerformanceYour DeFi API must be able to provide reliable and fast access to ensure a seamless user experience.  A digital asset is only as valuable as the story its metadata holds. When gauging efficacy of your DeFi solution, there are a few possible performance metrics to consider:  Total Value Locked (TVL): A standard metric used to measure overall health of a DeFi protocol. The total number of DeFi tokens is referred to as the “total value locked.”  Price-to-sales ratio (P/S ratio): This ratio compares market cap of an asset with its revenue. A P/S ratio is calculated by dividing a protocol’s capitalization by the total generated revenue.  Non-speculative usage: Reviewing digital transaction data to understand the real value of a decentralized finance solution. Transactions in a protocol that are performed for speculative use should be avoided.To analyze and iterate your DeFi solution, using an external analytics platform can save time and money, two invaluable resources in the ever changing Web3 landscape. Using an analytics platform to identify inconsistent latency can allow for optimization of connections and endpoints. Inconsistent response times, due to connections with varying latency, undermines the user experience and can lead to poor retention.  Additionally, identifying the transaction parameters that matter to your DeFi products versus ones that are just “vanity” metrics can allow for accelerated growth and business transparency. Measuring and acting on key metrics is vital to maintaining a smooth user experience.ScalabilityBecause the world of DeFi crypto exchanges is rapidly growing, your API must be able to handle increasing levels of usage. Considering how to make your infrastructure scalable early on can help to ensure your API can handle high levels of traffic. Using an analytics tool like Moesif can allow you to collect and analyze data on high volume APIs with no performance impact. Whether you are looking at token activation or endpoint usage, Moesif can handle your Web3 queries.ComplianceDeFi APIs must comply with relevant regulations and laws, including Anti-Money Laundering (AML) and Know Your Customer (KYC) regulations. Ensuring that you have a clear understanding of these requirements and designing your API accordingly to avoid any legal issues is paramount to the longevity and scalability of your DeFi project. It is unclear in the U.S. if DeFi projects are regulated for the purpose of AML compliance. The U.S. Department of Treasury’s Financial Crimes Enforcement Network (FinCEN) requires that any Money Service Business (MSB) implement and maintain a risk-based AML protocol.As of 2019, FinCEN considers crypto businesses as MSBs. The question of if a DeFi project is an MSB is slightly more unclear, as there is little regulation yet on the DeFi space. As it stands, a smart contract or DeFi application is not likely subject to these regulations. However, DeFi projects that consider themselves “decentralized” but are actually centralized in practice likely fall under the regulations set by FinCEN. Ensure that users of the traditional financial system are open to using your product by enacting a modern, risk-based DeFi framework.ConclusionWhile the world of traditional finance continues to remain unchanged, the world of crypto tokens and decentralized finance evolves at a rapid pace. Individuals with a crypto wallet want to know that their data is secure with your DeFi app. A decentralized financial product that is built to last starts with understanding how potential and current users interact with smart contracts and relevant blockchain technology, and ends with implementing changes based on your users’ data.Moesif provides user-centric analytics right down to the individual customer level, as well as deep insights into how your API is being used. Build highly-scalable, secure and performant DeFi APIs with help from Moesif today.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-analytics/api-product-management/5-Key-Considerations-for-Building-DeFi-APIs/",
          "author": "Rachael",
          "categories": "api-analytics, api-product-management"
        }
      
    ,
  
    
        "api-analytics-company-press-release-2023-product-awards": {
          "title": "Moesif Awarded Best B2B Tech Product by Products That Count",
          "content"	 : "Moesif has been named the Top B2B Tech Product in the 2023 Product Awards; Moesif won the Award as one of the Best Products for Product Managers. The Product Awards, presented by Products That Count in partnership with Mighty Capital and Capgemini, is the only awards show designed to celebrate the tools that help Product Managers build great products.Nominees are chosen by Products That Count’s product manager network, and winners are chosen by an Independent Awards Advisory Board composed of top product leaders. This year’s Board included product leaders from companies like Intuit, Oscar Health, and Macy’s.Moesif provides deep product analytics and effortless monetization for API-enabled companies. Through Moesif, product owners get detailed insights into how their solution is being used, from tracking and altering on key metrics, through to identifying issues in onboarding flows. APIs can be easily monetized with no-code needed - just connect Moesif to your billing provider and the rest is taken care of. Support for complex usage-based meters like prepaid, postpaid and PAYG, ensures that Moesif can handle the most involved billing requirements. Moesif’s self-service platform allows users to create custom dashboards to collaborate with teammates or surface to customers and partners. Turn your APIs into a business with Moesif API analytics.“The bar for what makes a great product gets higher every year,” said SC Moatti, founding CEO of Products That Count. “Moesif is a testament to that. We expect them to keep defining what it means to be at the cutting edge of product, not only in 2023 but also in the years to come.”“We at Moesif are dedicated to creating the best solution that enables our customers to build great products. It’s an honor and testament to that commitment to be recognized by The Product Awards.” said Derric Gilling, CEO of Moesif. “Moesif believes in the power of spurring business growth through API platform solutions. We look forward to continuing to enable our customers to activate and monetize their users.”About MoesifMoesif helps product-led teams grow and monetize their API products. With Moesif’s API analytics, product owners activate more customers, understand API usage, and better monetize customers. Moesif’s customers span financial, healthtech, logistics and enterprise software including fast-growing startups like Tomorrow.io and PandaDoc as well as large enterprises like UPS and Deloitte.About The Product AwardsThe Product Awards, produced by Products That Count in partnership with Capgemini and Mighty Capital, celebrate the best products for product managers, chosen by product leaders. Based on insights from thousands of product managers, the Product Awards showcase product managers’ favorite products within five distinct categories: B2B Tech, FinTech, Internet of Things, Life Sciences, and Sustainability. These categories were defined by our independent Awards Advisory Board, 12 product leaders committed to pushing forward the product conversation. Learn more at www.ProductsThatCount.com/Product-Awards.                Use API Analytics to Grow Your API              14 day free trial. No credit card required.              Learn More                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-analytics/company/Press-Release-2023-Product-Awards/",
          "author": "Rachael",
          "categories": "API-Analytics, Company"
        }
      
    ,
  
    
        "api-analytics-product-management-how-to-maximize-product-led-growth-with-customer-success-best-practices": {
          "title": "How to Maximize Product Led Growth with Customer Success Best Practices",
          "content"	 : "If you want to turbocharge your Product Led Growth (PLG), it’s time to take a long, hard look at your Customer Success Management practices. The way you approach your customer success activities can make a significant difference in how fast and how seamlessly you scale.What Is Product Led Growth?A product led growth strategy puts software, guides, demos and documentation at the center of a business’ growth strategy. The company’s success relies on the product to do much of the selling itself, through its ease of adoption, usability, features and performance. As your adoption funnel is  developed and optimized, customer experience will become more consistent, leading to less friction in your PLG company’s growth.An experienced product manager often runs businesses so that users are able to discover, experiment with, and adopt the product on their own terms and on their own timescale. That’s not to say that sales teams and customer support services aren’t required, they just tend to be layered in at a later stage of the customer journey.A product led approach can enable businesses to scale rapidly, particularly when they implement customer success management best practices alongside a product led growth mindset.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        What Is Customer Success Management and How Does it Underpin Product Let Growth?Customer Success Management (CSM) is a bridge between a business’ product and its customers. When used proactively, CSM best practices can ensure you understand your customers’ businesses, helps them stand up your product with minimal effort, and guarantees that your product provides real value at every stage of the customer journey.For product led growth companies, the customer success team acts as a guide to help customers achieve their goals. Engagement and improving the customer experience is at the heart of this approach, as engagement is the best indicator that a customer journey will end successfully - with happy customers, positive customer feedback and ideally a subscription plan.Regular, proactive engagement also means that customers can realize greater value from the product – if they engage with your customer success team to define the scope of their project, your team can help them with tools that achieves their goals faster and more efficiently. Sharing those insights with your product team results in a more cohesive PLG company and one where product led content will invariably improve customer experience.Top Tips on How to Maximize Product Led Growth with Customer Success Best PracticesA customer success manager uses a range of tools and techniques that empowers customers to be more efficient and realize additional value from the product. The first of these is to focus on data points, a focus which often reveals issues which customers can be helped with. By reviewing customers’ goals, SDKs, UTMs, and other hyper-personalized data points, the customer success team can quickly build up a deep understanding of each customer. The team then analyzes every step in customer onboarding, from discovery and signing up, through to growing usage and feature expansion. These instances serve as touch points to reach out to customers, cement relationships and ensure customer retention.Another important tip is to use a tool that supports detailed customer insights. With an in-depth analytics dashboard in place, it’s easy to assess account health and discover product usage trends. That data then forms the basis of conversations with customers, as pVerify observed when it deployed Moesif’s HIPAA-compliant API analytics for Covid testing:  “It’s completely pointless if you cannot pass your analytics data onto your clients. Embedded dashboards solved that for us. As a scientist, I’m all about data analytics. Finding, displaying and sharing API metrics like 400/500 errors, when you have thousands of customers and millions of API calls, is very difficult. Moesif solved that for us.” Rob Dejournett, CTO, COO, pVerify.With a proactive approach to CSM, analytics and monitoring, it’s possible to identify many customer problems at an early stage. This is another key tip for maximizing product led growth, as it can open up productive conversations with customers at just the right time in their growth journey, helping them overcome any unexpected bumps in the road. That’s not to say that upset customers won’t pop up, but when they do, a robust CSM approach means that you can immediately access key account metrics and take a laser-focused approach to the problem:  “It’s handy to have immediate access to key account metrics. You can see what’s happening and say, ‘Yes, you did this wrong. See all these failed calls? Let’s look into the body of those calls and see if we can reproduce that – make an autopsy of those fails.’” Oliver Burt, Customer Success Manager, MoesifA dashboard that aggregates your accounts’ health is the final CSM best practice for maximizing product led growth. An average CSM professional will manage perhaps 15 accounts, some doing well, potentially some badly and some with errors. A dashboard provides both oversight and peace of mind. Being able to produce dashboards by account or by cohorts such as company or vertical, and with a global perspective, enables CSM teams to share customer acquisition and customer retention statistics with their line managers, product management teams and across the company.Using Automation as Part of Your Product Led CSM StrategyAutomating certain CSM functions is advantageous to both the user and your CSM team. When certain conditions, such as onboarding difficulties, are found, it may make sense to show an in-app notification or email pointing users towards helpful content on behalf of the CSM team. This benefits the internal CSM team by taking that load off of their plate and potentially helping the user in a “hands-off” fashion. For the user, this means that help is dispatched immediately and can be used at their leisure, as needed. This approach lends itself well to the “product bumper” methodology that many PLG advocates point to.By using automation, users can be helped more immediately versus waiting for a call with the CSM team. By strategically pointing users to docs, tutorials, and new features based on events within the application or service, user experience is improved. Of course, there is always the fallback of users sidestepping the automation and engaging with the CSM team anyways. Having a highly-available CSM team plus automation is a great way to augment your PLG strategy.The Role of CSM Best Practices in Achieving Product Led GrowthCSM best practices can underpin product led growth in multiple ways. They can help customers stand up your product faster, reduce customer churn and accelerate product upsell. They can deliver greater recurring revenue and happier customers. And they can ensure that any growth trends are identified at an early stage, so that your business can support its customers and ensure they are ready and on the right path when usage explodes.Are you ready to maximize your product led growth with CMS best practices? You can find out more about customer success with Moesif or jump straight in and get started today.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-analytics/product-management/How-to-Maximize-Product-Led-Growth-with-Customer-Success-Best-Practices/",
          "author": "Larry",
          "categories": "API-Analytics, product-management"
        }
      
    ,
  
    
        "api-monetization-stripe-end-to-end-api-monetization-with-the-kong-developer-portal-stripe-and-moesif": {
          "title": "End-to-End API Monetization with The Kong Developer Portal, Stripe, and Moesif",
          "content"	 : "Many API developers and companies struggle to find ways to set up systems to monetize their APIs easily. Some are simple but not customizable, and some are complex and require massive engineering effort to get them all running.To make things easier, the Moesif platform includes Billing Meters which gives massive customizability to API monetization efforts with a minimal amount of code and engineering effort.For this example, which could be used out of the box, we will use Moesif, Kong, and Stripe to charge users for API usage. For this setup there are a few assumptions:  You have a running instance of Kong (with an endpoint and a route created)  Your Kong instance has key-auth enabled for your endpoint(s)  You have enabled the Kong Dev Portal (Enterprise License is required for this)  You have an active Stripe account  You have an active Moesif account  You have installed and configured the Moesif plugin in KongFor this particular instance, we will use the Kong Developer portal to register the user in Kong, Stripe, and Moesif. The user will then be able to generate API keys to access monetized endpoints. As traffic flows into the endpoint, Moesif will record and send the usage statistics over to Stripe so that users can be charged.Now, let’s first jump into creating some products and prices in Stripe that we can subscribe users to and begin to generate revenue.                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free        1 - Create Your Product and Price in StripeThe first step we will take is to create a product and price in Stripe. It’s best to do this step first because then when you integrate Stripe into Moesif you’ll already have some pricing plans for Moesif to pull in. A pricing plan can then be associated with specific billing criteria set up within a Billing Meter in Moesif.First make sure to meet these prerequisites.To create a product and price, log into Stripe and proceed to the Products page in the Stripe UI. Once there, click on the + Add Product button in the top right corner.You’ll then be able to add in the details for your product and price(s) for it. The form for your product will have a few fields to fill out.Product InformationName  This is the name of your product. In the example below, we use the name “My API”.Description  This field is optional but you could put a brief description of the product here. In the example below, we use a description of “This is a monetized API”.Image  Optionally upload an image that can help you easily recognize a item on the Products page. We’ll be using the default placeholder image in this example.Pricing InformationYou can choose between Recurring and One-off pricing for your product.Recurring PricinngIn recurring pricing, your customers pay an ongoing fee according to the pricing model you define. After selecting Recurring, you can enter the amount you want to charge and the billing period.To further configure your recurring pricing, select More pricing options. This allows you to specify the pricing model, amount, billing period, price description, and more.The following pricing models are available in Stripe for recurring pricing:  Flat rate  A fixed price for a single unit or package.  Package pricing  Charge for API usage by the package, or a group of units. For example, you could set it up to charge $10 for every 1000 API calls. Every time the user goes over the 1000 API call threshold, they are charged another $10.  Tiered pricing  Offer different price points for based on unit quantity.  Usage-based  Pay-as-you-go billing based on metered usage. You can charge per package, per unit , or per tier and define the prices and units accordingly. You can also set up a billing meter for the price to meter usage. See Creating a Product and Price in Stripe for instructions on how to set up a usage-based scheme.One-Off PricingIn one-off pricing, you charge a one-time fee rather than recurring amount in each billing period. After selecting One-off, you can enter the amount you want to charge in the Amount field.Similar to recurring pricing, you can select More pricing options and configure your pricing further by specifying the pricing model, amount, price description, and more.The following pricing models are available for one-off pricing:  Package pricing  Charge for API usage by the package, or a group of units. For example, you could set it up to charge $10 for every 1000 API calls. Every time the user goes over the 1000 API call threshold, they are charged another $10.  Flat rate  A fixed price for a single unit or package.  Customer chooses price  The customer sets a custom price. You can set a limit and define a preset amount to suggest to the customer.Billing periodThe billing period can be set as the following for recurring pricing models:  Daily  Weekly  Monthly  Every 3 months  Every 6 months  Yearly  CustomFor your configuration with Moesif, we recommend setting the billing period as Monthly.Price descriptionThis is an optional field but recommended. Here you can put a brief description of your price. This will allow you to more easily decipher which price you are selecting in the billing meter in Moesif, especially if you have multiple prices for a single product.Once you’ve input all of the details for your price, you can select Next and then select Add productAs you create products, you will be able to view and edit them on the Product Catalog screen.2 - Enable the Moesif-Stripe IntegrationOnce your products and prices are created, it’s time to begin to integrate Stripe with Moesif. To begin configuring Stripe in Moesif, go to the Billing Meters page and click the Edit Billing Provider dropdown in the top right corner of the screen.This will bring up the Stripe configuration screen walking you through the integration. From this screen, you can get all of the info needed to plug Stripe into Moesif. Each step for configuration is covered within the modal.Add the Moesif Webhook to StripeThe first step in the integration is to add the Moesif webhook into the configuration in Stripe. Adding this allows Stripe to send subscription updates to Moesif.To add the Moesif webhook to Stripe, from the upper right-hand side click on Developers, and then Webhooks in the left-side menu. This will bring you to the Webhooks page where you can view existing webhooks and add new ones. To add a new webhook we will click the Add an endpoint button at the bottom of the screen.From here, we will plug in our Moesif API endpoint URL and configure the events to listen to. You’ll want to copy your Moesif Webhook URL into the Endpoint URL field and then click the + Select Events button.  These details can all be found on the Stripe configuration page in Moesif mentioned in the previous section.You should select the option under Customer for Select all Customer events. After this, click the Add events button at the bottom of the screen.After this, you’ll be returned back to the original screen where you added the endpoint details. Scroll to the bottom of the screen and click Add endpoint to save the endpoint to Stripe.Plug the Stripe API Details into MoesifFor Moesif to add usage quantities to subscriptions in Stripe, we need to add the Stripe API details into Moesif. This is done in the Stripe configuration screen in Moesif, the same screen we’ve been working with previously.Currently, Moesif only supports version 2020-08-27 of the Stripe API so that field defaults for the Stripe API Version field.For the Stripe API Key field, you’ll need to retrieve the API key from Stripe to plug it in. From the Developers screen, the same one we used in the previous step, you’ll click on API Keys. You’ll then be able to see the private key for your API in either the Secret key or a generated Restricted keys field on the screen. Either key can be used.After copying the key from Stripe, you’ll paste this key into the Stripe API Key field back in Moesif. After doing this, back in Moesif you can scroll down to the bottom of the screen and click Save to save the configuration.Optionally, you have the ability to customize the company_id in Moesif as well. The default should work fine for most purposes. However, you can fully customize it to specify how to map Stripe Subscription and Customer objects to Subscription ID and Company ID in Moesif respectively:  A Stripe Customer maps to Moesif Company.  A Stripe Subscription maps to a Moesif Subscription.3 - Create a Billing MeterOnce you have the Stripe integration active in Moesif, you can begin to set up your billing meter. Billing meters created in Moesif do two things: track usage based on specific criteria and report that usage to the billing provider. Moesif allows you to set up very simple and very complex billing meters with relative ease.To create the Billing Meter, in Moesif you will navigate to the Billing Meter screen. You can do this from the left-side menu. On the Billing Meter’s screen, you’ll then click + Add Billing Meter in the top-right corner of the screen.The next screen is where you can actually input the criteria for your Billing Meter.Fields on this screen include:      Billing Meter Name          This is the Moesif internal name of your new Billing Meter            Billing Provider          In this dropdown you can choose the billing provider you want to send your usage metrics to.            Product (Stripe only)          Here you can choose which Product that you’ve set up in Stripe you want your usage metrics to be tied to.            Price (Stripe only)          The last field in the Billing Provider settings for the Billing Meter, here you will choose which Price you want to tie your usage metrics to.            Filters          Under the Filters configuration, you will configure your billing criteria to only include requests that fit a certain criteria.            Metrics          Here you can choose which metric you would like to bill on. Available options include:            Event Count          This will increment usage for every event that fits the criteria outlined in the Filter criteria.            Unique Users          This will increment usage whenever a unique user sends a request that fits the Filter criteria. For every unique user, the count will be incremented by 1 regardless of the event count for that user.            Unique Companies          This will increment usage whenever a unique company sends a request that fits the Filter criteria. For every unique company, the count will be incremented by 1 regardless of the event count for that company.            Unique Sessions/API Keys          This will increment usage whenever a unique session or API key is used to send a request that fits the Filter criteria. For every unique session or API key, the count will be incremented by 1 regardless of the event count for that particular session or API key.        There are other options under Metrics as well but the above 4 tend to be the most applicable to usage-based billing.As an example, for this guide we will create a Billing Meter that will filter traffic for a single endpoint, named /test-service, and where requests received a successful HTTP 200 response. We will use the Event Count metric to make sure that every request is added to the tally and sent to the billing provider.In Moesif, the billing meter will be configured as shown below.We will then click Create. This will create and activate the Billing Meter. A modal will appear notifying you that the billing meter has been created and presents a walk-through to ensure the meter is correctly configured.First, we will set up a flow to get users registered, subscribed, and create a JWT so they can use our monetized API. Once that is complete we will come back and proceed with the walk-through.4 - Setting Up the Kong Developer PortalNext, we will customize the Kong Developer Portal to accommodate monetization. For this, we will add a few files and change a few that are already created by default. The flow will allow users to sign up for access to the developer portal and the APIs within it. When the user signs up, they will automatically be subscribed to the “My API” product we created in the previous steps.The first step in customizing the portal for monetization is to log into the Kong Management UI. This usually is running on your Kong server on port 8002. Once you are in the Kong Management UI, click on the Dev Portals menu item at the top of the screen. Once on the Dev Portals screen, you’ll click the Dev Portal Overview link.Once on the Dev Portal Overview screen, in the navigation on the right, click the Editor menu item to go to the Dev Portal editor.Once in the Editor screen, we will add a monetization.js script. To do this, click the New File button in the top left of the screen.In the modal that pops up, we will select themes from the File Type dropdown. In the File Path, we will input base/assets/js/third-party/monetization.js to create our monetization.js file in the correct directory. Once the fields are filled out, click Create File.You should now see that the file has been added to the specified file path.At the top of the monetization.js file, we will add a credentials object so that we can add our Stripe API Key and Moesif Application ID.const credentials = {   stripe_api_key: 'Bearer STRIPE_API_KEY',   moesif_app_id: 'MOESIF_APP_ID',}Obtaining Your Stripe API and Price KeyYour Stripe API Key can be found in the same place we grabbed the key for our Stripe and Moesif integration we did earlier for the Billing Meter. You can actually use the same key for both or create a restricted key with just the scope needed for each function.While we’re at it, lets grab our Stripe product’s price key. We will need it in the next section. Your Stripe price key is an identifier for the price you created earlier in Stripe. This can be found by going to the product in Stripe and grabbing the value from the API ID column.Obtaining Your Moesif Application IDYour Moesif application ID is found in Moesif by going to the menu link in the bottom-left of the screen (which will show your name) and selecting API Keys.The key will then be on the page that appears under Collector Application Id.Under the credentials object, we will add a function to create a customer in Stripe. We will name the function createStripeCustomer and make a call to the Stripe /customers endpoint. The endpoint will return the Customer ID of the newly created Stripe customer. The fully-implemented function can be seen below.const createStripeCustomer = function(full_name, email) {   var stripeCustomerID = undefined;   $.ajax({       type: 'POST',       url: 'https://api.stripe.com/v1/customers',       async: false,       headers: {           Authorization: credentials.stripe_api_key       },       data: {           "email": email,           "name": full_name,       },       success: function(data) {           stripeCustomerID = data.id       },       error: function(xhr, ajaxOptions, throwError) {           console.log(xhr.responseJSON);           rej({           message: xhr.responseJSON ? xhr.responseJSON.message : null,           xhr: xhr,           ajaxOptions: ajaxOptions,           throwError: throwError           });       }   });   return stripeCustomerID;}Under our createStripeCustomer function, we will create another function to create a new Stripe subscription. We will call the /subscriptions Stripe endpoint that will subscribe the customer to the Stripe Price provided.const createStripeSubscription = function(stripeCustomerID, stripe_price_key) {   var stripeSubscriptionID = undefined;   $.ajax({       type: 'POST',       url: 'https://api.stripe.com/v1/subscriptions',       async: false,       headers: {           Authorization: credentials.stripe_api_key       },       data: {           "customer": stripeCustomerID,           "items": [               {                   "price": stripe_price_key               },           ],       },       success: function(data) {           stripeSubscriptionID = data.id;       },       error: function(xhr, ajaxOptions, throwError) {               rej({                   message: xhr.responseJSON ? xhr.responseJSON.message : null,                   xhr: xhr,                   ajaxOptions: ajaxOptions,                   throwError: throwError               });       }   })   return stripeSubscriptionID;}The last function to implement is the createMoesifUser function. This function will call the Moesif API and create a new user with the correct user credentials. As you can see, we are passing a payload that will create a user in Moesif with a UserId that is equal to their email and a CompanyId that is equivalent to their Stripe Subscription ID.const createMoesifUser = function(email, full_name, stripeSubscriptionID) {    $.ajax({       type: 'POST',       url: 'https://api.moesif.net/v1/users',       async: false,       headers: { 'Accept': 'application/json', 'X-Moesif-Application-Id': credentials.moesif_app_id },       data: JSON.stringify({ "user_id" : email, "company_id" : stripeSubscriptionID, "metadata" : { "name" : full_name } }),       success: function(data) {           console.log(data);       },       error: function(xhr, ajaxOptions, throwError) {           rej({               message: xhr.responseJSON ? xhr.responseJSON.message : null,               xhr: xhr,               ajaxOptions: ajaxOptions,               throwError: throwError           });       }    })}The complete monetization.js file will look like this:const credentials = {   stripe_api_key: 'Bearer sk_test_STRIPE_API_KEY',   moesif_app_id: 'MOESIF_APP_ID',}const createStripeCustomer = function(full_name, email) {   var stripeCustomerID = undefined;   $.ajax({       type: 'POST',       url: 'https://api.stripe.com/v1/customers',       async: false,       headers: {           Authorization: credentials.stripe_api_key       },       data: {           "email": email,           "name": full_name,       },       success: function(data) {           stripeCustomerID = data.id       },       error: function(xhr, ajaxOptions, throwError) {           rej({           message: xhr.responseJSON ? xhr.responseJSON.message : null,           xhr: xhr,           ajaxOptions: ajaxOptions,           throwError: throwError           });       }   });   return stripeCustomerID;}const createStripeSubscription = function(stripeCustomerID, stripe_price_key) {   var stripeSubscriptionID = undefined;   $.ajax({       type: 'POST',       url: 'https://api.stripe.com/v1/subscriptions',       async: false,       headers: {           Authorization: credentials.stripe_api_key       },       data: {           "customer": stripeCustomerID,           "items": [               {                   "price": stripe_price_key               },           ],       },       success: function(data) {           stripeSubscriptionID = data.id;       },       error: function(xhr, ajaxOptions, throwError) {               rej({                   message: xhr.responseJSON ? xhr.responseJSON.message : null,                   xhr: xhr,                   ajaxOptions: ajaxOptions,                   throwError: throwError               });       }   })   return stripeSubscriptionID;}const createMoesifUser = function(email, full_name, stripeSubscriptionID) {    $.ajax({       type: 'POST',       url: 'https://api.moesif.net/v1/users',       async: false,       headers: { 'Accept': 'application/json', 'X-Moesif-Application-Id': credentials.moesif_app_id },       data: JSON.stringify({ "user_id" : email, "company_id" : stripeSubscriptionID, "metadata" : { "name" : full_name } }),       success: function(data) {           console.log(data);       },       error: function(xhr, ajaxOptions, throwError) {           rej({               message: xhr.responseJSON ? xhr.responseJSON.message : null,               xhr: xhr,               ajaxOptions: ajaxOptions,               throwError: throwError           });       }    })}Once your code is complete, make sure to save the file.Next, we will navigate to Themes/base/partials/theme/required-scripts.html. In this file, we will need to add an entry to include the monetization.js script. That entry will look like this.<script src="assets/js/third-party/monetization.js"></script>In the file itself, just under the JQuery script tag, we will add the script tag for monetization.js.{# /! REQUIRED SCRIPTS. REMOVE / EDIT AT OWN RISK. /! #}<script src="assets/js/third-party/jquery.min.js"></script><script src="assets/js/third-party/monetization.js"></script><script src="assets/js/kong/kong.js"></script><script src="assets/js/kong/kong.utils.js"></script><script src="assets/js/kong/kong.auth.js"></script>  The import needs to be below the JQuery import since we use JQuery in the functions included in the monetization.js file.Finally, we can use the functions we defined in monetization.js as part of the developer portal registration flow. For this, we will need to go into the kong.auth.js file. Open the file by going to Themes/base/assets/js/kong/kong.auth.js. Once in the file, we will scroll down to the window.Kong.Auth.register and change the function to add a new Stripe customer, subscribe the new customer to a subscription, and add the user to Moesif.We will adjust the function by adding the following logic within the success callback.var customer_id = createStripeCustomer(JSON.parse(fields.meta).full_name, fields.email);var subscription_id = createStripeSubscription(customer_id, "price_YOUR_PRICE_KEY");createMoesifUser(fields.email, JSON.parse(fields.meta).full_name, subscription_id);  Note that currently the Stripe Price ID is being hard coded. You’ll need to replace this value with a price key that you’ve created in Stripe. Optimally, somewhere in the signup flow, you would have a dropdown populating this value or something similar. However, this hardcoded approach can work if you only have a single price.In the above code, the price_YOUR_PRICE_KEY value should contain the Stripe Price key that corresponds with the Billing Meter you set up. This will be the price key for the price you created earlier in Stripe. This can be found by going to the product in Stripe and grabbing the value from the API ID column.  The completed register function will look like this:window.Kong.Auth.register = function(options) { var type = options.type var fields = { meta: {} } for (var i = 0; i < options.fields.length; i++) {   var field = options.fields[i];   if (type.FIELDS.indexOf(field.name) < 0) {     fields.meta[field.name] = field.value;   } else {     fields[field.name] = field.value   } } fields.meta = JSON.stringify(fields.meta) return new Promise((res, rej) => {   $.ajax({     type: 'POST',     url: [options.baseUrl, window.Kong.Auth.REGISTER_ENDPOINT].join('/'),     data: fields,     success: function(data) {       var customer_id = createStripeCustomer(JSON.parse(fields.meta).full_name, fields.email);       // HARDCODE STRIPE PRICE VALUE BELOW       var subscription_id = createStripeSubscription(customer_id, "price_YOUR_PRICE_KEY");       createMoesifUser(fields.email, JSON.parse(fields.meta).full_name, subscription_id);       return res(data);     },     error: function(xhr, ajaxOptions, throwError) {       rej({         message: xhr.responseJSON ? xhr.responseJSON.message : null,         xhr: xhr,         ajaxOptions: ajaxOptions,         throwError: throwError       });     }   }); })}With your code added to the file, make sure to save your changes.Testing Out The Moesif Billing MeterNow that we have Moesif, Stripe, and the Kong Developer Portal set up, we can begin to test our setup to ensure that everything is working as expected.Start a Meter TestFor this, we will go back to our Billing Meter and click Test Meter.After clicking this, the Test Meter modal will appear. For now, we will leave this tab open in our browser since our first step is to use the Developer Portal in Kong to register a user. Registering a user will create the user in Stripe and create a subscription for them, which is the first requirement in the Test Meter.Sign Up in the Dev PortalTo create a user, we need to go into the Kong Dev Portal. Once you’ve navigated to the Kong Developer Portal, you’ll need to begin the registration process. To do this you can either click Sign Up in the top-right corner or click the Create a Developer Account button in the middle of the screen.On the next screen, there are a few fields required to sign up a new user. Once these fields are filled out, click Create Account.After the Create Account button is clicked, our amended Kong.register function will execute. This will create our user in Stripe and Moesif. Once the registration is complete, you’ll be taken to the Developer Dashboard screen.Just to ensure that our code worked successfully, we will navigate back to Stripe and check under the Customers screen that our new customer was created.By clicking on the customer, we can also confirm that they have been subscribed to the My API product as well.Now that we have confirmed that the user has been correctly registered and created in Stripe, let’s push some API traffic to our monetized /test-service endpoint.Create an API Key in the Developer PortalTo be able to access the /test-service endpoint, we will need to generate an API key. This API key will give us access to the endpoint and also attribute usage to our users in Kong, Moesif, and Stripe. To generate an API key, from the dashboard in the API Portal, click Create API Credential.A modal will appear. Ensure that the Credential Type is set to Key Auth Credential and we will leave the Key field blank so that one will be auto-generated for us. Then, click Create API Credential.The newly created API key will then be available on the dashboard screen.Copy this credential and have it ready to populate into the “apiKey” header of our request in Postman.Use Postman to Test The EndpointOur next step is to actually use our generated API key. We will then confirm that all the correct information is added to Moesif. The data we are confirming includes:  The users’ email is mapped to the Moesif User ID  The Stripe Subscription ID is mapped to the Moesif Company IDNext, let’s use Postman, or another platform, to send a request to the /test-service endpoint. This is the endpoint that we set up the billing meter in Step 3, above.In Postman, we will:  Put the /test-service API endpoint as the request URL  Select the Authorization tab  Select the Type as API Key  Populate the API key details  Set the Key as “apikey”, or whatever you have the Key set to in Kong  Set the Value as the generated API key  Set the Add to field value to “Header”Below is an example of the populated request configuration in Postman.To send the request to our endpoint, click Send.Once sent, your request should be proxied through Kong and the API call analytics should land in Moesif.Confirm The Request Landed in MoesifBack in Moesif, you’ll navigate to the Live Event Log where you should see the request you just sent. You should see the entry has both a User ID and Company ID populated with the user’s email and Stripe subscription ID respectively.Since your Meter Test is already underway, in a new tab in your browser, open up a second tab to view Moesif. To get to the Live Event Log, in Moesif, Click the + New button in the top-left corner of the screen. From the modal that appears, click Live Event Log.The entries should look like this:Resume the Meter TestBack in the Meter Test you started in the earlier steps, we can continue on to ensure that everything is working. With API call data landing in Moesif, we should be able to proceed.Part 1 of the Meter Test will confirm that our Subscription data has been synced into Moesif. As you can see in the screenshot below, you should see a subscription for the user you created in the portal. Once confirmed, click Next.Next, we should see that the API calls that we sent through are being correctly recorded for the billing meter/subscription. After you’ve confirmed this step, click the Next button.  This may take a minute or two but should show up relatively quickly.The last step in the Meter Test is Step 3. This step will ensure that usage data has been synced over to Stripe. Currently, as of the writing of this tutorial, Moesif syncs data to Stripe every 15 minutes. When you first get to the screen, you will likely see that it is waiting to sync data from Moesif to Stripe. The screen will look like this:On the screen, you should see when the next sync will occur. Once the sync window has elapsed and Moesif has sent the data to Stripe, you should see this screen update.Once completed, you can click Close. Your meter is now 100% functional and will accurately record and send usage data to Stripe.Check Stripe for UsageAs an extra step, you can also go into Stripe to confirm that usage is being added to a user’s subscription. Be sure that you’ve sent a few requests through in order to make sure you have some data that should be sent to Stripe.To check the usage, in Stripe you’ll want to navigate to the Customers screen and select the customer that you made the API call with.The subscription we are billing towards is the one we created earlier named My API. Click on the subscription entry on the customer details screen.On the next screen, click on View Usage beside the price entry.A modal should now pop up showing you the usage for the API that has been reported to Stripe from Moesif.Wrapping upMonetization has always been a tough hurdle to get past. Many custom solutions offered flexibility but at a very high engineering and support cost. With Moesif, API monetization is possible in an extremely minimal amount of time. As demonstrated in this article, With a little bit of configuration and minimal amount of code we can create a production-ready, post-paid monetization scheme in minimal time.                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue with your Kong managed APIs?            Monetize your Kong managed APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /api-monetization/stripe/End-To-End-API-Monetization-With-The-Kong-Developer-Portal-Stripe-And-Moesif/",
          "author": "Matthew",
          "categories": "API-Monetization, Stripe"
        }
      
    ,
  
    
        "api-analytics-api-product-management-the-4-best-dapp-frameworks-for-first-time-ethereum-developers": {
          "title": "The 4 Best dApp Frameworks for First-Time Ethereum Developers",
          "content"	 : "Ethereum has experienced dazzling growth in recent years. The programmable blockchain now has approximately 220 million unique addresses. Linked to the increase in users is an explosion in the number of dApps. Global companies and startups across finance, sales, HR, accounting, supply chain and manufacturing are using dApps to streamline processes and onboard new customers. Multiple frameworks exist that simplify the dApp development process for web2 developers who want to participate in web3. This post examines four of the most popular. But first, what is a dApp?What is a dApp?A dApp, or decentralized application, is serverless software that runs on a decentralized network and uses a programmable blockchain for security, transparency and immutability. A dApp combines smart contracts with a frontend user interface (html5, React, Angular). DApps can be used in a variety of industries and services, from social media to supply-chain management, payment tracking, complaint resolution and all manner of accounting software and (decentralized) financial services.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        How is a dApp different from an App?To the end client, a dApp shouldn’t feel any different to a traditional app. The differences are beneath the hood. Firstly, unlike a conventional app that has its backend code running on centralized servers such as AWS orAzure, dApps run on a decentralized peer-to-peer network (blockchain) such as Cardano, Algorand, Polkadot, Solana or Tezos. However, for this post we will focus on the most popular network - Ethereum. When designing your decentralized app, building for the Ethereum blockchain starts with selecting the right framework for your needs.What are dApp advantages?  Increased Privacy and censorship: Users don’t need to provide identity to interact with a dApp. This protects user data.  Better security: a conventional app runs on centralized servers that are more vulnerable to tampering and data breaches.  More Interoperability: traditional apps are mostly isolated, siloed software. dApps give interoperability across the same - and increasingly other - blockchain technology.Trustless: Smart contracts execute in predictable, pre-programmed ways, removing the need for intermediaries.How do dApps work with APIs?dApps use APIs to interact and access the functionality of other dApps - to retrieve financial, HR or accounts data, for example. They can also open their own APIs to the wider ecosystem of Ethereum dApps. Additionally, dApps use APIs to send transactions and interact with smart contracts on Ethereum.What are the common APIs for interacting with Ethereum?  JSON-RPC API: A popular API used to send transactions, read data and interact with smart contracts.  Web3.js: A JavaScript library that provides a user-friendly API for interacting with Ethereum. Web3.js is used to send transactions, read data and interact with smart contracts. Additional functionality includes event handling and contract abstraction.  Infura.API: Provides hosted Ethereum nodes so developers can interact with Ethereum without running their own.What are the best frameworks for developing Ethereum dApps?Solidity, the programming language of Ethereum, owes much to JavaScript and C++, as such, web2 developers should experience a shallow learning curve. However, there are numerous frameworks for developing decentralized apps that make the process for developers more straightforward. Picking the right one will go a long way to determining your success. Here are four of the best frameworks.TruffleTruffle is a popular development and testing framework for dApps, both for first time and experienced Ethereum developers. As well as containing a web3.js library, Truffle is simple, user friendly and, with over 56K GitHub users, trusted. To install Truffle you need to have Node, NPM and Python. You can install Truffle via NPM with the command ‘npm install -g truffle.’Truffle Pros:  User-friendly interface with a comprehensive suite of developer tools, including smart contract development, testing and debugging  Write tests in Solidity, JavaScript and TypeScript and use Drizzle front-end libraries for dApp UI  Layer 2 support - develop on EVM and JSON-RPC compatible blockchains such as Optimism, Polygon, Arbitrum and AvalancheTruffle Cons:  Steep learning curve and a potential complex testing and debugging environment for first time dApp developers  Reliance on JavaScript is a limitation for experienced Ethereum developersTruffle Use Cases:  Building and deploying smart contracts on the Ethereum blockchain  Developing and testing smart contracts locally before deploying them to the blockchain  Automating contract testing and deployment  Managing and interacting with multiple development networks and testnets  Creating and managing digital assets, such as tokens  Building decentralized autonomous organizations (DAOs)HardhatHardhat allows developers to build, test, and deploy smart contracts and dApps using a variety of tools and libraries. With over 114K users on GitHub and an active Discord community, Hardhat is a hugely popular framework for dApp developers. Much of its popularity can be attributed to its rich list of features, flexibility and the Hardhat Ethereum Virtual Machine for testing and debugging smart contracts.Hardhat Pros:  Intuitive debugging and testing environment. Developers get stack traces, console.log and explicit error messages when transactions fail  Test and deploy dApps via JavaScript, Rust and TypeScript integration  Active community and trusted by some of the biggest names in web3 - Yearn, Uniswap, Decentraland, ChainlinkHardhat Cons:  Steep learning curve and limited documentation compared to Truffle  Limited support for front-end frameworks for dApp UI design  Designed more for experienced web3 dApp developersHardhat Use Cases:  Developing and testing smart contracts on a local development network  Automating smart contract testing and deployment  Debugging and troubleshooting smart contract issues  Simulating and testing different network conditions, such as high network latency or low gas prices  Creating and managing multiple development networks for different stages of the development process  Interacting with smart contracts through a user-friendly command line interface (CLI)EmbarkSimilar to Truffle, Embark provides tools and libraries (web3.js, IPFS, EmbarkJS, Embark-testrpc) for developing, launching and maintaining dApps. Additional features of Embark include automatic contract deployment and a user interface for integration with other APIs. Embark is a sound choice for first time Ethereum dApp developers.Embark Pros:  User-friendly interface. Comes with Cockpit - web-based tools to facilitate the development and debugging of dApps  Multiple libraries, storage and integration with IPFS, Whisper and Swarm  Respected debugging and testing environment. Extensive plug-in customization options for full dApp developmentEmbark Cons:  Steep learning curve  Reliance on JavaScript  Limited Github community and not as popular in web3 as other frameworks such as TruffleEmbark Use Cases:  Building and deploying smart contracts on the Ethereum blockchain  Building front-end user interfaces for dApps using JavaScript frameworks such as AngularJS and ReactJS  Developing and testing smart contracts locally before deploying them to the blockchain  Integrating dApps with web3 wallets and other blockchain-related tools  Automating deployment and management of smart contracts and dApps.OpenZeppelinOpenZeppelin is a popular dApp framework with some of the biggest companies in web3 (Decentraland, Aave, ENS, The Sandbox). Its smart contract templates and rich reserve of libraries (Network.js, Hotloader) are tried and tested. The OpenZeppelin Starter Kits make the framework an ideal starting place for first-time Ethereum dApp developers.OpenZepplin Pros:  OpenZeppelin starter kits are a great way to build your first dApp - reuse community vetted code, upgrade and test smart contracts and create UI  Widely used open source framework with an active GitHub community and documentation  Extensive and trusted audit service - smart contracts will conform to established standardsOpenZepplin Cons:  Reliance on Solidity  Steep learning curve  Needs to be used in conjunction with other frameworks such as Truffle and Embark for the complete dApp development processOpenZepplin Use Cases:  Building decentralized applications on Ethereum  Creating and managing digital assets such as ERC-721, ERC-1155 and ERC-20 tokens  Implementing smart contract security best practices  Building decentralized autonomous organizations  Creating and managing digital identities on the blockchaindApp Frameworks SummaryWhen it comes to choosing which dApp framework is best for a first-time Ethereum developer, understanding the fundamentals of what  a dApp is are critical. It’s vital to consider documentation, how active the community (Reddit, GitHub, Discord) is and how geared the particular framework is to the needs of your decentralized app. That said, most frameworks offer similar tools, so getting familiar with one might be more beneficial than experimenting with two or three.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-analytics/api-product-management/The-4-Best-dApp-Frameworks-for-First-Time-Ethereum-Developers/",
          "author": "Rachael",
          "categories": "api-analytics, api-product-management"
        }
      
    ,
  
    
        "technical-api-development-building-rest-api-with-aws-gateway-and-python": {
          "title": "Building a REST API with AWS Gateway and Python",
          "content"	 : "AWS Gateway is a powerful tool for building APIs that scale to meet the demands of modern web and mobile applications. With AWS Gateway, you can create RESTful APIs that expose your data and business logic to developers who can then build rich, interactive applications that consume your API.REST API is an industry standard for building scalable, distributed web applications. With AWS Gateway, you can easily build a REST API that supports both GET and POST methods, as well as complex query parameters. You can also add support for other HTTP methods, such as PUT, DELETE, and HEAD.Using AWS Gateway, you can quickly create APIs that are secure and robust. You can also use it to deploy your code to a production environment with minimal effort. Additionally, AWS Gateway allows for seamless integration with other AWS services, such as S3 and DynamoDB, enabling you to easily add complex functionality to your APIs.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        PrerequisitesBefore building a RESTful API with AWS Gateway, you should have the following in place:      Create an AWS account if you don’t have one already.        Log in to the AWS Management Console and navigate to the Amazon API Gateway service.    Click on “Create API” and select “REST API”.  Click on “Actions” and define the resource and click “Create Resource”.  Select the newly created resource and click on “Create Method”.      Choose the HTTP verb (e.g. GET, POST, PUT, etc.) and click on the checkmark to create the method.        In the “Integration type” section, select “Lambda Function” and enter the name of the Lambda function you want to use to handle the API requests.        Click on “Save” to create the API.    Select Node from the Runtime Dropdown.Code Exampleimport json# Example datadata = {    "items": [        {"id": 1, "name": "Item 1", "price": 10.99},        {"id": 2, "name": "Item 2", "price": 15.99},        {"id": 3, "name": "Item 3", "price": 20.99},    ]}def lambda_handler(event, context):    # Determine the HTTP method of the request    http_method = event["httpMethod"]    # Handle GET request    if http_method == "GET":        # Return the data in the response        response = {            "statusCode": 200,            "body": json.dumps(data)        }        return response    # Handle POST request    elif http_method == "POST":        # Retrieve the request's body and parse it as JSON        body = json.loads(event["body"])        # Add the received data to the example data        data["items"].append(body)        # Return the updated data in the response        response = {            "statusCode": 200,            "body": json.dumps(data)        }        return response    # Handle PUT request    elif http_method == "PUT":        # Retrieve the request's body and parse it as JSON        body = json.loads(event["body"])        # Update the example data with the received data        for item in data["items"]:            if item["id"] == body["id"]:                item.update(body)                break        # Return the updated data in the response        response = {            "statusCode": 200,            "body": json.dumps(data)        }        return response         # Handle DELETE request    elif http_method == "DELETE":        # Retrieve the request's body and parse it as JSON        body = json.loads(event["body"])        # Find the item with the specified id in the example data        for i, item in enumerate(data["items"]):            if item["id"] == body["id"]:                # Remove the item from the example data                del data["items"][i]                break        # Return the updated data in the response        response = {            "statusCode": 200,            "body": json.dumps(data)        }        return response    else:        # Return an error message for unsupported methods        response = {            "statusCode": 405,            "body": json.dumps({"error": "Method not allowed"})        }        return responseThis code defines a Lambda function, lambda_handler, that handles different types of HTTP requests (GET, POST, PUT, DELETE) on some data. The data is an object containing an array of items, each item has an id, name, and price.When the function is called, it first determines the HTTP method of the request from the event object. Then it handles the request accordingly:  GET: returns the data in the response with a status code of 200.  POST: retrieves the request’s body and parse it as JSON, then add the received data to the example data, then returns the updated data in the response with a status code of 200.  PUT: retrieves the request’s body and parse it as JSON, then update the example data with the received data, then returns the updated data in the response with a status code of 200.  DELETE: retrieves the request’s body and parse it as JSON, then find the item with the specified id in the example data and remove it, then returns the updated data in the response with a status code of 200.  If the method is not supported, it will return an error message with a status code of 405.Deploy the API by clicking on “Actions” and selecting “Deploy API”.Select a deployment stage (e.g. “prod” or “test”) and click on “Deploy”.Use the generated API endpoint to make requests to your API.Running and Testing The Code in PostmanNow, our API is up and running. You can send a test HTTP request through Postman. By sending a request to your invoke URL, you should see a 200 OK status code. For this test, no request body is needed for the incoming request.Wrapping UpWith that, we’ve created a simple RESTful API using AWS Lambda and Python. This code can serve as a foundation for creating more complex APIs for your application. As you continue to develop the API, you may want to consider implementing security measures such as an API key, integrating with an API gateway, monitoring the usage of the API, or generating revenue through API monetization. If you are interested in exploring options for API analytics and monetization check out Moesif.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Building-Rest-API-With-AWS-Gateway-And-Python/",
          "author": "Preet",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-building-rest-api-with-aws-gateway-and-node": {
          "title": "Building a REST API with AWS Gateway and NodeJS",
          "content"	 : "AWS Gateway is a powerful tool for building APIs that scale to meet the demands of modern web and mobile applications. With AWS Gateway, you can create RESTful APIs that expose your data and business logic to developers who can then build rich, interactive applications that consume your API.REST API is an industry standard for building scalable, distributed web applications. With AWS Gateway, you can easily build a REST API that supports both GET and POST methods, as well as complex query parameters. You can also add support for other HTTP methods, such as PUT, DELETE, and HEAD.Using AWS Gateway, you can quickly create APIs that are secure and robust. You can also use it to deploy your code to a production environment with minimal effort. Additionally, AWS Gateway allows for seamless integration with other AWS services, such as S3 and DynamoDB, enabling you to easily add complex functionality to your APIs.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        PrerequisitesBefore building a RESTful API with AWS Gateway, you should have the following in place:      Create an AWS account if you don’t have one already.        Log in to the AWS Management Console and navigate to the Amazon API Gateway service.    Click on “Create API” and select “REST API”.  Click on “Actions” and define the resource and click “Create Resource”.  Select the newly created resource and click on “Create Method”.      Choose the HTTP verb (e.g. GET, POST, PUT, etc.) and click on the checkmark to create the method.        In the “Integration type” section, select “Lambda Function” and enter the name of the Lambda function you want to use to handle the API requests.Click on “Save” to create the API.    Select Node from the Runtime Dropdown.Code Examplelet user = {    firstName: "John",    lastName: "Smith",    location: "Bay Area"}export const handler = async(event) => {    // TODO implement    console.log(">>>> Inside Lambda Function....");    if(event.httpMethod === "GET")    {        getUserRecord(event);    }    if(event.httpMethod === "POST")    {        createUserRecord(event)    }    const response = {        statusCode: 200,        body: JSON.stringify({            user_details: user        })    };    return response;};function getUserRecord (event) {    const response = {        statuscode: 200,        body: JSON.stringify({            user_details: user        })    };    return response;}function createUserRecord(event) {    const body = JSON.parse(event.body);    const response = {        statusCode: 200,        body:JSON.stringify({            message: "successfully created",            details: body        })    };    return response;}The code first creates an object called user that contains some properties like firstName, lastName, and location.Then the handler function checks the httpMethod property of the event object, if it’s “GET” it calls the getUserRecord function and if it’s “POST” it calls the createUserRecord function.Both getUserRecord and createUserRecord functions takes the event object as input and returns the response object.In the getUserRecord function, it creates a response object with a statusCode of 200 and a body that contains a JSON object with user_details property which is the user object created at the beginning.In the createUserRecord function, it first parse the event.body which is a string, into a JSON object and then creates a response object with a statusCode of 200 and a body that contains a JSON object with message and details properties.Deploy the API by clicking on “Actions” and selecting “Deploy API”.Select a deployment stage (e.g. “prod” or “test”) and click on “Deploy”.Use the generated API endpoint to make requests to your API.Running and Testing The Code in PostmanNow, our API is up and running. You can send a test HTTP request through Postman. By sending a request to your invoke URL, you should see a 200 OK status code. For this test, no request body is needed for the incoming request.Wrapping UpWith that, we’ve created a simple RESTful API using AWS Lambda. This code can then be expanded on as needed to build APIs for your applications. Moving forward, you may want to secure the API with an API key, integrate the API with an API gateway, check out how your API is being consumed and used, or build revenue through API monetization? For a solution to your API analytics and monetization needs, check out Moesif today to explore all of this and more!                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Building-Rest-API-With-AWS-Gateway-And-Node/",
          "author": "Preet",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-building-a-restful-api-with-aws-lambda-and-express": {
          "title": "Building a RESTful API with AWS Lambda and Express",
          "content"	 : "AWS Lambda is a serverless computing service provided by Amazon Web Services (AWS) that allows you to run code without provisioning or managing servers. Node.js is a JavaScript runtime built on Chrome’s V8 JavaScript engine. Together, AWS Lambda and Node.js can be used to create a RESTful API that can be triggered by events such as an HTTP request.PrerequisitesBefore building a RESTful API with Express.js, you should have the following in place:  As Express.js is a JavaScript framework, you’ll need Node.js installed to run it. You can download Node.js from the official website  Text Editor or Integrated Development Environment (IDE): To write and edit your API code, you will need a text editor or IDE. Examples of popular text editors are Sublime Text and Visual Studio Code, while popular IDEs are WebStorm and Visual Studio.  In order to write your API, you should have a basic understanding of JavaScript, since Express.js is written in JavaScript.  A familiarity with Express.js: Express.js is a web framework for Node.js that helps you build web applications and APIs quickly and easily.  The RESTful APIs use HTTP for communication, so you should be familiar with the protocol. It is necessary to have a basic understanding of HTTP methods (GET, POST, PUT, DELETE) and their intended uses, status codes, and the format of HTTP requests and responses.  For keeping track of changes to your codebase, familiarity with version control systems (VCS) like Git is helpful.As soon as you have these prerequisites in place, you can start building your RESTful API with Express.js.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Adding in The Code  Create AWS_Node folder using the mkdir command in the terminal and cd into the directory.mkdir aws-lambda-express-democd aws-lambda-express-demo  Create app.js file in your AWS_Node folder.touch app.jsLibraries  We’ll use npm to download the latest version of the express package from the npm registry and store it in the node_modules folder in your project’s root directory. The package’s dependencies will also be installed and stored there as well.npm install express  Next we’ll install a middleware-compatible framework called serverless-http, a library for creating serverless applications. AWS Lambda allows you to write your application normally, and then wrap it around a function that is exported and executed using an HTTP request. Aside from Azure, Google Cloud, and others, it is also compatible with serverless providers.npm install serverless-http  You can install the package globally by running npm install -g serverless-httpHere’s an example of a RESTful API implemented using Node.js with the Express.js framework that implements the GET, POST, DELETE, and PUT methods:This code creates an Express.js app and adds routes for the GET, POST, DELETE, and PUT methods. It uses an in-memory items array to store data and uses the find and findIndex methods to retrieve and update items based on the id provided in the URL. Note that for the POST and PUT routes, you will have to parse the body of the request, which you can do with middleware such as body-parser.const express = require('express');const app = express();const serverless = require('serverless-http');const users = [    { id: 1, name: 'John', company: "ABC Company" },    { id: 2, name: 'Frank', company: "XYZ Inc." },    { id: 3, name: 'Ashley', company: "123 Company" },];app.get('/users', (req, res) => {    res.json(users);});app.get('/users/:id', (req, res) => {    const user = users.find(user => user.id === parseInt(req.params.id));    if (!user) res.status(404).json({ message: 'User not found' });    res.json(user);});app.post('/users', (req, res) => {    const user = {        id: users.length + 1,        name: req.body.name,        company: req.body.company,    };    users.push(user);    res.json(user);});app.delete('/users/:id', (req, res) => {    const userIndex = users.findIndex(user => user.id === parseInt(req.params.id));    if (userIndex === -1) res.status(404).json({ message: 'User not found' });    users.splice(userIndex, 1);    res.json({ message: 'User deleted' });});app.put('/users/:id', (req, res) => {    let user = users.find(user => user.id === parseInt(req.params.id));    if (!user) res.status(404).json({ message: 'User not found' });    user.name = req.body.name;    user.company = req.body.company;    res.json(user);});const handler = serverless(app);const startServer = async () => {    app.listen(3000, () => {      console.log("listening on port 3000!");    });}startServer();module.exports.handler = (event, context, callback) => {    const response = handler(event, context, callback);    return response;};We’ll write the line console.log("listening on port 3000!"); to indicate that your API is up and running. Finally, module.exports.handler is a function that takes in event, context, and callback arguments, and calls the handler function passing in the event, context, and callback arguments.Running and Testing The CodeStart the server by running the following:node app.jsNow, our API is up and running. You can send a test HTTP request through Postman. By sending a request to localhost:3000/users, you should see a 200 OK status code. For this test, no request body is needed for the incoming request.Wrapping UpWith that, we’ve created a simple RESTful API using AWS Lambda. This code can then be expanded on as needed to build APIs for your applications. Moving forward, you may want to secure the API with an API key, integrate the API with an API gateway, check out how your API is being consumed and used, or build revenue through API monetization? For a solution to your API analytics and monetization needs, check out Moesiftoday to explore all of this and more!                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Building-a-RESTful-API-With-AWS-Lambda-and-Express/",
          "author": "Preet",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-building-rest-api-with-java-sping-boot": {
          "title": "Building a RESTful API with Java Spring Boot",
          "content"	 : "Spring Boot is a popular framework for creating powerful RESTful APIs, and in this tutorial, we will use it to develop a simple API that simulates a credit score rating. The API endpoint we will create will allow a user to retrieve a credit score rating by sending a request to the API. However, it is important to note that we will not be linking up to any actual backend systems to pull a real credit score, instead we will use a random number generator to generate the score and return it to the user. Although this API is relatively simple, it will demonstrate the basics of REST API development using Java and Spring Boot. This tutorial will provide a hands-on introduction to building RESTful APIs with Java Spring Boot.PrerequisitesBefore we start, we must ensure that a couple of prerequisites are completed. To follow along and run this tutorial, you will need:  A working Java installation  Gradle or Maven build tools installed (we’ll be using Gradle)  An IDE or text editor of your choice  Postman to test our endpoint                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Creating the Initial ProjectWe’ll be using the Spring Initializr tool to create our initial project. The Spring Initializr generator provides many options allowing for quick customizations to our starter project.The options include:  Project          The type of build tool to be used with the project        Language          The language the project will use        Spring Boot          The Spring Boot version to use        Project Metadata          Group name                  Uniquely identifies your project across all projects                    Artifact name                  The name of the jar without version                    Name                  Name of the project                    Description                  A description for the project                    Package name                  The name for the package, typically a combination of the group and artifact name                    Packaging type                  The type of package that is created when building your project                    Java Version                  The version of Java to be used in the project                      Dependencies          Select and include many dependencies from developer tools and databases to web and security frameworks      We’ll be using the following settings for our project:Selecting Generate will initiate a download for the sample project. Unzip and open the folder generated by Spring Initializr, in our case called RESTDemo, in your favorite editor. This is where we will add our API code. Run the following command to ensure our project builds correctly out of the gate../gradlew buildAdding in The CodeUnder the path /src/main/java/com/example/RESTDemo we’ll create a file called APIController.java. This is where we will house our creditScore endpoint as well as the code to randomly generate the return value.Populate the newly created file with the following code:package com.example.RESTDemo;import java.util.Random;import org.springframework.web.bind.annotation.ResponseBody;import org.springframework.web.bind.annotation.RequestMapping;import org.springframework.web.bind.annotation.RestController;@RestControllerpublic class APIController {  @RequestMapping("/creditscore")  @ResponseBody  public String creditscore() {    int creditScoreMin = 500;    int creditScoreMax = 900;    Random rand = new Random();    int randomCreditScore = rand.nextInt(creditScoreMin, creditScoreMax);    return String.format("{ "credit_score": "%d" }", randomCreditScore);  }}The @RestController annotation is a specialized version of the Controller annotation in Spring MVC. It’s used to create RESTful web services using Spring. This annotation tells Spring that this class is a controller class that should handle incoming web requests.Inside this class, there’s a single method called creditscore() which is annotated with @RequestMapping("/creditscore"). The @RequestMapping annotation maps the method to a specific URI, in this case, it’s localhost:8080/creditscore.This method is also annotated with @ResponseBody. The @ResponseBody annotation tells Spring to bind the return value of this method to the body of the HTTP response.The method body of creditscore() declares two integers creditScoreMin and creditScoreMax. By utilizing the Random object instance we created, we will generate a number between creditScoreMin and creditScoreMax. Finally returns a JSON string representation of the generated credit score.Also, it’s worth noting that this code does not handle any request parameters as it does not take any input from the user, this make this end-point an example of a simple end-point.Running and Testing the APIWith our code written run the following command in order to compile our project into an executable .jar file../gradlew buildNow, let’s run our simple web API by using the following command in the root directory of the app.java -jar build/libs/RESTDemo-0.0.1-SNAPSHOT.jarThe API is now operational and ready for testing. You can use a tool like Postman to send an HTTP request to the API. The endpoint to get a credit score is localhost:8080/creditscore. When you send a request to this endpoint, you should receive a 200 OK status code as well as a credit score generated by the random number generator.Wrapping UpIn summary, we have developed a basic RESTful API using Java Spring Boot. This code can serve as a foundation for creating more complex APIs for your application. As you continue to develop the API, you may want to consider implementing security measures such as an API key, integrating with an API gateway, monitoring the usage of the API, or generating revenue through API monetization. If you are interested in exploring options for API analytics and monetization check out Moesif.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Building-REST-API-with-Java-Sping-Boot/",
          "author": "Dylan",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-building-a-restful-api-with-go": {
          "title": "Building a RESTful API with Go",
          "content"	 : "When it comes to building an API, Go is an extremely popular programming language choice to build powerful RESTful APIs with. The language is super lightweight, has many different libraries and frameworks available, and is easy to run. In this tutorial, we will build a simple REST API using Go. The API endpoint will allow a user to retrieve a credit score rating. Of course, we won’t be linking up to any backend systems to pull a credit score but will instead use a random number generator to generate the score and return it to the user. Although simple, this tutorial will show you the basics of  REST API development with Go.PrerequisitesBefore we start, we must ensure that a couple of prerequisites are completed. To follow along and run this tutorial, you will need to:  Install Go  Have an IDE to edit your code  Install Postman so that you can test the endpointAfter all 3 of these prerequisites are completed, we can begin!                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Creating The Base ProjectIn the directory of your choice, Create a folder named my-go-rest-api.With the new folder created, open a terminal in the root of the folder so that commands can be executed to build and run our go project.Once your terminal is pointed to the root directory of your project, run the go mod init command so you can initialize the go project and manage the dependencies.go mod init example/my-go-rest-apiLastly, in the root directory, create a file called main.go. This will be the file where we write all of our code.Adding in The CodeOnce the main.go file is created, we can add our application code to it. First, we will add a few lines of code to import our dependencies. At the top of the file, add the following code:package mainimport (  "encoding/json"  "log"  "math/rand"  "net/http")Next, under the imports code, we will add in our upper and lower bounds for generating the “credit score” and create a credit_rating struct to hold our credit rating data.const creditScoreMin = 500const creditScoreMax = 900type credit_rating struct {  CreditRating int `json:"credit_rating"`}With the parameters for our credit score defined, we will create a function that will randomly generate a credit score number. This function will also be our Http Handler Function so the function definition will include a http.ResponseWriter and an *http.request. Inside the function, a credit score will be generated, passed into the credit_rating struct, and then written to the HTTP response as a JSON object. The implemented function will go below the other credit score definition code and will look like this:func getCreditScore(w http.ResponseWriter, r *http.Request) {  var creditRating = credit_rating{      CreditRating: (rand.Intn(creditScoreMax-creditScoreMin) + creditScoreMin)  }  w.WriteHeader(http.StatusOK)  json.NewEncoder(w).Encode(creditRating)}Lastly, we will add a main function where our application will execute and a handleRequests function which will create our endpoint and expose our API service on port 8080. The code will look like this:func handleRequests() {  http.Handle("/creditscore", http.HandlerFunc(getCreditScore))  log.Fatal(http.ListenAndServe(":8080", nil))}func main() {  handleRequests()}The completed code in the main.go file, combined together, will look like this.package mainimport (  "encoding/json"  "log"  "math/rand"  "net/http")const creditScoreMin = 500const creditScoreMax = 900type credit_rating struct {  CreditRating int `json:"credit_rating"`}func getCreditScore(w http.ResponseWriter, r *http.Request) {  var creditRating = credit_rating{      CreditRating: (rand.Intn(creditScoreMax-creditScoreMin) + creditScoreMin)  }  w.WriteHeader(http.StatusOK)  json.NewEncoder(w).Encode(creditRating)}func handleRequests() {  http.Handle("/creditscore", http.HandlerFunc(getCreditScore))  log.Fatal(http.ListenAndServe(":8080", nil))}func main() {  handleRequests()}Running and Testing The CodeWith our code finally written, in the current folder, run go get in order to pull the packages and dependencies into the project.go get .After the command is completed, the project dependencies will be available to our code when we run it. Now, let’s run our simple web API by using the go run command in the root directory of the app.go run .Now, our API is up and running. You can send a test HTTP request through Postman. By sending a request to localhost:8080/creditscore, you should see a 200 OK status code and a credit score returned from the random number generator we created in the getCreditScore handler. For this test, no request body is needed for the incoming request.Wrapping UpWith that, we’ve created a simple RESTful API using Go. This code can then be expanded on as needed to build APIs for your applications. Moving forward, you may want to secure the API with an API key, integrate the API with an API gateway, check out how your API is being consumed and used, or build revenue through API monetization. For a solution to your API analytics and monetization needs, check out Moesif today to explore all of this and more!                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Building-A-RESTful-API-With-Go/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "technical-api-development-building-a-restful-api-with-go-and-gin": {
          "title": "Building a RESTful API with Go and Gin",
          "content"	 : "When it comes to building an API, Go is an extremely popular programming language choice to build powerful RESTful APIs with. The language is super lightweight, has many different libraries and frameworks available, and is easy to run. One of the frameworks support by Go is Gin. Gin is a web framework written in Golang that offers great performance and scalability, amongst other features. According to the Gin website, “Gin features a Martini-like API, but with performance up to 40 times faster than Martini”. In this tutorial, we will build a simple REST API using Go and Gin. The API endpoint will allow a user to retrieve a credit score rating. Of course, we won’t be linking up to any backend systems to pull a credit score but will instead use a random number generator to generate the score and return it to the user. Although simple, this tutorial will show you the basics of REST API development with Go and Gin.PrerequisitesBefore we start, we must ensure that a couple of prerequisites are completed. To follow along and run this tutorial, you will need to:  Install Go  Have an IDE to edit your code  Install Postman so that you can test the endpointAfter all 3 of these prerequisites are completed, we can begin!                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        Creating The Base ProjectIn the directory of your choice, Create a folder named my-go-rest-api.With the new folder created, open a terminal in the root of the folder so that commands can be executed to build and run our go project.Once your terminal is pointed to the root directory of your project, run the go mod init command so you can initialize the go project and manage the dependencies.go mod init example/my-go-rest-apiLastly, in the root directory, create a file called main.go. This will be the file where we write all of our code.Adding in The CodeOnce the main.go file is created, we can add our application code to it. First, we will add a few lines of code to import our dependencies, including our dependency for Gin. At the top of the file, add the following code:package mainimport (   "math/rand"   "net/http"   "github.com/gin-gonic/gin")Next, under the imports code, we will add in our upper and lower bounds for generating the “credit score” and create a credit_rating struct to hold our credit rating data.const creditScoreMin = 500const creditScoreMax = 900type credit_rating struct {  CreditRating int `json:"credit_rating"`}With the parameters for our credit score defined, we will create a function that will randomly generate a credit score number. Inside the function, which receives a *gin.Context struct, a credit score will be generated, passed into the credit_rating struct, and then written to the gin.Context as a JSON object. The implemented function will go below the other credit score definition code and will look like this:func getCreditScore(c *gin.Context) {    var creditRating = credit_rating{      CreditRating: (rand.Intn(creditScoreMax-creditScoreMin) + creditScoreMin)    }    c.IndentedJSON(http.StatusOK, creditRating)}Lastly, we will add a main function where our application will expose a GET endpoint for /creditscore and expose our API service on port 8080 (the default port for Gin). The code will look like this:func main() {   router := gin.Default()   router.GET("/creditscore", getCreditScore)   router.Run("localhost:8080")}The completed code in the main.go file, combined together, will look like this.package mainimport (   "math/rand"   "net/http"   "github.com/gin-gonic/gin")const creditScoreMin = 500const creditScoreMax = 900type credit_rating struct {   CreditRating int `json:"credit_rating"`}func getCreditScore(c *gin.Context) {    var creditRating = credit_rating{      CreditRating: (rand.Intn(creditScoreMax-creditScoreMin) + creditScoreMin)    }    c.IndentedJSON(http.StatusOK, creditRating)}func main() {   router := gin.Default()   router.GET("/creditscore", getCreditScore)   router.Run("localhost:8080")}Running and Testing The CodeWith our code finally written, in the current folder, run go get in order to pull the packages and dependencies into the project.go get .After the command is completed, the project dependencies will be available to our code when we run it. Now, let’s run our simple web API by using the go run command in the root directory of the app.go run .Now, our API is up and running. You can send a test HTTP request through Postman, or another service of your choice. By sending a request to localhost:8080/creditscore, you should see a 200 OK status code and a credit score returned from the random number generator we created in the getCreditScore handler. For this test, no request body is needed for the incoming request.Wrapping UpWith that, we’ve created a simple RESTful API using Go and Gin. This code can then be expanded on as needed to build APIs for your applications. Moving forward, you may want to secure the API with an API key, integrate the API with an API gateway, check out how your API is being consumed and used, or build revenue through API monetization. For a solution to your API analytics and monetization needs, check out Moesif today to explore all of this and more!                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /technical/api-development/Building-A-RESTful-API-With-Go-And-Gin/",
          "author": "Matthew",
          "categories": "technical, api-development"
        }
      
    ,
  
    
        "api-analytics-api-product-management-introducing-custom-functions-and-formulas": {
          "title": "Introducing Custom Functions and Formulas",
          "content"	 : "At Moesif we are always looking for ways for our users to derive even more valuable insights from their data. We pride ourselves on the fact that there is something for everyone on the platform, whether it’s engineering, marketing, customer success, or even sales departments. Recently we added a new way to drill even further into your metrics: Custom Functions and Formulas!Using custom formulas and functions can unlock new ways of looking at metrics. For instance, by using the Rolling Window custom function you can more easily plot engagement across various time intervals. You can also use the Rolling Average function to smooth out noisy metrics, making things more readable and digestible. Custom formulas can allow users to use different metrics in equations and plot them. Both new functionalities used together unlock many data science use cases that were previously unavailable. Let’s take a deeper dive into the specifics of what each new functionality is and how to use it in Moesif.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        What are Custom Functions?Custom Functions allow users to further augment their Custom Metrics within Time Series and Segmentation reports. Once you have created a custom metric, you are now able to apply even even functions against the selected metric. This includes applying common data science functions to the data such as a Rolling Window or Rolling Sum. You can also shift the data for a metric and further customize the custom function once you’ve added it. Available custom functions and their usages are listed below:Rolling Window  Returns the metric for X previous windows. For example, use this when you want to see a 7-day rolling window of unique users.Rolling Sum  Returns the sum of X previous intervals. For example, use this when you want to see the sum of the 2 previous periods.Rolling Max  Returns the rolling maximum value of the metric for each overlapping window.Rolling Min  Returns the rolling minimum value of the metric for each overlapping window.Rolling Unweighted Average  Returns the rolling average for each overlapping window. For example, You may use this function to see how your customers; usage is trending vs their trend line. You could use the formula to subtract a customer’s daily event count from their 7-day rolling average to see how their usage is trending.Rolling Linear Weighted Average  Returns the weighted average for each overlapping window where older data points contribute a linearly less amount to the total average.Rolling Standard Deviation  Returns the standard deviation of a rolling average for each overlapping window. The averaging function can be either the unweighted average or the linear related average.Exponentially Weighted Rolling Average  Returns the single exponential rolling average for each overlapping window where older data points become exponentially less important.Double Exponentially Weighted Rolling Average  Returns the double exponential rolling average for each overlapping window where older data points become exponentially less important.You can also shift the metric as well. The options for shifting the metric window include None, Shift Forward 1, and Shift Forward 2.What are Custom Formulas?Creating a Custom Formula allows you to use the values from other metrics or segments that you’ve defined and combine them into another metric via a formula. Now in Moesif Time Series and Segmentation reports, when you select the metrics you want to plot, each is assigned a letter to represent it. This can be used in the formula to create a new metric.When you create a metric or segment in Moesif, it will be assigned a Letter value which would allow it to be referenced in a custom formula. For example, if you wanted to plot the average event count per unique user you would want to divide the overall event count by the number of unique users.Average event count per user = event count / unique usersThis could then be plotted to see if each unique user is trending up or down in usage and allow you to follow that trend over time. Of course, this is an extremely simple example but simple and even extremely complex formulas can now be added and plotted in Moesif. We believe that many data scientists and users looking for deeper support of formulas will rejoice at this addition to the platform!Using Custom Functions and Formulas in The Real WorldIn the real world, a common use case for this new functionality would be for measuring DAU/WAU or DAU/MAU. What do these acronyms mean for those who are unfamiliar? Let’s break it down quickly:DAU = Daily Active Users  DAU is the number of unique users who did some action each dayWAU = Monthly Active Users  WAU measures the unique users who completed an action each weekMAU = Monthly Active Users  MAU is the number of unique users who completed an action within a monthNow, let’s create a chart in Moesif to display a comparison between DAU and WAU. To do this, create a new Time Series chart. In this example, we will use the /login endpoint as a marker for a user being active. This will be the filter and pick the metric as Unique Users. Ensure Daily is selected as the interval.Next, add a 2nd metric and pick Unique Users again. This time, also select the Rolling Window function from the Function dropdown and set the interval to 7 days. Setting it this way will plot the weekly unique users on a rolling basis for each day. if you want to compare DAU with MAU, you can simply change the rolling window to a 30-day interval.You can also select if you’d like to shift the metric at this time as well. The options for shifting the metric window include None, Shift Forward 1, and Shift Forward 2. For this example, we will keep the shift equal to the default value of None. Once input, click Apply.Lastly, add a Custom Formula by clicking Add Metric again and selecting Custom Formula.In the formula input, you will want to divide the DAU by the WAU. You can do this by inputting a / b. The letters used in the formula reference the letters assigned to each metric, shown to the left of each.  You can also refer to Segments in the custom formula as well by using the Segment Name. To use Segments as part of your formula, use the following syntax:  [SEGMENT_NAME].[METRIC_LETTER]Once all of these filters and inputs are set, the results will be shown on the time series.The DAU and WAU bars can be hidden by deselecting them on the bottom of the graph allowing the green bar, the results of our DAU/WAU calculation, to be meaningfully presented.You can also flip over to the table view so that the amounts can be interpreted in another way.At this point, we can now use the results to interpret trends. By using both custom functions and custom formulas, we have created some new metrics to track. The possibilities are endless and many new variations can be created to explore new metrics and trends in your API and business data.Try It Out TodayThis feature is now fully available on the Moesif platform and ready for our users to try out! If you want to dig into things a bit further you can check out our docs that show how to use the new functionality in a Time Series or Segmentation report. Using Custom Functions and Formulas can help you to derive insights and enhance your reporting capabilities. Don’t have a Moesif account? Sign up today to get started!«««< Updated upstream                 Simplify Log Analytics with Moesif              14 day free trial. No credit card required.              Learn More        =======                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required                                                                    Stashed changes                                          ",
          "url": " /api-analytics/api-product-management/Introducing-Custom-Functions-And-Formulas/",
          "author": "Matthew",
          "categories": "api-analytics, api-product-management"
        }
      
    ,
  
    
        "api-product-management-api-analytics-top-5-php-rest-api-frameworks": {
          "title": "Top 5 PHP REST API Frameworks",
          "content"	 : "PHP (Hypertext Preprocessor) is a programming language that is primarily used for web development. It is a server-side language, which means that it is executed on the server rather than in the user’s web browser. PHP is often used in combination with HTML, CSS, and JavaScript to create dynamic and interactive websites. One of the main advantages of PHP is that it is easy to learn and use, making it a popular choice for web developers of all skill levels. PHP also has a large and active developer community. This community provides a wealth of resources for those who want to learn more about the language or get help with specific issues.In PHP, there are several frameworks available that make it easy to create REST APIs, including Laravel, Slim, and Lumen. These frameworks provide a range of features and libraries to help developers create APIs quickly and efficiently, including support for routing, request and response handling, and data validation. Whether you are building an API for a small project or a large application, there is likely a PHP web development framework that can meet your needs.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        When choosing an API framework for PHP, there are a few key factors to consider:Performance: If your API will be handling a large number of requests or processing a lot of data, performance is an important factor to consider. Look for a web framework that is optimized for speed and efficiency.Ease of use: Consider the complexity of the framework and whether it is easy to learn and use. This is especially important if you are new to PHP or website development.Feature-rich and libraries: Think about the features and libraries that you will need for your API and whether the framework you are considering has built-in support for these. You may also want to consider the extensibility of the framework, in case you need to add custom functionality.Community and resources: Look for a  modern framework with a large and active community of developers and a wide range of resources available, including documentation, tutorials, and packages. This can make it easier to get help and find solutions to problems.Compatibility: Consider whether the framework is compatible with the version of PHP you are using and the hosting environment you will be deploying to.It is also a good idea to try out a few different frameworks to see which one is the best PHP framework for your needs and preferences as a web developer.The FrameworksLaravel 5A web application framework with expressive, elegant syntax, Laravel strives to make development an enjoyable, creative experience. The goal of Laravel is to make development easier by easing common tasks used in most web projects,  key features such as  Simple, fast routing engine.  Powerful dependency injection container.  Multiple back-ends for session and cache storage.  Database agnostic schema migrations.  Robust background job processing.  Real-time event broadcasting.Pros of using Laravel:  Ease of use: Laravel is known for its expressive syntax and intuitive design, which makes it easy for developers to get started and build applications quickly.  Large community and resources: Laravel framework has a large and active community of developers and a wide range of resources available, including documentation, tutorials, and packages. This makes it easier for developers to get help and find solutions to problems.  Built-in support for common tasks: Laravel has built-in support for common tasks such as  URL routing, database access, and form validation, which can save time and effort for developers.Cons of using Laravel:  Complexity: While Laravel is relatively easy to learn and use, it can be more complex than some other frameworks because it offers a wide range of features and libraries. This may make it more challenging for developers who are new to PHP or web development.  Performance: While Laravel is generally fast and efficient, it may not be the best choice for applications that need to handle a huge number of requests or process a large amount of data. In these cases, a lighter framework or microframework may be a better option.  Compatibility issues: Laravel is a relatively new framework, so it may not be compatible with older versions of PHP or certain hosting environments. This can limit its use for certain projects or teams.  To learn more about Laravel framework, you can check out the docs HereGuzzleSending HTTP requests and creating web service clients is made easy with Guzzle. In addition to service descriptions, resource iterators allow you to traverse paginated resources efficiently, batching allows you to send a large number of requests in a timely manner. It’s a framework that includes everything you need to build a robust web service client.Pros of using Guzzle:  HTTP request support: Guzzle is specifically designed to make it easy to send HTTP requests and handle responses, so it is a good choice for projects that need to interact with web services or APIs.  Flexibility: Guzzle is a standalone library that can be used in any PHP project, whether it is a full-stack framework or a custom application. This allows developers to use it in a variety of projects and environments.  Extensibility: Guzzle is designed to be modular and extensible, allowing developers to add custom functionality or extend existing features.  Documentation: Guzzle has comprehensive documentation and a strong community of users, making it easier for developers to get started and find answers to questions or problems.Cons of using Guzzle:  Limited features: Because Guzzle is a standalone library, it does not include many of the features and libraries found in full-stack frameworks like routing, database access, and form validation.Developers may have to use other libraries or write their own code to implement these features.  Complexity: While Guzzle is relatively easy to learn and use, it can be more complex than some other HTTP client libraries because it offers a wide range of options and features. This may make it more challenging for developers who are new to HTTP or web development.  Compatibility issues: Guzzle is a standalone library, so it may not be compatible with certain hosting environments or older versions of PHP. This can limit its use for certain projects or teams.  To learn more about Guzzle framework, you can check out the docs HereLeaf PHPThe Leaf MVC framework is a simple PHP framework for creating powerful web apps and APIs. Leaf MVC is powered by the Leaf Core and based on the Ruby on Rails and Laravel frameworks.Pros of using Leaf:  Lightweight framework and easy to learn: Leaf is a minimalistic framework, which makes it easy to learn and use for developers who are new to PHP or web development.  Performance: Leaf is designed to be fast and efficient, making it a good choice for applications that need to handle a large number of requests or process a lot of data.  MVC architecture: Leaf follows the Model View Controller (MVC) architecture, which helps to separate the presentation layer from the business logic and data management. This can make it easier to develop and maintain larger applications.  Built-in support for common tasks: Leaf comes with built-in support for common tasks such as routing, database access, and form validation, which can save time and effort for developers.Cons of using Leaf:  Limited community support: Leaf is a relatively new and lesser-known framework, which means that there may be less community support and resources available compared to more established frameworks like Laravel or CodeIgniter.  Limited features: Because Leaf is a minimalistic framework, it may not have as many built-in features and libraries as other frameworks. This means that developers may have to rely on external libraries or write their own code to implement certain features.  Limited documentation: Leaf has limited documentation compared to other frameworks, which can make it more challenging for developers to get started and find answers to questions or problems.  Compatibility issues: Because Leaf is a relatively new framework, it may not be compatible with older versions of PHP or certain hosting environments. This can limit its use for certain projects or teams.  To learn more about Leaf framework, you can check out the docs HereSlimThe Slim PHP micro-framework allows you to develop APIs and web applications quickly and easily. At its core, Slim is a microframework designed to receive HTTP requests, route the requests to the relevant controllers, and return the corresponding HTTP responses. This simplicity makes Slim easy to learn and performant.Pros of using Slim:  Lightweight and easy to learn: Slim is a minimalistic framework, which makes it easy to learn and use for developers who are new to PHP or web development.  Performance: Slim framework is designed to be fast and efficient, making it a good choice for applications that need to handle a large number of requests or process a lot of data.  Extensibility: Slim framework is designed to be modular and extensible, allowing developers to add custom functionality or extend existing features.Cons of using Slim:  Limited features: Because Slim is a minimalistic framework, it may not have as many built-in features and libraries as other frameworks. This means that developers may have to rely on external libraries or write their own code to implement certain features.  Limited documentation: Slim has limited documentation compared to other frameworks, which can make it more challenging for developers to get started and find answers to questions or problems.  Compatibility issues: Because Slim is a relatively new framework, it may not be compatible with older versions of PHP or certain hosting environments. This can limit its use for certain projects or teams.  To learn more about Slim framework, you can check out the docs HereLumenLaravel Lumen is a stunningly fast PHP micro-framework for building web applications with expressive, elegant syntax. By easing common tasks that are frequently encountered in most web projects, such as routing, database abstraction, queueing, and caching, Lumen aims to make development easier.Pros of using Lumen:  Performance: Lumen is designed to be fast and efficient, making it a good choice for building simple rest API development and microservices that need to handle a large number of requests or process a lot of data.  Compatibility with Laravel: Because Lumen is based on Laravel, developers who are familiar with Laravel will find it easy to learn and use. This also means that Lumen can benefit from the large community of Laravel developers and resources.  Extensibility: Lumen is designed to be modular and extensible, allowing developers to add custom functionality or extend existing features.Cons of using Lumen:  Limited features: Because Lumen is a minimalistic framework, it may not have as many built-in features and libraries as other frameworks. This means that developers may have to rely on external libraries or write their own code to implement certain features.  Limited documentation: Lumen has limited documentation compared to other frameworks, which can make it more challenging for developers to get started and find answers to questions or problems.  Compatibility issues: Lumen is a relatively new framework, so it may not be compatible with older versions of PHP or certain hosting environments. This can limit its use for certain web app projects.  To learn more about Lumen framework, you can check out the docs HereAdding in API Analytics and MonetizationBuilding an API is only the start. Once your API endpoint is built, you’ll want to make sure that you are monitoring and analyzing incoming traffic in addition to your API testing tool By doing this, you can identify potential issues and security flaws, and determine how your API design is being used. These can all be crucial aspects in growing and supporting your APIs.As your API platform grows, you may be focused on API products. This is making the shift from simply building APIs into the domain of using the API as a business tool. Much like a more formal product, an API product needs to be managed and likely will be monetized. Building revenue from your APIs can be a great way to expand your business’s bottom line.With Moesif, you can achieve all of the above. Moesif can easily integrate through either an SDK or plugin and be up and running in minutes. Once Moesif is integrated with your APIs, you’ll be able to explore charting and reporting to look at:  Live API traffic  Time-series reports inspecting usage  Conversion funnels  Retention reports  And much more…Moesif also enables API monetization by allowing you to track usage and sync it to a billing provider like Stripe, Recurly, or Chargebee. Within minutes, integrate your APIs and begin billing customers for usage. Moesif allows you to fine-tune exactly what you want to bill upon and is highly customizable to suit your exact needs.Wrapping UpIn this article, we covered 5 of the popular Golang framework for developing RESTful APIs with the Go programming language. We looked at a high-level overview of each and listed out some points for consideration. We also discussed some key factors in how to decide on which Go web application framework to use. Lastly, we looked at how Moesif can help you take your API development to the next level by implementing analytics and monetization.Looking to get started with Moesif? Simply log into Moesif and try these great features out. Don’t have an account yet? Sign up for a free trial and start exploring your user analytics and beyond in a few clicks.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-product-management/api-analytics/Top-5-PHP-REST-API-Frameworks/",
          "author": "Preet",
          "categories": "api-product-management, api-analytics"
        }
      
    ,
  
    
        "api-product-management-api-analytics-top-5-go-rest-api-frameworks": {
          "title": "Top 5 GO REST API Frameworks",
          "content"	 : "Go, also known as Golang, is a popular programming language that is performant and easy to learn. Go is known as a great language for building scalable and high-performance web applications. One key area where Go shines is in building REST APIs, which are essential for enabling communication between different systems and devices over the web.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        How to Pick an API FrameworkPicking the right API Golang framework is an important decision that can have a big impact on the success of your project. Here are some tips on how to pick the right API framework for your needs:Consider your goals: Before you start evaluating different frameworks, it’s important to have a clear understanding of what you want to achieve with your web API. Do you need a fast and efficient framework for handling a large number of requests? Or do you need a more flexible and customizable framework that can handle a wide range of use cases? Knowing your goals will help you narrow down your options and choose a framework that is well-suited to your needs.Evaluate the features and capabilities of each framework: Each API framework has its own set of features and capabilities, so it’s important to evaluate these carefully to see which one is the best fit for your project. Look for frameworks that have the features and capabilities you need, and consider whether they are easy to use and well-documented.Consider the learning curve: If you’re new to Go or web application development in general, you may want to choose a framework that has a gentle learning curve and good documentation. On the other hand, if you’re an experienced web developer, you may be more comfortable with a framework that has a steeper learning curve but more advanced features.Think about scalability: If you’re building an API that will need to handle a lot of traffic, it’s important to choose a framework that is designed for scalability. Look for frameworks that are known for their fast performance and ability to handle a large number of requests efficiently.Consider the size and complexity of your project: If you’re building a small, simple API, you may want to choose a minimalist framework that is easy to learn and use. On the other hand, if you’re building a larger and more complex API, you may want to choose a full-stack framework that provides a wider range of key features and capabilities.Overall, choosing the right API framework is a matter of balancing your goals, needs, and preferences with the features and capabilities of the different options available. By following these tips, you can find a framework that will help you build an efficient and successful API.In this post, we’ll take a look at the top 5 Go REST API frameworks that you can use to build robust and efficient APIs.The FrameworksGinGin is a high-performance Golang web framework that is designed for building APIs and microservices. It has a minimalistic design and focuses on simplicity and ease of use. Gin comes with a number of features such as routing, middleware, and request binding that make it easy to build APIs. It also has good documentation and a large user community, which makes it a great choice for developers new to Go.Pros of using Gin:  Fast performance: Gin is known for its high performance and is able to handle a large number of requests quickly and efficiently. This makes it a great choice for building APIs that need to handle a lot of traffic.  Minimalistic design: Gin has a minimalistic design and focuses on simplicity and ease of use. This makes it a great choice for developers who want a lightweight and easy-to-use framework.  Large user community: Gin has a large and active user community, which means you can find a lot of online resources and support if you need help with building your application.Cons of using Gin:  Limited flexibility: Gin framework has a more opinionated design compared to some other Go frameworks, which means it may not be as flexible and customizable as some alternatives.  Lack of some advanced features: Some developers may find that Gin is lacking in some advanced features that are available in other frameworks.  Steep learning curve: Gin has a relatively steep learning curve, which may make it more challenging for new developers to get started.  To learn more about Gin framework, you can check out the docs HereEchoEcho is another popular backend framework for building APIs in Go. It has a lightweight and flexible design and comes with a number of features such as routing, middleware, request validation, and more. Echo is known for its fast performance and easy-to-use API, and is a great choice for building scalable and high-performance APIs.Pros of using Echo:  Fast performance: Echo framework is known for its fast performance and is able to handle a large number of requests quickly and efficiently. This makes it a great choice for building APIs that need to handle a lot of traffic.  Lightweight framework and flexible design: Echo has a lightweight and flexible design, which makes it easy to use and customize.  Good documentation and support: Echo has good documentation and a large user community, which means you can find a lot of online resources and support if you need help with your web app.Cons of using Echo:  Limited framework capabilities: Echo is a minimalist framework that doesn’t provide many of the advanced features that you might find in a full-stack web framework. This means you’ll need to use it in combination with other packages to build a complete Golang rest API.  Steep learning curve: Echo has a relatively steep learning curve, which may make it more challenging for new developers to get started.  Lack of some advanced features: Some developers may find that Echo is lacking in some advanced features that are available in other frameworks.  To learn more about Echo framework, you can check out the docs HereGorilla MuxGorilla Mux is a powerful and flexible routing package for Go that is often used in combination with other web frameworks like Gin or Echo. It provides a number of features such as URL path matching, request handling, and middleware support, which make it easy to build complex and customizable APIs. Gorilla Mux is a popular choice among experienced Go developers due to its robustness and flexibility.Pros of using Gorilla Mux:  Powerful and flexible routing: Gorilla Mux is a powerful routing package that provides a number of features such as URL path matching, request handling, and middleware support. This makes it easy to build complex and customizable APIs.  Robust and reliable: Gorilla Mux has a solid reputation for being robust and reliable, which makes it a great choice for building APIs that need to handle a lot of traffic.  Widely used: Gorilla Mux is a popular choice among Go developers, which means you can find a lot of online resources and support if you need help with your backend development.Cons of using Gorilla Mux:  Limited framework capabilities: Gorilla Mux is just a routing package, so it doesn’t provide many of the other features that you might find in a full-stack web framework. This means you’ll need to use it in combination with other packages to build a complete API.  Steep learning curve: Gorilla Mux has a relatively steep learning curve, which may make it more challenging for new developers to get started.  Lack of some advanced features: Some developers may find that Gorilla Mux is lacking in some advanced features that are available in other frameworks.  To learn more about Gorilla Mux framework, you can check out the docs HereBuffaloBuffalo is a full-stack web development framework for Go that comes with everything you need to build web applications and APIs. It includes features such as routing, request handling, templating, and more. Buffalo is known for its simplicity and ease of use and is a great choice for developers new to Go who want a complete web development solution.Pros of using Buffalo:  Full-stack web development framework: Buffalo is a full-stack web development framework that comes with everything you need to build web applications and APIs. This makes it a great choice for developers who want a complete solution.  Simplicity and ease of use: Buffalo is known for its simplicity and ease of use, which makes it a great choice for developers new to Go who want to get up and running quickly.  Good documentation and support: Buffalo has good documentation and a large user community, which means you can find a lot of online resources and support if you need help with your app development.Cons of using Buffalo:  Limited flexibility: Buffalo has a more opinionated design compared to some other Go frameworks, which means it may not be as flexible and customizable as some alternatives.  Lack of some advanced features: Some developers may find that Buffalo is lacking in some advanced features that are available in other frameworks.  Steep learning curve: Buffalo has a relatively steep learning curve, which may make it more challenging for new developers to get started.  To learn more about Buffalo framework, you can check out the docs HereGojiGoji is a minimalist web framework for Go that is designed for building APIs and microservices. It has a lightweight design and focuses on simplicity and performance. Goji comes with features such as routing, middleware, and request handling that make it easy to build APIs and is a popular choice among Go developers who want a fast and efficient framework.Pros of using Goji:  Minimalist design: Goji has a minimalist design and focuses on simplicity and performance. This makes it a great choice for developers who want a lightweight and efficient framework.  Fast performance: Goji is known for its fast performance and is able to handle a large number of requests quickly and efficiently. This makes it a great choice for building APIs that need to handle a lot of traffic.  Widely used: Goji is a popular choice among Go developers, which means you can find a lot of online resources and support if you need help with your web app development.Cons of using Goji:  Limited framework capabilities: Goji is a minimalist framework that doesn’t provide many of the advanced features that you might find in a full-stack web framework. This means you’ll need to use it in combination with other packages to build a complete API.  Steep learning curve: Goji has a relatively steep learning curve, which may make it more challenging for new developers to get started.  Lack of some advanced features: Some developers may find that Goji is lacking in some advanced features that are available in other frameworks.  To learn more about Goji framework, you can check out the docs HereAdding in API Analytics and MonetizationBuilding an API is only the start. Once your API endpoint is built, you’ll want to make sure that you are monitoring and analyzing incoming traffic in addition to your API testing tool By doing this, you can identify potential issues and security flaws, and determine how your API design is being used. These can all be crucial aspects in growing and supporting your APIs.As your API platform grows, you may be focused on API products. This is making the shift from simply building APIs into the domain of using the API as a business tool. Much like a more formal product, an API product needs to be managed and likely will be monetized. Building revenue from your APIs can be a great way to expand your business’s bottom line.With Moesif, you can achieve all of the above. Moesif can easily integrate through either an SDK or plugin and be up and running in minutes. Once Moesif is integrated with your APIs, you’ll be able to explore charting and reporting to look at:  Live API traffic  Time-series reports inspecting usage  Conversion funnels  Retention reports  And much more…Moesif also enables API monetization by allowing you to track usage and sync it to a billing provider like Stripe, Recurly, or Chargebee. Within minutes, integrate your APIs and begin billing customers for usage. Moesif allows you to fine-tune exactly what you want to bill upon and is highly customizable to suit your exact needs.Wrapping UpIn this article, we covered 5 of the popular Golang framework for developing RESTful APIs with the Go programming language. We looked at a high-level overview of each and listed out some points for consideration. We also discussed some key factors in how to decide on which Go web application framework to use. Lastly, we looked at how Moesif can help you take your API development to the next level by implementing analytics and monetization.Looking to get started with Moesif? Simply log into Moesif and try these great features out. Don’t have an account yet? Sign up for a free trial and start exploring your user analytics and beyond in a few clicks.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-product-management/api-analytics/Top-5-GO-REST-API-Frameworks/",
          "author": "Preet",
          "categories": "api-product-management, api-analytics"
        }
      
    ,
  
    
        "api-product-management-api-analytics-top-5-nodejs-rest-api-frameworks": {
          "title": "Top 5 Node.js REST API Frameworks",
          "content"	 : "Node.js has seen meteoric growth in recent years, making it one of the most popular programming languages on the web. By combining Javascript on the front end with Node.js for backend development, JS developers can create powerful and scalable apps that offer benefits not found elsewhere.                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free        How To Pick an API FrameworkIf you’re a Node.js developer looking to create a REST API with Node.js, there are many different Javascript frameworks you can choose from. With so many options available, it can be difficult to know which one is right for your app development. In this article, we’ll go over some of the top 5 Node.js REST API frameworks and help you decide which one is best for your application programming interface (API) development.When choosing a Node.js REST API framework, there are a few things to keep in mind. First, consider what kind of functionality you need from your API development. Do you need a simple CRUD API or something more complex? Second, think about how much control you want over the structure of your API. Some Node.js frameworks provide more flexibility than others. Finally, take into account the size and scope of your application. Some frameworks are better suited for large web apps while others work better for small ones.  Ease of use: How easy is the framework to use? Is it well-documented?  Performance: How fast is the framework? Does it scale well?  Features: What features does the framework offer? Does it support everything you need?  Community: Is there a large and active web developer community around the framework?With all that in mind, let’s take a look at some of the top Node.js REST API frameworks:ExpressThe Express framework is a popular Node.js framework for building web app and mobile applications. It’s most commonly used as a router to create a single page application, multi-page, and hybrid applications. Express.js is built on top of Node.js and provides an all-in-one package for managing servers, routes, and more.Pros:  Links to databases like MySQL, MongoDB, etc  Use Middleware for request handling  Asynchronous  Express provides dynamic rendering of HTML Pages, allocated by passing the arguments to the template  Open source frameworkCons:  Issues with Callbacks  Errors are challenging to understand  Inability to process CPUs with the capacity for tasks that require large amounts of processing power  To learn more about Express framework, you can check out the docs HereFeatherJSFeathersJS is a JavaScript framework used for highly-responsiveness real-time apps. It simplifies JavaScript development while still being advanced. FeathersJS enables JS developers to control data through RESTful resources, meaning they don’t need external data stores or databases. Node.js Developers can also create REST APIs with Feather commands, so it’s easier to enable your web app to communicate with third-party applications and services like Twilio or Stripe. You can also integrate FeathersJS into various JavaScript frameworks.Pros:  Real-time API support  Good Documentation for the development process  Supports both Javascript and Typescript programming language  CLI scaffolding tool  Supports both Relational and Non-Relational DatabasesCons:  It uses PassportJS that which does not provide SAML authentication out of the box  Larger-scale real time application in FeathersJS could cause a WebSockets issue  To learn more about Feather.Js framework, you can check out the docs HereLoopBackLoopBack is a Node.js framework that can be used by JS developers and businesses to build on top of the service with TypeScript packages. It offers multiple advantages for application development, including the following:  Health Checks for monitoring  Metrics for collecting data about system performance  Distributed Tracing for tracing issues across microservices  Logging so you can gather insights about what’s going on within your applications  Built-in Docker files so you can quickly build new projects without having to worry about any of the infrastructureAll this combined makes LoopBack one of the few Node.js frameworks that support proprietary databases like Oracle, Microsoft SQL, IBM DB2, etc. It also provides an easy bridge between SOAP services, making it one of only a handful of Node.js frameworks providing integration with SOAP services.Pros:  Code is modular and structured  Good ORM with available connectors  Built-in user & access role feature  Built in API Explorer via SwaggerCons:  Monolithic architecture  Opinionated architecture  Not as much community support  Steep learning curve  To learn more about LoopBack framework, you can check out the docs HereNest.JsNest is a framework for building modern Node.js applications with a high-performance architecture that takes advantage of the latest JavaScript features by using progressive JavaScript (TypeScript), functional programming principles, and reactive programming. It combines the best of object oriented programming and functional reactive programming approaches so you can choose your preference without being forced to conform to one particular ideology.Pros:  NestJS includes a built-in Direct Injection container, which makes it easier to keep your code modular and readable  Can create software solutions where the components can be taken out and changed. This means there is no strong coupling between them  The use of modular structures simplifies the division of a project into separate blocks. It helps to use external libraries in a project  Easy to write simple API endpointsCons:  Developers know less about what’s going on under the hood, which means debugging is trickier and takes longer  NestJS may be lacking in features compared to frameworks in other languages, such as Spring in Java or .NET in C#  Complicated development process  To learn more about Nest.Js framework, you can check out the docs HereMoleculerMoleculer is a nodejs framework that helps you to build out microservices quickly and efficiently. It also gives you tools for fast recovery in the event of failure, so your services can continue running efficiently and reliably. Healthy monitoring ensures everything is up to date and any problems are quickly detected and fixed.Pros:  Fast performance  Open source framework  Durability  Fault-tolerant framework with CB and load-balancer featuresCons:  Lack of Documentation  Lack of Community Support  Limitations of an enterprise-grade API are that there are limited options for setting up APIs and other restrictions  Not as feature-rich as other frameworks  To learn more about Moleculer framework, you can check out the docs HereAdding in API Analytics and MonetizationBuilding an API is only the start. Once your API endpoint is built, you’ll want to make sure that you are monitoring and analyzing incoming traffic. By doing this, you can identify potential issues and security flaws, and determine how your API is being used. These can all be crucial aspects in growing and supporting your APIs.As your API platform grows, you may be focused on API products. This is making the shift from simply building APIs into the domain of using the API as a business tool. Much like a more formal product, an API product needs to be managed and likely will be monetized. Building revenue from your APIs can be a great way to expand your business’s bottom line.With Moesif, you can achieve all of the above. Moesif can easily integrate through either an SDK or plugin and be up and running in minutes. Once Moesif is integrated with your APIs, you’ll be able to explore charting and reporting to look at:  Live API traffic  Time-series reports inspecting usage  Conversion funnels  Retention reports  And much more…Moesif also enables API monetization by allowing you to track usage and sync it to a billing provider like Stripe, Recurly, or Chargebee. Within minutes, integrate your APIs and begin billing customers for usage. Moesif allows you to fine-tune exactly what you want to bill upon and is highly customizable to suit your exact needs.Wrapping UpIn this article, we covered 5 of the best Node.js frameworks for developing RESTful APIs with Javascript programming language. We looked at a high-level overview of each and listed out some points for consideration. We also discussed some key factors in how to decide on which API framework to use. Lastly, we looked at how Moesif can help you take your API development to the next level by implementing analytics and monetization.Looking to get started with Moesif? Simply log into Moesif and try these great features out. Don’t have an account yet? Sign up for a free trial and start exploring your user analytics and beyond in a few clicks.                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue with your Node REST APIs?            Monetize your Node APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /api-product-management/api-analytics/Top-5-NodeJs-REST-API-Frameworks/",
          "author": "Preet",
          "categories": "api-product-management, api-analytics"
        }
      
    ,
  
    
        "api-product-management-api-analytics-top-5-java-rest-api-frameworks": {
          "title": "Top 5 Java REST API Frameworks",
          "content"	 : "The Java programming language is a high-level, object-oriented language that enables developers to create robust, reusable code. Java is known for its portability and platform independence, which means that Java code can run on any system that supports the Java Runtime Environment (JRE).Java was originally developed by James Gosling at Sun Microsystems in 1995. Since then, the language has undergone several changes and has become one of the most widely used programming languages in the world. According to estimation, there are more than 9 million developers worldwide using Java for a variety of purposes.Java is a versatile and powerful programming language like Node js. It is widely used in a variety of application domains, including mobile applications, enterprise software development, web application development, and more. In recent years, the popularity of Java has grown significantly, making it one of the most popular programming languages for developing server-side applications.There are many reasons why java is so popular among developers. Some of the most notable reasons include:  Java is easy to learn and use  Java is versatile and can be used for a wide range of tasks  Java code is portable and can run on any platform that supports the JRE  Java is well-suited for the development of enterprise-scale applications and meets the spring security standards.What Are RESTful Web Services in Java?There are many different ways to define a RESTful web service in Java. In its most basic form, a RESTful web service is simply a web service that uses the Representational State Transfer (REST) architectural style. This means that the web service can be accessed via an HTTP request protocol and supports CRUD (Create, Read, Update, Delete) operations.A more specific definition of a RESTful web service in Java would be a web service that:  Is built on the JAX-RS API (Java API for XML Web Services)  Uses the @Path annotation to map URLs to resources  Supports CRUD operations via the @GET, @POST, @PUT, and @DELETE annotations  Is deployed to a Java EE-compliant application server, such as WildFly or TomcatHow to Pick an API FrameworkThere are many different Java API frameworks to choose from. So, how do you know which one is right for your project? Here are some things to keep in mind as a developer when choosing an API framework:      Make sure the framework is compatible with the versions of Java and other software your Java app requires like the data structure you are using.        Consider the size and complexity of your web application. Some frameworks are better suited for small projects, while others are more robust and can handle large, complex java applications.        Consider the type of API you need to create. Some frameworks focus on REST APIs, while others support SOAP or other API types.        Look at the API documentation, testing framework, and resources available for the framework to see if they meet your needs. Does the framework have good documentation? Are there plenty of resources available online (such as tutorials, articles, etc.)?        Ask other Java programmers what frameworks they recommend and can advise on other topics such as the java virtual machine.  Taking the above into consideration, let’s look at some of the most popular Java Frameworks for creating RESTful APIs.The FrameworksSpring Framework (Spring MVC)Spring MVC is the black sheep of the REST Frameworks, as it doesn’t implement the JAX-RS specification. However, at its roots, Spring has always been a framework that supports REST API, If you’re familiar with Spring’s Enterprise Java Application development, then you know how easy it is to replace the REST API with another compliant framework. In Spring, you use REST annotations to specify the different methods for interacting with your REST service. You place the @RestController annotation on a class so you can map it to any resources and commands.Pros:  Declarative support for caching, validation, transaction, and formatting.  Dependency injection is an excellent way to test frameworks. Spring isn’t required to be used in a serverless environment, while EJB and Struts applications require a server.  Dependent components can be injected without any knowledge of where they came from, making it easy for the system to be flexible and extensible.Cons:  To develop a Spring application, you need lots of XML.  Developers must spend a lot of time trying to figure out which features to use and which ones not to use.  Developers take for granted the importance of XSS and cross-site scripting. With this in mind, we need to figure out how to stop hackers from infiltrating your application independently.To learn more about Spring MVC  framework, you can check out the docs HerePlay FrameworkPlay Framework is a refreshingly unconventional and unique type of framework that uses RESTful architecture by default. It follows the convention over the configuration approach, which means Play is very easy to customize for your needs. Play is built on the MVC pattern and isn’t limited to Java and Scala. It’s similar to other frameworks like Django, Ruby on Rails, or ASP.NET MVC because it doesn’t follow J2EE web standards. It’s a high-performance java framework so errors are caught before you hit production with static typing and reactive processing principles. With Play2, you can integrate easily with Maven projects and generate simple JAR files.Pros:  Intuitive java server face  API Test and Unit Testing Application is easy  Fast developmentCons:  Unstable Plug-ins  Does not offer backward compatibility  Architecture is hard to understandTo learn more about Play  framework, you can check out the docs HereBladeBlade is an elegant and lightweight MVC framework that allows Java programmers to build fast web applications. Blade follows the RESTful style routing interface, allowing users to understand the whole framework in just a day. It has a low footprint with less than 500kb of total code and be accessed using Java 8. Blade also contains built-in security features such as CSRF(Cross-Site Request Forgery) and XSS Cross-Site Scripting).Pros:  Access the RESTful routing interface and deploy your application  Flexible framework, supporting plugin extensions  High-performance and lightweightCons:  Lack of Documentation  Optimization of code required  Social applications are generic and lack personalityTo learn more about Blade  framework, you can check out the docs HereGrailsGrails is a web framework written in the Groovy programming language that runs on Java. Grails is based on the Model-View-Controller design pattern and is compatible with Java syntax, though it has some additional features not found in Java. Grails is designed to be easy to learn if you know Java or another object-oriented language. Like JSP, GSP (Groovy Server Pages) is used to render data in Grails and it’s simple to create tags for the View. Grails also provide built-in support for RESTful APIs, making it easy to create such services, and you can use Hibernate instead of GORM as an ORM implementation.Pros:  The dynamic configuration feature allows you to configure changes without restarting the server. This is especially helpful when you have to make frequent adjustments.  Because there are fewer CSS framework plugins, it’s easier to configure the CSS.  Extensive DocumentationCons:  If you’re involved in a multi-threaded application, GORM might not work as well for you.  Java Developer primarily declares variables with “def” which is equivalent to “object.” This can be very difficult to maintain and could lead to errors.  Some interpreted languages to add a lot of weight to java code and directly affect the runtime.To learn more about Grails  framework, you can check out the docs HereDropwizardDropwizard a lightweight framework allows for very fast development times. Dropwizard’s out-of-the-box integration with advanced configurations, logging, and application metrics makes time-consuming tasks easy for programmers so they can focus on the code for their business logic. This framework is open-source and has libraries bundled with it to make configuring web RESTful applications a breeze. There are also integrations with security and performance-related libraries, so all developers need to worry about is writing their logic routines.Pros:  Metrics offers an insight-driven experience for monitoring  Support for configuration, application metrics, logging, operational tools, and template management  LightweightCons:  Developers tend to use external libraries for database access. This means you have to include additional code, which can make your project more complex.  Steep Learning Curve  No inbuilt ORM supportTo learn more about Dropwizard  framework, you can check out the docs HereAdding in API Analytics and MonetizationBuilding an API is only the start. Once your API endpoint is built, you’ll want to make sure that you are monitoring and analyzing incoming traffic in addition to your API testing tool By doing this, you can identify potential issues and security flaws, and determine how your API design is being used. These can all be crucial aspects in growing and supporting your APIs.As your API platform grows, you may be focused on API products. This is making the shift from simply building APIs into the domain of using the API as a business tool. Much like a more formal product, an API product needs to be managed and likely will be monetized. Building revenue from your APIs can be a great way to expand your business’s bottom line.With Moesif, you can achieve all of the above. Moesif can easily integrate through either an SDK or plugin and be up and running in minutes. Once Moesif is integrated with your APIs, you’ll be able to explore charting and reporting to look at:  Live API traffic  Time-series reports inspecting usage  Conversion funnels  Retention reports  And much more…Moesif also enables API monetization by allowing you to track usage and sync it to a billing provider like Stripe, Recurly, or Chargebee. Within minutes, integrate your APIs and begin billing customers for usage. Moesif allows you to fine-tune exactly what you want to bill upon and is highly customizable to suit your exact needs.Wrapping UpIn this article, we covered 5 of the best Java frameworks for developing RESTful APIs with Java programming language. We looked at a high-level overview of each and listed out some points for consideration. We also discussed some key factors in how to decide on which Java REST API framework to use. Lastly, we looked at how Moesif can help you take your API development to the next level by implementing analytics and monetization.Looking to get started with Moesif? Simply log into Moesif and try these great features out. Don’t have an account yet? Sign up for a free trial and start exploring your user analytics and beyond in a few clicks.                Easily Monetize APIs with Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /api-product-management/api-analytics/Top-5-Java-REST-API-Frameworks/",
          "author": "Preet",
          "categories": "api-product-management, api-analytics"
        }
      
    ,
  
    
        "python-rest-api-frameworks": {
          "title": "5 Best Python REST API Frameworks in 2026 (Tested & Compared)",
          "content"	 : "Python is still one of the most popular languages for building REST APIs. The reason has shifted, though. Five years ago, Python was the convenient choice for backend developers; today, it is also the default for serving AI and ML models, which means a meaningful share of new Python APIs live behind a model rather than a database. That changes which frameworks are worth picking up first.                Monetize APIs with Moesif              14 day free trial. No credit card required.              Try for Free        This post walks through the five frameworks we recommend for new Python REST APIs in 2026, based on the JetBrains State of Python 2025 survey (published September 2025) and the real-world patterns we see across Moesif and WSO2 customers.How to pick a Python REST API frameworkBefore the list, the criteria. The right framework depends on three things.What you are serving. A pure REST API on top of a database is one job. Serving a machine learning model in production is another. A real-time WebSocket app is a third. Different frameworks optimize for different shapes.Sync or async. Modern Python (3.8+) supports both. Async is the right default for APIs that fan out to other services or models; sync is fine for CRUD-heavy APIs against a single database.Batteries included or bring-your-own. Some frameworks ship with authentication, ORM, admin panel, and more. Others give you a router and let you assemble the rest. Match the framework’s opinion to how much your team wants to make their own choices.A fourth criterion that matters less than it used to: raw performance. Modern Python frameworks are all fast enough for almost any business workload. Unless you are serving millions of requests per second, framework choice is not the bottleneck.For the broader design decisions that shape your API regardless of framework, see our API design principles guide.1. FastAPIBest for: new APIs, ML model serving, async-first workloads, teams that value type safety.FastAPI is the highest-ranked Python web framework in JetBrains’ most recent State of Python survey, with adoption that has climbed sharply year over year. It was built around three modern Python ideas: async/await for concurrency, type hints for validation, and OpenAPI for documentation.The result is a framework that auto-generates an OpenAPI spec and an interactive Swagger UI directly from your function signatures, validates requests against Pydantic models automatically, and runs on the ASGI stack for native async support.Advantages:  Async-native, ideal for ML serving and streaming endpoints  Auto-generated OpenAPI spec and interactive docs  Type hints catch bugs at editor-time  Strong community momentum among new projectsTrade-offs:  Steeper learning curve for developers new to async/await  “Batteries not included,” meaning you choose your own auth, ORM, and admin tools  Smaller ecosystem of integrations than DjangoIf you are starting a new Python REST API in 2026 and the team is comfortable with async, FastAPI is the safe default. The 2025-2026 ecosystem moves matter here: the framework’s creator launched FastAPI Cloud (a managed deployment platform; private beta with a public waitlist at fastapicloud.com at the time of writing) for deploying a FastAPI app with a single fastapi deploy command, and the fastapi[standard] install now includes the fastapi dev CLI command that smooths out the gap between local development and production runs.2. Django REST FrameworkBest for: teams already using Django, enterprise backends, content-heavy applications.Django REST Framework (DRF) is the API extension to Django, the long-standing full-stack Python web framework. The JetBrains survey shows Django itself at 35% usage and DRF specifically at 20% usage in 2024.DRF builds directly on Django’s models, views, and authentication, which makes it the natural choice when you are already shipping a Django app and want to expose a REST API on top. The browsable API interface (an in-browser explorer for your endpoints) is a feature long-time DRF users miss when they leave for other frameworks.Advantages:  Deep Django integration; reuse models, auth, and middleware  Browsable API for interactive testing during development  Built-in permissions and serialization out of the box  Mature ecosystem with documentation and third-party packagesTrade-offs:  Tightly coupled to Django; not the right choice if you do not already use it  Heavier setup than lightweight alternatives  Sync by default; async support is partial and less polished than FastAPI’sIf your team is already in Django, DRF is the path of least resistance.3. FlaskBest for: small APIs, microservices, internal tools, ML prototypes that need to ship behind an HTTP endpoint quickly.Flask remains one of the most popular Python web frameworks (34% usage in the same survey). It is a “microframework”: a small router on top of Werkzeug (WSGI) and Jinja2 (templating), with everything else added by extensions when you need it.For REST APIs specifically, most teams pair Flask with an extension like Flask-RESTful or Flask-Smorest, which add API-specific features (resource classes, automatic serialization, OpenAPI generation).Advantages:  Lightweight, easy to understand from the first line of code  Huge community and a large catalog of extensions  Excellent for small services and data-science prototypes  Beginner-friendlyTrade-offs:  Sync by design; not the right choice for high-concurrency async workloads  “Bring your own everything,” so auth, ORM, and admin must be added manually  Without conventions, large Flask apps can become hard to maintainFlask is a good first framework and a good choice for small APIs. For new large APIs in 2026, FastAPI usually wins.4. aiohttpBest for: async-first APIs, WebSocket-heavy applications, servers that also need to make many outbound HTTP calls.aiohttp is an asynchronous HTTP server and client toolkit. The JetBrains survey shows 13% usage in the same survey. Unlike Flask or DRF, it is async by default; unlike FastAPI, it does not lean on type hints for validation, which makes it lower-level and more flexible.Teams reach for aiohttp when they want close control over the async event loop, native WebSocket support, or a single library that serves both incoming and outgoing HTTP traffic.Advantages:  Native async/await throughout  Built-in WebSocket support  Functions as both a server and a client library  Mature and well-maintainedTrade-offs:  Lower-level than FastAPI; less convenience for typical REST APIs  Validation, OpenAPI, and middleware are all bring-your-own  Smaller pool of tutorials and Stack Overflow answers compared to Flask or FastAPIIf you are building an async microservice that does heavy WebSocket or outbound-HTTP work, aiohttp is worth a serious look.5. FalconBest for: high-throughput REST APIs, microservices where every millisecond matters.Falcon is a minimalist Python web framework designed specifically for REST APIs at scale. It does not appear in the top of JetBrains’ general survey because it is intentionally niche, but it ships in production at companies that need raw throughput. It is one of the fastest Python web frameworks on standard benchmarks, partly because it does very little for you.Falcon’s philosophy is opposite Django’s. Where Django ships everything, Falcon ships nothing beyond routing and request/response objects. The result is a small surface area and predictable performance.Advantages:  Extremely fast for a Python framework  Small, predictable, easy to reason about  Stable API that has changed slowly over yearsTrade-offs:  No batteries included; you assemble auth, validation, and serialization yourself  Smaller community and ecosystem than FastAPI or Flask  The performance edge matters less in 2026 now that other frameworks have maturedPick Falcon when you have a clear performance need and your team is comfortable assembling its own stack.What about Litestar, Sanic, or Starlette?Three honorable mentions worth knowing about:  Starlette is the ASGI toolkit that FastAPI is built on top of. You can use Starlette directly if you want async routing and middleware without FastAPI’s Pydantic-and-OpenAPI opinions. It is the right choice when you need an async foundation and want to build the framework you actually want.  Sanic is an async framework similar in spirit to Flask but designed for async from the start. It has a dedicated user base for high-concurrency workloads but has lost mindshare to FastAPI for new projects.  Litestar (formerly Starlite) is an ASGI framework that emphasizes type safety, performance, and developer ergonomics. It is younger than FastAPI but well-regarded among teams that want FastAPI’s ideas with different design trade-offs (full DI container, plugins, slightly different validation philosophy).For most teams, the five primary frameworks above cover the practical choices. These three are good to know exist for the cases where the primary five do not fit.Side-by-side comparison            Framework      Async      Batteries      Best for      Adoption signal                  FastAPI      Native      Light      New APIs, ML serving      Top-ranked in JetBrains survey, growing fast              Django REST Framework      Partial      Heavy      Enterprise, existing Django teams      Default in Django shops              Flask      No      Light      Small APIs, prototypes      Long-time top of the JetBrains survey              aiohttp      Native      None      Async microservices, WebSocket apps      Smaller, focused user base              Falcon      Optional      None      High-throughput REST APIs      Specialized, performance-driven      For the underlying HTTP method conventions that all five frameworks expose, our API methods reference covers GET, POST, PUT, PATCH, and DELETE.Adding observability and monetizationBuilding the API is only the start. Once your Python API is in production, you will want to monitor and analyze incoming traffic to identify performance issues, security flaws, and how your endpoints are actually being used.Moesif integrates with Python frameworks through an SDK and is running in minutes. Once integrated, you can see:  Live API traffic with per-endpoint, per-customer breakdowns  Time-series reports on usage and latency  Conversion funnels from signup to first successful call  Retention reports across customer cohortsMoesif also handles API monetization, syncing usage to billing providers like Stripe, Recurly, or Chargebee. If you are serving an ML model or any API where customers expect granular billing, this is the layer that closes the loop between the framework you chose and the business outcomes you care about.Wrapping upThe Python framework landscape is in a clearer place than it was a few years ago. FastAPI is the new default for new APIs, Django REST Framework holds the enterprise position, Flask remains the lightweight choice, and aiohttp and Falcon serve specific async or performance use cases. Pick based on your team’s existing skills, your throughput needs, and whether you want a framework with opinions.Once your framework is in place, the next step is seeing what your API is actually doing in production. Start a 14-day Moesif free trial to get per-endpoint, per-customer analytics on the Python API you ship. No credit card required.Frequently asked questionsWhat is the best Python framework for REST APIs in 2026? For most new projects, FastAPI. If you are already on Django, Django REST Framework. Flask remains a strong choice for small APIs and prototypes.Is FastAPI better than Flask? For new REST APIs, generally yes. FastAPI is async-native, type-safe, and auto-generates docs. Flask is still excellent for small synchronous APIs, internal tools, and prototypes where you want minimal opinion.Is Django REST Framework still relevant? Yes. DRF is the default at enterprises that already use Django. It is not displacing FastAPI for new projects, but it is the right choice for the millions of existing Django codebases that need to expose REST APIs.What about Pyramid or Web2py? They still exist and have communities, but neither appears in the top of mainstream surveys in 2025 or 2026. For new projects, the five frameworks above cover the practical choices.Which Python framework is best for serving ML models? FastAPI is the dominant choice for ML serving. Its async support, type validation, and Pydantic integration map well to the inputs and outputs of ML inference endpoints.Do these frameworks all support OpenAPI? FastAPI generates OpenAPI specs automatically. Flask (with Flask-Smorest), DRF (with extensions like drf-spectacular), and Falcon can generate specs with additional setup. aiohttp does not generate them by default.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /python-rest-api-frameworks/",
          "author": "Preet",
          "categories": "api-product-management, api-analytics"
        }
      
    ,
  
    
        "api-product-management-api-analytics-how-to-sell-your-apis": {
          "title": "How to Sell Your APIs",
          "content"	 : "As we all know, APIs are absolutely everywhere. APIs power almost every aspect of a modern tech business and even non-tech businesses. You may have an internal API that is used by developers to power internal systems and external APIs which expose functionality more publicly. As with any functionality, APIs can also be used to drive revenue by selling them to users in need. Whether you’re selling a REST API, GraphQL API, or other API, learning how to sell your API has become a popular ask. let’s take a look at a few considerations for selling your APIs.How Do You Build an API and Sell It?Building an API with the intent to sell it requires one massive thing: value. When looking at the API offering, you should be able to determine if there is an external need for that API, the volume of businesses that are in need of such a service, and most importantly, whether they are  willing to pay for it. If you have determined that a person or business is willing to pay for your service, this is a great starting point.Once an API is built, to sell it, you’ll need to do a few things. The first order of business is to put in authentication and authorization. This is needed so that you can make sure that paying users are granted access and that no unauthorized or non-paying users can access the API.Next, you’ll want to ensure that proper rate limiting and quota limits are set in place. This ensures that users are only accessing the API based on the terms in their agreement. It also ensures that the endpoint is not overwhelmed with traffic, protecting against a denial of service attack.After this, you will need to find a way to charge for API usage. This includes deciding on a pricing strategy, a registration/sign-up flow, and a way to tally up usage and charge for it. This component is usually referred to as API monetization and is where the actual revenue is derived from.What Are the Challenges of Selling APIs?The challenges of selling APIs include correctly implementing monetization, service availability, and security. These challenges are covered in the above section and can usually be remedied by using a suitable API management tool, creating and deploying secure APIs, and using an easy-to-use monetization platform.The challenge of selling APIs, aside from the monetization component, is very similar to simply offering APIs for consumption in general. There is a constant battle for security and keeping tight authorization. Having services constantly available through a high-availability setup is also crucial since having a service down, especially if the API is driving revenue for the business using it, could be disastrous. Reliability is a massive factor in keeping users on a platform or continuing to pay for your API.The last challenge to consider is how much the API is costing you internally. For instance, let’s say you have created a credit rating API that retrieves a user’s credit score from various credit bureaus. You are charging your users $3 per API call, post-paid. The credit bureaus are also charging you $1 per API call, meaning that your net revenue per call is $2. What if a user uses your service and decides not to pay their invoice at the end of the month? You will still be responsible for the API cost to the credit bureaus even if your customer refuses to pay. This challenge isn’t specific to APIs but is something you should consider when looking at potential challenges.How Do You Monetize an API?You’ve built an API, decided to sell it, and now need to figure out how to charge and collect the revenue. For this, you will need to have a system that logs API traffic and can charge upon it. There is always the opportunity to build a custom solution to handle monetization but it can come with pitfalls. As per any homegrown solution, engineering and ongoing support costs must be accurately forecasted. Generally, these costs bloat, especially as you scale.An alternative is to use an out-of-the-box solution that is customizable to fit your use case. Moesif offers such a solution through the Billing Meter feature on the platform. With a Moesif Billing Meter, API traffic is logged, filtered, and sent to a billing provider, like Stripe or Recurly, so that revenue can be collected. This approach is highly flexible since Moesif can work with multiple API integrations and billing providers.With the monetization capabilities in place, the last step is to decide on a pricing model for your APIs. Once pricing is decided and included in the monetization setup, you’ll be on your way to creating revenue.API Marketplace vs API PortalAn API portal is a place where developers can register and manage access to an API. Much of the time, an API Gateway will be used to generate this customer-facing API portal so users can access published APIs. API portals are an extremely popular way to manage API access and offer users a direct way to use your API. A typical API portal will allow users to browse available APIs a particular company offers, enroll, and generate an API key for access. The portal may also contain API documentation or other API knowledge base articles to help users figure out how to use the API.An API Marketplace is a place where developers can list their APIs to bring in more users. Examples of popular API marketplaces include Rapid API, APILayer, and many others. API Marketplaces allow developers to browse through APIs that are available from any companies that are listed on the marketplace. Marketplaces also generally offer an API portal as well so that users can manage their API access in a self-serve way.Getting Developers To Use Your APIWith everything in place for selling your API, the last step is to get developers to use it. This can be done by listing it on an API marketplace, building a website and marketing around it, or many other ways you’d normally promote a product.Once you’ve attracted some developers, making the API onboarding process is crucial for getting developers to use your service. If the API is difficult to use or sign up for, developers may churn out and not use the service. Keeping track of how your onboarding process is performing can be a great way to make sure users are getting value from your service quickly and easily. Onboarding and overall ease of use are two things to keep an eye on when attempting to attract and retain developers using your API.Wrapping UpSelling APIs to drive business revenue is becoming more and more common. Hopefully, the above overview gives some insight into exactly how to do it and the challenges to overcome. In this post, we looked at how to sell an API, the challenges, the differences between an API portal and an API marketplace, and implementing monetization. Lastly, we looked at a few factors for attracting and retaining the developers that are using your APIs.To make sure you’re tracking and analyzing API traffic and monetizing your APIs effectively, check out Moesif. Sign up today to try out Time Series charts, Retention reports, Billing Meters, and other great tools to help you sell your APIs.                Easily Monetize APIs with Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /api-product-management/api-analytics/How-To-Sell-Your-APIs/",
          "author": "Matthew",
          "categories": "api-product-management, api-analytics"
        }
      
    ,
  
    
        "api-product-management-api-analytics-10-most-popular-frameworks-for-building-restful-apis": {
          "title": "10 Most Popular Frameworks For Building RESTful APIs",
          "content"	 : "As with many engineering problems, there are many ways to build RESTful APIs. Most of the time, when building RESTful APIs, engineers prefer to use frameworks. API frameworks provide an excellent platform for building APIs with most of the components necessary straight out of the box.In this post, we will explore the 10 most popular REST API frameworks for building web APIs. These frameworks span multiple languages and varying levels of complexity and customization. First, let’s dig into some key factors in deciding which framework to begin building with.                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free        How to Pick an API FrameworkThe first factor in choosing an API framework is usually deciding which language you want to work in. For many projects, depending on the organization you work with or your experience, choices may be limited. The usual recommendation is to go with a language you are already familiar with since learning a new language and a new framework can lead to less-than-optimal implementations. If you’re already familiar with the language, your main focus can be on understanding the framework and building efficiently.Once the language is decided, you may have multiple choices of frameworks that support your language of choice. At this point, you will need to decide based on what types of functionality you require from your APIs. Some frameworks will have plugins and dependencies that allow for easy integration with other platforms, some may support your use case more precisely, and others may be limited in functionality that you require, automatically disqualifying them. Making sure that your use case and functionalities are supported by the framework is key.Last but not least, you should also consider the learning curve and educational materials and docs available. As a developer, the availability of good documentation and examples are massive factors in how quickly you and your team can scale up your APIs. Before deciding on a framework, browse the documentation, and do a quick search to ensure that you can find examples that can guide you and inform you on how much effort is needed to build APIs in the framework of your choosing.Now that we have a few factors to consider, let’s take a look at some popular framework options.Spring BootSpring Boot is an open-source framework that helps developers build web and mobile apps. Developed by Pivotal Software, Spring Boot is a framework that’s intended to make the original Spring framework more user-friendly. You can easily start using Spring Boot out of the box, without spending time configuring any of its libraries.Programming Language: JavaPros:  Quick to load due to enhanced memory allocation  Can be easily configured with XML configurations and annotations  Easy to run since it includes a built-in serverCons:  Not backward compatible with previous Spring projects and no tools to assist with migration  Binary size can be bloated from default dependencies  To learn more about Spring Boot framework, you can check out the docs HereRuby on RailsRuby on Rails was originally developed as MVC framework, which gives it the name “the startup technology” among developers. The main purpose of the framework is to deliver apps with high performance. The high-performance standards of Ruby on Rails excited developers using Python and PHP and many of its concepts are replicated in popular Python and PHP frameworks.Programming Language: RubyPros:  Great framework for rapid development with minimal bugs  Open-source with many tools and libraries available  Modular design with efficient package management systemCons:  Can be difficult to scale compared to other frameworks like Django and Express  Limited multi-threading support for some libraries  Documentation can be somewhat sparse, especially for 3rd party libraries  To learn more about Ruby on Rails framework, you can check out the docs HereFlaskFlask is a Python framework developed by Armin Ronacher. Flask’s framework is more explicit than Django and is also easier to learn. Flask is based on the Web Server Gateway Interface toolkit and Jinja2 template engine.Programming Language: PythonPros:  Built-in development server and fast debugger  Integrated support for unit testing  RESTful request dispatching  WSGI 1.0 compliant  Unicode baseCons:  Included tools and extensions are lacking and custom code is often required  Security risks  Larger implementations more complex to maintain  To learn more about Flask framework, you can check out the docs HereDjango RESTDjango REST framework is a customizable toolkit that makes it easy to build APIs. It’s based on Danjgo’s class-based views, so it can be an excellent choice if you’re familiar with Django.Programming Language: PythonPros:  The web browsable API is a huge win for web developers  Developers can authenticate people on their web app with OAuth2.  Provide both ORM and non-ORM serialization.  Extensive Documentation  Easy DeployCons:  Learning Curve  Does not cover Async  Serializers are slow and impractical for JSON validation  To learn more about Django REST framework, you can check out the docs HereExpress JsExpress.Js is an open-source framework for Node.js that simplifies the process of development by offering a set of useful tools, features, and plugins.Programming Language: JavascriptPros:  Well Documented  Scale application quickly  Widely used and good community supportCons:  Lack of Security  Issues in the callbacks  Request problems encountered with the middleware system  To learn more about Express.Js framework, you can check out the docs HereFastifyFirst created in 2016, Fastify is a web framework that is highly dedicated to providing the best developer experience possible. A powerful plugin architecture and minimal overhead also help make this framework a great choice for developers.Programming Language: JavascriptPros:  Easy development  Performant and highly scalable  The low overhead web framework that grounds this system minimizes operation costs for the entire application.Cons:  Lack of Documentation and community support  Not readily used in the industry  To learn more about Fastify framework, you can check out the docs HerePlay FrameworkPlay is a web application framework for creating modern, robust applications using Scala and Java. Based on Dynamic Types, Play integrates the components and APIs required for modern web application development.Programming Language: Java, ScalaPros:  Intuitive User Interface  Testing the Application simplified  Faster development on multiple projectsCons:  Steep Learning Curve  Too many plug-ins which are unstable  Maybe it doesn’t offer any features for backward compatibility.  To learn more about Play framework, you can check out the docs HereGinGin is a fast framework for building web applications and microservices in the programming language Go. It provides a martini-like API and enables users to build versatile and powerful applications with Go. It contains common functionalities used in web development frameworks such as routing, middleware support, rendering, etc.Programming Language: GolangPros:  Performance  Easy to track HTTP method status code  Easy JSON validation  Crash-freeCons:  Lack of documentation  Syntax not concise  To learn more about Gin framework, you can check out the docs HerePhoenixPhoenix is written in Elixir and works to implement the MVC pattern. It will seem similar to frameworks like Ruby on Rails and Django. One interesting thing about Phoenix is that it has channels for real-time features which pre-compile templates. These templates work quickly, making the site smooth and easy to scroll through.Programming Language: ElixirPros:  Filters data that is safe and efficient  Elixir runs on the Erland VM for improved web app performance.  ConcurrencyCons:  Expensive  Processing Speed  Prior Erlang knowledge required  To learn more about Phoenix framework, you can check out the docs HereFast APIFast API is a web framework for developing RESTful APIs in Python. It fully supports asynchronous programming, so it can run with product servers such as Uvicorn and Hypercorn. It has support for Fast API into popular IDEs, such as Jetbrains PyCharm.Programming Language: PythonPros:  High Performance  Easy to Code with few bugs  Short Development time  Supports asynchronous programmingCons:  Poor Request Validation  Does not support singleton instances  Main File is crowded  To learn more about Fast API framework, you can check out the docs HereAdding in API Analytics and MonetizationBuilding an API is only the start. Once you’re API is built, you’ll want to make sure that you are monitoring and analyzing incoming traffic. By doing this, you can identify potential issues and security flaws, and determine how your API is being used. These can all be crucial aspects in growing and supporting your APIs.As your API platform grows, you may be focused on API products. This is making the shift from simply building APIs into the domain of using the API as a business tool. Much like a more formal product, an API product needs to be managed and likely will be monetized. Building revenue from your APIs can be a great way to expand your business’s bottom line.With Moesif, you can achieve all of the above. Moesif can easily integrate through either an SDK or plugin and be up and running in minutes. Once Moesif is integrated with your APIs, you’ll be able to explore charting and reporting to look at:  Live API traffic  Time-series reports to inspect usage  Conversion funnels  Retention reportsAnd much more…Moesif also enables API monetization by allowing you to track usage and sync it to a billing provider like Stripe, Recurly, or Chargebee. Within minutes, integrate your APIs and begin billing customers for usage. Moesif allows you to fine-tune exactly what you want to bill upon and is highly customizable to suit your exact needs.Wrapping UpIn this article we covered 10 of the most popular frameworks for developing RESTful APIs. We looked at a high-level overview of each and listed out some points for consideration. We also discussed some key factors in how to decide on which API framework to use. Lastly, we looked at how Moesif can help you take your API development to the next level by implementing analytics and monetization.                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue with your REST APIs?            Monetize your RESTful APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /api-product-management/api-analytics/10-Most-Popular-Frameworks-For-Building-RESTful-APIs/",
          "author": "Preet",
          "categories": "api-product-management, api-analytics"
        }
      
    ,
  
    
        "api-product-management-api-analytics-top-8-blockchain-apis-for-developers": {
          "title": "Top 8 Blockchain APIs for Developers",
          "content"	 : "Developers are always looking for new ways to make their applications more secure and efficient. Blockchain APIs are one way to do this. A blockchain API is an application programming interface that allows developers to interact with a blockchain. By using a blockchain API, developers can access the data and functionality of a blockchain without having to build their own blockchain platform. This can save time and resources, as well as provide a more secure environment for development. In this post, we will explore the best blockchain APIs for developers. We will look at the features and benefits of each blockchain API, as well as how they can be used to create more secure and efficient decentralized applications. Blockchain APIs are a crucial part of the expanding blockchain ecosystem, enabling scalability and diversity in blockchain technologies and applications.What Is Blockchain Technology?A blockchain is a digital ledger of all cryptocurrency transactions. It is constantly growing as “completed” blocks are added to it with a new set of recordings. Each block contains a cryptographic hash of the previous block, a timestamp, and transaction data. Bitcoin nodes use the block chain to differentiate legitimate Bitcoin transactions from attempts to re-spend coins that have already been spent elsewhere.The API allows the blockchain developer to interact with the blockchain in a variety of ways. For example, they can develop digital wallets for users, send and receive payments, and check balances. The API also enables developers to monitor markets and trends, as well as create applications that can be used to track prices or manage investments. A wallet API can be used to manage transaction histories, facilitate cryptocurrency transfers, and check wallet balances, making it essential for building applications like e-commerce platforms that require cryptocurrency payments.The most popular blockchain is the one that powers Bitcoin, but there are many other types of blockchains being developed for a variety of purposes. Some of these include Ethereum, Litecoin, and Monero.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        What Are the Benefits of Using Blockchain APIs?Blockchain APIs offer a number of benefits for developers. They can help to streamline the process of developing applications and make it easier to integrate with other systems. In addition, Blockchain APIs can provide access to data that is stored on the blockchain, making it easier for developers to create applications that make use of this crypto data. Reliable data is crucial in blockchain development, and blockchain APIs ensure secure and accurate data retrieval, mitigating risks related to data manipulation.Blockchain APIs can also help to reduce the costs associated with developing applications. By making it easier to access data stored on the blockchain, developers can avoid the need to build their own infrastructure to support their decentralized application. This can lead to significant savings in both time and money. A market data API can provide real-time market data related to cryptocurrencies, which is essential for applications like trading platforms.In addition, Blockchain APIs can provide developers with a way to monetize their applications. By charging for access to data or functionality provided by an API, developers can generate revenue from their applications. This can help to offset the costs of developing and maintaining a decentralized application.Finally, Blockchain APIs can help to create a more open and accessible ecosystem for applications. By making it easier for developers to access data and functionality provided by other applications, Blockchain APIs can help to create an environment where applications can interoperate with each other. This can lead to new and innovative app development being created that would not be possible without the use of APIs.The Best Blockchain APIs for Blockchain DevelopmentThere are a number of different blockchain APIs available for developers, each with its own advantages and disadvantages. In this article, we’ll take a look at some of the best blockchain APIs currently available and provide a brief overview of each one. Many of these APIs are REST APIs, which are widely used for their simplicity and efficiency.BlockCypher APIThe BlockCypher API is one of the most popular blockchain APIs available today. It offers a simple interface that makes it easy to get started with blockchain development. allowing the developer to interact with Bitcoin, Etherum, Litecoin, and Dogecoin on a variety of platforms. The versatile development tools gives you the ability to interact with a smart contract, get notified about an unconfirmed transaction or create a multi signature transaction. Other features include:  Data Addresses. Transactions, Blocks, Smart contracts.  Interactions. Create Transactions. Decode transactions. Interact with contracts. Deploy contracts.  Notifications. Transactions webhook. Block webhook. Double spend webhook. Websocket.  Advanced Features, Multisig. Segwit Support. Confidence factor.Chain APIThe Chain API is another popular option for blockchain developers. It offers a more comprehensive set of features than BlockCypher, making it a good choice for more experienced developers. Chain also has good documentation and support for multiple programming languages. ChainAPI has a user-friendly interface that will allow API providers ro set up first-party oracles easily. Interface is used by the API2 employees to do integrations on behalf of API providers with necessary greatly improving their efficiency and correctness. Chain API will make it easier for API providers and requesters to interact with the Airnode protocol across multiple chains, including a node dashboard.CoinBase APIThe CoinBase API can be a great alternative option for blockchain development. Coinbase Pro provides an API that makes it easy to carry out various tasks. This includes sourcing real-time prices, storing digital currency safely, buying or selling cryptocurrencies, and processing digital wallets. They also offer a premium option with a much more advanced API for your blockchain solution. Some of the features CoinBase API offers:  Generate bitcoin cash wallets and addresses.  Securely store the coins.  Obtain real-time and/or historical price data.  Get notified when the payments arrive.  Send/receive or sell/buy bitcoin cash, bitcoin, litecoin and ethereum.Crypto APICrypto APIs is a blockchain infrastructure provider that makes developing and managing Web 3 solutions easy and efficient. They provide Wallet as a Service (WaaS), Blockchain Data, Blockchain Events, Blockchain Automations, Blockchain Tools, and Market Data to make development easier. Developers use their SDK to access over 100 end points from a single provider for diverse solutions including digital banks, exchanges, wallets, custodians, lending products and more. With Node as a Service, you’ll be able to quickly deploy your blockchain technology with shared or dedicated node infrastructure. Chainlink, Ledger, Nexo and Paypal have already tried Crypto APIs’ company’s REST APIs. They love the white-glove support and features that are effective fast. Blockchain developers appreciate how reliable they are. Some features include….  MPC’s Wallet as a Service is the best digital wallet on the market - it incorporates the top features, protection, and authorization process currently available.  Blockchain Data- Unified access to complex and dynamic data from a single point using REST APIs. Data is often stored and exchanged in JSON format, highlighting its accessibility and ease of use for developers.  With Node as a Service, you’ll be able to quickly deploy your blockchain technology with shared or dedicated node infrastructure. Utilizing JSON-RPC, it can be plugged into existing infrastructure and allows for an easy development environment in Javascript.  Blockchain Automations - automatically forward any coins or tokens that are received to a preferred main deposit address.Blockchain APIThe Blockchain API is an additional option worth considering for blockchain development. Blockchain API is the perfect solution for providing cryptocurrency payments to your projects. The high quality of their services and capabilities make it easy to integrate. Blockchain API has been successful in integrating with over 25,000 developers. They offer many different APIs that are tailored to satisfy the needs of different customers. They have APIs for wallets, payment processing, querying data, exploring the blockchain network, analyzing crypto data and more.It has several aspects that make it a competitive provider in the market. This includes data storage in blockchain, which is done in block form. The result of this is JSON data, which deals with transactions. Blockchain’s offline-first approach also means that they don’t need additional cryptocurrency storage services. It has a vast developer community and low timeouts, as well as an accessible JSON data format. You can also access the blockchain network through e-wallet accounts.Block.io APIThe Block.io API offers a simple interface that makes it easy to get started with blockchain development. Block.io also provides support for multiple programming languages, making it a good choice for developers who want to write code in their language of choice. Don’t forget to test your application thoroughly and always keep your private key confidential.BitPay APIThe BitPay API an international digital asset, BitPay’s API allows you to perform a wide range of tasks. BitPay provides a standards-based REST interface that enables application developers to interact in powerful and secure ways with their BitPay account. Using the BitPay API, clients can manage invoices, issue refunds, view merchant records, and more.Developers may choose to call the API over HTTPS using the language of their choice, or take advantage of our code libraries. And, if their preferred language isn’t listed, they can still customize the integration.GetBlock APIThe GetBlock API is another popular choice for a blockchain developer to explore. GetBlock provides a simple, easy way to use the power of blockchains, including smart contract functionality. Discover their high-speed running nodes and secured access to API for bitcoin and Binance’s Smart Chain on blockchains, like Bitcoin, that allow you to run a decentralized app efficiently. GetBlock offers API, smart contract, and explorer data services. It also provides a blockchain development program which has access to raw data. When you use our service, you are guaranteed speedy access over blocks, transactions, and contracts that can be achieved by simply using the base API data. GetBlock API has helpful technical guides and documents and offers custom SLAs that are tailored solutions to suit your business needs.Factors to consider when determining on a blockchain APIDevelopers and development teams have preferences when it comes to choosing the best programming languages, architecture patterns, frameworks, or libraries. Blockchain API’s are similar.Technology:  It is important to use open-source codes which help avoid mistakes and improve overall security as it can be tested by other developers.Performance: Blockchain APIs vary in performance and capacity, so it’s important to pick one that meets the needs of your application. A few transactions per second may be enough if you just need to encrypt a couple of documents, but apps with thousands of transactions per second will require subsystems called microservices. These will allow for handling high loads and continued responsiveness when processing user requests.Compatibility: You’ll want to make sure the API you choose can support the coins you need.How To Use Blockchain APIsIf you’re a blockchain developer looking to get started with blockchain technology, one of the first things you’ll need is a blockchain API. This will allow you to interact with the blockchain and build your blockchain application on top of it.There are many different blockchain APIs available, so it’s important to choose one that’s right for your needs. Some factors to consider include the programming language you’re using, the features you need, and the level of development tools offered.Once you’ve chosen a blockchain API, you’ll need to register for an account and get an API key. Then, you can start building your app development.Be sure to test your application thoroughly before deploying it. And, always monitor the blockchain for changes that could affect your application. Tools such as Moesif can help leverage a browser SDK to access API call data from the client side. From there, we can generate debugging and monitoring reports to ensure your site’s running smoothly. If something does go wrong, we’ll alert you immediately so that it doesn’t have time to expand into a larger problem.Support and Monitoring with MoesifMoesif has unique features that allow you to associate each event that occurs in your app with a user and/or company. This gives a more nuanced understanding of the developer experience and how APIs are being used by each individual. A perfect example of this is Funnels and Retention.Funnels are a step-by-step breakdown of a flow within your app. For example, you might be optimizing your sign-up process and looking for ways to speed it up. This analysis provides you with insights and how long it takes users to make their way through the steps and what percentage of customers complete them. This is important, as it helps establish a baseline you can use as you try to improve conversions and API usage.A retention analysis can help determine when customers are quitting or becoming inactive with your product and APIs. This can help to improve retention and allow you to know when there is a problem with your developer experience. This will enable you to establish baselines to correlate improvements or deterioration of your developer experience with the retention of users.Another way to track your API usage is to use alerts to let you now when users are having difficulties or to let you know about anomalies in traffic. For example, an alert might be used to let a customer success team know about issues that users are experiencing so they can reach out and help. Alerts also allow product teams to learn from feedback on issues in the short-term and make improvements in the future. Alerts are made up of two types: Static and Dynamic.Static alerts can be set as a specific threshold, like having more than 3 401 - Unauthorized errors in an hour. Dynamic alerts allow you to set a threshold such as noticing when a user is experiencing a surge in 401 errors compared to their average amount. It allows you to monitor trends or spikes without putting a specific number in for the threshold.Both forms of alerts can improve the user experience within your product, especially if it’s high quality and supportive from its team members. For more info on how to create an alert in Moesif, check out our docs.Moesif allows you to set up automated email flows based on events your users are experiencing. This is done through creating behavioral emails. For instance, if a user has a high number of 401 - Unauthorized responses then you may want to send them an email with suggestions on how to fix the issue or a guide. This takes the pressure off the support team and also makes it possible for instant and proactive action to be taken.Enabling API Monetization with MoesifAs an API provider, you may want to create some type of revenue with your APIs. When first starting the monetization process, you may find that the challenges are steep and complex. To solve these issues and create a smooth experience, significant customization and testing are going to be required. Thankfully, Moesif is a robust API monetization platform that can help. With unlimited options available through the platform, it’s easy for you as an API provider to drive adoption and revenue from your APIs.With Moesif, you will have the flexibility to make well-informed decisions to increase revenue and profits. You’ll also have other tools that can supplement your user journey and improve your API product.With Moesif, usage data can be synced to the provider. This calculated API usage is sent to the billing provider and is used to generate invoices for users. Alternatively, you can support pre-paid billing, where a developer buys credits as they need them.For more info on how to monetize your APIs in Moesif, check out our docs.Customer Success using MoesifLeading customer success for API-first or developer-focused businesses is much different from traditional enterprise software, The best API products are built in a way that make them easy to use and hands-off, meaning customers don’t have to log in to the platform once it’s implemented.When customer success works with API-first businesses, they should be prepared for two different experiences with their customers. The first experience is when the customer signs up and logs into your portal. Second, there is the customer’s experience when integrating with and interaction with your API platform. Both are considered to be part of the onboarding experience, and metrics should be recorded for each one. What’s more, it is the API that has the potential to offer long-term value. Customers who sign up but never fully integrate with or use that platform can churn quickly (if they’re paying already).In Moesif, every API call can be attributed to a user and/or company. Moesif creates a profile for each of these companies and users which allows customer success teams to have a CRM-style view of the customers attributes.Customer success teams can also easily track customer interaction with the platform and keep an eye on key customer health metrics. These metrics will show under the user or companies profile dashboard. Having the ability to see customer metrics in a visual dashboard which shows customer usage, including errors and integration problems that may be occurring.ConclusionThe Blockchain APIs on this list provide a wide range of capabilities, from allowing developers to create their own digital currency wallets, to helping them interact with smart contracts on the Ethereum blockchain. While some of these APIs are still in development, and therefore may not be as reliable as others, they all show great promise for the future of blockchain technology. In the meantime, we encourage you to experiment with all of these APIs to see which one(s) work best for your project.When creating APIs for yourself, using a solution like Moesif to monitor and improve your users API experience is crucial. The adoption of APIs can be easily tracked and enhanced using many of the features talked about in this article. The features we covered included API analytics, API monitoring, and API metered billing. To try out these features for yourself for your own Web3 APIs, log in to Moesif or sign up today to get started.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Grow Your API Business with Moesif            Ship better API products with a powerful API analytics and monetization platform.            Try for Free            No credit card required            ",
          "url": " /api-product-management/api-analytics/Top-8-Blockchain-APIs-For-Developers/",
          "author": "Preet",
          "categories": "api-product-management, api-analytics"
        }
      
    ,
  
    
        "customer-success-monitoring-the-secret-to-building-an-effective-customer-success-dashboard": {
          "title": "The Secret to Building an Effective Customer Success Dashboard",
          "content"	 : "The secret to customer success is making your customers happy and supporting them to succeed. A simple concept – until you try to implement it. You’ll need a great product and a great customer service team, for starters. You’ll also need an in-depth understanding of the customer experience, including their pain points and how you can help solve them.This is where things get trickier, but the answer could already be tucked away in your data. All you need is an effective customer success dashboard to automate the identification of issues and then support you to solve them – as the dashboard example below demonstrates.Customer Success KPI Dashboard Example  ”A couple of years ago, when we encountered errors from sub-optimal client API practices, we might share a permalink with a team member so they could look at logs. That worked reasonably well, but you’re still sifting through logs. Now with Moesif, events that belong together can be grouped and easily shared in a dashboard. Issues can then be proactively addressed, resulting in better customer experiences.” Conrad Caplin, Co-Founder, Pronto CX.Pronto CX  is an API-first FinTech company specializing in loyalty and payment services for diverse industries, from transit to live events. As they were going through rapid growth, Pronto CX looked for tools that could maintain their outstanding 100% customer retention rate. The firm began using Moesif to identify data validity issues and then troubleshoot them, using a customer success API dashboard to achieve maximum results.What is a Customer Success Dashboard?When it comes to understanding and enabling customer success, implementing an effective customer success dashboard that enables swift key performance indicator (KPI) analysis should be at the heart of your approach.A customer success dashboard is a visual tool that helps you to track everything from how your product is being used to your retention rate, churn rate, monthly revenue and more.Why are Customer Success Dashboards Important?Pronto CX’s customer success KPI dashboard is a testament to the difference that such a dashboard can make to customer satisfaction. It enabled Pronto CX to implement both reactive and proactive measures to drive up its customer success levels.Retaining users starts with understanding the steps at which they get stuck when using your product and why. Pronto CX had previously employed a reactive strategy to API problems, giving their customers an opportunity to grow impatient and become at risk for canceling their contracts. Through Moesif they were able to automate the monitoring and display of API usage, gaining customer insights and identifying situations where Pronto CX could proactively intervene and help their customers be successful.How Do You Measure Customer Success KPI?Customer success dashboards can support the journey from new customer to loyal, retained customer. To monitor that journey, you need to measure various customer success KPIs. Some of the most important include:  Customer churn rate  Customer lifetime value  Monthly recurring revenue  Customer support tickets  Net promoter score  Customer satisfactionYou can also add in any KPI that will support understanding your own customers’ success and monitoring it, such as API calls as in the customer success API dashboard example above.Customer Success Overview Dashboard TemplateYour customer success overview dashboard template will be shaped around your specific product. That said, it’s possible to outline some broad examples of what such a dashboard should include.What Should Be on a Customer Success Dashboard?Start by understanding what success means to your customers and what your goals are. After all, customer success stories vary hugely from business to business.What you monitor is up to you. Say you have a sales rep who has implemented behavioral emails to try and accelerate API integration. You can monitor the impact on your dashboard.Your dashboard will be a mix of customer health indicators and KPIs, monitoring both positive and negative customer success metrics. Positive metric examples include number of new customers and monthly recurring revenue. Churn rate, meanwhile, is an example of a negative metric.How Do You Measure the Effectiveness of a Dashboard?You can measure the effectiveness of your customer success dashboard by monitoring your overall customer satisfaction and success levels. Another measure of effectiveness is the return on investment that your dashboard delivers.For digital mobile payments platform Reloadly, one measure of their dashboard’s effectiveness was the company’s ability to proactively identify upcoming issues, before they came to a head.  In terms of ROI, one way to look at it is that with Moesif we can get advanced knowledge of issues that allows us to be proactive and take action, which leads to greater customer satisfaction.” Emmanuel Piard, Co-founder and CTO, ReloadlyWhat are 3 Benefits of a Dashboard?When it comes to customer success, a dashboard delivers multiple benefits. Three of the most useful are:  Reducing customer churn  Creating more stable revenue  Increasing customer satisfactionBy supporting your customers’ success, you’re supporting your own.View Customer Success Metrics and Gain InsightsWe touched on customer success metrics when talking about KPIs above. Monitoring rates such as customer churn  provides an overview of customer success that can drive unique insights into your customer experience and your own business.Key to identifying the customer success metrics that will provide the most useful and comprehensive oversight is understanding what your customer needs. You can then track your success in meeting those needs through relevant metrics.How Do You Track Customer Success?Being able to view customer success metrics and gain insights is hugely valuable. But how do you do it?In the case of the Amware example, tracking success meant monitoring the number of errors that customers were making when ordering using the Amware API, then working with individual customers to reduce the number of errors.To track customer success, you need to implement a range of metrics that can provide an overview of your progress. Let’s take a look at a few of these.What are the Important API Customer Success Metrics?The most important API customer success metrics for your business will be those that dive into the detail of what you’re achieving. Examples of such metrics include:Customer retention rateIt’s all very well winning new business, but if your customers don’t stick with your product, you have a problem. Tracking what proportion of your customers you retain over time is key not just to understanding your customer success performance but to supporting the long-term viability of your enterprise.Customer health scoreYou can use a customer health score to determine what proportion of your customers are ‘healthy’ and what proportion are ‘at risk’. On an individual customer basis, this helps you identify which customers are likely to grow and which you are at risk of losing. At an organizational level, it can serve as an early warning system if you identify a growing proportion of customers at risk.Net promoter scoreA long-established tool in market research, as well as in monitoring customer success and business growth, your net promoter score relates to the proportion of people who would recommend your business (or product) to a friend or colleague. Tracking your net promoter score can reveal a great deal about how positively (or otherwise!) your customers view your business.Customer lifetime valueMonitoring customer lifetime value tells you what a customer is worth over the entire period of their relationship with your business. Calculating your customer lifetime value and then monitoring it to ensure that it increases is an effective way to track your growing customer success.Customer churn rateThe lower your customer churn rate, the better. After all, it costs your business more to go out and find new customers than it does to retain existing ones. As such, working to reduce your churn rate can deliver long-term rewards.Monthly recurring revenueYour monthly recurring revenue is the amount of income that you get from your customers each month. Tracking it is essential to understanding your customer success performance and to monitoring the financial viability of your business.Customer retention costsWe mentioned above that it’s cheaper to retain customers than go out and find new ones, but retaining customers is far from cost-free. By measuring the cost of your customer success work and comparing it to your number of customers, you can monitor how much it costs to retain each customer.Customer satisfaction scoreA customer satisfaction score measures, quite simply, how satisfied your customers are with your business. It is an excellent indicator of customer success, particularly when combined with the metrics detailed above. Together, they provide a comprehensive overview of your customer success performance.Making a Success of Customer Success Dashboards for API ProductsCustomer success dashboards for API products are a hugely important tool for understanding the impact that those products can deliver and the implications of that impact for the overall success of your business.If you’re ready to implement a customer success API dashboard, why not get in touch with the Moesif team to discover how we can help you analyze, interrogate and monitor your customer success data?                What You Should Track to Reduce Churn              14 day free trial. No credit card required.              Learn More        ",
          "url": " /customer-success/monitoring/The-Secret-to-Building-an-Effective-Customer-Success-Dashboard/",
          "author": "Larry",
          "categories": "Customer-Success, Monitoring"
        }
      
    ,
  
    
        "customer-success-monitoring-how-customer-success-transforms-your-saas-business": {
          "title": "How Customer Success Transforms Your SaaS Business",
          "content"	 : "Focusing on customer success could be the difference between your SaaS business ticking along nicely or achieving stratospheric growth. Increasingly, customer success is seen as a foundation of growth for Software as a Service (SaaS) businesses. If your enterprise isn’t achieving the desired outcome in terms of growth, perhaps it’s time to reassess your thoughts around the customer relationship and the importance of happy customers.What is Customer Success?When it comes to SaaS businesses, customer success is a foundation of growth. Why? Customer success is all about supporting your existing customers to achieve their goals. This is at the core of what customer success professionals set out to achieve.Customer success is more than customer support or service. It is a non-transactional relationship building between company and customer. In terms of structure, customer success sits between a business’ sales and support functions. It interacts with and overlaps both of these functions in complementary ways.A strong customer success program supports:  onboarding  activation  integration  subscription renewal  subscription upgrade  customer retention  customer advocacyCustomer Success is a Foundation of GrowthDone well and in line with industry best practices, Customer Success Management (CSM) can help  any business to reduce customer churn and achieve a better reputation, greater recurring revenue, happier customers, and more. It is an essential component of any modern SaaS business that wants to maximize its opportunities for growth.Why is Customer Success Important for SaaS?Customer success is important for any business, but particularly for those in SaaS. Once upon a time, a business sold a product. The customer would implement the product, perhaps with a little help from customer support, and the interaction was largely finished. Not anymore.Today’s SaaS customers expect to lead their buying journey before, during, and after their initial purchase of a subscription plan. Today’s SaaS customer is a self-led buyer. This means that outbound, traditional methods of selling no longer apply. Customers would rather discover and vet a product themselves than receive unsolicited emails. This model is called Product-Led Growth, or PLG.Product-Led Growth is the New Champion of SaaSEnter customer success. By establishing organic relationships with existing customers, customer success creates a new channel of communication between company and customer. This means a way to speak directly to the self-led buyer.This is why customer success has become such an important element of the SaaS business model. Customer success managers, data driven customer success, and their teams can significantly increase the likelihood that a customer will continue using their service, or even upgrade their plan. They serve as the voice of the customer within the SaaS operation. Doing so involves a range of touchpoints with the customer, both at set stages within the customer journey and in response to anomalous occurrences along the way.Relationships are the New DifferentiatorWithin a SaaS business, customer success management can ensure customers feel listened to. It can drive product changes. And it can maximize the value of each customer not just at the point you acquire them, but over their full lifetime. Which, with SaaS products, could be an actual full lifetime. The potential for this in terms of customer value, customer lifetime value realization is huge.One other reason that customer success is important for SaaS is that happy customers have the potential to become referrers. Who better to recruit new clients for your business than those who are already using your product happily and getting maximum value from it?How is Customer Success Different from Customer Support?The key difference between customer success and customer support is that customer success is proactive and non-transactional. Your customer success team should be one step ahead of the customer’s needs at every stage, anticipating how your product can support the customer to do things better and achieve their desired outcome.  A range of customer success metrics can support your team to do this, so that it identifies both set points when the customer will benefit from interaction, and personalized opportunities for maximizing each customer’s growth and success.Customer support, on the other hand, is a reactive service. It is there to respond rapidly and helpfully when the customer reaches out.Make Customer Success an Early Priority for Your SaaS BusinessWith any business function, the earlier you implement it, the easier it is to embed as a fundamental part of your operations. That’s why it is important to make customer success an early priority for your SaaS business, why customer success is a priority for saas. Doing so means you can put the people and tools you need in place to ensure that your customer success effort yields maximum results.Support During Customer OnboardingYour customer success team is there to support every new customer to evolve into a long-term, loyal customer. The customer onboarding process is the first step in this journey. First impressions count for a lot, so onboarding is your chance to impress your customer with how easy your product is to use and how helpful and pleasant your team is to work with.Getting onboarding right means supporting a customer to set up your product correctly, in the way that best suits their business and their desired outcomes. It means providing technical hand holding (appropriate to the individual client’s needs) during the product activation phase. It also means supporting the client with any integration between your product and their existing infrastructure. After all, when your SaaS product is the newcomer, you need to ensure it plays nicely with others.Support During Integration and BeyondOnce the customer is onboarded, it’s time to support them to get the most out of your product. Did you know that many SaaS customers never finish integration? [INCLUDE: fact] This is where customer success can encourage repeat business, by ensuring that customers are able to properly integrate your SaaS product into their offerings.Key to that is understanding your customer’s business. You will have gathered plenty of information on how the client likes to operate during the customer onboarding and activation phase, so use that to deliver a personalized approach to customer success. This turns their business growth into a partnership experience with your SaaS company, with the result that both businesses benefit.The more you understand the customer’s business, the more effectively you can highlight features in your own product that they could benefit from using. And the more likely you are to anticipate any roadblocks that they might run into. Using customer data to identify these needs and potential issues puts you in a powerful position to support the client to realize maximum value from your product. This is a great way to gain loyal, happy clients through a proactive virtual assistant for customer service approach that’s embedded as a core part of your business.A Customer Cared for is a Customer KeptBy taking this approach to customer engagement in your SaaS business, you’re doing much to improve retention and lower your customer churn rates. Customer engagement lowers churn. Left to their own devices, customers are unlikely to use your product to its full potential, or to keep up with your latest feature releases and the benefits that those could deliver. With your customer success team on hand though, each customer can achieve real value from your SaaS product on an ongoing basis – and will thus be a great deal more likely to renew their subscription when the time comes.What Challenges Do Companies Face Around Customer Success?Customer success, like any business function, takes work, thought and expertise to get right. Doing so involves overcoming a range of challenges.Putting Data at the Heart of Customer SuccessData plays a core role in enabling any customer success manager to deliver their best results. Using the right customer success metrics can flag up the most effective times for the customer success team to engage with the client.Making customer success data driven is the first of these challenges – which is where Moesif comes into the picture. By providing visibility, moesif for customer success of a range of customer success metrics, along with alerts that can trigger customer engagement interactions, the platform supports businesses to act on each and every opportunity to level up their customer experience.After all, data can’t lie. In a field as subjective as relationship management, measuring what metrics you can is necessary. It’s important to note that some metrics are more useful than others.SaaS Integration is Hard - That’s Why You Need to Measure itOne such metric is “Time to First Hello World” (TTFHW). This is the time it takes for a customer to move from signup to full integration. The faster they get there, the happier they are likely to be with your product.When you know how long the TTFHW process can and should take, it’s easy to identify when a customer gets stuck along the way. Moesif can flag this up to your customer success team through an automated alert, so they can proactively step in, find out what the issue is and support the client to move ahead.Monitoring TTFHW for all customers can also help you spot trends. If your TTFHW is increasing, you may need to let your product and engineering team know, for example, as their changes could be impacting the user journey by making customer onboarding more complex.This data-driven approach can also alert you when customers are struggling with new features, identify success gaps, flag up instances of decreasing usage (and thus customers who are a churn risk), and identify opportunities to upsell and cross-sell. All while ensuring the customer feels supported by a partnership approach.Monitoring How Effective Your Customer Success Effort isAnother challenge is monitoring the impact of your customer success efforts. This type of monitoring is highly dependent on the nature of your SaaS product. However, segmenting your onboarding journey with user funnels as well as setting up alerts when customers encounter errors can help you proactively manage customer issues. With regard to measuring ongoing effort, logging engagement with your platform through metrics like API call volume reveals if customers value their experience with you.You can also monitor a range of customer success metrics through tools such as Moesif, to gain a business-wide overview of usage statistics and trends. This further embeds the role of customer success in your business.4 Strategies to Achieve Customer Success in SaaSYou can implement various strategies to ensure the success of your customer success efforts. The following 4 strategies to achieve customer success in SaaS can help you do so, but there are plenty of other customer success strategy options out there, so be sure to implement the ones that make most sense for your business model.Identify Customers Struggling to IntegrateThe example above, of Moesif flagging up customers who may be having issues with their setup and integration, is part of a prescient customer success management strategy. This is all about identifying issues and reaching out to the customer before they even realize how much they could benefit from an interaction with your team.The idea of a prescient customer support approach is that you reach out and engage the customer before they become frustrated with your product. You don’t wait until they’ve tried something ten times and got stuck. Instead, you are readily available to help via the landline service or provide chat or email support long before they reach the point of irritation. You smoothly support them past whatever issue they are having, leaving them delighted with both your product and your team.Make Good Customers into Great CustomersAnother excellent SaaS customer success approach is to personalize your upselling and cross-selling activities. As much of software sales now relies on a buyer-led journey, your customer success people have privileged access to your customer’s attention. Thus, a customer success manager is often in the best position to upsell on certain services.This relies on an understanding of each customer’s needs and the way in which your product supports them to achieve their desired outcomes. It’s not about going in with the hard sell just because you’ve developed a new feature that you think is great. Rather, it’s about taking a thoughtful approach to how your new features could benefit your customers and reaching out to those who could achieve genuine value from them.This personalized customer success strategy ticks a range of boxes. It shows your customers that you care about their individual business needs and that you understand their pain points and goals. And it means you can upsell in a way that boosts your customer retention and recurring revenue rates, rather than diminishing them through hard selling tactics.Keep Customers Engaged with EmailA third strategy for proactive customer success is building a winning lifecycle email program. This is another task that Moesif can support you with, through integration with your CRM. The goal is to send timely, stage-appropriate emails to your customers that are designed to maximize the value they get from your SaaS product.Forget monthly, all-customer emails with generic product and feature recommendations. They won’t be helpful to the majority of customers and will irritate some due to their irrelevance. Instead, you can track customer behavior with your product and use that data to send emails with relevant, genuinely helpful features and usage suggestions at the ideal time in the customer journey.Customer success strategies like this also deliver an added benefit. The fact that they are data-driven and automated means that they can be implemented at scale. So no matter how successful your customer acquisition strategy may have been, you can still reach out to each customer at just the right point in their journey to maximize your chances of cross-selling or upselling – and to maximize the value they get from your service.Turn Customers into FriendsAnother key customer success strategy – perhaps the most traditional of them all – is to talk to your customers. They are the people who can tell you what success looks like for them. They can tell you what’s working brilliantly for them and what they love about your product, as well as what they don’t.A sound customer feedback process can do much to boost your overall customer happiness. But beware – a token effort at asking for customer feedback can backfire. After all, your customers are taking the time to share their views on your product. They need to see that you appreciate the time they’ve put in and that you are doing something with the data they provide.This is where advocating for the customer within your business comes in. Customer success teams, building a great customer success team can feed back to product and engineering teams on the pain points customers are facing and how you could help overcome them. It could be something as simple as changing a default setting or as complex as adding a whole new feature. But with the right feedback loops in place, you can show your customers that you care, support greater customer success, and reduce the likelihood of customer churn.Curious About What Makes a Great Customer Success Operation at a SaaS Company?Creating a great customer success operation at a SaaS company is all about early implementation – about embedding a genuine commitment to customer success as a core business principle.It’s also about finding the right tools and the right people. The right tools mean you can monitor your customer success metrics in sufficient depth to take prescient action and deliver a personalized, stage-appropriate customer success experience. The right people, meanwhile, will mean that every interaction your customer has with your business leaves them feeling positive. Even a customer who is encountering problems can feel satisfied when you have the right team with the right attitude.Do I Need to Hire a Customer Success Team or Manager?If you operate a SaaS business model and you want to maximize your recurring revenue, then yes – as soon as you can afford to. Doing so will add to your outgoings, of course, but effective customer success professionals can quickly cover their own costs by reducing your customer churn rate and increasing the likelihood that your customers will renew, or even upgrade,  their subscriptions. They can also enhance your company reputation, leveling up your net promoter score by creating happier customers.Knowing where to start when building a customer success team is key to hiring the right people, so be sure to do your homework before compiling your job specs and interview questions. Consider deploying a reliable Cloud recruitment software to streamline the hiring process. Think about personalities as well as skills and knowledge, and ensure that your hiring process is sufficient to identify those who will contribute to the long-term success of your customers’ businesses and your own.The other thing to bear in mind when building a customer success team is that it is not a team that operates in isolation. Your customer success function won’t be very effective if it sits in a silo, cut off from your other business functions. Given that customer success bridges the work of the customer support, product and engineering teams, you need a structure in place that encourages positive interaction and collaboration between those teams.Where to Start When Building a Customer Success TeamIf you’re ready to start building a customer success team, it’s time to think about the skills that you need. Yes, you’ll likely need technically minded people if you’re a SaaS business, but the success of your approach to customer success management will rely on far more than that. You’ll need a team that cares about the customer experience, that believes in the role and value of customer success, and that has the diplomacy, positivity, and empathy to serve as a bridge between the customer and your product and engineering teams.What Does a Customer Success Manager Do in SaaS?A customer success manager is responsible for the daily operation and delivery of the customer success function in a SaaS business. On the personnel side, they are in charge of managing the team. On the operational side, customer success managers, steps to building a customer success team are in charge of ensuring the team hits its targets.Customer success managers will usually report to a chief customer officer or VP of customer success (depending on the size of the organization), who is responsible for the overall performance of the business’ customer success program. It is the customer success manager’s responsibility to oversee the day-to-day operation and achievement of that customer success plan, making a plan for customer success.Developing Customer Success Advocates in Your CompanyMuch of the strength of your customer success program will lie in the quality of the customer success advocates you hire. Customer success advocates are the team members who will be working one-on-one with your customers. Their responsibilities include building strong customer relationships, providing technical assistance and insights, and advocating on the client’s behalf within your business. In this way, they will ensure customers achieve maximum value for your product and thus improve your customer retention and renewal rate. As that rate is the lifeblood of your SaaS business, it’s easy to understand the immense value of this role.Your customer success manager will need to work closely with each customer success advocate to ensure they are fully supported. This should include appropriate mentoring and coaching to ensure that all support is provided in line with your business values and practices. To streamline this processes, consider using mentoring software, as it allows to track and assess important KPIs, set performance goals, and provide feedback to improve the customer success advocates’ overall performance.Feedback from customer success advocates is invaluable in creating connections with your customer support, product and engineering teams. The more effective those interactions are, the greater the scope will be for developing your product and your business in a way that best supports your existing customers. Doing so can not only reduce your rate of customer churn but can also boost your potential for new customer acquisition, as your product development will remain relevant to the evolving needs of your target audience. And what SaaS business can afford to ignore the potential benefits of that?Final ThoughtsCustomer success isn’t some “nice to have” function that your SaaS business should maybe think about one day in the future. It’s an essential component of the modern SaaS model that can transform a business from something run-of-the-mill to one that stands out from the crowd – when it’s done well, that is.From the right tools to drive insights and actions based on customer success metrics, to the quality of the people you hire and the customer success strategies you implement, you have the potential to scale your SaaS business rapidly. All through putting a commitment to customer success at the heart of your operations and approach. Because customer success is what underpins the recurring revenue that every SaaS business relies on for growth – and, indeed, for its continued existence.                What You Should Track to Reduce Churn              14 day free trial. No credit card required.              Learn More        ",
          "url": " /customer-success/monitoring/How-Customer-Success-Transforms-Your-SaaS-Business/",
          "author": "Savannah",
          "categories": "Customer-Success, Monitoring"
        }
      
    ,
  
    
        "product-management-api-analytics-whats-the-best-way-to-determine-the-price-of-an-api": {
          "title": "What's the Best Way to Determine the Price of an API?",
          "content"	 : "As an API provider, once you’ve decided to bring in revenue from your APIs, the next step is to figure out how you will price the usage. As with any business decision, there are plenty of ways to go about pricing an API. There are many short-term strategies to establish initial pricing and then iterate to find which pricing model and price points work best for your customers. Long term, there are also steps that can be taken to ensure your pricing stays relevant and balances retention and revenue.In this article, we will take a deep dive into all the relevant questions when it comes to API pricing. I will aim to give you some points to think on as well as some best practices. Hopefully, by the end of the article, you will have a better understanding of API pricing and how you can apply the techniques below to your organization’s monetized APIs. Lastly, we will take a look at how Moesif can help you to figure out what APIs to monetize, how to price them, and how to actually charge users for usage. Let’s jump in!How does API pricing work?API pricing, as with most product pricing, consists of two major questions: What metric will be charged upon, and what pricing model will be implemented? For example, you may choose to bill upon every single API call and charge a set price per call. You may also involve a discounted rate per call when usage exceeds a certain amount. Maybe you’ll decide to just charge per user or the API key used to access the APIs.Each company may have a different metric that they would like to bill upon and different pricing models they may want to implement. These choices are all very dependent on the APIs you are monetizing and the needs of your business.What are common metrics to charge upon?There are an infinite number of metrics that one could charge upon. Generally, though, there are a few common scenarios that come to mind. Here are a few of the most common metrics used to measure API usage:Per eventThis metric is applied to every call that comes into your API. Each call is tallied up and counted towards the total for the billing period. An example may be if you want to charge $0.10 per API call. If a user calls the API 5 times, they will receive $0.50 in charges on their bill. This metric is one of the most popular to start with as it is easiest to implement.Per request/response body countIn some cases, you may want to count the number of elements in an API request or response body. This is great if you want to charge users based on the payload going into or out of the API. An example would be if we wanted to charge $0.10 per element in a request body (since each element will be processed by our service). In this case, if a developer sends a request with 3 elements to be processed in the request body, they would be charged $0.30 for their API call.Per unique userThis metric works well when you want to charge for usage based on a unique user accessing the API. Unlike using API keys, where a user may have several, instead, you charge at the user level. An example would be if you wanted to charge $10 per user, per month. In this case, regardless of how many API keys a user is using, possibly one per environment or app, they will only be charged $10 for access to the platform’s APIs.Per API keySimilar to per unique user, the charge would be based on the number of unique API keys used to access the API. At the end of the billing period, the total API key usage would be added up and billed for. An example would be if there was a cost of $5 per API key per month. In this case, if a developer has 3 API keys they have used to access the API then they would be charged $15 for usage that month.On top of the metrics discussed here, there are plenty of others. Some of these may offer a good place to start and as you expand, you may find that measuring more complex metrics is required to fit your future use cases.What pricing models can be implemented?Pricing models vary widely, although there are some staples that many companies use. Let’s quickly check out some of the more standard pricing models that could be used when charging for API usage below.Standard pricingThis pricing model can be used when you want to charge the same price for each API call or for each unique user. Essentially, every unit of usage that is recorded will be charged at the same price point. This is the simplest pricing model in existence and may be a great place to start if you don’t want to get too complex off the hop.PackageThis pricing model can be used when you want to charge for API usage by the package, or a group of units. For example, you could set it up to charge $10 for every 1000 API calls. Every time users go over the 1000 API call threshold, they are charged another $10.GraduatedA graduated pricing works if you want to use pricing tiers to charge for usage. For example, in a graduated pricing scheme, you might charge $10.00 per unit for the first 100 units and then $5.00 per unit for the next 50.VolumeUsing volume-based pricing can be used to charge the same price for each unit based on the total number of units sold. For example, you might charge $10.00 per unit for 50 units or $7.00 per unit for 100 units. At the end of the billing period, usage is totaled up and the overall volume of usage will determine the price applied to each unit of usage.Of course, this is not an exhaustive list. Depending on the customer or product, you may also use different pricing models for each. Choosing the pricing model which makes the most sense for your specific API should balance driving customer usage while still making sure revenue is maximized.What’s the best way to determine the price of an API?Once you’ve decided on the metrics and pricing model, you’ll then have to determine what you will charge customers. This, of course, can be quite complex depending on factors such as what metric and pricing model you are using. The recommendations below are meant to guide you on some important considerations when thinking about the price of your API.Calculating the cost of your API for your businessOne thing you need to take into consideration is the cost of your API to your business. This cost could be the ongoing support costs, development cost for new features, or even third-party APIs or services that your API leverages that add to the cost.The easiest cost to determine is other services that your API uses and the costs associated with them. The goal should be to at least cover these costs that will directly cause an expense for your business. You may factor in third-party API costs, maybe other services that your API leverages to provide its functionality, or maybe even your infrastructure costs if API call volume will make the cost increase or decrease.Creating a product will also cost time and dollars for support and ongoing engineering effort. This should also be calculated into the cost of ownership for your APIs. This is especially true if the resources used for support and maintenance are spending the majority of their time working on the monetized API. Figuring out the base cost, or total cost of ownership, of the API, can help to give you an idea of where your price should start.How much value are you adding through your API?Is your API bringing a lot of value to potential users? For instance, the value may come in many different forms but the one that most frequently comes to mind is how much savings a company is getting from using your API. The savings generally come in the form of how much it would cost for them to develop a similar service in-house. If the service would take them 2 days to create and deploy, maybe you’ll need to add a bit more depth to your product in order to extract a high dollar amount for usage. On the other hand, if it would take months and a massive budget for them to build what you’ve already created then you may be in a very good position.It may be hard to quantify the exact value but you should have a good idea of what it takes to build an equivalent service in-house and how much companies may be willing to pay in order to use your service versus building their own. Another way to measure value maybe if you are offering a service through your API that is outside of the expertise of your potential market. This means they would likely need to hire an entire suite of staff just to create what you already have available. Use the value you’re adding to give a range for your pricing and eventually determine the price for your API.What pricing model should you use?Generally, you have two big decisions on this front: will you charge a subscription fee or will you charge based on actual usage? Both come with pros and cons, as well as ways to somewhat combine both paradigms. For instance, you may charge a subscription fee that allows for 1000 API calls per month and then charge for usage overages in the form of overage fees. If you go decide to charge on overages, on top of the pricing model you will also want to think about your overage model as well.Once you’ve determined the higher-level pricing question above, you can then decide if you will implement any volume-based pricing, tiered pricing, and other potential pricing schemes available. Some companies strive to offer simple pricing strategies while others have a huge amount of flexibility and options. Based on your customers, and potentially looking at other products that they may be using, you can probably get a feel for which pricing model makes the most sense.Should you be factoring in SLA and support into the cost of usage?On top of charging for usage, some companies also charge a premium for different levels of support. This could be another way to drive revenue for your APIs that can be factored into the overall price you charge customers. You may still charge for your API based on a strict pricing scheme but charge for upgrades such as an improved support package for companies that require such an agreement.Keep testing your price pointsYour price points don’t have to be set in stone forever. As with most products and services, over time, the cost tends to increase. Customers generally expect an increase at some point and as long as the value is still being delivered in an equivalent or exceeding manner, they won’t churn out due to cost.When should you assess your current pricing model?The answer to this question is heavily dependent on your business model. If you are doing month-to-month or pay-for-usage type models, price points can technically be changed at any time. Obviously, you want to make sure customers are aware to shield yourself from any blowback and give customers a chance to adjust to new rates. However, if you have an annually-paid enterprise plan or more strict time-based contracts, you’ll likely only be able to increase the price upon renewal.It’s always good to be assessing your prices against your competitors as well. If your service is more expensive, you may want to dig hard into differentiators and push from that side to ensure you are showing the value of the cost to consumers.Overall, pricing should be assessed and revisited frequently. This should include looking at how much you charge, what pricing models are available to customers, and if new features warrant a price increase across the entire pricing scheme or if new packages or add-ons should be added to purchase options.What’s the best way to roll out pricing changes?The best way to roll out pricing changes is to make sure that you have accurate feedback loops set in place. When a price changes you want to have monitoring in place so that customer success and sales teams can try and bring back any customers who churn out. This is more of a reactive approach and not every customer will come back, some may be afraid of further price hikes in the future.You also want to make sure that any price changes are well communicated. If a customer is coming up for renewal, start negotiations early. If customers are paying monthly by credit card, try and let them know well in advance before the next billing cycle starts so they aren’t surprised. One thing to be careful of is not over communicating the “why” of the price change. You can state something simple about new features, improved SLAs, or another high-level value add that justifies the increase. However, by listing out all of the features you’ve improved, it may give customers a chance to say “I don’t use that so why am I paying for it?” and other possible objections to price increases.Lastly, you may also add a question on price to customer surveys to check if they are happy with the current price or if they think it is too high or low. Asking a customer “What is the upper limit of what you would pay for our service?” is actually a great way to see the perceived value for the customer and to test what the upper limits might be without experimenting with your live prices.Free trial, free tier, or nothing?Assessing your trial period pricing is something you may also play around with. If you have a free trial, playing around with the length of it may drive more paying customers as well. You may find that your free tier is “too good” and many customers stay on it. This prevents customers from jumping to a paid plan since the free plan already covers everything they require. Maybe scrapping any trial at all is in favor of your business?As much as trials can be a major driver of adoption, assessing how your trial period perks are working for you and your customers is part of the pricing strategy. If you do play around with the trial offerings within your product, make sure to have heavy analytics input so that you can track the true value of the changes.Using Moesif for conversion analysis and billingWith Moesif, using API analytics to help figure out which APIs to monetize and what price to charge becomes a more precise science. Of course, you could guess around all of these factors and through trial and error, find what works. This comes with a great amount of risk and wasted opportunity to drive more revenue from the start.Using the Moesif platform can help you with finding the right price for your API, navigate price changes, and optimize onboarding. The platform can easily be integrated through your existing API code using an SDK or through your API gateway by leveraging one of our plugins.Researching API usage for API endpoints to monetizeAre you familiar with what APIs are receiving the most traffic? Do you know what companies that traffic is coming from? By asking and answering key questions in your API usage, you can quickly determine which endpoints to monetize. You may even be able to accurately predict revenue for when the API does become monetized. Moesif can help to generate reports that show API usage in ways you may have never looked at before.A bar chart from Moesif demonstrating where each customer’s users generated the greatest number of API eventsBy using Moesif to dig deep into your API usage, you can uncover the answers to many questions which can help to determine the value of an API and potentially how much to charge for it. After you’ve implemented monetization, these reports can help you to see revenue at the user and company level to drive long-term organizational goals and inform your API product roadmap.User funnels and retentionA user funnel report from Moesif, showing the percentage of users completing three phases of integrationAfter you’ve implemented or changed your pricing, you’ll want to make sure customers are still converting to paid customers. This can be tracked with a user funnel in Moesif. The user funnel will allow you to outline each step in your conversion funnel and see what percentage of users convert throughout each step and how long it takes.API retention chart showing which users used which language at different stages of integration. Languages include node.js, python, go, ruby, phpRetaining customers is also important, especially after a price change. With a retention report, you can see the historical retention rate of your product and ensure that recent changes in pricing strategy are benefiting your business and not driving down retention rates.Billing MetersFlowchart showing data moving from your app or API to Moesif, and then to payment processors Recurly, Stripe, or ChargebeeMoesif can also help with implementing monetization on your APIs as well. By using Billing Meters, you can establish which endpoints you’d like to monetize and the pricing model, and send the usage totals to a billing provider such as Stripe, Recurly, or Chargebee. Moesif’s Billing Meters give end-to-end monetization capabilities with minimal engineering effort. If you need a solution to monetize your APIs easily, securely, and quickly then Moesif’s Billing Meter feature is exactly what you need.Wrapping UpThe pricing plan that works best for your business will always need to be tailored. Pricing API calls can be extremely tough to do, but following the guidelines above can help you find ways to make it easier and more precise. Making sure that any endpoint you decide to monetize is bringing value to developers and their organizations can be a win-win scenario for your business and theirs.Using Moesif can help with many aspects of API monetization including helping you make informed pricing decisions, figuring out what endpoints to monetize, and tracking customer retention throughout the process. To get started, sign up today and try Moesif’s API analytics and monetization features for yourself.                Monetize APIs in Seconds with Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /product-management/api-analytics/Whats-The-Best-Way-To-Determine-The-Price-Of-An-API/",
          "author": "Matthew",
          "categories": "product-management, API-Analytics"
        }
      
    ,
  
    
        "technical-stripe-end-to-end-api-monetization-with-django-stripe-and-moesif": {
          "title": "End-to-End API Monetization with Django, Stripe, and Moesif",
          "content"	 : "Many API developers and companies struggle to find ways to easily set up systems to monetize their APIs. Some are simple but not customizable, some are complex and require massive engineering effort to actually get it all running.To make things easier, Moesif created a feature called Billing Meters which gives massive customizability but with a minimal amount of code and engineering effort.For this blog post, which could actually be used out of the box, we will use Moesif, Django, and Stripe to charge users for API usage. To complete this Stripe Django monetization example, there are a few detail assumptions:  You have a working Python environment installed on your machine          We are using Python 3.10.5, the latest version available at the time of writing      We are also using pipenv to manage our project within a virtual environment        You have an active Stripe account with access to the Stripe Dashboard  You have an active Moesif accountThe setup is pretty simple from the outside. We will create a /register endpoint which:  Registers a new user in Stripe  Subscribes that user to a product  Registers the User and Company in Moesif  Creates a JWT to authenticate/authorize calls to our monetized endpointI’ve also created a frontend using Django Forms that is houses a simple form that registers a user by calling the /register endpoint and then displays the generated JWT for the newly registered user.                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free        1 - Create Your Product and Price in StripeThe first step we will take is to create a product and price in Stripe. It’s best to do this step first because then when you integrate Stripe into Moesif you’ll already have some pricing plans for Moesif to pull in. A pricing plan can then be associated with specific billing criteria set up within a Billing Meter in Moesif.First make sure to meet these prerequisites.To create a product and price, log into Stripe and proceed to the Products page in the Stripe UI. Once there, click on the + Add Product button in the top right corner.You’ll then be able to add in the details for your product and price(s) for it. The form for your product will have a few fields to fill out.Product InformationName  This is the name of your product. In the example below, we use the name “My API”.Description  This field is optional but you could put a brief description of the product here. In the example below, we use a description of “This is a monetized API”.Image  Optionally upload an image that can help you easily recognize a item on the Products page. We’ll be using the default placeholder image in this example.Pricing InformationYou can choose between Recurring and One-off pricing for your product.Recurring PricinngIn recurring pricing, your customers pay an ongoing fee according to the pricing model you define. After selecting Recurring, you can enter the amount you want to charge and the billing period.To further configure your recurring pricing, select More pricing options. This allows you to specify the pricing model, amount, billing period, price description, and more.The following pricing models are available in Stripe for recurring pricing:  Flat rate  A fixed price for a single unit or package.  Package pricing  Charge for API usage by the package, or a group of units. For example, you could set it up to charge $10 for every 1000 API calls. Every time the user goes over the 1000 API call threshold, they are charged another $10.  Tiered pricing  Offer different price points for based on unit quantity.  Usage-based  Pay-as-you-go billing based on metered usage. You can charge per package, per unit , or per tier and define the prices and units accordingly. You can also set up a billing meter for the price to meter usage. See Creating a Product and Price in Stripe for instructions on how to set up a usage-based scheme.One-Off PricingIn one-off pricing, you charge a one-time fee rather than recurring amount in each billing period. After selecting One-off, you can enter the amount you want to charge in the Amount field.Similar to recurring pricing, you can select More pricing options and configure your pricing further by specifying the pricing model, amount, price description, and more.The following pricing models are available for one-off pricing:  Package pricing  Charge for API usage by the package, or a group of units. For example, you could set it up to charge $10 for every 1000 API calls. Every time the user goes over the 1000 API call threshold, they are charged another $10.  Flat rate  A fixed price for a single unit or package.  Customer chooses price  The customer sets a custom price. You can set a limit and define a preset amount to suggest to the customer.Billing periodThe billing period can be set as the following for recurring pricing models:  Daily  Weekly  Monthly  Every 3 months  Every 6 months  Yearly  CustomFor your configuration with Moesif, we recommend setting the billing period as Monthly.Price descriptionThis is an optional field but recommended. Here you can put a brief description of your price. This will allow you to more easily decipher which price you are selecting in the billing meter in Moesif, especially if you have multiple prices for a single product.Once you’ve input all of the details for your price, you can select Next and then select Add productAs you create products, you will be able to view and edit them on the Product Catalog screen.2 - Enable the Moesif-Stripe IntegrationOnce your products and prices are created, it’s time to begin to integrate Stripe with Moesif. To begin configuring Stripe in Moesif, go to the Billing Meters page and click the Edit Billing Provider dropdown in the top right corner of the screen.This will bring up the Stripe configuration screen walking you through the integration. From this screen, you can get all of the info needed to plug Stripe into Moesif. Each step for configuration is covered within the modal.Add the Moesif Webhook to StripeThe first step in the integration is to add the Moesif webhook into the configuration in Stripe. Adding this allows Stripe to send subscription updates to Moesif.To add the Moesif webhook to Stripe, from the upper right-hand side click on Developers, and then Webhooks in the left-side menu. This will bring you to the Webhooks page where you can view existing webhooks and add new ones. To add a new webhook we will click the Add an endpoint button at the bottom of the screen.From here, we will plug in our Moesif API endpoint URL and configure the events to listen to. You’ll want to copy your Moesif Webhook URL into the Endpoint URL field and then click the + Select Events button.  These details can all be found on the Stripe configuration page in Moesif mentioned in the previous section.You should select the option under Customer for Select all Customer events. After this, click the Add events button at the bottom of the screen.After this, you’ll be returned back to the original screen where you added the endpoint details. Scroll to the bottom of the screen and click Add endpoint to save the endpoint to Stripe.Plug the Stripe API Details into MoesifFor Moesif to add usage quantities to subscriptions in Stripe, we need to add the Stripe API details into Moesif. This is done in the Stripe configuration screen in Moesif, the same screen we’ve been working with previously.Currently, Moesif only supports version 2020-08-27 of the Stripe API so that field defaults for the Stripe API Version field.For the Stripe API Key field, you’ll need to retrieve the API key from Stripe to plug it in. From the Developers screen, the same one we used in the previous step, you’ll click on API Keys. You’ll then be able to see the private key for your API in either the Secret key or a generated Restricted keys field on the screen. Either key can be used.After copying the key from Stripe, you’ll paste this key into the Stripe API Key field back in Moesif. After doing this, back in Moesif you can scroll down to the bottom of the screen and click Save to save the configuration.Optionally, you have the ability to customize the company_id in Moesif as well. The default should work fine for most purposes. However, you can fully customize it to specify how to map Stripe Subscription and Customer objects to Subscription ID and Company ID in Moesif respectively:  A Stripe Customer maps to Moesif Company.  A Stripe Subscription maps to a Moesif Subscription.3 - Create a Billing MeterOnce you have the Stripe integration active in Moesif, you can begin to set up your billing meter. Billing meters created in Moesif do two things: track usage based on specific criteria and report that usage to the billing provider. Moesif allows you to set up very simple and very complex billing meters with relative ease.To create the Billing Meter, in Moesif you will navigate to the Billing Meter screen. You can do this from the left-side menu. On the Billing Meter’s screen, you’ll then click + Add Billing Meter in the top-right corner of the screen.The next screen is where you can actually input the criteria for your Billing Meter.Fields on this screen include:      Billing Meter Name          This is the Moesif internal name of your new Billing Meter            Billing Provider          In this dropdown you can choose the billing provider you want to send your usage metrics to.            Product (Stripe only)          Here you can choose which Product that you’ve set up in Stripe you want your usage metrics to be tied to.            Price (Stripe only)          The last field in the Billing Provider settings for the Billing Meter, here you will choose which Price you want to tie your usage metrics to.            Filters          Under the Filters configuration, you will configure your billing criteria to only include requests that fit a certain criteria.            Metrics          Here you can choose which metric you would like to bill on. Available options include:            Event Count          This will increment usage for every event that fits the criteria outlined in the Filter criteria.            Unique Users          This will increment usage whenever a unique user sends a request that fits the Filter criteria. For every unique user, the count will be incremented by 1 regardless of the event count for that user.            Unique Companies          This will increment usage whenever a unique company sends a request that fits the Filter criteria. For every unique company, the count will be incremented by 1 regardless of the event count for that company.            Unique Sessions/API Keys          This will increment usage whenever a unique session or API key is used to send a request that fits the Filter criteria. For every unique session or API key, the count will be incremented by 1 regardless of the event count for that particular session or API key.        There are other options under Metrics as well but the above 4 tend to be the most applicable to usage-based billing.As an example, for this guide we will create a Billing Meter that will filter traffic for a single endpoint, named /test-service, and where requests received a successful HTTP 200 response. We will use the Event Count metric to make sure that every request is added to the tally and sent to the billing provider.In Moesif, the billing meter will be configured as shown below.We will then click Create. This will create and activate the Billing Meter. A modal will appear notifying you that the billing meter has been created and presents a walk-through to ensure the meter is correctly configured.First, we will set up a flow to get users registered, subscribed, and create a JWT so they can use our monetized API. Once that is complete we will come back and proceed with the walk-through.4 - Create The /register EndpointInstead of using a pre-built onboarding flow, such as through a Developer Portal within an API gateway, we will build our own. We will create an endpoint called /register which we can then use to onboard our users who want to use the API. The result will be that the user receives a JWT that they can use that will track their usage.Since we are using Moesif, Stripe, and Python as part of our overall solution, we need to make sure each of the components is working together properly.Here’s what the endpoint will do:  Create a customer in Stripe  Subscribe the new customer to the API subscription in Stripe  Create the CompanyID in Moesif (which will be the Stripe subscription ID)  Create the UserID in Moesif (which will be the Stripe Customer ID)  Create a JWT with an id field that contains the Stripe Customer ID  If you already have User and Company identifiers in Moesif and other systems that you want to use, instead of using Stripe’s customer and subscription as your IDs, you can do that in Moesif under the Stripe configuration settings.In this example, I will create a simple Django API with the Django REST framework to do the above.Install the necessary python dependenciesAs previously mentioned, we’ll be utilizing pipenv to safely manage our dependencies in a virtual environment. Pipenv offers developers an easy way to setup a working environment. Using pipenv grants us some more advanced features but most importantly includes built in support for environment variables.I’ve created a folder called moesif-monetization-django which I’ll be working in but feel free to call yours whatever you would like. Open this directory in your favorite IDE or text editor. I will be using Visual Studio Code for the remainder of this tutorial for all coding. Open Code’s integrated terminal and use pipenv to install the following dependencies for use in your virtual environment.pipenv install django djangorestframework moesifdjango stripe pyjwtYou will see that both Pipfile and Pipfile.lock files have been created.These dependencies will help us to make calls to REST endpoints, connect to Stripe and Moesif, generate and validate JWTs, and various other capabilities we will build into our app.  Using pip and installing your dependencies globally is a perfectly fine alternative, if you prefer.If you are using pip you will need to install the django-environ package to manage environment variables. The command you will use will look like this: pip install django moesifdjango stripe django-environ.Launch the pipenv shellLet’s hop into our virtual environment’s shell and start setting up our project.pipenv shellCreate the Django projectNext, we’ll use the django-admin startproject command create a folder called moesif_monetization and prepare our project. This is where we will add our API code. Run the following command:django-admin startproject moesif_monetization .You’ll see a moesif_monetization folder in your current working directory along with a manage.py file.Finally, we’ll apply the necessary migrations by running the following:python manage.py migrateConfirm project dependenciesYou can double check your Pipfile file includes the correct dependencies:[[source]]url = "https://pypi.org/simple"verify_ssl = truename = "pypi"[packages]django = "*"moesifdjango = "*"stripe = "*"djangorestframework = "*"pyjwt = "*"moesifapi = "*"[dev-packages][requires]python_version = "3.10"Your dependencies will be brought into the project and installed to our virtual environment. These dependencies will help us to make calls to REST endpoints, connect to Stripe and Moesif, generate and validate JWTs, and various other capabilities we will build into our app.Create the .env fileInstead of hard-coding the Stripe keys and other static values into our app, we will abstract them into a .env file. Again, we’ll be utilizing pipenv’s built in support for environment variables.In the root directory, create a file named .env. Within this file, we will add a few entries that will contain the keys and values used in our code.STRIPE_API_KEY="sk_test_XXX"STRIPE_PRICE_KEY="price_XXX"MOESIF_APPLICATION_ID="YOUR_MOESIF_APPLICATION_ID"TOKEN_SECRET_KEY="YOUR_TOKEN_SECRET_KEY”The values that are here can be found in the following places:Obtaining Your Stripe API and Price KeyYour Stripe API Key can be found in the same place we grabbed the key for our Stripe and Moesif integration we did earlier for the Billing Meter. You can actually use the same key for both or create a restricted key with just the scope needed for each function.While we’re at it, lets grab our Stripe product’s price key. We will need it in the next section. Your Stripe price key is an identifier for the price you created earlier in Stripe. This can be found by going to the product in Stripe and grabbing the value from the API ID column.Obtaining Your Moesif Application IDYour Moesif application ID is found in Moesif by going to the menu link in the bottom-left of the screen (which will show your name) and selecting API Keys.The key will then be on the page that appears under Collector Application Id.Creating Your Token SecretThis will be the secret that is used as part of generating and validating your JWTs. This could be any 256-bit string you’d like, however, for production purposes you are best off using some sort of generation and obviously keeping this key stored elsewhere.Once you’ve populated the file with the four key-value pairs, save the file. We won’t need to touch this file again for the remainder of the tutorial.Set up the settings.py fileWe will need to make a few changes to the default settings file that Django has generated for us. We’ll add a few applications, which is a term that encompasses python packages, models, views, templates, template tags, static files, URLs, and middleware for use in our project.First, add the following imports to the file.import os, jwtNext, we’ll add the rest_framework to our INSTALLED_APPS definitions.INSTALLED_APPS = [    'django.contrib.admin',    'django.contrib.auth',    'django.contrib.contenttypes',    'django.contrib.sessions',    'django.contrib.messages',    'django.contrib.staticfiles',    'rest_framework',]Comment out the CsrfViewMiddleware and add moesif_middleware to the MIDDLEWARE definitions.MIDDLEWARE = [    'django.middleware.security.SecurityMiddleware',    'django.contrib.sessions.middleware.SessionMiddleware',    'django.middleware.common.CommonMiddleware',    # 'django.middleware.csrf.CsrfViewMiddleware',    'django.contrib.auth.middleware.AuthenticationMiddleware',    'django.contrib.messages.middleware.MessageMiddleware',    'django.middleware.clickjacking.XFrameOptionsMiddleware',    'moesifdjango.middleware.moesif_middleware']Next, we’ll define our identifyUser function which enables us to track users within Moesif and configure our Moesif Middleware.def identifyUser(req, res):    customerID = ""    try:        jwt_token = req.headers['Authorization']        tokenArray = jwt_token.split(" ");        decodedJWT = jwt.decode(tokenArray[1], os.environ['TOKEN_SECRET_KEY'], algorithms=["HS256"])        customerID = decodedJWT.get("id")    except Exception as exception:        print(exception)        customerID = ""    if customerID != "":        # Sending customerID to Moesif...        return customerID    else:        # CustomerID not found - possibly not included in request authorization header        return NoneMOESIF_MIDDLEWARE = {    'APPLICATION_ID': os.environ['MOESIF_APPLICATION_ID'],    'CAPTURE_OUTGOING_REQUESTS': False, # Set to True to also capture outgoing calls to 3rd parties.    'IDENTIFY_USER': identifyUser, # Optional hook to link API calls to users}We are a bit ahead of the game here with our identifyUser function. When our Django server receives an API call, it’s contents are then forwarded to Moesif. Within this function we’ll extract the JWT from the request’s Authorization Header. We’ll parse it and check it against our token secret key allowing us to extract the customerID to enable user tracking within Moesif.We also define and configure the Moesif middleware with our application ID. We’ll need to revisit this file later but for now lets move on to standing up our API.Edit the urls.py fileIn the root directory of our app, we will edit the generated urls.py file. In this file we will add the following code that simply defines our endpoints:from django.contrib import adminfrom django.urls import pathfrom moesif_monetization import viewsurlpatterns = [    path('admin/', admin.site.urls),    path('test-service/', views.test_service),    path('register/', views.register),]We’ll also come back to this later in order to configure the endpoints for our frontend.Create the views.py fileOur next step is to implement the logic for our /register and /test-service endpoints.The /register endpoint will essentially create the binding between our generated JWT, Stripe, and Moesif. The outcome will be a generated JWT which will associate usage with a user in Moesif, which will then be reported to Stripe.First, lets define our imports and configure our api clients.import os, stripe, jwt, jsonfrom moesifapi.moesif_api_client import *from moesifapi.models import *from django.http.response import JsonResponsefrom rest_framework import statusstripe.api_key = os.environ['STRIPE_API_KEY']api_client = MoesifAPIClient(os.environ['MOESIF_APPLICATION_ID']).apiNow we will define our register function. Our first step in the flow is to create the customer in Stripe. We will use Stripe python package to do just that. We will use the parameters from the request body (email, first name, last name) to create the customer in Stripe using the stripe.Customer.create function. We will then store the created customer in a custom variable so we can access the customer ID generate in Stripe.def register(request):  request_body = json.loads(request.body.decode('utf-8'))  # Create Stripe Customer  customer = stripe.Customer.create(    email = request_body['email'],    name = request_body['firstname'] + ' ' + request_body['lastname'],    description = 'Customer created through /register endpoint'  )  print("Creating Stripe Customer Complete. Returned ID: " + customer.id)In the same function body, we will subscribe this new user to our API subscription we created in Stripe earlier. We will use the stripe.Subscriptions.create function and use the generated customer ID from the previous function call to subscribe them. This will return back a subscription object containing an ID we will use later.  # Create Stripe Subscription  subscription = stripe.Subscription.create(    customer = customer.id,    items = [{'price': os.environ['STRIPE_PRICE_KEY']}]  )  print("Creating Stripe Subscription Complete. Returned ID: " + subscription.id)After our customer and subscription are created in Stripe, we will then use the Moesif middleware to create the user and add their relevant details into Moesif. First we will call the Moesif middleware’s update_company function to map the Stripe subscription.id to the companyId in Moesif.  # Create Company in Moesif  company = {'company_id': customer.id}  update_company = api_client.update_company(company)Our next step is to generate a JWT with the Stripe Customer ID attached. This line will create a JWT for us to use with our endpoints.  token = jwt.encode({'id': customer.id}, os.environ['TOKEN_SECRET_KEY'])We will then do a similar step with the update_user function and use it to map the Stripe customer.id to the userId and companyId, and some other metadata we collected on the user into Moesif.Optionally we will add our JWT to our Moesif user metadata for ease of use. This isn’t recommended for production environments but can help when testing your setup instead of generating a new JWT if you lose the previously generated one.  # Create User in Moesif  user = {    'user_id': customer.id,    'company_id': customer.id,    'metadata': {      'email': request_body['email'],      'first_name': request_body['firstname'],      'last_name': request_body['lastname'],      'metadata': {        'jwt': token      }    }  }  update_user = api_client.update_user(user)Next, we will add the subscription data into Moesif too. To do this, we will take our Stripe subscription ID and map it into Moesif using the api_client’s update_subscription method that is exposed through the SDK.  # Create the subscription in Moesif  subscription = {    'subscription_id': subscription.id,    'company_id': customer.id,    'status': 'Active'  }  update_subscription = api_client.update_subscription(subscription)Lastly, we will return a 200 OK JsonResponse back to the caller with the JWT in the response body.  return JsonResponse(data=token, status=status.HTTP_200_OK, safe=False, encoder=json.JSONEncoder)The completed function will look like this:def register(request):  request_body = json.loads(request.body.decode('utf-8'))  # Create Stripe Customer  print("Creating Stripe Customer")  customer = stripe.Customer.create(    email = request_body['email'],    name = request_body['firstname'] + ' ' + request_body['lastname'],    description = 'Customer created through /register endpoint'  )  print("Creating Stripe Customer Complete. Returned ID: " + customer.id)  # Create Stripe Subscription  print("Creating Stripe Subscription")  subscription = stripe.Subscription.create(    customer = customer.id,    items = [{'price': os.environ['STRIPE_PRICE_KEY']}]  )  print("Creating Stripe Subscription Complete. Returned ID: " + subscription.id)  # Create User and Company in Moesif  print("Creating Company in Moesif")  company = {'company_id': customer.id}  update_company = api_client.update_company(company)  print("Creating Company in Moesif Complete.")  print("Creating JWT")  token = jwt.encode({'id': customer.id}, os.environ['TOKEN_SECRET_KEY'])  print("JWT Created")  print("Creating User in Moesif")  user = {    'user_id': customer.id,    'company_id': customer.id,    'metadata': {      'email': request_body['email'],      'first_name': request_body['firstname'],      'last_name': request_body['lastname'],      'metadata': {        'jwt': token      }    }  }  update_user = api_client.update_user(user)  subscription = {    'subscription_id': subscription.id,    'company_id': customer.id,    'status': 'Active'  }  update_subscription = api_client.update_subscription(subscription)  print('Creating User in Moesif Complete.')  # Having issues with these two responses below  # return HttpResponse(data=token)  # return render(request, 'index.html', token)  return JsonResponse(data=token, status=status.HTTP_200_OK, safe=False, encoder=json.JSONEncoder)Next we’ll implement the logic for our /test-service endpoint.def test_service(request):  try:    # Parse request headers and jwt token array    jwt_token = request.headers['Authorization']    token_array = jwt_token.split(" ")    # Attempt to decode JWT    print("JWT tokenized: " + token_array[1]);    decoded = jwt.decode(token_array[1], os.environ['TOKEN_SECRET_KEY'], algorithms=["HS256"])    print("Decoded: " + decoded.get("id"))  except Exception:    return JsonResponse(data="Bad Auth Token", status=status.HTTP_401_UNAUTHORIZED, safe=False)  return JsonResponse(data={token_array[0]: token_array[1]}, status=status.HTTP_200_OK, safe=False)We’ll attempt to parse the Authorization Header for the JWT that we have passed along. We’ll then try and decode the JWT using our TOKEN_SECRET_KEY from our .env file. Depending on the outcome we’ll return the proper JsonResponse object, successfully including our JWT token for auditing purposes.With that, we can now actually try out our endpoints to make sure that each piece is working as expected. The outcome should be a registered user with an associated JWT which will record and report usage data to Stripe. Let’s move onto testing it. Fire up your server by running the following command:python manage.py runserver5 - Send a Test Request to the /register EndpointOnce your /register endpoint has been coded and deployed, it’s time to test it. For right now we will simply use Postman to send a request. Our request will contain a JSON request body that will contain a:  First name  Last name  EmailOf course, this is the minimal amount of information we would want to configure our system and profiles in Stripe and Moesif, plus, generate the JWT. You can easily add more fields as needed for your specific use case.In Postman, we will create our request with the following information:Request Type: POSTEndpoint URL: http://localhost:{port}/registerRequest Body:{  "firstname": "Userfirstname",  "lastname": "Userlastname",  "email": "test@test.com"}  Replace port in your endpoint URL with the assigned port number.Once everything is plugged into Postman, it should look like the following:Once the request is sent, the response should contain an JWT that the newly registered user can use.We will now check Stripe to ensure that the information we registered the customer with is correctly entered into Stripe.Logging back into Stripe, you’ll navigate to Customers screen. You should see your newly created user in the list.Click on the newly added customer in the list. On the next screen, you should see that the customer is also subscribed to your APIs subscription.With this check completed, we can safely assume that our /register endpoint is correctly setting up our users accounts and subscriptions in Stripe.6 - Call Your API Using the Generated JWTOur next step is to actually use our generated JWT. We will then confirm that all the correct information is added into Moesif. The data we are confirming includes:  The Stripe Customer ID is mapped to the Moesif User ID  The Stripe Subscription ID is mapped to the Moesif Company ID  Moesif contains the Stripe metadata in the users profileUse Postman to Send the RequestNext, let’s use Postman, or another platform, to send a request to the /test-service endpoint. This is the endpoint that we set up the billing meter for in Step 3, above.In Postman, we will:  Put the /test-service API endpoint as the request URL  Select the Authorization tab  Select the Type as Bearer Token  Populate the token details  Set the Token as the JWT received from our /register callBelow is an example of the populated request configuration in Postman. To send the request to our endpoint, click Send.Once sent, the API call analytics should land in Moesif.Confirm That Moesif Received the Request Info Using Profile DashboardsBack in Moesif, you’ll navigate to the Live Event Log screen. You can do this by clicking the New button and selecting Live Event Log.On this screen, you should see the request you just sent. You should see the entry has both a User ID and Company ID populated with the Stripe user and subscription ID’s. The entries should look like this:  The customer ID will look like “cus_XXXX” and the subscription ID will look like “sub_XXXX”.If you click on the User ID shown in the entries on the Live Event Log screen, you will come to the users profile page. On this page, we will confirm that the Stripe metadata is present. We will need to add a new column to our profile to display the Stripe data. To do this, from the profile page, click on the … More Actions button and click Customize Profiles’ Layout.We will then add a new column for the Stripe metadata. You will click the + button on the far right of the screen to create a new column where we will add the Stripe metadata.  You may need to scroll to the right to see it depending on your resolution and screen size to see the + button.You will then drill down to Metadata > stripe > customer > created and use this field in the new row. I’ve also changed the column image to one more fitting. You can customize this by clicking on the image and selecting whichever one fits best.You can also add other fields, but for right now just this single field is enough to tell us that Moesif is correctly receiving data from Stripe.  If you don’t see the Stripe metadata entry as an available field, wait a few minutes. If after a few minutes the Stripe metadata isn’t present, ensure that your Stripe configuration is correct in Moesif. After confirming or editing it, try creating a new user and sending a request again to confirm that the integration is working.At this point, we now have confirmed that our API call is working and is stamped with the correct user and company details in Moesif. We also confirmed that Stripe is sending data back to Moesif which is correctly being mapped to the corresponding user profile, confirmed through the Stripe metadata in Moesif.7 - Create the frontendNext, we want to add a simple little frontend so we don’t need to call for our JWT through Postman. We will make a quick little registration form that will then return a JWT for our newly registered user to use.First, we’ll create a few template files and then edit our settings.py file to add our moesif_monetization project as an application itself and to let Django find our template files. We will then update our urls.py file to introduce some new endpoints. Next, we’ll implement the markup for our html pages. Additionally we’ll create Django form used to coordinate passing data between our views. Finally we’ll update our views.py file and make some changes to our functions to accommodate receiving data from our frontend.Adding frontend filesIn the moesif_monetization directory of the application, we will create a folder called templates. We will then add a base.html, an index.html, and a registered.html file into that folder.In addition, create a forms.py file within the moesif_monetization directory.Edit the settings.py fileAdd moesif_monetization, or whatever your project name may be, to the INSTALLED_APPS array. This allows Django to see our project as an application itself and present our frontend.INSTALLED_APPS = [    'django.contrib.admin',    'django.contrib.auth',    'django.contrib.contenttypes',    'django.contrib.sessions',    'django.contrib.messages',    'django.contrib.staticfiles',    'rest_framework',    'moesif_monetization']Additionally, add the following to the DIRS section of the TEMPLATES section. This allows Django to locate our template files.TEMPLATES = [    {        'BACKEND': 'django.template.backends.django.DjangoTemplates',        'DIRS': [os.path.join(BASE_DIR, 'templates')],        'APP_DIRS': True,        'OPTIONS': {            'context_processors': [                'django.template.context_processors.debug',                'django.template.context_processors.request',                'django.contrib.auth.context_processors.auth',                'django.contrib.messages.context_processors.messages',            ],        },    },]Edit the url.py fileIn the urls.py file, we will add in routes to serve the static HTML files. Replace what we entered earlier with the following the following code:urlpatterns = [    path('', views.index, name='index'),    path('admin/', admin.site.urls),    path('test-service/', views.test_service),    path('register/', views.register),    path('registered/', views.registered, name='registered'),]This code will now load the website (once we have the code plugged in) when you navigate to http://127.0.0.1:8000/.Code the frontend formFinally, let’s add the code for our frontend HTML and Django form functionality. In the base.html file, we will add markup that looks like this:<!DOCTYPE html><html lang="en"><head>    <meta charset="UTF-8">    <meta http-equiv="X-UA-Compatible" content="IE=edge">    <meta name="viewport" content="width=device-width, initial-scale=1.0">    <title>Moesif Monetization Demo</title>    <!-- Bootstrap CSS -->    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/css/bootstrap.min.css" integrity="sha384-Gn5384xqQ1aoWXA+058RXPxPg6fy4IWvTNh0E263XmFcJlSAwiGgFAW/dAiS6JXm" crossorigin="anonymous">    <script src="https://code.jquery.com/jquery-3.2.1.slim.min.js" integrity="sha384-KJ3o2DKtIkvYIK3UENzmM7KCkRr/rE9/Qpg6aAZGJwFDMVNA/GpGFF93hXpG5KkN" crossorigin="anonymous"></script>    <script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.12.9/umd/popper.min.js" integrity="sha384-ApNbgh9B+Y1QKtv3Rn7W3mgPxhU9K/ScQsAP7hUibX39j7fakFPskvXusvfa0b4Q" crossorigin="anonymous"></script>    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/js/bootstrap.min.js" integrity="sha384-JZR6Spejh4U02d8jOt6vLEHfe/JQGiRRSQQxSfFWpi1MquVdAyjUar5+76PVCmYl" crossorigin="anonymous"></script>    <script src="https://unpkg.com/htmx.org@1.6.1"></script></head><body>    <div class="container mt-4">        {% block content %}        {% endblock %}    </div></body></html>Additionally, in the index.html file.{% extends 'base.html' %}{% block content %}<form method="post">  {{ form.as_p }}  <input type="submit" value="Register"></form>{% endblock %}Finally, the registered.html file.{% extends 'base.html' %}{% block content %}<p>  {{ jwt }}</p><a href="../">Go Back</a>{% endblock %}Create the Registration FormIn our forms.py file, let’s define our Registration Form.from django import formsclass RegistrationForm(forms.Form):  first_name = forms.CharField(label='First Name', max_length=100)  last_name = forms.CharField(label='Last Name', max_length=100)  email = forms.EmailField(label='Email', max_length=100)This markup in combination with our forms.py file will display a form which allows users to input an email, first name, and last name. It also has a Register button that will call the new register_frontend python function we’ll implement. That function will be in our views.py file.Edit the views.py fileWe’ll add the following three functions and imports to complete our views.py file....from django.shortcuts import renderfrom .forms import RegistrationForm...def index(request):  context = {'form': RegistrationForm()}  if request.method == 'POST':    # create a form instance and populate it with data from the request:    form = RegistrationForm(request.POST)    # check whether it's valid:    if form.is_valid():      response = register_frontend(form.cleaned_data['email'], form.cleaned_data['first_name'], form.cleaned_data['last_name'])      print("get_registration_info - Valid")      jwt = (response.content.decode("utf-8").strip('"'))      request_context = {'jwt': jwt}      return render(request, 'registered.html', request_context)    else:      print("get_registration_info - not valid")  else:      print("get_registration_info - Not Post")  return render(request, 'index.html', context)def registered(request):  return render(request, 'registered.html')def register_frontend(email, firstname, lastname):  # Create Stripe Customer  customer = stripe.Customer.create(    email = email,    name = firstname + ' ' + lastname,    description = 'Customer created through /register endpoint'  )  print("Creating Stripe Customer Complete. Returned ID: " + customer.id)  # Create Stripe Subscription  subscription = stripe.Subscription.create(    customer = customer.id,    items = [{'price': os.environ['STRIPE_PRICE_KEY']}]  )  print("Creating Stripe Subscription Complete. Returned ID: " + subscription.id)  # Create Company in Moesif  company = {'company_id': subscription.id}  update_company = api_client.update_company(company)  token = jwt.encode({'id': customer.id}, os.environ['TOKEN_SECRET_KEY'])  # Create User in Moesif  user = {    'user_id': customer.id,    'company_id': subscription.id,    'metadata': {      'email': email,      'first_name': firstname,      'last_name': lastname,      'metadata': {        'jwt': token      }    }  }  update_user = api_client.update_user(user)  return JsonResponse(data=token, status=status.HTTP_200_OK, safe=False, encoder=json.JSONEncoder)The register_frontend function is essentially the same as the register function we implemented earlier with some minor changes to accommodate how the frontend passes data to our backend.8 - Test the frontendTo test the frontend, save your code changes and restart the server. Then, in a browser, navigate to http://127.0.0.1:8000/. You will then see the form show up.Fill out the form fields and submitNow that the form is loaded on the screen, fill in the fields and click the Register button. This will take the info, post it to our /register_frontend endpoint, and give us the generated JWT.  It is suggested that you use a different email than you used earlier when you created a JWT directly through the /register endpoint.Confirm the JWT is returnedOnce the submit button is clicked, after a few seconds, the JWT should be returned back to the UI.9 - Send a Request to Your Monetized APIWe will once again want to make sure that everything is working with our UI, through to our backend systems. For this, simply repeat the steps from Step 6 to confirm that the user and company IDs are populated correctly and that the Stripe metadata is returned for this user and the new JWT. We should see these calls populated within our Live Event Log as well.10 - Confirm All the Pieces are Working CorrectlyAlthough this is optional, this step may help with troubleshooting any issues that may have came from our previous steps. Here are a few things to check to make sure that all is working as it should. After creating a new user through the UI and using the generated JWT to place a call to your API, confirm the following:In Stripe  Confirm that a customer has been created in Stripe with the details you entered into the UI  Confirm that the customer has been subscribed to the correct product and priceIn Moesif  Your API call was recorded in Moesif in the Live Event Log  Your API call has the Stripe Customer ID and Subscription ID in the User and Company fields in Moesif, respectively.  Confirm that the Stripe metadata is populated in Moesif  All Billing Meter test conditions have passed11 - Check Stripe for UsageLastly, After a few hours, it’s best to go into Stripe to confirm that usage is being added to a users subscription. Be sure that you’ve sent a few requests through in order to make sure you have some data that should be sent to Stripe.  It may take a few hours for usage to make its way from Moesif to Stripe. If data still isn’t in Moesif after a few hours, ensure you’ve followed all the steps outlined within this guide. This includes making sure that your user and company ID’s from Moesif are correctly mapped to the corresponding keys in Stripe.To check the usage, in Stripe you’ll want to navigate to the Customers screen and select the customer that you made the API call with. Once selected, you should see some active subscriptions for the users that you’ve registered through the /register endpoint. The one we created earlier is called My API. Click on the subscription entry.On the next screen, click on View Usage beside the price entry.A modal should now pop up showing you the usage for the API that has been reported to Stripe from Moesif.  Remember, there is a delay in Moesif’s reporting to Stripe. If you data isn’t there yet, check back in a bit later.12 - Determining If the Billing Meter is Working CorrectlyTesting the created Billing Meter is easy with out Test Meter function. Navigate to your created Billing Meter from the left side navigation pane and selecting your Stripe Test billing meter. Select Test Meter on the top right.We will first confirm the meter that you are attempting to test. Click the Next button at the bottom of the modal.Moesif will wait for Subscriptions to created within Stripe and those subscriptions to be associated within Moesif itself. This page will update automatically, no need to refresh.Moesif will then wait for an API call to our any endpoint associated with our billing meter using our the JWT that has been created for us.Finally, Moesif will sync all usage data to Stripe every 15 minutes. This step may take a few minutes depending on when the API call was initiated but will update on the given interval.Wrapping upMonetization has always been a tough hurdle to get past. Many custom solutions offered flexibility but at a very high engineering and support cost. With Moesif, monetization  of your digital product is possible in an extremely minimal amount of time. As demonstrated in this article, With a little bit of configuration and minimal amount of code we can create a production-ready, post-paid monetization scheme in minimal time.                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue with your Django REST APIs?            Monetize your Django APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/stripe/End-To-End-API-Monetization-With-Django-Stripe-And-Moesif/",
          "author": "Dylan",
          "categories": "technical, stripe"
        }
      
    ,
  
    
        "customer-success-monitoring-how-to-define-measure-analyze-and-predict-customer-churn": {
          "title": "How to Define, Measure, Analyze and Predict Customer Churn",
          "content"	 : "It costs your business more to acquire a new customer than it does to retain an existing one. Analysis varies when it comes to just how much more, but it’s somewhere in the ballpark of 5X to 25X. As such, defining, measuring and analyzing customer churn – then predicting and proactively reducing it – can save your business money. A lot of money. Here’s how.The cost of acquiring a customer is up to 25 times more expensive than retaining oneCustomer Churn is the Most Important MetricIf you want to get maximum value out of your customers, you need to retain them. Doing so is a core part of your customer success strategy. That means you need to understand your customer churn rate.What is Churn in Customer Success?Customer churn is the rate at which customers stop using your product or service. It is often stated as a rate over a fixed time period, such as 2% churn per month or 6% churn per quarter.This kind of revenue churn is costly to your business. You invest a whole heap of time and effort, only to lose the customer further down the line.Keeping your customers happy and engaged with your product over the longer-term is key to reducing your customer churn rate. This maximizes the initial investment you made in acquiring those customers.How Does User Churn Rate Affect Other Metrics?So, why is user or customer churn the most important metric? To understand your customer churn rate, you need to monitor what your customers are doing. You also need alerts when users are struggling to integrate or use your products and APIs. The range of metrics you put in place to achieve this will be driven by the need to reduce customer churn.To understand your customer churn rate, you need to measure and analyze:  Number of API calls and retention  Error events  Cohort and product retentionIdeally, all of these should have automatic alerts so that if a customer drops below a certain level, a notification is generated.What Are the Main Two Reasons Customers Churn?There are many causes of customer churn. Perhaps your marketing attracted the wrong type of customers, so your product isn’t fulfilling their needs. Maybe your product is great, but your customer support is terrible. Or maybe the support is spot on, but your product is glitchy. Price is key too – if your price is too high compared to your product’s perceived value, your customers will be eyeing-up the competition pretty quickly.Although the main two reasons customers churn will be unique to your business and product, they will be centered on issues in: customer fit, product features, customer support effectiveness, reliability or pricing. That’s why doing a deep dive into customer churn is so important.Why Is Churn Especially Important in SaaS and APIs?SaaS and API businesses rely on regular revenue from subscribers. If there’s an increasing SaaS churn rate, the business will quickly begin to struggle. As such, monitoring customer success, using customer churn as the key metric, is essential to the business’ financial health.What Tools Can I Use to Keep Track of Customer Churn?Keeping track of customer churn is about getting the best out of your data. It can show you which pain points are causing customers to churn, providing insights into customer retention issues that allow you to take timely, decisive action.So, what tools can I use to keep track of customer churn? There are various tools out there to help you track your churn rate. They work by providing you with the data visibility you need to understand your level of churn. Then, it’s up to you to take that data and turn it into action.Moesif is an excellent example of such a tool. It delivers:  A 360° view into your customers’ account health  Comprehensive churn analysis across a wealth of data points  Alert automation  Ease of useEach of these elements plays an important role in churn analysis and reduction. With 360° visibility, you can visualize what is happening with every customer and spot trends that indicate when customers are ripe for expansion or at risk of churn. Analyzing a wealth of data points enables this. They include:  New account sign-ons  Daily log-ins  Daily usage  Daily API growth rate  Feature utilization  Most active users  API error log  MRR growthHaving a wide range of metrics is the foundation of comprehensive churn analysis. The particular metrics under the microscope will depend on your business. For a particularly helpful analysis, look for tools that allow you to customize the metrics you report on. Ensure automated alerts are built in too, so that you don’t miss any warning signs that a customer is at risk of churn.Throughout the process, whether it’s customizing reports or drilling down into different metrics, the easier your churn analysis tool is, the faster you can support your customers to stick with your service.Who Is Responsible for Churn?Putting a strategy in place for reducing churn isn’t down to one single individual in your business. In fact, a range of teams needs to be involved if you are serious about implementing ways to stop churn and preserve the health of your customer base.You’ll need your engineers and other technical folk on board in order to implement your chosen tool for reducing customer churn, of course. They can also help ensure the right metrics are being monitored, working along with your core operations team to ensure that the right data is being analyzed.Then you’ll need your customer success team’s input, to help understand the customer pain points that the data is flagging and suggest strategies for better supporting those customers. Some of those fixes will no doubt also need significant input from your technical team.Your finance team will also need to be looped in to discuss any expenditure designed to reduce customer churn, as well as to analyze the potential financial benefits of doing so.Such a wide-ranging churn analysis project, which spans several teams, will also need oversight from management to ensure it aligns with the company’s long-term direction. In short, the responsibility for reducing churn and increasing customer retention is very much a team effort.Reducing Customer Churn with Targeted Proactive RetentionGone are the days of losing customers and wondering why. At least, those days are gone if you have the right tools in place to analyze the reasons behind your churn rate. With the right platform in place, you can take a targeted, proactive approach to customer retention.Track retention and keep your focus on minimizing churnWhat Is Customer Churn and Retention?We’ve considered customer churn in terms of the rate at which customers are walking away from your service. The counterbalance to that is customer retention, which is all about preventing customers from ceasing their usage.Customer retention is at the core of any strategy designed to stop churn. It is about engaging your customers over the longer-term and ensuring they are satisfied with your product and your support.How to Improve Customer Retention and Reduce Churn with Proactive EngagementMonitoring each customer in a way that alerts your engineering, customer success, and sales teams to potential churn means you can reach out to every single customer at risk of churn. With Moesif, for example, you can fire alerts over email, SMS, Slack, PagerDuty, or a custom webhook – whatever works best for your business. Then your team can spring into action.There can be many reasons why a customer is using your product/service less, from scaling down their operations to integration issues you aren’t even aware of. With a proactive retention strategy in place using dynamic alerts, you can rapidly identify a customer’s drop in use, find out the cause and support the customer to move past it. You can set up alerts for key customers or for every customer, depending on your business model and need.Happily, a proactive approach to customer engagement can help to build trust – which is essential to long-term retention. Say a customer’s use of your API has dropped by 2X. If dynamic alerts flag this, meaning you can deliver relevant consultancy at the moment the customer needs it, not only will you support them to overcome their issue, you will also build trust and appreciation. This is all part of helping prevent churn.What Are the Top Customer Retention Strategies?There are numerous strategies for reducing customer churn with targeted proactive retention. At the core of them is one simple task – delivering customer success. This is what will reduce churn.Strategies for keeping your customers happy and retaining them center around creating positive interactions (from onboarding to overcoming roadblocks), building trust in your company and product, and delivering a sense of value. Get these fundamentals right and you’re doing much to reduce your churn rate.Each of these top-level goals can be broken down into various customer retention-focused tasks. They could be things as simple as sending out a newsletter with tips for overcoming common product integration issues, or something far more personalized in terms of delivering a unique customer experience. Again, your business will dictate your particular needs and strategy, but if you keep customer happiness at the core of it, then add a proactive approach to reducing churn on top, you’re likely to see positive results.View Customer Success Metrics and Gain InsightsUnderlying all of this proactive engagement is the ability to view the right metrics. That is what enables you to identify which customers are at risk of churn and to investigate the reasons behind the customer’s decreasing engagement with your service. It perhaps comes as no surprise, then, that monitoring customer success is also all about the right metrics.Customer success is intrinsically linked to customer churn. If you’re not supporting your customers to succeed, they will churn. If the customer experience that your product provides is terrible, they will churn. And if your customer service is poor? That’s right, more churn. As such, you need a robust and carefully considered customer success strategy in place, along with a customer success management team that delivers all the knowledge and care that customers could wish for. And one that understands that different customers will wish for a different level and style of customer care.With those elements in place, it’s time to consider your customer success metrics. Moesif’s approach to this is to enable you to monitor the complete, end-to-end customer experience. This allows you to not only understand your overall customer health score, but to identify any changes in customer behavior. Changes which could ultimately lead to customer churn.How Do You Determine if a Customer Will Churn?Using Moesif, the first step towards determining if a customer will churn is learning what normal behavior looks like. After all, you can’t flag deviations from the norm if you don’t know what that norm is. To do this, you need to monitor functional and performance issues continuously, to build up a picture of what normal means for your business and your customers.Then it’s time for Real time User Monitoring (RUM). This isn’t just about infrastructure metrics, but about key customer criteria relating to adoption, engagement and retention. Moesif’s advanced anomaly detection algorithms work with all this data to discover deviations from the norm – behavior patterns that could indicate a customer is going to churn. It picks up ‘unknown unknowns’ in customer behavior that mean it’s time for your customer service team to investigate and likely provide additional support.With a customer success agent investigating anomalies in your customer success metrics, you can take a proactive, almost prescient approach to better supporting your users. In some cases, you will be able to identify problems before your customers are even aware of them. This can introduce an unexpected element of delight into the customer experience, particularly when your customer success agent flags it to the user along with a simple fix that’s easy to implement.You can introduce dynamic alerts to flag indicators that customers will churn. It’s also a good idea to view reports on your most important accounts on a regular basis, so that you can monitor the general direction of their customer success metrics. This proactive approach to customer health scores can determine which customers are most likely to churn and enable you to do something about it before they do. You can use the insights gleaned from your customer success metrics to identify problems and implement solutions, all of which can reduce your churn rate and, ultimately, ensure you achieve maximum value from the investment you made in acquiring that customer in the first place.Stop Churn by Defining, Measuring, Analyzing and Ultimately Predicting itCustomer churn occurs when your customers are unhappy. Understanding why your customers are churning is key to unlocking your ability to stop them doing so. That’s why defining, measuring, analyzing and predicting churn is important. Unless you are proactively doing so, you are likely costing your business dearly. So much so that your customer churn rate could just be the most important metric.Thankfully, with the right tools in place, you can track your customer churn rate, gain insights and put measures in place to reduce it, with associated benefits for your bottom line.                What You Should Track to Reduce Churn              14 day free trial. No credit card required.              Learn More        ",
          "url": " /customer-success/monitoring/How-to-Define-Measure-Analyze-and-Predict-Customer-Churn/",
          "author": "Larry",
          "categories": "Customer-Success, Monitoring"
        }
      
    ,
  
    
        "product-management-api-analytics-how-to-drive-valuable-insights-from-api-analytics-for-web3": {
          "title": "Drive Valuable Insights About Your Web3 Application Using API Analytics",
          "content"	 : "What’s your API data telling you about your Web3 App? By lifting relevant information from your App’s API transactions and call logs, you can identify and proactively catch issues before they’re surfaced by your customers. Keep your customers happy and reduce churn - make your customer success team performant.Moesif provides API analytics uniquely targeted to Web3 Apps and developer platforms. By simply connecting Moesif through our API gateway partners, or a quick-to-install SDK, your teams can start analyzing the calls made over that API today. Moesif’s customizable dashboards allow for tracking and alerting you to deviations from the norm. This global view of behavior can deliver real-time API analytics for Web3 tools and apps, providing you with an enhanced view of your business.Generate Insights From Transaction DataWeb3 adoption is increasing. Between 2015 and 2022, blockchain adoption increased at the same pace as internet adoption did between 1991 and 1998. If that continues on the same trajectory, then Web3 users will hit one billion by 2027.For early Web3 adopters, there are plenty of opportunities, which insights from transaction data can help to identify. Moesif helps with this by elevating transactional data from crypto companies and other Web3/blockchain adopters, to visualize the data and generate insights.What Information Can Transaction Data Hold?Transaction data can provide a wealth of information when you have the right tools in place. Your data can reveal insights into conversion rates, popular endpoints and API payload anomalies. It can even help identify instances of fraud. As Bloomberg points out, working with a leading provider can reveal the stories in your data. You can then use what the data reveals to your advantage and further your business, improve your product and gain more customers.Actionable insights gleaned through your analytics can help you to operate your business more efficiently and enhance your customer experience. The insights from transaction data from your Web3 application can unlock hidden business growth potential, just as analyzing Web2 API payloads can.Where is Transaction Data Stored?Blockchain transaction data is stored in different ways by different blockchains.  Because of the open-data structure that sustains blockchains, they are not suitable for storing large amounts of data. This is why many dApps offer decentralized storage. The blockchains themselves, as decentralized applications, are stored in ‘nodes’ around the world.Bitcoin, for example, uses an Unspent Transaction Output database to store the change from Bitcoin transactions. The Ethereum blockchain, meanwhile, uses a trie (digital tree) data structure that includes state tries, storage tries, transaction tries, and receipt tries.Transaction KPIs That MatterAre people using your service effectively or not? If not, you are at risk of them heading over to the competition. Measuring your transactions’ Key Performance Indicators (KPIs) can provide plenty of insight here. That means you’ll need to spend some time working out which are the transaction KPIs that matter.The differences between Web2 and Web3 can appear very stark at this point. The decentralized nature of Web3 means that many key performance indicators that were essential for Web2 might not make sense for Web3.What KPIs Should You Measure for Payment Processing?When it comes to payment processing, getting your financial metrics in order is key – they are something that every successful business needs to track. Web3 payments use secure ledgers to process money movements as a form of decentralized finance. Payments tend to be processed without the personal data requirements and fees that are associated with big data, centralized banks, and Web2.Whether it’s a financial KPI or a developer KPI, management consultant Peter Drucker points out that, “If you can’t measure it, you can’t manage it.” This is the cornerstone of deciding which KPIs to measure. Whether it’s customer satisfaction, customer retention, customer acquisition, or lifetime value that you’re digging into, the right KPIs will unlock detailed insights.For Web3 payment processing, relevant metrics include transactional KPIs, security KPIs and reconciliation KPIs. Transactional KPIs might include measures such as number of miners, number of transactions, or hash rate. Security KPIs, meanwhile, could include the number of fraud detection/alerts or number of data breaches prevented. On the reconciliation front, you could measure metrics such as on-time reconciliations, the number of aging reconciling items or the percentage of automated reconciliations.Retaining users is just as important as gaining new ones, so monitoring your conversion rate is a fundamental part of your Web3 API analytics. Whether as a means of gauging customer satisfaction or operational efficiency, your user behavior can be used to inform your product decisions.How Do KPIs Fit Into Your Engineering Roadmap to Ensure Customer Success?The way you build your engineering road map underpins everything from operational efficiency to customer success. After all, there’s only so much you can do to provide winning customer service if you haven’t got the fundamentals right. Land on the nose, though, and your net promoter score could go through the roof.Thinking about KPIs early on will help. Which are the transaction KPIs that matter to you? Only by knowing that can you ensure you build in the capacity to track and monitor the right elements of your Web3 products.Fitting KPIs into your engineering roadmap therefore needs consideration both when you are prioritizing your major themes and when you are integrating with project management, data analysis and other tools. You first need to identify the KPIs you want to track, then implement the right data analytics for Web3 to enable you to do so.How Can KPIs Detected by Moesif Help You Understand Deviations in User Data?Moesif offers API analytics for Web3 developer platforms. We serve the Web3 community by ensuring Web3 tool/service providers retain their customers and conform to governance rules.Part of this involves using KPIs to identify any deviations in user data. We do this by enabling you to create dashboards, with workspaces representing different metrics. You can analyze each workspace, reviewing the historic performance of each metric. The graphical nature of the data means that any deviation is quickly apparent, allowing for quick action to solve business and product issues .You can also set up alert rules that notify you when a metric passes a certain threshold. This instantly flags any deviations beyond the thresholds you set and communicates the deviation instantly via our Slack integration or built-in behavioral email tool.Grow Your Product With Real-Time User MonitoringBy strengthening your Web3 business with the right product analytics, you can grow your product with real-time user monitoring. Monitoring real user data can be far more effective than synthetic monitoring, as it dives into the real user experience you are delivering and flags any performance issues. This means that you can make changes to your product, strategy and delivery model to deliver an enhanced customer experience. This can lead to accelerated growth, since the decisions made are backed up by real user data.How Do Analytics Accelerate Growth?Analytics accelerate growth by helping you understand how people use your product and where they get stuck. You can understand where they are facing challenges, identify patterns that lead to customer churn and see what happens when they hit your paywalled services.By understanding [consumer behavior]](https://online.ben.edu/programs/mba/resources/evaluating-consumer-behavior-to-boost-your-business){:target=”_blank” rel=”noopener”} in greater depth, you can identify ways in which you can better serve your customers and support them to change their behaviors in ways that benefit both them and your business. You can identify the need to iron out bugs in your product and find new ways to enhance the user experience. Essentially, the analytics mean you can convert your log data into actionable information that you can use to elevate your product.RUM vs Synthetic DataReal-time user monitoring (RUM) means using real user monitoring to analyze your product’s performance and underpin accelerated growth. It uses the logs to deliver data-based customer feedback and insights into what your users are actually doing.Many companies let you test your product using synthetic data instead, but those fake endpoints and fake analytics can’t tell you what true users of your service will actually do when they face a challenge or hit a paywall. This means that testing with synthetic data can only take you so far. It’s a theoretical approach to what your users might do, rather than feedback based on the reality of what’s happening.This is why there is so much more scope to grow your product with real-time user monitoring. RUM delivers value that synthetic data can’t. It delivers insights into what’s happening in your business at any given moment and on an ongoing basis.For example, if the number of developers who convert to paying customers is lower than expected, you can use RUM to discover their pain points. Then you can solve those pain points and witness the impact of your changes through real user monitoring.Alongside other forms of customer feedback, this provides you with a powerful ability to optimize your product to deliver an outstanding user experience – one that supports long-term user loyalty and continuous product evolution. It’s precisely why Moesif provides real-time user monitoring; because doing so puts you in a better position to grow your product and to grow your business.If you’re ready to take your API analytics for Web3 in hand, Moesif is here to help. We can support your blockchain technology business with insightful data analytics and actionable information that drives growth. Get in touch to find out more about our support for Web3 startups.                Simplify Log Analytics with Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /product-management/api-analytics/How-To-Drive-Valuable-Insights-From-API-Analytics-For-Web3/",
          "author": "Rachael",
          "categories": "product-management, API-Analytics"
        }
      
    ,
  
    
        "developer-marketing-api-analytics-how-can-moesif-help-you-improve-your-platforms-developer-experience": {
          "title": "How Can Moesif Help you Improve your Platform's Developer Experience",
          "content"	 : "A good developer experience can make or break your product. Developers, myself included, are extremely particular about the tools they use to build and monitor their projects. With the vast array of developer tools available, the option to be picky is easier than ever.The great part about product experiences is that with proper tracking and analytics, you can slowly make impactful small changes. These small changes can rapidly transform your product’s usability and developer experience for the better.This is where Moesif can help with everything from efficiently gathering metrics to helping you to make sense of them. Moesif is an analytics tool that can gather metrics from your API calls and your front end to accurately track your entire customer journey. Then, as you make changes, Moesif will also help you track the results to make sure you are headed in the right direction. Let’s take a look at a few areas where Moesif can help you improve your developer experience.User and Company trackingA building block of the Moesif platform is the ability to associate each event that occurs in your application with a user and/or company. This allows for a level of detailed reporting and analysis that you can’t attain from metrics that can’t be tied to a particular user journey. Two key analyses that can help gauge developer experience are Funnels and Retention. Let’s look at how each feature works and how it can be used to improve the developer experience.FunnelsFunnels are essentially a step-by-step breakdown of a specific flow within your application. For example, you may be looking at your sign-up flow and trying to optimize it.With this analysis, you can see how long it takes for the user to make it through each step and also what percentage of users actually complete it. This is important since it allows you to establish a baseline and an idea of where users are getting hung up. Then, as you make changes you can check to make sure that the conversions throughout the funnel are improving. At the very least, you should check to make sure your “improvements” are not causing further issues for your users.RetentionUsers that extract value from your platform and enjoy their experience with it tend to be retained. A Retention analysis can help you determine when customers are dropping off and becoming inactive with your product. Again, this can allow you to create a baseline to correlate the improvement, or deterioration, of your Developer Experience to the retention of users.AlertingBeing proactive and engaging users can also be a great way to improve the developer experience on your platform. Sometimes developer experience is not simply about having the slickest UI or onboarding but can also come down to how the company helps you if you do get stuck.Improvements to the UI or physical experience with the product can take a while to change, this is where alerts can come in handy. Alerts can be used to inform the customer success team, engineering team, or any other team concerned with customer experience, about issues that users may be experiencing. The team can then do two things:  Reach out to the customer to help them resolve their issue before they abandon the product  Send feedback into the product team’s feedback loop to help improve the experience in the futureThere are two types of alerts that are available in Moesif: Static and Dynamic. Let’s take a look at both.Static AlertsIf there is a specific threshold that you have in mind, such as if a user experiences more than 5 401 - Unauthorized errors in a 1-hour time period, you can use a static alert. This is a static criterion with a static amount of occurrences that you will use as the threshold.Dynamic AlertsIf you are unsure of the exact number of occurrences you’d like to use as your alert threshold, a dynamic alert may be best. A Dynamic alert allows you to set a threshold such as alerting when a user is experiencing a spike in 401 errors compared to their normal amount. A dynamic alert allows you to monitor for trends and spikes without putting a specific number in for the threshold.Both of these alerting styles can be used to improve the developer experience within your product. Although not directly associated with the product, the team behind the product being helpful, knowledgeable, and proactive can be a significant plus.Behavioral EmailsIf you aren’t able to handle a massive amount of high-touch email support or just want to optimize it through automation, Behavioral Emails are the way to go. Moesif allows you to set up behavioral email flows which are triggered by events in your application. For instance, if a user is receiving a high number of 500 - Internal Server Error responses then you may want to send them an email outlining why and possible fixes. This takes the onus off the support team and also allows for instant and proactive action to be taken.You may also use behavioral emails to nudge developers towards resources that can assist them with features or APIs they are using. This can help to increase developer productivity and developer effectiveness when they are using your product. By guiding them to the correct documentation, you can help a user get to their first “Hello World” moment with your APIs more quickly or help them explore more advanced features within your API or tool.Billing MetersSelf-service can be a great way to improve the developer experience within your product. Many developers don’t like going through hoops or sales calls to access and use a product. By putting a mandatory “talk to sales” hurdle between the developer and your product, you could be losing customers. Instead, allowing users to sign up, pick a plan, add a credit card, or be invoiced for usage can be a game-changer for attracting new users.Moesif’s Billing Meters can help with this. The Billing Meters feature allows you to quickly build a monetization model and send usage to a billing provider, such as Stripe or Recurly. The action of signing up, billing, and invoicing can all be automated to make a seamless process for developers.Self-serve API usageAlthough using a Billing Meter may have many purposes, the great part about leveraging billing meters is that it enables companies to quickly create a self-serve consumption model. Instead of having developers go through sales to get started, they can simply sign up and use the platform. Most developer tools enable a self-serve model so that bottoms-up adoption can happen. Because this is the norm, this will be expected on your platform as well.If you have APIs or another part of your product you are monetizing, you can simply set up criteria to bill upon and have Moesif calculate the usage and create an invoice for the developer to pay. You could also support pre-paid billing where a developer can buy credits and have them “burn down” as they use the service. Having self-service in place for API usage is a great way to improve the ease with which developers can utilize your platform.Wrapping UpA positive developer experience is rooted in good DX, good documentation, and a consistent aim to always improve the developer journey within your product. Poor developer experience can not just make developers leave your product, but also could stop other software engineers from trying it if word-of-mouth factors into product adoption. That is why tracking metrics and using tactical changes for improving developer experience is a must for any company that has created a tool or service used by a development team.Moesif is one of the best tools to not only track developer experience but also to directly improve it. As mentioned, using Moesif’s user and company tracking, alerting, behavioral emails, and billing meters are all great ways to augment your offering to developers. By tracking and improving developer experience using Moesif you should see better activation and retention across your product. To try out Moesif for yourself, sign up today to begin tracking and improving the developer experience within your platform.                Build Better Relationships With Developers Using Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /developer-marketing/api-analytics/How-Can-Moesif-Help-You-Improve-Your-Platforms-Developer-Experience/",
          "author": "Matthew",
          "categories": "developer-marketing, api-analytics"
        }
      
    ,
  
    
        "podcasts-developers-podcast-supporting-10-million-developers": {
          "title": "Supporting 10 Million Developers",
          "content"	 : " Ep. 13: Phil Nash, developer evangelist at Twilio.Moesif’s Podcast Network: Providing actionable insights for API product managers and other API professionals.Joining us is Phil Nash, leading developer evangelist at Twilio, a Google Developer Expert and a member of the Live Coders team on Twitch. He’s a regular conference speaker where he sometimes writes code on stage and hopes everything just works.Derric Gilling, Moesif’s CEO, is your host today.Moesif · 13. Supporting 10 Million DevelopersListen to the episode on SoundCloud above, watch it on our YouTube Channel or download it on Apple or Google. Table of Contents   1:00 Developer Evangelism Evolves with Growth    3:40Twilio Scales Through Acquisitions    6:55Language Varies From Product to Product    8:38Make APIs Standard    12:17OpenAPI Aids Library Creation    14:19Devs Want CLIs    18:00Offer a Serverless Platform for Local Dev    21:50Customers and Developer Platforms Share Best Practices    25:18Segment Data to Ensure API Privacy    27:20Developer First Equals Developer Empathy    30:19Supporting Developers Requires Community Building    35:25Remote Access Enables Diversity at Conferences    38:04API Success is More Difficult Than Ever  Derric Gilling (Moesif): Welcome to another episode from Moesif’s Podcast Network APIs over IPAs. We provide analytics for API product managers and other API professionals. Joining me today is Phil Nash, from Twilio. He’s one of the earliest developer experience professionals at the API-first pioneer. Twilio focuses on communication SMS and now is getting into video and a lot of other things. Super happy to have you here Phil.Phil Nash (Twilio): So good to be here. Thank you for the invitation. I want to point out that I’m one of the earliest developer experience professionals still at Twilio, but I joined in 2014, and we’ve been going since 2008. Some of the earliest hires were on the evangelism team and I get to build on top of a lot of the work they did in those first six years. It’s a pleasure to be a part of the team and carry on their work.Developer Evangelism Evolves with GrowthHow do you support developers as your product becomes more successful? Twilio split the original evangelism team into two: education and docs, to handle devs at scale.Derric: I would love to hear a little bit more around that story. Twilio was a couple hundred people when you joined them, building out the developer evangelism team, and now Twilio is up to 4,500 people.Phil Nash (Twilio): It’s something like that. I think we probably just released bigger numbers recently, but we’ll stick with 4,500 for now. We just keep buying companies at the moment and that number goes up fast. It’s been an incredible journey really. When I joined Twilio, there were about 300 people in the company in 2014.It was the biggest company that I had worked at, at that time, which is a weird thing for me. I’d gone from a company like an agency that had never gotten bigger than 50 people into this 300 person company. It felt like a big place to me.Now that it’s into the thousands it’s overwhelming at times. It’s been nice for me because I worked initially in the London office and now I’m down in Australia.Always sort of part of the satellite teams, just always felt like a bit smaller and a bit more like family for me. It’s incredible to know that there’s this huge sort of machine behind me to do the work and produce the product we do.When I joinedthe evangelism team, it was just in the process of actually splitting into two teams, which was the start of ourjourney into building what we now call our developer network team. Which encompasses all sorts offacets of the developer kind of experience with Twilio and the community experience at Twilio as well.At that time we were considering what became our developer education team, which is a team solely focused on the documentation, platform for the documentation and buildingthat kind of thing into apractice that we could focus on. I think that over the last seven years, the building of Twilio and certainly building the experience of Twilio has been a case of more and more focusof things. Like I said, we’ve gone from that original evangelism team, and then two guys split out from there to do the education team and built out the docs platform.Since then they’ve built a game-they built TwilioQuest. Have you seen that?Derric: I don’t think so, but it sounds interesting, I’ll have to check it out.                Grow Your APIs With Moesif              14 day free trial. No credit card required.              Learn More        Twilio Scales Through AcquisitionsIn order to best serve their customer base during growth, Twilio continues to add additional products via acquisitions. At a certain size, Twilio needed to develop more products than they could handle internally, leading them to purchase companies like Segment or SendGrid to offer their customer’s more capabilities.Phil: Yes, please do. We’re coming up to a fancy new release sometime soon. It’s a game, a full 2D top down kind of RPG adventurethat also teaches you how to code and how to use the Twilio API, as well as introductions to other programming languages at the moment.We’re just working on it and it’s possible now to actually build your own levels for that as well, so we’re hoping to see a lot more stuff coming out of that.We’ve built that and then we have a sort of more community focus team, which has kind of been in and out of things that used to handle social and things like that.Now it’s focused on forums, champions programs and things like that. Meanwhile the evangelism team continues to try and go out towhere developers are in the world or where they are online. More likely in the last 18 months, in order to take the word of Twilio to them, but also bring that experience back.In the meantime, we’ve also grownenormous numbers of products. When I started there was voice and SMS. You could make phone calls from a browser andmobile apps on the devices. Since then we’ve added chat, video andall sorts of other additions that has spread the knowledge quite thin. And then, of course, brought in other companies. So we brought in Auth0 and added two-factor authentication and verification. We brought in more recently SendGrid, then Segment, and I’m still catching up. I have no idea how you use Segment yet, but I’ll get there. Getting better at SendGrid though; email delivery ability is not as easy as one might think,when you do so many emails during the day.So yeah, there’s been a lot of that. I think the nice thing is the product and everything at Twilio has always remained developer first. That allows us to support and help developers along the way, all the way from seven years ago to today.Derric: Well it’s funny that you mentioned acquiring all these different companies. We’re SendGrids’ customer, we’re Twilios’ customer to power some of our learning features, and we’re partner with Segments, so it’s really intertwined with Twilio over there.Phil: Seems that way, I think that’s what we’re trying to do. We noticed that these things fit well together.I think SendGrid is fairly obvious that it’s a communications channel that we didn’t have and getting SendGrid involved waspicking the best company out there on the market. It was doing it to become part of Twilio.Segment might seem a little bit weird but at the end of the day, behind all of the communications thing is customer journeys and customer data that you want to expose the way you then communicate with people.I think it’s an exciting feature for how that all ties together.                Easily Gain API Product Insights              14 day free trial. No credit card required.              Learn More        Language Varies From Product to ProductThe term “API” may feel like a buzzword without proper documentation and explanation. Twilio’s messaging comes together because they keep their products under one roof.Derric: Has that made developer evangelism a challenge? Going from just talking about SMS and how to send a message, versus SendGrid and all these differentways about thinking about marketing to developers. Is it a common process or common language that you have or is it all different to each product or each area?Phil: That’s a good question. I think it’s become more difficult in sort of the elevator pitch, when you used to be able to say, “You can send or receive text messages, make or receive phone calls. Go have at it, it’s an API; go!”Now it’s  this wide expanse of things that you need longer elevators for that kind of thing.It’s difficult to say, the expense of the marketing team and getting the message out for this has been an important part of it as well. On the other hand, we are still working to integratesome of these purchases. Segment, for the most part, continues to look after itself and continue its own messaging and reach out to people. So that’s been easy for now.It’ll be interesting to look to Signal, which is our conference in October, for how that messaging comes together because that’s everything Twilio under one roof at that point.We’ll see some interesting things there I think. Nothing I can give away right now, as far as I’m aware.                Manage Your APIs With Moesif              14 day free trial. No credit card required.              Learn More        Make APIs StandardHow does Twilio make life easier for developers? Twilio’s developer experience and relations teams are focused on making sure APIs are standard and have consistent quality across the business.Derric: I would love to hear more around how you structure developer relations. We’ve got so many different roles these days: developer advocate, developer experience, developer relations itself. How was that sourced at Twilio and what have you seen work well in terms of responsibilities?Phil: That’s a good question and I will be upfront with you. Let’s step one step back to developer experience. I think developer experiences are important to all of Twilio, right? If the API teams aren’t producing great products for developers, then everything falls down on that.Everything else becomes layers on top of that, to make life better, easier for developers. There is a developer experience team that is not within the developer relations team.This is what we call our teams, they are focused on making sure those APIs are indeed standard and have consistent quality across the business, now there’s so many parts of it.Like I said, in the sort of developer relations side of things, we have this development network team, which deals with evangelism. That’s going out to communities and developers out there, education, building the docs platform and TwilioQuest as a game.The community is looking after the forum. We also have within there, an enterprise evangelism team which is a relatively new addition to this.We’re taking the tactics and practices of regular evangelism and community events to larger businesses. Enterprises, effectively take them through hackathons, innovation sessionsand give those businesses the opportunity to share a problem with their developers thathopefully, communications and the joy platform can help solve. Having Twilio experts on hand. Just set them going for a day or two, to try and build their own solutions and solve their own problems. We findwithin enterprises that tends to be a place where developers maybe don’t get that decision making opportunity that often.But when you put a problem in front of them with a tight deadline, in a small team to work on it,the developers have a great time building and finding this out, even if it’s their first experience with the API.That’s what our enterprise evangelism team does, it takes Twilio out to those companies like that. Like I said, it’s that same kind of experience as regular in the Community kind of thing butmoved to see that enterprise experience and it makes those developers a lot happier, it’s amazing.What else do we have? The startups team as well, that’s certainly fostering those newer and smaller companies where everybody in the team,everybody in the company could be a developer, or is at least a builder.  Somebody who’s wanting to produce something in the world and the support,a little bit of funding, some credits go into the Twilio startup program, kind of helps them get off the ground and that’s great. There’s so many parts of this.                Debug Your APIs With High Cardinality              14 day free trial. No credit card required.              Learn More        OpenAPI Aids Library CreationBefore OpenAPI spec library generators existed, Twilio wrote libraries against their own specifications, which was a tedious endeavor.I’ve been talking about and working on the Twilio CLI. for example. That itself as a project came out of the developer education team, so the team that’s working with the docs. We were like, “we think we should have this as well”.That now, I think, has been taken over bya developer experience team, actually, which is good.It’s funny that some of these projects just came out of people wanting them to exist in the world. Then turning that into something we can callGA or generally available, is a different matter, it’s not just a version one on the NPM module sadly.When you won’t be able to deal with it, there are those developer experience team things there and a developer interfaces team as well, which works on all the helper libraries and other stuff like that.The helper libraries themselves are written by a program that looks at our specsso the developer interfaces team deals with the bits around the edges of that, as well as theprogram outputting it as well. I think before OpenAPI speclibrary generators existed, we sat down and wrote against our own specifications.That’s gotits own interesting things to deal with as well, it’s a big Python program that writes all our libraries.It can be a pain if you want to change the javascript library, you have to change to Python writing javascript in order to fix it. But for the most part, it doesn’t need that much fixing anymore, it just generates stuff and that’s nice.There’s lots of details, lots of things going on that specifically focus on this experience side of things.That structure is ever growing as we find other places to focus on.                Faster API Integration With Moesif              14 day free trial. No credit card required.              Learn More        Devs Want CLIsThe Command Line Interface (CLI) itself came out of a desire of the developer education team. Twilio’s CLI was created to help customers interact with a Twilio account that isn’t just through the UI in the console.Derric: So what motivated creating the CLI and how do you measure the experience of it? I mean, it’s not like a web app where you can just install Amplitude or something.Phil: That’s true, and like I said, the CLI itself came out of a desire of the developer education team to build. I thinkas developers we don’t necessarily like to get away from the keyboard that often andgiving people an option to interact with a Twilio account that isn’t just through the UI in the console was one of the plans there. Also, the idea that maybe you can use this CLI as part of a build process or build pipeline as well.Those are the two things that they wanted to solve for.They built that and some of the early ways they got feedback on it was actually at our Signal conference back insay 2018 perhaps, where we would take people off and have proper user experiencetesting sessions with them. Like sit them in front of a keyboard with the early version of the CLI, and say “just go try achieve this with it andhow did you get on with that?”. That was really good for early learning on that, but then, how you measure like CLI usage after that?It’s hard, you’re right, you can’t just whack analytics in it. Although, I thinkwe are working more on that.The rough thing is, CLI is published as both a NPM module and package on Homebrew right now.We are trying to add more packaging options to that so you don’t need to have Node installed in order to run it on most systems, we’re getting to that. The NPM install is a blunt kind of tool to ask if people are using? Are people getting involved in this? Following that we are working on ways to work at people who are using it so we’ve had to rearrange our user agent strings for ourhelper libraries, so they all report a little bit more on where they’re being used. Which we think is going to be really useful becauseultimately the CLI is using the Node package, but not all calls from the Node package are obviously from the CLI, there’s plenty of people who have installed the Twilio nodepackage elsewhere. So finding out which ones are going from where, and if that is growing usage, that’s going to be really interesting but we haven’t quite got there yet.Then we have layers on top of that, there’s plugins to the CLI,some of which call the API, some of which don’t need to. Being able to tell if the call came not just from the CLI, not from the Node but from a plugin that was on top of the CLI is also going to be part and parcel of that as well.Measuring this is ongoing, but we definitely know from our user interviews and talking to customers that people are using it and using it forvarious things so we know that it is useful at least. We’re pretty happy with that.Personally, I don’t normally work on the core of the CLI myself but on one of the more bigger plugins, which is our serverless plugin, serverless toolkit we like to call it.                Collaborate On Your APIs              14 day free trial. No credit card required.              Learn More        Offer a Serverless Platform for Local DevWhere does development actually happen? Customers wanted to develop on their own computers rather than via tunneling and ngrok, requiring Twilio to create a lightweight serverless platform.Stepping back again, one of the things we released at Twilio was a lightweight serverless platform as well, like Twilio Functions and Twilio Assets. Whichallow people to host javascript and static assets in our infrastructure and use them by functions. The javascript can be called, like an AWS Lambda, a thing like that.We decided that was a good thing because this is another developer experience thing really, in working with web hooks,whilst probably the simplest way you could deal with real time interactions, with a phone call for example,was still something when you start developing and you want to do it on your own computer, suddenly it becomes difficult.We’ve alwayspushed, like tunneling things and ngrok to us as part of thatto make that easier. Even then people still find it hard, so we decide if you just write javascript, write something inside the Twilio console and have that deal with yourincoming web hooks, that’ll be easier. Of course people wanted to do that, but then you find people wanting toturn that into a moredevelopment focused thing. Our first version of that was literally a text box, with a bit of syntax highlighting and validation inside the console. You want to be able tosource control that and not just every time you save your redeploying something, your source control and then set up other deployments.We built a second version of that which had an API. Meanwhile, myself and one of my colleagues, Dominic Kendall,had been working on various different tools.Dominic had worked on a tool called Twilio Run, which allows you tobasically mimic the functions environment locally. So, we’ve gone all the way from “we need this remote environment, you can just use on the console”, to, “but I want to develop my own laptop with that”, so he built this Node wrapper for that.He built that and then I could never remember how to set up a project that would work with it, so I built aproject generator for that, and that was the first two bits to it. Then the serverless team added an API. So we added an API set to that, Twilio run became able to deploy it, and then we packaged it all up into a Twilio plugin, a Twilio CLI plugin.After all that, like I said, all of this becomes layers upon layers of what are people doing right now, where would people like to getto, as we’re building the little tools for it. I think we do actually get to improve that experience. Once again, I’m looking forward tohaving a bit more data fromwherever those API calls are coming from to know how much this is being used. I know some of our customers are definitely using these things, particularly the serverless plugin, I’ve had to support them at times. We got an interesting bug a few months ago, which was based on what we now deem as a breaking change that happened in one of our dependencies, but not at a major version whichcaused us some problems.Semantic version everyone, it’s a great thing.Having supported those teams that this bug caused issues for meant I definitely know that people using this and it’s exciting to see that. It’s going to be exciting to see more data as well.                Analyze API Logs With Moesif              14 day free trial. No credit card required.              Learn More        Customers and Developer Platforms Share Best PracticesHow do you ensure that SDKs are robust and trackable? Twilio put the CLI into the hands of a team that is dedicated to improving the customer experience given customer feedback.Derric: Definitely, and I’m glad you brought up the user agent string concept which is always important to track, all the different SDKs and different plugins that are using these APIs and get that uses data.You talked about multiple different layers on top of the CLI. What are things that people can be doing to the SDKs in different things, using these APIs to get the right metrics and just make sure that they’re able tofigure out whether there’s bugs or not? Sometimes it could be a single SDK or a single weird use case that you never saw before.Phil: Not quite sure what you mean by the question.Derric: Is there anything else in terms of best practices that acustomer could be doing or a developer platform can be doing with the SDKs to ensure they’re robust, and to ensure they’retrackable.Phil: Right, I get you. That’s a good question. As you say, if you had a web application, you can stick some error tracking in there and things like that. Idon’t think we have that kind of thing, we don’t phone home with our CLI,for anything other than actual API calls. Not to say that we might not do that in the future. That kind of thing is interesting. But I think if a bug occurs, if something goes wrong then the thing will stack trace on theterminal. I’m not sure that’s a bad thing, when you consider it, the product is for developers, the CLI is for developers and seeing a stack trace hopefully understandable for that. We encourage you to raise that as an issuein the repo.Developers for the most part, are very willing to raise issues and tell you what’s wrong with the thing,almost too willing, at times. I think it’ll be interesting to see where we go with this and as we put the CLI into the hands of a team that is dedicated to it and dedicated to improving things like that.I’m always wary of the idea of more tracking and things like that. If there are CLIs where you have to pass a flag or a command in order to turn offsharing that data and maybe if you don’t know about the command or flag, you’ve been sharing your usage of it without knowing and that’s potentially privacy encroaching. I’m not a huge fan of that kind of tracking.I’m comfortable with the user agent stuff because knowing where something came from is a good thing but any more than that, like looking after the usage is,I think there’s that line between making a better product and encroaching on other people’s work, their privacy and their data.I don’t want to see that much more tracking like that, I prefer it to be opt in rather than opt out as well.We’ll see about that.                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This is communications data betweenbusinesses and their customers.We have recommendations on what data should and shouldn’t be sent over to various channels, of course, you don’t text in your credit card number.It’s not a good idea.We do have strong protections on how that data is kept and looked afterwards within Twilio.Many parts of the business, HIPAA compliant now and there’s a lot of work to deal with GDPR compliance back when that was a huge lift for the company at the time.I think one of the important things here is, we are as open about that and about how we look after the data as we possibly can.To the point that it’s part of our API definitions and that gets generated into our documentation to say whichproperties on a resource, for example,are deemed personally identifiable information and then how long we intend to keep that for. When that’s going to expire, when it’s going to go out the system and that’s all in the docs. It’s allthere at your fingertips, as you’re looking through that kind of thing. I think that’s really important and anything going through the CLI is also just calling those API endpoints.It ishandled yet centrally, as far as I can tell.                Protect Your API Data              14 day free trial. No credit card required.              Learn More        Developer First Equals Developer EmpathyHow do you ensure a successful customer experience if you’re “developer first”? When developers are your predominant customer base, being empathetic to their needs is a must for developer onboarding and customer retention.Derric: Definitely, and taking a step back and discussing more around developer experience. What does developer empathy mean over at Twilio? How is it used or how do you think about it across different functions? Whether it’s the developer experience team or as a developer relations team, and so on.Phil: Yeah I mean,I said at the start, everything in Twilio is developer focused, because they’re the first customer.We have a bunch of company values at Twilio and again company values, coming from a small company.Before I joined, Twilio was something I’d never seen before; they have a list of company values, one of which is to wear the customer’s shoes.That’s really important to us because it’s not just developer focused, of course, it’s empathy for all of our customers.Including the fact that, increasingly we have people who are not developers signing up for Twilio accounts and trying to work out what they can do witha platform that they told is really useful for communications. Having empathy for that non developer persona has also been really important.Butthe empathy for developers is very muchthe way that wedeal with our products as developers ourselves, we try and use it ourselves. I think it’s really important and useful.I think the business to havethis developer network, developer relations team, that is not working on the core products themselves but tends to be or can be like those first users of the products and be that voice of the developer within the company, voice of the external developer, at least.Because we don’t know how it’s made, we just want to hear that we’re building a cool new product, we want to hear that experience and use it ourselves. A lot of feedback, early feedback we can give like that.At the same time, it comes from the top as well.Jeff Lawson, the CEO, is still a developer.He doesn’t write production code for Twilio anymore, at least not that I’m aware of. But he is alwaysbuilding. I think in the last year he’s been producing,I think, zoom cube. It’s a whole different set of buttons that you can press to make different things in your video chat occur; 10 years on and off, mute, and a big button to hang up that kind of thing.It’s good to see that he is finding time to continue to be a developer, and a builder,and that filters down through the rest of the company as well.                Drive Developer Adoption With Moesif              14 day free trial. No credit card required.              Learn More        Supporting Developers Requires Community BuildingHow does Twilio support their large range of developers at various experience levels? By engaging with their community of citizen and professional developers alike, Twilio is able to share information that helps developers create better applications.Derric: That’s an interesting point and speaking on both supporting customers and developers, at the same time, and wearing their hat or wearing their shoes.Nowadays developers are not a single persona, right? We have some folks who are expert developers, we also have folks who are just learning to code for the very first time or using maybe a no code solution. How do you support all those different developers at the same time?Phil: It’s increasingly hard to do that, you’re right. We have a whole range of developers using or trying to use the platform.We areactually trying to support that from the ground up. I mentioned TwilioQuest earlier and a lot of the TwilioQuest team are doing work toadd more early stages of how to develop content. I believe there’s a lesson coming out on working with APIs, not specifically the Twilio API but like what is an API and how to work with oneas a new lesson for the game. We’re trying to get TwilioQuest into universities starting in the US, to try to help out and teach people who are new to programming. And need to get on board with that because it’s such a useful skill for almost any role these days, so we’re working from there. We alsodid a lot of work in the last couple of years to produce and sample usable applications that people can build and install into their Twilio account without having to know what the code is. Although, there is a sort of code behind it, this is what we call the Twilio code exchange.The code exchange itself is a sample project, and then there are quick deploy apps within that. Like when you hit a button, fill in a couple of details and you now have a functional Twilio application that you can go and use.We’ve released a couple recently that have been around notifications and information for vaccine hesitancy kind of stuff. As usual, Twilio has a bit of a plan to try and help as many people get vaccinated as possible during this pandemic.We have samples for that, but we have simple ones for one click just to get a voicemail thing going as well.Those one click and instant applications, quick deploy applications,they’re there for what we’re calling citizen developers, people who aren’t necessarily professional developers but want to achieve something.The code is there behind it as well, and if you do deploy it and you want to change it, you can get a developer involved and update that. You mentioned low-code tools and Twilio Studio has been that kind oflow code, I say. In order to do a few things, a couple of widgets within the drag and drop interface that can either call a Twilio functional or send off an HTTP request.But, really like being able to build an entire communications flow in drag-and-drop has beenenormously good for unlockingproduct managers and other things on the customer side to achievea lot, not even just the basics, but a lot of the communication flow before having to get developers involved. That is really useful because as developers we don’t want to be doing the same thing over and over again, so just being able tobuild the interesting parts of those systems, I think has been really good, too.We’re also fully happy to support expert developers. They’re the ones that want the API references, they want the libraries and want to be left to go about it as well, and that’s exciting.And this is where I think we’ve now got more focused on ourcommunity team these days as well. We have forums and beta, which is kind of weird to think Twilio is athirteen, fourteen year old company that hasn’t really had a forum for the community, at least a first party one for all that time.I think we haven’t had one a different time butwe’re going to renew our focus on that so that people can lead, come together, help each other, get a bit of help from people at Twilio and then we take that out as well.You’ll rarely find or I plan for it to be rarely found, that a stack overflow question tag Twilio doesn’t have an answer from one of us as well.We’re there to support people wherever and however they want to build with Twilio and there’s many more ways than that, but those are the ones that come to mind.                Enable Customer Success With Moesif              14 day free trial. No credit card required.              Learn More        Remote Access Enables Diversity at ConferencesHow has the COVID-19 pandemic impacted DevRel given the lack of in-person events? The transition to online conventions has led to a unique opportunity for event goers and companies alike: the ability to take part in events from any part of the globe, which allows for a far more diverse attendee group than possible before.Derric: Definitely. I love the one click install or those types of installation processes.The example of batteries included but optional, you can modify as needed but just get started as quickly as possible.You mentioned COVID and how you’ve been thinking about that. I’m just curious, how has DevRel changed since COVID hit, especially with events no longer happening in person. And has it been accelerated? Has it been beneficial in a way, in terms of reaching developers?Phil: Yes and no.The difference for me, certainly is I’ve been right here for a lot more of my time, rather than out and seeing people in person. We’redefinitely missing in person, talking to people and getting people excited about things.On the other hand, I think it actually has led to reaching a whole bunch of different people in different places as well. The way events, meetups and other communities have gone online, has led to people from all over, being able to take part in them. Not just people who live in the right place or can afford to get there at the right time. I think that’s what has democratized things a lot.For developer relations, it makes it a little bit more difficult, I think I’ve yet to seean online event that has the same sponsor experience as an in person event.I think that’s understandable, when you have a bunch of talksand a gap in the middle, and people are at home watching it. They’re going to go make a cup of tea or coffee in that break, they’re not going to hang out at the virtual booths and that’s fine. We have to work on better ways to engage with people.I think we still are but that’s an ongoing thing to work on within events as well.Derric: That was a really good point around the events.The fact is that it’s hard to network, right? Going to a conference or something, one of the easiest ways to meet new people is by hanging around the booths, the happy hours and all the other activities going on, like hackathons and such.Phil: Exactly, I do all my best work at the after party.                Improve Your Customer Success              14 day free trial. No credit card required.              Learn More        API Success is More Difficult Than EverBreaking into the world of APIs is no longer about providing an answer to a problem but rather a complete solution package as an API platform. But, for API developers, toolkits will continue to provide flexibility in building applications thanks to the plethora of tooling available.Derric: Funny enough, right? My last question is around the future of APIs and DevRel. What do you see next and where do you see API’s going?Phil: That’s tough, I think within the bigger API companies a lot of consolidation right now. I’m obviously saying that from the point of view of Twilio, where we buy in the occasionalother API company, but there’s a bit more of that going around.I feel like more recently there’s been a bunch of more back to basics, things like databases and other connectivity thingsare new and exciting, which is sort of a surprise to me. I guessthere’s new and better fundamental technology that’s driving this.Though of course anybody trying to do a completely fundamental thing like a database is fighting against your big three cloud operators regardless. So, you really have to have a very impressivereason to exist. I haven’t managed to play with many of them myself but I’m excited to do so at some point.What’s the future for it though? I think it’s getting more difficult to enter the API space because there’s so many things you  need to provide to bring the same experience that somebody who’s been doing it for a decade or more has been able to provide.Of course, there are open source tools that have come along with this kind of thing, which make it easier to get there but there’s just a lot to think about.One thingthat even we have shied away from is sort of providing UI kits to deal with that kind of thing as well. I think Stripe is very good at providing actual UI for things as well, butbeing able to do thatacross not only browsers.Being able to provide UIs across, not just browsers and javascript front ends and the kind of frameworks that are there.But then the necessary native applications, their framework, then their cross platform frameworks, yourflutter and react native that kind of thing. There’s a lot to maybe produce when you come out with this.I kind of wish anyone well, who is building new things in APIs basis because there’s a lot to consider and a lot to think about.Even though as a developer, I think it’s a wonderful time to be building products and building applications because there’s such a lot of tooling available to you to build the way you want to.As an API company, being able to provide everything for those people is kind of difficult though.Derric: There is a lot of tooling out there, and a lot of new API first or developer first companies coming about, that requires avery critical skill set right. Well, thank you very much Phil for joining us today on our podcast and looking forward to seeing what’s next over at Twilio and over at Signal.Phil: Thank you very much.Derric: Have a good one.                Make Your API Platform Successful With Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /podcasts/developers/Podcast-Supporting-10-Million-Developers/",
          "author": "Larry",
          "categories": "Podcasts, Developers"
        }
      
    ,
  
    
        "technical-api-analytics-4-ways-to-leverage-user-metrics-in-moesif": {
          "title": "4 Ways to Leverage User Metrics in Moesif",
          "content"	 : "Without users, products mean nothing. So why is it that so many organizations analyze API and user behavior metrics as an afterthought? My theory would be that they just don’t have the right tools in place to actually collect, analyze, and use these usage metrics.With so many products aiming to improve their user experience and customer satisfaction, tools like Moesif are exactly what is needed. With Moesif we can collect meaningful user data, find value in that data, and even action some of that data right within the platform. The impressive part is how easy it is to do.This post will look at 4 core functionalities within Moesif that can help you, your organization, and your users have better outcomes. The only prerequisites required are that you have integrated your APIs with Moesif and have set up user tracking.Once user tracking is set up, navigating to the Users screen will give you access to 4 options that you can use to leverage your user-based metrics:  User Lookup (and cohorts)  User Funnel  User Retention  User CompositionEach of these can give you great insight into how your product is doing, how to make your product better, who’s using your product, and much more.Let’s take a look at each of these key features, one by one.User Lookup (and cohorts)Being able to identify and explore the users who are using your system is important. Knowing who is using your system, how they are using it, and their individual trends over time can bring great value.With Moesif, you can use the User Lookup screen to filter out specific users and dig in to explore at a more granular level. When you first come to the screen you’ll see an unfiltered list of users:From this list, you can create filters that filter by individual user criteria, like “GeoIP” or “Time Last Seen”, or filter users that meet certain event criteria, like users who have experienced an HTTP 4XX error in the last 24 hours.To implement the first example, we could look for users whose “Last Seen Time” is greater than one week ago. That would look like this:Once the filter is created, the users displayed at the bottom of the page will be automatically updated. We could also add in the second criteria under the “Who Performed” filter to add even more filtering to the query. To do this we will additionally add a filter to only include users who live in the US (by using their request’s GeoIP.Country_Code). Now our filter will look like this:Now we will only display users in the US region whose “Last Seen Time” is more than a week ago.If we found that these criteria might be useful for other functions, or is something we want to keep an eye on, we could save this as a cohort.  A saved cohort is a dynamic list of users (or companies) that match some specific criteria. Those criteria can be specific user/company properties or based on what the user/company performed in terms of API calls and user actions. Because these lists are dynamic, Moesif continuously updates them in the background.We can also look at particular users. To do this, we can simply find a user in the list, click on their user_id, and then view an overview page that shows some key metrics and details. Here you can see things like “First Seen Time”, “Last Seen Time”, user metadata, and other important information.The User Lookup functionality and cohorts allow you to filter and explore your users easily. If you’re looking to dig in more, check out our docs.User FunnelBeing able to track a user as they navigate through different stages of your applications funnel is a great way to see what is and what isn’t working and where users drop off or leave the funnel.With Moesif, you will set up different stages of your funnel, possibly certain API calls, to track the user’s progress. An example of this may be a flow where users perform 3 steps:  Login          The user logs into the system by calling the /login endpoint        Purchase          The user decides to purchase a product by calling the /purchases endpoint.        Subsequent purchases          The user decides to do an additional 2 purchase transactions through the /purchases endpoint.      Each of these steps represents a crucial part of the funnel. By tracking the movement of users from one stage of the funnel to the next you can more accurately determine pain points or issues, strategically make changes to the flow to increase conversions, and test changes to the flow accurately by viewing metric changes.Setting this up in Moesif is very simple. Once in the app’s Users dashboard, we will click User Funnel.Once here, we will define our 3 steps/criteria which determine each step of the funnel.Specifically, I will make sure that each call to purchases returns an HTTP 200 OK response so that we know the purchase was successful. We wouldn’t want to count unsuccessful calls towards our funnel metrics.From this, you can see each step of the funnel that users complete and how long it takes on average to go to the next step.We can see from this funnel that for users to make it through the entire funnel it takes an average of almost 33 days. We also see that only about 9.21% of all users make it through our entire funnel.To dig deeper into User Funnels, dive into our docs.User RetentionAnother way to leverage user metrics is to dig deep into user retention. These metrics allow you to track how well you are retaining your users but go well beyond just checking to see if they are still signed up for the service.Active users tend to stick around, upgrade, and promote your product. Making sure that your product is fostering these types of interactions is critical to retention.With Moesif, you’ll first need to figure out your criteria to show active and returning users. For example, you may say that the first action is when a user first logs into the application and every subsequent login is their returning action. As time progresses, you’ll be able to see how many users are returning each day.You may also dig in a little further, especially if your application or business is transaction-based. In a scenario like this, you may make the users first call to /purchases endpoint their first action. When they perform another call to /purchases, this will be their returning action. This would signify that the users are deriving value since they are continuing to actively purchase something within your product.  Since you know your business best, you will be the best person to establish when a user first perceived value and what events after can be perceived as them continuing to receive value.To set up a retention analysis in Moesif, you’ll need to navigate to the Users screen and select the User Retention tab.Once here, you’ll define your first event. In this example, we will use the first event as the first time a user makes a purchase. This is defined as a call to the /purchases endpoint. Our returning event will be when a user performs an additional purchase.We will also group the results by user_agent.name so that we can see which platforms are experiencing the best and worst retention.Lastly, we would like to include metrics from the last 12 weeks and view the retention on a daily basis. For that, we will set the following values in the dropdowns to the upper-right side of the chart to “daily” and “Last 12 Weeks”.From the above output, we can see the daily retention rate based on the User-Agent being used to access the platform.An accurate retention analysis can help to track changes in your retention rates as your organization experiments and aims to improve. Tracking retention is one key metric to look at when analyzing your business’s health.For more info on how to create a retention analysis in Moesif, check out our docs.User CompositionOnce you’ve explored your individual users, your funnel metrics, and your retention rates, you may want to figure out your user composition.User composition allows you to take a deep look at your user base. You can stay very high-level and group large segments together or can go very low level by filtering your users by extremely particular metrics and criteria.User composition can be filtered by a specific user property, such as company or location, or by users who performed specific events.Here is an example where I am filtering by users who are located in Canada who have successfully received an HTTP 200 OK response from an API:Once you’ve set your filter criteria, you can dig further by grouping the results by a specific criterion as well by setting the Group By criteria. An example of this could be grouping by user-agent (what platform the traffic is coming from), by country, by company, or any other possible groupings. You can choose whether you want to show the top or bottom of the grouping and how many entries to include. For instance, you may only want to show the user composition of the top 10 companies within your analysis.If you choose a Group By criteria which involves a time metric, you can then group the data by date_histogram which includes time intervals of 1 day, 7 days, 1 week, and 1 month.To add to the example above, I have added a Group By which will group the results by a user’s Company Domain and show results for the top 5.Lastly, the results of this query will be shown based on whatever metric you choose. Moesif has some predefined ones as well as the ability to create a custom approach. This will be the metric that will be shown in the resulting visualization or chart. Predefined metrics include:  Unique Users  Unique Sessions/API Keys  Unique Companies  Average TTFHW  Max TTFHWAdding to the above example, we will make sure that the Unique Users metric is the one that is selected.With all the filters selected, the result will be Users in Canada who have received an HTTP 200 OK status code. This resultset will then be grouped by users Company Domain and display the top 5 results. Lastly, the results displayed will show the unique users that each of these companies has that have matched the filters.The result would look something like this:In this chart, we can see the results of the above criteria based on a sample dataset.By using the User Composition feature in Moesif, you can gain valuable insights which show your current user demographics and can manipulate filters to find very precise details about different segments within your user base.Bringing it all togetherWith these 4 tools within Moesif, you’ll be able to see everything that you need to empower your business. You will easily gain powerful insights into your users and their interactions with your product. To recap, we covered:  User Lookups/cohorts          Quickly and easily look up users based on what they did with your platform and find details about them. Very similar to a CRM-style solution, but with capabilities to look up based on what they did, not just their attributesCreate filters and save the resulting lists, also known as a saved user cohort        User Funnels          Define steps within your application to create your user funnel and track conversion rates through each step.        User Retention          Create an analysis that tracks users retention to ensure that users are actively using the product. This goes beyond just tracking subscriptions or similar metrics but actually allows you to define criteria to ensure your users are deriving value from the product, the main factor in retention.        User Composition          Easily explore the composition of your user base by setting up criteria, filtering, and sorting the results. Create great visuals and charts which show who is using your product and how they are using it.      Looking to get started? Simply log into Moesif and try these great features out. Don’t have an account yet? Sign up for a free trial and start exploring your user analytics and beyond in a few clicks.",
          "url": " /technical/api-analytics/4-Ways-To-Leverage-User-Metrics-In-Moesif/",
          "author": "Matthew",
          "categories": "technical, api-analytics"
        }
      
    ,
  
    
        "api-product-management-api-analytics-using-time-series-charts-to-explore-api-usage": {
          "title": "Using Time Series Charts to Explore API Usage",
          "content"	 : "One major reason for digging into API and product analytics is to be able to easily identify trends in the data. Of course, trends can be very tough to see when looking at something like raw API call logs but can be much easier when looking at a chart aimed at easily allowing you to visualize trends. Enter the Time Series chart.A Time Series chart looks at trends over a specified interval of time. You’re probably familiar with these types of charts in everyday life. Charts showing the average returns per month from the S&P 500 index, for instance, are usually shown in a time series chart. Imagine if you could have this type of chart to display API metrics like call volume, average latency, or even something more business-centric like the number of new users who got on-boarded per day or week. With Moesif Time Series capabilities, you can do exactly this.What Is a Time Series?In the context of API and product analytics, a Time Series analysis allows you to view aggregated trends for the API traffic over a certain time period. In general, a time series chart is a way of displaying a data set that measures some quantity over time. Time Series charts are used for statistics, signal processing, econometrics, mathematical modeling, and many other uses.Benefits of Using Time SeriesThere are many benefits of using time series reports to show API usage. The benefit to time series data is that it is easy to collect and track over time. Using a Time Series chart is a great way to visually identify trends and easily compare historical data. The other benefit is that a Time Series chart can also help companies to identify possible trends in the future as well by allowing the to visually predict where trends may be headed.In the context of APIs and product analytics, Time Series charts are also an easy way to display data to those who are less technical. Unlike other types of reports that may take some technical knowledge to decipher, the visual nature of a Time Series report makes it easily digestible.Time Series can be created for different time periods, such as hourly, daily, weekly, monthly, or yearly. This allows you to compare usage over different time periods. This can help developers plan for future traffic and optimize their API for performance. This information can be used to improve API design, optimize performance, and troubleshoot issues. Additionally, time series can be used to track customer satisfaction or monitor SLAs (service-level agreements).How To Create a Time Series ChartCreating a Time Series chart with Moesif is extremely simple. The first step is to navigate to the Time Series screen which can be done by clicking on the New button and selecting Time Series under the Metrics option.Once you’re on the Time Series screen, you can build filters based on the data you’d like to track within your Time Series chart. Moesif allows users to easily build filters by parsing through your data to make this more efficient.For example, you may want to display the Time Series data to show trends in 404 - Not Found errors that users are experiencing. To do this, you would add a filter for response.Status Code = 404 Not Found. Below is how this would look when input into Moesif.If you want to be even more specific in your event criterion, you also have the option to filter on multiple events by selecting the + Where button, and adding additional criteria.The results will appear as shown below using the bar chart style. Moesif is able to accommodate almost infinitely complex filtering since there is no limit to the amount of additional where clauses you may add.  If you filter events on your Live API Log, the same filters will work for filtering time series charts. For more specifics, check out our docs.How Time Intervals Work with a Time Series ChartMoesif will align the time intervals by matching the bottom and top of the interval. You can change your global time zone by going to Apps and Team settings. By default, the browser’s own time is used as the timezone, but you can make any change to this as you want. Moesif always aligns to a calendar interval, so the very last one may not be completely reported. For example, if the current time is 5:55pm and you selected an interval of “Daily,” then the last interval would only include data from 12:00 AM to 5:55pm. This incomplete interval will usually be shown as a dashed-line on the Time Series, showing that interval is not completely reported.Adding a Group By for Deeper InsightsMoesif Time Series charts support Group By functionalities as well. Using Group By allows you to group data by a specific criteria. This could be something along the lines of grouping data by the Response.StatusCode, Company Domain, UserId, or even something like Initial UTM Source. The chart below is showing a Time Series that is grouping by the Response.StatusCode for each API call to the /login endpoint. This could help users to see trends in specific responses and aid teams in understanding how to potentially solve problems if the trends are negative, such as a high occurrence of error status codes.If there are too many lines on the chart and it is getting crowded, you can select the name of one of the time series’ grouped metrics in the legend below the chart to show or hide an entire line. This can be extremely useful when there are large amounts of data an lines plotted in the Time Series chart.How To Use API Metrics Within a Time Series ChartYou can plot additional metrics in addition to event count. You can choose from predefined metrics or create custom metrics for your own data needs. There are various options available:  Event Accumulation  Unique Users  Unique Companies  Unique Sessions/API Keys  Avg Latency  Max Latency  P90 Latency  Req Body Count  Res Body Count  Sum Req Content-Length  Custom MetricFields that are numeric or have dates will show the min, max, and average of data fields and the distinct values. Fields that are string types will only show the distinct values.For example, adding a custom metric allows you to use more than one metric on your chart. This can be done using the custom metric option in the Metrics dropdown. Additionally, you can add more than one metric on your chart by clicking the button + Add Metric again.How To Change Chart StyleTime Series charts can be rendered using three different styles, Bar, Line or Table.Click the top left of a Time Series to get the bar, line, or table view.You can also view your data using the following options of Log Scale, Percent Breakdown, Growth Rate, and Add Marker.Percent Breakdown plots a calculated value of each group over the sum of all groups for each interval. Growth Rate plots a calculated value comparing previous intervals, starting on the second interval. Add Marker lets you interact with the graph by adding a line.Saving Your ChartSaving to CSVIf you simply want to extract the plotted data, Moesif gives the option to download the current events via a CSV file.  If you are downloading all terms, we recommend using the Bulk Export feature to get this data.Saving work as a public link or embedded templateMoesif allows you to save your work with your peers multiple ways.  Private  Team  Public Link  Embed TemplateTo create one, click on the share button at the top of the Time Series dashboard.To create a private workspace, in the modal will appear make sure Private is selected. You can then name your new workspace, and select the dashboard you would like to add it to. Lastly, click on the Save button on the bottom left of the modal to save the workspace to the selected dashboard.To create a workspace to share with your team, the steps are very similar to creating a Private dashboard. Make sure Team is selected, name your workspace, and add it to the desired dashboard. Make sure to click Save to save your workspace to the dashboard.Public LinkA Public Link can be used to securely share metrics. A Public Link allows you to embed charts in an iFrame. To create one, click on the Share button at the top of the Time Series Log screen.Click on Get Share Link to generate the link to be embedded where you need it.Embedded TemplateWith Embedded Templates, you can embed dynamic charts into customizable layouts. And if it’s a more complicated data visualization, Dynamic Fields will let you restrict the data only to those you authorize.To create a template and embed it in an app, click on the Embed button at the top of the Time Series screen, then select Create Template.Headers and bodies are private by default, but if you need to filter based on those fields, you can change the settings. It’s important to note that all customers that use the template will see the same filter applied to the data.Access via APIWith Embedded Templates, you can customize the app for your customers. For example, you can create a button to generate a chart with their data by entering in the dynamic fields.You can get the following data, with a subscription to the Management API, by generating a Management API Key with the read:events scope. Replace YOUR_MANAGEMENT_API_KEY in the below command and execute.  Accessing the data via API can allow for more complex customization. If complex customization is not what you’re after, the simplest route will be to leverage Embedded Templates instead.Creating AlertsAlert rules are an important feature for users that want to monitor and proactively take steps to keep customers happy. For instance, you may want to be alerted when a large number of customers have their traffic decrease or latency increase. You can use alert rules to monitor the API’s you have created. Docs for Alerting hereTo navigate to Alerts to the top right of your Time Series Screen.The filters that you have applied will pop up in the alerts tab to the right of the screen.Moesif gives you the option of creating either a Dynamic Alert or a Static Alert. Dynamic alerts look for spikes and trends in regards to API usage, rather than looking for predetermined thresholds.Static Alerts allow you to receive notifications when an endpoint reaches 1,000 calls per hour. When this happens, a notification will be sent to a predetermined channel.Set Up Your New Channel ModalIf Moesif detects a potential problem, it will notify you by email for example, or on any application that you have configured. If you would like to configure a new destination for alerts to go to, you will do this by adding another channel. To add a new channel, click on New Channel on the alert pane.Then fill out the fields and select which channel you would like to send the alerts to. Lastly, click Save to save the details and create the channel.The newly created channel can then be added as a channel for your new nd existing alerts.Try It Out!To try out Time Series functionality for yourself, Log in or sign up today to get started with Moesif!                API Usage with Time Series              14 day free trial. No credit card required.              Learn More        ",
          "url": " /api-product-management/api-analytics/Using-Time-Series-Charts-to-Explore-API-Usage/",
          "author": "Preet",
          "categories": "api-product-management, api-analytics"
        }
      
    ,
  
    
        "api-monetization-azure-end-to-end-api-monetization-with-azure-apim-stripe-and-moesif": {
          "title": "End-to-End API Monetization with Azure APIM, Stripe, and Moesif",
          "content"	 : "Many API developers and companies struggle to find ways to set up systems to monetize their API product easily. Some are simple but not customizable and some are complex and require massive engineering effort to actually get it all running. Some may fit your needs but not your payment provider.To make things easier, Moesif created the Billing Meters feature which gives massive customizability but with a minimal amount of code and engineering effort to collect payment and monetize APIs.For this example, which could actually be used out of the box, we will use Moesif, Azure API Management, and Stripe to charge users for API usage. To complete this Stripe Azure monetization example,  there are a few detail assumptions:  You have a running instance of Azure API Management service (with an endpoint/API created)  Your Azure API Management instance is enforcing Subscription Required on your endpoints and header name configured to api-key          This can be configured by going to your API in Azure APIM, clicking the Settings tab, and configuring the values under Subscription        You have an active Stripe account with access to Stripe docs  You have an active Moesif account  You have installed and configured the Moesif plugin in for Microsoft Azure API ManagementThe setup is pretty simple from the outside. We will create a /register endpoint which:  Registers a user in Stripe  Subscribes that user to a product  Creates a user in Azure Api Management  Registers the User and Company in Moesif  Creates a subscription in Azure API Management for the user and generates an API keyI’ve also created a little frontend for it that is a simple form that registers a user by calling the /register endpoint and then displays the generated API key for the newly registered user.                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free        1 - Create Your Product and Price in StripeThe first step we will take is to create a product and price in Stripe. It’s best to do this step first because then when you integrate Stripe into Moesif you’ll already have some pricing plans for Moesif to pull in. A pricing plan can then be associated with specific billing criteria set up within a Billing Meter in Moesif.First make sure to meet these prerequisites.To create a product and price, log into Stripe and proceed to the Products page in the Stripe UI. Once there, click on the + Add Product button in the top right corner.You’ll then be able to add in the details for your product and price(s) for it. The form for your product will have a few fields to fill out.Product InformationName  This is the name of your product. In the example below, we use the name “My API”.Description  This field is optional but you could put a brief description of the product here. In the example below, we use a description of “This is a monetized API”.Image  Optionally upload an image that can help you easily recognize a item on the Products page. We’ll be using the default placeholder image in this example.Pricing InformationYou can choose between Recurring and One-off pricing for your product.Recurring PricinngIn recurring pricing, your customers pay an ongoing fee according to the pricing model you define. After selecting Recurring, you can enter the amount you want to charge and the billing period.To further configure your recurring pricing, select More pricing options. This allows you to specify the pricing model, amount, billing period, price description, and more.The following pricing models are available in Stripe for recurring pricing:  Flat rate  A fixed price for a single unit or package.  Package pricing  Charge for API usage by the package, or a group of units. For example, you could set it up to charge $10 for every 1000 API calls. Every time the user goes over the 1000 API call threshold, they are charged another $10.  Tiered pricing  Offer different price points for based on unit quantity.  Usage-based  Pay-as-you-go billing based on metered usage. You can charge per package, per unit , or per tier and define the prices and units accordingly. You can also set up a billing meter for the price to meter usage. See Creating a Product and Price in Stripe for instructions on how to set up a usage-based scheme.One-Off PricingIn one-off pricing, you charge a one-time fee rather than recurring amount in each billing period. After selecting One-off, you can enter the amount you want to charge in the Amount field.Similar to recurring pricing, you can select More pricing options and configure your pricing further by specifying the pricing model, amount, price description, and more.The following pricing models are available for one-off pricing:  Package pricing  Charge for API usage by the package, or a group of units. For example, you could set it up to charge $10 for every 1000 API calls. Every time the user goes over the 1000 API call threshold, they are charged another $10.  Flat rate  A fixed price for a single unit or package.  Customer chooses price  The customer sets a custom price. You can set a limit and define a preset amount to suggest to the customer.Billing periodThe billing period can be set as the following for recurring pricing models:  Daily  Weekly  Monthly  Every 3 months  Every 6 months  Yearly  CustomFor your configuration with Moesif, we recommend setting the billing period as Monthly.Price descriptionThis is an optional field but recommended. Here you can put a brief description of your price. This will allow you to more easily decipher which price you are selecting in the billing meter in Moesif, especially if you have multiple prices for a single product.Once you’ve input all of the details for your price, you can select Next and then select Add productAs you create products, you will be able to view and edit them on the Product Catalog screen.2 - Enable the Moesif-Stripe IntegrationOnce your products and prices are created, it’s time to begin to integrate Stripe with Moesif. To begin configuring Stripe in Moesif, go to the Billing Meters page and click the Edit Billing Provider dropdown in the top right corner of the screen.This will bring up the Stripe configuration screen walking you through the integration. From this screen, you can get all of the info needed to plug Stripe into Moesif. Each step for configuration is covered within the modal.Add the Moesif Webhook to StripeThe first step in the integration is to add the Moesif webhook into the configuration in Stripe. Adding this allows Stripe to send subscription updates to Moesif.To add the Moesif webhook to Stripe, from the upper right-hand side click on Developers, and then Webhooks in the left-side menu. This will bring you to the Webhooks page where you can view existing webhooks and add new ones. To add a new webhook we will click the Add an endpoint button at the bottom of the screen.From here, we will plug in our Moesif API endpoint URL and configure the events to listen to. You’ll want to copy your Moesif Webhook URL into the Endpoint URL field and then click the + Select Events button.  These details can all be found on the Stripe configuration page in Moesif mentioned in the previous section.You should select the option under Customer for Select all Customer events. After this, click the Add events button at the bottom of the screen.After this, you’ll be returned back to the original screen where you added the endpoint details. Scroll to the bottom of the screen and click Add endpoint to save the endpoint to Stripe.Plug the Stripe API Details into MoesifFor Moesif to add usage quantities to subscriptions in Stripe, we need to add the Stripe API details into Moesif. This is done in the Stripe configuration screen in Moesif, the same screen we’ve been working with previously.Currently, Moesif only supports version 2020-08-27 of the Stripe API so that field defaults for the Stripe API Version field.For the Stripe API Key field, you’ll need to retrieve the API key from Stripe to plug it in. From the Developers screen, the same one we used in the previous step, you’ll click on API Keys. You’ll then be able to see the private key for your API in either the Secret key or a generated Restricted keys field on the screen. Either key can be used.After copying the key from Stripe, you’ll paste this key into the Stripe API Key field back in Moesif. After doing this, back in Moesif you can scroll down to the bottom of the screen and click Save to save the configuration.Optionally, you have the ability to customize the company_id in Moesif as well. The default should work fine for most purposes. However, you can fully customize it to specify how to map Stripe Subscription and Customer objects to Subscription ID and Company ID in Moesif respectively:  A Stripe Customer maps to Moesif Company.  A Stripe Subscription maps to a Moesif Subscription.3 - Create a Billing MeterOnce you have the Stripe integration active in Moesif, you can begin to set up your billing meter. Billing meters created in Moesif do two things: track usage based on specific criteria and report that usage to the billing provider. Moesif allows you to set up very simple and very complex billing meters with relative ease.To create the Billing Meter, in Moesif you will navigate to the Billing Meter screen. You can do this from the left-side menu. On the Billing Meter’s screen, you’ll then click + Add Billing Meter in the top-right corner of the screen.The next screen is where you can actually input the criteria for your Billing Meter.Fields on this screen include:      Billing Meter Name          This is the Moesif internal name of your new Billing Meter            Billing Provider          In this dropdown you can choose the billing provider you want to send your usage metrics to.            Product (Stripe only)          Here you can choose which Product that you’ve set up in Stripe you want your usage metrics to be tied to.            Price (Stripe only)          The last field in the Billing Provider settings for the Billing Meter, here you will choose which Price you want to tie your usage metrics to.            Filters          Under the Filters configuration, you will configure your billing criteria to only include requests that fit a certain criteria.            Metrics          Here you can choose which metric you would like to bill on. Available options include:            Event Count          This will increment usage for every event that fits the criteria outlined in the Filter criteria.            Unique Users          This will increment usage whenever a unique user sends a request that fits the Filter criteria. For every unique user, the count will be incremented by 1 regardless of the event count for that user.            Unique Companies          This will increment usage whenever a unique company sends a request that fits the Filter criteria. For every unique company, the count will be incremented by 1 regardless of the event count for that company.            Unique Sessions/API Keys          This will increment usage whenever a unique session or API key is used to send a request that fits the Filter criteria. For every unique session or API key, the count will be incremented by 1 regardless of the event count for that particular session or API key.        There are other options under Metrics as well but the above 4 tend to be the most applicable to usage-based billing.As an example, for this guide we will create a Billing Meter that will filter traffic for a single endpoint, named /test-service, and where requests received a successful HTTP 200 response. We will use the Event Count metric to make sure that every request is added to the tally and sent to the billing provider.In Moesif, the billing meter will be configured as shown below.We will then click Create. This will create and activate the Billing Meter. A modal will appear notifying you that the billing meter has been created and presents a walk-through to ensure the meter is correctly configured.First, we will set up a flow to get users registered, subscribed, and create a JWT so they can use our monetized API. Once that is complete we will come back and proceed with the walk-through.4 - Create the /register endpointInstead of using a pre-built onboarding flow, such as through a Developer Portal within an API Instead of using a pre-built onboarding flow, such as through a Developer Portal within an API gateway, we will build our own. We will create an endpoint called /register which we can then use to onboard our users who want to use the API. The result will be that the user receives an API key that they can use that will track their usage.Since we are using Moesif, Stripe, and Azure APIM as part of our overall solution, we need to make sure each of the components is working together properly.Here’s what the endpoint will do:  Create a user in Stripe  Subscribe the new user to the API subscription in Stripe  Create the user in Azure APIM (with the Stripe Customer ID in the user’s Name field)  Create the CompanyID in Moesif (which will be the Stripe subscription ID)  Create the UserID in Moesif (which will be the Stripe Customer ID)  Create an API key  If you already have User and Company identifiers in Moesif and other systems that you want to use, instead of using Stripe’s customer and subscription as your IDs, you can do that in Moesif under the Stripe configuration settings.In this example, I will create a simple NodeJS API with Express to do the above.Create the npm projectFirst, we will create a folder called moesif-monetization where we will add our API code. We will then run npm init to turn moesif-monetization so we can use npm in our project. For that, you’ll run the following command in the moesif-monetization directory.$ npm init  You can fill out the details or use the defaults as needed when creating the npm project.You should then see a package.json file in your moesif-monetization folder.Now, open this directory in your favorite IDE or text editor. I will be using VS Code for the remainder of this tutorial for all the coding.Add in the project dependenciesWe will now edit our package.json file with our correct dependencies. In the package.json we will add the following entries under the dependencies object."dependencies": {  "@stripe/stripe-js": "^1.29.0",  "body-parser": "^1.20.0",  "dotenv": "^16.0.0",  "express": "^4.17.1",  "http": "0.0.1-security",  "moesif-nodejs": "^3.5.8",  "node-fetch": "^2.6.5",  "path": "^0.12.7",  "stripe": "^8.219.0"}Save the file, then navigate to the terminal and run:$ npm installNow, your dependencies will be brought into the project and added to the node_modules folder. These dependencies will help us to make calls to REST endpoints, connect to Stripe and Moesif, and various other capabilities we will build into our app.Create the .env fileInstead of hardcoding the Stripe keys and other static values into our app, we will abstract them into a .env file. We can do this using the dependency we added in our package.json called dotenv.In the root directory, create a file named .env. Within this file, we will add a few entries that will contain the keys and values used in our code.STRIPE_KEY="sk_test_XXX"STRIPE_PRICE_KEY="price_XXX"MOESIF_APPLICATION_ID="YOUR_MOESIF_APP__ID"AZURE_URL="https://management.azure.com/subscriptions"AZURE_MANAGEMENT_API_TOKEN="Bearer my_token"AZURE_RESOURCE_GROUP_NAME="MY_RGN"AZURE_SERVICE_NAME="MY_SN"AZURE_SUBSCRIPTION_ID="MY_SUB_ID"AZURE_API_VERSION="2021-08-01"The values that are here can be found in the following places:Obtaining Your Stripe API and Price KeyYour Stripe API Key can be found in the same place we grabbed the key for our Stripe and Moesif integration we did earlier for the Billing Meter. You can actually use the same key for both or create a restricted key with just the scope needed for each function.While we’re at it, lets grab our Stripe product’s price key. We will need it in the next section. Your Stripe price key is an identifier for the price you created earlier in Stripe. This can be found by going to the product in Stripe and grabbing the value from the API ID column.Obtaining Your Moesif Application IDYour Moesif application ID is found in Moesif by going to the menu link in the bottom-left of the screen (which will show your name) and selecting API Keys.The key will then be on the page that appears under Collector Application Id.Your AZURE_URLThis will be the Azure APIM Management API URL. Likely, this value will be “https://management.azure.com/subscriptions”Your AZURE_MANAGEMENT_API_TOKENThis will be a bearer token which will authenticate your Azure APIM Management API calls. Long term, you’ll likely want to create a function which will automatically renew this bearer token based on the expiry of the current token being used. More details on that can be found hereObtaining Your AZURE_RESOURCE_GROUP_NAMEThis is the resource group name that your service is deployed under. This can be found in the Azure APIM Dashboard on the Overview page for your gateway service, under the Essentials tab.Obtaining Your AZURE_SERVICE_NAMEThis is the actual name of your gateway service. This can also be found in the Azure APIM Dashboard on the Overview page for your gateway service, in the top left of the screen.Obtaining Your AZURE_SUBSCRIPTION_IDThis is your Azure subscription ID. This can also be found in the Azure APIM Dashboard on the Overview page for your gateway service, under Essentials.Your AZURE_API_VERSIONThis is the Management API version that you will be using. For this example, we are using 2021-08-01Once you’ve populated the file with the nine key-value pairs, save the file. We won’t need to touch this file again for the remainder of the tutorial.Create the app.js fileIn the root directory of our app, we will create an app.js file (if not already created). In this file we will add the following code that adds our dependencies, defines our Azure APIM Management API URL, and creates a base REST endpoint for /register.const express = require('express')const path = require("path");require('dotenv').config()var bodyParser = require('body-parser')const moesif = require('moesif-nodejs');const Stripe = require('stripe');// npm i --save node-fetch@2.6.5const fetch = require('node-fetch');const app = express();app.use(express.static(path.join(__dirname)));const port = 5000;const stripe = Stripe(process.env.STRIPE_KEY);var jsonParser = bodyParser.json();const AZURE_MANAGEMENT_API_ROUTE = `${process.env.AZURE_URL}/${process.env.AZURE_SUBSCRIPTION_ID}/resourceGroups/${process.env.AZURE_RESOURCE_GROUP_NAME}/providers/Microsoft.ApiManagement/service/${process.env.AZURE_SERVICE_NAME}`;const moesifMiddleware = moesif({ applicationId: process.env.MOESIF_APPLICATION_ID});app.use(moesifMiddleware);app.post('/register', jsonParser, async (req, res) => { })app.listen(port, () => { console.log(`Example app listening at http://localhost:${port}`);})In the above code we are:  Importing a few dependencies  Configuring the Stripe dependency  Configuring the Moesif middleware  Create the /register endpoint  Set our app to run on port 5000 and start our Node app  You will notice that we have process.env.STRIPE_KEY and process.env.MOESIF_APPLICATION_ID. These values will come from the .env file that we created in the last step.Implement the /register endpointOur next step is to implement the /register endpoint. This endpoint will essentially create our binding between Azure APIM, Stripe, and Moesif. The outcome will be a generated API Key from Azure APIM which will associate usage with a user in Moesif, which will then be reported to Stripe.  You will notice that we are not using Azure APIM’s JavaScript client but instead are using pure HTTP calls. This is to allow portability if you are using something other than JavaScript to build your /register endpoint.Our first step in the flow is to create the customer in Stripe. We will use our Stripe JS dependency to do just that. We will use the parameters from the request body (email, first name, last name) to create the customer in Stripe using the stripe.customers.create function. We will then store the created customer in a custom variable so we can access the customer ID generate in Stripe.const customer = await stripe.customers.create({  email: req.body.email,  name: `${req.body.firstname} ${req.body.lastname}`,  description: 'Customer created through /register endpoint',});Next, we will subscribe this new user to our API subscription we created in Stripe earlier. We will use the stripe.subscriptions.create function and use the generated customer ID from the previous function call to subscribe them. This will return back a subscription object containing an ID we will use later.const subscription = await stripe.subscriptions.create({  customer: customer.id,  items: [    { price: process.env.STRIPE_PRICE_KEY },  ],});Then, we will create the user in Azure APIM. This will allow us to later generate a Subscription/API key for this user. We will send a PUT request to Azure APIM’s /users endpoint through the Azure APIM Management APIs. This will generate a user with an ID that is equal to their Stripe Customer ID (created above) and populate their user details for their email, first name, and last name.var body = {  properties: {    firstName: req.body.firstname,    lastName: req.body.lastname,    email: req.body.email,  }};var response = await fetch(`${AZURE_MANAGEMENT_API_ROUTE}/users/${customer.id}?api-version=${process.env.AZURE_API_VERSION}`, {  method: 'put',  body: JSON.stringify(body),  headers: {    'Content-Type': 'application/json',    'Authorization': process.env.AZURE_MANAGEMENT_API_TOKEN  }});var data = await response.json();Once the user is created in Azure APIM, we will now use the Moesif middleware to create the user and add their relevant details into Moesif. First we will call the Moesif middleware’s updateCompany function to map the Stripe customer.id to the companyId in Moesif.  var company = { companyId: customer.id };  moesifMiddleware.updateCompany(company);We will then do a similar step with the updateUser function and use it to map the Stripe customer.id to the userId and companyId and some other metadata we collected on the user into Moesif. This will link the user to the company as well.  var user = {     userId: customer.id,     companyId: customer.id,     metadata: {       email: req.body.email,       firstName: req.body.firstname,       lastName: req.body.lastname,     }   };   moesifMiddleware.updateUser(user);At this point, we now have all of our user details plugged into the necessary platforms. Our next step is to generate a Subscription/API key for this user in Azure APIM. For that, we will call the Azure APIM Management API again using the /subscriptions/{subscriptionId} endpoint. This will create a new subscription, with an ID that matches our Stripe Subscription ID generated above, and return a Subscription object that contains an API key to pass back to the user being registered. We access that API key through data.properties.primaryKey.body = {  properties: {    displayName: subscription.id,    ownerId: `/subscriptions/${process.env.AZURE_SUBSCRIPTION_ID}/resourceGroups/${process.env.AZURE_RESOURCE_GROUP_NAME}/providers/Microsoft.ApiManagement/service/${process.env.AZURE_SERVICE_NAME}/users/${customer.id}`,    scope: `/subscriptions/${process.env.AZURE_SUBSCRIPTION_ID}/resourceGroups/${process.env.AZURE_RESOURCE_GROUP_NAME}/providers/Microsoft.ApiManagement/service/${process.env.AZURE_SERVICE_NAME}/apis`,    state: "active"  }};var response = await fetch(`${AZURE_MANAGEMENT_API_ROUTE}/subscriptions/${subscription.id}?api-version=${process.env.AZURE_API_VERSION}`, {  method: 'put',  body: JSON.stringify(body),  headers: {    'Content-Type': 'application/json',    'Authorization': process.env.AZURE_MANAGEMENT_API_TOKEN  }});var data = await response.json();var apiKey = data.properties.primaryKey;Optionally, we will add the users API key to our metadata in Moesif as well. For testing purposes, this makes it easy to get the key if you’ve lost track of it and don’t want to go back into Azure APIM to retrieve it. We will use the updateUser function again in the Moesif middleware to do this, supplying the generated API key in the metadata object.var user = {  userId: customer.id,  metadata: {    apiKey  }};moesifMiddleware.updateUser(user);Lastly, we will return a 200 OK response back to the caller with the API key in the response body.res.status(200)res.send({ apiKey });The completed function, end-to-end, will look like this:app.post('/register', jsonParser, async (req, res) => {    // create Stripe customer    const customer = await stripe.customers.create({      email: req.body.email,      name: `${req.body.firstname} ${req.body.lastname}`,      description: 'Customer created through /register endpoint',    });    // create Stripe subscription    const subscription = await stripe.subscriptions.create({      customer: customer.id,      items: [        { price: process.env.STRIPE_PRICE_KEY },      ],    });    //create Azure APIM user    var body = {      properties: {        firstName: req.body.firstname,        lastName: req.body.lastname,        email: req.body.email,      }    };    var response = await fetch(`${AZURE_MANAGEMENT_API_ROUTE}/users/${customer.id}?api-version=${process.env.AZURE_API_VERSION}`, {      method: 'put',      body: JSON.stringify(body),      headers: {        'Content-Type': 'application/json',        'Authorization': process.env.AZURE_MANAGEMENT_API_TOKEN      }    });    var data = await response.json();    // create user, company, and subscription in Moesif    var company = { companyId: customer.id };    moesifMiddleware.updateCompany(company);    var user = {      userId: customer.id,      companyId: customer.id,      metadata: {        email: req.body.email,        firstName: req.body.firstname,        lastName: req.body.lastname,      }    };    moesifMiddleware.updateUser(user);    // Create subscription and API Key in Azure APIM    body = {      properties: {        displayName: subscription.id,        ownerId: `/subscriptions/${process.env.AZURE_SUBSCRIPTION_ID}/resourceGroups/${process.env.AZURE_RESOURCE_GROUP_NAME}/providers/Microsoft.ApiManagement/service/${process.env.AZURE_SERVICE_NAME}/users/${customer.id}`,        scope: `/subscriptions/${process.env.AZURE_SUBSCRIPTION_ID}/resourceGroups/${process.env.AZURE_RESOURCE_GROUP_NAME}/providers/Microsoft.ApiManagement/service/${process.env.AZURE_SERVICE_NAME}/apis`,        state: "active"      }    };    var response = await fetch(`${AZURE_MANAGEMENT_API_ROUTE}/subscriptions/${subscription.id}?api-version=${process.env.AZURE_API_VERSION}`, {      method: 'put',      body: JSON.stringify(body),      headers: {        'Content-Type': 'application/json',        'Authorization': process.env.AZURE_MANAGEMENT_API_TOKEN      }    });    var data = await response.json();    var apiKey = data.properties.primaryKey;    // update Moesif user with API key    var user = {      userId: customer.id,      metadata: {        apiKey      }    };    moesifMiddleware.updateUser(user);    // return API key to caller    res.status(200)    res.send({ apiKey }); })With that, we can now actually try out our endpoint to make sure that each piece is working as expected. The outcome should be registered API consumers, each with an individual API key, which will record and report usage data to Stripe. Let’s move onto testing it.5 - Send a test request to the /register endpointOnce your /register endpoint has been coded and deployed, it’s time to test it. For right now we will simply use Postman to send a request. Our request will contain a JSON request body that will contain:  First name  Last name  EmailOf course, this is the minimal amount of information we would want to configure our system and profiles in Azure APIM, Stripe, and Moesif correctly.You can easily add more fields as needed for your specific use case.  Note: for the /register call, we will just call it through localhost. Optimally, you may proxy this through Azure APIM and access it through there. For brevity, we will just be using the endpoint locally. The actual functionality would not change if you added it as an endpoint in Azure APIM, but the URL would be different.In Postman, we will create our request with the following information:  Request Type: POST  Endpoint URL: http://localhost:5000/register  Request Body:{  "firstname": "Userfirstname",  "lastname": "Userlastname",  "email": "test@test.com"}Once everything is plugged into Postman, it should look like the following:Once the request is sent, the response should contain an API key that the newly registered user can use.We will now check Azure APIM and Stripe to ensure that the information we registered with is correctly entered into each of the destination systems. The first one we will check is Stripe.Logging back into Stripe, you’ll navigate to Customers screen. You should see your newly created user in the list.Click on the newly added customer in the list. On the next screen, you should see that the customer is also subscribed to your APIs subscription.Once these two entries are confirmed in Stripe, you can move over to Azure APIM to also make sure that the user was correctly set up there too.Using the Azure APIM UI, navigate to the Users screen. Here you should see an entry where the Name, Full name, and Email fields are set to the supplied parameters and the Name field should be match the Stripe Customer ID.Clicking on the entry will bring you to the User Details screen. Once here, clicking on the Subscription menu item will show you the generated API keys for this user and you’ll notice that the Stripe Subscription ID is listed as the Subscription ID in Azure APIM as well. The Primary Key/API key shown should match the one returned in Postman.With these checks completed, we can safely assume that our /register endpoint is correctly setting up our users accounts and subscriptions in Azure APIM and Stripe.6 - Call your API using the generated API keyOur next step is to actually use our generated API key. We will then confirm that all the correct information is added into Moesif. The data we are confirming includes:  The Stripe Customer ID is mapped to the Moesif User ID  The Stripe Subscription ID is mapped to the Moesif Company ID  Moesif contains the Stripe metadata in the users profileUse Postman to send the requestNext, let’s use Postman, or another platform, to send a request to the /test-service endpoint. This is the endpoint that we set up the billing meter for in Step 3, above.In Postman, we will:  Put the /test-service API endpoint as the request URL  Add a Request header for Authorization and add your API key into the value fieldBelow is an example of the populated request configuration in Postman.To send the request to our endpoint, click Send.Once sent, your request should be proxied through Azure APIM and the API call analytics should land in Moesif.Confirm that Moesif received the request infoBack in Moesif, you’ll navigate to the Events screen where you should see the request you just sent. You should see the entry has both a User ID and Company ID populated with the Stripe user and subscription ID’s. The entries should look like this:  The customer ID will look like “cus_XXXX” and the subscription ID will look like “sub_XXXX”.If you click on the User ID shown in the entries on the Live Event Log screen, you will come to the users profile page. On this page, we will confirm that the Stripe metadata is present. We will need to add a new column to our profile to display the Stripe data. To do this, from the profile page, click on the … More Actions button and click Customize Profiles’ Layout.We will then add a new column for the Stripe metadata. You will click the + button on the far right of the screen to create a new column where we will add the Stripe metadata.  You may need to scroll to the right to see it depending on your resolution and screen size to see the + button.You will then drill down to Metadata > stripe > customer > created and use this field in the new row. I’ve also changed the column image to one more fitting. You can customize this by clicking on the image and selecting whichever one fits best.You can also add other fields, but for right now just this single field is enough to tell us that Moesif is correctly receiving data from Stripe.  If you don’t see the Stripe metadata entry as an available field, wait a few minutes. If after a few minutes the Stripe metadata isnt present, ensure that your Stripe checkout configuration is correct in Moesif. After confirming or editing it, try creating a new user and sending a request again to confirm that the integration is working.At this point, we now have confirmed that our API call, proxied through Azure APIM, is working and is stamped with the correct user and company details in Moesif. We also confirmed that Stripe is sending data back to Moesif which is correctly being mapped to the corresponding user profile, confirmed through the Stripe metadata in Moesif.7 - Create the frontendNext, we want to add a simple little frontend so we don’t need to call for our API key through Postman. We will make a quick little registration form that will then return an API key for our newly registered user to use.Add your frontend files to the appIn the root directory of the application, we will add two files. We will add both an index.html and an index.js.Add in your route to serve the static html filesIn the app.js file, we will add in a route to serve the static HTML files. Underneath our code for the /register endpoint, we will add another endpoint. Add the following code:pp.get("/", function (_req, res) { res.sendFile(path.join(__dirname, "index.html")); res.sendFile(path.join(__dirname, "index.js"));});This code will now load the website (once we have the code plugged in) when you navigate to http://localhost:5000/ .Code the frontend form and logicFinally, let’s add the code for our frontend HTML and JavaScript functionality. In the index.html file, we will add markup that looks like this:<!DOCTYPE html><html lang="en"> <head>   <meta charset="utf-8" />   <meta name="viewport" content="width=device-width, initial-scale=1" />   <meta name="theme-color" content="#000000" />   <meta     name="description"     content="Moesif Monetization example"   />   <title>None React App Example</title> </head> <body>   <noscript>You need to enable JavaScript to run this app.</noscript>   <h1>     Moesif Monetization Example   </h1>   <div id="form-input">     email: <input id="email-input" placeholder="email" />     first name: <input id="firstname-input" placeholder="first name" />     last name: <input id="lastname-input" placeholder="last name" />     <button onClick="submitRegistration()">Register</button>   </div>   <div id="apikey-output"></div>   <p id="error-message"></p>   <script src="index.js"></script> </body></html>This markup will display a form which allows users to input an email, first name, and last name. It also has a Register button that will call the JavaScript function for submitRegistration(). That function will be in our index.js JavaScript file.The index.js file will look like this:async function submitRegistration() {  const email = document.getElementById("email-input").value;  const firstname = document.getElementById("firstname-input").value;  const lastname = document.getElementById("lastname-input").value;  const errorElement = document.getElementById("error-message");  const apikey = document.getElementById("apikey-output");  var body = { email, firstname, lastname };  console.log(body);  var response = await fetch('http://localhost:5000/register', {    method: 'post',    body: JSON.stringify(body),    headers: {'Content-Type': 'application/json'}  });  var data = await response.json();  console.log(data);  apikey.innerHTML = data.apikey;}This function will take the input from the form, post it to our /register endpoint, and display the returned API key on the screen.8 - Test the frontendTo test the frontend, save your code changes and restart the server. Then, in a browser, navigate to http://localhost:5000/. You will then see the form show up.Fill out the form fields and submitNow that the form is loaded, fill in the fields and click the Register button. This will take the info, post it to our /register endpoint, and give us the generated API key.  It is suggested that you use a different email than you used earlier when you created an API key directly through the /register endpoint.Confirm the API key is returnedOnce the submit button is clicked, after a few seconds, the API key should be returned back to the UI.9 - Send a request to your monetized APIWe will once again want to make sure that everything is working with our UI, through to our backend systems. For this, simply repeat the steps from Step 6 to confirm that the user and company IDs are populated correctly and that the Stripe metadata is returned for this user and the new API key.10 - Confirm all the pieces are working correctlyAlthough this is optional, this step may help with troubleshooting any issues that may have came from our previous steps. Here are a few things to check to make sure that all is working as it should. After creating a new user through the UI and using the generated API key to place a call to your API, confirm the following:  In Stripe          Confirm that a customer has been created in Stripe with the details you entered into the UI      Confirm that the customer has been subscribed to the correct product and price        In Azure APIM          The User has been created      The Users Name field matches the customer ID from Stripe (which begins with cus_XXXX)      The users Subscription is created and the Subscription ID is equal to the Stripe subscription ID        In Moesif          Your API call was recorded in Moesif in the Live Event Log      Your API call has the Stripe Customer ID and Subscription ID in the User and Company fields in Moesif, respectively.      Confirm that the Stripe metadata is populated in Moesif      11 - Check Stripe for usageLastly, After awhile, it’s best to go into Stripe to confirm that usage is being added to a users subscription. Be sure that you’ve sent a few requests through in order to make sure you have some data that should be sent to Stripe.  It may take a few hours for usage to make its way from Moesif to Stripe. If data still isn’t in Moesif after a few hours, you can do a Billing Meter Test from the billing meter screen to ensure that the system is all working as expected, if you haven’t already done so when you first created the Billing Meter.To check the usage, in Stripe you’ll want to navigate to the Customers screen and select the customer that you made the API call with. Once selected, you should see some active subscriptions for the users that you’ve registered through the /register endpoint. The one we created earlier is called My API. Click on the subscription entry.On the next screen, click on View Usage beside the price entry.A modal should now pop up showing you the usage for the API that has been reported to Stripe from Moesif.  Remember, it may take up to an hour before usage is reported to Stripe. If your data isn’t there yet, check back a bit later for the updates.12 - Determining If the Billing Meter is Working CorrectlyTesting the created Billing Meter is easy with out Test Meter function. Navigate to your created Billing Meter from the left side navigation pane and selecting your Stripe Test billing meter. Select Test Meter on the top right.We will first confirm the meter that you are attempting to test. Click the Next button at the bottom of the modal.Moesif will wait for Subscriptions to created within Stripe and those subscriptions to be associated within Moesif itself. This page will update automatically, no need to refresh.Moesif will then wait for an API call to our any endpoint associated with our billing meter using our the JWT that has been created for us.Finally, Moesif will sync all usage data to Stripe every 15 minutes. This step may take a few minutes depending on when the API call was initiated but will update on the given interval.Wrapping upMonetization has always been a tough hurdle to get past. Many custom solutions offered flexibility but at a very high engineering and support cost. With Moesif, monetization  of your API access is possible in an extremely minimal amount of time. As demonstrated in this blog post, with a little bit of configuration and minimal amount of code we can create production-ready, post-paid smart monetization strategies in minimal time.                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue with your Azure APIM managed APIs?            Monetize your Azure APIM managed APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /api-monetization/azure/End-To-End-API-Monetization-With-Azure-APIM-Stripe-And-Moesif/",
          "author": "Matthew",
          "categories": "API-Monetization, Azure"
        }
      
    ,
  
    
        "product-management-api-analytics-how-can-moesif-help-you-understand-api-usage": {
          "title": "How Can Moesif Help You Understand API Usage?",
          "content"	 : "Are you looking to grow your business? Deliver more value to your customers? Well, understanding API usage should be at the top of your priority list, right after the ability to track API usage metrics and API monitoring.By using Moesif for understanding your customer’s API usage, you can unlock many different insights, such as  Your most popular APIs  Which APIs are having the most errors  How individual users and companies are using your APIs  And much more…Understanding these key insights can help make informed business and engineering decisions. With Moesif, there are many different ways you can slice and dice API analytics to reveal multiple angles of API usage. Not only that, but you can also use certain features to take action to improve retention and drive even more usage from users while driving down errors.Let’s take a look at some of the features in Moesif that can help with understanding and improving API usage.Charts  Docs for exploring different Metric reports can be found hereBeing able to visualize API usage is a great way to bring context to the data and make it more digestible. A core focus of Moesif is to provide easily configurable charts which enable users to really drill down into specifics with custom metrics. This is done by applying filters to the data to get the exact output you want. For example, you may want to see a Time Series chart that shows error trends for the last 2 weeks across all your APIs. You may want to dig even further and look at 401 errors that occurred when users from a specific company tried to access a certain endpoint in the last 12 hours. Being able to dial in precisely what you need is what Moesif does best.Although lots of different charting styles are available, let’s review the 2 most popular ones that can help to understand key API metrics and improve API retention.Live Event LogThe Live Event Log is a great way to see and dive into each individual event coming into Moesif. By clicking on each event, you can explore all the details about the request and response. This is much more efficient than looking through plain-text API logs.You can also easily dig into the user and company profile attached to the event if you have User and Company tracking set up and enabled. To show a quick example of how the Live Event Log can be used, let’s filter some demo data to identify unsuccessful API calls that are occurring on the /purchase route.You can see here that we can easily see which calls are erroring out and can even inspect or generate a sample API request to replay the request for troubleshooting or testing purposes.The benefit of using the Live Event Log to understand API usage metrics is that each event can be seen and interacted with. It’s a great way to troubleshoot issues and see trends that need to be explored at the most fine-grained levels.Time SeriesFor looking at trends over time, a Time Series chart is the best way to visualize this. With a Time Series, you will determine which events you want to track and then let Moesif display the trends over a set period of time. An example of this might be that we want to look at the top 5 endpoints where unsuccessful API calls are happening and view that trend over time. Here is an example of what that may look like below.The Time Series chart allows you to visually see trends more easily while the Live Event Log allows you to dig into each individual API call. Both charts can be extremely useful in understanding  your API usage data and exploring each call in detail.Dashboards  Docs for Dashboards can be found hereNow that you’ve found some charts that are helping you look deeply at API metrics within your organization, let’s bring them all into one place. Dashboards within your API monitoring tool allow you to bring all of your favorite charts together in one spot for easy viewing and management. If you have multiple aspects of API product metrics that you’d like to view in a single place, a dashboard is the way to do it.Default dashboardsWhen you first create your Moesif account, your Moesif instance will be populated with a few dashboards out of the box. These dashboards cover the core areas of most businesses and are prepopulated with workspace tiles that may be helpful in each area. The areas include:  Product  Engineering  Security  Customer Success  Sales  MarketingAs an example, in the Product dashboard that comes by default, we can see the included workspace tiles.These can act as a great starting point where you can add more workspace tiles based on the charts and reports you like or you can edit each of the tiles to be more tailored to your specific use case.Custom dashboardsIf the dashboards included by default with Moesif are not exactly what you need or you just feel like creating something from scratch, custom dashboards can also be created. Custom dashboards are a great way to create streamlined ways to access your favorite charts and reports that are more tailored to your exact needs. For specifics on creating Dashboards, including custom ones, check out our docs.User and company tracking  Docs for User and Company tracking features can be found hereA major differentiator between Moesif and other analytics platforms is the ability for Moesif to tie every event to a user and/or a company with real user monitoring. This opens up a lot of deep analytics use cases that are not possible when looking at large data sets which are only looking at events anonymously.Two of the most popular features to use with User and Company tracking are Funnel and Retention analysis. Both can provide answers about how customers are using the product, how effective your onboarding is, current user performance, and if users are leaving your product. Let’s take a closer look at both of these features.FunnelsFunnels allow Moesif users to see how each step in their conversion funnel is working. For most businesses, this can give very important hints about what should be improved and how the latest improvements are improving or hindering the conversion process.A simple example would be for us to look at 3-step funnel that explores the user logins. We will look at a funnel where:  Users log in once  Users log in a second time  Users log in 3 more times after their second loginWhat this will do is allow us to see how our customers are returning to the platform. The funnel would look like this:RetentionAnother crucial part of understanding API performance is knowing how users are being retained. Getting users to begin using your APIs is part of the battle, the bigger value to the company is to keep user traffic coming into the APIs.With Moesif, unlike subscription retention which merely tracks whether a customer has an active subscription, Moesif can be used to look into product retention. This is more accurate since it tracks whether users are coming back and actively using your product. To calculate the retention of your users, Moesif requires an initial event and a returning event. If we had an API that allowed users to checkout with a purchase, we may make both the initial and return event equal to when they call the /purchase API.Essentially, this type of retention analysis would outline that users are making continuous purchases and we consider a user who is making repeat purchases as an active/retained customer. In Moesif, the above weekly retention analysis would look like this.We may go even further and look at the retention of different segments or groups who are using our platform. An example of this might be looking at the difference in retention for Enterprise vs. Non-Enterprise customers. Adding that to our report would look like this:In both Funnels and Retention analysis, you can also dig into specific user segments, such as looking at retention trends by user or company. This can help you to understand the API usage at the individual level and see how various users and companies are using the platform and if they are returning to the platform.Alerts  Docs for Alerting and Monitoring can be found hereWhen certain things are happening to your customers or within your product, it’s important to get a notification. To rely on dashboards and constant manual polling to uncover issues is extremely inefficient and also unreliable. With alerts, you can configure Moesif to alert you through email, SMS, Slack, PagerDuty, or another custom channel to notify you when a specific criterion is met.In terms of understanding API usage, you will likely want to know when APIs are receiving an abnormally high or low amount of traffic. This can be done by Moesif by using two distinctive types of alerts.Static AlertsStatic Alerts allow you to configure an alert to send a notification when a static threshold is hit. An example may be to alert when a specific API endpoint is receiving more than 1,000 calls per hour. Once the endpoint is receiving that amount of traffic, a notification will be sent to whatever channels are configured. In Moesif, this is how that would look:Dynamic AlertsIf you don’t know exactly what static amount you’d like to set as your threshold to alert on, you can use a Dynamic Alert. The setup for a dynamic alert is similar to the static alert setup above except you will specify the type as a Dynamic Alert and then set the sensitivity for factors such as Abrupt Spike Sensitivity, Unusual Change Sensitivity, and Trending Higher Sensitivity.When the Dynamic Alert is enabled, Moesif will look at trends and spikes to alert users about anomalies in their customers’ API usage. This may be a better way to monitor API usage compared to a static threshold.Governance rules  Docs for Governance Rules can be found hereMuch of the time, creating reports and monitoring APIs is essential but being able to use the insights in an automated fashion can be extremely powerful and augment those capabilities. This means that understanding API usage is only part of the battle. Being able to use those insights to govern access to the APIs is the next level of understanding API usage and proactively using the data. With Moesif’s Governance Rules feature, you can do exactly that.Blocking unwanted behaviors and usageThe Governance Rules feature can be used to detect when unwanted behaviors or conditions are occurring and stop users from using the API. There are 2 types of governance rules that can be applied: blocking and non-blocking. Non-blocking rules still allow the request to go through to the upstream API but allow the response body, response status code, and response headers to be overridden. For blocking rules, the request does not get received by the upstream API but is blocked at the plugin/gateway or SDK level and an overridden response will be sent back to the user that called the API.An example of when this might be useful is in the context of API monetization. In Moesif, we could create a Governance Rule that will block users with overdue invoices from accessing the monetized API. The users would also receive a response that tells them that they have an overdue invoice and returns a 429 - Payment Required status code to the caller.  Governance Rules are not supported on every SDK or Plugin. Please check the docs for your plugin to see if it supports Governance Rules.Governance Rules are a great way to leverage metrics around your API usage and automate governance to make your APIs more secure and user-friendly.Try it out!Moesif is full of plenty of features to enhance your API business and understand API and product usage. We’ve covered some key features that can help with understanding API usage above and showed some examples of what each feature looks like in Moesif. The Moesif platform enables your entire organization, from engineers to API product managers, to more deeply understand API usage. Moreover, the platform also allows you to use those insights to take action automatically through Governance Rules and even through other features such as Behavioral Emails or Metered Billing. Log in or sign up today to get started with Moesif!                Analyze API Usage with Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /product-management/api-analytics/How-Can-Moesif-Help-You-Understand-API-Usage/",
          "author": "Matthew",
          "categories": "product-management, API-Analytics"
        }
      
    ,
  
    
        "api-monetization-stripe-end-to-end-api-monetization-with-java-spring-stripe-and-moesif": {
          "title": "End-to-End API Monetization with Java Spring, Stripe, and Moesif",
          "content"	 : "Many API developers and companies struggle to find ways to easily set up systems to monetize their APIs. Some are simple but not customizable, some are complex and require massive engineering effort to actually get it all running.To make things easier, Moesif created a feature a few months ago called Billing Meters which gives massive customizability but with a minimal amount of code and engineering effort.For this example, which could actually be used out of the box, we will use Moesif, Java Spring, and Stripe to charge users for API usage. For this setup there are a few assumptions:  You have Java installed on your machine          We will be using openJDK 17 and Gradle        You have an active Stripe account  You have an active Moesif accountThe setup is pretty simple from the outside. We will create a /register endpoint which:  Registers a user in Stripe  Subscribes that user to a product  Registers the User and Company in Moesif  Create a JWT to authenticate/authorize calls to our monetized endpointI’ve also created a little frontend for it that is a simple form that registers a user and then displays the generated JWT for the newly registered user.                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free        1 - Create Your Product and Price in StripeThe first step we will take is to create a product and price in Stripe. It’s best to do this step first because then when you integrate Stripe into Moesif you’ll already have some pricing plans for Moesif to pull in. A pricing plan can then be associated with specific billing criteria set up within a Billing Meter in Moesif.First make sure to meet these prerequisites.To create a product and price, log into Stripe and proceed to the Products page in the Stripe UI. Once there, click on the + Add Product button in the top right corner.You’ll then be able to add in the details for your product and price(s) for it. The form for your product will have a few fields to fill out.Product InformationName  This is the name of your product. In the example below, we use the name “My API”.Description  This field is optional but you could put a brief description of the product here. In the example below, we use a description of “This is a monetized API”.Image  Optionally upload an image that can help you easily recognize a item on the Products page. We’ll be using the default placeholder image in this example.Pricing InformationYou can choose between Recurring and One-off pricing for your product.Recurring PricinngIn recurring pricing, your customers pay an ongoing fee according to the pricing model you define. After selecting Recurring, you can enter the amount you want to charge and the billing period.To further configure your recurring pricing, select More pricing options. This allows you to specify the pricing model, amount, billing period, price description, and more.The following pricing models are available in Stripe for recurring pricing:  Flat rate  A fixed price for a single unit or package.  Package pricing  Charge for API usage by the package, or a group of units. For example, you could set it up to charge $10 for every 1000 API calls. Every time the user goes over the 1000 API call threshold, they are charged another $10.  Tiered pricing  Offer different price points for based on unit quantity.  Usage-based  Pay-as-you-go billing based on metered usage. You can charge per package, per unit , or per tier and define the prices and units accordingly. You can also set up a billing meter for the price to meter usage. See Creating a Product and Price in Stripe for instructions on how to set up a usage-based scheme.One-Off PricingIn one-off pricing, you charge a one-time fee rather than recurring amount in each billing period. After selecting One-off, you can enter the amount you want to charge in the Amount field.Similar to recurring pricing, you can select More pricing options and configure your pricing further by specifying the pricing model, amount, price description, and more.The following pricing models are available for one-off pricing:  Package pricing  Charge for API usage by the package, or a group of units. For example, you could set it up to charge $10 for every 1000 API calls. Every time the user goes over the 1000 API call threshold, they are charged another $10.  Flat rate  A fixed price for a single unit or package.  Customer chooses price  The customer sets a custom price. You can set a limit and define a preset amount to suggest to the customer.Billing periodThe billing period can be set as the following for recurring pricing models:  Daily  Weekly  Monthly  Every 3 months  Every 6 months  Yearly  CustomFor your configuration with Moesif, we recommend setting the billing period as Monthly.Price descriptionThis is an optional field but recommended. Here you can put a brief description of your price. This will allow you to more easily decipher which price you are selecting in the billing meter in Moesif, especially if you have multiple prices for a single product.Once you’ve input all of the details for your price, you can select Next and then select Add productAs you create products, you will be able to view and edit them on the Product Catalog screen.2 - Enable the Moesif-Stripe IntegrationOnce your products and prices are created, it’s time to begin to integrate Stripe with Moesif. To begin configuring Stripe in Moesif, go to the Billing Meters page and click the Edit Billing Provider dropdown in the top right corner of the screen.This will bring up the Stripe configuration screen walking you through the integration. From this screen, you can get all of the info needed to plug Stripe into Moesif. Each step for configuration is covered within the modal.Add the Moesif Webhook to StripeThe first step in the integration is to add the Moesif webhook into the configuration in Stripe. Adding this allows Stripe to send subscription updates to Moesif.To add the Moesif webhook to Stripe, from the upper right-hand side click on Developers, and then Webhooks in the left-side menu. This will bring you to the Webhooks page where you can view existing webhooks and add new ones. To add a new webhook we will click the Add an endpoint button at the bottom of the screen.From here, we will plug in our Moesif API endpoint URL and configure the events to listen to. You’ll want to copy your Moesif Webhook URL into the Endpoint URL field and then click the + Select Events button.  These details can all be found on the Stripe configuration page in Moesif mentioned in the previous section.You should select the option under Customer for Select all Customer events. After this, click the Add events button at the bottom of the screen.After this, you’ll be returned back to the original screen where you added the endpoint details. Scroll to the bottom of the screen and click Add endpoint to save the endpoint to Stripe.Plug the Stripe API Details into MoesifFor Moesif to add usage quantities to subscriptions in Stripe, we need to add the Stripe API details into Moesif. This is done in the Stripe configuration screen in Moesif, the same screen we’ve been working with previously.Currently, Moesif only supports version 2020-08-27 of the Stripe API so that field defaults for the Stripe API Version field.For the Stripe API Key field, you’ll need to retrieve the API key from Stripe to plug it in. From the Developers screen, the same one we used in the previous step, you’ll click on API Keys. You’ll then be able to see the private key for your API in either the Secret key or a generated Restricted keys field on the screen. Either key can be used.After copying the key from Stripe, you’ll paste this key into the Stripe API Key field back in Moesif. After doing this, back in Moesif you can scroll down to the bottom of the screen and click Save to save the configuration.Optionally, you have the ability to customize the company_id in Moesif as well. The default should work fine for most purposes. However, you can fully customize it to specify how to map Stripe Subscription and Customer objects to Subscription ID and Company ID in Moesif respectively:  A Stripe Customer maps to Moesif Company.  A Stripe Subscription maps to a Moesif Subscription.3 - Create a Billing MeterOnce you have the Stripe integration active in Moesif, you can begin to set up your billing meter. Billing meters created in Moesif do two things: track usage based on specific criteria and report that usage to the billing provider. Moesif allows you to set up very simple and very complex billing meters with relative ease.To create the Billing Meter, in Moesif you will navigate to the Billing Meter screen. You can do this from the left-side menu. On the Billing Meter’s screen, you’ll then click + Add Billing Meter in the top-right corner of the screen.The next screen is where you can actually input the criteria for your Billing Meter.Fields on this screen include:      Billing Meter Name          This is the Moesif internal name of your new Billing Meter            Billing Provider          In this dropdown you can choose the billing provider you want to send your usage metrics to.            Product (Stripe only)          Here you can choose which Product that you’ve set up in Stripe you want your usage metrics to be tied to.            Price (Stripe only)          The last field in the Billing Provider settings for the Billing Meter, here you will choose which Price you want to tie your usage metrics to.            Filters          Under the Filters configuration, you will configure your billing criteria to only include requests that fit a certain criteria.            Metrics          Here you can choose which metric you would like to bill on. Available options include:            Event Count          This will increment usage for every event that fits the criteria outlined in the Filter criteria.            Unique Users          This will increment usage whenever a unique user sends a request that fits the Filter criteria. For every unique user, the count will be incremented by 1 regardless of the event count for that user.            Unique Companies          This will increment usage whenever a unique company sends a request that fits the Filter criteria. For every unique company, the count will be incremented by 1 regardless of the event count for that company.            Unique Sessions/API Keys          This will increment usage whenever a unique session or API key is used to send a request that fits the Filter criteria. For every unique session or API key, the count will be incremented by 1 regardless of the event count for that particular session or API key.        There are other options under Metrics as well but the above 4 tend to be the most applicable to usage-based billing.As an example, for this guide we will create a Billing Meter that will filter traffic for a single endpoint, named /test-service, and where requests received a successful HTTP 200 response. We will use the Event Count metric to make sure that every request is added to the tally and sent to the billing provider.In Moesif, the billing meter will be configured as shown below.We will then click Create. This will create and activate the Billing Meter. A modal will appear notifying you that the billing meter has been created and presents a walk-through to ensure the meter is correctly configured.First, we will set up a flow to get users registered, subscribed, and create a JWT so they can use our monetized API. Once that is complete we will come back and proceed with the walk-through.4 - Creating the APIInstead of using a pre-built onboarding flow, such as through a Developer Portal within an API gateway, we will build our own. We will create an endpoint called /register which we can then use to onboard our users who want to use the API. The result will be that the user receives a JWT that they can use that will track their usage.Since we are using Moesif, Stripe, and Java Spring as part of our overall solution, we need to make sure each of the components is working together properly.Here’s what the endpoint will do:  Create a customer in Stripe  Subscribe the new customer to the API subscription in Stripe  Create the CompanyID in Moesif (which will be the Stripe subscription ID)  Create the UserID in Moesif (which will be the Stripe Customer ID)  Create a JWT with a jti field that contains the Stripe Customer ID  If you already have User and Company identifiers in Moesif and other systems that you want to use, instead of using Stripe’s customer and subscription as your IDs, you can do that in Moesif under the Stripe configuration settings.In this example, we will create a simple Java Spring API to do the above.Create the Java Spring ProjectWe’ll be using the Spring Initializr tool to help create our initial project. The Spring Initializr generator provides many options allowing for quick customizations to our starter project.The options include:  Project          The type of build tool to be used with the project        Language          The language the project will use        Spring Boot          The Spring Boot version to use        Project Metadata          Group name                  Uniquely identifies your project across all projects                    Artifact name                  The name of the jar without version                    Name                  Name of the project                    Description                  A description for the project                    Package name                  The name for the package, typically a combination of the group and artifact name                    Packaging type                  The type of package that is created when building your project                    Java Version                  The version of Java to be used in the project                      Dependencies          Select and include many dependencies from developer tools and databases to web and security frameworks      We’ll be using the following settings for our project:Selecting Generate will initiate a download for the sample project. Unzip and open the folder generated by Spring Initializr, in our case called monetizationDemo, in your favorite editor. This is where we will add our API code. Run ./gradlew build to ensure our project builds correctly out of the gate.Add in the Project DependenciesWe will now edit our build.gradle file with our correct dependencies. In the build.gradle we will add the following entries under the dependencies object.dependencies { implementation 'org.springframework.boot:spring-boot-starter-thymeleaf' implementation 'org.springframework.boot:spring-boot-starter-web' implementation 'com.moesif.servlet:moesif-servlet:1.7.4' implementation 'com.stripe:stripe-java:21.8.0' implementation 'io.jsonwebtoken:jjwt-api:0.11.5' implementation "jakarta.xml.bind:jakarta.xml.bind-api:2.3.2" implementation "org.glassfish.jaxb:jaxb-runtime:2.3.2" implementation 'io.jsonwebtoken:jjwt-impl:0.11.5' implementation 'io.jsonwebtoken:jjwt-jackson:0.11.5' testImplementation 'org.springframework.boot:spring-boot-starter-test'}Save the file, then navigate to the terminal and run:./gradlew buildNow, your dependencies will be brought into the project and compiled if necessary. These dependencies will help us to make calls to REST endpoints, connect to Stripe and Moesif, generate and validate JWTs, and various other capabilities we will build into our app.Due to our choice to use the Web MVC framework, we have a lot of classes that depend and interact with others. In the following sections we will be building out each of these classes. If your project starts throwing errors they should be sorted out after continuing with the guide.Defining the Moesif Filter  Going forward we will refer to /src/main/java/com/username/monetizationDemo as our working directory.In our working directory, we will create file called MoesifConfig.java. In this file we will add the following code which will allow us to identify users within moesif through the use of JSON Web Tokens (JWT) as well as some other code that enables the filter to function.import java.util.Base64;import javax.servlet.Filter;import javax.servlet.http.HttpServletRequest;import javax.servlet.http.HttpServletResponse;import com.moesif.servlet.MoesifFilter;import com.moesif.servlet.MoesifConfiguration;import org.json.JSONObject;import org.springframework.context.annotation.*;import org.springframework.web.servlet.config.annotation.*;import org.springframework.web.servlet.view.InternalResourceViewResolver;@Configuration@EnableWebMvc@ComponentScanpublic class MoesifConfig {  public String applicationId = "YOUR_MOESIF_APPLICATION_ID";  @Bean  public Filter moesifFilter() {    MoesifConfiguration config = new MoesifConfiguration() {      @Override      public String identifyUser(HttpServletRequest request, HttpServletResponse response) {        String customerID = null;        try {          // Obtain Stripe customerID from JWT included in header          String token = request.getHeader("Authorization");          String[] chunks = token.split(".");          Base64.Decoder decoder = Base64.getUrlDecoder();          String payload = new String(decoder.decode(chunks[1]));          JSONObject jsonObject = new JSONObject(payload);          customerID = jsonObject.getString("jti");        } catch (Exception e) {          System.out.print("Auth payload could not be parsed into JSON object: " + e.toString());        }        if (customerID == null) {          return null;        }        return customerID;      }      @Override      public String getSessionToken(HttpServletRequest request, HttpServletResponse response) {        return request.getHeader("Authorization");      }      @Override      public String getApiVersion(HttpServletRequest request, HttpServletResponse response) {        return request.getHeader("X-Api-Version");      }    };    MoesifFilter moesifFilter = new MoesifFilter(applicationId, config, true);    // Set flag to log request and response body    moesifFilter.setLogBody(true);    return moesifFilter;  }  @Bean  public InternalResourceViewResolver viewResolver() {    InternalResourceViewResolver resolver = new InternalResourceViewResolver();    resolver.setPrefix("/WEB-INF/jsp/");    resolver.setSuffix(".jsp");    return resolver;  }}Here we are configuring the Moesif filter, including implementing the identifyUser function to pull the customer ID from the JWT to enable user tracking in Moesif.We also declare our Moesif application ID. This can be found within the Moesif interface.Obtaining Your Moesif Application IDYour Moesif application ID is found in Moesif by going to the menu link in the bottom-left of the screen (which will show your name) and selecting API Keys.The key will then be on the page that appears under Collector Application Id.Creating the Moesif ServiceIn our working directory, we will create file called MoesifService.java. We’ll create a config object that utilizes the MoesifConfig we created in the previous step, a filter object that is used to intercept and process requests before they are sent, as well as some helper methods.import com.moesif.api.APIHelper;import com.moesif.api.models.UserModel;import com.moesif.api.models.UserBuilder;import com.moesif.api.models.CompanyModel;import com.moesif.api.models.CompanyBuilder;import com.moesif.servlet.MoesifFilter;import com.moesif.servlet.MoesifConfiguration;import io.jsonwebtoken.io.IOException;public class MoesifService {  MoesifConfig config = new MoesifConfig();  MoesifFilter moesifFilter = new MoesifFilter(config.applicationId, new MoesifConfiguration(), true);  public String updateUser(String customerId, String subscriptionId, String email, String firstname, String lastname, String jwt) {    try {      UserModel user = new UserBuilder()          .userId(customerId)          .companyId(subscriptionId)          .metadata(APIHelper.deserialize("{" +              ""email": "" + email + ""," +              ""first_name": "" + firstname + ""," +              ""last_name": "" + lastname + ""," +              ""metadata": {" +              ""jwt": "" + jwt + """ +              "}" +              "}"))          .build();      moesifFilter.updateUser(user);    } catch (IOException ex) {      ex.printStackTrace();    } catch (Throwable t) {      System.out.println("Error while updating the user profile.");      t.printStackTrace();    }    return "user updated, check moesif";  }  public String updateCompany(String subscriptionId) {    try {      CompanyModel company = new CompanyBuilder()          .companyId(subscriptionId)          .metadata(APIHelper.deserialize("{" +              ""org_name": "Stripe Subscription"" +              "}"))          .build();      moesifFilter.updateCompany(company);    } catch (IOException ex) {      ex.printStackTrace();    } catch (Throwable t) {      System.out.println("Error while updating the company profile.");    }    return "company updated, check moesif";  }}The methods we have defined will create both users and companies within Moesif with the information returned from Stripe like customer ids, subscription ids, and price keys.Creating the Registration ServiceIn our working directory, create file called RegistrationService.java. This class provides methods that will be use to create customers and subscriptions within Stripe through the use of createCustomer and createSubscription, respectively.import java.util.Map;import java.security.Key;import java.util.HashMap;import com.stripe.Stripe;import com.stripe.model.Customer;import com.stripe.model.Subscription;import io.jsonwebtoken.Jwts;import io.jsonwebtoken.JwtBuilder;import io.jsonwebtoken.SignatureAlgorithm;import javax.crypto.spec.SecretKeySpec;import javax.xml.bind.DatatypeConverter;public class RegistrationService {  public String createCustomer(String email, String firstname, String lastname) {    String id = null;    try {      Stripe.apiKey = "STRIPE_SECRET_OR_RESTRICTED_KEY";      Map<String, Object> customerParams = new HashMap<>();      customerParams.put("description", "Customer created through /register endpoint");      customerParams.put("email", email);      customerParams.put("name", String.format("%s %s", firstname, lastname));      // create the new customer      Customer customer = Customer.create(customerParams);      id = customer.getId();    } catch (Exception ex) {      ex.printStackTrace();    }    return id;  }  public String createSubscription(String customerId, String plan) {    String id = null;    try {      Stripe.apiKey = "STRIPE_SECRET_OR_RESTRICTED_KEY";      Map<String, Object> item = new HashMap<>();      item.put("plan", plan);      Map<String, Object> items = new HashMap<>();      items.put("0", item);      Map<String, Object> params = new HashMap<>();      params.put("customer", customerId);      params.put("items", items);      Subscription sub = Subscription.create(params);      id = sub.getId();    } catch (Exception ex) {      ex.printStackTrace();    }    return id;  }   public String generateJWT(String id) {    SignatureAlgorithm signatureAlgorithm = SignatureAlgorithm.HS256;    byte[] apiKeySecretBytes = DatatypeConverter.parseBase64Binary("TOKEN_SECRET");    Key signingKey = new SecretKeySpec(apiKeySecretBytes, signatureAlgorithm.getJcaName());    JwtBuilder builder = Jwts.builder().setId(id)        .signWith(signingKey, signatureAlgorithm);    return builder.compact();  }}We’ll also create a generateJWT method that will, well, generate a JWT for our authorization purposes. We will include the JWT as a bearer token which will attribute API calls to specific users.Obtaining Your Stripe API and Price KeyYou will see in the code example above that we need to provide a secret or restricted key for our createCustomer and createSubscription methods.Your Stripe API Key can be found in the same place we grabbed the key for our Stripe and Moesif integration we did earlier for the Billing Meter. You can actually use the same key for both or create a restricted key with just the scope needed for each function.While we’re at it, lets grab our Stripe product’s price key. We will need it in the next section. Your Stripe price key is an identifier for the price you created earlier in Stripe. This can be found by going to the product in Stripe and grabbing the value from the API ID column.Creating Your Secret TokenThis will be the secret that is used as part of generating and validating your JWTs. This could be any 256-bit string you’d like, however, for production purposes you are best off using some sort of generation and obviously keeping this key stored elsewhere and brought in using Java Spring’s Externalized Configuration support.Creating the API ControllerIn our working directory, we will create file name APIController.java. In this file we define our MoesifConfig and MoesifFilter objects as well as our RegistrationService and MoesifService objects.We will also create three endpoints for our API.  A base endpoint - /          Used simply to test if our API is working        A register endpoint - /register          This endpoint will essentially create the binding between our generated JWT, Stripe, and Moesif. The outcome will be a generated JWT which will associate usage with a user in Moesif, which will then be reported to Stripe        A test-service endpoint - /test-service          Returns a simple string, but more importantly, implements the @Request Header annotation to pass along the JWT bearer token      import java.util.Map;import java.util.HashMap;import com.moesif.servlet.MoesifFilter;import com.moesif.servlet.MoesifConfiguration;import org.springframework.web.bind.annotation.*;import org.springframework.web.bind.annotation.PostMapping;@RestControllerpublic class APIController {  MoesifConfig config = new MoesifConfig();  MoesifFilter moesifFilter = new MoesifFilter(config.applicationId, new MoesifConfiguration(), true);  private RegistrationService registrationService = new RegistrationService();  private MoesifService moesifService = new MoesifService();  @RequestMapping("/")  public String hello(@RequestParam(value="name", defaultValue="") String name) {    return "{ "message": "Hello World!" }";  }  @PostMapping("/register")  public Map<String, Object> registerUser(@RequestBody Map<String, Object> payload) {    String email = payload.get("email").toString();    String firstname = payload.get("firstname").toString();    String lastname = payload.get("lastname").toString();    // Create Stripe customer and subscription    String customerId = registrationService.createCustomer(email, firstname, lastname);    String subscriptionId = registrationService.createSubscription(customerId, "price_1LpI5yGa4tYEM7sSR5G1QWP0");    // Generate JWT for user using customerID    String jwt = registrationService.generateJWT(customerId);    // Create Moesif User and Company    moesifService.updateCompany(subscriptionId);    moesifService.updateUser(customerId, subscriptionId, email, firstname, lastname, jwt);    // Build json response    Map<String, Object> jsonResponse = new HashMap<>();    jsonResponse.put("JWT", jwt);    return jsonResponse;  }  @RequestMapping("/test-service")  @ResponseBody  public String simpleString(@RequestHeader (name="Authorization") String token) {    return "this is a simple string";  }}The /register endpoint receives our users name and email and our RegistrationService then creates a customer and subscription within Stripe returning the necessary identifiers. A JWT is then created using the customer identifier and finally passes all this data along to our MoesifService to create a user and company within Moesif. The JWT is passed along in the response we get from the call to the /register endpoint which we will use as the bearer token in our call to the /test-service endpoint.With that, we can now actually try out our endpoint to make sure that each piece is working as expected. The outcome should be a registered user with an associated JWT which will record and report usage data to Stripe. Let’s move onto testing it.Build your project:./gradlew buildand then deploy it:java -jar build/libs/monetizationDemo-0.0.1-SNAPSHOT.jar  If you are having issues with dependencies building, try: ./gradlew clean build --refresh-dependencies5 - Send a Test Request to the /register EndpointOnce your /register endpoint has been coded and deployed, it’s time to test it. For right now we will simply use Postman to send a request. Our request will contain a JSON request body that will contain a:  First name  Last name  EmailOf course, this is the minimal amount of information we would want to configure our system and profiles in Stripe and Moesif, plus, generate the JWT. You can easily add more fields as needed for your specific use case.In Postman, we will create our request with the following information:Request Type: POSTEndpoint URL: http://localhost:{port}/registerRequest Body:{  "firstname": "Userfirstname",  "lastname": "Userlastname",  "email": "test@test.com"}  Replace port in your endpoint URL with the assigned port number.Once everything is plugged into Postman, it should look like the following:Once the request is sent, the response should contain an JWT that the newly registered user can use.We will now check Stripe to ensure that the information we registered the customer with is correctly entered into Stripe.Logging back into Stripe, you’ll navigate to Customers screen. You should see your newly created user in the list.Click on the newly added customer in the list. On the next screen, you should see that the customer is also subscribed to your APIs subscription.With this check completed, we can safely assume that our /register endpoint is correctly setting up our users accounts and subscriptions in Stripe.6 - Call Your API Using the Generated JWTOur next step is to actually use our generated JWT. We will then confirm that all the correct information is added into Moesif. The data we are confirming includes:  The Stripe Customer ID is mapped to the Moesif User ID  The Stripe Subscription ID is mapped to the Moesif Company ID  Moesif contains the Stripe metadata in the users profileUse Postman to Send the RequestNext, let’s use Postman, or another platform, to send a request to the /test-service endpoint. This is the endpoint that we set up the billing meter for in Step 3, above.In Postman, we will:  Put the /test-service API endpoint as the request URL  Select the Authorization tab  Select the Type as Bearer Token  Populate the token details  Set the Token as the JWT received from our /register callBelow is an example of the populated request configuration in Postman. To send the request to our endpoint, click Send.Once sent, the API call analytics should land in Moesif.Confirm That Moesif Received the Request Info Using Profile DashboardsBack in Moesif, you’ll navigate to the Live Event Log screen. You can do this by clicking the New button and selecting Live Event Log.On this screen, you should see the request you just sent. You should see the entry has both a User ID and Company ID populated with the Stripe user and subscription ID’s. The entries should look like this:  The customer ID will look like “cus_XXXX” and the subscription ID will look like “sub_XXXX”.If you click on the User ID shown in the entries on the Live Event Log screen, you will come to the users profile page. On this page, we will confirm that the Stripe metadata is present. We will need to add a new column to our profile to display the Stripe data. To do this, from the profile page, click on the … More Actions button and click Customize Profiles’ Layout.We will then add a new column for the Stripe metadata. You will click the + button on the far right of the screen to create a new column where we will add the Stripe metadata.  You may need to scroll to the right to see it depending on your resolution and screen size to see the + button.You will then drill down to Metadata > stripe > customer > created and use this field in the new row. I’ve also changed the column image to one more fitting. You can customize this by clicking on the image and selecting whichever one fits best.You can also add other fields, but for right now just this single field is enough to tell us that Moesif is correctly receiving data from Stripe.  If you don’t see the Stripe metadata entry as an available field, wait a few minutes. If after a few minutes the Stripe metadata isn’t present, ensure that your Stripe configuration is correct in Moesif. After confirming or editing it, try creating a new user and sending a request again to confirm that the integration is working.At this point, we now have confirmed that our API call is working and is stamped with the correct user and company details in Moesif. We also confirmed that Stripe is sending data back to Moesif which is correctly being mapped to the corresponding user profile, confirmed through the Stripe metadata in Moesif.7 - Create the FrontendNext, we want to add a simple little frontend so we don’t need to call for our JWT through Postman. We will make a quick little registration form that will then return a JWT for our newly registered user to use.Add the Html Files to the AppIn the src/main/resources directory of the application, create a folder named templates, we will add in two files. We will add both a registration.html and an result.html file.Code the Frontend Form and LogicLet’s add the code for our frontend HTML. In the registration.html file, we will add markup that looks like this:<!DOCTYPE HTML><html xmlns:th="https://www.thymeleaf.org"><head>    <title>Moesif Monetization Example</title>    <meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /></head><body>  <h1>Moesif Monetization Example</h1>    <form action="#" th:action="@{/registration}" th:object="${registration}" method="post">      <p>Email: <input type="text" th:field="*{email}" /></p>      <p>First Name: <input type="text" th:field="*{firstname}" /></p>      <p>Last Name: <input type="text" th:field="*{lastname}" /></p>      <form:hidden path="jwt" />      <p><input type="submit" value="Register" /> <input type="reset" value="Reset" /></p>    </form></body></html>This markup will display a form which allows users to input an email, first name, and last name. It also has a Register button that will pass along the entered data to the RegistrationController we will create in a minute.Next’ we’ll add the following markup to our result.html file:<!DOCTYPE HTML><html xmlns:th="https://www.thymeleaf.org"><head>    <title>Moesif Monetization Example</title>    <meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /></head><body>  <h1>Moesif Monetization Example Results</h1>    <p th:text="'Email: ' + ${registration.email}" />    <p th:text="'First Name: ' + ${registration.firstname}" />    <p th:text="'Last Name: ' + ${registration.lastname}" />    <p th:text="'JWT: ' + ${registration.jwt}" />    <a href="/registration">Submit another registration</a></body></html>The result.html file will present the data received from the controller after our registration flow is completed.Create the Registration ModelHead back into our “working directory” (/src/main/java/com/username/monetizationDemo) and create the following two files, Registration.java and RegistrationController.java.In the Registration.java file, we will create a class that is used to model our user:public class Registration {  private String email;  private String firstname;  private String lastname;  public String jwt;  public String getEmail() {    return email;  }  public void setEmail(String email) {    this.email = email;  }  public String getFirstname() {    return firstname;  }  public void setFirstname(String firstname) {    this.firstname = firstname;  }  public String getLastname() {    return lastname;  }  public void setLastname(String lastname) {    this.lastname = lastname;  }  public String getJWT() {    return jwt;  }  public void setJWT(String jwt) {    this.jwt = jwt;  }}Create the Registration ControllerThe RegistrationController is used to present our form, receive the data entered from the front end, and pass it along to out backend RegistrationService. It’s results will then be displayed to the end user with the returned JWT used to authorize calls.import java.util.Map;import java.util.HashMap;import org.springframework.ui.Model;import org.springframework.stereotype.Controller;import org.springframework.web.bind.annotation.GetMapping;import org.springframework.web.bind.annotation.PostMapping;import org.springframework.web.bind.annotation.ModelAttribute;@Controllerpublic class RegistrationController {  @GetMapping("/registration")  public String registrationForm(Model model) {    model.addAttribute("registration", new Registration());    return "registration";  }  @PostMapping("/registration")  public String registrationSubmit(@ModelAttribute Registration registration, Model model) {    // Create payload from form data for registration    Map<String, Object> payload = new HashMap<>();    payload.put("email", registration.getEmail());    payload.put("firstname", registration.getFirstname());    payload.put("lastname", registration.getLastname());    // Register User and add generated JWT to result form response    Map<String, Object> jsonResponse = new APIController().registerUser(payload);    registration.setJWT(jsonResponse.get("JWT").toString());    model.addAttribute("registration", registration);    return "result";  }}8 - Test the FrontendTo test the frontend, save your code changes and restart the server. Then, in a browser, navigate to http://localhost:8080/registration. You will then see the form show up.Fill out the Form Fields and SubmitNow that the form is loaded on the screen, fill in the fields and click the Register button. This will take the info, pass it along to our RegistrationController, and give us the generated JWT.  It is suggested that you use a different email than you used earlier when you created a JWT directly through the /registration endpoint.Confirm the JWT is ReturnedOnce the submit button is clicked, after a few seconds, the JWT should be returned back to the UI.9 - Send a Request to Your Monetized APIWe will once again want to make sure that everything is working with our UI, through to our backend systems. For this, simply repeat the steps from Step 6 to confirm that the user and company IDs are populated correctly and that the Stripe metadata is returned for this user and the new JWT. We should see these calls populated within our Live Event Log as well.10 - Confirm All the Pieces are Working CorrectlyAlthough this is optional, this step may help with troubleshooting any issues that may have came from our previous steps. Here are a few things to check to make sure that all is working as it should. After creating a new user through the UI and using the generated JWT to place a call to your API, confirm the following:In Stripe  Confirm that a customer has been created in Stripe with the details you entered into the UI  Confirm that the customer has been subscribed to the correct product and priceIn Moesif  Your API call was recorded in Moesif in the Live Event Log  Your API call has the Stripe Customer ID and Subscription ID in the User and Company fields in Moesif, respectively.  Confirm that the Stripe metadata is populated in Moesif  All Billing Meter test conditions have passed11 - Check Stripe for UsageLastly, After a few hours, it’s best to go into Stripe to confirm that usage is being added to a users subscription. Be sure that you’ve sent a few requests through in order to make sure you have some data that should be sent to Stripe.  It may take a few hours for usage to make its way from Moesif to Stripe. If data still isn’t in Moesif after a few hours, ensure you’ve followed all the steps outlined within this guide. This includes making sure that your user and company ID’s from Moesif are correctly mapped to the corresponding keys in Stripe.To check the usage, in Stripe you’ll want to navigate to the Customers screen and select the customer that you made the API call with. Once selected, you should see some active subscriptions for the users that you’ve registered through the /register endpoint. The one we created earlier is called My API. Click on the subscription entry.On the next screen, click on View Usage beside the price entry.A modal should now pop up showing you the usage for the API that has been reported to Stripe from Moesif.  Remember, there is a delay in Moesif’s reporting to Stripe. If you data isn’t there yet, check back in a bit later.12 - Determining If the Billing Meter is Working CorrectlyTesting the created Billing Meter is easy with out Test Meter function. Navigate to your created Billing Meter from the left side navigation pane and selecting your Stripe Test billing meter. Select Test Meter on the top right.We will first confirm the meter that you are attempting to test. Click the Next button at the bottom of the modal.Moesif will wait for Subscriptions to created within Stripe and those subscriptions to be associated within Moesif itself. This page will update automatically, no need to refresh.Moesif will then wait for an API call to our any endpoint associated with our billing meter using our the JWT that has been created for us.Finally, Moesif will sync all usage data to Stripe every 15 minutes. This step may take a few minutes depending on when the API call was initiated but will update on the given interval.Wrapping upMonetization has always been a tough hurdle to get past. Many custom solutions offered flexibility but at a very high engineering and support cost. With Moesif, API monetization is possible in an extremely minimal amount of time. As demonstrated in this article, With a little bit of configuration and minimal amount of code we can create a production-ready, post-paid monetization scheme in minimal time.                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue with your Java Spring REST APIs?            Monetize your Java Spring APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /api-monetization/stripe/End-To-End-API-Monetization-With-Java-Spring-Stripe-And-Moesif/",
          "author": "Dylan",
          "categories": "API-Monetization, Stripe"
        }
      
    ,
  
    
        "product-management-moesif-product-using-moesifs-live-event-log-to-filter-and-inspect-api-calls-and-events": {
          "title": "Using Moesif’s Live Event Log to Filter and Inspect API Calls and Events",
          "content"	 : "As you may know, event logs are a common feature in operating systems and other software that keep track of system and application errors. When you have API traffic to follow or front-end actions you want to watch, using Moesif’s Live Event Log is a simple way to filter and find the data you need. In this article, we will go over the basics of Live Event Log, including how to filter for the data you want, different actions you can perform on the logs, and how to use them for troubleshooting and debugging.What Is a Live Event Log?The Live Event Log in Moesif is a real-time running record of all the API events and actions that are taking place within your application or services. The Live Event Log allows you to filter on specific attributes within the received API Calls and Actions. The Live Event Log gives you a raw and easy-to-navigate way to explore user-centric insights into API usage and user behaviors on your platform.Why Use a Live Event Log?Live Event Log can be an incredibly useful tool for tracking what’s happening on your website or application in real time. Using the Live Event Log can help you troubleshoot errors, track user activity, and gain insights into how your site or app is being used.When looking at traditional logs, searching and filtering can be extremely difficult to do. Moesif supports extremely simple or complex queries with ease. Moesif allows you to build queries based on the data that is in the logs so that there is no guessing. For example, if you are filtering on an endpoint, Moesif will pre-populate the filter dropdown with the endpoints that are contained in the current data.Moesif also supports Body and Header analytics which means that you can have a Moesif filter based on a specific body field and its contents. This level of granularity is part of what sets Moesif apart from other API and product analytics platforms.Using the Live Event LogNow that you know what the Live Event Log is and what it does, let’s take a look at how to use it in Moesif. Although this isn’t an exhaustive list of all the ways you can create and use Live Event Log, we will show you some common ways to create and use them.To get to the Live Event Log screen, click the New button and select Live Event Log.How to create a filterCreating a filter is the best way to view events that are in the Live Event Log screen. Once you are in the Live Event Log screen, the filter input is at the top.You can filter by a variety of API properties like URI route, query parameters, HTTP headers, and body fields. You can also filter based on whether an event is an API call or an Action. Actions show the activity of the customer in your UI, such as “logged-in” or “Viewed-Guides”.Live Event Log will also allow you to display the actions of new users and companies only. This can be done by using the New Users Only and New Companies Only filter buttons.A filter can be extremely simple or complex depending on the data you are looking for. For more information on how to build filters, check out our docs.Displaying the event dataYou can also customize how you view the Live Event Log. You have the ability to view the data from Live Event Log as a Stream or Table. The Stream view displays each API call or Action as clickable elements.The Table view displays the events in a fashion similar to a spreadsheet.Exporting the dataOther features offered by Moesif include the ability to download events through the browser or to export the events to run in Postman.To export events, select some events and click on the Bulk Export button.You will then choose if you want the output to be in JSON, CSV, or Parquet format and an email to send. Once the form is completed, click the Start Export button.Once the export is complete, you’ll get an email with a download link to the email(s) specified in the previous modal.For Postman, simply select the API calls you want to export and click the Run in Postman button at the top of the screen.You’ll then be prompted to Download Postman Collection. The collection will save and can then be uploaded to Postman. For more specifics, check out our docs which show the step-by-step procedure.Comparing events using Diff 2 EventsSometimes it makes sense to compare the request and response of 2 different calls. This is a common scenario when trying to debug issues within an API call. You may want to check the difference between a successful call and one that is causing an error. To do this in Moesif, you can use the Diff 2 Events function to compare two API calls.Once two calls are selected and the Diff 2 Events button is clicked, you will be able to see the red highlighted text showing changes removed while green showing implemented changes.Using this functionality takes out the manual work of trying to find differences and easily shows the user in an easy-to-read fashion.Saving your search to a DashboardOnce you can create a Live Event Log filter that you like, you may want to add it to a dashboard in Moesif. Moesif gives you the option to save and share your created logs with your team members or privately. To save a Live Event Log to a dashboard in Moesif, click the Save button.In the modal that appears, name your new workspace (the Live Event Log you just created), choose if you want the workspace to be private or for your team, and select the Dashboard you would like to add it to.Lastly, click the save button at the bottom of the modal to save the workspace to a dashboard.Exporting the Live Event Log as a public link or embedded templateIf you’d like to share your Live Event Log to users who don’t have access to Moesif, including your customers, you can use a Public Link or Embedded Template.A Public Link can be used to securely share metrics with external parties or statically embed charts in an iFrame. A public link has a static filter that can not be changed. To create one, click on the Share button at the top of the Live Event Log screen.In the modal that appears ensure that Public Link is selected.Grab the link or the static embed code from the modal.If you’d like to allow the user to adjust some filters, you can use an Embedded Template. This feature allows you to embed dynamic charts in customer-facing apps. Dynamic fields, such as authenticated users, can be created to limit data access. To create one, click on the Embed button at the top of the Live Event Log screen and select Create Template.Here you can add in your Sandbox Policy and then click Get Embed Code.Now you can take the generated code and embed it into your application.Try It OutMoesif’s Live Event Log can be an incredibly useful tool for businesses and developers alike. We hope that this guide has given you a better understanding of how Moesif’s Live Event Log works and some of its most useful features. Whether you just want an easy way to analyze every event that is occurring on your platform or are trying to troubleshoot your application and APIs, Live Event Log can be an indispensable tool. To try out Live Event Log for yourself, log in to Moesif or sign up today to get started.                Inspect API Calls with Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /product-management/moesif-product/Using-Moesifs-Live-Event-Log-to-Filter-and-Inspect-API-Calls-and-Events/",
          "author": "Preet",
          "categories": "Product-Management, Moesif-Product"
        }
      
    ,
  
    
        "customer-success-moesif-product-user-funnels-are-vital-to-the-customer-experience": {
          "title": "User Funnels Are Vital to the Customer Experience",
          "content"	 : "Happy Customer + Customer loyalty = Brand AdvocateIf you’re looking for ways to improve customer satisfaction, experience, and loyalty, you need to focus on how customers are using and adopting your product. For this, you’ll need to focus on your user funnel, sometimes referred to as a conversion funnel. In this article, we’ll show you seven ways that user funnels can improve some key areas of your business to boost all of the above!User funnels are the defined path that your customers take as they interact with your product or service. This may track steps from when they first discover your product, sign up for it, and begin using specific features. By understanding and optimizing the user funnel, you can make targeted improvements to boost your customer satisfaction and loyalty. Here are seven ways that user funnels can help you do just that:1. Improve the onboarding processThe onboarding process is crucial for new users. The onboarding experience is a “make-or-break” step for many customers and is where you’ll see the bulk of people drop out of the funnel. if users have a good experience, they’re more likely to stick around and continue to use the product. If not, they’re more likely to churn, or worse, talk negatively about your product even though they didn’t have a chance to truly experience it.User funnels can help you optimize the onboarding process by helping you understand where users are dropping off and what’s causing them to do so. An onboarding process usually contains multiple steps which can be segmented. By checking out the conversion at each step in the process, versus looking at it as a single entity, you can quickly improve the experience and make more informed decisions about where effort should be expended. A great marketing strategy and the sales team can only do so much to get people to sign up, onboarding is where you can truly stand out and create evangelists for your product2. Increase engagementEngagement is key to keeping customers happy, allowing them to provide product feedback and insight. Providing feedback can help the product team make feature improvements, keeping up with competitors and building lasting relationships. Engagement can come in many different forms, including using customer feedback surveys at different stages in your customer lifecycle. You may find that sending out a post-trial email survey to new customers can keep customers engaged and hopefully signing up for paid accounts for your product or service.Attempts at increasing engagement may also come from product emails. These emails may show the latest features or a customized email flow which points out features which would bring the most value to that specific user based on other behaviors. When using a sequence to try and drive adoption, it’s important to keep an eye on your user funnel to see if your actions to drive engagement are leading to conversions and adoption.3. Reduce churnWhen the customer is engaged, they are less likely to look at other competitors or seek our alternative solutions. Relevant documentation and support is vital for the client to succeed.Analyzing why customers are churning is vital for the growth of the company and important to to understand where new features can be created and existing features can be improved. By collecting data to understand why customers may be churning, you can then track changes to your user experience to see if churn is being reduced or if it is increasing due to the changes. User funnels allow you to see at exactly which stages in the customer journey are most likely to cause churn.4. Improve Customer SatisfactionKeeping customer satisfaction is a key factor in keeping customers happy. If they’re not satisfied with your product or service, they will keep churning. Identifying negative experiences within the user funnel can help with creating guides that can help customers better utilize your product and services. Customer satisfaction can be increased or decreased based on factors such as ease of use, documentation, or feature availability. A user funnel can help to pinpoint the pain points, remedy them, and track how the improvements are impacting the customer.5. Improve loyaltyCustomer Loyalty is key to customer retention. Building a customer base that enjoys your product and continues to return to the platform is one of the best ways to build recurring revenue. As most people know, one of the most expensive parts of growing a business is attracting and onboarding new customers. By keeping customers loyal, you can continue to grow revenue with those who have already adopted your product.To build customer loyalty, the ability to understand where the customer needs support is crucial. This support can come from ongoing marketing and product documentation or directly from a customer success team. Loyalty can signal, most importantly, that your company has the ability to deliver a product or service that the client is interested in using and is getting repeated value from.6. Increase revenueIncreased revenue is a natural result of improved customer satisfaction and customer loyalty. By improving the user funnel and conversions, you can improve customer satisfaction and loyalty, which will lead to increased revenue.7. Improve the overall customer experienceThe overall customer experience is key to keeping customers engaged. The effort starts from the sales rep to customer success teams.The Seven Key Stages in a Typical User JourneyAwareness stageThis is when a prospect becomes aware of your brand or product. For example, they might see an ad, read an article about your product, or be referred by a friend.Interest and consideration stageOnce they are aware of your product, they need to develop some level of interest in it. This is where you need to provide more information about what your product can do for them and why they should care. This can come from different sources such as, guides, influencer marketing, social media, email marketing, digital marketing, Reddit, Twitter and/or LinkedIn. Any social media platform can play a big part in the customer journey. The key is to know when to post on LinkedIn and other social media platforms to make sure your posts reach the maximum audience engagement and effectively contribute to the customer journey.DesireAt this stage, the prospect really wants your product and is considering making a purchase. They might compare different products, read reviews, or contact the sales team so marketing strategy is very important to any new customer.ActionFinally, the prospect takes action and buys your product. But their journey doesn’t end with the sales team.RetentionEven after someone has bought your product, you need to  keep them engaged so they don’t become a one-time customer. You can do this by providing great customer service, offering additional products or services, or sending out rewards creating customer loyalty.ReactivationIf a customer has been inactive for a while, you can try to reactivate them with special offers or adding them to the company email list.ReferralThe best customers are those who tell their friends about your product and help you bring in new business leads. You can encourage this behavior with referral programs or other incentives.User funnels are an essential part of creating happy potential and existing customers who keep coming back for more. By understanding the journey that your customers take and optimizing each stage, you can ensure that they have a smooth and enjoyable experience from the awareness stage to the referral stage and increase customer loyalty. You want your customer to be a brand advocate.Why User Funnels?There are many reasons why user funnels are a great way to make your new customers happier. First, user funnels help you guide your customers through your website or app in a way that is ideally easy and intuitive for them. This helps to reduce frustration and confusion, and makes it more likely that they will find what they are looking for.Second, user funnels can help you collect important data about your customers that you can use to improve their experience. For example, you can use data from user funnels to understand which pages or features are most popular with your customers, and make changes accordingly.Third, user funnels can help you segment your customers so that you can tailor your marketing and communications to the target audience. This ensures that your customers only receive messages that are relevant and interesting to them, which makes them more likely to engage with your brand.Overall, user funnels offer many benefits that can make your new customers happier. By making it easier for them to find what they need on your website or app, and by providing valuable data that you can use to improve their experience, user funnels can help you create a better experience for your customers from start to finish.How To Implement User FunnelsUser funnels are a great way to make your new customers happier. By implementing user funnels, you can give your customers a better experience by guiding them through your website or app in a more efficient way.User funnels help you to track your users’ behavior and see where they are dropping off. This way, you can make changes to your website or app to improve the user experience. Additionally, user funnels can also help you to up-sell and cross-sell to your customers.There are a few different ways to implement user funnels. For front-end analysis in the B2C space, one way is to use Google Analytics. You can set up user funnels in Google Analytics by going to the Admin section and selecting the “Goals” menu. From there, you can create a new goal and select “Funnel” as the type.Another way to implement user funnels is through A/B testing. A/B testing allows you to test different versions of your website or app with a small group of users. This way, you can see which version works best for your customers.Moesif is a great tool to track users throughout their journey. You can see where the users have registered and when they used the product and made a purchase or subscribed to the product. Click here for a help guide to using Moesif’s user funnels.Overall, user funnels are a great way to improve the customer experience on your website or app by tracking user behavior.Types of User FunnelsThere are several different types of user funnels that businesses can use to make their new customers happier.One type of user funnel is the free trial funnel. This type of funnel offers new users a free trial period of a product or service. After the free trial period is over, the user is then asked to pay for the product or service. This type of funnel is a great way to let new customers try out your product or service before they commit to paying for it.Another type of user funnel is the subscription funnel. This type of funnel requires new users to sign up for a subscription in order to access your product or service. Once the subscription is over, the user will no longer have access to your product or service. This type of funnel is a great way to get new customers to commit to using your product or service on a regular basis.The last type of user funnel is the one-time purchase funnel. This type of funnel allows new users to purchase your product or service outright. There is no commitment required from the user, and they will have access to your product or service for as long as they want. This type of funnel is a great way to get new customers to make a one time purchase.The 7 Tips To Increase Customer Happiness  Keep your user funnel simple and user friendly  Make it easy for a new customer to find what they are looking for.  Offer a great first impression, create brand awareness  Provide excellent customer service  Customer support to understand their needs creating a loyal customer  Be proactive by creating ongoing educational content supporting them through their customer journey.  Show appreciation for customer retentionFinal ThoughtsUser funnels can be a great way to increase sales. By understanding how users interact with your product, you can design a funnel that leads them towards a purchase.User funnels can also be used for onboarding new users. By showing new users the most important features of your product, you can help them get started and increase the chance that they will continue using your product.User funnels can be designed in many different ways. The important thing is to think about the user’s journey and what you want them to do at each step. By designing a user funnel, you can make it more likely that users will take the actions you want them to take potentially leading to referrals and brand awareness.Overall, user funnels can be a great way to improve customer satisfaction. By providing a clear path for users to follow, you can help them to achieve their goals more easily. This can lead to a better experience for your customers, and ultimately, happier customers.                Reduce Churn With User Funnels              14 day free trial. No credit card required.              Learn More        ",
          "url": " /customer-success/moesif-product/User-Funnels-Are-Vital-To-The-Customer-Experience/",
          "author": "Preet",
          "categories": "Customer-Success, Moesif-Product"
        }
      
    ,
  
    
        "api-monetization-api-strategy-tiered-pricing-strategy-definition-examples-and-benefits": {
          "title": "Tiered pricing strategy - definition, examples and benefits",
          "content"	 : "The right tiered pricing strategy can ensure you effectively monetize your APIs and other digital products, encouraging your customers to spend more whilst ensuring they are happy about doing so. But get it wrong and you can irritate your customers and push them towards your competitors. In this post we’ll explain how to get your tiered pricing right and maximize your API’s revenue.How to Implement a Tiered Pricing StrategyTiered pricing is a way of packaging your product into fixed-cost bundles, rather than using a usage-based pricing model. Each tier delivers a fixed range of features: number of users, volume of data, depth of support, etc. Customers can choose the tier that best suits their requirements and their budget. Then, once the customer expands their usage of your product and exceeds the levels in their chosen tier, they move up to the next. It’s a common pricing strategy for Software as a Service (SaaS) products, such as APIs.Understanding how to implement a tiered pricing strategy involves clearly and carefully defining your tiers. It also means considering how best to incentivize your customers to progress from one tier to the next.What is a tiered pricing strategy?Tiered pricing strategies center on what’s included in each tier and how much is charged for those capabilities. To set your pricing strategy, analyze your product offering and determine which features are germane to your product, which are add ons and which could be considered custom. Divide up those features into tiers and determine the price for those tiers using the analysis presented in our companion blog post.As a sanity check, you could look at your competitors and see what they’re offering and at what price. You may need to reconsider your strategy if you’re pricing yourself out of the market.An effective tiered pricing strategy ensures your customers feel they get good value on every tier. Consider starting with a free tier to encourage product adoption, with the aim of moving those who adopt your product onto paying tiers as their usage increases.What is a tiered pricing structure?A tiered pricing structure relates to how you price each tier and what you include in it. This is crucial to the success of your whole tiered pricing model.The nature of your product and the way in which your customers use it will impact what you provide on each tier. Whether your business model is B2C or B2B will also impact your custom pricing approach, as these models require different strategies.What is an example of tiered pricing?You can set up your tiered pricing structure through a solution such as Moesif and Stripe, or you can implement a custom solution, although the latter would take longer and involve greater complexity).Let’s take the example of an API that you’re charging your customers to use. You can charge customers for each query/call that they send to the endpoint. You can then offer a cheaper price per call based on increasing usage, thus incentivizing customers to move up to more expensive tiers in order to enjoy discounted rates.What Are the Benefits of Tiered Pricing?Implemented well, a tiered pricing structure can make it easy for your customers to understand what your product will cost them. It can also make them feel like they gain additional value each time they move up a tier and increase their spend.When you get your tiered pricing right the benefits are multifaceted, as follows.Pricing structure is easy to understandWith your pricing arranged into tiers, it’s easy for your customers to understand precisely what they get for their money. You can define the offering in each tier with a couple of bullet points and a comparison table to show the tiers side-by-side.This kind of pricing structure makes costs predictable for your customers. And by making it easy for them to budget for your product each month, you’re reducing friction in the adoption process. Everyone wins.Commonly accepted pricingThe tiered pricing model is well understood within the SaaS industry. As such, you’re presenting your customers with something known and familiar. Again, this reduces friction in terms of them deciding to adopt your product.Using a commonly accepted pricing structure also supports those who want to adopt your product but need their managers to sign off on its adoption. Managers will see clearly what the product will cost and have the comfort of working within a familiar pricing model.Easy to implementAll you need to implement your tiered pricing is a subscription billing software. It’s super simple to put in place and won’t drain your time or resources. You can use the software to set up a lower priced tier, plus one or more at a higher price - an established strategy that means you can capitalize on a downward sloping demand curve.Reduced complexities around billingFixed pricing, as opposed to dynamic pricing, also means you can reduce complexities around the billing process. You don’t need to implement any metering or usage tracking through analytics. You simply decide the price of each tier and bill accordingly.So far, so good. But there is also a downside to tiered pricing…Shortcomings of Tiered Pricing StrategiesIt’s only fair to unpack the cons of tiered pricing strategies, as well as to consider the benefits. After all, using tiers is just one kind of pricing model. Before you decide to implement tiered pricing, take the following into account.Price jumps can cause frictionThe predictability of tiered pricing means that your customers will quickly get used to what your product costs them each month. It also means that they may feel irritated by the price increase when they move up a tier.Value perception comes into play here. Your next tier up might include a whole bunch of exciting features. However, if your customer is moving up to it simply because they’ve hit a limit (such as a certain number of users or number of events), then they may not want those additional features. As such, a major jump in cost could cause discontent, as the customer doesn’t perceive they are getting sufficient additional value to justify the increase. This is certainly something to factor in when deciding your pricing strategy.Too many variablesIf each of your tiers has too many variables, it may trigger a customer to move up a tier before they feel they are ready to do so. After all, a customer is unlikely to exceed all of the plan limits at once. As such, it’s easiest to stick with just a couple of volume pricing metrics. This makes it easier for the customer to understand when they will hit the tier’s limits and reduces the likelihood that the tier jump will come as a surprise.Too many tiersIt can also be a problem if you have too many tiers. Make things confusing and it becomes hard for potential customers to see which tier would suit them best. Analysis paralysis kicks in and you’ve slowed down or even lost a potential sale. Offer three, or four tiers at most, to avoid this.Lack of flexibilityA tiered pricing strategy doesn’t allow for personalized pricing, where you can take into account the differing amounts that your customer base can afford to pay. By using fixed-price tiers, you cut out the ability to price your product differently to suit each customer.Loss of revenueIf you misprice your tiers, you could miss out on revenue. You need to price your tiers in a way that not only appeals to customers, but also covers your costs sufficiently. This means thinking carefully about each tier, including the top tier, where any kind of “unlimited” offering could end up being a major drain on your time and resources.Having taken the time to unpack the cons of tiered pricing strategies, as well as review the benefits, it’s worth taking a quick look at alternative pricing models. The main alternative to tiers is pay as you go (PAYG) pricing.Popular Alternatives to Tiered Pricing with API LeadersNo decision to adopt a tiered pricing strategy should be made without a thorough pay as you go explanation and examples. The main alternative to tiered pricing popular with API leaders, PAYG means that customers only pay for precisely what they use.Pay as you go explanation and examplesFrom pay-as-you-go (PAYG) cell phones, to utilities such as gas and electric bills, usage-based billing – another way of saying pay as you go – is a familiar concept. It is based on metering, which can be applied to a range of digital products, as well as to services such as utilities.PAYG explained in simple terms means billing based on something you have measured. That could be anything from transaction volume to unique users. Obviously, you would need to do some serious number crunching to find out what would work best in terms of monetizing your product, just as would if deciding what to offer at each level of a tiered pricing model.Benefits of PAYG billingPAYG billing offers a range of benefits. One is that, thanks to extensive PAYG implementations in SaaS products, it has become a familiar payment structure to many of those who will be making decisions about whether or not to use your product. As we noted above, delivering a sense of reassurance and familiarity when it comes to your payment structure is never a bad thing!Perhaps the greatest benefit of the PAYG pricing model is that it reduces friction as customers increase their expenditure. Instead of customers experiencing a sudden jump in cost, along with associated issues such as feeling they’re not ready or not getting sufficient additional value, their costs increase and decrease in harmony with their usage. Whether they spend more or less in any given month, they will understand that the cost is justified by the degree to which they have used your product.Consumption metrics for PAYG billingIf you’re going down the PAYG pricing route, then you will need to decide on your consumption metrics. Your thinking on this will likely be driven by the kind of product you are offering. If it’s an API, for example, or an event-based platform (such as SMS and analytics), then you could consider a metric such as the number of API calls or the number or messages sent. Platforms handling financial functions such as payments or expense reporting, meanwhile, might be better to use a percentage of revenue or transaction fee.Other consumption metrics for PAYG billing include data volume (gigabytes sent, minutes made), user volume (number of users/seats) and resource factors (computer units, active hours).This is where Moesif comes into its own. Working with Recurly, Stripe or Chargebee, you can use our analytics platform to implement PAYG billing and invoicing to monetize your APIs effortlessly, driving revenue in a few clicks.The decision to implement either a tiered pricing strategy or a PAYG pricing model should not be taken lightly. If in doubt, why not talk the options through with the Moesif team.                Grow And Monetize Your API Products              14 day free trial. No credit card required.              Learn More        ",
          "url": " /api-monetization/api-strategy/Tiered-Pricing-Strategy-Definition-Examples-and-Benefits/",
          "author": "Larry",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "api-product-management-retention-analysis-what-is-product-retention-and-how-can-you-improve-it": {
          "title": "What is Product Retention and How Can You Improve It?",
          "content"	 : "Product retention is an extremely important metric to check the health of your business. Calculating the retention rate of your business will return the percentage of users who continue using your product or service over a given time period. Retention can really be seen as a gauge for customer loyalty and a good reflection of the quality of your customer relationship with a new or existing customer. How to increase customer retention is a huge concern for many businesses and developing a great customer retention strategy is the best way to solve it. Being great at customer acquisition is only meaningful if you can also manage to keep your customer churn low and continue retaining customers.A low customer retention rate can be a major indicator of something being wrong with your product, including too high of a price for the value the product delivers. A high customer retention rate means your current customers value your product and the continued usage of the product leads to a sustainable source of revenue.Retention Analysis is quite easy to perform but using a platform like Moesif to create one is the best way to track customer retention over time. Let’s take a look at the specifics of each part of a retention report and how to create and interpret them in Moesif.Product Retention for API-first businessesMeasuring product retention is a bit different if your business is an API. Measuring logins to your developer portal may not be accurate as typically developers don’t log into admin portals daily or weekly. The best way to measure retention for API products is through consistent API activity. However, not all API traffic is equal. Using API traffic as the basis for your retention report requires considering traffic that creates value for the customer and not just including any API call. The traffic that brings customers value may be the same events that you charge on if you have a usage-based billing model. Customers of API businesses may be using different APis to meet different requirements, this means you’ll likely want a way to filter down on specific fields or volume a customer may be sending. For example, 4xx or 5xx HTTP error response codes shouldn’t be considered in retention reports as these are typically error conditions and can skew a retention analysis.Determining First Event and Returning EventsWhen we are looking at customer retention, there are 2 important events that are used to determine if a user is being retained. The initial event is known as the “First Event” and this will be where your retention report starts. The First Event can be determined by determining when a user first receives value from the product. With an e-commerce platform, this could be the first time the user actually creates a purchase order. If you are a communications platform, the first event may a user sending a text message through the platform. You could also generalize this to be any API call.The second part of the retention analysis is when a user performs a Returning Event, signaling that they are a repeat customer. The returning event is when a customer returns back to the platform and performs an event where they receive value from the product. Typically, this is the same type of event that was performed in the First Event, but it could be something completely different. Every time a user performs the Returning Event they are considered a retained user. When a user no longer performs the Returning Event, the user is no longer retained and likely is no longer using the product.Creating a Retention Report in MoesifCreating a Retention Report in Moesif is easy to do. The first step is to navigate to the Users screen. Once on the screen, the next thing you will do is pick Retention from the dropdown in the top left of the screen.Now on the Retention screen, you can specify both the First Event and the Returning Event. In this example, we will be looking at creating a simple retention analysis where the First Event and Returning Event will be when a user makes a purchase and a repeat purchase on the platform. We will also make sure to only include successful checkouts by specifying that the response Status Code is equal to 200 or 201, denoting a successful call. Below is what it would look like in Moesif.  In Moesif, if events are not specified, Moesif will calculate the retention based on any event being received for that user. This can sometimes be useful but is generally more accurate to pick specific First and Returning events.As you can see, Moesif automatically creates a chart that plots out the customer retention curve.Adding in Segmentation and GroupingTo break down the retention report even further, we can also look at adding segmentation and grouping to the data.For segmentation, we may want to only include retention for customers who performed a certain action or belong to a certain segment of our customer base. For example, building on top of the example we built before, we may add a segmentation clause where we want to look at the differences in retention for both Enterprise and Non Enterprise customers. For that, we will add in segments for to distinguish between both types of customers. And example can be seen below.Another angle to look at the analysis from is to use the Group By capability. This allows us to see what the retention looks like across different groupings of users. For example, we can change the previous example to only look at Non Enterprise customers and add a Group By to group the customers within this segment by UTM Source. This will show us what customer retention looks like for users depending on what UTM source they came from. This could help us to see which tactics are working and which ones aren’t when it comes to creating loyal customers. Here’s what that would look like.With these two additional features, we can really dig into each aspect of customer retention and infer deeper and more specific insights than we could without segmentation and grouping.Interpreting the Retention ReportWhen actually looking at a retention report, it’s important to know what you are looking at. There are a few scenarios that show healthy retention and others that show unhealthy retention or retention that could use improvement.Retention is high/optimalThe optimal retention rate varies by industry so it’s hard to say exactly where you want your retention rate to land. This being said, there are plenty of studies that show average retention by industry including this one done in 2020 by ProfitWell. Inside this report we can see the following average retention rates by industry:  Retail: 63%  Banking: 75%  Telecom: 78%  IT: 81%  Insurance: 83%  Professional services: 84%  Media: 84%Depending on your industry, you obviously want to make sure that your retention curve begins to flatten quickly and stays roughly around these targets. It’s also important to take a look at what you are doing to have retention be so high and continue to do so to keep loyal customers retained. If retention starts to drop as you make changes to onboarding, business processes, marketing, and other factors, you may want to look at reverting back to tactics when retention was higher. Likely the experience of your service or app is not meeting the customer expectation. Your customer retention strategy should always aim to keep retention as high as possible.Retention drops off quicklyIf retention drops quickly to low levels, this usually means that customers are not satisfied with the product or are not getting the value out of the product to justify returning. If you are looking at retention on a monthly level and by the second month retention is dropping significantly, you may want to look at retention in a smaller time segment, such as weekly or even daily. With poor retention, the silver lining is that small changes may lead to massive differences. When retention is poor, you’ll want to continue to keep a close eye on potential improvements since low retention can spell disaster for many businesses.The retention report doesn’t show much historyIf your business has only been around for 4 weeks or you’ve only been tracking retention for 4 weeks, it’s tough to look at retention on a 12-month basis since the data doesn’t exist. If you find that your retention report stops sooner than you’d expect, you’ll need to make sure that you have enough data to support the time span that you are trying to look at. If your data is limited, consider using a smaller unit of time such as looking at retention on a daily or weekly basis until your data grows big enough to assess at a monthly or yearly level.Of course, every company is going to have different expectations for where their retention should be. You will likely fall into one of the three categories mentioned above, maybe even multiple. Once you’ve assessed your retention, the next step is to either maintain it or, more likely, aim to improve it. Next, let’s look at some ways that retention can be improved.Tips for Improving RetentionRetention can be a bit of a puzzle but tracking the metrics around retention is the first step to improving it. There are plenty of ways to try and improve retention, some may be more applicable than others depending on what industry you are in. Here are a few customer retention strategies that you can try out to build your customer base out of more loyal customers.Refine Your Onboarding ProcessNothing scares a new customer off more quickly than a bad customer onboarding process. It’s hard to retain customers if they never fully get onboarded and find value in your product. This is why improving your onboarding can be the single best way to improve your user retention. A great onboarding experience usually leads to a great customer experience that quickly delivers solutions for customer needs. To see how your onboarding process looks like in its current state, you may want to look at metrics such as:  How many users are beginning the onboarding process and not finishing?  Where most people are abandoning the onboarding process?  At what point in the onboarding process are users more likely to follow through to the end?An extremely complex and long onboarding process can quickly kill retention. The shorter the time it takes for a user to find value, the more impactful your onboarding will be. To dig into onboarding with Moesif, you may want to use our User Funnels feature to look at each step in the onboarding process, how many folks convert to the next step, and how long it takes to complete each step. A better onboarding experience could be the secret to really boosting retention through improving overall customer experience.Engage customers through customer feedback surveysGreat customers who enjoy your product can answer some of the questions around why they continue to use your product. Customers who dropped off your product can also offer just as much insight. Getting customer feedback via a customer survey for both types customers can unlock insights into why users are staying and leaving, the core of the retention equation. Using a customer survey to to build up qualitative customer data can help you figure out how happy customers have established their brand loyalty.For users who are staying, it’s important to ask questions about what they like the best about the product, information about what demographics they are part of, and what parts of the product would potentially get them to leave (this could be questions around features, reliability, or even price). Once you’ve discovered what is keeping users around and find some trends, you can double down to try and spread these good vibes to new and other existing customers. As for negative feedback, this could be prioritized to make sure issues are resolved which could lead to a boost in retention.More importantly, especially if your retention is already low, is figuring out why previous users are leaving the platform. In a short survey, you could ask these customers why they left and what it would take to get them to return. This should give you insight into issues and potential solutions, setting you on the right path to improve the product and hopefully bump up retention. Once the product is improved, you may even think to reach back out to these previous users to let them know their feedback has been listened to and you’d love to have them try out your new and improved experience.Customer feedback surveys can be an extremely effective way to get succinct feedback to some of your biggest customer experience and retention problems and likely help you uncover issues that you never even knew about that are impacting customer satisfaction.Don’t stop marketing once a customer is acquiredMany companies stop marketing once they have someone signed up, onboarded, and using the product. Logically this may make sense, but in practice, it couldn’t be further from the truth. You should always aim to be engaging users to keep your product and services top-of-mind. This could include newsletters, product release notes, and other great content to keep users engaged and discovering new value within your product. Keeping in touch with customers and continuing to market your product is a great way to get customers to return to the product and discover new ways to unlock value with it. This should be a cornerstone in almost all customer retention strategies that businesses implement.A big caveat to this approach is to make sure that you are using smart automation to continually market to existing customers. Using personalized emails that contain content relevant to the user is crucial to maintaining the line between receiving value and being annoyed. Using events from within your application to drive specific emails can be a great way to do this, such as when a user uses a feature for the first time and you email them an overview or link to the docs so that they can dig deeper if they want. In Moesif, doing this type of email marketing can be done through the use of Behavioral Emails where you can trigger specific emails based on application usage or specific events or customer behavior within the application. This can act as a way to keep marketing to existing customers while also making sure that you are offering great automated customer support. You should also make sure that the volume of emails being sent are not overwhelming to users, potentially getting you on the blocked or unsubscribed list.Improving Retention With MoesifNow that you know what retention is, why it’s important, and a few tips on how to potentially improve it, you can start tracking customer retention metrics using Moesif. Aren’t using Moesif yet? sign up today to get started and in a matter of minutes begin tracking and improving customer retention and customer loyalty. Whether you are using our Retention Analysis to see track your customer retention rate or using Behavioral Emails or other features to help increase customer retention, Moesif can help to supercharge your customer success and retention. Let Moesif help you build a customer retention strategy that works since retaining a loyal customer is the most efficient way to build revenue and increase customer lifetime value.",
          "url": " /api-product-management/retention-analysis/What-Is-Product-Retention-And-How-Can-You-Improve-It/",
          "author": "Matthew",
          "categories": "API-Product-Management, Retention-Analysis"
        }
      
    ,
  
    
        "api-monetization-api-pricing": {
          "title": "What You Need to Know About API Pricing in 2026",
          "content"	 : "Pricing APIs correctly is a key part of your API monetization strategy. That means understanding how you should charge for usage, whether it is better to set your pricing by month or quarter, whether data tiers or a pay-as-you-go model would work best, and a whole host of other elements. In 2026 it also means competing with how buyers think about LLM token pricing, designing for AI agents that consume your API at machine speed, and giving enterprise customers the chargeback views their finance teams now expect.                Monetize in Minutes with Moesif              14 day free trial. No credit card required.              Try for Free        This post walks through everything you need to know when pricing APIs in 2026.Why API pricing got harder in 2026Three shifts in the last 18 months reshaped the conversation.LLM APIs reset the price floor. When buyers compare a generic REST API against GPT-class endpoints, they expect prices measured in fractions of a cent per call. A $0.10-per-call transactional API now feels expensive even when the underlying value is high.AI agents consume APIs autonomously. A single human session can trigger dozens of downstream API calls through tool-using agents. Pricing models built for “one user, one call, one bill” leak revenue when an agent is doing the calling.Chargeback is back in the conversation. With LLM spend ballooning inside enterprises, finance teams want engineering to attribute API and AI costs back to the business unit that drove them. That changes what your customers want to see in their bill, not just what they pay.Every section below is written with those three pressures in mind.Building a pricing strategy for your APIsWho pays for an API?At the most basic level it is important to ask who actually pays for an API. As an API provider, monetizing your existing API starts with charging the end user, the businesses that are utilizing your API to provide business value. So, by understanding the value that your API provides, you will be better able to build a favorable pricing strategy.One of the founding principles when treating your API as a Product is to understand your customer. By seeing how they use your product, you will not only be better able to price your service, you will also get the insights you need to decide what to ship next.How do you calculate API price?There are several ways to determine what your API is worth. Charging by API call is one common pricing metric, but it is no longer the only option. Different usage-based billing approaches to consider when mapping out your complete API strategy:  Transaction volume, based on API call count. Ideal for APIs and event-based platforms in communications (Twilio) or analytics (Moesif).  Token or unit volume, based on input and output tokens. Mandatory for any LLM-wrapping API, and increasingly common for any API that returns variable-sized payloads.  Revenue or cost share, where you charge the end user a percentage of revenue or transaction fees. Well suited to payment platforms (Stripe is the canonical example).  Data volume, based on gigabytes sent or minutes processed. A good approach for platforms focused on data, such as logging or storage (the AWS S3 model).  User-centric, pricing based on the number of monthly active users. A modern version of per-seat licensing.  Resource-based, by compute units or active hours. Ideal for compute-heavy infrastructure such as databases or virtual machines.  Hybrid or value-metric, combining two of the above (for example, a base subscription with per-call overage). The dominant model among public API companies above $10M ARR.The product you are offering plays a key role in your pricing strategy. There is much to be learned from SaaS pricing, since most API pricing models inherit from it. Leading thinking on SaaS pricing emphasizes setting prices based on three main factors: the cost of delivering your product, what your competitors charge, and the value metrics your customers care about. Feedback from your first ten paying customers is also key, because they will have plenty of thoughts on which plan will and will not work for them.  Where Moesif fits: the harder problem after picking a model is metering on the unit your buyer values. Moesif’s usage-based billing lets you bill on any field inside the request or response, so you can ship a token-based or hybrid model without rebuilding your billing pipeline.Internal versus external APIsExternal-facing APIs, the ones available to other companies outside your organization, often perform actions on behalf of users and are often charged for. Internal APIs, which you might use to interconnect different services within your organization, are not usually charged for. The 2026 wrinkle is internal chargeback: even when you do not charge another company for an internal API, the team that owns it increasingly needs to attribute its cost back to the business units that consume it. We have a full walkthrough on building an internal chargeback model if you need one.Pay-as-you-go pricingThe pay-as-you-go billing model means that customers pay only for what they consume. That could be measured by a range of metrics, such as number of messages sent. This consumption-based subscription model is well suited to APIs, which are naturally transaction-based.With pay-as-you-go API billing, you choose which metrics to charge on based on your customers’ usage. You can also implement volume discounts depending on how you monetize your API.The pay-as-you-go model is easy to implement, which makes it a popular choice for many companies devising their first API monetization strategy. From your customers’ perspective, pay-as-you-go billing is cheap to adopt. However, it can be more expensive for them in the long run if usage is not capped. You will also need to implement a system with the capability to alert your users around quota limits to avoid surprise bills. Such a system works to your credit, because it avoids surprise bills prompting your customers to shop around for cheaper alternatives. Pairing pay-as-you-go with behavioral emails for usage alerts is the standard pattern.How are API calls calculated?There are many different ways to calculate how you bill for your APIs. This is where Moesif really shines, since the platform can bill based on any parameter in your API. It means you can shape your API monetization strategy around your customers’ precise requirements. If you have decided on usage-based billing, possible consumption metrics include number of API calls, gigabytes sent, minutes consumed, monthly active users, active hours, or any field inside the request body.How much can an API make?Another key question, regardless of pricing strategy, is how much your API can generate. APIs are a subset of SaaS and have proven time and time again to be capable of scaling at hypergrowth speeds.The two original poster-children for API success are still instructive. Stripe and Twilio both built their businesses on an API-first model. After launching its product in 2011, Stripe scaled from $40M in revenue in 2016 to a multi-billion-dollar net revenue figure within a decade, all on transaction-fee pricing. Twilio grew from $50M in revenue in 2013 to several billion in annual revenue, on usage-based pricing applied to every API surface they sell.The 2026 entrants worth studying are the LLM API providers (OpenAI, Anthropic, Google) who used token-based pricing with cached-input discounts to grow API revenue faster than any SaaS company in history. The lesson is not to copy LLM pricing. The lesson is that pricing models are malleable, and the providers who reprice fastest grow fastest.API or enterprise software implications?If you have the option of offering an API product or an enterprise software solution, this decision affects how you deploy and generate revenue. One strategy is to focus on offering just your API to end users, which lets you get to market faster and focus all of your engineering talent on the business logic. Going the enterprise software route means investing in a frontend too, and by focusing all of your resources on differentiated business features, you may be able to charge more.Tiered pricing: decide price points and package accordinglyWhen developing a tiered pricing model, it is up to you to decide price points and package your tiers accordingly. It has become increasingly common to adopt a “freemium” pricing strategy, where the starter tier is free and the later tiers are paid. In Moesif’s case, we have four tiers (Free, Grow, Pro, and Enterprise), providing a concrete example of how to structure pricing.There are trade-offs between transactional and tiered product pricing models. If you are using the tiered approach, you will need to define which features and usage quotas are included in each tier. You will also need to address customers’ primary concern with this model:What happens if I reach the API plan’s limit?Each tier defines usage, number of users, and features. If a customer needs more, they have the option to subscribe to the next tier as part of their planned growth. It is a simple model that makes it easy for the customer to budget and administer, so it is certainly one to consider as part of your API pricing strategy.What is a premium API business model?The other benefit of tiered pricing is that you can introduce premium elements. A premium API business model is one where top-tier customers benefit from features that are not available on any other tier. In Moesif’s case, premium extensions include the ability to sync API usage data to on-premise warehouses, Salesforce integration, MarTech tools, and more. The premium API elements you can offer will be based on your unique business model and use case.Hybrid: tiered base plus metered overageThe model most public API companies converge on as they scale. A flat base fee covers a quota of usage, and overage is charged per unit. This gives the customer predictability for normal usage and gives you upside on growth.If you only build one pricing model in 2026, build this one. It is the model behind the majority of API businesses above $10M ARR. The trick is metering the overage in a way the customer can predict and see in-product.Department chargeback (new in 2026)Inside large enterprises, the buyer is increasingly the platform team, and the platform team’s job is to redistribute API and AI cost to the business units that consume it. The WSO2 AI Gateway governs API, LLM, and MCP traffic from one control plane with token-level visibility and the ability to allocate token budgets to teams and workloads. Moesif sits behind the gateway and attributes every API and LLM call back to a user, organization, or cost center. If your buyer is Fortune 500, expect chargeback to be in the RFP.Pricing for AI agents and MCP serversA meaningful share of API traffic in 2026 comes from AI agents, not humans. When you expose your API through an MCP server, the consumer of your endpoints is a model, not a developer. That changes pricing in three ways.Volume is higher and burstier. An agent can make dozens of calls to resolve what a human would have made one call for. Per-call pricing extracts more revenue from agent traffic, but it also raises the perceived cost, because buyers compare it directly against their LLM bill.Per-agent attribution matters. Buyers want to know which agent or workflow drove which spend. This is the chargeback story above, repeated inside a single customer’s account.Caching is a pricing lever, not just a performance lever. OpenAI and Anthropic both discount cached input tokens, and the WSO2 AI Gateway uses semantic caching to reduce repetitive calls. If your API benefits from caching, exposing a cache-hit discount is now an expected part of the menu.If you are exposing an MCP server, the safest 2026 default is hybrid pricing (base subscription plus metered overage) with separate metering on cache hits and per-agent attribution on top.Does the API address a unique need?The final element of pricing APIs relates to the value your product delivers. Ask yourself: does this API address a unique need? If it does, you have greater freedom in terms of pricing than if your API serves a need that many competitors also address.Ease of use also comes into play, so consider how accessible your API is. Is it a REST API or another format? Is it cloud-based or does it run on premises? Will developers need an API key, and if so, is the process of obtaining it frictionless? Customers will be happier to pay for an API that is easy to use and comes with clear code examples than an API that causes them headaches.Mistakes that kill API pricing strategiesThese are the patterns we see most often when WSO2 and Moesif customers come to us mid-implementation.Pricing on a unit nobody owns. Charging per “transaction” when nobody on the customer’s side can predict their own transaction count is how you create surprise bills. Pick a unit your buyer can forecast.Launching with one model and refusing to change it. The fastest-growing API businesses reprice every six to twelve months. Treat your pricing page like a product surface, not a contract.Hiding overage charges in the fine print. Customers will tolerate overage; they will not tolerate finding out about it at the end of the billing cycle. Show running spend in-product, alert customers at 80% of their quota, and let them upgrade in two clicks.Forgetting the buyer’s CFO. Engineering buys APIs; finance pays for them. If your bill is not exportable and attributable to a cost center, the renewal conversation gets harder than it needs to.Pricing AI workloads like REST workloads. A model can consume your API a hundred times to satisfy one human intent. If your pricing extracts revenue per call but the perceived value lives at the intent level, you are setting up a renegotiation. Meter on the unit the customer values, not the unit you serve.How Moesif and WSO2 cover end-to-end API monetizationMost API monetization stories stop at “pick a model and bill for it.” The harder problems show up later: attributing cost to the right user, exposing live usage to the customer, and metering on a unit that did not exist when you wrote your terms of service.Moesif handles those problems as part of the WSO2 API Platform. WSO2 secures and governs every API, LLM, and agent call through its API Manager and AI Gateway, across any gateway (WSO2, Kong, AWS, Azure, Envoy) and any deployment model (SaaS, hybrid, self-hosted). Moesif sits behind the gateway and meters every call on any parameter, attributes cost to the right user, organization, or cost center, surfaces live usage and quota alerts to your customers, and syncs billing events to Stripe, Chargebee, Salesforce, or a data warehouse.If you are pricing a B2B API, an internal AI gateway, or an MCP-based agent product in 2026, this is the integrated stack to evaluate.Set up your pricing todayAPI pricing is a strategy problem, not a math problem. Get the unit right, build a model your customer can predict, and instrument every call so you can change the model when the market does. The earlier you instrument, the more options you have.Take the plunge today. Start a 14-day Moesif free trial and meter your APIs on any parameter in the request or response. No credit card required. If you are pricing an AI or MCP product at enterprise scale, the WSO2 API Platform team can walk through the integrated architecture with you.Frequently asked questionsWhat is API in pricing? “API pricing” is the strategy and mechanics by which an API provider charges customers for access. It covers what unit you charge on (calls, tokens, users, revenue share), how you package those units into plans, and how you bill and report on them.How much does it cost to have an API? As a consumer, costs range from free tiers (Gemini and OpenAI both have generous free quotas) to several dollars per million tokens for high-end models, to flat $50-$500/month subscriptions for productivity APIs. As a provider, the cost to build and operate a paid API is dominated by gateway and observability tooling, payments infrastructure, and engineering time; the marginal cost per call is usually a fraction of a cent.How is API pricing calculated? Three inputs: the cost of delivering one unit of the API, what comparable APIs charge, and the value the customer gets per unit. The price per unit lives somewhere between cost-plus and value-based. Most providers start cost-plus and migrate toward value-based as they understand their customer better.What is the best pricing model for an API in 2026? Hybrid: a subscription base that covers a baseline quota, plus metered overage above it. This is the model most public API companies converge on by the time they pass $10M ARR. For AI and MCP-exposed APIs, layer per-agent attribution and a cache-hit discount on top.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /api-monetization/api-pricing/",
          "author": "Larry",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "technical-api-analytics-what-is-dynamic-sampling-and-how-it-works": {
          "title": "What is Dynamic Sampling and How it Works",
          "content"	 : "Sometimes you may want to limit the amount of analytics data coming into Moesif. This could be because you want to exclude specific traffic, such as internal or health check traffic, or you may want to reduce unnecessary data to control cost. Dynamic Sampling, available to customers on our Enterprise plan, was built to do just this. Dynamic Sampling lets you control which API calls are logged to Moesif based on customer or API behavior. Moesif intelligently extrapolates metrics for accurate reporting even with multiple sample rates. That means that no matter what rules or sample rates you have set up, you can be sure you are still seeing an accurate representation of your data.Dynamic Sampling is controlled by setting up sampling rules in the Moesif Dashboard. You can create rules based on criteria such as request path, response status codes, user or company behaviors, and more. You can also control the applied sample rate for each rule. The sample rate is the percentage of events that will make their way into Moesif. For example, if you set a rule to sample 50% of events, Moesif will log half of the matching events intelligently (and not just log every other event to meet the 50% sampling rate).Why use Dynamic SamplingOur Dynamic Sampling feature serves two use cases for customers:  Gives users the capability to trim down excessive or uninteresting calls populating within Moesif while retaining the insight you gain from the platform.  Allows users to reduce their Enterprise subscription costs and save money. Moesif considers the sampling rules you have created and intelligently extrapolates usage metrics.  Sampled API calls do not count against your quota; for example, if your API usually sees 1 million API calls per month but the global sample rate is set to 25%, only the 250 thousand events per month coming into Moesif will count towards that quota.When not to use Dynamic SamplingIt would be best if you avoided Dynamic Sampling on specific events moving into Moesif. These use cases include when you are leveraging with other Moesif features like Metered Billing or Governance Rule where you’ll want every call to be assessed in Moesif.Dynamically sampling the number of API calls coming into Moesif would impact a monetized API because it would fail to track the exact usage, likely missing some events depending on the sampling rate applied. This will affect your billing meters and the invoices being sent to customers. However, a way around this is to apply Dynamic Sampling to endpoints that are not monetized and let events counted within your Billing Meter sample at 100%.Another reason not to use Dynamic Sampling is when your logged API calls are less than the number of calls your subscription plan supports. For example, suppose you are making fewer API calls than your subscription plan includes. In that case, dynamic sampling is unnecessary unless you are simply trying to exclude data you don’t want in Moesif to keep data cleaner. Examples of this may be not including traffic from health check probes or internal testing.Four types of sample ratesDynamic Sampling rules are categorized into four kinds of sample sets and are prioritized in the following manner:  Sample rates for a single user or company  Sample rates based on regex rules  Sample rates based on user behaviors and demographics (Saved Cohorts)  Sample rates applied globallyThis means that if a sample rate is set for a specific customer, this will take priority over a sample rate associated with a behavioral cohort that a customer belongs to. Global sample rates have the lowest priority and are, by default, set to 100% for all customers. If an API call matches multiple rules, it gets processed by the first kind of sample rate in the prioritization hierarchy.How to create Dynamic Sampling rules in MoesifNow that you’ve learned what Dynamic Sampling is and what it can do. Let’s briefly look at how to set up some Dynamic Sampling rules. First, navigate to the Dynamic Sampling screen within Moesif.Here is where we can access all of the sampling rates that have been created.Selecting the + Rule button will allow us to create new user or company, regex, or cohort sampling rules.Specific users or companiesAfter selecting either New User Rule or New Company Rule, select the user lookup or company lookup button, respectively.  In this example we will be showcasing the user lookup however the process is the same for companies.Proceed to select an ID from the user list to enter a their Profile View. You can use the Users Where filter to quickly get at the user you are looking for.On the Profile View under Sample Rate, select the pencil icon to change the specific user’s sample rate.Disable the inherit toggle to allow adjustments of the user’s sample rate. Set the desired sample rate by typing in the rate manually, or by using the slider, and select save.Regex rulesReturning to the Dynamic Sampling rules page, select New Regex Rule in the + New dropdown, enter your desired criteria and sample rate and save it. In this example; we have set the sampling rate to zero to exclude all GET requests to any of our health endpoints. These calls will not appear in the UI or count against our event quota.Behaviors or demographics via Saved Cohorts  Behavior and demographic sampling is accomplished by utilizing Saved Cohorts; if you are unfamiliar with Saved Cohorts check out our blog post, written guide, or video guide to learn more.Setting sampling rates for cohorts is quite easy. Select the + New button on the Dynamic Sampling rules page then either New User Rule or New Company Rule. A modal will appear letting you select a perviously saved cohort. Continue to set your desired sample rate as well as the priority. Priorities are ordered within user or company cohorts. User sample rules always takes precedence over company sample rules.GloballyYou can apply global rules by clicking the Settings button on the Dynamic Sampling rules page. The resulting modal that appears allows you to set the global sample rate, suppress known bot traffic, as well as stop traffic from given IP addresses either expressly or through regex rules. The bottom of the modal showcases all users or companies that have their sample rates set within their respective user profiles.Try it outFor many reasons, Dynamic Sampling can be an essential part of your Moesif configuration. We also reviewed some limitations and scenarios where Dynamic Sampling would not be the best choice, such as when applying a Billing Meter or Governance Rule to an endpoint. Lastly, we went through how to set up different types of rules within Moesif itself.If you are already on a Moesif Enterprise plan, Dynamic Sampling is available to you and can be applied. Feel free to check out our in-depth written tutorial or video guide. If you are not on an Enterprise plan, you can contact our Sales team to unlock this powerful capability. If you’re new to Moesif, sign up today to get started on unlocking the power of API analytics for your organization.                Easily Monetize Your APIs              14 day free trial. No credit card required.              Learn More        ",
          "url": " /technical/api-analytics/What-Is-Dynamic-Sampling-And-How-It-Works/",
          "author": "Dylan",
          "categories": "technical, API-Analytics"
        }
      
    ,
  
    
        "technical-api-analytics-using-event-tags-in-moesif": {
          "title": "Using Event Tags in Moesif",
          "content"	 : "Sometimes you may have related events that you want to group together or want to give a friendly name to events. This could be especially true if you have a group of API calls you want to combine into a single API product. Fortunately with Moesif you can do exactly that, so that groups of events can be looked at as a single unit, like a SKU. In Moesif, this can be done by using the Event Tag feature.Creating An Event TagThe first step in creating an event tag is to navigate to the Live Event Log screen. This can be done by going to the + New button and selecting Live Event Log.Once on the Live Event Log screen, we can determine our tag’s filter. In this example, we will filter upon two endpoints that are related. With the 2 routes added to the filter, we can now create the tag. Click on the Create Tag button.A modal will then appear which shows the filter for the tag. We will type in a name, Categories API, for the tag and then click Create “Categories API”.Lastly, you will click the Create Tag button.Now, the tag is created and can be used in Moesif so that events can be viewed using a single criterion.Updating a tag will also update old events. However, this may take a few minutes. You can view the processing status of tags from the Tag manager.Using An Event TagUsing an event tag can be used just as any other attribute. A good example of this would be using the tag in a live event log query. Instead of having to specify the endpoints, you can then just specify to include events that match the tags. Below is a quick demonstration of what that filter would look like and the output.You’ll also notice that entries also show the tags that they are part of. Each of the entries shows the Categories API tag.Similarly, you can group by tag names just like any other attribute. Below we can see the traffic volume for each of our APIs. This can be helpful to organize your endpoints into API products when creating billing meters.Accessing and Editing Existing TagsTo edit existing tags, you can access them through the Tags Manager. This can be accessed through the Settings menu at the bottom of the screen in Moesif.Once clicked, you’ll be able to see previously created tags, edit them, and delete them.To edit a tag, simply click the ellipsis button at the end of the entry and click Edit.A modal will then pop up and allow you to edit the filter for the tag. Once completed, click the Save button.To delete a tag that you no longer want to use, simply click on the ellipsis button for the entry and click Delete.A modal will then appear to confirm the deletion. Click Yes to delete the tag.Generating Tags from OpenAPI SpecAnother feature available in the Tags Manager screen is the ability to create tags automatically based on your OpenAPI spec. To do this, simply upload or drag and drop your OpenAPI Spec and tags will be automatically generated.Wrapping UpAnd that’s all there is to creating tags so that you can combine event metrics together under a single label. Whether just trying to make looking up related events easier or adding the concept of an API Product into your metrics, Event Tags are a great way to accomplish this.Not using Moesif yet? Easily create an account and integrate Moesif with your favorite programming language or API management tool in a matter of minutes. Try out Moesif and Event Tags today and unlock the power of API and product analytics.                Catalog and Analyze your APIs With Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /technical/api-analytics/Using-Event-Tags-In-Moesif/",
          "author": "Matthew",
          "categories": "technical, API-Analytics"
        }
      
    ,
  
    
        "business-compliance-what-is-the-difference-between-data-compliance-and-data-privacy": {
          "title": "What Is Data Compliance? A Practical Guide for 2026",
          "content"	 : "Data compliance is the practice of collecting, storing, processing, and sharing data in a way that meets all applicable laws, regulations, and industry standards, including GDPR, HIPAA, CCPA, PCI DSS, and SOC 2. It covers data security, privacy, and governance, and it protects organizations from fines, breaches, and reputational damage.If you build APIs or run a SaaS platform, data compliance is no longer a back-office checkbox owned by a legal team. Every request your service handles is a potential compliance event: a webhook that ships PII to a third-party processor, a debug log that captures a credit card number, a support engineer who queries production data without an audit trail. This guide covers what data compliance means today, the regulations you need to know, and how API-first teams build programs that survive an audit.What Is Data Compliance?Data compliance is the discipline of making sure every piece of data your business handles, from a customer email to a payment token to a patient record, moves through your systems in a way that satisfies the laws and standards that apply. That covers how data is collected, where it is stored, who can read it, how long it is kept, and how it gets deleted.A compliance program covers three intertwined surfaces:  Security: the technical controls that keep unauthorized parties out (encryption, key management, access controls, network segmentation).  Privacy: the rules that decide what data you collect, what you do with it, and how you honor user rights like access, correction, and deletion.  Governance: the policies, audits, and evidence that prove to a regulator (or a customer’s procurement team) that the controls actually work as documented.The three overlap but are not the same. A team can have strong security and still fail compliance if it has no documented retention policy, or be technically compliant and still leak data because no one wrote a privacy rule for a new debug endpoint.Compliance is also continuous. SOC 2 Type II evaluates controls over six to twelve months, per the AICPA’s SOC suite guidance, and GDPR obligations are ongoing duties. Treating compliance as a project that ends when the certification arrives is the most reliable way to fail the next audit.                Grow Your API Business with Moesif              14 day free trial. No credit card required.              Try for Free        Why Data Compliance MattersFine avoidance is the most visible driver. GDPR Article 83 sets administrative fines up to €20 million or 4% of an organization’s worldwide annual turnover, whichever is higher, for the most serious infringements (basic processing principles, consent, data subject rights, international transfers), per the official text of Article 83. Lower-tier infringements run up to €10 million or 2% of turnover. HIPAA penalties are tiered and inflation-adjusted annually under the HITECH Act, per the HHS HITECH summary. As of the 2026 adjustment, the maximum annual caps now exceed the older $1.5M shorthand (current maximum tier is approximately $2.19M, but check the HHS Office for Civil Rights penalty adjustment page or current Federal Register for the latest figure). Through October 2024, OCR had resolved cases totaling $144.8 million in settlements and civil money penalties, per HHS’s Enforcement Highlights.Fines rarely turn out to be the largest cost. The bigger drivers:  Breach economics. IBM’s Cost of a Data Breach Report 2025 tracks the global average and the shifts driven by faster identification, AI use in security operations, and the growing share of AI-related incidents.  Procurement gating. Enterprise buyers won’t sign without a current SOC 2 report or ISO 27001 certificate. A missing attestation kills six- and seven-figure deals.  Customer trust. Public breaches damage retention long after the legal exposure clears.  API blast radius. A single endpoint that leaks PII can trigger dozens of DSARs, notification obligations across multiple jurisdictions, and weeks of incident response. Every endpoint is a compliance surface.That last point is what makes data compliance an engineering problem, not just a legal one. Teams that get it right treat compliance the way they treat reliability: instrument everything, alert on deviations, and never trust that a control is working without evidence.Key Data Compliance Regulations and StandardsA small set of regulations and frameworks shapes most data programs. They overlap on basics like encryption at rest and breach notification, and diverge sharply on data subject rights and audit cadence.            Regulation      Region / Scope      Who it applies to      Maximum penalty                  GDPR      EU / EEA, plus any org processing EU resident data      Controllers and processors of EU personal data      €20M or 4% global turnover              CCPA / CPRA      California      For-profit businesses meeting revenue, or data-volume thresholds (e.g., processing personal information of 100,000 or more California residents or households)      Civil penalties per violation, plus private right of action for breaches              HIPAA      United States, healthcare      Covered entities and business associates handling PHI      Tiered, inflation-adjusted annually; current caps exceed the older $1.5M shorthand (verify current HHS figure)              PCI DSS      Global, payments      Anyone storing, processing, or transmitting cardholder data      Contractual; set by acquirers and card brands              SOC 2      Global, B2B service orgs      Service organizations holding customer data      None (audit attestation, not a law)              ISO/IEC 27001      Global      Any org operating an ISMS      None (certification, not a law)              SOX      United States, public companies      SEC registrants and their auditors      Criminal and civil under SEC enforcement      GDPRThe General Data Protection Regulation governs the processing of personal data of people in the EU and EEA, regardless of where the processing organization sits. It establishes data subject rights (access, rectification, erasure, restriction, portability, objection), requires lawful bases for processing, and obliges controllers to sign Data Processing Agreements with processors.For API teams, the high-friction work is responding to DSARs and right-to-erasure requests against production datasets. If your API logs every request and response payload to a third-party tool, you need a way to find and delete a specific user’s events on demand. Our GDPR-focused features help teams handle right-to-erasure with one-click user deletion, per-user collection blocking for right-to-object, and per-user event export for right-to-access. These controls can help support data minimization and consumer-rights workflows, but compliance still depends on the customer’s configuration, legal basis, retention rules, and operating procedures.CCPA / CPRAThe California Consumer Privacy Act, as amended by the California Privacy Rights Act, grants California residents the right to know what personal information a business collects, to delete it, to opt out of its sale or sharing, to correct inaccurate data, and to limit use of sensitive personal information, per the California Attorney General’s CCPA guidance. Note that the statute is written in terms of “consumers,” but the threshold and rights apply to California “residents or households,” and the precise statutory term matters when scoping which records are in or out of obligations. Sensitive personal information explicitly includes Social Security numbers, financial account details, precise geolocation, and genetic data, all of which routinely flow through API request bodies. APIs handling behavioral data for cross-context advertising also need to respect global privacy control (GPC) signals and provide do-not-sell mechanisms.HIPAAThe Health Insurance Portability and Accountability Act covers protected health information (PHI) handled by covered entities (providers, health plans, clearinghouses) and their business associates. Any vendor processing PHI needs a Business Associate Agreement and must implement the technical, physical, and administrative safeguards in the Security Rule. For an API analytics or observability layer, that means stripping or hashing PHI before it leaves the application, restricting which engineers can view request payloads, and producing audit logs that prove who accessed what. Our walkthrough on building HIPAA-compliant APIs covers the engineering playbook.PCI DSSThe Payment Card Industry Data Security Standard applies to any entity that stores, processes, or transmits cardholder data, per the PCI Security Standards Council. Validation requirements scale with annual transaction volume; the largest merchants require on-site assessment by a Qualified Security Assessor. The most effective way to reduce PCI scope is to keep cardholder data out of your servers: tokenize at the edge through your payment processor so your APIs never store the primary account number. Where data still crosses your boundary (refund APIs, fraud-scoring webhooks), you need encryption in transit, segmentation of the cardholder data environment, and logging that captures every access attempt.SOC 2SOC 2 is an attestation report issued by an independent CPA firm against the AICPA’s Trust Services Criteria: Security (required), Availability, Processing Integrity, Confidentiality, and Privacy. Type I tests control design at a point in time; Type II tests operating effectiveness over six to twelve months. Most enterprise procurement teams want Type II because it is a single document a buyer can hand to their security team to short-circuit a custom review. We maintain an active SOC 2 Type 2 attestation through our Trust Center.ISO/IEC 27001ISO/IEC 27001:2022 specifies the requirements for an information security management system (ISMS), per ISO’s standard page. Unlike SOC 2, it is an internationally recognized certification, which makes it the default in European and Asian procurement. An ISMS is the documented system through which an organization identifies information security risks and selects controls to mitigate them; the audit checks both its design and the evidence that it operates continuously.SOX, FISMA, FERPAA few additional frameworks shape data programs in specific sectors. SOX requires US public companies and their auditors to maintain controls over financial reporting. FISMA governs information security for US federal agencies and contractors, mapped to NIST 800-53. FERPA protects student education records at institutions receiving US Department of Education funding. If you sell into multiple sectors, expect to maintain several certifications in parallel; most controls overlap, but the evidence each auditor wants is different.Data Compliance vs. Data Privacy vs. Data SecurityThese three terms get used interchangeably, and the confusion has real operational consequences. They describe three different things:  Data compliance is meeting external rules: the obligation imposed by laws and standards, plus the evidence you produce to demonstrate you met them.  Data privacy is protecting individuals’ rights and expectations about their data. It’s the practical concern of making sure data is only seen and used by people who genuinely need it.  Data security is the technical defense against unauthorized access: encryption, authentication, network controls, intrusion detection.Compliance frequently requires privacy and security controls, but it’s the documentation and audit layer on top. Strong security does not automatically produce privacy: a perfectly encrypted database that any employee can decrypt with a shared key has security but not privacy. Strong privacy does not automatically produce compliance: you can have excellent data hygiene and still fail GDPR because you never documented a lawful basis for processing.The privacy bar is usually higher than the compliance bar. Frameworks define a floor, not a ceiling. Teams that genuinely protect user data go beyond compliance by limiting which staff can view sensitive fields, using client-side encryption so even the vendor cannot read customer data, and minimizing what is collected in the first place.How Data Compliance Applies to APIs and Cloud PlatformsAPIs are the primary movement layer for sensitive data in modern systems. A single integration can fan out request payloads to a logging service, an analytics platform, a CRM, a feature flag system, and a queue, each becoming a sub-processor under GDPR and an in-scope system under SOC 2.This creates compliance surfaces that traditional security reviews miss:  Endpoint sprawl. Every new endpoint can collect or expose regulated data. Inventory and classification has to be continuous, not annual.  Sub-processor risk. Every third party your API integrates with inherits the obligations of the data it handles. GDPR requires you to maintain a current sub-processor list and notify customers of changes.  Data residency. EU customers increasingly require data to stay inside the EEA, which constrains where your API can write logs, run AI inference, or queue background jobs.  Cloud shared responsibility. Your provider secures the underlying infrastructure; you secure everything on top (IAM, keys, network rules, data classification, access logs).  Non-production environments. Staging and QA databases often hold copies of production PII, are under-instrumented and under-audited, and are a frequent breach origin.Teams that handle this well treat their API gateway and analytics layer as a compliance enforcement point: privacy rules, payload masking, access controls, and audit logs sit at the request boundary, not in twenty downstream applications. For one common pattern, see our writeup on a secure proxy for HIPAA-compliant API analytics.How to Achieve Data Compliance: A Step-by-Step FrameworkCompliance programs that survive audit follow a consistent shape. Adapt these steps to whichever regulations apply.1. Inventory your data and your obligationsStart with two lists. The first is every data store, queue, log destination, and third-party processor in your system, with the categories of data each holds. The second is every regulation that applies based on customer location, industry, and contractual commitments. The overlap is your compliance scope.2. Implement access controls and encryptionEncrypt data at rest and in transit by default. Use customer-managed keys for the highest-sensitivity data so even the platform vendor cannot decrypt it. Replace shared credentials with RBAC tied to identity provider groups, and require MFA for all privileged access.3. Establish data handling policiesDocument classification levels (public, internal, confidential, restricted), retention periods per class, and approved destinations for each. Build the policies into pipelines: a confidential payload should never be allowed to land in an analytics service that lacks a Business Associate Agreement.4. Build audit logging and observabilityEvery regulator and SOC 2 auditor wants evidence: who accessed what, when, from where. That means tamper-evident audit logs for application access, infrastructure changes, and admin actions on your compliance tools themselves, queryable on demand. If a regulator asks who exported a user’s data last March, the answer needs to take hours, not weeks.5. Train your team and run drillsCompliance fails on the human edges. Run quarterly training on phishing, secure data handling, and incident reporting, and track completion against access provisioning. Run tabletop breach drills to test whether the incident response runbook actually works under pressure.6. Run regular auditsInternal audits should run on a fixed cadence (quarterly is typical), not just before the external audit. The goal is to find the gaps before a regulator or auditor does.7. Maintain a breach response planA documented runbook, a defined incident commander, pre-drafted notification templates, and an up-to-date contact list for legal counsel and cyber insurance. GDPR requires notification of supervisory authorities within 72 hours of becoming aware of a personal data breach, and US state breach notification laws set their own clocks. The work to meet those clocks happens before the breach.Common Data Compliance ChallengesMost programs hit the same walls. None are unsolvable, but they all require deliberate engineering.Multi-jurisdiction data residency. A customer in Frankfurt requires data to stay in the EU; a customer in Sydney requires Australian residency. Solving this means regional deployments, data routing logic at the API edge, and clear contractual scope so customers know which subsystems touch their data.Third-party and sub-processor visibility. Every vendor you add inherits your obligations. The misconception that you cannot store data with any third party is wrong: a tool with audited SOC 2 and ISO 27001 controls can be more compliant than an in-house data store with shared root credentials and no audit logging. The right question isn’t internal versus external, it’s whether the tool applies the same rigor as the rest of your system.DSAR response at scale. Manual processes work for ten requests a month and collapse at a thousand. Automate the lookup, extraction, and deletion paths in your APIs and data platforms, and tie each DSAR to a ticket for an audit trail of the response time.Compliance in non-production environments. Staging databases often hold anonymized production data with incomplete anonymization. Production-grade access controls and logging should apply to any environment that touches real customer data, including ephemeral test instances.Compliance is not just an engineering concern. GDPR, SOC 2, and similar frameworks touch sales, support, and legal alongside engineering. A “secure” AWS environment does not make you SOC 2 compliant; an in-house database does not make you GDPR compliant. The Data Processing Addendum two companies sign defines controller, processor, processing rules, SLAs, and breach procedures. Engineering controls without the operational paperwork won’t pass an audit.How Moesif Helps API Teams Stay CompliantBecause we sit in the API request path, the events we surface (who accessed what, from where, with what authentication) are useful inputs to data minimization, consumer-rights workflows, and audit logging. We don’t make a company compliant, but the request-level visibility helps teams operationalize the controls their compliance program already calls for. The features that map to specific obligations:  Privacy rules with role-based access control. Restrict which team members can view specific HTTP headers, payload fields, or PHI. A support engineer sees request metadata without seeing the body of a healthcare claim; an analyst pulls aggregate metrics without raw PII.  Customer-managed client-side encryption. Bring Your Own Key keeps sensitive data private to your organization. Even Moesif employees cannot decrypt customer data, since the keys live in your AWS KMS, CloudHSM, or IAM environment with automatic rotation.  Audit logs. Every dashboard view, query, and configuration change is logged, which is the evidentiary fit SOC 2 and CCPA auditors look for.  GDPR data subject workflows. One-click user deletion that can help support right-to-erasure, per-user collection blocking that can help support right-to-object, and per-user event export that can help support right-to-access, all through the UI or API.  SOC 2 Type 2 attestation and ISO/IEC 27001 + 27017 compliant data centers. Available through our Trust Center, with HIPAA and GDPR-ready deployment options for regulated industries.These features can help support compliance workflows; they do not by themselves make a company GDPR, HIPAA, or CCPA compliant. Compliance depends on the customer’s configuration, legal basis, retention rules, contractual commitments, and operating procedures. The goal is to push controls to the API edge where sensitive data first crosses the boundary, so downstream systems inherit a smaller compliance footprint.Building a Compliance Program That Holds UpData compliance is operational work, not a binder on a shelf. Teams that pass audits and keep customer trust are the ones that instrument their APIs, enforce policy at the request boundary, and produce evidence on demand. Pick the regulations that apply, build the controls into the systems handling the data, and review them on a cadence matched to the risk.If you’re evaluating where to enforce those controls at the API layer, see how privacy rules, audit logs, and customer-managed encryption fit together in Moesif’s security and compliance overview.This article is informational only and not legal advice.Frequently Asked QuestionsWhat is the difference between data compliance and data privacy?Compliance is meeting external legal and regulatory requirements with documented evidence. Privacy is the practical concern of protecting individuals’ rights over their data. Compliance frameworks define a minimum; strong privacy practice goes further. You can be compliant without being genuinely private, and vice versa.Who is responsible for data compliance in an organization?Compliance is shared. Executives own ultimate accountability, a DPO or privacy officer coordinates policy, engineering owns technical controls, legal owns contracts and notifications, and sales and support own operational handling of customer requests. Treating compliance as solely a legal or engineering problem is the most common cause of audit failure.What happens if a company is not data compliant?Direct penalties (fines, sometimes personal liability for executives), indirect costs (lost deals, churn, breach remediation), and downstream effects (insurance premium increases, regulatory consent orders). GDPR fines reach €20M or 4% of global turnover; HIPAA caps are tiered and inflation-adjusted annually, with current ceilings well above the older $1.5M shorthand (check HHS for the current figure).What are the 7 pillars of data compliance?The most common framing covers data inventory and classification, access controls, encryption, audit logging, retention and disposal policies, training and awareness, and breach response. Different consultancies number them differently, but the substance is consistent.How often should data compliance audits run?External audits typically run annually for SOC 2 Type II and ISO 27001. Internal audits should run quarterly for high-risk programs. Continuous monitoring through automated control checks is the modern best practice, since once-a-year audits miss drift between assessments.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /business/compliance/What-Is-the-Difference-Between-Data-Compliance-and-Data-Privacy/",
          "author": "Derric",
          "categories": "Business, Compliance"
        }
      
    ,
  
    
        "business-company-meeting-moesif-with-head-of-developer-relations-matt-tanner": {
          "title": "Meeting Moesif with Head of Developer Relations Matt Tanner",
          "content"	 : "Matt is the Head of Developer Relations at Moesif. He currently splits his time between Ontario and Prince Edward Island, Canada. Before getting into Developer Relations, Matt previously worked as a software developer, team lead, and architect at various large enterprises. As part of our Meeting Moesif blog series, we ask him about his role at Moesif, how he handles working remotely in a different country, and his views on Moesif fitting into the API landscape of today and in the future.What is your job title and what does your job entail?My job title is Head of Developer Relations here at Moesif. The role is quite diverse since you help out all aspects of the company including marketing, sales, engineering, and product. Being the glue between these departments and being a subject matter expert on the product is paramount to our roles in Developer Relations. We do everything from write technical blog, talk with other companies for potential partnership opportunities, to even sitting on client onboarding calls to help explain the product from a developer lens. In the developer relations jobs here at Moesif, we have one goal: to help make Moesif the best it can be for developers (and for all Moesif users).You work fully remote; how do you separate yourself from work?Working remotely has its benefits and drawbacks. It is very much your responsibility to make sure that you get your work done by being productive, but you also need to be a gatekeeper to insure that you take sufficient time away from work. I’m a firm believer that when people enjoy the work they do, are well rested, and appreciated, that they do their best work. This is what I strive for in my own work and life, as well as for anyone on our team. Moesif does a great job with promoting this. All that being said, I enjoy working with Moesif in the Developer Relations space so much, that most days it truly doesn’t feel like work anyways.What do your workspaces look like?My workspace looks different depending on the work that I’m doing. If I need to do some deep work for video or written content, I will usually use a desk with a large ultra-wide monitor. If I’m on sales calls or other meetings, I’ll generally take them at various areas throughout the house to keep my environment different throughout the day.Gear wise, I use a 17-inch MacBook Pro and if I’m working away from my desk I will use Sidecar to use my iPad as a second screen. The ability to do that gives a huge amount of flexibility when you’re away from your desk.As Moesif is a small company, have you felt called to lead in ways you hadn’t before?Working for many smaller startups over the years, this was expected. The great part about Moesif is the collaboration that happens as you begin to stretch and work with different parts of the company. There truly is a culture of learning here that gives everyone the chance to learn and lead in the ways that they want to.Moesif is a company where everyone is encouraged to ask questions and fully understand what we are building. Even our Sales and Marketing teams are fully aware of the product vision and the problems we are aiming to solve. If someone wants to lead, there is always trust in allowing a person to do so and the support to make sure that they are successful with it as well.Where do you see Moesif fitting into the future of APIs?I see Moesif fitting into the future of all products, APIs included. Our platform allows any business to break down what they are doing well and what they need to improve on. Plus, we give these users the tools to start to action upon these insights and track to make sure that the changes they are making are leading to improvements for their users. If someone is building a product or looking to improve an existing one, whether that’s onboarding, retention, etc., then Moesif can give them the tool set to improve.A final piece that I’m really excited about is our Billing Meter functionality. Working to monetize APIs in the past I can appreciate how hard it can be to do effectively. With Billing meters, we can send usage data to Stripe, Recurly, and Chargebee through a really simple integration (that takes about 5 mins to set up), set up the criteria to bill on, and have usage reported to the providers in a few minutes. To me, this is really a feature that helps to solidify a direct impact that Moesif can make on the future of APIs and API products.About Meeting MoesifAt Moesif, we help our customers understand how their APIs are used and how they can grow their API platform. Meeting Moesif highlights the people making that vision a reality. Our people-first culture values open communication, initiative, and impact — not to mention, perks like flexible working options that ensure employee wellness.If you’re interested in joining, you can start by learning more about what it’s like to be part of Moesif. Our Meeting Moesif blog series offers a glimpse into our office life and how we expand API observability for all of our customers. Follow the link below to see other Meeting Moesif interviews with team members.                Grow And Monetize Your API Products              14 day free trial. No credit card required.              Learn More        ",
          "url": " /business/company/Meeting-Moesif-With-Head-Of-Developer-Relations-Matt-Tanner/",
          "author": "Matthew",
          "categories": "Business, Company"
        }
      
    ,
  
    
        "business-company-meeting-moesif-with-developer-advocate-dylan-frankcom": {
          "title": "Meeting Moesif with Developer Advocate Dylan Frankcom",
          "content"	 : "Dylan is an associate Developer Advocate here at Moesif. He is a recent grad from Ontario, Canada, and is new to developer advocacy. He has experience as an independent software developer and has contributed to many open source projects and released software for the Mac and iOS. As part of our Meeting Moesif blog series, we ask him about his role at Moesif, how he handles working remotely in a different country, and his views on Moesif fitting into the API landscape of today and the future.What is your job title and what does your job entail?I’m an associate developer advocate here at Moesif. I’m a developer at heart, and as a developer advocate, I’m responsible for detailing our product and platform to other developers. The job entails showcasing our product through various mediums and working with other developers to help them make the most of our company’s product and services.There are many different roles a developer advocate can assume. Some advocate for developers internally, working with the engineering team to help them understand the developer community’s needs. Others work externally, engaging with developers through events, social media, and other channels. At Moesif, I try my best to accomplish both. I feel my role here at Moesif is pretty well defined: to help make Moesif the best it can be for developers.You work fully remote; how do you separate yourself from work?Working remotely is a great opportunity that allows for an excellent work/life balance. This is my first fully remote job, and initially, I was worried about how I would manage my time and stay productive. However, I quickly realized that remote work is a great way to manage your time and stay productive.There are a few key things that I have learned about remote work that has helped me stay productive and maintain a good work/life balance. First, it is essential to have dedicated workspaces. This can be a desk or specific areas in your house or apartment dedicated to your work. A dedicated workspace helps to create a boundary between your work and personal life. Second, it is vital to develop a daily routine and stick to it. This routine can include taking breaks, setting aside time for deep work, and scheduling time for personal life activities.One way to help achieve this balance is to utilize unlimited paid time off (PTO). This allows you to take the time you need to recharge and secure an identity outside of your professional life. Moesif provides unlimited PTO, which can be used for various purposes, such as vacation, personal time, and sick days.A healthy work/life balance is a significant hurdle when working remotely because, if you’re always working, you will get burned out, plain and simple. So it’s essential to take time for yourself to recharge, so you can be productive when you’re at work.What do your workspaces look like?I read that there are many benefits to splitting work between multiple workspaces. For example, when you have designated areas for specific tasks, it can help you stay more focused and effective and less likely to get distracted or side-tracked. Another benefit is that it can help you stay organized. Having separate areas for different types of work can help you keep track of your progress and make the most efficient use of your time. Lastly, splitting your work between two workspaces can also help to boost your productivity. For example, when you have a dedicated space for each task, you are more likely to complete it promptly. This can free up your time for other tasks or give you a sense of accomplishment.As for the workspaces themselves, they are all designed around a MacBook Pro with docking in mind. This keeps my developer environment consistent while being able to work from multiple locations. My main workspace in the office consists of your typical developer monitor setup: one landscape and the other in portrait. My partner and I share an office, and we both work remotely on some days. So we created what we like to call the “Agnostic Workstation”. The “Agnostic Workstation” has a sole external monitor, external keyboard, trackpad, and mouse. We’ll hop between the office and the workstation depending on how each of our days are lined up. I also love starting my mornings with a coffee on the patio while catching up on some missed Slack threads and Notion ticket updates from the previous day, due to the time difference.As Moesif is a small company, have you felt called to lead in ways you hadn’t before?Yes, definitely. I love working at a small company because it allows me to feel like I can make a huge impact. Small companies are usually more agile and can move faster to take advantage of new opportunities. They are also more open to new ideas and willing to take risks. This means that there are often more opportunities for employees to impact company success directly. I feel like my work makes a difference and that I can help shape the direction of the company. Of course, this also comes with more responsibility, but I think it’s worth it.When you work at a small company or start-up, you need to be able to wear many hats. It helps to become comfortable being someone that people can come to for various tasks, whether it’s answering customer questions, developing proof of concepts, or trying something that may not be in your particular domain.As a developer advocate, I’m responsible for talking to our customers, understanding their needs, and then advocating for them internally. I then work with our engineering and marketing teams to aid them in understanding how our customers are using our product. This also informs how I can develop content that helps our customers get the most out of our product. I have supported our company’s goals by writing guides and blog posts, creating videos, and helping to connect our developers with our customers. It’s been a great experience so far! I also enjoy the close-knit community formed when everyone works together towards a common goal.Where do you see Moesif fitting into the future of APIs?Currently, Moesif can help in several ways when developing an API product. First, by understanding how your API is being used, you can make informed decisions about improving it. Second, Moesif can aid in developing a monetization strategy by providing insights into which features are used most often and by whom. Finally, by understanding which features are most valuable to users, developers can price their API accordingly and ensure that they generate revenue from their most popular features.I think Moesif has a unique advantage in where it sits within the API world. The features that accompany Moesif’s suite of analytics like Metered Billing, Governance, and Behavioral Alerting offer so much more than just statistics and logs. The ability to be able to grow your API product with the insights given by analytics, instant customer support provided by behavioral emails, and the income generated via billing meters all on one platform is genuinely game-changing. The future looks promising as we continue to build out and add functionality to these features.About Meeting MoesifAt Moesif, we help our customers understand how their APIs are used and how they can grow their API platform. Meeting Moesif highlights the people making that vision a reality. Our people-first culture values open communication, initiative, and impact — not to mention, perks like flexible working options that ensure employee wellness.If you’re interested in joining, you can start by learning more about what it’s like to be part of Moesif. Our Meeting Moesif blog series offers a glimpse into our office life and how we expand API observability for all of our customers. Follow the link below to see other Meeting Moesif interviews with team members.                Grow And Monetize Your API Products              14 day free trial. No credit card required.              Learn More        ",
          "url": " /business/company/Meeting-Moesif-With-Developer-Advocate-Dylan-Frankcom/",
          "author": "Dylan",
          "categories": "Business, Company"
        }
      
    ,
  
    
        "best-practices-api-product-management-5-tips-for-recovering-revenue-with-apis": {
          "title": "5 Tips For Recovering Revenue With APIs",
          "content"	 : "Recovering revenue is an important part of running a successful venture in the modern API economy. With an API product it can be easy to undervalue your services and, ultimately, your business. This is why many API providers turn to billing customers for their usage, but which API monetization method is best for your product stack? Moesif enables you to make smart, informed decisions around your customers and maximize the monetization of your business model. Here’s how to approach your API monetization structure to minimize churn risk, and maximize API revenue and customer satisfaction.1. Monitor Customer Behavior and Usage to Create GuidelinesUnderstanding the business value of your API and how to structure revenue recovery and usage guidelines starts with data. Be informed about what your users generally do and how many calls they normally take or receive over your API. Benchmark user behavior with analytics on your user-base with data collected and stored over your API. It is important to note that users behave differently depending on what your API is meant for, but understanding the base needs and wants of your users will help your developers set up meaningful internal protocols for dealing with API consumer issues.Setting up informed internal protocols and alerts for flagging outliers allows you to contact customers more quickly around possible usage or billing concerns or manage support needs before customers are aware of possible issues, reducing customer churn.2. Direct Monetization with Usage-Based BillingDeciding on an invoicing strategy is no easy feat, but monetizing your APIs is one way to recoup lost revenue without extending your product line. Moesif can empower your subscription business needs with easily integrated API monetization support. Determine the right API monetization strategy for your product by examining your current users - what features do they rely on most? What metrics do they base their business on? Understanding your user’s top needs will enable you to implement revenue generators.Usage-based billing enables you to achieve the real business value of your APIs. If you’re following the traditional way of billing for API usage (a tiered subscription model), then you’re leaving money on the table. Whether you implement a prepaid or postpaid model, monetization allows you to select the most relevant metrics (API call count, active users, gigabytes sent, etc.) to capitalize on, bringing value to your customers while allowing you to charge for their API access. Pricing out your rate plan structure begins with a look at all the features your API management platform or product offers. Look at the most necessary tools and the cost to the business to offer them to build your pricing options, leading to direct revenue.3. Appeal to DevelopersAPIs are used by developers, so it makes sense to take a developer-first approach to documentation, your developer and general messaging. Creating revenue opportunities starts with happy users, which starts pointed API resources. Your API may be the quickest, smartest way to solve a given problem, but if integrating it is too convoluted, users will move on to the next option. Documentation should be clear and straight to the point to avoid confusion during implementation. The more straightforward your initial documentation is, the more likely developers will be to successfully stand up and use your API - developers value ease of use. When considering how to attract users, consider messaging dedicated for the individuals who will use your API in earnest. After all, attracting stakeholders begins with developer advocacy.Be sure to take advantage of API users willing to test your product, as it will be easier to make a sale when developers have had a chance to test and advocate for implementing your API. Offering trials and flexibility around feature exploration is one way to push companies to make informed decisions around increasing API usage and create extra monetization opportunities.4. Personalize to Your Differing UsersDifferent users will behave differently because they use your API product for differing purposes. The behavior and usage of a developer in fintech, for example, is vastly different from one in healthcare. Understanding how your API is used is one thing, but who is using it? Data offers insight into consumer patterns, customer retention, ideal follow-up frequency, even how often to retry a payment method. Personalize these insights to the different companies and business sectors utilizing your app. As more businesses undergo a digital transformation, make sure your messaging is true to different customer’s API needs.Building a comprehensive impression of your users is not easy, but Moesif makes it simpler with real-time data and advanced user usage analysis. Understand your user journey down to the individual user and make smarter, data-driven decisions around your API monetization model.5. Give Customers Leeway Within ReasonNothing speaks to users the way patience and nudging can. When payment fails, do not be afraid to contact your customers. When it comes to subscriptions where customers payment methods fail to complete a transaction, you may choose to:  Keep the subscription active,  Terminate the subscription, or  Pause or temporarily deactivate itCustomers are valuable and important to the ecosystem of an API company, but so is trust and revenue. This is why it can be better to pause the subscription, withholding future services while giving the customer the benefit of the doubt to correct a failed payment method. To resume API consumption, a digital business will rectify their payment issue after being alerted to the problem. Unless abuse occurs, air on the side of alerting your users to possible issues rather than jumping straight to subscription cancellation.It is important for your customer success team to balance your business needs with customer desires by maintaining regular communication around account status. This could be as simple as [setting up an external alerts with Moesif’s behavioral emails to let customers know their usage is exceeding their quota to avoid surprise charges, or an email nudge around failed payment and timeline to subscription deactivation.Concluding ThoughtsBuilding a winning business strategy for your product starts with using data to make informed decisions around revenue growth and customer communication. Learn more about how Moesif can help your APIs generate further revenue and support your customer solutions to scale.                Get A Big Business Impact From Your APIs With Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /best-practices/api-product-management/5-Tips-For-Recovering-Revenue-With-APIs/",
          "author": "Rachael",
          "categories": "Best-Practices, API-Product-Management"
        }
      
    ,
  
    
        "best-practices-api-product-management-why-twilio-customers-are-not-going-anywhere": {
          "title": "Why Twilio Customers Are Not Going Anywhere",
          "content"	 : "It’s no secret: we’re fans of API first companies like Twilio. With more than ten million customers using their platform, it seems we’re not alone.Longtime the darling of developers, Twilio’s approachable platform makes it easy to send voice, video, and SMS messages across nearly any context.Today, we’re diving into how Twilio still offers the best in customer engagement.Low-Code/No-Code Functionality“Build or Buy,” that is the question. As in the classic Shakespearean dilemma, “to be or not to be,” a choice must be made. For SaaS companies, the cost to add new features once meant building those features in-house, or paying an external vendor for added functionality. Both options posed serious challenges.There is now a third option. Low-Code/No-Code (LC/NC) tools offer added capability without the need to write complex code. Software that may normally take months to build can be released in weeks using LC/NC tools. Naturally, the option is already gaining popularity.The trend isn’t lost on Twilio, who rolled out Twilio Studio in late 2017. Twilio Studio lets customers enjoy robust communication infrastructure without any need to write code. This is great for non-technical users, as well as lean engineering teams looking to build quickly.Streamlined User InterfaceTwilio’s interface provides both ease and control. Developers and product managers alike need not fear decision fatigue here. Like a Bentley, Twilio drives smoothly while packing lots of power under the hood.In onboarding alone, it’s easy to see what makes Twilio’s interface so great.Setting ExpectationsFrom the beginning, Twilio lets the user know what to expect. Anticipating that someone might wonder, “how much is Twilio,” Twilio immediately lets users know that no upfront payment is due.This is a gentle introduction to the transparency they deliver on every aspect of the platform. Twilio is also quick to let users know what they can do with their product. See the below element from their early onboarding process as an explanation.In just these first few minutes of onboarding, you will already know that Twilio doesn’t plan to surprise you. Deeper into the platform, they continue to present new information in the same way.Less is More  “Perfection is achieved, not when there is nothing more to add, but when there is nothing left to take away.” - Antoine de Saint-ExuperyIf there is one word to describe Twilio’s interface, it is “precise.” Uncluttered menus, well-placed tool tips, and strong visual design create a clean user interface.Every choice presented to the user is intentionally simplified, without losing meaning. Once a user completes the above steps, they offer focused quizlets to customize the user’s new account. The wording is direct, unambiguous, and approachable. Meanwhile, the number of choices presented to the user offers customization without overwhelm.After those few steps, initial onboarding is complete. From here, users are directed to their dashboard for the first time.Users immediately get to test-drive Twilio’s full functionality. A free user has the chance to set up a Twilio phone number, add teammates, and to try out Twilio Studio for a LC/NC experience.Diversified Help ResourcesMeanwhile, accessible support resources ensure that anyone who needs the details can find them. These resources are available through multiple, thoughtful links, ensuring that all users get the information they need.Users may opt to learn about Twilio through help articles, docs, a guided tour, and even a game, Twilio Quest.Overall, Twilio’s interface provides exactly what it needs to, without compromising user experience.They Were Among the First “Developer-First”Before Twilio was a post-IPO unicorn, they weren’t known for selling like a typical enterprise SaaS company. They often didn’t “sell” at all. Yet, they still grew.One famous story about founder Jeff Lawson illustrates this perfectly.“ABOUT A YEAR AFTER LAWSON and two friends founded Twilio in 2008, Lawson was invited to introduce it at a popular networking mixer called the SF New Tech Meetup. Rather than talk about an inherently difficult-to-explain technology, Lawson decided to let the Twilio software speak for itself. In front of a thousand people Lawson began telling his story while simultaneously coding a Twilio app—a simple conference line.In just a few minutes he opened an account and secured a phone number, and after writing a handful of lines of code that everyone in the room could understand, his conference line was up and running. Lawson then asked everyone to phone in, and just like that a mob of developers was on a giant conference call. Lawson then added some more code, and his app called everyone back to thank them for participating. As phones throughout the room began buzzing, the crowd went wild with enthusiasm. “ – Miguel Helft, ForbesTry Before You BuyTwilio offered developers the chance to try their product without expectation of purchase. Though counter to the dominant enterprise sales model of the time, skipping upfront costs earned the trust of developer communities. Quickly, Twilio developed a reputation as an accessible tool for any project.Twilio doesn’t see developers only as potential customers, they see a community. Some even credit Twilio for popularizing Developer Evangelism. Though their payment model is just one part of their developer-led strategy, it’s one that’s still making their product great.Any free user creating a Twilio account will find a robust suite of powerful features. Free trial users are entitled to a Twilio phone number, allowing them to try out voice call, video conferencing, and SMS messaging functionality. As a result, even Twilio’s free capabilities provide a realistic preview of their paid plans.Flexible Pricing OptionsIf a user decides to exceed their $15.50 trial balance, provided complimentary with signup, their bill scales seamlessly with PAYG pricing. To learn more about the PAYG model, see our other post here.When a new user wants to become a customer, they’ll find plan options for any situation. This is great for nearly any type of user, whether they are looking to build projects experimentally, for fun, or for vital infrastructure. Almost any use case can be accommodated.Why We Like TwilioIf anything is constant in the world of software, it’s change. As a company staking its future on our belief that every company will become an API company, we look forward to seeing other API-first companies grow.Twilio continues to adapt to the changing realities of SaaS - it meets every new challenge with adaptation. We aspire to similar resiliency here at Moesif, which is why we offer transparent pricing features and streamlined onboarding.  Moesif offers four plan tiers with flexible PAYG billing on each tierOur pricing model combines the flexibility of Pay-As-You-Go with the reliability of subscriptions. Users can benchmark their monthly use with one of our four pricing tiers. But, if they use far less or more of our service than anticipated, then their monthly bill will reflect that actual usage.Similarly to Twilio, we too aim for ease in our onboarding process. For most installations, Moesif needs only a few lines of code to add our SDK. Each onboarding workflow is easily customized to the user’s existing tech stack. Like Twilio, our platform aims to reduce decision fatigue by offering targeted information.Like Twilio, we also offer a “try before you buy” model. This way, Moesif users can experience the full capabilities of our product before making a decision. Anyone can become a user by signing up for a free trial here.Overall, we think Twilio is a “leader” in customer engagement for good reason. Its clean user experience, commitment to developer communities, and speed of innovation certainly make it a great choice for communication infrastructure. In other words, those ten million customers aren’t leaving anytime soon!                Build Products Devs Like With Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /best-practices/api-product-management/Why-Twilio-Customers-Are-Not-Going-Anywhere/",
          "author": "Savannah",
          "categories": "Best-Practices, API-Product-Management"
        }
      
    ,
  
    
        "technical-stripe-aws-api-gateway-end-to-end-api-monetization-with-aws-api-gateway-stripe-and-moesif": {
          "title": "End-to-End API Monetization with AWS API Gateway, Stripe, and Moesif",
          "content"	 : "Many API developers and companies struggle to find ways to easily set up systems to monetize their APIs. Some are simple but not customizable, some are complex and require massive engineering effort to actually get it all running.To make things easier, Moesif created a feature a few months ago called Billing Meters which gives massive customizability but with a minimal amount of code and engineering effort.For this example, which could actually be used out of the box, we will use Moesif, AWS API Gateway, and Stripe to charge users for API usage. For this setup there are a few assumptions:  You have an active AWS Account  You have a running instance of AWS API Gateway (with an endpoint created)  Your AWS API Gateway instance is protecting your endpoint(s) with an API key  You have created a Usage Plan in your AWS API Gateway  You have an active Stripe account  You have an active Moesif account  You have configured the Moesif and AWS API Gateway Integration  When configuring Moesif in the AWS console, the log format must include the follow:  "user": "$context.identity.apiKeyId"  this will map the API Key ID to the Moesif UserId This is a crucial configuration step or the example code below will not work as expected.The setup is pretty simple from the outside. We will create a /register endpoint which:  Generates an API key using the AWS API Gateway Client SDK  Added the API key to a usage plan  Registers the user in Stripe  Subscribes the user to a product  Registers the User and Company in Moesif  Returns the API key to the userI’ve also created a little frontend for it that is a simple form that registers a user by calling the /register endpoint and then displays the generated API key for the newly registered user.                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free        1 - Create Your Product and Price in StripeThe first step we will take is to create a product and price in Stripe. It’s best to do this step first because then when you integrate Stripe into Moesif you’ll already have some pricing plans for Moesif to pull in. A pricing plan can then be associated with specific billing criteria set up within a Billing Meter in Moesif.First make sure to meet these prerequisites.To create a product and price, log into Stripe and proceed to the Products page in the Stripe UI. Once there, click on the + Add Product button in the top right corner.You’ll then be able to add in the details for your product and price(s) for it. The form for your product will have a few fields to fill out.Product InformationName  This is the name of your product. In the example below, we use the name “My API”.Description  This field is optional but you could put a brief description of the product here. In the example below, we use a description of “This is a monetized API”.Image  Optionally upload an image that can help you easily recognize a item on the Products page. We’ll be using the default placeholder image in this example.Pricing InformationYou can choose between Recurring and One-off pricing for your product.Recurring PricinngIn recurring pricing, your customers pay an ongoing fee according to the pricing model you define. After selecting Recurring, you can enter the amount you want to charge and the billing period.To further configure your recurring pricing, select More pricing options. This allows you to specify the pricing model, amount, billing period, price description, and more.The following pricing models are available in Stripe for recurring pricing:  Flat rate  A fixed price for a single unit or package.  Package pricing  Charge for API usage by the package, or a group of units. For example, you could set it up to charge $10 for every 1000 API calls. Every time the user goes over the 1000 API call threshold, they are charged another $10.  Tiered pricing  Offer different price points for based on unit quantity.  Usage-based  Pay-as-you-go billing based on metered usage. You can charge per package, per unit , or per tier and define the prices and units accordingly. You can also set up a billing meter for the price to meter usage. See Creating a Product and Price in Stripe for instructions on how to set up a usage-based scheme.One-Off PricingIn one-off pricing, you charge a one-time fee rather than recurring amount in each billing period. After selecting One-off, you can enter the amount you want to charge in the Amount field.Similar to recurring pricing, you can select More pricing options and configure your pricing further by specifying the pricing model, amount, price description, and more.The following pricing models are available for one-off pricing:  Package pricing  Charge for API usage by the package, or a group of units. For example, you could set it up to charge $10 for every 1000 API calls. Every time the user goes over the 1000 API call threshold, they are charged another $10.  Flat rate  A fixed price for a single unit or package.  Customer chooses price  The customer sets a custom price. You can set a limit and define a preset amount to suggest to the customer.Billing periodThe billing period can be set as the following for recurring pricing models:  Daily  Weekly  Monthly  Every 3 months  Every 6 months  Yearly  CustomFor your configuration with Moesif, we recommend setting the billing period as Monthly.Price descriptionThis is an optional field but recommended. Here you can put a brief description of your price. This will allow you to more easily decipher which price you are selecting in the billing meter in Moesif, especially if you have multiple prices for a single product.Once you’ve input all of the details for your price, you can select Next and then select Add productAs you create products, you will be able to view and edit them on the Product Catalog screen.2 - Enable the Moesif-Stripe IntegrationOnce your products and prices are created, it’s time to begin to integrate Stripe with Moesif. To begin configuring Stripe in Moesif, go to the Billing Meters page and click the Edit Billing Provider dropdown in the top right corner of the screen.This will bring up the Stripe configuration screen walking you through the integration. From this screen, you can get all of the info needed to plug Stripe into Moesif. Each step for configuration is covered within the modal.Add the Moesif Webhook to StripeThe first step in the integration is to add the Moesif webhook into the configuration in Stripe. Adding this allows Stripe to send subscription updates to Moesif.To add the Moesif webhook to Stripe, from the upper right-hand side click on Developers, and then Webhooks in the left-side menu. This will bring you to the Webhooks page where you can view existing webhooks and add new ones. To add a new webhook we will click the Add an endpoint button at the bottom of the screen.From here, we will plug in our Moesif API endpoint URL and configure the events to listen to. You’ll want to copy your Moesif Webhook URL into the Endpoint URL field and then click the + Select Events button.  These details can all be found on the Stripe configuration page in Moesif mentioned in the previous section.You should select the option under Customer for Select all Customer events. After this, click the Add events button at the bottom of the screen.After this, you’ll be returned back to the original screen where you added the endpoint details. Scroll to the bottom of the screen and click Add endpoint to save the endpoint to Stripe.Plug the Stripe API Details into MoesifFor Moesif to add usage quantities to subscriptions in Stripe, we need to add the Stripe API details into Moesif. This is done in the Stripe configuration screen in Moesif, the same screen we’ve been working with previously.Currently, Moesif only supports version 2020-08-27 of the Stripe API so that field defaults for the Stripe API Version field.For the Stripe API Key field, you’ll need to retrieve the API key from Stripe to plug it in. From the Developers screen, the same one we used in the previous step, you’ll click on API Keys. You’ll then be able to see the private key for your API in either the Secret key or a generated Restricted keys field on the screen. Either key can be used.After copying the key from Stripe, you’ll paste this key into the Stripe API Key field back in Moesif. After doing this, back in Moesif you can scroll down to the bottom of the screen and click Save to save the configuration.Optionally, you have the ability to customize the company_id in Moesif as well. The default should work fine for most purposes. However, you can fully customize it to specify how to map Stripe Subscription and Customer objects to Subscription ID and Company ID in Moesif respectively:  A Stripe Customer maps to Moesif Company.  A Stripe Subscription maps to a Moesif Subscription.3 - Create a Billing MeterOnce you have the Stripe integration active in Moesif, you can begin to set up your billing meter. Billing meters created in Moesif do two things: track usage based on specific criteria and report that usage to the billing provider. Moesif allows you to set up very simple and very complex billing meters with relative ease.To create the Billing Meter, in Moesif you will navigate to the Billing Meter screen. You can do this from the left-side menu. On the Billing Meter’s screen, you’ll then click + Add Billing Meter in the top-right corner of the screen.The next screen is where you can actually input the criteria for your Billing Meter.Fields on this screen include:      Billing Meter Name          This is the Moesif internal name of your new Billing Meter            Billing Provider          In this dropdown you can choose the billing provider you want to send your usage metrics to.            Product (Stripe only)          Here you can choose which Product that you’ve set up in Stripe you want your usage metrics to be tied to.            Price (Stripe only)          The last field in the Billing Provider settings for the Billing Meter, here you will choose which Price you want to tie your usage metrics to.            Filters          Under the Filters configuration, you will configure your billing criteria to only include requests that fit a certain criteria.            Metrics          Here you can choose which metric you would like to bill on. Available options include:            Event Count          This will increment usage for every event that fits the criteria outlined in the Filter criteria.            Unique Users          This will increment usage whenever a unique user sends a request that fits the Filter criteria. For every unique user, the count will be incremented by 1 regardless of the event count for that user.            Unique Companies          This will increment usage whenever a unique company sends a request that fits the Filter criteria. For every unique company, the count will be incremented by 1 regardless of the event count for that company.            Unique Sessions/API Keys          This will increment usage whenever a unique session or API key is used to send a request that fits the Filter criteria. For every unique session or API key, the count will be incremented by 1 regardless of the event count for that particular session or API key.        There are other options under Metrics as well but the above 4 tend to be the most applicable to usage-based billing.As an example, for this guide we will create a Billing Meter that will filter traffic for a single endpoint, named /test-service, and where requests received a successful HTTP 200 response. We will use the Event Count metric to make sure that every request is added to the tally and sent to the billing provider.In Moesif, the billing meter will be configured as shown below.We will then click Create. This will create and activate the Billing Meter. A modal will appear notifying you that the billing meter has been created and presents a walk-through to ensure the meter is correctly configured.First, we will set up a flow to get users registered, subscribed, and create a JWT so they can use our monetized API. Once that is complete we will come back and proceed with the walk-through.4 - Create the /register endpointInstead of using a pre-built onboarding flow, such as through a Developer Portal within an API gateway, we will build our own. We will create an endpoint called /register which we can then use to onboard our users who want to use the API. The result will be that the user receives an API key that they can use that will track their usage.Since we are using Moesif, Stripe, and AWS API Gateway as part of our overall solution, we need to make sure each of the components is working together properly.Here’s what the endpoint will do:  Use the AWS API Gateway JS Client to create an API key and add it to a usage plan  Create a user in Stripe  Subscribe the new user to the API subscription in Stripe  Create the CompanyID in Moesif (which will be the API Key ID)  Create the UserID in Moesif (which will be the Stripe Customer ID)  Create an API key  If you already have User and Company identifiers in Moesif and other systems that you want to use, instead of using Stripe’s customer and subscription as your IDs, you can do that in Moesif under the Stripe configuration settings.In this example, I will create a simple NodeJS API with Express to do the above.Create the npm projectFirst, we will create a folder called moesif-monetization where we will add our API code. We will then run npm init to turn moesif-monetization so we can use npm in our project. For that, you’ll run the following command in the moesif-monetization directory.$ npm init  You can fill out the details or use the defaults as needed when creating the npm project.You should then see a package.json file in your moesif-monetization folder.Now, open this directory in your favorite IDE or text editor. I will be using VS Code for the remainder of this tutorial for all the coding.Add in the project dependenciesWe will now edit our package.json file with our correct dependencies. In the package.json we will add the following entries under the dependencies object. "dependencies": {   "@aws-sdk/client-api-gateway": "^3.131.0",   "@stripe/stripe-js": "^1.29.0",   "body-parser": "^1.20.0",   "dotenv": "^16.0.0",   "express": "^4.17.1",   "http": "0.0.1-security",   "moesif-nodejs": "^3.5.8",   "node-fetch": "^2.6.5",   "path": "^0.12.7",   "stripe": "^8.219.0" }Save the file, then navigate to the terminal and run:$ npm installNow, your dependencies will be brought into the project and added to the node_modules folder. These dependencies will help us to make calls to REST endpoints, connect to Stripe and Moesif, and various other capabilities we will build into our app.Create the .env fileInstead of hardcoding the Stripe keys and other static values into our app, we will abstract them into a .env file. We can do this using the dependency we added in our package.json called dotenv.In the root directory, create a file named .env. Within this file, we will add a few entries that will contain the keys and values used in our code.STRIPE_KEY="sk_test_XXX"STRIPE_PRICE_KEY="price_XXX"MOESIF_APPLICATION_ID="YOUR_MOESIF_APP__ID"AWS_INVOKE_URL="https://your-aws-api-gateway.execute-api.region.amazonaws.com"AWS_REGION="aws-region"AWS_ACCESS_KEY_ID="awsaccesskeyid"AWS_SECRET_ACCESS_KEY="awssecretaccesskey"AWS_USAGE_PLAN_KEY_TYPE="API_KEY"AWS_USAGE_PLAN_ID=”usageplan"The values that are here can be found in the following places:Obtaining Your Stripe API and Price KeyYour Stripe API Key can be found in the same place we grabbed the key for our Stripe and Moesif integration we did earlier for the Billing Meter. You can actually use the same key for both or create a restricted key with just the scope needed for each function.While we’re at it, lets grab our Stripe product’s price key. We will need it in the next section. Your Stripe price key is an identifier for the price you created earlier in Stripe. This can be found by going to the product in Stripe and grabbing the value from the API ID column.Obtaining Your Moesif Application IDYour Moesif application ID is found in Moesif by going to the menu link in the bottom-left of the screen (which will show your name) and selecting API Keys.The key will then be on the page that appears under Collector Application Id.Your AWS_INVOKE_URLThis will be the URL of your AWS API Gateway. This can be found by navigating to your API in the AWS API Gateway Console, navigating to Stages, selecting your stage, and copying the Invoke URL on the screen.Your AWS_REGIONThis value will be based on the AWS region where you have your AWS API Gateway running. An example value would be “us-east-2”.Obtaining Your AWS_ACCESS_KEY_IDInfo on this and the Secret Access Key mentioned below can be found here.{:target=”_blank” rel=”noopener”}Obtaining Your AWS_SECRET_ACCESS_KEYThis will be your AWS Secret Access Key. You will likely need to generate a new one and can check out the instructions from the AWS team here on how to do it.{:target=”_blank” rel=”noopener”}Your AWS_USAGE_PLAN_KEY_TYPEAWS allows for multiple usage plan key types but for our purposes we will use API_KEY as the value here.Obtaining Your AWS_USAGE_PLAN_IDThis value can be accessed by going to your Usage Plan in the AWS API Gateway console. You can navigate here by going to your API in the AWS console, clicking Usage Plans, selecting your plan, and copying the ID.Once you’ve populated the file with the nine key-value pairs, save the file. We won’t need to touch this file again for the remainder of the tutorial.Create the app.js fileIn the root directory of our app, we will create an app.js file (if not already created). In this file we will add the following code that adds our dependencies and creates a base REST endpoint.const express = require('express')const path = require("path");require('dotenv').config()var bodyParser = require('body-parser')const moesif = require('moesif-nodejs');const Stripe = require('stripe');// npm i --save node-fetch@2.6.5const fetch = require('node-fetch');// npm install @aws-sdk/client-api-gatewayconst { APIGatewayClient, CreateApiKeyCommand, CreateUsagePlanKeyCommand } = require("@aws-sdk/client-api-gateway");const app = express();app.use(express.static(path.join(__dirname)));const port = 5000;const stripe = Stripe(process.env.STRIPE_KEY);var jsonParser = bodyParser.json();config = { invokeUrl: process.env.AWS_INVOKE_URL, region: process.env.AWS_REGION, credentials: {   accessKeyId: process.env.AWS_ACCESS_KEY_ID,   secretAccessKey: process.env.AWS_SECRET_ACCESS_KEY }}const client = new APIGatewayClient(config);const moesifMiddleware = moesif({ applicationId: process.env.MOESIF_APPLICATION_ID});app.use(moesifMiddleware);app.post('/register', jsonParser, async (req, res) => { })app.listen(port, () => { console.log(`Example app listening at http://localhost:${port}`);})In the above code we are:  Importing a few dependencies  Configuring the Stripe dependency  Configure our AWS API Gateway Client  Configuring the Moesif middleware  Create the /register endpoint  Set our app to run on port 5000 and start our Node app  You will notice that we have process.env.STRIPE_KEY, process.env.MOESIF_APPLICATION_ID, and other environment variables in the code. These values will come from the .env file that we created in the last step.Implement the /register endpointOur next step is to implement the /register endpoint. This endpoint will essentially create our binding between AWS API Gateway, Stripe, and Moesif. The outcome will be a generated API Key from AWS API Gateway which will associate usage with a user in Moesif, which will then be reported to Stripe.The first bit of code we will add to our__/register__ endpoint will be to create the API key and associate that API key with a Usage Plan. To do this we will use the AWS API Gateway Client SDK and it’s CreateApiKeyCommand and CreateUsagePlanKeyCommand.   const params = {     name: req.body.email,     enabled: true,   };   const command = new CreateApiKeyCommand(params);   const response = await client.send(command);   const awsApiKeyId = response.id;   const awsApiKey = response.value;   const usageKeyCommand = new CreateUsagePlanKeyCommand({keyId: response.id,keyType: process.env.AWS_USAGE_PLAN_KEY_TYPE,usagePlanId: process.env.AWS_USAGE_PLAN_ID   });   const usageKeyResponse = await client.send(usageKeyCommand);The next step in the flow is to create the customer in Stripe. We will use our Stripe JS dependency to do just that. We will use the parameters from the request body (email, first name, last name) to create the customer in Stripe using the stripe.customers.create function. We will also store the AWS API Gateway generated API Key ID in the Stripe metadata object. We will then store the created customer in a custom variable so we can access the customer ID generated in Stripe, if needed.   const customer = await stripe.customers.create({     email: req.body.email,     name: `${req.body.firstname} ${req.body.lastname}`,     description: 'Customer created through /register endpoint',     metadata: {       'awsAPIKeyId': awsApiKeyId,     }   });Next, we will subscribe this new user to our API subscription we created in Stripe earlier. We will use the stripe.subscriptions.create function and use the generated customer ID from the previous function call to subscribe them. This will return back a subscription object containing an ID we will use later.   const subscription = await stripe.subscriptions.create({     customer: customer.id,     items: [       { price: process.env.STRIPE_PRICE_KEY },     ],   });Once the user and subscription are created in Stripe, we will now use the Moesif middleware to create the user and add their relevant details into Moesif. First we will call the Moesif middleware’s updateCompany function to map the Stripe customer.id to the companyId in Moesif.  var company = { companyId: customer.id };  moesifMiddleware.updateCompany(company);We will then do a similar step with the updateUser function and use it to map the Stripe customer.id to the userId and companyId and some other metadata we collected on the user into Moesif. This will link the user to the company as well.  var user = {     userId: customer.id,     companyId: customer.id,     metadata: {       email: req.body.email,       firstName: req.body.firstname,       lastName: req.body.lastname,     }   };   moesifMiddleware.updateUser(user);Lastly, we will return a 200 OK response back to the caller with the API key in the response body.   res.status(200)   res.send({ apikey: awsApiKey });The completed function, end-to-end, will look like this:app.post('/register', jsonParser, async (req, res) => {    console.log(req.body);    // Generate API key    const params = {      name: req.body.email,      enabled: true,    };    const command = new CreateApiKeyCommand(params);    const response = await client.send(command);    const awsApiKeyId = response.id;    const awsApiKey = response.value;    console.log(response);    // Associate the API key with a usage plan    const usageKeyCommand = new CreateUsagePlanKeyCommand({  keyId: response.id,  keyType: process.env.AWS_USAGE_PLAN_KEY_TYPE,  usagePlanId: process.env.AWS_USAGE_PLAN_ID    });    const usageKeyResponse = await client.send(usageKeyCommand);    console.log(usageKeyResponse);    // create Stripe customer    console.log('create stripe customer');    const customer = await stripe.customers.create({      email: req.body.email,      name: `${req.body.firstname} ${req.body.lastname}`,      description: 'Customer created through /register endpoint',      metadata: {        'awsAPIKeyId': awsApiKeyId,      }    });    console.log('stripe customer created');    // create Stripe subscription    console.log('create stripe subscription');    const subscription = await stripe.subscriptions.create({      customer: customer.id,      items: [        { price: process.env.STRIPE_PRICE_KEY },      ],    });    console.log('stripe subscription created: ' + subscription.id);    // create user, company, and subscription in Moesif    var company = { companyId: customer.id };    moesifMiddleware.updateCompany(company);    var user = {      userId: customer.id,      companyId: customer.id,      metadata: {        email: req.body.email,        firstName: req.body.firstname,        lastName: req.body.lastname,      }    };    moesifMiddleware.updateUser(user);    res.status(200)    res.send({ apikey: awsApiKey }); })With that, we can now actually try out our endpoint to make sure that each piece is working as expected. The outcome should be a registered user with an API key which will record and report usage data to Stripe. Let’s move on to testing it.5 - Send a test request to the /register endpointOnce your /register endpoint has been coded and deployed, it’s time to test it. For right now we will simply use Postman to send a request. Our request will contain a JSON request body that will contain a:  First name  Last name  EmailOf course, this is the minimal amount of information we would want to configure our system and profiles in AWS API Gateway, Stripe, and Moesif correctly. You can easily add more fields as needed for your specific use case.In Postman, we will create our request with the following information:  Request Type: POST  Endpoint URL: http://localhost:5000/register      Request Body:    {  "firstname": "Userfirstname",  "lastname": "Userlastname",  "email": "test@test.com"}      Once everything is plugged into Postman, it should look like the following:Once the request is sent, the response should contain an API key that the newly registered user can use.We will now check Stripe to ensure that the information we registered with is correctly entered into the systems.Logging back into Stripe, you’ll navigate to Customers screen. You should see your newly created user in the list.Click on the newly added customer in the list. On the next screen, you should see that the customer is also subscribed to your APIs subscription. You should also see under Metadata that the awsAPIKeyID is also present.  Having the awsAPIKeyID present is important since that is part of the mechanism we use to map usage data over to Stripe from Moesif. If this is not present, make sure the code above has been correctly configured to add it to the Stripe customer metadata.With these checks completed, we can safely assume that our /register endpoint is correctly setting up our users accounts and subscriptions in Stripe.6 - Call your API using the generated API keyOur next step is to actually use our generated API key. We will then confirm that all the correct information is added into Moesif. The data we are confirming includes:  The Stripe Customer ID is mapped to the Moesif Company ID  The Stripe Subscription ID is mapped to the Moesif Subscription ID  Moesif contains the Stripe metadata in the company profileUse Postman to send the requestNext, let’s use Postman, or another platform, to send a request to the endpoint on AWS API Gateway. This is the endpoint that we set up the billing meter for in Step 3, above.In Postman, we will:  Put the invoke URL for your AWS API Gateway API endpoint as the request URL  Select the Authorization tab  Select the Type as API Key  Populate the API key details          Set the Key as “x-api-key”      Set the Value as the generated API key      Set the Add to field value to “Header”      Below is an example of the populated request configuration in Postman.To send the request to our endpoint, click Send.Once sent, your request should be proxied through AWS Api Gateway and the API call analytics should land in Moesif.Confirm that Moesif received the request infoBack in Moesif, you’ll navigate to the Live Event Log screen where you should see the request you just sent. You should see the entry has both a User ID and Company ID populated with the Stripe user and subscription ID’s. The entries should look like this:  The customer ID should match your AWS API Gateway API Key ID and the subscription ID will look like “sub_XXXX”.If you click on the User ID shown in the entries on the Live Event Log screen, you will come to the users profile page. On this page, we will confirm that the Stripe metadata is present. We will need to add a new column to our profile to display the Stripe data. To do this, from the profile page, click on the … More Actions button and click Customize Profiles’ Layout.We will then add a new column for the Stripe metadata. You will click the + button on the far right of the screen to create a new column where we will add the Stripe metadata.  You may need to scroll to the right to see it depending on your resolution and screen size to see the + button.You will then drill down to Metadata > stripe > customer > created and use this field in the new row. I’ve also changed the column image to one more fitting. You can customize this by clicking on the image and selecting whichever one fits best.You can also add other fields, but for right now just this single field is enough to tell us that Moesif is correctly receiving data from Stripe.  If you don’t see the Stripe metadata entry as an available field, wait a few minutes. If after a few minutes the Stripe metadata isnt present, ensure that your Stripe configuration is correct in Moesif. After confirming or editing it, try creating a new user and sending a request again to confirm that the integration is working.At this point, we now have confirmed that our API call, proxied through AWS API Gateway, is working and stamped with the correct user (API Key ID) and company (Stripe Subscription ID) details in Moesif. We also confirmed that Stripe is sending data back to Moesif which is correctly being mapped to the corresponding user profile, confirmed through the Stripe metadata in Moesif.7 - Create the frontendNext, we want to add a simple little frontend so we don’t need to call for our API key through Postman. We will make a quick little registration form that will then return an API key for our newly registered user to use.Add your frontend files to the appIn the root directory of the application, we will add two files. We will add both an index.html and an index.js.Add in your route to serve the static html filesIn the app.js file, we will add in a route to serve the static HTML files. Underneath our code for the /register endpoint, we will add another endpoint. Add the following code:pp.get("/", function (_req, res) { res.sendFile(path.join(__dirname, "index.html")); res.sendFile(path.join(__dirname, "index.js"));});This code will now load the website (once we have the code plugged in) when you navigate to http://localhost:5000/ .Code the frontend form and logicFinally, let’s add the code for our frontend HTML and JavaScript functionality. In the index.html file, we will add markup that looks like this:<!DOCTYPE html><html lang="en"> <head>   <meta charset="utf-8" />   <meta name="viewport" content="width=device-width, initial-scale=1" />   <meta name="theme-color" content="#000000" />   <meta     name="description"     content="Moesif Embedded Dashboard example"   />   <title>None React App Example</title> </head> <body>   <noscript>You need to enable JavaScript to run this app.</noscript>   <h1>     Moesif Monetization Example   </h1>   <div id="form-input">     email: <input id="email-input" placeholder="email" />     first name: <input id="firstname-input" placeholder="first name" />     last name: <input id="lastname-input" placeholder="last name" />     <button onClick="submitRegistration()">Register</button>   </div>   <div id="apikey-output"></div>   <p id="error-message"></p>   <script src="index.js"></script> </body></html>his markup will display a form which allows users to input an email, first name, and last name. It also has a Register button that will call the JavaScript function for submitRegistration(). That function will be in our index.js JavaScript file.The index.js file will look like this:async function submitRegistration() {  const email = document.getElementById("email-input").value;  const firstname = document.getElementById("firstname-input").value;  const lastname = document.getElementById("lastname-input").value;  const errorElement = document.getElementById("error-message");  const apikey = document.getElementById("apikey-output");  var body = { email, firstname, lastname };  console.log(body);  var response = await fetch('http://localhost:5000/register', {    method: 'post',    body: JSON.stringify(body),    headers: {'Content-Type': 'application/json'}  });  var data = await response.json();  console.log(data);  apikey.innerHTML = data.apikey;}This function will take the input from the form, post it to our /register endpoint, and display the returned API key on the screen.8 - Test the frontendTo test the frontend, save your code changes and restart the server. Then, in a browser, navigate to http://localhost:5000/. You will then see the form show up.Fill out the form fields and submitNow that the form is loaded, fill in the fields and click the Register button. This will take the info, post it to our /register endpoint, and give us the generated API key.  It is suggested that you use a different email than you used earlier when you created an API key directly through the /register endpoint.Confirm the API key is returnedOnce the submit button is clicked, after a few seconds, the API key should be returned back to the UI.9 - Send a request to your monetized APIWe will once again want to make sure that everything is working with our UI, through to our backend systems. For this, simply repeat the steps from Step 6 to confirm that the user and company IDs are populated correctly and that the Stripe metadata is returned for this user and the new API key.10 - Confirm all the pieces are working correctlyAlthough this is optional, this step may help with troubleshooting any issues that may have came from our previous steps. Here are a few things to check to make sure that all is working as it should. After creating a new user through the UI and using the generated API key to place a call to your API, confirm the following:  In Stripe          Confirm that a customer has been created in Stripe with the details you entered into the UI      Confirm that the customer has been subscribed to the correct product and price      Confirm that the generated AWS API Key ID is listed in the Customer Metadata        In AWS API Gateway          The API Key has been created and attached to a Usage Plan        In Moesif          Your API call was recorded in Moesif in the Live Event Log      Your API call has the AWS API Gateway API Key ID and Stripe Subscription ID in the User and Company fields in Moesif, respectively.      Confirm that the Stripe metadata is populated in Moesif      11 - Check Stripe for usageLastly, After a few hours, it’s best to go into Stripe to confirm that usage is being added to a users subscription. Be sure that you’ve sent a few requests through in order to make sure you have some data that should be sent to Stripe.  it may take a few hours for usage to make its way from Moesif to Stripe. If data still isn’t in Moesif after a few hours, ensure you’ve followed all the steps outlined within this guide. This includes making sure that your user and company ID’s from Moesif are correctly mapped to the corresponding keys in Stripe.To check the usage, in Stripe you’ll want to navigate to the Customers screen and select the customer that you made the API call with. Once selected, you should see some active subscriptions for the users that you’ve registered through the /register endpoint. The one we created earlier is called My API. Click on the subscription entry.On the next screen, click on View Usage beside the price entry.A modal should now pop up showing you the usage for the API that has been reported to Stripe from Moesif.  Remember, it may take up to an hour before usage is reported to Stripe. If you data isn’t there yet, check back a bit later for the updates.12 - Determining If the Billing Meter is Working CorrectlyTesting the created Billing Meter is easy with out Test Meter function. Navigate to your created Billing Meter from the left side navigation pane and selecting your Stripe Test billing meter. Select Test Meter on the top right.We will first confirm the meter that you are attempting to test. Click the Next button at the bottom of the modal.Moesif will wait for Subscriptions to created within Stripe and those subscriptions to be associated within Moesif itself. This page will update automatically, no need to refresh.Moesif will then wait for an API call to our any endpoint associated with our billing meter using our the JWT that has been created for us.Finally, Moesif will sync all usage data to Stripe every 15 minutes. This step may take a few minutes depending on when the API call was initiated but will update on the given interval.Wrapping upMonetization has always been a tough hurdle to get past. Many custom solutions offered flexibility but at a very high engineering and support cost. With Moesif, API monetization is possible in an extremely minimal amount of time. As demonstrated in this article, With a little bit of configuration and minimal amount of code we can create a production-ready, post-paid monetization scheme in minimal time.                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue with your AWS managed APIs?            Monetize your AWS managed APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/stripe/aws-api-gateway/End-To-End-API-Monetization-With-AWS-API-Gateway-Stripe-And-Moesif/",
          "author": "Matthew",
          "categories": "technical, stripe, AWS-API-Gateway"
        }
      
    ,
  
    
        "developer-platforms-chargebee-easily-monetize-your-apis-with-moesif-plus-chargebee": {
          "title": "Easily Monetize Your APIs with Moesif Plus Chargebee",
          "content"	 : "It’s always great to build something that makes money. The most successful businesses often find the easiest and most efficient ways to make money, while keeping costs and support to a minimum. After all, the best businesses and products are simply the ones that know how to build revenue. Many companies now look to monetizing their APIs as part of their overall monetization strategy.API monetization isn’t always easy though. It generally takes a lot of integrations, a fair amount of code and customization, and can also lead to a large support burden. This is especially true when billing issues arise. In short, there are challenges both during implementation, and once the billing system is up and running.What if a simpler approach was possible? At Moesif, we recently introduced a feature for Billing Meters. This is a way to use the data coming into Moesif to monitor usage for a user, send this data to a billing provider, and have the users be presented with accurate bills, all with a fraction of the work required to implement a custom solution.To illustrate how it works, let’s assume we have an API that we would like to charge customers to use. As an example, let’s pretend that we have created a new credit score API that companies can use to bring consumers’ credit ratings back to their app. Our API will be /getCreditScore, and users will be charged for each query/call they send to the endpoint. Our API monetization model will be pretty simple, we will charge $0.10 for every call to our /getCreditScore endpoint.Chargebee will be the billing provider that we will use to invoice and charge customers for their usage. Chargebee is simple to use and will allow us to easily set up plans and pricing to adhere to the pricing scheme above.We will now use Moesif to tally up the usage for the endpoint and send the usage metrics to Chargebee. Chargebee can then use these metrics to bill the customer accordingly, based on the tier that their metrics coincide with, and collect payment.Integrate your app and APIs with MoesifIn order to use the Billing Meters feature in Moesif, you need to have your APIs integrated with Moesif. This is because Moesif will use the metrics stored within it and then feed Chargebee the info it needs. Once your APIs are integrated with Moesif, you can also use other features which pair well with our Billing Meters feature, including behavioral emails, governance rules, and alerts.If you are not currently using Moesif to monitor your APIs, integration can be done in a few different ways. If you are using an API gateway or API management platform, you can use one of our many plugins which allow you to quickly feed analytics to Moesif. If you are not using a third-party gateway or management platform, or want to do it at the API code level, you can use one of our SDKs. A Moesif SDK will allow you to easily integrate Moesif with your Node, Python, or Java APIs (plus many, many more languages and frameworks) directly from your code. Either way is easy and simple to support.Another Moesif feature that you need to deploy in order to make billing work correctly, is to implement user and company tracking. Generally, this can be set up in a few simple steps. We need this feature enabled so that usage data in Moesif can be tied to specific users and companies. This is how Chargebee will map usage to a customer within Chargebee, so they can be billed accordingly.Once you have integrated with Moesif, and have user and customer tracking enabled, your next step would be to actually create your plans in Chargebee, so they can be used in Moesif.Create plans and prices in ChargebeeAfter creating a Chargebee account and logging in, you can begin to create your plans. For our purposes, we need to create a plan which includes an price that uses standard pricing.You’ll need to create a plan in Chargebee which that is metered. the price should be set to a monthly billing interval so that the usage is totalled up at the end of the month. The unit price would also need to be set to $0.10.Setting the plan up, for example, will look like this:The monthly pricing details will look like this:Now, at the end of the month, when Chargebee creates the invoice, it will add up the total usage and bill the customer accordingly. Of course, at this point, we only have the plans defined, but no one will be charged since we don’t yet have any usage data being sent to Chargebee.                Integrate Metered Billing Easily              14 day free trial. No credit card required.              Learn More        Integrate Chargebee with MoesifWe still need to actually get the data from Moesif over to Chargebee, and vice versa. There are two mechanisms that are used for this: a webhook and the Chargebee API. Each has a distinct part in facilitating the data sharing between the platforms.By adding the webhook into Chargebee, subscription updates can be sent back to Moesif. By using the Chargebee API, Moesif can send usage details to Chargebee and can also retrieve details about available plans and prices in Chargebee. These two points of contact are all that’s needed for the Chargebee and Moesif integration. Thankfully, Moesif walks you through this when you set up Chargebee as a billing provider and provides all the needed details. For the specifics, you can also check out our docs on the Chargebee integration.Set your billing parametersOnce you’ve integrated Chargebee in Moesif, you can set up your billing meter. For our example, it will be very simple. What we will do is send usage metrics to Chargebee whenever an API call to /getCreditScore returns a “200 OK” response. This means we will only bill for successful calls and won’t accidentally charge for calls where an error was experienced.Once we create the billing meter, every hour the usage for each customer will be sent to Chargebee. At the end of the month, Chargebee will create an invoice based on our pricing structure and bill the user.  You can also use Moesif to automatically send behavioral emails for every successful call, or even if they are about to cross into the next discount tier. You could also use Moesif’s Governance Rules to block users with overdue invoices from accessing the API until their invoice is settled.Try it out for yourselfAs you can see, you are just a few steps away from robust API monetization that is simple to implement and support. By using Moesif and Chargebee together you’ll be able to charge customers for usage in a matter of minutes, manage subscriptions, and can even use other Moesif features to create the ultimate customer experience. Our billing setup is so simple that it can even be done without having any developer skills.The Billing Meters feature is available for all Moesif users. Sign up for Moesif today to instantly access our Billing Meters feature to begin billing for your customers’ API usage. If you’re already using Moesif, click on Billing Meters in the left-side navigation menu and take a look at our docs to show the exact steps to get you to monetize your APIs.                Easily Monetize Your APIs              14 day free trial. No credit card required.              Learn More        ",
          "url": " /developer-platforms/chargebee/Easily-Monetize-Your-APIs-With-Moesif-Plus-Chargebee/",
          "author": "Matthew",
          "categories": "Developer-Platforms, Chargebee"
        }
      
    ,
  
    
        "best-practices-api-product-management-the-stripe-developer-experience-and-docs-teardown": {
          "title": "Stripe Developer Experience Teardown: What to Steal in 2026",
          "content"	 : "Stripe’s developer experience is the benchmark most API teams quietly measure themselves against. Over the past decade, the company has built one of the most-cited examples of how an API product should treat its developers, and the docs are the most visible piece of that work. Stripe processes hundreds of millions of API requests per day across its integrations, and a meaningful share of the conversion from “I am evaluating a payments provider” to “I have shipped a working integration” happens entirely inside the docs.This is a 2026 teardown of why the experience works, what has changed since the patterns first became famous, and a practical list of what to copy if you are building an API product yourself. Stripe still rewards study, and the bar has moved.                Ensure a Great Developer Experience              14 day free trial. No credit card required.              Try for Free        What makes Stripe’s developer experience the benchmarkThree things, layered on top of each other:  A docs architecture that respects how developers actually read. Stripe’s documentation is laid out so a developer’s questions get answered in the order they ask them, not in the order Stripe’s internal product hierarchy would suggest.  An authoring system that lets product and engineering co-own the docs. Stripe writes its docs in Markdoc, a Markdown-superset framework Stripe open-sourced in 2022. Code samples in the docs are real, runnable, and integrated with the page’s content.  A consistent design language across the brand, the docs, the API reference, and the dashboard. Developers move between those four surfaces dozens of times in a single integration session, and Stripe’s design system makes the transitions feel like one product.The composite is hard to copy in one project. But individual pieces are within reach for any API team willing to invest, and that is what the rest of this teardown is about.The three-column documentation layoutStripe’s documentation is famous for a three-column layout that organizes navigation, content, and live code in parallel.  Left column: product-area navigation (Payments, Billing, Connect, Issuing, Terminal, and so on), with a per-product Quickstart and topic tree nested underneath. Developers scope themselves to one product, then drill in.  Middle column: the actual documentation prose: concepts, walkthroughs, and step-by-step guides written for the developer’s current task.  Right column: runnable code in the developer’s chosen language, kept in sync with the prose. Hovering over a paragraph highlights the code that paragraph describes, and vice versa.The pattern works because it preserves context. A developer reading about how to charge a saved payment method does not have to leave the page to find the code; the code is already there, in their language, and the highlighting connects each concept to the line that implements it.For a deeper treatment of how this layout fits into a broader developer portal, we have a separate guide. The three-column pattern is one part of a larger discipline around developer-first information architecture.Markdoc: how Stripe authors interactive docsThe visible polish of Stripe’s docs is built on a less-visible piece of infrastructure called Markdoc. Stripe open-sourced Markdoc in 2022 (the launch post lives at stripe.dev/blog/markdoc), and it is now used at companies far beyond Stripe.Markdoc extends standard Markdown with three things every API docs system needs:  Custom components. Code samples, parameter tables, status badges, and interactive widgets become first-class authoring primitives in the Markdown source. Authors write {% code_block %} rather than copy-pasting HTML.  Conditionals and variables. Documentation can branch by language, product version, or feature flag without splitting into separate pages that drift over time.  Validation at build time. Broken links, missing components, and invalid syntax fail the build, not the user. This is why Stripe’s docs almost never show a stale or 404’d internal link.The point is not that every team should adopt Markdoc. The point is that interactive, on-brand docs require a content pipeline that treats the docs as software. Free-form CMS authoring does not produce Stripe-quality output, even with Stripe’s design assets.Hover-and-highlight code interactionsOne of the most-cited details in Stripe’s docs is the synchronized highlighting between prose and code. A developer reading “the customer field on the Charge object is the customer this charge belongs to” sees the corresponding line in the right-pane code sample highlight as they hover or scroll past the explanation.The interaction is doing something specific: it is removing the translation step between concept and implementation. Without it, the developer reads a paragraph, looks down at the code, looks back at the paragraph to remember which field was being described, and finally maps the two together. With it, the mapping is automatic.Small detail, but it is one of the most-copied patterns in modern API docs. If you are picking one Stripe interaction to clone first, this is it.Interactive testing inside the docsThe right-side code pane in Stripe’s docs is not just for reading. For many endpoints, developers can paste their test API key and run the call from the page, with the response rendering inline. The integration moves from “I read the docs and then went to my terminal” to “I tried it in the docs and saw it work.”This is one of the highest-friction-removing features in any API product. Two design points matter:  The test always runs against Stripe’s test mode (signaled visually so developers do not accidentally charge a real card).  The pasted key is stored only in browser session storage, not server-side, so developers do not have to worry about leaving credentials behind.The closest non-Stripe equivalent is Postman’s “Try it” pattern or the Swagger/Redoc “Try it out” buttons. Stripe’s version is tighter because it runs inside the docs page rather than redirecting to a separate tool.Stripe’s docs in the agent era: llms.txt and AI-readable patternsThis is the part of the teardown that did not exist five years ago and that most older “Stripe docs analysis” posts miss.By 2026, a meaningful share of API documentation traffic is non-human. AI agents, IDE-integrated assistants, and LLM-based search engines all read documentation to figure out how to call an API on a user’s behalf. Stripe has been one of the more visible adopters of patterns that make docs agent-friendly:  llms.txt or equivalent index files. A machine-readable index of the docs that an LLM crawler can use to navigate the docs the way a sitemap helps search engines navigate a site. Stripe exposes structured navigation at the OpenAPI spec level and at the docs level.  Consistent operationId, summary, and description fields in the OpenAPI spec. Agents use these literally when deciding which endpoint to call. Stripe treats these fields as developer-facing copy, not as internal labels. Vague descriptions cause wrong tool selection.  Stable parameter naming across the API and the docs. Agents that read the docs and then call the API need the same field names in both places. Stripe’s snake_case discipline across the entire surface is one reason agent integrations work as cleanly as they do.The 2026 takeaway: developer experience now has two audiences, the human reading the docs and the model reading the spec. Stripe’s docs work for both because the underlying content is structured well enough that either audience can navigate it.How Slate comparesSlate has been the most-recommended open-source alternative for teams that want Stripe-style docs without building from scratch. It produces a three-column layout with prose on the left, navigation in the middle, and code samples on the right. Many of Stripe’s visual conventions originated in Slate or vice versa.What Slate gets right:  Out-of-the-box three-column layout with sensible defaults  Markdown-based authoring that any technical writer can pick up quickly  Built-in code samples in multiple languages with tabbed switchingWhat Slate does not solve:  Interactive testing from within the docs (Slate does not run live calls)  Versioning across product areas with conditional content (Slate uses a single config file per docs project)  Build-time validation of internal links and component usage (basic Markdown error checking only)For small APIs or internal docs, Slate is still the fastest way to get a Stripe-shaped layout live. For a fast-growing API product, the gap between Slate’s defaults and what Stripe ships becomes visible somewhere around the third or fourth product line.What to steal: a 7-point developer-docs checklistStripe’s full stack is unrealistic for most teams. The individual patterns are not. A practical checklist of what to copy first, ranked by ROI:  Three-column layout with persistent navigation, prose, and code. The lowest-effort piece to copy; the highest-ROI.  Hover-and-highlight prose/code synchronization. Cheap to implement on top of any docs framework; instantly improves comprehension.  OpenAPI-first reference generation. Your reference docs should be generated from the spec, not hand-written, so they cannot drift.  Real, runnable code samples. Even if the sample is just a curl command, make it copy-able with a single click and ensure it works with the developer’s actual API key.  Versioning by header or URI plus a clear deprecation policy. Stripe’s Stripe-Version header is the most-cited example of a clean versioning scheme that survives years of breaking changes.  Agent-readable spec quality. Treat operationId and description as user-facing copy. They are.  A “test mode” channel. A way for developers to run real API calls without consequences. Stripe’s test cards and test-mode segregation make integration safe at the experimentation stage.Most teams that try to copy Stripe’s developer experience focus on the visible design polish first. The above order reverses that, because the visible polish is meaningless if developers cannot test what they are reading.How Moesif observes the developer journeyBuilding the docs is only half the work. The other half is knowing whether developers are getting through them.Moesif instruments the API itself, which is where the developer journey actually shows up. Per-customer analytics make visible the points where new integrations stall: which endpoints get the first call, where the customer’s error rate peaks, how long the gap is between signup and first successful production call, and which customer cohorts churn before they ever see a 200. Pairing the docs work with the observability work is how high-DX API products tighten the funnel quarter over quarter.The principles that drive both the docs and the observability are the same ones we cover in our API design principles guide. Developer experience is a property of the whole product, not just the documentation page.Next stepsStripe’s developer experience is the result of a decade of compounded discipline across docs, design, API behavior, and dashboard. The patterns are studyable, the structural ones are copyable, and the bar moves up every year.If you are building an API product and want to know how developers are actually moving through the experience you ship, start a 14-day Moesif free trial for per-customer, per-endpoint analytics. No credit card required.Frequently asked questionsWhy is Stripe’s developer experience considered the best? A tight feedback loop between docs, API design, and dashboard. Developers can read about a behavior, test it inline, see the result in the dashboard, and ship the integration without leaving the Stripe surface. The polish matters, but the underlying tightness matters more.What software does Stripe use for their documentation? Stripe authors their docs in Markdoc, an open-source Markdown-superset framework they released in 2022. The three-column layout, interactive code panes, and component-based authoring are all built on Markdoc.Is Markdoc free to use? Yes. Stripe open-sourced Markdoc in 2022 under the MIT license. Several non-Stripe teams now run their own docs on it.What is the three-column docs layout? A documentation pattern with three vertical panels: navigation on the left, written content in the middle, and runnable code samples on the right. Used by Stripe, several other API-first products, and many Slate-based docs.Can I make my docs as good as Stripe’s without their budget? The visible polish is the expensive part. The structural patterns (three columns, OpenAPI-first reference, real code samples, agent-readable spec, clear deprecation policy) are within reach for any small team and produce most of the developer-experience gain.Do AI agents read API docs? Yes, increasingly. LLM-based coding assistants, agent runtimes, and search engines all parse API documentation to figure out how to call an API. Spec-level fields like operationId and description are now user-facing copy for the agent audience.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /best-practices/api-product-management/the-stripe-developer-experience-and-docs-teardown/",
          "author": "Larry",
          "categories": "Best-Practices, API-Product-Management"
        }
      
    ,
  
    
        "technical-api-analytics-saved-cohorts-the-what-why-and-how": {
          "title": "Saved Cohorts: The What, Why, and How",
          "content"	 : "Being able to group a set of users or companies based on their behaviors or traits can be extremely useful. In Moesif, we have a feature called Saved Cohorts which allows you to do exactly this. Saved Cohorts can help businesses power marketing campaigns and other sales initiatives, help engineers and customer success teams manage ongoing issues, and a host of other use cases.What are Saved Cohorts?Saved Cohorts are essentially a grouping of users or companies which fit specific criteria. As a condition is met, a user or company can be added to the cohort. When a user or company no longer fits the cohort definition, they are removed from the cohort. In order to use Saved Cohorts in Moesif, you must have user and/or company tracking enabled. This functionality will not work for anonymous users or companies.In Moesif, a User Cohort will add or remove individual users from a Saved Cohort. This can be useful when trying to identify when an individual or group is having an issue or error, new users, or another specific grouping. For instance, it may be useful to the Customer Success team to know when new users are experiencing a specific error, such as an HTTP 401 Unauthorized response when trying to access an API. A cohort could be created to keep track of all of the users that fit this condition.A Company Cohort is similar to a User Cohort with the exception that it looks at all of the users within a company as a single unit. Going back to our previous example, we could set up a Company Cohort which would monitor companies that have received an HTTP 401 Unauthorized response. This means that any user within that company that received a response fitting this criterion would mean that the company is then added to the cohort. Using both user and Company Cohorts can be useful depending on what types of insights you are trying to derive from your data.What are Cohort Notifications?Unless you are constantly refreshing your cohort list, it may be useful to be notified when a new user or company is added to a specific cohort you’ve set up. For this, you would use a Cohort Notification. Using a Cohort Notification will allow you to know exactly when a new user or company has fit specific criteria without the need to constantly monitor your cohort list manually.Cohort Notifications are supported across a few different channels and can be created when you create a new cohort or can be added to an existing cohort. Channels supported for Cohort Notifications include Email, Slack, or a Custom Webhook. Having a few options gives the flexibility to deliver notifications to the most effective channel for your team, or even multiple channels if needed. When a user or company is added to the cohort, the selected channels will then have notifications delivered to them to help make your team aware.Why use Saved Cohorts?Using Saved Cohorts is great for viewing groupings of users that have similar traits or trends in product usage. Saved Cohorts can be used for reporting purposes, as well to quickly see which users belong to the specific segment or criteria you have outlined. In Moesif, beyond just reporting or viewing the members of a cohort, many of our most powerful features are driven by Saved Cohorts. For instance, features such as Behavioral Emails and Governance Rules are powered by the use of cohorts.For Behavioral Emails, when a user is added to a cohort they may automatically be sent an email. A simple example may be when a user makes their first API call or signs up for your platform, you may automate an email to be sent to them as part of an onboarding email flow. You may also do the same with users experiencing specific errors. By creating a cohort for them, you may then sending an email to them when they experience that specific error condition.As in the Behavioral Email scenario above, using Governance Rules, entails creating a cohort that outlines a specific behavior that you may want to block. A great example of this would be if you had a monetized API and wanted to block users with overdue invoices. You could simply create a cohort to add users who have an invoice that is more than 7 days overdue. Once the users join this cohort, the Governance Rule could return an HTTP 402 Payment Required status back to the user, including a request body telling users to pay their invoice.Cohorts can also be used for Dynamic Sampling. By having traffic be associated with a Saved Cohort, you can choose to customize or down sample the amount of traffic that gets logged in Moesif. By doing this, you can help to make sure your most important traffic is being logged in Moesif while excluding your less important traffic. You may want to make sure that 100% of calls which return an error response make it into Moesif, while only sampling 20% of the successful calls. To do this, you’d simply set up a Saved Cohort for each condition as part of your sampling criteria.As you can see, cohorts in Moesif extend far beyond just simply being used as a reporting tool but can also drive some really great functionality within the platform.How to create a Saved Cohort in MoesifNow that you’ve learned what Saved Cohorts are and what they can do. Let’s take a brief look at how to set up a User Cohort. First, you’ll want to navigate to the Users screen in Moesif.After this, we will set up a criterion. In this example, I will filter on users who have received an HTTP 401 Unauthorized response at least 5 times in the past few months. That will look like this:Now that my filter is created, I will click Create Cohort in the upper right corner of the screen.From here, enter your Cohort Name and then click the Create Cohort button in the bottom right of the modal.  You can also click Receive Cohort Notifications as well to set up notifications at this point. Alternatively, you can also set this up later once the cohort is created as well.Now, you will see that the cohort has been created. You can leverage some other features, like Behavioral Emails and Governance Rules, by clicking on the entries on the right side of the screen. You can also configure Cohort Notifications through this panel as well.If you need to edit or want to check out the cohort in the future, it is available through the User Cohorts menu item on the left-side navigation.  The same steps should be taken if you need to create a Company Cohort. You can follow these same steps to create a__Company Cohort__ by going to the Company screen instead of the Users screen as we did above.Try it out!Interested in trying out Saved Cohorts for yourself? If you’re already signed up with Moesif, simply log in and follow the instructions above or check out our guide that goes a step further. If you’re new to Moesif, sign up for an account today and unlock all of our advanced features including Behavioral Emails and Governance Rules that are all powered by Saved Cohorts.",
          "url": " /technical/api-analytics/Saved-Cohorts-The-What-Why-And-How/",
          "author": "Matthew",
          "categories": "technical, API-Analytics"
        }
      
    ,
  
    
        "api-monetization-api-strategy-how-to-accelerate-api-product-revenue": {
          "title": "How to Accelerate API Product Revenue",
          "content"	 : "APIs can have a huge impact on your business, creating value and delivering new income streams. But they don’t always do so in the way you might have had envisioned at the outset. Which means, when it comes to API monetization, it can pay to be prepared for the unexpected.Laying the Foundations for API MonetizationIf you’re just starting out dabbling in APIs, or you have an existing product whose revenue has plateaued, it’s time to kick off a program to get things moving again. Analytics sits at the start of this process. You can use analytics to drill down into how people are using your APIs, and armed with that knowledge, pull out popular features, endpoints in API parlance, and monetize them. Generating revenue growth as an API provider starts with understanding API usage.Using analytics in this way can enable you to generate another business revenue line from your APIs by spinning out endpoints from your existing product. It’s a way to monetize individual elements of your product, rather than focusing solely on the product as a whole.Monetization in ActionIt’s always helpful to see theory put into practice, so let’s take a quick look at NexHealth’s API monetization example. NexHealth started out as a SaaS platform delivering online patient scheduling and two-way communication for doctor and dentist offices. To do this, the company interfaced with many different databases.When NexHealth’s clients saw the potential that those connected databases offered – that is, the ability to interface easily with any database that contains medical records – they were pretty excited about the possibilities. So NexHealth launched an API that it spun out from its existing product. The new API enabled clients to interface with all of those databases, with Moesif on board to help NexHealth’s developers monetize the API interface.NexHealth’s success resulted from its willingness to spin out a single feature of its original product, flexing and scaling in response to market demand and thus maximizing and accelerating the monetization of its APIs. By April 2022, NexHealth’s series C funding round saw the company bank $125 million. Its valuation, at the time of writing, is $1 billion.  “The API will be a bigger driver of revenue long-term because we have a bigger market to sell to. It’s not just going to be doctors, it’s going to be patients as well. It’s going to be consumers, it’s going to be people in labs and pharmacies… anyone that needs access to healthcare data. That’s going to be our market; that’s going to be huge.” Waleed Asif, CTO NexHealthFocusing on API MonetizationThere are different ways in which you can monetize your app. Freemium models, subscriptions, pay-as-you-go and revenue share models can all bolster your bottom line. Using your API to drive traffic to your website or inspire third-party providers or a new partner to adopt your product can also help to fill your coffers, albeit less directly. Driving customer loyalty and customer retention requires treating your API platform as a product.Key to all of this is a monetization mindset. After all, what use is it to build a product if you can’t sell it? Focusing on a solution is all about offering APIs as products to drive income – about optimizing your business so that your APIs deliver the maximum possible impact. After that, it’s a question of drilling down into how people are using those APIs, and how best you might be able to profit from that. Collecting payment is part of the user experience for a monetized API, so ensuring that your banking process is smooth and easy is critical.This focus on APIs and what they can deliver – known as the API economy – is nothing new. Established global behemoths have been taking his approach for years. Jeff Bezos, for example, issued an API mandate at Amazon way back in 2002, requiring that all capabilities must be designed and exposed as APIs. It’s worked out pretty well for Amazon since.APIs can enable a vast array of business models and digital experiences. But it’s not just Meta, Google and the like who are able to achieve mighty things by putting APIs at the heart of their strategy. Any company with an API, anywhere in the world, has the potential to maximize the value of its API program by spinning out individual features.Maximizing API ValueAccording to McKinsey’s analysis​​​, there are three primary sources of value in API programs:  Simplifying the back end  Personalizing offers  An ecosystem of innovation and engagementSimplifying the back end allows for easy API integration, task automation and enhanced speed to market. Personalizing offers, meanwhile, can drive up customer engagement through well-considered data aggregation and reporting. And an ecosystem of innovation and engagement can support an improved customer experience and the creation of new products and services.Taking this approach means you are in the best possible position to spin out new features and accelerate the revenue of your new API product. Embracing the API-first mindset is central to this. Jeannie Hawrysz, Lead API Architect at business analytics company SAS, explains:  “All APIs should be designed with the intent that one day they’ll be externalized, so the product manager’s business rationale has to answer the question: ‘Is this API appropriate for the audience that I’m looking to target?’”Putting APIs first in this way, in order to accelerate revenue from them, is possible for companies of all shapes and sizes. Older, more established enterprises may have to deal with competing priorities, but this isn’t something that will necessarily impede API revenue acceleration, so long as the mindset in the company is focused firmly on APIs. Hawrysz continues:  “Focusing on something that’s brand new… that has potential and that hasn’t been proven yet, especially in a 40+ year old company, that’s naturally going to compete with other business priorities. Many of those other priorities that we’ve had over time, those were already making real money. And, until it’s real, we’re theorizing that the APIs are going to make money. The tide is definitely turning though. We’ve gotten a lot of use cases from our field. We’ve been connecting with leaders in the field. They’ve been asking for high-value, easy-to-use APIs.”Finding the Right PartnersAs NexHealth has so ably demonstrated, working with the right partners is essential when it comes to monetizing APIs and spinning out features to accelerate revenue. NexHealth was originally getting less than 10% of its recurring revenue from its API customers. Moving forward, the majority of revenue is expected to result from its new API product, with analytics playing a key role in driving that growth. CTO Waleed Asif succinctly sums up the importance of working with the right partners in reaching that outcome:  “Before Moesif, we had no idea who was using our platform and who wasn’t. We couldn’t even bill our customers until we got Moesif in place.”Nor is it just external partners who can help. SAS’s Jeannie Hawrysz emphasizes the importance of finding allies within the business:  “On the business side, especially if you’re at the beginning of forming a program, find allies… It’s one thing to have the API cult, which is what we kind of call ourselves internally, ‘APIs are important rah, rah, rah’, but when other teams, especially customer-facing teams, started supporting us, that’s when things really started to accelerate.”Another great example of API revenue acceleration is Okra. The African enterprise is scaling rapidly thanks to its Open Finance API, which is enabling financial service apps in thousands of businesses across the continent. The company is on its way to connecting a billion Africans to the global economy, using an API to help businesses speed onboarding, assess risk, verify transactions, examine spending patterns, and build personalized financial service solutions for their customers.Moesif’s API analytics are supporting Okra to visualize what’s going on, at scale, across its client base. By partnering with Moesif, Okra has been able to lighten its support tickets by more than 80% and reduce customers’ time from sign up to production rollout by over 2x. The result? Faster API revenue generation.Across the globe, the digital economy is moving fast. Companies that are ready to take a fresh look at their existing product, and be ready to spin out some of its features in new ways, could just be the ones to push their enterprises from plateaus into unicorn territory.                Grow And Monetize Your API Products              14 day free trial. No credit card required.              Learn More        ",
          "url": " /api-monetization/api-strategy/How-To-Accelerate-API-Product-Revenue/",
          "author": "Larry",
          "categories": "API-Monetization, API-Strategy"
        }
      
    ,
  
    
        "developer-platforms-self-service-starting-an-api-first-company": {
          "title": "Starting an API-First Company",
          "content"	 : "You’ve developed an amazing API product. Fantastic. But that’s just the beginning of your journey. Starting an API-first company brings with it a whole host of unique challenges for you to face along the way. Based on Moesif’s own experience of founding an API-first company, we’ve shared some insights below to help you avoid some of the pitfalls and make the most of the opportunities that lie before you.“API-first” can mean different things to different people. However, for the purposes of this article, we’re referring to companies that publish APIs as their primary product and make money by selling their APIs to other businesses.What Is Unique About Starting an API-First Company?When you start an API-first company, you are selling your product to developers (B2D). This is one of the key differences between an API-first business and traditional enterprise businesses. You’re not a B2C company nor a B2B one – you occupy a unique position somewhere between the two which has its own set of challenges.This means some of the traditional playbooks for lead generation and sales don’t always apply. You’re selling to an audience of developers who are, largely, known for being less responsive to traditional sales and marketing efforts. At the same time, the purchase of software is much more decentralized than before. Individual developers have tremendous autonomy when choosing which tools or APIs to leverage. More buying power is now dictated by the individual developers who discover and integrate your APIs.This means your go-to-market (GTM) must be aligned to reaching and selling to developers. A popular playbook is to land developers first and get them to pay a token amount (such as $50/month). This requires having a robust content and inbound marketing strategy to pull in developers. See Why Content Is the Key to Unlocking Your Developer-First Marketing Strategy.At the outset, you need to drive discovery and engage with developers on their own turf. Content is your friend in this respect, so it’s time to focus on positioning yourself as an authority on your particular subject area. You’ll need to demonstrate your expertise while speaking to developers in their own language, showing you understand their frustrations and supporting them to find genuine solutions – one of which is, ideally, your product!Going in heavy-handed, recruiting a full sales team, will likely yield poor results without mastering your self-service adoption and activation funnel. You need to make it easy and affordable for developers to try your product on their own terms and pace. Get that right and you can layer in sales at an appropriate stage later in your company’s growth.Customer DiscoveryLikely you started the company as an engineer-turned-founder, which likely makes you familiar with the problem/solution space. However, your experience is just one of many experiences. It’s important to perform proper customer discovery to understand what each customer’s pain points and what gets them to adopt an API like yours. Different customers have different needs depending on size, industry, and technologies. While getting developers to initially jump on a call can be tricky, it can work if they start to see value first (such as testing your API or reading a piece of valuable content). One of the best ways to get product feedback is by spending plenty of time getting to know your first few customers through support tickets and associated calls helping them get up and running.It’s very valuable for the CEO/CoFounder to be handling initial support tickets while building an initial MVP rather than to delegate. This is where some of your best customer discovery will come from, including outstanding insights into the specific ways in which customers are using your product and finding value in it.Connecting with your early adopters can therefore provide you with a wealth of information on use cases, pain points and more.These insights will be invaluable when it comes to growing your fledgling business.Learning About SalesA startup founder has to learn a bit of everything along the way – about finance and legal, about human resources and about marketing and sales. You may be an engineer, but you have to embrace a whole load of other business disciplines too, including sales.Developer-first (or product-led growth) doesn’t mean zero sales headcount. You’ll still need to assist customers get up and running and make them successful. The CEO/CoFounder should perform the initial sales calls as he or she will know the product and use cases best, at least until around ten customers are landed. Those early sales discussions will be super valuable to see first-hand what a potential customer’s issues or objections to adopting your API. A formal sales process is not necessarily required at this stage. However, you should be focused on creating a steady stream of qualified developers signing up and trying the API. We say qualified because it’s important to ensure your marketing strategy targets developers at organizations with potential to buy.The other thing to bear in mind when it comes to sales when you start an API-first company is how easy you need to make it for developers to trial your product. By providing a cheap introductory plan that a developer can stick on a credit card, you’re reducing the time for developers to get up and running with your API. Making it too complicated or too expensive can lengthen your sales process heavily as it will likely require approval from many stakeholders. For example, instead of a lengthy SaaS agreement that requires legal review, leverage a click-through Terms of service. Instead of expensive POCs or pilots, leverage a trial that’s self-service so developers can get up and running at their own pace. This also means your sales process might not get engaged until a later point once they have shown intent and or tested your API.Finding FundingOne of the things that will likely be near the top of your To Do List when you start an API-first company is finding funding. API-first companies can have good unit economics and not require a ton of startup capital, but you still need a structured approach like any sales process.When looking to fundraise, start with your existing professional contacts. You’ll have a much easier time getting investment from those who already know you vs a complete stranger. Many professionals in tech like to angel invest, which is a good starting point before venture capital. An angel investor is someone who is investing their own money, but you must confirm they are accredited. Of course, startups are inherently risky. Never accept money from someone who could be heavily impacted by a complete loss. The best way to navigate angel investors is to have an informal coffee date. No deck, no formal pitch. Just talk about your problem/solution space and gauge if there is mutual interest.Usually angel investors can close after a single meeting or maybe a few follow up emails. Once you decide to target venture capital, there is a slightly more involved diligence process depending on stage. Angel (or pre-seed) money can help you hire marketing freelancers or a few key roles to build an initial MVP (minimal viable product).Create a Product VisionThe final piece in the puzzle when it comes to starting your own API-led company is creating your product vision. Without one, you run the risk of building a whole heap of uncomplimentary features and ending up with a confusing solution.The crawl, walk, run approach can work well to formalize this. For example think about what you can do first, such as a single API with one function. After that, you can think about additional features that could turn it into a larger product in the medium-term. Finally, how do you turn your product into a larger platform that wins customers and the market. This approach means you can move logically from having a single API to adding complimentary APIs and features that create more value around your platform. A clear vision and plan will help you get there smoothly.In addition, ensure your company has a mission statement which encompass your vision. This can be helpful to ensure your product and marketing roadmap aligns to that vision. While a company’s mission can change over time, it should be well understood by the company at any given time. As you propose new product features, ask yourself: does this align to our company’s vision? In addition, look into which features are missing that prevent you from achieving that vision.Build and Iterate on ProductIf there are multiple players in your space, it can be easy to get caught up with ‘the competition’ and become overly secretive with your idea. The worst you can do is build an entire API platform in stealth without any validation from customers. The great thing about API-first businesses is that APIs are like Lego building blocks. Instead of building and releasing all functionality at once, get a minimal viable product/minimal viable platform (MVP) in the hands of customers which could be just a small set of APIs. You can add additional APIs later which could expand functionality. It’s surprisingly hard for another company to completely change its product strategy and replicate your entire vision. It’s better to embrace the competition and see what kind of innovative partnerships you can create instead.Remember, too, that many tech founders like to blog. This means you can keep up not just with the tech developments of the other players in your sector but also with whatever they care to share in terms of the operational and cultural development of their businesses. When you’re in the early stages of your own startup, some of those insights could be particularly helpful.Rapid Decision MakingWhen you start your API-first company, you’ll have a whole heap of decisions to make. Sorting these decisions into groups based on their outsized impact can help to focus how much time you allocate to making those decisions.For example, should you spend more time planning your backend architecture or your product messaging? Changes to the former could be hard to implement and have large scope, while changes to the latter can be rolled out quite quickly. Thinking about the implications of the decision is a great guide to how much of your attention that decision warrants.Further InspirationStarting an API-first company is a learning experience every step of the way. If you’re after further inspiration, this podcast on How to Build an API-First Company with Nick Patrick, CEO of Radar, is a great place to start.                Build Products Devs Like With Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /developer-platforms/self-service/Starting-an-API-First-Company/",
          "author": "Derric",
          "categories": "developer-platforms, self-service"
        }
      
    ,
  
    
        "api-monetization-stripe-end-to-end-api-monetization-with-nodejs-stripe-and-moesif": {
          "title": "End-to-End API Monetization with NodeJS, Stripe, and Moesif",
          "content"	 : "Many API developers and companies struggle to find ways to easily set up systems to monetize their APIs. Some are simple but not customizable, some are complex and require massive engineering effort to actually get it all running.To make things easier, Moesif created a feature a few months ago called Billing Meters which gives massive customizability but with a minimal amount of code and engineering effort.For this example, which could actually be used out of the box, we will use Moesif, NodeJS, and Stripe to charge users for API usage. For this setup there are a few assumptions:  You have NodeJS installed on your machine  You have an active Stripe account  You have an active Moesif accountThe setup is pretty simple from the outside. We will create a /register endpoint which:  Registers a user in Stripe  Subscribes that user to a product  Registers the User and Company in Moesif  Create a JWT to authenticate/authorize calls to our monetized endpointI’ve also created a little frontend for it that is a simple form that registers a user by calling the /register endpoint and then displays the generated JWT for the newly registered user.                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free        1 - Create Your Product and Price in StripeThe first step we will take is to create a product and price in Stripe. It’s best to do this step first because then when you integrate Stripe into Moesif you’ll already have some pricing plans for Moesif to pull in. A pricing plan can then be associated with specific billing criteria set up within a Billing Meter in Moesif.First make sure to meet these prerequisites.To create a product and price, log into Stripe and proceed to the Products page in the Stripe UI. Once there, click on the + Add Product button in the top right corner.You’ll then be able to add in the details for your product and price(s) for it. The form for your product will have a few fields to fill out.Product InformationName  This is the name of your product. In the example below, we use the name “My API”.Description  This field is optional but you could put a brief description of the product here. In the example below, we use a description of “This is a monetized API”.Image  Optionally upload an image that can help you easily recognize a item on the Products page. We’ll be using the default placeholder image in this example.Pricing InformationYou can choose between Recurring and One-off pricing for your product.Recurring PricinngIn recurring pricing, your customers pay an ongoing fee according to the pricing model you define. After selecting Recurring, you can enter the amount you want to charge and the billing period.To further configure your recurring pricing, select More pricing options. This allows you to specify the pricing model, amount, billing period, price description, and more.The following pricing models are available in Stripe for recurring pricing:  Flat rate  A fixed price for a single unit or package.  Package pricing  Charge for API usage by the package, or a group of units. For example, you could set it up to charge $10 for every 1000 API calls. Every time the user goes over the 1000 API call threshold, they are charged another $10.  Tiered pricing  Offer different price points for based on unit quantity.  Usage-based  Pay-as-you-go billing based on metered usage. You can charge per package, per unit , or per tier and define the prices and units accordingly. You can also set up a billing meter for the price to meter usage. See Creating a Product and Price in Stripe for instructions on how to set up a usage-based scheme.One-Off PricingIn one-off pricing, you charge a one-time fee rather than recurring amount in each billing period. After selecting One-off, you can enter the amount you want to charge in the Amount field.Similar to recurring pricing, you can select More pricing options and configure your pricing further by specifying the pricing model, amount, price description, and more.The following pricing models are available for one-off pricing:  Package pricing  Charge for API usage by the package, or a group of units. For example, you could set it up to charge $10 for every 1000 API calls. Every time the user goes over the 1000 API call threshold, they are charged another $10.  Flat rate  A fixed price for a single unit or package.  Customer chooses price  The customer sets a custom price. You can set a limit and define a preset amount to suggest to the customer.Billing periodThe billing period can be set as the following for recurring pricing models:  Daily  Weekly  Monthly  Every 3 months  Every 6 months  Yearly  CustomFor your configuration with Moesif, we recommend setting the billing period as Monthly.Price descriptionThis is an optional field but recommended. Here you can put a brief description of your price. This will allow you to more easily decipher which price you are selecting in the billing meter in Moesif, especially if you have multiple prices for a single product.Once you’ve input all of the details for your price, you can select Next and then select Add productAs you create products, you will be able to view and edit them on the Product Catalog screen.2 - Enable the Moesif-Stripe IntegrationOnce your products and prices are created, it’s time to begin to integrate Stripe with Moesif. To begin configuring Stripe in Moesif, go to the Billing Meters page and click the Edit Billing Provider dropdown in the top right corner of the screen.This will bring up the Stripe configuration screen walking you through the integration. From this screen, you can get all of the info needed to plug Stripe into Moesif. Each step for configuration is covered within the modal.Add the Moesif Webhook to StripeThe first step in the integration is to add the Moesif webhook into the configuration in Stripe. Adding this allows Stripe to send subscription updates to Moesif.To add the Moesif webhook to Stripe, from the upper right-hand side click on Developers, and then Webhooks in the left-side menu. This will bring you to the Webhooks page where you can view existing webhooks and add new ones. To add a new webhook we will click the Add an endpoint button at the bottom of the screen.From here, we will plug in our Moesif API endpoint URL and configure the events to listen to. You’ll want to copy your Moesif Webhook URL into the Endpoint URL field and then click the + Select Events button.  These details can all be found on the Stripe configuration page in Moesif mentioned in the previous section.You should select the option under Customer for Select all Customer events. After this, click the Add events button at the bottom of the screen.After this, you’ll be returned back to the original screen where you added the endpoint details. Scroll to the bottom of the screen and click Add endpoint to save the endpoint to Stripe.Plug the Stripe API Details into MoesifFor Moesif to add usage quantities to subscriptions in Stripe, we need to add the Stripe API details into Moesif. This is done in the Stripe configuration screen in Moesif, the same screen we’ve been working with previously.Currently, Moesif only supports version 2020-08-27 of the Stripe API so that field defaults for the Stripe API Version field.For the Stripe API Key field, you’ll need to retrieve the API key from Stripe to plug it in. From the Developers screen, the same one we used in the previous step, you’ll click on API Keys. You’ll then be able to see the private key for your API in either the Secret key or a generated Restricted keys field on the screen. Either key can be used.After copying the key from Stripe, you’ll paste this key into the Stripe API Key field back in Moesif. After doing this, back in Moesif you can scroll down to the bottom of the screen and click Save to save the configuration.Optionally, you have the ability to customize the company_id in Moesif as well. The default should work fine for most purposes. However, you can fully customize it to specify how to map Stripe Subscription and Customer objects to Subscription ID and Company ID in Moesif respectively:  A Stripe Customer maps to Moesif Company.  A Stripe Subscription maps to a Moesif Subscription.3 - Create a Billing MeterOnce you have the Stripe integration active in Moesif, you can begin to set up your billing meter. Billing meters created in Moesif do two things: track usage based on specific criteria and report that usage to the billing provider. Moesif allows you to set up very simple and very complex billing meters with relative ease.To create the Billing Meter, in Moesif you will navigate to the Billing Meter screen. You can do this from the left-side menu. On the Billing Meter’s screen, you’ll then click + Add Billing Meter in the top-right corner of the screen.The next screen is where you can actually input the criteria for your Billing Meter.Fields on this screen include:      Billing Meter Name          This is the Moesif internal name of your new Billing Meter            Billing Provider          In this dropdown you can choose the billing provider you want to send your usage metrics to.            Product (Stripe only)          Here you can choose which Product that you’ve set up in Stripe you want your usage metrics to be tied to.            Price (Stripe only)          The last field in the Billing Provider settings for the Billing Meter, here you will choose which Price you want to tie your usage metrics to.            Filters          Under the Filters configuration, you will configure your billing criteria to only include requests that fit a certain criteria.            Metrics          Here you can choose which metric you would like to bill on. Available options include:            Event Count          This will increment usage for every event that fits the criteria outlined in the Filter criteria.            Unique Users          This will increment usage whenever a unique user sends a request that fits the Filter criteria. For every unique user, the count will be incremented by 1 regardless of the event count for that user.            Unique Companies          This will increment usage whenever a unique company sends a request that fits the Filter criteria. For every unique company, the count will be incremented by 1 regardless of the event count for that company.            Unique Sessions/API Keys          This will increment usage whenever a unique session or API key is used to send a request that fits the Filter criteria. For every unique session or API key, the count will be incremented by 1 regardless of the event count for that particular session or API key.        There are other options under Metrics as well but the above 4 tend to be the most applicable to usage-based billing.As an example, for this guide we will create a Billing Meter that will filter traffic for a single endpoint, named /test-service, and where requests received a successful HTTP 200 response. We will use the Event Count metric to make sure that every request is added to the tally and sent to the billing provider.In Moesif, the billing meter will be configured as shown below.We will then click Create. This will create and activate the Billing Meter. A modal will appear notifying you that the billing meter has been created and presents a walk-through to ensure the meter is correctly configured.First, we will set up a flow to get users registered, subscribed, and create a JWT so they can use our monetized API. Once that is complete we will come back and proceed with the walk-through.4 - Create the /register endpointInstead of using a pre-built onboarding flow, such as through a Developer Portal within an API gateway, we will build our own. We will create an endpoint called /register which we can then use to onboard our users who want to use the API. The result will be that the user receives a JWT that they can use that will track their usage.Since we are using Moesif, Stripe, and NodeJS as part of our overall solution, we need to make sure each of the components is working together properly.Here’s what the endpoint will do:  Create a customer in Stripe  Subscribe the new customer to the API subscription in Stripe  Create the CompanyID in Moesif (which will be the Stripe subscription ID)  Create the UserID in Moesif (which will be the Stripe Customer ID)  Create a JWT with an id field that contains the Stripe Customer ID  If you already have User and Company identifiers in Moesif and other systems that you want to use, instead of using Stripe’s customer and subscription as your IDs, you can do that in Moesif under the Stripe configuration settings.In this example, I will create a simple NodeJS API with Express to do the above.Create the npm projectFirst, we will create a folder called moesif-monetization where we will add our API code. We will then run npm init to turn moesif-monetization so we can use npm in our project. For that, you’ll run the following command in the moesif-monetization directory.npm init  You can fill out the details or use the defaults as needed when creating the npm project.You should then see a package.json file in your moesif-monetization folder.Now, open this directory in your favorite IDE or text editor. I will be using VS Code for the remainder of this tutorial for all the coding.Add in the project dependenciesWe will now edit our package.json file with our correct dependencies. In the package.json we will add the following entries under the dependencies object. "dependencies": {   "@stripe/stripe-js": "^1.29.0",   "body-parser": "^1.20.0",   "cors": "^2.8.5",   "dotenv": "^16.0.0",   "express": "^4.17.1",   "express-jwt": "^5.3.1",   "http": "0.0.1-security",   "jsonwebtoken": "^8.5.1",   "moesif-nodejs": "^3.5.8",   "path": "^0.12.7",   "stripe": "^8.219.0" }Save the file, then navigate to the terminal and run:npm installNow, your dependencies will be brought into the project and added to the node_modules folder. These dependencies will help us to make calls to REST endpoints, connect to Stripe and Moesif, generate and validate JWTs, and various other capabilities we will build into our app.Create the .env fileInstead of hard-coding the Stripe keys and other static values into our app, we will abstract them into a .env file. We can do this using the dependency we added in our package.json called dotenv.In the root directory, create a file named .env. Within this file, we will add a few entries that will contain the keys and values used in our code.STRIPE_KEY="sk_test_XXX"STRIPE_PRICE_KEY="price_XXX"MOESIF_APPLICATION_ID="YOUR_MOESIF_APP__ID"TOKEN_SECRET="YOUR_TOKEN_SECRET”The values that are here can be found in the following places:Obtaining Your Stripe API and Price KeyYour Stripe API Key can be found in the same place we grabbed the key for our Stripe and Moesif integration we did earlier for the Billing Meter. You can actually use the same key for both or create a restricted key with just the scope needed for each function.While we’re at it, lets grab our Stripe product’s price key. We will need it in the next section. Your Stripe price key is an identifier for the price you created earlier in Stripe. This can be found by going to the product in Stripe and grabbing the value from the API ID column.Obtaining Your Moesif Application IDYour Moesif application ID is found in Moesif by going to the menu link in the bottom-left of the screen (which will show your name) and selecting API Keys.The key will then be on the page that appears under Collector Application Id.Creating Your Token SecretThis will be the secret that is used as part of generating and validating your JWTs. This could be any string you’d like, however, for production purposes you are best off using something like the NodeJS Crypto library to generate this.Once you’ve populated the file with the four key-value pairs, save the file. We won’t need to touch this file again for the remainder of the tutorial.Create the app.js fileIn the root directory of our app, we will create an app.js file (if not already created). In this file we will add the following code that adds our dependencies and creates a base REST endpoint.require('dotenv').config();const express = require('express');const path = require("path");const bodyParser = require('body-parser');const moesif = require('moesif-nodejs');const Stripe = require('stripe');// npm install express-jwt@5.3.1const ejwt = require('express-jwt');// npm install jsonwebtokenconst jwt = require('jsonwebtoken');const app = express();app.use(express.static(path.join(__dirname)));const port = 3000;const stripe = Stripe(process.env.STRIPE_KEY);var jsonParser = bodyParser.json();const moesifMiddleware = moesif({ applicationId: process.env.MOESIF_APPLICATION_ID, identifyUser: function (req, _res) {   return req.user ? req.user.id : undefined; },});app.use(moesifMiddleware);app.post('/register', jsonParser, async (req, res) => { })app.get('/test-service/',  ejwt({ secret: process.env.TOKEN_SECRET, algorithms: ["HS256"] }),  (_req, res) => {    res.status(200)    res.send('this is a response');  })app.listen(port, () => { console.log(`Example app listening at http://localhost:${port}`);})In the above code we are:  Importing a few dependencies  Configuring the Stripe dependency  Configuring the Moesif middleware, including implementing the identifyUser function to pull the id field from the JWT to enable user tracking in Moesif  Creating the /register endpoint (without any logic yet)  Creating a simple /test-service endpoint (this will be our monetized API)  Set our app to run on port 3000 and start our Node app  You will notice that we have process.env.STRIPE_KEY, process.env.MOESIF_APPLICATION_ID, and process.env.TOKEN_SECRET. These values will come from the .env file that we created in the last step.The /test-service endpoint uses the express-jwt dependency to validate the JWT attached to the request. If you’re unfamiliar with how express-jwt works, check out their github to find more information.Implement the /register endpointOur next step is to implement the /register endpoint. This endpoint will essentially create the binding between our generated JWT, Stripe, and Moesif. The outcome will be a generated JWT which will associate usage with a user in Moesif, which will then be reported to Stripe.Our first step in the flow is to create the customer in Stripe. We will use our Stripe JS dependency to do just that. We will use the parameters from the request body (email, first name, last name) to create the customer in Stripe using the stripe.customers.create function. We will then store the created customer in a custom variable so we can access the customer ID generate in Stripe.const customer = await stripe.customers.create({     email: req.body.email,     name: `${req.body.firstname} ${req.body.lastname}`,     description: 'Customer created through /register endpoint',   });Next, we will subscribe this new user to our API subscription we created in Stripe earlier. We will use the stripe.subscriptions.create function and use the generated customer ID from the previous function call to subscribe them. This will return back a subscription object containing an ID we will use later.   const subscription = await stripe.subscriptions.create({     customer: customer.id,     items: [       { price: process.env.STRIPE_PRICE_KEY },     ],   });Once the user and subscription are created in Stripe, we will now use the Moesif middleware to create the user and add their relevant details into Moesif. First we will call the Moesif middleware’s updateCompany function to map the Stripe customer.id to the companyId in Moesif.  var company = { companyId: customer.id };  moesifMiddleware.updateCompany(company);We will then do a similar step with the updateUser function and use it to map the Stripe customer.id to the userId and companyId and some other metadata we collected on the user into Moesif. This will link the user to the company as well.  var user = {     userId: customer.id,     companyId: customer.id,     metadata: {       email: req.body.email,       firstName: req.body.firstname,       lastName: req.body.lastname,     }   };   moesifMiddleware.updateUser(user);Our next step is to generate a JWT with the Stripe Customer ID attached. To do this, first, we will create a function that will call the jwt.sign function from the jsonwebtoken dependency. This method, named generateAccessToken, will create a JWT for us to use with our endpoints.const generateAccessToken = async (id) => { const token = await jwt.sign(id, process.env.TOKEN_SECRET); console.log(token); return token;}In our /register endpoint, we will call our generateAccessToken function and pass the Stripe Customer ID to inject it into the JWT. We will do this just below the last code we added to add the user and company to Moesif.const token = await generateAccessToken({ id: customer.id });Optionally, once the JWT is created, we will add it to our Moesif user metadata for ease of use. This isn’t recommended for production environments but can help when testing your setup instead of generating a new JWT if you lose the previously generated one.var user = {     userId: customer.id,     metadata: {       jwt: token,     }   };   moesifMiddleware.updateUser(user);Lastly, we will return a 200 OK response back to the caller with the JWT in the response body.   res.status(200)   res.send({ jwt: token });The completed function(s), end-to-end, will look like this:const generateAccessToken = async (id) => {  const token = await jwt.sign(id, process.env.TOKEN_SECRET);  console.log(token);  return token;}app.post('/register', jsonParser, async (req, res) => {   // create Stripe customer   const customer = await stripe.customers.create({     email: req.body.email,     name: `${req.body.firstname} ${req.body.lastname}`,     description: 'Customer created through /register endpoint',   });   // create Stripe subscription   const subscription = await stripe.subscriptions.create({     customer: customer.id,     items: [       { price: process.env.STRIPE_PRICE_KEY },     ],   });  // create user, company, and subscription in Moesif  var company = { companyId: customer.id };  moesifMiddleware.updateCompany(company);  var user = {    userId: customer.id,    companyId: customer.id,    metadata: {      email: req.body.email,      firstName: req.body.firstname,      lastName: req.body.lastname,    }  };  moesifMiddleware.updateUser(user);  // generate new jwt for user  const token = await generateAccessToken({ id: customer.id });  // [optional] update user profile with jwt  var user = {    userId: customer.id,    metadata: {      jwt: token,    }  };  moesifMiddleware.updateUser(user);  res.status(200)  res.send({ jwt: token }); })With that, we can now actually try out our endpoint to make sure that each piece is working as expected. The outcome should be a registered user with an associated JWT which will record and report usage data to Stripe. Let’s move onto testing it.5 - Send a Test Request to the /register EndpointOnce your /register endpoint has been coded and deployed, it’s time to test it. For right now we will simply use Postman to send a request. Our request will contain a JSON request body that will contain a:  First name  Last name  EmailOf course, this is the minimal amount of information we would want to configure our system and profiles in Stripe and Moesif, plus, generate the JWT. You can easily add more fields as needed for your specific use case.In Postman, we will create our request with the following information:Request Type: POSTEndpoint URL: http://localhost:{port}/registerRequest Body:{  "firstname": "Userfirstname",  "lastname": "Userlastname",  "email": "test@test.com"}  Replace port in your endpoint URL with the assigned port number.Once everything is plugged into Postman, it should look like the following:Once the request is sent, the response should contain an JWT that the newly registered user can use.We will now check Stripe to ensure that the information we registered the customer with is correctly entered into Stripe.Logging back into Stripe, you’ll navigate to Customers screen. You should see your newly created user in the list.Click on the newly added customer in the list. On the next screen, you should see that the customer is also subscribed to your APIs subscription.With this check completed, we can safely assume that our /register endpoint is correctly setting up our users accounts and subscriptions in Stripe.6 - Call Your API Using the Generated JWTOur next step is to actually use our generated JWT. We will then confirm that all the correct information is added into Moesif. The data we are confirming includes:  The Stripe Customer ID is mapped to the Moesif User ID  The Stripe Subscription ID is mapped to the Moesif Company ID  Moesif contains the Stripe metadata in the users profileUse Postman to Send the RequestNext, let’s use Postman, or another platform, to send a request to the /test-service endpoint. This is the endpoint that we set up the billing meter for in Step 3, above.In Postman, we will:  Put the /test-service API endpoint as the request URL  Select the Authorization tab  Select the Type as Bearer Token  Populate the token details  Set the Token as the JWT received from our /register callBelow is an example of the populated request configuration in Postman. To send the request to our endpoint, click Send.Once sent, the API call analytics should land in Moesif.Confirm That Moesif Received the Request Info Using Profile DashboardsBack in Moesif, you’ll navigate to the Live Event Log screen. You can do this by clicking the New button and selecting Live Event Log.On this screen, you should see the request you just sent. You should see the entry has both a User ID and Company ID populated with the Stripe user and subscription ID’s. The entries should look like this:  The customer ID will look like “cus_XXXX” and the subscription ID will look like “sub_XXXX”.If you click on the User ID shown in the entries on the Live Event Log screen, you will come to the users profile page. On this page, we will confirm that the Stripe metadata is present. We will need to add a new column to our profile to display the Stripe data. To do this, from the profile page, click on the … More Actions button and click Customize Profiles’ Layout.We will then add a new column for the Stripe metadata. You will click the + button on the far right of the screen to create a new column where we will add the Stripe metadata.  You may need to scroll to the right to see it depending on your resolution and screen size to see the + button.You will then drill down to Metadata > stripe > customer > created and use this field in the new row. I’ve also changed the column image to one more fitting. You can customize this by clicking on the image and selecting whichever one fits best.You can also add other fields, but for right now just this single field is enough to tell us that Moesif is correctly receiving data from Stripe.  If you don’t see the Stripe metadata entry as an available field, wait a few minutes. If after a few minutes the Stripe metadata isn’t present, ensure that your Stripe configuration is correct in Moesif. After confirming or editing it, try creating a new user and sending a request again to confirm that the integration is working.At this point, we now have confirmed that our API call is working and is stamped with the correct user and company details in Moesif. We also confirmed that Stripe is sending data back to Moesif which is correctly being mapped to the corresponding user profile, confirmed through the Stripe metadata in Moesif.7 - Create the frontendNext, we want to add a simple little frontend so we don’t need to call for our JWT through Postman. We will make a quick little registration form that will then return a JWT for our newly registered user to use.Add your frontend files to the appIn the root directory of the application, we will add two files. We will add both an index.html and an index.js.Add in your route to serve the static html filesIn the app.js file, we will add in a route to serve the static HTML files. Underneath our code for the /register endpoint, we will add another endpoint. Add the following code:app.get("/", function (_req, res) { res.sendFile(path.join(__dirname, "index.html")); res.sendFile(path.join(__dirname, "index.js"));});This code will now load the website (once we have the code plugged in) when you navigate to http://localhost:3000/ .Code the frontend form and logicFinally, let’s add the code for our frontend HTML and JavaScript functionality. In the index.html file, we will add markup that looks like this:<!DOCTYPE html><html lang="en"> <head>   <meta charset="utf-8" />   <meta name="viewport" content="width=device-width, initial-scale=1" />   <meta name="theme-color" content="#000000" />   <meta     name="description"     content="Moesif Embedded Dashboard example"   />   <title>None React App Example</title> </head> <body>   <noscript>You need to enable JavaScript to run this app.</noscript>   <h1>     Moesif Monetization Example   </h1>   <div id="form-input">     email: <input id="email-input" placeholder="email" />     first name: <input id="firstname-input" placeholder="first name" />     last name: <input id="lastname-input" placeholder="last name" />     <button onClick="submitRegistration()">Register</button>   </div>   <div id="apikey-output"></div>   <p id="error-message"></p>   <script src="index.js"></script> </body></html>This markup will display a form which allows users to input an email, first name, and last name. It also has a Register button that will call the JavaScript function for submitRegistration(). That function will be in our index.js JavaScript file.The index.js file will look like this:async function submitRegistration() {   const email = document.getElementById("email-input").value;   const firstname = document.getElementById("firstname-input").value;   const lastname = document.getElementById("lastname-input").value;   const errorElement = document.getElementById("error-message");   const apikey = document.getElementById("apikey-output");   var body = { email, firstname, lastname };   console.log(body);   var response = await fetch('http://localhost:3000/register', {     method: 'post',     body: JSON.stringify(body),     headers: {'Content-Type': 'application/json'}   });   var data = await response.json();   console.log(data);   apikey.innerHTML = data.apikey; }This function will take the input from the form, post it to our /register endpoint, and display the returned JWT on the screen.8 - Test the frontendTo test the frontend, save your code changes and restart the server. Then, in a browser, navigate to http://localhost:3000/. You will then see the form show up.Fill out the form fields and submitNow that the form is loaded on the screen, fill in the fields and click the Register button. This will take the info, post it to our /register endpoint, and give us the generated JWT.  It is suggested that you use a different email than you used earlier when you created a JWT directly through the /register endpoint.Confirm the JWT is returnedOnce the submit button is clicked, after a few seconds, the JWT should be returned back to the UI.9 - Send a Request to Your Monetized APIWe will once again want to make sure that everything is working with our UI, through to our backend systems. For this, simply repeat the steps from Step 6 to confirm that the user and company IDs are populated correctly and that the Stripe metadata is returned for this user and the new JWT. We should see these calls populated within our Live Event Log as well.10 - Confirm All the Pieces are Working CorrectlyAlthough this is optional, this step may help with troubleshooting any issues that may have came from our previous steps. Here are a few things to check to make sure that all is working as it should. After creating a new user through the UI and using the generated JWT to place a call to your API, confirm the following:In Stripe  Confirm that a customer has been created in Stripe with the details you entered into the UI  Confirm that the customer has been subscribed to the correct product and priceIn Moesif  Your API call was recorded in Moesif in the Live Event Log  Your API call has the Stripe Customer ID and Subscription ID in the User and Company fields in Moesif, respectively.  Confirm that the Stripe metadata is populated in Moesif  All Billing Meter test conditions have passed11 - Check Stripe for UsageLastly, After a few hours, it’s best to go into Stripe to confirm that usage is being added to a users subscription. Be sure that you’ve sent a few requests through in order to make sure you have some data that should be sent to Stripe.  It may take a few hours for usage to make its way from Moesif to Stripe. If data still isn’t in Moesif after a few hours, ensure you’ve followed all the steps outlined within this guide. This includes making sure that your user and company ID’s from Moesif are correctly mapped to the corresponding keys in Stripe.To check the usage, in Stripe you’ll want to navigate to the Customers screen and select the customer that you made the API call with. Once selected, you should see some active subscriptions for the users that you’ve registered through the /register endpoint. The one we created earlier is called My API. Click on the subscription entry.On the next screen, click on View Usage beside the price entry.A modal should now pop up showing you the usage for the API that has been reported to Stripe from Moesif.  Remember, there is a delay in Moesif’s reporting to Stripe. If you data isn’t there yet, check back in a bit later.12 - Determining If the Billing Meter is Working CorrectlyTesting the created Billing Meter is easy with out Test Meter function. Navigate to your created Billing Meter from the left side navigation pane and selecting your Stripe Test billing meter. Select Test Meter on the top right.We will first confirm the meter that you are attempting to test. Click the Next button at the bottom of the modal.Moesif will wait for Subscriptions to created within Stripe and those subscriptions to be associated within Moesif itself. This page will update automatically, no need to refresh.Moesif will then wait for an API call to our any endpoint associated with our billing meter using our the JWT that has been created for us.Finally, Moesif will sync all usage data to Stripe every 15 minutes. This step may take a few minutes depending on when the API call was initiated but will update on the given interval.Wrapping upMonetization has always been a tough hurdle to get past. Many custom solutions offered flexibility but at a very high engineering and support cost. With Moesif, API monetization is possible in an extremely minimal amount of time. As demonstrated in this article, With a little bit of configuration and minimal amount of code we can create a production-ready, post-paid monetization scheme in minimal time.                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue with your NodeJS REST APIs?            Monetize your NodeJS APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /api-monetization/stripe/End-To-End-API-Monetization-With-NodeJS-Stripe-And-Moesif/",
          "author": "Matthew",
          "categories": "API-Monetization, Stripe"
        }
      
    ,
  
    
        "api-strategy-monetization-when-should-you-consider-metered-billing": {
          "title": "When Should you Consider Metered Billing",
          "content"	 : "Have you ever paid a bill for electricity, internet, or water? Was that bill based upon the amount of resources you used? If so, the pricing model your provider used was “metered billing.”If you’re familiar with software tools, you might have heard of this model by different names: “Pay As You Go,” “Usage-Based Billing,” “Consumption-based Billing,” “PAYG.” In short, this payment model allows customers to pay only for services rendered. It’s not just for utilities. This value-based pricing model pairs well with customer-driven growth strategies, such as product-led growth. Plus, this revenue model scales in real-time.SaaS companies are taking notice. Metered billing reduces the barrier to entry for new customers, and it’s becoming more common in software. For healthcare innovation platform provider NexHealth, usage-based billing from Moesif enabled them to start billing customers immediately and helped their API product quickly become a revenue generator.So, should you consider adopting a metered-billing model?If you’re building a SaaS platform, and your customers are……developers, or are highly technical…early adopters of your service…paying to use your API productthen, metered billing will likely accelerate your growth.Buying any software service or feature, especially a new kind, is not a simple decision for your potential customers. It’s a decision that most will only make with buy-in from technical leaders of their organization. A frictionless, transparent billing experience builds customer acquisition and loyalty. This is particularly true for developer-led, and B2B SaaS companies you’d like to convert into customers.Metered billing caters to the culture of autonomy amongst software professionals. Offering a technical decision-maker full control of their discovery process empowers them to integrate faster. Even so, success with any SaaS pricing model depends upon how you measure your core business offerings.If you can track value metrics such as……number of API calls…unique users…unique sessions…API keysthen metered billing can absolutely expand your customer base.Tracking the key value metric for your service will ensure that you and your customers get the most out of your product. For example, an API product specializing in SMS offers value each time it sends a text message. Let’s say that the API sends out an average of 100 messages in one API call, as a batch. In this case, the value of the service maps to a multiple of the number of API calls sent. By attaching this value metric to the customer’s bill, they pay for the true value of the service.The platform now can automatically bill customers who send more text messages, without penalizing those with lower usage rates. This transparency in pricing can improve revenue and build loyalty with existing customers.It’s worth noting that metered billing carries the risk of misuse. Some customers will incur costs they don’t intend to pay. You can mitigate this issue by implementing rate limits using Moesif. Alternatively, you can build such functionality yourself, though doing so is often time-intensive and costly.Despite that flaw, a metered billing model can mean the difference between growth and stagnation.Can you afford to ignore developer buy-in?If you are considering a usage-based billing model for your business, take a look at our complete implementation guide here. There, you can thoroughly qualify if this billing model is right for your business.If you have more specific questions, we can help with that too.  How can I use Moesif and Stripe for pay as you go billing?  How does metered billing help me monetize my API?   What is product-led growth?Or, refer to our metered billing page. Whatever pricing model you choose, we hope it’s your winning strategy.                Get the Biggest Business Impact from your APIs with Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /api-strategy/monetization/When-Should-you-Consider-Metered-Billing/",
          "author": "Savannah",
          "categories": "API-Strategy, Monetization"
        }
      
    ,
  
    
        "technical-security-5-security-tips-for-your-graphql-api": {
          "title": "5 Security Tips for Your GraphQL API",
          "content"	 : "In 2015 GraphQL was created by Facebook as an alternative to REST APIs to give more power to frontend developers by making API calls more flexible. GraphQL achieves this goal by providing its API consumers with a query language that allows them to query just the data they need.While GraphQL can improve frontend developer experience, its specification doesn’t have opinions on security. A GraphQL API needs to translate queries to the data fetches, which adds another layer of complexity to your API architecture.We already wrote an article about the top ten API security threads, and they all hold for GraphQL APIs. In this article, you will learn about additional enhancements to improve your GraphQL API security.New privacy laws in the US and EU make every security hole a huge financial danger for API companies.1. Comprehensive Query AuthorizationAlthough small APIs often provide an HTTP accessible way to access databases, GraphQL is generally data-source agnostic. This means GraphQL doesn’t care where the data comes from. The data can come from a database, or it can be fetched from another API. This also means, GraphQL isn’t a data-store, so it has to authenticate against the actual data-sources and check if the users are authorized to access specific data from these sources.Suppose one GraphQL server that serves many different clients is authenticated as one user in an upstream service or database. In that case, the GraphQL server has to do the authorization checks inside the resolves by itself. Otherwise, this can lead to a leak of private or, even worse, medical data regulated by CCPA laws and makes you liable.You can check permissions in every resolver if needed:const resolvers = {  Query: {    adminResolver: async (parent, args, context) => {      if(!context.user || !context.roles.includes("admin")) throw new Error("Permission denied!");      ...      return data;    },  },};2. Input Validation and NormalizationBack in the days, developers would generate SQL queries in the frontend and send them to the API to fetch data from an SQL database; thus, SQL injections were born. An attacker could write SQL and send it to the API, which would execute it without asking further questions.While nobody prevents a GraphQL API developer from creating a type that accepts an SQL string that gets blindly executed on the server, this is seldom the case.But accepting data from clients is always a risk. This is why all inputs should be validated and normalized before any data-fetching happens. Especially custom scalars are susceptible to this threat since they don’t do default validations.3. Introspection RestrictionGraphQL provides its API consumers with a convenient introspection feature, which allows GraphQL clients to ask the API what types of data it provides. This is great, because now a client developer doesn’t have to look into the documentation, but can directly ask the API server what data is available.But introspection can also be abused when not controlled rigorously. For example, when GraphQL types that provide administrative functionality can be discovered and used by regular users.GraphQL API creators have to use rigid authorization schemes for introspections to make it harder for attackers to find API vulnerabilities. If external developers don’t use your API, it can also be a good idea to disable the introspection feature in production environments.Apollo server, for example, allows to disable introspection with a simple configuration flag:const IS_PRODUCTION = process.env.ENVIRONMENT === "production";const server = new ApolloServer({  typeDefs,  resolvers,  introspection: !IS_PRODUCTION,});server.listen();4. Query LimitationsGraphQL queries give API consumers much flexibility to fetch exactly the data they want with just one request, but this feature can also be abused in multiple ways.Malicious clients can create queries that are very deep, complex, or generally long-running for various reasons. If these attackers find an edge case that leads to heavy computations or exploit the N+1 query problem, they can overload the API and degrade performance for other clients drastically.That’s why a GraphQL API should limit query execution time to a sane maximum. Even better, another way to achieve this goal is to restrict query depth or complexity so it makes it even tougher to execute common GraphQL DDoS attacks which tend to use recursive and deep querying only meant to disrupt services.5. Upstream Error HidingAs said before, a GraphQL API isn’t a data-storage mechanism; this means it uses upstream services like databases or other APIs to fetch the actual data. These services can have errors too. If you deliver the upstream services’ errors to your clients, an attacker can use them to get insights about your architecture.To mitigate this threat, you should always process upstream errors before delivering them to a client; this way, you can hide the services you get your data from and prohibit attackers from taking advantage of bugs or security holes these services could have.Let’s look at the following code example:const resolvers = {  Query: {    myResolver: async (parent, args, context) => {      try {        const data = await fetchFromRemoteDataSource();        return process(data);      } catch (upstreamError) {        const cleanError = analyzeUpstreamError(upstreamError);        throw cleanError;      }    },  },};The resolver tries to fetch some data from a remote data-source, but this could fail. The error we catch comes from the remote data source, so it could contain information about the data-source.We need to analyze the error and clean it of any upstream information before delivering it to the client.Keep Your GraphQL APIs SecureGraphQL APIs are an excellent way to improve the developer experience for the frontend team. It helps to optimize data fetches by giving clients a way to specify what they need. But this comes at the cost of higher complexity in the API architecture, which increases the attack surface of an API.With the rise of GDPR and CCPA laws, which stipulate fines of multiple million dollars, it’s more crucial than ever for API creators to keep the common API threats in mind when developing an API and also look out for GraphQL specific weaknesses that could appear.                Protect from Threats and Abuse With Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /technical/security/5-Security-Tips-for-Your-GraphQL-API/",
          "author": "Kay",
          "categories": "Technical, Security"
        }
      
    ,
  
    
        "developer-marketing-api-analytics-developer-experience-the-metrics-that-matter-most": {
          "title": "Developer Experience: The Metrics That Matter Most",
          "content"	 : "Developer experience. If you provide APIs or API-first products, you likely hear that term a lot. After all, you need developers for an API to succeed — and if they don’t have a great experience, they’ll move on. Software development requires tools that provide the most value for the least amount of upkeep. Understanding your key performance indicator for your API product or software quality metrics allows you to improve on your developer experience.What Is Developer Experience?Developer Experience (DevEx or DX) is an extension of user experience (UX) where the focus is on users impacted by the technical side of things — for example, tooling, languages, and workflows. But DevEx is far more than “UX for developers”: it means ensuring that developers can easily understand and leverage an API for their own applications and use cases. Great DevEx happens when you communicate with your developer users, understanding and meeting their needs directly by looking at and iterating off of  your software metrics. If you can win over developers, you can build a large and thriving ecosystem around your software engineering products.What Metrics Matter? It Depends on Your RoleCelebrated management consultant Peter Drucker may have said it best: “If you can’t measure it, you can’t manage it.”Improving DevEx starts with the right metric analysis. You’ll want to measure the things that matter most to DevEx, avoiding vanity metrics and linking specific API transactions to business value. What matters most to you, however, depends on your role in improving DevEx. Finding quality metrics that are meaningful to your API product will aid your development teams in valuing your users in the software development process.There may be three distinct roles in your organization focused on DevEx: developer experience manager, developer relations professional, and API product manager. The concerns of each of these roles overlap in project management, but they differ in focus. These distinct focuses will be represented in the metrics they care about most.Developer Experience ManagerDevEx managers focus on the effectiveness of the APIs or API-first products developers use. They also look at ways to improve processes so that developers achieve success. The DevEx manager is responsible for running everything developers access and use — from the developer portal and documentation to code samples and SDKs.DevEx managers make sure developers have a great experience with the tools and processes associated with the product. As such, the lean metrics they most care about include the following.Time to User ActivationHow long does it take a development team to start using the API once they complete the registration — hours, days, weeks? If a developer doesn’t integrate or activate promptly, it could indicate a problem.  Lead time is a major determining factor in integration success. Perhaps your documentation is confusing, or you don’t have SDKs for popular languages. If an engineering team does not integrate or activate within a few days, a good DevEx manager will take notice. Aiding developer productivity starts with a solid initial integration.Time to First Hello WorldTime to First Hello World (TTFHW) measures how long it takes a new developer to get up and running, achieving the minimal level of value from your API. Every developer has different criteria for what constitutes that first success. It could be calling the API for the first time and getting a response. It could be creating a simple test app and completing a transaction that verifies the API will suit the developer’s needs. But in any case, the mean time a developer requires to achieve a modicum of success with your API is a key indication of whether they’ll continue using it and convert to a paying customer. Understanding the project metrics that have the most value will allow you to more appropriately triage issues and speed up your development process and deployment frequency.Number of Devs Needing SupportHow many developers have reached out to get support on an issue? Are many developers experiencing the same quality assurance problem? DevEx managers should log all integration issues internally and track how many developers need help improving a specific performance metric or productivity.Time to Support ResolutionIf you have a support system in place, how long does it take your agile teams to resolve technical support or code quality issues? Developers don’t want to wait to resolve their issues with your API or the integration. They want prompt answers to help them move forward and achieve their development goals.Developer Relations ProfessionalDeveloper relations (DevRel) professionals aim to get developers to adopt an API or API platform and complete their goals with it. Typical DevRel roles include developer evangelist, developer advocate, and community manager.DevRel professionals focus on building and maintaining relationships with developers and the developer communities served by the product or service. People in DevRel positions care about making connections with developers, listening to their thoughts, and figuring out how to address their needs. They also help developers understand how to use the API or product, providing demonstrations and leading educational events, like hackathons and meetups.Two north star metrics for DevRel professionals are TTFHW and Weekly Active Tokens (WAT).Weekly Active Tokens (WAT)DevRel professionals want to see how much traction the API gets and whether the developer community is growing. To help figure this out, they can look at the weekly active tokens (WAT) for the API. Most APIs limit access to authenticated users, so they can track how many unique tokens access the API platform weekly. If they narrow this metric to Weekly Active Integrated Companies, the DevRel team can figure out where to invest more time and effort in developer outreach and marketing.Other DevRel MetricsWhile TTFHW and WAT are the two main DORA metrics DevRel professionals focus on, they may also be interested in measures such as  How much coverage does the company have with docs, guides, and quickstart resources?  How often and how long are devs interacting with reference documentation? Which parts?  How satisfied are devs with the support they receive?  How frequently are teams creating content to support devs?  Are devs engaging with the instructional content the company offers?API Product ManagersAPI product management is somewhat new, so the responsibilities of this role vary depending on the company and the API products it offers. In general, an API product manager understands the business justification for the API or API platform and the high-level objectives for the company’s API initiatives. They also need to balance the needs of internal stakeholders and product developers with requests from customers.API product managers focus primarily on user growth, user retention, and usage patterns. They need to know which features and endpoints developers use the most (and the least) to prioritize development. They also need to understand the impact of API changes, like deprecated endpoints and new API versions, on customers.API product managers look at a range of metrics, but the two most common are API Usage Growth and Unique API Consumers.API Usage GrowthAPI product managers need to measure API adoption, and they do that by looking at API usage growth. They measure API usage over extended periods, like weeks or months, to discover key trends in growth.Unique API ConsumersSometimes an increase in API usage occurs because of a single customer account instead of unique user growth. API product managers should measure the number of monthly or daily unique consumers of their APIs. They should also monitor these users daily or monthly to see if they remain active. Looking at monthly or daily active users (MAU/DAU) can tell you if increased API usage comes from new customers, existing customers, or both.Other metrics API product managers look at include  When, how, and how long do developers use the API?  Which API features are developers using or not using?  What kind of feedback are specific product features getting?  How does the money spent compare to overall product adoption?Tracking DevEx MetricsWhile each of your team’s DevEx professionals cares about different things, they all need a tool that will help them efficiently track and measure relevant metrics. Is your current role focused on DevEx? Then you need an effective API analytics tool that will track and measure the metrics you care about and the metrics that matter to other roles.The tool you choose should provide visibility into all APIs — e.g., REST, RPC, SOAP, Hypermedia — and APIs running on query languages like GraphQL. It should include plug-ins and SDKs so you can integrate with popular servers without having to write a lot of custom code. Finally, it should include pre-built dashboards to track the metrics every team cares about — from engineering and security to product and customer success.You don’t have to look far to find an analytics tool with these features — the Moesif API Experience Platform has them all. You can try out the platform for free for 14 days.                Build Better Relationships With Developers Using Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /developer-marketing/api-analytics/Developer-Experience-the-Metrics-That-Matter-Most/",
          "author": "Larry",
          "categories": "developer-marketing, api-analytics"
        }
      
    ,
  
    
        "api-product-management-developer-platforms-what-is-an-api-product": {
          "title": "What Is An API Product?",
          "content"	 : "We’ve all heard the terms API product or API-as-a-product. The terms themselves seemed to be used quite freely, leaving some of us with an assumption of what is meant but without a really solid grasp. As joining and contributing to the API economy becomes more desirable, API products are a crucial part of any business that is looking to tap into it. So asking “what is an API product?” is a really relevant question with many different angles.An API product is no different than any other product: it delivers value in some way, shape, or form. In the context of an application programming interface (API), we are essentially saying that we have created and packaged an API that can bring value to internal and external organizations. You may decide to build an API product from inception or you may choose to productize different APIs as part of a new business model spurred by digital transformation.A few examples of API products you may be familiar with are:Google MapsThe google maps API is probably one of the widest used APIs out there. Most websites and mobile apps that require any type of location lookup, geo-tracking, or any other location-based functionality are likely using the Google Maps API. Easy onboarding, usage, and great documentation are some hallmarks of this product strategy.Tomorrow.ioMoesif customer Tomorrow.io is also a great example of an API Product with their Weather API. Used by organizations big and small, this API enables a true API Product approach with an abundance of developer docs, multiple pricing tiers, and the ability for an API consumer to sign-up and self-serve/onboard.As you can see, API products usually focus on a specific core functionality, such as location lookup or the current weather. These companies have found a way to create APIs that deliver value to other developers and their organizations. This is what it truly means to build an API product.What makes a successful API product?Since any API that provides value to a developer could be transformed into an API product, it’s important to look at what makes a good product API for both the developers and the company that built it.  Creating a comprehensive API product strategy requires knowing where your API product fits into your overall business vision.Self-serve integrationThe ability for developers to move from sign-up through to actually using the API in a self-serve fashion is crucial. Most developers do not like complex onboarding or an onboarding that is led by the sales team. A developer should be able to register and subscribe to your API and integrate it in a very “low-touch” manner, only reaching out to support if they have a unique situation or an error that they are unable to move past after going through your API reference library. This could also be improved by using an API gateway or API management platform to ensure API integration and API access is uniform across all of your API resources.Developer docsSince APIs are highly technical, good API documentation is paramount to supporting a good product. Developer docs to help API consumers should be relatively diverse and include tips on integrating, how to use features, general product information, and even how to troubleshoot common scenarios that developers may face. You may also think to enhance your docs further by adding in comprehensive written and video tutorials that show an API consumer how to achieve common outcomes.API MonetizationTo develop a product and keep it afloat, you need to drive revenue. With an API, this means figuring out what to charge RESTful API developers for and how you are going to implement that process. As an API provider, usually, you will need to determine if you’ll have a free trial or free tier if you’ll be doing pre-paid or post-paid billing, and which billing provider you will use. These considerations are key aspects of your API monetization strategy. Of course, API monetization can be very complex but making it easy to checkout, manage charges, and collect payment is key to a good customer experience.Ability to gather user feedbackAs you build your product, you’ll want to constantly improve and add value in key areas that customers are using. This means that you’ll want to gather critical operational data, user data, and usage metrics for API calls. Combining these factors together will help you to develop a more stable API product that is performant and delivers on all the areas that customers use the most. It’s also a great way to dive deep into your onboarding experience and uncover challenges that customers may be having in the early stages of adopting your API.How can Moesif Help?Moesif can help companies build great API products by leveraging some of the platform’s core features. These features can help to enable some of the points we made above which are part of a successful API program. Moesif can help with streamlining onboarding, API monetization, helping developers navigate the product, and much more. Let’s take a look at some of the specifics below.User FunnelsUsing Moesif’s user funnel analysis, you will be able to see certain trends and conversion rates throughout each step of the user journey. This can be very revealing if you point the spotlight at onboarding. For example, you may have a user funnel which goes like:  Register  1st API Call  2nd API Call  1000th API CallBy tracking this you’ll be able to see a few key insights:  How many people sign up and never make an API call?  How many people make their first API call and no subsequent ones?  What percentage of developers make their 1000th API call and beyond?You’ll also be able to see the time it takes to move between phases of the customer journey and ideate possible ways to improve it. For instance, you may believe that from the time of registration to their first API call, users should only take about 5 minutes. But, when looking at the data you see that it is actually taking more than an hour on average. This could hint that you need to improve your integration steps or perhaps just simply add some better documentation.As you make these improvements, you can continually check for improvement in conversion and the time it takes between steps. This will help you to determine if you’re moving towards optimization or away from it.Billing MetersMoesif Billing Meters allow you to easily monetize your APIs. This is possible because Moesif allows you to dial in your billing criteria and Moesif will then send API usage statistics to the provider of your choice, such as Stripe, Recurly, or Chargebee. A big hurdle to making your API available as a product is the step of actually implementing an end-to-end solution to implement your API monetization model. With Moesif, this is no longer an issue. As an example, check out this ready-to-go API monetization example using Moesif, Stripe, and Kong. Billing Meters also pair well with API Governance Rules that can be created in moesif. These rules could be used for something like blocking an API request to an endpoint based on an unpaid invoice.Behavioral EmailsWhether it’s a “Welcome” email, a “Next Steps” email, or an email to assist developers who are stuck, creating a proactive line of communication to the customer is essential. These types of email can keep customers engaged and can also help them to solve issues without having to reach out for support.With Moesif, you can create specific criteria for when to send an email. This could be when the users sign up for your API when they receive more than five 401 responses within 30 minutes from an endpoint when they use a newly added API feature or a multitude of other scenarios. Setting up these emails is extremely simple and can add value to your customer’s experience with minimal effort.AlertsInstead of actively monitoring developers in a manual way, Moesif allows you to set up conditions in which your team will be alerted to things such as new customer sign-ups, onboarding issues, consistent errors, and more. These alerts can be sent to many different channels including email, SMS, PagerDuty, Slack, or a custom webhook.By setting up alerts, your support team (and others) can proactively take steps to help customers before they come to them, or worse, decide to give up on using the API.Embedded TemplatesYou may build a custom dashboard for users to view things such as a live log or a time series of their usage. Moesif can help to expedite this with the embedded templates feature. With embedded templates, you can take your favorite charts or product data and make them available even for those who don’t have access to Moesif. For example, you could have a time series chart embedded in your APIs dashboard that has a quota line added to it to show how close developers are to reaching their quota and how their API request volumes are trending over time.Wrapping UpAPI products are no different than any other product, all of them require users to have an easy and enjoyable experience using the product. Like most products, analytics can help ensure that you are moving in the right direction in terms of adoption and customer satisfaction. Moesif can help with digging into those analytics, gaining insights, and allowing you to take action through features like behavioral emails or alerts. To up your API product game, sign up for Moesif today and unlock deep insights coupled with features to help grow and improve your platform.                Track and Improve your Developer Experience With Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /api-product-management/developer-platforms/What-Is-An-API-Product/",
          "author": "Matthew",
          "categories": "API-Product-Management, Developer-Platforms"
        }
      
    ,
  
    
        "business-company-meeting-moesif-with-adops-manager-rachael-kiselev": {
          "title": "Meeting Moesif with AdOps Manager Rachael Kiselev",
          "content"	 : "Can you imagine explaining API observability with two cats meowing in your lap? Meet our resident multitasking expert, Rachael Kiselev. She’s the mastermind spreading awareness about an entirely new way to analyze APIs, in 300 words or less. As part of our Meeting Moesif blog series, we ask her about the future of APIs, and what it took to transition from new grad to tech marketer during a global pandemic.What is your job title and what does your job entail?I’m a content marketing manager, but really that’s a pretty fluid title. I handle digital marketing as it relates to content. A lot of the digital ads that you might see on sites like Reddit are mine. Well, they’re a joint effort on marketing and design to create a cohesive ad front. I handle some of the SEO practices like UTM tagging blog posts or adding calls-to-action in pieces that other Moesif employees write. Supporting the marketing team in general as a generalist is what I do in my free time, too. So anything from transcribing a podcast to doing competitor research, etc. I try to focus on supporting the sales funnel as much as possible.We’re just coming out of a global pandemic that changed everything. How did your working life evolve with the rest of your life?Prior to COVID, I had actually just graduated from university. So really, I didn’t have any true “job” experience in the working world, more like prior in-person internships. When I graduated I was working at a non-profit managing their Google AdWords, newsletters, the like. I ended up working for a small, boutique agency right around when the first lockdown started. While there, I was fully remote and like a lot of people, really missed face-to-face interaction. It was kind of lonely, only communicating via chat or Zoom.When I left that position, I knew I wanted to be somewhere that allowed for flexibility around remote work but offered an in-person space for collaboration. Thanks to the dropping numbers in the Bay, Moesif employees have been able to work in the office at their discretion. After my initial onboarding experience, I became a kind of “flex” worker, so I come up at least once a week but no more than two or three days of the week. It’s good for me, because I live pretty far down the peninsula, and it wasn’t until recently that our public transit systems added better air filters. In short, the commute to the office can be a little crowded, so it’s preferable not having to do it every day. Also, working from home means that I can deal with issues that arise in my house in real time instead of being pleasantly surprised by the gifts (and messes) my cats leave me.Where do you see Moesif fitting into the future of APIs?I guess in an ideal world, APIs will look nothing like they do today, meaning there’s a good chance that the APIs of today will be somewhat different than the APIs of tomorrow. I see Moesif as being an integral part of the shifts companies will roll with as the API landscape evolves. I see Moesif continuing to further customer’s understandings of their APIs, therefore enabling evolution across market segments. I think Moesif’s greatest value is being able to dig into APIs to understand and monetize API usage. So being able to be at the forefront of things, giving companies a chance to iterate and improve on their APIs will help further technology as a whole. Again, we already see that the API economy has shifted away from REST APIs and towards GraphQL, so there’s already a change to the API landscape. I think in the next few years, we will expand our support capabilities and become even more integral for API-first companies to remain iterative and at the forefront of technology.What do your workspaces look like?Messy, to be frank. My desk at work looks like no one lives in it, except for a small acrylic stand I have. My keyboard and mouse are still in their boxes. At home I have a few monitors at my desk but generally split my time between my desk and my couch. I’ve mastered the art of working efficiently and ruining my posture in one go. Also, my cats follow me everywhere I go, so they’re my bonus coworkers.Can you tell us a bit about your background, and how you got to Moesif?My past experience was digital marketing inclined. I have an advertising degree and an information science minor, but ended up in marketing. A lot of my minor revolved around data aggregation and visualization (with a focus on ethics and policy in regards to information). I think that my analytical background strengthens my marketing and advertising skillset because data powers everything. Moesif being an “analytics first” product makes us an inherently data-driven company. As such, it feels like a natural fit for me.About Meeting MoesifAt Moesif, we help our customers understand how their APIs are used and how they can grow their API platform.Meeting Moesif highlights the people making that vision a reality. Our people-first culture values open communication, initiative, and impact — not to mention, perks like flexible working options that ensure employee wellness.If you’re interested in joining, you can start by learning more about what it’s like to be part of Moesif. Our Meeting Moesif blog series offers a glimpse into our office life and how we expand API observability for all of our customers. Follow the link to see other Meeting Moesif interviews with team members.                Get the Biggest Business Impact from your APIs with Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /business/company/Meeting-Moesif-with-AdOps-Manager-Rachael-Kiselev/",
          "author": "Rachael",
          "categories": "Business, Company"
        }
      
    ,
  
    
        "technical-monitoring-understanding-api-error-codes-10-status-errors-when-building-apis-for-the-first-time-and-how-to-fix-them": {
          "title": "10 Most Common API Error Codes (and How to Fix Them in 2026)",
          "content"	 : "When your first API call fails, the response body is often less useful than the status code. Documentation typically focuses on success paths; the error paths are where you spend the actual debugging time. This guide walks through the ten HTTP error codes you will see most often when building or integrating against an API, what each means, and how to fix it.                Debug And Fix API Issues Quickly With High-cardinality API Logs              14 day free trial. No credit card required.              Try for Free        For the complete status code reference across all five families, see our HTTP status codes guide. For the question of which code to return for each CRUD action when you are designing the API, see which status code for CRUD.Understanding HTTP status codesHTTP status codes are three-digit numbers returned with every response. They tell the client what happened before any parsing of the response body. The codes are divided into five families: informational (100-199), success (200-299), redirection (300-399), client errors (400-499), and server errors (500-599). Codes from 400 to 599 indicate that the request did not produce the expected result.The number is for programmatic recognition; the reason phrase (OK, Not Found, Internal Server Error) is for humans. Both follow the same rule, even for custom codes.Error types and causesHTTP errors fall into two broad classes:  Client-side errors (4xx) happen when the request itself is wrong: malformed syntax, missing or invalid auth, insufficient permissions, exceeded rate limits, or addressing a resource that does not exist. The client needs to change something before retrying.  Server-side errors (5xx) happen when the server fails to process a valid request. Overload, internal exceptions, unreachable upstream services, or maintenance downtime. The client did nothing wrong; only the server can fix it.Distinguishing the two is the first step in debugging. A 4xx tells you to inspect your request; a 5xx tells you to retry with backoff and, if it persists, contact the API provider.Client-side status codesStatus codes in the 4xx range typically indicate client-side errors, though changes to the API can also trigger them. Here are the five most common, and how to resolve each.400 Bad RequestThe 400 Bad Request error indicates that your API request was not properly formatted. The server could not parse what you sent.If the response body does not include error details, consult the API’s documentation. The cause is usually one of: a missing query parameter, a missing required field in the request body, an invalid header field, or incorrect syntax in the JSON itself.A related code that is sometimes returned instead: 422 Unprocessable Entity. The difference is that 400 means the request body could not be parsed (invalid JSON, missing required field at parse-time); 422 means the body parsed cleanly but failed business validation (the email is not in your allowed domain, the price is negative).401 UnauthorizedThe 401 Unauthorized status indicates that the API could not authenticate the request. Either no credentials were sent, or the credentials are invalid or expired.To fix: confirm you are sending an Authorization header in the format the API documents (Bearer <token> is the most common, but Stripe historically used HTTP Basic auth with the API key as the username (and most SDKs now use Bearer) and AWS uses signed requests). If the credentials look correct, the token may have expired and need to be refreshed. 401 means the request could succeed with valid credentials, which differentiates it from 403.403 ForbiddenThe 403 Forbidden status indicates that you authenticated successfully but the credentials you sent do not have permission to access the requested resource.This is different from 401: re-authenticating with the same identity will not help, because the identity itself is not authorized. Common causes include using an API key with insufficient scope, attempting to access a resource owned by another customer, or trying to use a feature your subscription plan does not include.To fix: review the API’s permission model and confirm the credentials you are using have the necessary scope. For subscription-based APIs, check whether the endpoint requires a higher tier.404 Not FoundThe 404 Not Found error signifies that the URL specified in your request does not exist on the API server. This is one of the most common HTTP errors and can have several causes:  A typo in the URL path  A change to the API’s URL structure after a version update (look for redirects via 301, 308, or Sunset headers)  A resource ID that does not exist (calling /users/9999 when user 9999 was never created or was deleted)  An API path that exists on a different base URL (staging vs production)Some APIs also return 404 in place of 403 when they do not want to leak the existence of a protected resource to an unauthorized caller. If you suspect this, check your authentication first before assuming the resource truly does not exist.429 Too Many RequestsThe 429 Too Many Requests status indicates that your client has exceeded the API’s rate limit. The response should include a Retry-After header telling you how long to wait before sending another request.To fix: implement client-side throttling that respects the rate limit headers (X-RateLimit-Remaining, X-RateLimit-Reset). For long-running clients, exponential backoff with jitter is the standard pattern. If you hit 429 consistently, you may need a higher subscription tier or a more efficient access pattern (fewer calls with larger payloads, caching, batching).A related code worth knowing: 412 Precondition Failed, returned when conditional headers (If-Match, If-None-Match) do not match the current resource state. Often paired with retries against rate-limited or versioned resources.Server-side status codesThe 5xx range indicates server-side errors. Sometimes an invalid client request that should have produced a 4xx ends up returning a 5xx because the server did not validate the input before processing. Five most common, with fixes:500 Internal Server Error500 can mean anything; the server encountered an unexpected exception. The cause might be related to your request (an edge case the server did not handle) or completely unrelated (a backend bug, a database hiccup).To fix: double-check that your request matches the documented format (query fields, body fields, headers). If your request is well-formed and the issue persists, contact the API’s support team. Quote any X-Request-Id or trace ID in the response, since that is how the API’s team will find your specific call in their logs.501 Not ImplementedThe 501 Not Implemented status indicates that the HTTP method you used is not supported for any resource on the API. Distinct from 405 (Method Not Allowed), which means the specific URL does not accept that method.To fix: try a different HTTP method. If you are calling a PATCH and getting 501, the API might only support PUT for updates. The API’s documentation should specify which methods are valid.502 Bad GatewayThe 502 Bad Gateway response indicates that the server you reached is a proxy or load balancer, and the upstream API server did not respond with a valid HTTP response. The proxy reached the upstream service but received something it could not relay.Causes: the upstream API server crashed, the network between the proxy and the API server failed, or the upstream service is in the middle of a deployment. 502 is generally a transient problem on the API provider’s side. Retry with backoff; if it persists for more than a few minutes, contact the provider.503 Service UnavailableThe 503 Service Unavailable status signifies that the server is temporarily unable to handle the request, usually because it is overloaded or undergoing maintenance. The response should include a Retry-After header telling you when to try again.To fix: implement a delay before retrying. If the error persists for an extended period, reach out to the API provider. 503 is what a well-behaved API returns during planned maintenance or capacity events; if you see it consistently outside of maintenance windows, the provider may be under-resourced.504 Gateway TimeoutThe 504 Gateway Timeout response is similar to 502, except the upstream API server did not respond in time rather than responding incorrectly. The proxy waited, hit its timeout, and returned 504 to the client.Causes: high network latency between the proxy and the upstream API, the API server taking too long to process the request (long-running queries, slow upstream dependencies), or a deliberate timeout on the proxy that does not match the upstream’s actual response time. 504 can also occur when you request an excessive amount of data, so trimming the request size is a useful diagnostic.To fix: if your request is large or computationally heavy, consider breaking it into smaller requests. If it is reasonable and the 504 persists, contact the provider.Best practices for error handlingError handling is one of the parts of API design that compounds the most over time. Three patterns that hold up across APIs:  Use meaningful error messages. Pair every error code with a machine-readable identifier (e.g., payment_method_declined) and a human-readable message. Avoid generic “An error occurred” responses; consumers cannot debug against them.  Log errors centrally. Track and analyze error patterns. A spike in a particular error code across customers indicates a systemic issue worth addressing; a spike on a specific customer’s traffic indicates a customer-support issue. Both need visibility.  Use standardized HTTP codes. Custom codes outside the 100-599 range break HTTP clients, proxies, and frameworks. Stay within standard codes and put detail in the response body.  Centralize error handling on the server side. A single middleware layer that maps internal exceptions to HTTP status codes plus consistent JSON error envelopes is much easier to maintain than per-endpoint error code.  Test error paths. Simulate the conditions that produce each error code. The error paths are where most production support tickets come from, so they deserve the same test coverage as success paths.Error handling for AI agent retriesThis is the 2026 update worth knowing about even if everything above stays the same.AI agents call APIs as part of multi-step tasks, and they retry aggressively on transient failures. Two behaviors matter for your error handling:  429 Too Many Requests is the agent-safe signal for rate limiting. Agents respect 429 with a Retry-After header and slow down their retry cadence accordingly. Returning 503 for rate-limited traffic confuses the agent into treating the situation as a server outage and retrying aggressively, which makes the overload worse. Reserve 503 for genuine outages and 429 for capacity management.  Idempotency keys reduce duplicate writes. When an agent retries a POST (because the network blipped, or because the agent runtime is conservative), without idempotency you get duplicate orders, duplicate charges, and duplicate emails. Accept an Idempotency-Key header on every POST and PATCH, cache the response keyed on it, and return the same response on retry. The convention was popularized by Stripe’s API and is now widely used across payments, fintech, and infrastructure APIs.These two patterns are now table stakes for any API that expects agent traffic, which in 2026 is most of them.How Moesif monitors API errors in productionA list of error codes is only useful if you can see which ones are actually occurring on your live API. Moesif records the status code distribution per endpoint and per customer in real time, with alerting when error rates spike on a specific endpoint or for a specific customer.The kinds of questions Moesif’s error monitoring answers in seconds rather than hours:  Which customer is getting the most 4xx errors today? (Likely an integration that needs support.)  Which endpoint has the highest 5xx rate this week? (Likely a server-side problem to investigate.)  Did the 429 rate spike after the latest deploy? (Likely a regression in rate-limit configuration.)  Are agent-driven retries hitting 503 instead of 429? (Likely a misconfiguration that is amplifying load.)Pair the monitoring with the design-time guidance covered above (status code semantics + CRUD-action mapping) and the loop closes: pick the right code at design time, return the right code at runtime, observe the actual code distribution in production.SummaryYou will see many error codes when building or integrating against APIs, and most have reasonable fixes. Some are related to server errors and some to client-side errors; sometimes one causes the other. Read the API’s documentation carefully, log errors centrally for analysis, and reach out to the provider when issues persist.If you want to see your live API’s error distribution per endpoint, per customer, in real time, start a 14-day Moesif free trial. No credit card required.Frequently asked questionsWhat does an API error code mean? It is the HTTP status code returned with an unsuccessful API response. Codes from 400 to 499 mean the client’s request was wrong; codes from 500 to 599 mean the server failed to process the request.What is the difference between a 400 and a 422 error? 400 Bad Request means the request body could not be parsed (invalid JSON or missing required field). 422 Unprocessable Entity means the body parsed cleanly but failed business validation. Use 400 for syntax errors and 422 for semantic ones.Why am I getting a 401 error? Your credentials are missing, invalid, or expired. Confirm you are sending the Authorization header in the format the API documents, and that your token has not expired.Why am I getting a 429 error? You are exceeding the API’s rate limit. Check the Retry-After header in the response, wait that long, then retry. Implement client-side throttling that respects X-RateLimit-Remaining to avoid the limit altogether.What is the difference between a 502 and a 504? Both indicate that an upstream API server failed to deliver a valid response to a proxy. 502 means the upstream returned something invalid; 504 means the upstream did not respond in time. Both are transient on the provider side and typically resolve with backoff.Should I retry on 5xx errors? Yes, with exponential backoff and a maximum retry count. 5xx errors are typically transient. Do not retry on 4xx errors unless you have changed the request, because the same request will produce the same error.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /technical/monitoring/Understanding-API-Error-Codes-10-Status-Errors-When-Building-APIs-For-The-First-Time-And-How-To-Fix-Them/",
          "author": "Kay",
          "categories": "Technical, Monitoring"
        }
      
    ,
  
    
        "customer-success-api-strategy-ready-set-product-led-growth": {
          "title": "Ready, Set, Product Led Growth",
          "content"	 : "The meteoric rise of Product-Led Growth, or PLG, has transformed business within the world of technology. One sector in particular has found incredibly fertile ground, that being, API-driven companies. This is in part because API-driven companies offer an easy-to-adopt platform that is highly customizable. Top product led growth companies Twilio and Stripe are often listed as the luminaries in the API space, because of their disruptive approach and their wildly high valuations.Core to these companies’ growth strategies is customer API usage. It’s driving all levels of business, from acquisition and retention, to expansion. In a Product Led Growth model, customers can try the product for free and realize the value themselves without being locked into the sales cycle. It’s a simple concept, but it’s one that has flipped traditional enterprise sales on its head.With the rise of Product Led Growth strategies, business teams are reimagining their growth frameworks and looking for new tooling that will allow them to better support and develop their customers. The goal is to create a pipeline of free customer sign-ups and then flip them into high-paying customers.To properly operationalize a Product Led Growth model, innovative teams look to Moesif to gain powerful business insights into how their customers are interacting with their API platform. Moesif’s platform enables business teams to gain customer-driven insights into API usage by allowing business teams to help more active users, expand customer revenue, and thwart customer churn.A great place to start with Moesif and your Product Led Growth strategy is monitoring your company’s free trial. Typically, free-trial customers pass through a series of product usage milestones. These milestones often indicate that a customer has reached a new value point, such as 1 API call or 1,000 API calls. Once your customer reaches a defined product milestone, Moesif can notify your sales team with an automated alert, letting the team know that there is a great prospect to engage with. This allows your sales team to reach your free-trial customers at the right time to convert them into paying customers.From there, Moesif will be able to help you detect which accounts are about to hit their plan’s usage caps. Typically, Product Led Growth companies have a tiered pricing model, such as a “Grow-Pro-Enterprise” plan. Monitoring your customers’ account usage will enable you to identify companies that are growing quickly and ripe for expansion.For example, it would be wise to reach out to a “Grow Customer” who has blown through their monthly credit within the first week. Armed with the necessary API usage metrics, your business team can provide your customers with the right contextual information that will help guide them towards an upsell.Moesif’s identification of a short-term spike can also be another key signal that your customers have hit a new product milestone. For example, spikes in usage can indicate that a customer has just released a new feature, onboarded a ton of new customers of their own, or is simply using your platform in a different way. It’s critical to get alerts when there has been a dramatic change in customer behavior and API usage. This will help your business team reach your customers at a critical juncture of their lifecycle, enabling your team to make opportune product recommendations that drive revenue expansion.Finally, Product-Led Organizations need to monitor customer API usage to help prevent customer churn. Business teams need to be aware when the “hockey stick” growth flips downward, as this sends important signals that your customer’s sense of value has diminished, or that they have run into an unexpected roadblock. In either case, your business team must intervene before your customer jumps ship. For instance, it would be beneficial to your business team to reach out to a customer who has seen a 2x drop in API usage. This will position your business team as a trusted consultant that is bringing relevant account-level information to the table, which can ultimately help prevent customer churn.With PLG transforming how API-driven companies conduct business, API platform teams are quickly trying to adapt and modernize their growth strategies. Fortunately, Moesif can help fill in the gaps with powerful customer API usage insights to help accelerate customer activation, expand customer revenue, and prevent customer churn. If you are looking to implement an API Product Led Growth strategy, you can sign up for a free trial today with Moesif.                Accelerate Activation, Expand Revenue and Prevent Churn              14 day free trial. No credit card required.              Learn More        ",
          "url": " /customer-success/api-strategy/Ready-Set-Product-Led-Growth/",
          "author": "Oliver",
          "categories": "customer-success, API-strategy"
        }
      
    ,
  
    
        "developer-platforms-self-service-what-is-product-led-growth-and-why-is-it-critical-for-api-first-companies": {
          "title": "What Is Product-Led Growth and Why Is It Critical for API-First Companies?",
          "content"	 : "There’s a common misconception that product-led growth means a business doesn’t focus on sales. That’s not the case. It’s just that product-led growth layers in sales later in the customer journey. This can be a hugely effective approach, particularly when it’s combined with deep data insights to drive that growth.For API-first companies, this product-led approach is a cornerstone of success. We’ll dive into the detail of why it’s so critical below, but first let’s start with the basics…What Is Product-Led Growth?Product-led growth is precisely what the name implies – it is where acquisition, activation, expansion, and retention is led primarily by the product. Customers either use a free version of the product or pay a nominal amount to use it, with sales coming into the equation only after the customer has had the opportunity to see the product’s value firsthand.With the product-led model, it is the product itself that is driving sales. By using the product, supported by the company’s customer support team and, at the relevant time, its sales team, the customer moves almost seamlessly through the four stages of product adoption: activation, expansion, retention and adoption.A Different Approach to SalesUsing the product-led growth model doesn’t mean assuming the product sells itself and having zero sales staff. It just means that sales teams are focused on what people are actually doing with the product, rather than sitting there analyzing target industries and creating ideal customer personas. Their goal is to grow the accounts of existing customers, rather than to sell to cold prospects.By layering sales in at a later stage of the customer journey, the nature of the task fundamentally changes. There is less effort required to pitch the product, as customers are already familiar with it and the value it can provide, so the effort of pushing something new is removed. Instead, sales work becomes more about creating more value for the organization. This could be getting additional teams to use the product, increasing its usage, or finding additional use cases so that customers move up to a higher value plan.There is a strong link between sales staff and customer success teams when it comes to product-led growth. Customer success staff are dedicated to ensuring that customers get the best out of the product – that they understand its value, how to utilize it fully and how it can be of maximum benefit to their business. While the focus is on ensuring customers get value, rather than making a sale, that showcasing of the product’s potential can result in deeper integration and growing usage. The product becomes increasingly critical to the customer’s success.Why Is Product-Led Growth So Important to API-First Companies?Product-led growth isn’t suitable for every business. If you’re selling solar panels, for example, the upfront costs and installation process preclude using a product-led growth approach. However, for API-first companies, where the cost and effort of enabling a customer to try the product are minimal, the product-led growth model can be particularly effective.One reason for this is that they are selling their products in a sector where buying has become much more decentralized. Individual developers will be looking for solutions that suit their particular use case and that they can trial for minimal cost. Those same developers are often skeptical of sales and marketing teams, so the hands-on nature of using a product before making any major commitment to it works on multiple levels.Selling to technical teams as a whole can be hard. They have a constant stream of higher priority tasks to juggle and you’re battling the ‘not invented here syndrome’ when trying to introduce something new. Along with the fact that many developers are naturally inclined to wonder if they can build it themselves when you try and sell them a new product!Product-led growth cuts out this uphill battle and replaces it with engineers discovering the product in their own way and at their own pace. Provided the integration is easy, by taking a product-led approach, API-first companies are empowering individual developers to make decisions around the API solutions that work for them. And once the developers are on board, conversations with the c-suite execs become that much easier.Selling to DevelopersProviding a developer with a quick and cost-effective way to trial an API means they can experiment with it and prove the concept. A product that’s affordable on a trial plan means popping something on a manager’s credit card, rather than going through an onerous procurement and legal review process. API-first companies that get their pricing and swift integration right can hugely reduce the friction involved in getting their product through the door.This isn’t about bypassing procurement altogether. Far from it. It’s about providing developers with an easy opportunity to get to know the product – to experience it firsthand and see what a good fit for the business it is. Give them click-through terms of service, fast integration and a price that they can pop on a manager’s credit card, and you’ve laid the foundations for a small proof of concept trial that has the potential to deliver a sudden skyrocketing of volume further down the line. With all the associated procurement activities undertaken and legal hoops jumped through at that later stage, naturally.Getting Billing RightThis style of product-led growth requires some thought around your billing model. This is where usage-based billing comes into its own. A developer can pay a token amount to get started with the product and trial it in their test account while they complete their proof of concept. After that, when the developer takes the product into production, the usage-based billing model needs to scale alongside the customer’s growing usage.APIs are a natural fit for this pay-as-you-go style of billing, as there are various ways that their use can be tracked. Just be sure that you’re using the right metrics! Broadly speaking, value metrics can be focused around:  Transaction volume – number of API calls, messages sent  Revenue/cost share – percentage of revenue, transaction fee  Data volume – gigabytes sent, minutes made  Users – monthly active unique users  Resources – compute units, active hoursThese examples hint at the huge variety that can be built into usage-based billing. They also highlight the complexity of getting billing right.Take the example of an email automation API that allows customers to create email templates and send emails to leads. Usage-based pricing centered on the number of templates could see the customer store 1,000 templates and send zero emails. The customer would get zero value from the product while receiving a huge bill, making them a very high-risk customer. Bills based on the number of emails sent or different contacts reached out to, however, would be closely linked with the customer getting true value from the product – and thus more likely to continue using it. This is why implementing the right value metric(s) is so important.Data-Driven SalesUsing data to drive your sales is key here. The support from your customer success team needs to merge seamlessly into conversations with sales at just the right point in the customer’s growing usage. For that, API-first companies need data.With tracking in place for account health and usage growth, sales teams can open discussions with customers at just the right moment to ensure that the customer gets more value out of their product. They can create a plan that factors in both current usage and future growth plans.This is where Moesif comes into its own, providing a scalable solution with automated resources that can flag up increases in usage and suggest (for example) a discussion to ensure the customer is still on the best value plan to meet their needs.But tracking usage data isn’t just about finding the right sales moment. It’s also about ensuring that the customer doesn’t get hit with an enormous surprise bill following an increase in usage – and then cancel due to the unexpected jump in cost. Instead, a well-timed call from a salesperson to discuss a more appropriate plan can ensure the customer feels they’re still getting value for money, even if their bill goes up.How to Engage Developers with Your ProductAPI-first companies that are looking to their products to lead their growth don’t have to sit back and wait for developers to discover them. They will need to proactively market the product to ensure that developers hear about it and see enough potential in it to try it out.This means a whole heap of investment into developer experience needs to go into encouraging initial engagement with the product and into making it easy for developers to carry out that first proof of concept. That can be achieved through a mix of tutorials, demos, technical documentation, blog posts, webinars and more. Content of this nature becomes an integral part of the sales funnel, because it supports developers to trial the product in the first place.Ease of use becomes a top priority with this product-led model. From sign-up guides to integration walkthroughs, onboarding needs to be super easy, as every stumbling block reduces the potential for the product’s success. At the same time, the onboarding and trialing process needs to be as self-service as possible, as otherwise it’s not easily or rapidly scalable.Again, the pricing and packaging model is important here. A self-service trial can be used by a wide spectrum of companies, so billing options need to be built around a huge variety of needs. At the core of each of those models, though, needs to be a low price to activate customers initially and support them to engage with the product. After that, once they realize how valuable the product is to their business, it’s time for API-first companies to let their sales teams work their magic.",
          "url": " /developer-platforms/self-service/What-Is-Product-Led-Growth-and-Why-Is-It-Critical-for-API-First-Companies/",
          "author": "Derric",
          "categories": "developer-platforms, self-service"
        }
      
    ,
  
    
        "api-management-customer-success-you-are-measuring-api-active-users-wrong": {
          "title": "You are Measuring API Active Users Wrong",
          "content"	 : "API providers need to understand how their consumers are using their APIs. Usage metrics are essential because they tell you about API adoption, how your API is growing over time, and which endpoints are seeing more (or less) use. When you look at API usage metrics, you should be measuring the active users on your API in the sense that most closely aligns with your service. This informs where your organization allocates compute resources, which API endpoints you decide to develop, and which API endpoints you document.What is an active user? At first glance it may seem like simply tracking the number of users using an endpoint. But API users can’t be tracked in the same way as website users. Instead, you need to look at how your users access your API in the context of your product.What does an active user on an API look like? An active API user is any user that regularly makes calls to your API to achieve a real outcome. This doesn’t mean that they have to constantly make API calls to be considered active. The frequency with which an API consumer makes API calls depends on the context of your API. An active user might make many API calls everyday, if that’s what they need to gain value from your API, or they may only need to make API calls once a week if all they need from your API is weekly data processing. One common practice is to divide your API usage metrics into daily active users and monthly active users to get a clearer picture of how often your users are active over different time frames of interest.While it can be tempting to take a one size fits all approach to measuring active API users, you need to look at the context of your API when you decide what to measure. Your metrics should reflect how your API creates value for users in the real world. For example, an API that allows you to send SMS messages should track the number of SMS messages a user sends. An API that compresses video would track the size of the data files compressed and sent for each user. The metrics should directly relate to the value of the service being provided.Bad Metric, Good MetricWhile it’s common for API providers to measure active users by tracking the number of unique users, this is a vanity metric that makes things look good without really conveying much information. Since the number of unique users will usually increase over time, it’s tempting to point to metrics like this because it can tell a positive story to internal stakeholders. Unfortunately, a simple count of unique users active is a bad metric because it doesn’t give you details about what your users are doing with your API. It’s not too different from counting the number of window shoppers if you’re a department store. Interesting, but it does not tell you how many came in to buy something, what they bought, or how much they spent. Correspondingly, you want to know if users go on to use your API in a meaningful way, how much value is being created for your API consumers, and which API endpoints and functionality are most valuable to your users. Without meaningful information about your API consumers, your organization can’t decide where to apply new development or how to allocate resources.Scaling your API metrics as your user base grows is important, but it’s often overlooked. When you have a small number of API consumers and a small number of usage patterns, measuring active users can be straightforward. But, as your API gains complexity, it becomes more challenging to accurately measure meaningful user activity. You need a forward-looking metrics strategy that develops alongside your API as you grow. This has the added benefit of making sure that your organization doesn’t leave old users behind as your API grows. Metrics like the unique number of API keys your organization has created, or the total number of calls made to your API endpoints, don’t always indicate what they originally measured. A single developer may create multiple API keys, for example.Let’s examine what good metrics look like. Good metrics should give you meaningful information about how your API consumers interact with your API. Your metrics should be able to answer questions like:  How much data is delivered in your API responses? Is it all useful?  How long does each user remain connected to your API?  Which endpoints are getting the most activity?  How successful are users when making API calls?Good metrics tell you more than just how many developers have used your API. The questions above are different ways to look at how engaged your API consumers are with your API. If your API is focused on transferring data between different devices, then the volume of data transferred is a great indicator of how engaged your users are. On the other hand, if your API provides a real-time service, like live captions for video content, the length of the connection would be a better engagement metric. Learn more about how Moesif can help you track engagement metrics.API Active Users, For RealA good starting point for good metrics is aggregating your API calls into meaningful events, instead of looking at the raw number of API calls, which may be a noisy signal. An event can be a more detailed look at an API call, combining an API call and its outcome.Measuring events instead of API calls also allows you to look at how different types of events compare, including what they have in common. Looking at failed events can reveal common sources of failure. If your users encounter failed events because there’s too much traffic going to your API endpoints, for example, your organization can handle this by allocating more resources to your API. Alternatively, a pattern of failed events associated with your developer onboarding may point to trouble your users have getting started with your API. This may point to a need for more comprehensive documentation, or your users may need additional support to integrate your API.Let’s look at some real-world examples of API event metrics. An API that performs two-factor verification would have at least four basic API events. A successful send verification SMS would be the first API event, paired with a failed to send SMS event. The other two events are when a verification code is successfully validated, and when the verification code fails to be validated. These events taken together show how often API consumers succeed at using the API to achieve their goal (in this example, verifying a phone number). Distinguishing between successful and failed API interactions by bucketing them into events makes it easy to understand the real activity happening with your API.Best PracticesWhile we’ve discussed how measuring active API users with events can better inform how your API consumers are using your API, there are additional benefits. For example, you can use your active API usage metrics to bill your users based directly on the value your service creates for them. Your organization can bill for successful events, thereby tying billing directly to real-world outcomes. For example, an API that makes phone calls can bill based on time spent on a call, and an API that sends text messages can bill for the number of SMS messages sent.Real-time reporting is critical when relying on active user metrics. There are many valuable insights to gain from measuring your users while they use your API. For example, you can spot failed events as soon as your users encounter them, making it possible for your team to address failures in real-time. Users are less likely to leave your service if they receive timely support for their issues. Real-time analysis also allows your organization to look at how the number of successful events compares to the number of failed events at a given moment, such as during times of heavy load. With real-time event metrics, your team can rapidly respond to your user’s needs, monitor failed events, and see which endpoints provide the most value to your users. Build better API products with deep insights powered by Moesif.Success in Real-timeMeasuring the total number of API users can be misleading because it doesn’t give you contextual information about how your API consumers gain value from your API. Batching your API calls into events will give your organization informative metrics at scale for your API. You’ll be able to evaluate failed events so that your organization can understand which of your API endpoints needs more attention and resources. You’ll also be able to compare patterns of successful events so that your organization can understand which endpoints are creating the most value for your API consumers.To really understand your users, focus your metrics on what they use your API for and whether they are truly achieving their desired outcomes. Your metrics should focus on the API events that create the most value for your users so that you understand where to focus your resources. Understand your users by measuring their activity in real-time, giving yourself the best opportunities to address their needs. If you’ve been measuring API active users wrong all this time, now is the time to rethink your strategy. Your internal stakeholders and your customers alike will thank you for it.",
          "url": " /api-management/customer-success/You-Are-Measuring-API-Active-Users-Wrong/",
          "author": "Matthew",
          "categories": "API-Management, Customer-Success"
        }
      
    ,
  
    
        "developer-marketing-inbound-content-why-content-is-the-key-to-unlocking-your-developer-first-marketing-strategy": {
          "title": "Why Content Is the Key to Unlocking Your Developer-First Marketing Strategy",
          "content"	 : "Founding a developer-first startup isn’t quite the same as starting a regular company. When your primary focus is on creating products to sell to developers, you need to build a sales strategy around those developers’ needs. The occasional email with a ‘click here for a demo’ button just won’t cut it. Instead, it’s time to work on your content strategy.Why take our word for it? Because content is Moesif’s number one strategy when it comes to connecting with developers. And if you’re reading this blog post, that strategy’s working.Feed Developers’ CuriositySelling to a developer audience is all about building your credibility. They need to know they can trust your product to deliver reliably and efficiently. That means you have to prove you know what you’re talking about.But there’s more to it than that. Developers, by and large, are a curious bunch – they like to undertake their own research, dive deeply into topics and discover products from different angles. This means that you need to provide them with multiple routes to your product and feed their need for input.Once you’ve built up your credibility and established yourself as a thought leader in your particular space, then it’s time to think about layering in sales. And if you’ve got your content strategy right, the sales part will slot easily into place.So, Are We Just Talking About a Few Blog Posts Here?Certainly not. A robust content strategy has multiple strands. You can hold in-person events, record them and turn them into webinars. Then you can transcribe those webinars and turn them into articles – for your own blog and for other sites that your developer audience visits regularly. It is when you tie all these forms of content together that you can get maximum value from your strategy.At every point, it’s essential that your content is educational and helpful. It needs to deliver value to the reader, not just marketing messages. It is through this valuable content that you’ll have the chance to position yourself as a thought leader in developers’ eyes for your particular product area. Becoming the number one article for a very specific topic can be immensely valuable.Multi-layered ContentA good content strategy has several layers. At the top of the funnel sits content that looks at general problems and solutions – top ten roundups, blog posts, infographics and more. The aim is to create an initial awareness of your brand, putting yourself on developers’ radars. Many tools can help you with this; for example, for your blog post, you can use an AI writing tool, or for your infographics, you can leverage graphic design software to enhance visual appeal and effectivenessNext comes content that shows developers how your product/platform/tool can be used to accomplish something. In essence, how it can make their lives easier in practical terms. This is about showing the real-world value of your product in different scenarios, all in a gentle, non-promotional way, of course.And once your developers are ready to discover more? That’s when you can share your bottom of the funnel content – your in-depth case studies, comparison guides, white papers and more.Sharing Is CaringThe work doesn’t end with creating your content. In fact, the biggest mistake that many content creators make is not bothering to share their content and get backlinks. Those lovely, valuable backlinks that will speak to your expertise and showcase your content as the resource that developers need.Contrary to many people’s beliefs, content isn’t a volume game. It’s about expertise and shareability. Create an article that makes it onto DZone and you’ve got developers’ eyeballs on your content straight away. Make it onto Stack Overflow or InfoQ and again, you’re front and center when developers are looking for solutions. The same is true of any site that appeals to the developer audience that you’re targeting. By incorporating powerful AI tools, you can further enhance the content and functionality of your website.Clarity and infographics are your friends here. The more readable and usable your content, the more likely people are to share it. The holy grail is to get developers sharing your content among themselves, using it on Reddit threads and within community forums to answer each other’s questions. That’s when you know you’ve nailed it.There are some quick wins when it comes to creating shareable content, too. A top ten roundup where you tag each of the ten companies you mention, for example, is a great way to enjoy ten easy shares on social media. Just remember to deliver value to the reader with every piece of content you produce.Data is also important when it comes to using your content to connect with developers. The more you can understand about which topics and which angles are getting developers to sign up to your tool, or actively engage with your platform, the better. You’re then perfectly positioned to create complementary content that feeds the same subject that developers are finding so valuable. By leveraging an AI-powered article writer, you can optimize your content strategy and establish a stronger connection with your developer audience.So, About Sales…Once you’ve created this outstanding, interconnected web of content, what’s next? Well, then it’s time to finally get your sales team to engage – and to engage in a meaningful way. Salespeople who genuinely care about developers’ pain points and understand their intent when it comes to the content that they’re consuming will be those who turn prospects into paying customers.A developer-first company that does this well will stand the best chance of driving people to sign up for its platform/product and, ultimately, grow its bottom line.                Track and Improve your Developer Experience With Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /developer-marketing/inbound-content/Why-Content-Is-the-Key-to-Unlocking-Your-Developer-First-Marketing-Strategy/",
          "author": "Derric",
          "categories": "Developer-Marketing, Inbound-Content"
        }
      
    ,
  
    
        "business-company-meeting-moesif-with-seo-manager-savannah-whitman": {
          "title": "Meeting Moesif with SEO Manager Savannah Whitman",
          "content"	 : "Meeting Moesif - SEO Manager Savannah WhitmanGrowing your career can be both scary and exciting, and our SEO manager Savannah Whitman experienced both before joining Moesif. Now she’s nurturing search rankings to bring the latest in API observability to technologists who need it. In this installment of Meeting Moesif, we talk to her about where API technology is heading, and what it takes to keep up.How would you describe your role at Moesif?On paper? I’m the SEO manager in-house, making sure anyone interested in API observability finds our advice.The truth? Everyone in SaaS has an opinion about where APIs are headed. Not everyone has a category-creating product like Moesif’s user-centric API analytics. It’s proof enough that our knowledge base is targeted at technologists operating on the edge of what’s possible. That’s why I leverage my (not so) magical powers to make our content pop up on Google, DuckDuckGo, or wherever the API enthusiast searches for answers.Like any startup employee, I’m usually wearing more than one hat. I actually started out in PR and digital marketing, so I’m often treated to projects outside of SEO.As Moesif is a small company, have you felt called to lead in ways you hadn’t before?Absolutely. In my past roles, I offered behind-the-scenes support for larger teams. During the first five years of my career I played PR assistant, marketing freelancer, ghostwriter, you name it. Those experiences taught me what a mutually beneficial brand-customer relationship looks like. At the same time, I started to spend time with brands I was truly interested in - API-first companies like Auth0 and Postman. Eventually, I realized I knew what I wanted, and how to get it. It was time to enter the next stage of my career.Taking that leap is easier said than done, however. I think for female and gender diverse tech workers, there’s a sense that you need to fight for your seat at the table. It’s important to remember that not every company operates that way.At Moesif, there’s a seat for everyone, whether we are sitting down for our catered lunch or a planning meeting. I’ve been surprised by how easy it is to grow in this team. Here, I’m free to design and run my own projects, rather than only executing on that of others’. Even more refreshingly, I’m doing so with the support of an entire team, instead of doing so alone as a freelancer.What are the most exciting challenges facing API products today?I’m eager to see APIs trade their identity as a new, unvetted technology for that of a reliable, mature one.API-first companies came of age at about the same time I did, in the late 00’s. Facebook and Twitter announced their APIs for the first time, setting a new standard. Back then, I was a FIRST Robotics student communicating technology for the first time, hawking consumer tech to sponsors. Call it sentimentality, but I hoped my career would grow alongside the tech trends born in that era.We’re now in the 2020’s, and I’ve gotten my wish. APIs power our interconnected world of apps and microservices, worldwide. As it is in any trend-turned-revolution, mass adoption took place before the details were sorted out. For example, APIs remain uniquely vulnerable to emerging security threats, and still aren’t sure how to handle regulation.More than seeing any one issue solved, I’m excited to see a complete, reliable ecosystem emerge. API analytics are just one part of that picture, but every small improvement counts. I’m looking forward to spreading the message, “APIs are here to stay, and they can run perfectly, everytime.”What are the values that drive you?I’m a lifelong technophile, but more than anything, I value treating human beings like human beings. That’s not a value Silicon Valley is known for. There are valid reasons for that, but I believe in the potential of technology to serve the common good.Understandably, there’s a spotlight on the ugly parts of big tech. Manipulative software. “Tech bro” culture. Runaway energy usage. Even amongst tech workers, I hear concerns about ethics. Who wants to believe their work is bad for the world? When we frame unethical businesses as the pinnacle of “innovation,” it’s easy to feel the entire tech industry is that way.But, innovation itself is impartial. When I see headlines about “evil” tech companies, I see a social problem, not a problem with new tools. That’s why I seek companies that embrace the unconventional for the benefit of the many, not just the few, and are honest about their ability to do that. That means awareness of their impact on employees, communities, and technology ecosystems. I do think the game is currently weighted towards bad-faith actors, but if anything is constant in tech, it’s evolution - and big changes often start with small steps.Even with a small footprint, every company daring to do better sets an example. I like to think we’re doing that, here at Moesif. We’re building towards progress in computing instead of seeking short-term rewards. Moesif functions as a “missing piece” to managing APIs. Whatever happens to the product, the niche we’re establishing will further maturation of API technology as a whole. We do so while treating employees like real people, who sometimes face challenges or need extra support. That balance between achievement and responsibility makes me feel at home.About Meeting MoesifAt Moesif, we help our customers understand how their APIs are used and how they can grow their API platform. Meeting Moesif highlights the people making that vision a reality. Our people-first culture values open communication, initiative, and impact—not to mention, perks like flexible working options that ensure employee wellness.If you’re interested in joining, you can start by learning more about what it’s like to be part of Moesif. Our Meeting Moesif blog series offers a glimpse into our office life and how we expand API observability for all of our customers. Follow the link below to see other Meeting Moesif interviews with team members.",
          "url": " /business/company/Meeting-Moesif-with-SEO-Manager-Savannah-Whitman/",
          "author": "Savannah",
          "categories": "Business, Company"
        }
      
    ,
  
    
        "developer-platforms-api-analytics-how-to-monetize-your-apis-choosing-your-api-monetization-stack": {
          "title": "API Monetization: The Ultimate Guide",
          "content"	 : "The technology you choose to start your project with determines what your product is capable of now and what it will be capable of in the future. Finding the right stack to build on top of is one of the biggest engineering challenges you can face. Picking a stack that allows you to build a product and get to market rapidly is excellent unless that same choice limits the scalability and features of a product in the future. Pick a stack well beyond your current and future needs, which could lead to difficulties in building the initial project you’ve set out to build. A balance must be struck between your current needs and the needs of your product and customers in the future.When making money from your APIs, picking the right stack can help you get up and running quickly and allow flexibility for your business and its customers. Let’s look at a few ways to assess which stack best fits your API monetization needs.What is API monetization?Everything runs on APIs, and the API economy continues to grow. Do you have an API product that other individuals or companies may want to use? Then, usage-based billing and API monetization are something you’ve likely thought about. When we speak of API monetization, we look at how we can bring in revenue from our APIs, whether through charging per user, per API call, or another metric. If your business model doesn’t include using your API resources to drive additional revenue, you could leave cash on the table. API revenue could be a swift win, especially if you have public APIs that could be useful to other businesses.How you charge for usage and when will depend on your billing model. You may make money by charging an API consumer after they have used your API through a post-paid billing model. You may also charge an API consumer up-front for usage in a pre-paid or pay-as-you-go billing model. There are many different ways to make money from your existing APIs and from the ones that are being built.Once you’ve decided to use your APIs to generate further revenue, you must build a solution. The solution will likely have a few moving parts, and each of these parts will play a crucial role in your API monetization stack. First, let’s look at different monetization models, as they can sometimes impact the technologies we will use within our API monetization stack.What are API monetization models?API monetization models refer to the various strategies businesses adopt to generate revenue from their API offerings. As an API provider, there are many ways to do this, and many businesses will use one or multiple models to sell their APIs. These models are crucial for sustaining API-driven business initiatives and ensuring a return on the investment for the time and money allocated for developing and maintaining the monetized APIs. Let’s take a brief look at some monetization models that may apply to your APIs.      Freemium Model: In this model, the basic version of the API is offered for free with limitations on usage, features, or data access. Users who require advanced capabilities, higher rate limits, or premium data can upgrade to a paid version. This model acts as a gateway to attract potential customers, giving them a taste of the API’s potential before they commit to a paid plan.        Subscription Model: This is one of the most common monetization strategies. Here, users pay a recurring fee, often monthly or annually, to access the API. The pricing can be tiered based on usage limits, features, or the level of support offered.        Pay-as-you-go Model: Users are billed based on their API usage. This model is particularly appealing to businesses that have fluctuating API usage needs. They pay for what they use without being tied to a fixed subscription cost.        Transaction-based Model: This model charges users for every transaction made via the API. It’s suitable for APIs where each request has a significant value, such as payment gateways or e-commerce platforms.        Affiliate Model: Here, API providers earn a commission for driving business to third-party services. For instance, a travel API might provide hotel booking capabilities and earn a percentage of every booking made through their system.        Data Monetization: In this model, businesses monetize the data accessed through the API. Users might pay to access unique datasets, analytics, or insights the API provides.        Indirect Monetization: Not all APIs are monetized directly. Some are offered for free to drive engagement to the main product or service. For instance, a company might provide API access to a particular user base to drive sales (especially when the API is only available on an “enterprise” plan) or to create an ecosystem around its platform.        One-time Purchase: Less common but still viable, in this model, users pay a one-time fee for perpetual access to the API. It’s more suited to APIs that don’t require continuous maintenance or updates and have relatively low volume.  When choosing an API monetization model, it’s essential to consider your target audience, the perceived value of your API, and the costs associated with maintaining and updating the API. It’s also beneficial to remain flexible as business needs and market dynamics change. As mentioned at the start of this section, a hybrid approach, combining two or more models, might be the best fit for some businesses, while others can pick a single approach and run with it.Getting started with API monetizationWhen it comes to implementing an API monetization solution, you’ll need to build a technical stack to do so. In its simplest form, an API monetization stack has two components: your API and a billing provider. Unfortunately, to make the combination of these work, it becomes quite difficult to implement. Customization and expert knowledge are required to make this work efficiently and accurately.A more practical approach is to break the solution into 3 parts: your API, a platform to track API usage, and a billing provider. Let’s look into the role of each of these components.The APIThe API is where the value to users will sit. Here, when we talk about “the API,” we refer to everything to do with the API, including securing the API, rate-limiting, transformations, etc. Usually, part of the API setup includes some API management layer, like an API proxy or an API gateway. Most modern businesses are using API gateways such as Kong, Tyk, AWS Gateway, or one of many other alternatives.API Analytics (for tracking usage)As the API is being used, you’ll want to gather metrics about that usage. Since the analytics platform is seeing API consumption metrics around every API call, it makes sense for this to be the system of record for tracking usage and sending it to the billing provider.Billing ProviderThe billing provider will receive usage amounts for each customer, tally up the amounts over the billing period, issue an invoice, and collect on the amount owing. With Moesif in the picture as the API Analytics piece, this is roughly how things look at a high level.Choosing gateway or proxy for your APIWhen choosing a gateway or proxy for API management, it’s essential to understand exactly what functionality you will need. Almost every gateway on the market has some differentiators which may make it more or less suitable for your use case. To really dig into the gateway versus proxy argument, check out this quick breakdown we put together.One thing is for sure: using an API gateway will make securing and managing your APIs much more straightforward than doing it without. Most gateways support various authorization and authentication protocols to control API access, tools for generating and managing an API key, great support for rate limiting and quotas, and other features such as the ability to apply transformations to requests and responses. An API management platform also usually provides a developer portal so that API users can easily see which APIs are available and get access to them.Although APIs can be managed without an API gateway or proxy, having one means that you can establish a more uniform way to secure and manage the APIs. Essentially it allows you to manage all of your API-related concerns under a single pane of glass. From a scalability and security perspective, there’s no better solution.Popular API management tools include Kong, Tyk, AWS Gateway, Azure API Management, and many others. Each will have its own pros and cons, just like any other piece of software or infrastructure. For more information on how some of these compare, check out this article we wrote to help with precisely that.Choosing a billing providerWhen choosing a billing provider, as with any technology you incorporate into your stack, you should be aware of current and future use cases and how the technology can support them. Take stock of your product roadmap and map out the technical pros and cons of each solution. A great overall picture of the differences is mapped out here. You’ll also want to be aware of what it takes to integrate the billing provider with your current solution. Different providers have different ways to integrate, making them more or less suitable for your project. Another detail is ensuring the billing provider can support your API monetization model.On top of the technical requirements, there are a few other operational factors that should also go into choosing your provider. These factors are highlighted below.CostFor most companies, this will be the main factor outside of comparing the capabilities of the technologies. If both billing platforms will technically work for your project, the next focus will be on cost.The cost of a platform could be calculated in many different ways. It could be done as a flat rate, percentage, or a mixture of both. You’ll also need to factor in the volume you’ll be passing through the system since that can also lead to discounts, which may make a certain provider more attractive.ReportingThe reporting tools built into the platform can help the business side of an organization easily see the results of API monetization. Most platforms have a minimal amount of reporting, while others have incredibly detailed reports available. If you’d like out-of-the-box reports versus homegrown ones, this should be an important factor.Customer SupportIf an issue does occur, it’s great to know that the provider can provide quick support to remedy the problem. You’ll want to make sure that you look at different support packages the provider offers, what the SLA is for turnaround on minor and major issues, and also look at some customer reviews to see if existing users are happy with the support.How to set up API monetization using MoesifBridging the gap between your APIs and the chosen billing provider is where Moesif comes in. Moesif has the unique ability to keep track of usage, add it up, and then send the usage records to the billing provider to charge upon. This is all available in Moesif right out of the box.Moesif allows for full-service API monetization. This means that Moesif can cover all of the gaps that are crucial to creating a strong API monetization stack. This includes tracking usage, blocking usage on accounts with overdue invoices, keeping customers informed of usage, and alerting internal teams about any concerning behaviors or possible upsell opportunities.Billing metersBilling meters are the heart of the Moesif monetization solution. A billing meter allows you to describe how you want to charge users and specify that criterion. These could be criteria such as billing on a specific endpoint, a specific part of the request, a response code, a combination of all three, or any other criteria imagined.Once the billing meter is set up, it will report this usage to the billing provider at scheduled intervals. This can be done in a matter of minutes and takes absolutely no code to create. Billing meters in Moesif can currently be integrated with Stripe, Recurly, and Chargebee. These options for integration will continue to grow as we learn more about our customer’s favorite billing providers.GovernanceGovernance can allow Moesif to block calls when an invoice is overdue, or, if you’re doing a pre-paid setup, can block an API consumer once they have run out of credits on their account. Again, all of this can be done with no code, no engineering support, and can go live in a matter of minutes.Governance rules can be set up by configuring a filter that suits your exact needs and specifying what you would like to override the response with. For instance, if a user has run out of credits to use the API or has an overdue invoice, you may return a 402 Payment Required status and a JSON body that goes into more detail.Behavioral emailsAutomated emails become handy when a user is going into the next usage tier, getting close to a rate limit or quota, or is out of credits. Of course, there are plenty of other scenarios where automated emails can also help. The great part about using behavioral emails in Moesif is that it keeps customers informed about key events while reducing the support and sales team burden. In contrast, in the past, they would need to reach out directly.AlertsOn top of behavioral emails, it also may make sense to send notifications to different internal teams based on a company or user’s actions. For this, we can use Moesif to set up alerts to be triggered by specific criteria. These alerts can be delivered through email and SMS, as well as alternatives like Slack, PagerDuty, or a custom webhook.For monetization, this might consist of letting the sales team know that a user is ready to move to a new plan based on their volume or behavior. It may also mean sending your finance or collections department an alert letting them know that a delinquent customer is trying to access an API.Advantages of using API monetizationAPI monetization not only provides a revenue stream for businesses but also comes with a suite of other benefits. Below is a brief list of key advantages of implementing API monetization:      New Revenue Channels: One of the most direct benefits is the creation of new revenue streams. Whether through a subscription model, pay-per-use, or affiliate earnings, monetizing your API can contribute significantly to the bottom line.        Increased ROI: For organizations that invest heavily in API development, monetization can ensure a return on investment by generating profits over and above the operational costs.        Boost in API Adoption: A monetized API often comes with better documentation, support, and quality assurance. This can lead to increased trust in your product and, consequently, higher adoption rates among developers and businesses who want to use it.        Enhanced Market Reach: Since third-party developers can use the monetized APIs to create applications that cater to niche markets or specific audiences, API usage can help expand beyond the reach of the original service.        Building Ecosystems: Monetized APIs can foster a community or ecosystem of developers and partners. This ecosystem can lead to collaborative ventures, shared innovations, and a network effect, enhancing the value of your APIs and the associated products or services.        Improved Feedback Loop: A monetization model often means more engagement with end-users and developers since they pay for access. This can lead to valuable feedback, which can be used to refine and improve the API, ensuring it meets market needs and fits into applicable use cases.  Incorporating monetization into your API strategy not only makes financial sense but also paves the way for growth, collaboration, and continuous improvement of your APIs. As APIs and API monetization become more mainstream, businesses that leverage and monetize their APIs effectively will be better positioned to thrive in such an ecosystem.ConclusionMonetizing APIs is a complex project, but it doesn’t have to be complicated. Along with an excellent API strategy and robust API design, by using reputable and proven technologies to get you to market quickly while keeping quality high, you can quickly drive revenue from your APIs. Choosing the right stack for your monetized API is crucial for the needs of today but also for making sure that you have what it takes to expand into tomorrow confidently.No matter what you choose, using Moesif as the hub for your API monetization strategy saves time and effort while delivering scalability and stability for your newest revenue stream. Try out Moesif’s API monetization platform today by signing up and starting your API monetization journey.",
          "url": " /developer-platforms/api-analytics/How-To-Monetize-Your-APIs-Choosing-Your-API-Monetization-Stack/",
          "author": "Matthew",
          "categories": "Developer-Platforms, api-analytics"
        }
      
    ,
  
    
        "graphql-api-development-graphql-versus-restapi-which-is-better-for-api-observability": {
          "title": "GraphQL Versus RESTAPI Which is Better for API Observability",
          "content"	 : "API ObservabilityAPI providers need to observe their APIs to get meaningful data about whether and how they are consumed in practice. API observability is a form of monitoring that passively logs API traffic to an observability service. Different from traditional API monitoring, with API observability you:Monitor interactions to improve developer experienceUnderstand how customers use your APITroubleshoot your APIObserving REST APIs is well understood and supported, but not every API is a REST API. In particular, GraphQL APIs behave very differently when it comes to observability. Let’s dive into what that means and how to take advantage of it.GraphQL APIs Offer Advantages, But Challenge Observability Many organizations approach API observability by monitoring the endpoints that users access, logging error messages, and measuring the latency of endpoint requests. This strategy is fine for REST APIs, but it is not enough for API observability with GraphQL APIs.API consumers love GraphQL because it is flexible enough to query data from multiple sources in a single request and handle complex state and cache management, making for a great developer experience.The benefits from GraphQL aren’t just for API consumers. With GraphQL, API providers can update their APIs without impacting existing queries. Serving data with GraphQL is also platform-agnostic, so API providers can choose the data provider that works best for them including bundling multiple data resources into a single query. Supporting GraphQL is important for API providers committed to building a great developer experience, where queries are efficient, powerful and flexible.GraphQL’s flexibility is an important benefit for both API providers and API consumers, but this flexibility also means that you need a different approach to observability for GraphQL APIs. Let’s take a deeper look at the challenges that API observability presents in a GraphQL context and how you can handle them so that your organization can observe your GraphQL API.Observing GraphQL in PracticeTo access data from a REST API, a developer calls multiple endpoints separately to patch together all the data that a client needs. For complex apps a GraphQL approach is more appealing because requests to different data sources all happen in a single query. This means that developers only need to request the data that they need when they use GraphQL, whereas REST query structures don’t allow this level of customizable query. For example, a shopping app that displays information about a product, who is selling it, and how long delivery will take, would consist of three separate REST API calls like this:fGET /category//productInfoGET /seller//GET /delivery//It takes multiple GET calls to receive all the data necessary to load a product page. Data is called from the product endpoint, which returns information including a seller-id. With the seller-id, the app makes a data call to the seller endpoint to get information about the seller, including a location-id. Finally, a call is made to the delivery endpoint with the location-id so that the page knows how long delivery should take for this product. Each response relies on the information from the API call before it, and this approach frequently fetches too much or too little data. A GraphQL call to get only the needed information from multiple sources can be done with a single query:{  category{    name    product(product-id){      name      productInfo{        size        price        seller-id      }      seller(seller-id){        name        location-id        delivery(location-id){          name        }      }    }  }} While the flexibility of a single query for the GraphQL approach is great for developers, this presents a challenge when it comes to monitoring data queries with GraphQL. If there is a partial failure during a transaction with a REST API or if an endpoint fails to return data, the HTTP request returns a failed status response. Both the API provider and the API consumer receive HTTP 500 or 400 errors when a call is unsuccessful and a successful HTTP 200 for successful endpoint calls. A REST API failure from our shopping example would look like this:HTTP/1.1 400 Unprocessable Entity{  "error":     {      "code": "400",      "message": "Product ID does not exist"    }}With GraphQL, a partial failure will resolve successfully and return HTTP 200 even if the API call didn’t return any of the queried data. The successful HTTP response is a result of the query reaching the server. If the query calls data that doesn’t exist or that the consumer doesn’t have access to, the HTTP response code won’t give you this information. This can make handling errors more difficult because more effort is needed to diagnose errors. API consumers can check the response object for an errors object, which will show which endpoint experienced an error and what caused the error. Here’s what a partial failure would look like in our GraphQL shopping app query example:{  "errors": [    { "path": [ "product" ],      "locations": [ { "line": 3, "column": 12 } ],      "extensions": {        "message": "Object not found",        "type": 2      }    }  ]}An API consumer has the ability to diagnose errors by checking client-side for a GraphQL errors object like the one shown here. Unfortunately, on the server side you may not catch these errors if you aren’t inspecting every call. For API providers to resolve this, they need to monitor the GraphQL payloads their API returns so that they can check for error objects like the developers using their APIs. Monitoring API payloads has the added benefit of letting you know how developers are consuming your API, what queries are most common, which queries have the lowest latency, and whether API consumers are successfully completing queries.Learn by ObservingAn additional benefit to monitoring your GraphQL payloads is that you can log metrics so that you can make long-term decisions about your API based on how developers access it. Logs can tell you which GraphQL queries were successful, who your most loyal users are, which queries first-time users make, and which queries are often bundled together, among other things. This can inform your organization’s customer success strategy, which would allow you to write more effective documentation, find users who are having challenges with their API calls, and offer more proactive support to the developers using your API. With detailed logs, you can get detailed insight into your API. Filtering queries is a powerful technique to aggregate the information from your GraphQL logs.Aggregating the GraphQL queries to your API then filtering them by categories like field name and argument value will give you actionable information about the majority of the queries being made to your GraphQL API. This tells you what parts of your API are creating the most value for your customers, so you know what to focus on when you write documentation, which queries to reach out to developers about, and which additional features are likely to create value for your users. If your organization has both GraphQL and REST APIs, filtering your queries will allow you to make direct comparisons between how developers use your different APIs.We’ve covered how your organization should approach GraphQL API observability, and how important it is to an organization that is focused on promoting customer success. While REST API observability can be more intuitive to implement, and there are more existing REST API analytics tools, GraphQL observability has a few unique advantages over REST API observability.Advantages of GraphQL API ObservabilityGraphQL queries indicate exactly what information is needed. REST API consumers often underfetch or overfetch data because they can’t choose what specific data they need. GraphQL queries only call the data a consumer needs, which tells you exactly what data is creating value for your consumers. With REST API analytics it can be difficult to determine if multiple successive API endpoint calls are part of a single transaction from your consumer, or if they are unrelated events. GraphQL analytics bundle all the calls needed for a single transaction into a single query, so you know more about how consumers are using your API.GraphQL API analytics can give you an edge over REST API analytics in product development, and they can make it easier for your organization to focus on customer success. If your organization understands how first-time users are attempting to query your API when they run into challenges, your customer support can play a more active role in resolving the issues your customers run into and therefore offer more detailed and constructive support. If a customer fails to query your API because they call a REST endpoint that no longer exists, your organization may not observe this failure and your customer support won’t be able to reach out to this customer. Contrast this with GraphQL API calls, where queries go to a single endpoint. If a customer tries to query data that no longer exists, your monitoring will record this failure and your customer support can reach out to the user directly to help your customer make successful API calls.GraphQL has grown very popular over the years, and more teams are choosing to use GraphQL APIs. As an API provider, observing your GraphQL API can be challenging. The vast majority of API analytics tools were built to support REST APIs, and GraphQL is often an afterthought. Moesif is different, built to observe both GraphQL and REST APIs so that your organization can promote customer success and create value for your customers. Moesif provides all of the advantages of GraphQL observability, built-in to the product. You don’t need to forgo powerful analytics tools when you implement a GraphQL API!",
          "url": " /graphql/api-development/GraphQL-Versus-RESTAPI-Which-is-Better-for-API-Observability/",
          "author": "Adam",
          "categories": "graphql, API-development"
        }
      
    ,
  
    
        "developer-platforms-api-analytics-benefits-of-an-api-program": {
          "title": "Benefits of an API Program",
          "content"	 : "APIs can help you achieve things faster, save you money and enhance your business decisions. If you’re considering using an API program to help scale your business, read on.Adding ValueResearch shows that, over a four-year period, APIs can deliver 12.7% more growth in market capitalization for firms that use them, compared to those that don’t. Over a 16-year period, that growth extends to 38%.APIs can help with a wide range of business operations. If you have a product or service and are looking into API adoption, there’s plenty to consider. We’ve detailed some of the benefits below, along with some of the challenges, to help you establish whether adopting an API program makes sound business sense.Benefits of an API Program?APIs enable new use cases and customizations, whether your users are internal or external. As the above statistics indicate, they can also be an excellent source of revenue while supporting business growth.One reason for this is that it’s fairly easy to monetize APIs and get paid. You can bill your customers based on how many times they call your API or on a range of other, more granular factors. Building monetization into your thinking at an early stage can help to shape your API program and ensure that you create a bulletproof strategy.  “We were soon able to start billing our customers for API usage and it quickly became a revenue generator for us.” Paul Fung, API GM at NexHealth, from their case study.Another benefit of APIs is that your customers are developers, not end users. This means that they’re making decisions from an architectural perspective. Your APIs can quickly become deeply ingrained into your customers’ operations, enabling you to scale rapidly. And once your APIs are integrated, the fact that such architectures can be hard to detangle means that your customers should stick around for the long haul – provided that you deliver a great developer experience.It’s also true that APIs tend to accelerate a product’s go-to-market. Part of that is because you don’t need to expend any effort on the User Interface (UI), because there usually isn’t one. APIs tend to be headless products, slotting into the customer’s backend, rather than needing a UI.This means that you can focus your engineering effort on building differentiated business logic. Through your API, you’re providing a building block for a developer to adopt best-in-class business logic to fulfill certain functions. By not expending resources providing a UI for your customer to use your product, you’ll use your engineering resources more cost-effectively to deliver maximum value.ChallengesEvery new business venture or approach has its challenges. Introducing an API program is no exception.While you don’t need to expend effort on delivering a UI as part of your API, you will need to invest time and energy into producing documentation. You will need to document standards, guidelines, patterns, recommendations and more, all in a user-friendly fashion. That requires a technical resource who can write engaging content.This is all part of delivering a great customer experience, which, along with superb customer service, is essential to ensuring you don’t get a bad reputation when it comes to your API program.The other resource you’ll need is an evangelist – someone who can sell the concept of API adoption and the value of the API program to internal stakeholders. It can take time to bring everyone up to speed and gain their buy-in, so the sooner you can start evangelizing, the better.  “Be patient when building your API program. Be empathetic to others who don’t see things the same way that you do – try to build bridges to help them understand. Have a vision and work towards it.” Jeannie Hawrysz, Lead API Architect, SAS from our podcast.Security ConcernsAPIs are immensely versatile, suiting a huge range of verticals and business models. The FinTech and HealthTech sectors in particular have been quick to grasp their potential. This means that security is paramount, with HIPAA/SOC2/ISO27001 compliance high on the agenda for enterprises in these sectors. It also means that there are plenty of API products and tools out there that deliver this level of compliance and accreditation – so you can sleep easy when it comes to security concerns.Harnessing the Power of APIsA well-considered and implemented API program can enable new applications and go-to-market strategies. Complementing your APIs with powerful API analytics and tools can then ensure you activate, understand and monetize customers fully.If you’re ready to explore what an API program could do for your business, and how to get maximum value out of it, it’s time to talk to Moesif.                Get the Biggest Business Impact from your APIs with Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /developer-platforms/api-analytics/Benefits-of-an-API-Program/",
          "author": "Larry",
          "categories": "Developer-Platforms, api-analytics"
        }
      
    ,
  
    
        "technical-stripe-tyk-end-to-end-api-monetization-with-tyk-stripe-and-moesif": {
          "title": "End-to-End API Monetization with Tyk, Stripe, and Moesif",
          "content"	 : "Many API developers and companies struggle to find ways to easily set up systems to monetize their APIs. Some are simple but not customizable, some are complex and require massive engineering effort to actually get it all running.To make things easier, Moesif created a feature a few months ago called Billing Meters which gives massive customizability but with a minimal amount of code and engineering effort.For this example, which could actually be used out of the box, we will use Moesif, Tyk, and Stripe to charge users for API usage. For this setup there are a few assumptions:  You have a running instance of Tyk (with an API endpoint already created)  Your API endpoints Authentication Mode in Tyk is set to Authentication Token  You have an active Stripe account  You have an active Moesif account  You have installed and configured the Moesif plugin in TykThe setup is pretty simple from the outside. We will create a /register endpoint which:  Registers a user in Stripe  Subscribes that user to a product  Registers the User and Company in Moesif  Uses Tyk to generate an API keyI’ve also created a little frontend for it that is a simple form that registers a user by calling the /register endpoint and then displays the generated API key for the newly registered user.  Want to view the completed code in GitHub? Check out our repository here                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free        1 - Create Your Product and Price in StripeThe first step we will take is to create a product and price in Stripe. It’s best to do this step first because then when you integrate Stripe into Moesif you’ll already have some pricing plans for Moesif to pull in. A pricing plan can then be associated with specific billing criteria set up within a Billing Meter in Moesif.First make sure to meet these prerequisites.To create a product and price, log into Stripe and proceed to the Products page in the Stripe UI. Once there, click on the + Add Product button in the top right corner.You’ll then be able to add in the details for your product and price(s) for it. The form for your product will have a few fields to fill out.Product InformationName  This is the name of your product. In the example below, we use the name “My API”.Description  This field is optional but you could put a brief description of the product here. In the example below, we use a description of “This is a monetized API”.Image  Optionally upload an image that can help you easily recognize a item on the Products page. We’ll be using the default placeholder image in this example.Pricing InformationYou can choose between Recurring and One-off pricing for your product.Recurring PricinngIn recurring pricing, your customers pay an ongoing fee according to the pricing model you define. After selecting Recurring, you can enter the amount you want to charge and the billing period.To further configure your recurring pricing, select More pricing options. This allows you to specify the pricing model, amount, billing period, price description, and more.The following pricing models are available in Stripe for recurring pricing:  Flat rate  A fixed price for a single unit or package.  Package pricing  Charge for API usage by the package, or a group of units. For example, you could set it up to charge $10 for every 1000 API calls. Every time the user goes over the 1000 API call threshold, they are charged another $10.  Tiered pricing  Offer different price points for based on unit quantity.  Usage-based  Pay-as-you-go billing based on metered usage. You can charge per package, per unit , or per tier and define the prices and units accordingly. You can also set up a billing meter for the price to meter usage. See Creating a Product and Price in Stripe for instructions on how to set up a usage-based scheme.One-Off PricingIn one-off pricing, you charge a one-time fee rather than recurring amount in each billing period. After selecting One-off, you can enter the amount you want to charge in the Amount field.Similar to recurring pricing, you can select More pricing options and configure your pricing further by specifying the pricing model, amount, price description, and more.The following pricing models are available for one-off pricing:  Package pricing  Charge for API usage by the package, or a group of units. For example, you could set it up to charge $10 for every 1000 API calls. Every time the user goes over the 1000 API call threshold, they are charged another $10.  Flat rate  A fixed price for a single unit or package.  Customer chooses price  The customer sets a custom price. You can set a limit and define a preset amount to suggest to the customer.Billing periodThe billing period can be set as the following for recurring pricing models:  Daily  Weekly  Monthly  Every 3 months  Every 6 months  Yearly  CustomFor your configuration with Moesif, we recommend setting the billing period as Monthly.Price descriptionThis is an optional field but recommended. Here you can put a brief description of your price. This will allow you to more easily decipher which price you are selecting in the billing meter in Moesif, especially if you have multiple prices for a single product.Once you’ve input all of the details for your price, you can select Next and then select Add productAs you create products, you will be able to view and edit them on the Product Catalog screen.2 - Enable the Moesif-Stripe IntegrationOnce your products and prices are created, it’s time to begin to integrate Stripe with Moesif. To begin configuring Stripe in Moesif, go to the Billing Meters page and click the Edit Billing Provider dropdown in the top right corner of the screen.This will bring up the Stripe configuration screen walking you through the integration. From this screen, you can get all of the info needed to plug Stripe into Moesif. Each step for configuration is covered within the modal.Add the Moesif Webhook to StripeThe first step in the integration is to add the Moesif webhook into the configuration in Stripe. Adding this allows Stripe to send subscription updates to Moesif.To add the Moesif webhook to Stripe, from the upper right-hand side click on Developers, and then Webhooks in the left-side menu. This will bring you to the Webhooks page where you can view existing webhooks and add new ones. To add a new webhook we will click the Add an endpoint button at the bottom of the screen.From here, we will plug in our Moesif API endpoint URL and configure the events to listen to. You’ll want to copy your Moesif Webhook URL into the Endpoint URL field and then click the + Select Events button.  These details can all be found on the Stripe configuration page in Moesif mentioned in the previous section.You should select the option under Customer for Select all Customer events. After this, click the Add events button at the bottom of the screen.After this, you’ll be returned back to the original screen where you added the endpoint details. Scroll to the bottom of the screen and click Add endpoint to save the endpoint to Stripe.Plug the Stripe API Details into MoesifFor Moesif to add usage quantities to subscriptions in Stripe, we need to add the Stripe API details into Moesif. This is done in the Stripe configuration screen in Moesif, the same screen we’ve been working with previously.Currently, Moesif only supports version 2020-08-27 of the Stripe API so that field defaults for the Stripe API Version field.For the Stripe API Key field, you’ll need to retrieve the API key from Stripe to plug it in. From the Developers screen, the same one we used in the previous step, you’ll click on API Keys. You’ll then be able to see the private key for your API in either the Secret key or a generated Restricted keys field on the screen. Either key can be used.After copying the key from Stripe, you’ll paste this key into the Stripe API Key field back in Moesif. After doing this, back in Moesif you can scroll down to the bottom of the screen and click Save to save the configuration.Optionally, you have the ability to customize the company_id in Moesif as well. The default should work fine for most purposes. However, you can fully customize it to specify how to map Stripe Subscription and Customer objects to Subscription ID and Company ID in Moesif respectively:  A Stripe Customer maps to Moesif Company.  A Stripe Subscription maps to a Moesif Subscription.3 - Create a Billing MeterOnce you have the Stripe integration active in Moesif, you can begin to set up your billing meter. Billing meters created in Moesif do two things: track usage based on specific criteria and report that usage to the billing provider. Moesif allows you to set up very simple and very complex billing meters with relative ease.To create the Billing Meter, in Moesif you will navigate to the Billing Meter screen. You can do this from the left-side menu. On the Billing Meter’s screen, you’ll then click + Add Billing Meter in the top-right corner of the screen.The next screen is where you can actually input the criteria for your Billing Meter.Fields on this screen include:      Billing Meter Name          This is the Moesif internal name of your new Billing Meter            Billing Provider          In this dropdown you can choose the billing provider you want to send your usage metrics to.            Product (Stripe only)          Here you can choose which Product that you’ve set up in Stripe you want your usage metrics to be tied to.            Price (Stripe only)          The last field in the Billing Provider settings for the Billing Meter, here you will choose which Price you want to tie your usage metrics to.            Filters          Under the Filters configuration, you will configure your billing criteria to only include requests that fit a certain criteria.            Metrics          Here you can choose which metric you would like to bill on. Available options include:            Event Count          This will increment usage for every event that fits the criteria outlined in the Filter criteria.            Unique Users          This will increment usage whenever a unique user sends a request that fits the Filter criteria. For every unique user, the count will be incremented by 1 regardless of the event count for that user.            Unique Companies          This will increment usage whenever a unique company sends a request that fits the Filter criteria. For every unique company, the count will be incremented by 1 regardless of the event count for that company.            Unique Sessions/API Keys          This will increment usage whenever a unique session or API key is used to send a request that fits the Filter criteria. For every unique session or API key, the count will be incremented by 1 regardless of the event count for that particular session or API key.        There are other options under Metrics as well but the above 4 tend to be the most applicable to usage-based billing.As an example, for this guide we will create a Billing Meter that will filter traffic for a single endpoint, named /test-service, and where requests received a successful HTTP 200 response. We will use the Event Count metric to make sure that every request is added to the tally and sent to the billing provider.In Moesif, the billing meter will be configured as shown below.We will then click Create. This will create and activate the Billing Meter. A modal will appear notifying you that the billing meter has been created and presents a walk-through to ensure the meter is correctly configured.First, we will set up a flow to get users registered, subscribed, and create a JWT so they can use our monetized API. Once that is complete we will come back and proceed with the walk-through.4 - Create the /register endpointInstead of using a pre-built onboarding flow, such as through a Developer Portal within an API gateway, we will build our own. We will create an endpoint called /register which we can then use to onboard our users who want to use the API. The result will be that the user receives an API key that they can use that will track their usage.Since we are using Moesif, Stripe, and Tyk as part of our overall solution, we need to make sure each of the components is working together properly.Here’s what the endpoint will do:  Create a user in Stripe  Subscribe the new user to the API subscription in Stripe  Create the CompanyID in Moesif (which will be the Stripe subscription ID)  Create the UserID in Moesif (which will be the Stripe Customer ID)  Create an API key (with the Stripe Customer ID as the tokens Alias)  If you already have User and Company identifiers in Moesif and other systems that you want to use, instead of using Stripe’s customer and subscription as your IDs, you can do that in Moesif under the Stripe configuration settings.In this example, I will create a simple NodeJS API with Express to do the above.Create the npm projectFirst, we will create a folder called moesif-monetization where we will add our API code. We will then run npm init to turn moesif-monetization so we can use npm in our project. For that, you’ll run the following command in the moesif-monetization directory.npm init  You can fill out the details or use the defaults as needed when creating the npm project.You should then see a package.json file in your moesif-monetization folder.Now, open this directory in your favorite IDE or text editor. I will be using VS Code for the remainder of this tutorial for all the coding.Add in the project dependenciesWe will now edit our package.json file with our correct dependencies. In the package.json we will add the following entries under the dependencies object. "dependencies": {   "@stripe/stripe-js": "^1.29.0",   "body-parser": "^1.20.0",   "dotenv": "^16.0.0",   "express": "^4.17.1",   "http": "0.0.1-security",   "moesif-nodejs": "^3.5.8",   "node-fetch": "^2.6.5",   "path": "^0.12.7",   "stripe": "^8.219.0" }Save the file, then navigate to the terminal and run:npm installNow, your dependencies will be brought into the project and added to the node_modules folder. These dependencies will help us to make calls to REST endpoints, connect to Stripe and Moesif, and various other capabilities we will build into our app.Create the .env fileInstead of hardcoding the Stripe keys and other static values into our app, we will abstract them into a .env file. We can do this using the dependency we added in our package.json called dotenv.In the root directory, create a file named .env. Within this file, we will add a few entries that will contain the keys and values used in our code.STRIPE_KEY="sk_test_XXX"STRIPE_PRICE_KEY="price_XXX"MOESIF_APPLICATION_ID="YOUR_MOESIF_APP_ID"TYK_AUTH_KEY="TYK_AUTH_ID"TYK_API_ID="TYK_API_ID"TYK_API_NAME="API_NAME"TYK_URL="http://TYK_URL:3000"The values that are here can be found in the following places:Obtaining Your Stripe API and Price KeyYour Stripe API Key can be found in the same place we grabbed the key for our Stripe and Moesif integration we did earlier for the Billing Meter. You can actually use the same key for both or create a restricted key with just the scope needed for each function.While we’re at it, lets grab our Stripe product’s price key. We will need it in the next section. Your Stripe price key is an identifier for the price you created earlier in Stripe. This can be found by going to the product in Stripe and grabbing the value from the API ID column.Obtaining Your Moesif Application IDYour Moesif application ID is found in Moesif by going to the menu link in the bottom-left of the screen (which will show your name) and selecting API Keys.The key will then be on the page that appears under Collector Application Id.Obtaining Your TYK_AUTH_KEYThis key is the key that you’ll require in order to send requests to the Tyk Dashboard API. This can be found in Tyk by navigating to the Users page and clicking on a user’s entry (likely your own).Once you’ve clicked on the user, you will be able to see the user’s Tyk Dashboard API Access Credentials. This is the key you’ll plug into the config.Obtaining Your TYK_API_IDThe API ID for the endpoint can be found by going to APIs and copying the ID for your endpoint.Obtaining Your TYK_API_NAMEThe API Name for the endpoint can be found in the same place as the API ID.Your TYK_URLThis will be the URL of the Tyk Dashboard API. Usually, this will be hosted on port 3000 but it may be different if you have a custom configuration.Once you’ve populated the file with the seven key-value pairs, save the file. We won’t need to touch this file again for the remainder of the tutorial.Create the app.js fileIn the root directory of our app, we will create an app.js file (if not already created). In this file, we will add the following code that adds our dependencies and creates a base REST endpoint.require('dotenv').config();const express = require('express');const path = require("path");const bodyParser = require('body-parser');const moesif = require('moesif-nodejs');const Stripe = require('stripe');// npm i --save node-fetch@2.6.5const fetch = require('node-fetch');const app = express();const port = 5000;const stripe = Stripe(process.env.STRIPE_KEY);var jsonParser = bodyParser.json();const moesifMiddleware = moesif({ applicationId: process.env.MOESIF_APPLICATION_ID});app.use(moesifMiddleware);app.post('/register', jsonParser, async (req, res) => { })app.listen(port, () => { console.log(`Example app listening at http://localhost:${port}`);})In the above code we are:  Importing a few dependencies  Configuring the Stripe dependency  Configuring the Moesif middleware  Create the /register endpoint  Setting our app to run on port 5000 and start our Node app  You will notice that we have process.env.STRIPE_KEY and process.env.MOESIF_APPLICATION_ID. These values will come from the .env file that we created in the last step.Implement the /register endpointOur next step is to implement the /register endpoint. This endpoint will essentially create our binding between Tyk, Stripe, and Moesif. The outcome will be a generated API Key from Tyk which will associate usage with a user in Moesif, which will then be reported to Stripe.Our first step in the flow is to create the customer in Stripe. We will use our Stripe JS dependency to do just that. We will use the parameters from the request body (email, first name, last name) to create the customer in Stripe using the stripe.customers.create function. We will then store the created customer in a custom variable so we can access the customer ID generate in Stripe.const customer = await stripe.customers.create({     email: req.body.email,     name: `${req.body.firstname} ${req.body.lastname}`,     description: 'Customer created through /register endpoint',   });Next, we will subscribe this new user to our API subscription we created in Stripe earlier. We will use the stripe.subscriptions.create function and use the generated customer ID from the previous function call to subscribe them. This will return back a subscription object containing an ID we will use later.   const subscription = await stripe.subscriptions.create({     customer: customer.id,     items: [       { price: process.env.STRIPE_PRICE_KEY },     ],   });Once the user and subscription are created in Stripe, we will now use the Moesif middleware to create the user and add their relevant details into Moesif. First we will call the Moesif middleware’s updateCompany function to map the Stripe customer.id to the companyId in Moesif.  var company = { companyId: customer.id };  moesifMiddleware.updateCompany(company);We will then do a similar step with the updateUser function and use it to map the Stripe customer.id to the userId and companyId and some other metadata we collected on the user into Moesif. This will link the user to the company as well.  var user = {     userId: customer.id,     companyId: customer.id,     metadata: {       email: req.body.email,       firstName: req.body.firstname,       lastName: req.body.lastname,     }   };   moesifMiddleware.updateUser(user);At this point, we now have all of our user details plugged into the necessary platforms. Our next step is to generate an API key for this user. For that, we will call the Tyk Dashboard API using the /api/keys endpoint. This will return an API key to us for that user in the response. We access that API key through data.key_id.var body = {     alias: customer.id,     last_check: 0,     allowance: 1000,     rate: 1000,     per: 60,     expires: 0,     quota_max: 10000,     quota_renews: 1424543479,     quota_remaining: 10000,     quota_renewal_rate: 2520000,     access_rights: {       [process.env.TYK_API_ID]: {         api_id: process.env.TYK_API_ID,         api_name: process.env.TYK_API_NAME,         versions: [           "Default"         ]       }     }   }   var response = await fetch(`${process.env.TYK_URL}/api/keys`, {     method: 'post',     body: JSON.stringify(body),     headers: {       'Content-Type': 'application/json',       "Authorization": process.env.TYK_AUTH_KEY     }   });   console.log(response);   var data = await response.json();   console.log("Tyk create API key");   console.log(data);   var tykAPIKey = data.key_id;  You will see in the body of the request that we are creating a pretty large JSON object. A few important fields are:  alias - this will be our Stripe Customer ID that will be mapped to the User ID in Moesif  access_rights          api_id - This will pull the API ID from the .env file      api_name - This will pull the API Name from the .env file.      The remainder of the fields can be customized as needed.Optionally, we will add the user’s API key to our metadata in Moesif as well. For testing purposes, this makes it easy to get the key if you’ve lost track of it and don’t want to go back into Tyk to retrieve it. We will use the updateUser function again in the Moesif middleware to do this, supplying the generated API key in the metadata object.   var user = {     userId: customer.id,     metadata: {       apikey: tykAPIKey,     }   };   moesifMiddleware.updateUser(user);Lastly, we will return a 200 OK response back to the caller with the API key in the response body.   res.status(200)   res.send({ apikey: tykAPIKey });The completed function, end-to-end, will look like this:app.post('/register', jsonParser, async (req, res) => {    // create Stripe customer    const customer = await stripe.customers.create({      email: req.body.email,      name: `${req.body.firstname} ${req.body.lastname}`,      description: 'Customer created through /register endpoint',    });    // create Stripe subscription    const subscription = await stripe.subscriptions.create({      customer: customer.id,      items: [        { price: process.env.STRIPE_PRICE_KEY },      ],    });    // create user, company, and subscription in Moesif      var company = { companyId: customer.id };      moesifMiddleware.updateCompany(company);      var user = {        userId: customer.id,        companyId: customer.id,        metadata: {          email: req.body.email,          firstName: req.body.firstname,          lastName: req.body.lastname,        }      };      moesifMiddleware.updateUser(user);    // send back a new API key for use    var body = {      alias: customer.id,      last_check: 0,      allowance: 1000,      rate: 1000,      per: 60,      expires: 0,      quota_max: 10000,      quota_renews: 1424543479,      quota_remaining: 10000,      quota_renewal_rate: 2520000,      access_rights: {        [process.env.TYK_API_ID]: {          api_id: process.env.TYK_API_ID,          api_name: process.env.TYK_API_NAME,          versions: [            "Default"          ]        }      }    }    var response = await fetch(`${process.env.TYK_URL}/api/keys`, {      method: 'post',      body: JSON.stringify(body),      headers: {        'Content-Type': 'application/json',        "Authorization": process.env.TYK_AUTH_KEY      }    });    var data = await response.json();    var tykAPIKey = data.key_id;    var user = {      userId: customer.id,      metadata: {        apikey: tykAPIKey,      }    };    moesifMiddleware.updateUser(user);    res.status(200)    res.send({ apikey: tykAPIKey }); })With that, we can now actually try out our endpoint to make sure that each piece is working as expected. The outcome should be a registered user with an API key that will record and report usage data to Stripe. Let’s move on to testing it.5 - Send a test request to the /register endpointOnce your /register endpoint has been coded and deployed, it’s time to test it. For right now we will simply use Postman to send a request. Our request will contain a JSON request body that will contain a:  First name  Last name  EmailOf course, this is the minimal amount of information we would want to configure our system and profiles in Tyk, Stripe, and Moesif correctly.You can easily add more fields as needed for your specific use case.In Postman, we will create our request with the following information:  Request Type: POST  Endpoint URL: http://localhost:5000/register  Request Body:    { "firstname": "Userfirstname", "lastname": "Userlastname", "email": "test@test.com"}      Once everything is plugged into Postman, it should look like the following:Once the request is sent, the response should contain an API key that the newly registered user can use.We will now check Tyk and Stripe to ensure that the information we registered with is correctly entered into each of the destination systems. The first one we will check is Stripe.Logging back into Stripe, you’ll navigate to Customers screen. You should see your newly created user in the list.Click on the newly added customer in the list. On the next screen, you should see that the customer is also subscribed to your APIs subscription.Once these two entries are confirmed in Stripe, you can move over to Tyk to also make sure that the user was correctly set up there too.Using the Tyk Dashboard, navigate to the Keys screen. Here you should see an entry for a key where the Alias is equal to the users Stripe Customer ID.With these checks completed, we can safely assume that our /register endpoint is correctly setting up our user’s accounts and subscriptions in Tyk and Stripe.6 - Call your API using the generated API keyOur next step is to actually use our generated API key. We will then confirm that all the correct information is added to Moesif. The data we are confirming includes:  The Stripe Customer ID is mapped to the Moesif User ID  The Stripe Subscription ID is mapped to the Moesif Company ID  Moesif contains the Stripe metadata in the user’s profileUse Postman to send the requestNext, let’s use Postman, or another platform, to send a request to the /my_api endpoint. This is the endpoint that we set up the billing meter for in Step 3, above.In Postman, we will:  Put the /my_api API endpoint as the request URL  Select the Authorization tab  Select the Type as API Key  Populate the API key details  Set the Key as “Authorization”  Set the Value as the generated API key  Set the Add to field value to “Header”Below is an example of the populated request configuration in Postman.To send the request to our endpoint, click Send.Once sent, your request should be proxied through Tyk and the API call analytics should land in Moesif.Confirm that Moesif received the request infoBack in Moesif, you’ll navigate to the Events screen where you should see the request you just sent. You should see the entry has both a User ID and Company ID populated with the Stripe user and subscription ID’s. The entries should look like this:  The customer ID will look like “cus_XXXX” and the subscription ID will look like “sub_XXXX”.If you click on the User ID shown in the entries on the Live Event Log screen, you will come to the users profile page. On this page, we will confirm that the Stripe metadata is present. We will need to add a new column to our profile to display the Stripe data. To do this, from the profile page, click on the … More Actions button and click Customize Profiles’ Layout.We will then add a new column for the Stripe metadata. You will click the + button on the far right of the screen to create a new column where we will add the Stripe metadata.  You may need to scroll to the right to see it depending on your resolution and screen size to see the + button.You will then drill down to Metadata > stripe > customer > created and use this field in the new row. I’ve also changed the column image to one more fitting. You can customize this by clicking on the image and selecting whichever one fits best.You can also add other fields, but for right now just this single field is enough to tell us that Moesif is correctly receiving data from Stripe.  If you don’t see the Stripe metadata entry as an available field, wait a few minutes. If after a few minutes the Stripe metadata isn’t present, ensure that your Stripe configuration is correct in Moesif. After confirming or editing it, try creating a new user and sending a request again to confirm that the integration is working.At this point, we now have confirmed that our API call, proxied through Tyk, is working and is stamped with the correct user and company details in Moesif. We also confirmed that Stripe is sending data back to Moesif which is correctly being mapped to the corresponding user profile, confirmed through the Stripe metadata in Moesif.7 - Create the frontendNext, we want to add a simple little frontend so we don’t need to call for our API key through Postman. We will make a quick little registration form that will then return an API key for our newly registered user to use.Add your frontend files to the appIn the root directory of the application, we will add two files. We will add both an index.html and an index.js.Add in your route to serve the static html filesIn the app.js file, we will add in a route to serve the static HTML files. Underneath our code for the /register endpoint, we will add another endpoint. Add the following code:app.get("/", function (_req, res) { res.sendFile(path.join(__dirname, "index.html")); res.sendFile(path.join(__dirname, "index.js"));});This code will now load the website (once we have the code plugged in) when you navigate to http://localhost:5000/ .Code the frontend form and logicFinally, let’s add the code for our frontend HTML and JavaScript functionality. In the index.html file, we will add markup that looks like this:<!DOCTYPE html><html lang="en"> <head>   <meta charset="utf-8" />   <meta name="viewport" content="width=device-width, initial-scale=1" />   <meta name="theme-color" content="#000000" />   <meta     name="description"     content="Moesif Embedded Dashboard example"   />   <title>None React App Example</title> </head> <body>   <noscript>You need to enable JavaScript to run this app.</noscript>   <h1>     Moesif Monetization Example   </h1>   <div id="form-input">     email: <input id="email-input" placeholder="email" />     first name: <input id="firstname-input" placeholder="first name" />     last name: <input id="lastname-input" placeholder="last name" />     <button onClick="submitRegistration()">Register</button>   </div>   <div id="apikey-output"></div>   <p id="error-message"></p>   <script src="index.js"></script> </body></html>This markup will display a form that allows users to input an email, first name, and last name. It also has a Register button that will call the JavaScript function for submitRegistration(). That function will be in our index.js JavaScript file.The index.js file will look like this:async function submitRegistration() {   const email = document.getElementById("email-input").value;   const firstname = document.getElementById("firstname-input").value;   const lastname = document.getElementById("lastname-input").value;   const errorElement = document.getElementById("error-message");   const apikey = document.getElementById("apikey-output");   var body = { email, firstname, lastname };   var response = await fetch('http://localhost:5000/register', {     method: 'post',     body: JSON.stringify(body),     headers: {'Content-Type': 'application/json'}   });   var data = await response.json();   apikey.innerHTML = data.apikey; }This function will take the input from the form, post it to our /register endpoint, and display the returned API key on the screen.8 - Test the frontendTo test the frontend, save your code changes and restart the server. Then, in a browser, navigate to http://localhost:5000/. You will then see the form show up.Fill out the form fields and submitNow that the form is loaded on the screen, fill in the fields and click the Register button. This will take the info, post it to our /register endpoint, and give us the generated API key.  It is suggested that you use a different email than you used earlier when you created an API key directly through the /register endpoint.Confirm the API key is returnedOnce the submit button is clicked, after a few seconds, the API key should be returned back to the UI.9 - Send a request to your monetized APIWe will once again want to make sure that everything is working with our UI, through to our backend systems. For this, simply repeat the steps from Step 6 to confirm that the user and company IDs are populated correctly and that the Stripe metadata is returned for this user and the new API key.10 - Confirm all the pieces are working correctlyAlthough this is optional, this step may help with troubleshooting any issues that may have came from our previous steps. Here are a few things to check to make sure that all is working as it should. After creating a new user through the UI and using the generated API key to place a call to your API, confirm the following:  In Stripe          Confirm that a customer has been created in Stripe with the details you entered into the UI      Confirm that the customer has been subscribed to the correct product and price        In Tyk          The API Key is created in Tyk      The keys Alias field matches the customer ID from Stripe (which begins with cus_XXXX)        In Moesif          Your API call was recorded in Moesif in the Live Event Log      Your API call has the Stripe Customer ID and Subscription ID in the User and Company fields in Moesif, respectively.      Confirm that the Stripe metadata is populated in Moesif      11 - Check Stripe for usageLastly, After a few hours, it’s best to go into Stripe to confirm that usage is being added to a users subscription. Be sure that you’ve sent a few requests through in order to make sure you have some data that should be sent to Stripe.  it may take a few hours for usage to make its way from Moesif to Stripe. If data still isn’t in Moesif after a few hours, ensure you’ve followed all the steps outlined within this guide. This includes making sure that your user and company ID’s from Moesif are correctly mapped to the corresponding keys in Stripe.To check the usage, in Stripe you’ll want to navigate to the Customers screen and select the customer that you made the API call with. Once selected, you should see some active subscriptions for the users that you’ve registered through the /register endpoint. The one we created earlier is called My API. Click on the subscription entry.On the next screen, click on View Usage beside the price entry.A modal should now pop up showing you the usage for the API that has been reported to Stripe from Moesif.  Remember, there is a delay in Moesif’s reporting to Stripe. If you data isn’t there yet, check back in a bit later.12 - Determining If the Billing Meter is Working CorrectlyTesting the created Billing Meter is easy with out Test Meter function. Navigate to your created Billing Meter from the left side navigation pane and selecting your Stripe Test billing meter. Select Test Meter on the top right.We will first confirm the meter that you are attempting to test. Click the Next button at the bottom of the modal.Moesif will wait for Subscriptions to created within Stripe and those subscriptions to be associated within Moesif itself. This page will update automatically, no need to refresh.Moesif will then wait for an API call to our any endpoint associated with our billing meter using our the JWT that has been created for us.Finally, Moesif will sync all usage data to Stripe every 15 minutes. This step may take a few minutes depending on when the API call was initiated but will update on the given interval.Wrapping upMonetization has always been a tough hurdle to get past. Many custom solutions offered flexibility but at a very high engineering and support cost. With Moesif, API monetization is possible in an extremely minimal amount of time. As demonstrated in this article, With a little bit of configuration and a minimal amount of code we can create a production-ready, post-paid monetization scheme in minimal time.                Monetize your APIs with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Want to generate revenue with your Tyk managed APIs?            Monetize your Tyk managed APIs in minutes with Moesif.            Try for Free            No credit card required            ",
          "url": " /technical/stripe/tyk/End-To-End-API-Monetization-With-Tyk-Stripe-And-Moesif/",
          "author": "Matthew",
          "categories": "technical, stripe, Tyk"
        }
      
    ,
  
    
        "developer-platforms-api-analytics-maximize-your-api-revenue": {
          "title": "Maximize Your API Revenue",
          "content"	 : "The International Monetary Fund (IMF) is projecting a significant slowdown over the coming years, with global growth dropping sharply from an estimated 6.1 percent in 2021 to 3.6 percent in 2022 and 2023. Meanwhile, Reuters polls reveal that the global streak of high inflation is far from over.As such, businesses need to be doing all they can to maximize their revenue – not through increasing  spend on new infrastructure, but by getting maximum business value out of their existing product. You’ve expended significant treasure on building your APIs, don’t sell it short by undercharging.Usage-based billing enables you to achieve the real business value of your APIs. Instead of basing income on crude, raw counts, it allows you to create highly-unique filters to bill for specific endpoints and how they are used. Moesif has long led the field when it comes to inferring actionable insights  from data in your APIs. Now, you can use that intense granular scrutiny to monetize your APIs as well. Utilizing an API management tool like Moesif will allow you to diversify your revenue stream by enabling success of your API product.The bottom line? A better bottom line.How to Maximize the Business Value of Your APIsUsage-based billing from Moesif means you can monetize any parameter at all in your API call or your API payload – you can meter specific endpoints, transaction counts, unique users, data volume, dollar volume, or any other usage-based metric around your platform. Through Moesif’s billing meters, you can quickly experiment with different pricing decisions to see how you can extract the most revenue growth and expansion revenue from your existing customers.Getting up and running is easy. When real-time patient booking platform NexHealth implemented usage-based billing, it took one person less than a week to integrate, fully test and deploy the system. And thanks to Moesif’s HIPAA compliant security measures, personal information stays private.  “Moesif allows us to offer the flexibility of having API usage-based, call-based or really any kind of pricing model. We were soon able to start billing our customers for API usage and it quickly became a revenue generator for us.” Paul Fung, GM, NexHealth.Moesif sits between your apps/APIs and your preferred billing provider, Stripe, Recurly, Chargebee, etc. Our platform calculates and sends data to the billing provider. This means you can bill your customers accurately, based on their usage, while an easy-to-use dashboard allows you to visualize usage and track payments. Using real-time data to refine your pricing strategy is a critical component of a scalable API monetization strategy.We also deliver an embedded, non-branded dashboard that you can share with your customers or guests, providing them with clear insight into everything they need. Behavioral emails, meanwhile, keep their customers informed and updated regarding their usage or demand levels. Improving customer retention starts with being able to drive valuable insights from your API data.With no code required, you can be up and running with Moesif’s billing system after just a few clicks in the configuration settings. As an API provider, you can then track your customers’ use of your APIs in outstanding granular detail, resulting in a hugely flexible and customizable range of usage-based API billing options.Delivering for Today, Planning for TomorrowWhen it comes to your APIs and your customers, data security is of paramount importance. That’s why Moesif delivers the reassurance of SOC 2 compliance, with data encrypted at rest and in transit. Companies can also leverage Moesif client-side encryption feature to keep their data private to their organization or meet regulatory requirements. We’re delivering the security you need today, to enable you to build a more profitable, forward-looking business for tomorrow.By building our usage-based billing product on top of our analytics platform, we’re putting Moesif’s years of knowledge and experience at your fingertips. Our professional services in analytics and API platform performance is what feeds the level of granular detail in our usage-based pricing offering, which means you can now maximize the business value of every single API by driving repeat purchases with enhanced functionality.Not only does it have the potential to increase your profits, but unlocking revenue potential can appeal to your customers as well. The past couple of years have been a powerful lesson in the need for companies to be flexible and the importance of basing decisions in real customer behavior. With this kind of metered billing arrangement, your customers have the flexibility to scale up and down as they need to. They can do so whilst sticking with your business, minus any awkward negotiations around trying to scale back fixed-rate contracts that no longer apply to their altered circumstances. The result? Happier customers and enhanced loyalty. Win-win.Time to Maximize Your PotentialIf you’re following the traditional way of billing for API usage, then you’re leaving money on the table. Given the IMF’s rather dreary global outlook for the next couple of years, is it really the time to be coasting along, leaving potential revenue uncollected? Even aside from the current economic climate, doesn’t it make sense to get the most out of the infrastructure you’ve already invested in building?Moesif’s usage-based API billing makes it possible to monetize your API products like never before. Fast and easy to implement, it can maximize the business value of your APIs while keeping your developer and product teams happy, along with your customers.You can read about usage-based API billing in more detail here or contact Moesif today to talk about your needs or to schedule a demo. We’re here to support you to take your API monetization to the next level.                Get the Biggest Business Impact from your APIs with Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /developer-platforms/api-analytics/Maximize-your-API-Revenue/",
          "author": "Larry",
          "categories": "Developer-Platforms, api-analytics"
        }
      
    ,
  
    
        "api-product-management-stripe-using-moesif-and-stripe-for-pay-as-you-go-api-billing": {
          "title": "Using Moesif and Stripe for Pay-As-You-Go API Billing",
          "content"	 : "Offering customers a variety of ways to pay for your product allows for flexibility and ease. In general, there are two ways for companies to invoice for subscription-based API access: Post-paid and pre-paid. Pre-paid is sometimes also referred to as PAYG, or Pay-As-You-Go.Post-paid usage can sometimes lead customers to have billing surprises if they are not tracking their usage or payment details closely. This can cause headaches for these users and can also cause issues for the subscription provider. The result could be lost customers and lost revenue due to bills adding up that a customer is unable to settle. There are plenty of positive and useful situations to use post-paid billing as well. Many of the services you use right now are likely post-paid services.Pre-paid, or PAYG, services can add an extra peace of mind for users. Essentially, the users will buy credits and when those credits are used their access to the service is cut. This is great for budgeting and ensuring that bills won’t soar with subscription usage. This approach allows teams to pre-pay their bills, which is great for the service provider since they know that usage revenue is guaranteed.Moesif’s latest billing feature can support both use cases. If you’re unfamiliar with Billing Meters in Moesif, you can check out some more details about them here. We will use a few features in the platform to enable a PAYG flow. This tutorial will walk through accepting payment by integrating Moesif with a payment provider, the Stripe API.  #1 - We will set up a Company Cohort that will contain users who are out of credits  #2 - We will set up a Governance Rule that blocks users who are out of credits  #3 - We will set up a Behavioral Email that will let users know that they are out of creditsWe use a Company cohort instead of a User cohort because Stripe Customers map to Companies in Moesif. For more information about how different Stripe entities map to Moesif entities, see Provider Mapping.Let’s take a look at implementing a PAYG monetization scheme in Moesif.PrerequisitesThere are a few things we need to do in order to implement the above PAYG flow. This includes:  An active Moesif-Stripe integration          You will require the Moesif and Stripe integration to be configured and active for taking Stripe payments.        An active billing meter          Once the Stripe integration is active, you’ll also need to make sure that you have an active billing meter set up.        To dig into a working example of this using Kong API gateway, check out our example code here and our docs on billing meters.Create custom dashboards to monitor consumption and success metrics across your entire API customer base.Set up the cohortTo set up the cohort in Moesif, you will navigate to the Companies screen using the navigation on the left.Then we will create our filter for the cohort so that if their balance in Stripe is greater than or equal to 0.  In Stripe, a negative balance would show that a user has credits that can be used. The balance is represented in cents. So a user with $100 in credits would show in the Stripe API data as -10000.Our filter will look like this:This is all we need to set up our cohort filter. Our next step is to create the cohort. To do that, we will click the Create Cohort button in the top right of the screen.You’ll then be shown a modal that will allow you to name the Cohort (and set up a Cohort Notification if you’d like). Enter a name for the cohort and click the Create Cohort button in the bottom right of the modal.After this, you’ll be prompted to use the newly created cohort. On this next screen in the modal, choose Governance Rule.This will then bring you to the Governance Rule screen where you can create the rule.Integrate new users with your platform and resolve issues quickly with Moesif.Set up the Governance RuleFor the governance rule, we will see that our newly created cohort has been added under the Apply To Users field. We also need to make sure the Blocking checkbox is checked, the Override Response Status should be set to 402 Payment Required, and the Override Response Body dropdown should be set to Merge Tags and should have a JSON error message populated in the text input.You’ll then click Create in the top right corner.Once created, you will then need to make sure that you enable the rule by toggling it to On. By default, a newly created cohort is always turned on.Your Governance Rule will now be active.  The rule may take a few minutes to be enforced. If the rule isn’t working instantly, give Moesif up to 15 minutes to propagate the rule and take effect.Set up the Behavioral EmailIn order to proactively let users know that they have run out of credits, we will also send them a behavioral email which is triggered the same way the governance rules is, through the cohort.To do this, we will click on the Behavioral Email menu item in the left-side navigation. Then we will click Create Template.  For this example, I used Sendgrid as the SMTP server. For more instructions on how to set that up, check out our blog all about it.You’ll need to set up your email server before your emails will be able to be sent.After you’ve clicked Create Template, you will then be prompted for the type of email you’d like to set up. Moesif offers many different “pre-canned” options you could go with but for our purposes today we will choose Blank. Clicking this will bring us to the email design screen.On the email design screen, we will:  name our email  pick our cohort to trigger the email (the one we created earlier)  Add our subject line  Add our From Address, and optionally, our From Name  Check the box for Recurring workflow  Set the Eligible for re-enrollment after field to “1 hour”Below is an example of how the configuration will look once completed.Next we will focus on the template itself. We will click the Add Row button to begin adding some elements to the template.Then, we will click Add Content.Next, we will drag and drop a text element from the right-side menu onto the template.Click into the newly added text element. We will begin to type our email content within it. So that we can add a more customized feel, we will click on Merge Tags in the editor and select First Name.Our completed email will look like the one displayed below. We will then click Test to send a test email to our account to verify everything is working from an email configuration as expected. Lastly, we will click on Create to actually create the email.Once you go back to the Behavioral Emails menu, you’ll also want to ensure that the email is turned on in the Is Active column. You can use the slider to toggle between states.The cohort, governance rule, and email are all now set up and ready to be tested.Reduce analytics costs with Moesif - zero maintenance with no performance impact.Test the setupTesting the setup is simple to do in the Stripe dashboard. First, we will log into Stripe, select a customer, and give them some credits.We can then send a request through for this user and see a 200 OK response returned in Postman.We will then take away the credits from that user via Stripe API, leaving their balance at $0.The user will then be added to the cohort in Moesif.  It’s important to note that because of how Moesif works, it may take a few minutes for members to be added/removed from a cohort. If there is a yellow caution badge on the right of the table, the user has not been synced into the cohort yet. If it is the green check, the user has been successfully synced to the cohort.We will then send an API request again with the user having no credits remaining. Moesif will then block the call and return a 402 Payment Required to the user.This will effectively stop the user from being able to make an API call until they have added more credits to their Stripe account and completed payment with a successful checkout session.Lastly, the user should also receive the “out of funds” email we created earlier as well. That will be received and look like this:With that, we can confirm that our PAYG monetization scheme is in full effect. For future payments, users will now be required to top up their accounts in order to use the API and will also be emailed to let them know. By directly sending invoice reminders to your users, you can shorten the amount of time a bank transfer takes by staying top of mind. For card errors, sending payment information can keep your users from hitting surprise governance quotas.Of course, many other considerations could be made in order to customize this for your organization’s needs. You may put a minimum credits threshold for your governance rule where users must have Stripe credit > $10 in order to use the API, or something similar. You may also notify customers before they hit a 0 balance to ensure that they do not have their usage suspended and cause service interruption for their applications.Try it out!Want to try out PAYG billing or post-paid billing for your APIs and apps? Sign up for Moesif today to begin using billing meters, behavioral emails, governance rules, and powerful analytics, out-of-the-box. Moesif is a “one-stop-shop” for all your monetization needs with a growing number of payment gateway platforms we support including Stripe, Recurly, and Chargebee.                Easily Monetize APIs with Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /api-product-management/stripe/Using-Moesif-And-Stripe-For-Pay-As-You-Go-API-Billing/",
          "author": "Matthew",
          "categories": "api-product-management, stripe"
        }
      
    ,
  
    
        "api-product-management-api-analytics-how-to-customize-your-profile-view-experience-in-moesif": {
          "title": "How to Customize Your Profile View Experience in Moesif",
          "content"	 : "At Moesif, we’ve put a lot of work into improving viewing details and trends for specific users and companies. This includes our recent addition of adding profile dashboards to add individual and reusable charts to user and company profilesNow, we’ve added even further functionality by allowing users to customize what each individual user and companies profile view looks like. This makes it easier to show all the data that is relevant to your organization and hide stuff that isn’t.What’s changed?The old profile view screen was static and could not be customized. Although much of the data was useful, it may not be relevant to the needs of every organization using Moesif. Previously, the profile view looked like this:Now, we’ve added a new look and functionality which makes the profile view much more usable and fully-customizable. When you click on a user or company profile, you’ll now see the following profile view at the top of the screen.How to customize the profile viewCustomizing fields within the profile view is extremely easy to do. Once the profile view is edited, it can be saved and applied to the profile view whenever it is opened. Both the user and company profile views can be edited independently. This can help with further customization since you may have specific fields you want to see on a company profile that you may not want to see on a user profile, and vice versa.Create custom dashboards for the  metrics that matter most to your API with Moesif.Adding and editing fieldsTo edit which fields appear on the profile view, simply click on More Actions in the top right of the pane and then select Customize Profiles’ Layout.Once selected, the screen will now become editable.To add a new field to a column, simply click on the + button at the top of each column.From here, select which field you would like to add. The field will then be added to the selected column. You can also reposition new and existing fields by clicking and dragging them to their desired position.  You’ll need to make sure that you click and hold the entry on the left to move the field.To save your changes, click on the Save button in the top-right of the screen.Adding columnsTo add a new column, scroll to the end of the already created columns and click on the + button.Then, select the first field you’d like to add to your new column. For example, I will choose Initial Utm Medium. Once selected, the new column will be created.Organize your API logs and metrics with Moesif.To edit the icon used for the column, you can click on the current icon. You will then see the available library of icons that can be used.Click on the icon you want to use and it will be assigned to the column.To save your changes, click on the Save button in the top-right of the screen.Try it for yourselfEnable your organization to easily view the most important details for each user and company that you have in Moesif. Use the latest customization functionality in profile view to help your sales, customer success, and other teams get quick access to the data that matters most. To see this latest feature in action, log in or create your Moesif account today.                Scale Support with Confidence              14 day free trial. No credit card required.              Learn More        ",
          "url": " /api-product-management/api-analytics/How-To-Customize-Your-Profile-View-Experience-In-Moesif/",
          "author": "Matthew",
          "categories": "api-product-management, api-analytics"
        }
      
    ,
  
    
        "api-product-management-api-analytics-introducing-profile-dashboards": {
          "title": "Introducing Profile Dashboards in Moesif",
          "content"	 : "We are excited to announce that Profile Dashboards are now live within Moesif! We have designed Profile Dashboards to enable customer-facing teams with a convenient way to monitor and analyze your customer’s account health. This new feature provides customer specific information in an easy and consistent fashion. Profile Dashboards allow you to see a personalized dashboard for specific users and companies.Profile Dashboards are the easiest way to see key metrics for specific users and companies. Let’s discover how to view and take advantage of Profile Dashboards in Moesif!What are Profile Dashboards?Moesif’s Profile Dashboards are a newly added feature that provide individualized account metrics across your entire customer base. This is accomplished by providing a customizable template that is applied to each customer’s profile page.Profile Dashboards give Moesif users a great visual breakdown of customer account health. Previously, Moesif users could view a Live Event Log under each user and company profile which showed that user’s activity for easy viewing. Profile Dashboards expands on this by allowing you to create a dashboard template with your favorite charts that automatically filter and display metrics for the specific user or company selected.Why use Profile Dashboards?Profile Dashboards allow you to see everything you want to know about a customer’s usage and interactions with your platform by simply navigating to their profile page. All of your Profile Dashboards are derived from a single template that can be customized to your specific use case. Previously, you would either need to override your existing dashboards to filter on a specific user or company or create a brand new dashboard for each unique user or company manually. Now, you can create the template once and have the Profile Dashboard automatically filtered and populated when viewing a user or company profile.The flexibility of Profile Dashboards can aid many areas of your business including your sales and customer success teams. Instant access to account health can make certain tasks much easier compared to manually filtering for the data you need or manipulating existing dashboard filters.A great example may be when a customer reaches out to your team for support for a 400 status code response they are receiving from an API. It can take a few minutes for your support rep to get their information filtered and pulled up. They would first need to duplicate an existing workspace or might even have to create a new one from scratch, as mentioned previously. They then need to set up the required filters for the issue that the customer is having as well as filtering the results based on the customer’s ID. Profile Dashboards could make this easier by allowing the customer success rep to go to the user’s profile and view a dashboard tile showing 4XX/5XX Errors. The rep can then easily view the error and offer troubleshooting advice quickly.Once set up, Profile Dashboards are a great addition to your team’s arsenal of tools to help with viewing and maintaining good account health. Build beautiful, customer facing dashboards with Moesif.Where can I find Profile Dashboards?Profile Dashboard templates are located on the left navigation pane at the bottom of the Saved Dashboard heading. This is where we can customize our layouts for each respective profile. Clicking on the Profile Dashboards drop down menu reveals two sub categories, User Profile and Company Profile.Let’s head into the User Profile sub-section. The dashboard preview shows what our workspaces currently look like. It also gives us insight into what will be displayed on our individual user Profile Dashboards.Similar to User Profiles, Company Profiles can be accessed and edited in the same way by clicking Company Profile.  Using the left navigation pane we can access the User Profile or Company Profile dropdown. We can see a list of all of our configured workspaces. Clicking any of these will bring us directly into the configuration view where we can customize each to our specific use case.  How to View Profile DashboardsThere are multiple ways to navigate to a specific user or company’s profile. You can do so by:  Going to the Events screen and clicking on a User ID or Company ID in the Live Event Log  Navigating to the Users or Companies screen and clicking on a User/Company ID in the LookupOnce you bring up a user or company profile, scroll to the bottom of the screen to see your Profile Dashboard for the selected user or company. On this screen, you will see the Profile Dashboard template you created (or the default one if you haven’t touched it) with metrics and charts filtered for that specific user/company. No extra work is needed, this is all filtered automatically for you.Try it outAs you can see, Profile Dashboards can be a great tool to assist many areas of your business with personalized charts and metrics for each user and company using your platform. We’ve given you a great set of default dashboard tiles to get you started but you can easily customize your Profile Dashboards to your needs. To get started with Profile Dashboards simply sign in to Moesif or sign up today. If you’re interested in monetizing your API, take a look at our new metered billing feature, too.                Scale with Confidence              14 day free trial. No credit card required.              Learn More        ",
          "url": " /api-product-management/api-analytics/Introducing-Profile-Dashboards/",
          "author": "Dylan",
          "categories": "api-product-management, api-analytics"
        }
      
    ,
  
    
        "technical-api-debugging-debugging-your-apis-with-postman-and-moesif": {
          "title": "Debugging Your APIs with Postman and Moesif",
          "content"	 : "Debugging APIs can be a challenge for any developer dealing with RESTful APIs. Trying to create an exact API request, especially for highly complex requests with large API request bodies and multiple headers, is essential but also tough to do. By using a tool like Postman to create a request for debugging purposes and as an API client, you can easily replay an API request with the exact configuration of the original request. This can enable developers to reproduce the scenario they are trying to debug in a consistent manner.Moesif can also help to make the process of debugging APIs easier as well. Since Moesif receives data around every aspect of a request, it makes it a great platform to export all of the details of a request so it can easily be replayed to assist with debugging. In Moesif, you can easily export all of your API call data into a Postman Collection. This helps developers to replicate the calls in Postman automatically by importing the generated Collection.Debugging best practices aim to ensure that the conditions in which you are debugging an error, match exactly with the conditions that caused the error. By using Moesif’s export functionalities as part of your debugging method, you’re guaranteed that the API endpoint and debugger are receiving the same request data that caused the error.Exporting the calls in MoesifTo export calls from Moesif, first, navigate to the Live Event Log screen.From there, you can select the calls that you want to export into the Postman Collection. Simply select the checkboxes beside the calls you want to export and click Run in Postman to export them.  Exporting the calls into a Postman collection will include everything you need to recreate an API call. This will include the body, headers, params, etc.Once you click the Run in Postman button, a modal will appear to allow you to download the collection. Click Download Postman Collection to download the collection file to your local machine.After this, the Postman Collection will be downloaded and ready to load into Postman.Loading the collection into PostmanIn Postman, click the Collections tab on the right side of screen and then click Import.Next, a modal will appear where you can either pick the Postman Collection file from a file picker or drag-and-drop the file onto the modal to import it. Once the file is selected, you’ll see the contents in the modal.Now, you’ll click Import to actually bring the collection into Postman. You should now see the collection displayed in the Collections pane in Postman.Replaying the requests and debuggingWith the collection now available in Postman, select one of the requests to replay.Every detail of the request will be populated including the params, headers, and body. From here, you can click the blue Send button beside the URL to send an exact copy of the web API request you are trying to debug. Now, your debugger will be loaded with the same data as the error call you are currently debugging since it has been loaded from the exported Postman Collection.Try it out for yourself!Next time you’re debugging RESTful API code, use Moesif to quickly create a Postman Collection to easily replicate the requests causing your issue. Don’t worry about missing a single detail by manually inputting the request into Postman. By exporting the request from Moesif and replaying the request through Postman, you are debugging your API in the exact conditions that caused the error in the first place. Simply select the requests you need, export, and debug. This ensures that you’ll be hitting your debugger breakpoint with the exact data included in the original request. Debugging errors in your REST API should be easy and consistent. With Postman and Moesif, it is!To get started today, log in or sign up for Moesif to add this great tool to your debugging arsenal. While you’re at it, also check out other great features like our alerts, embedded templates, and our latest billing features to help you monetize your APIs.                Debug APIs with Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /technical/api-debugging/Debugging-Your-APIs-With-Postman-And-Moesif/",
          "author": "Matthew",
          "categories": "Technical, API-Debugging"
        }
      
    ,
  
    
        "podcasts-developer-marketing-podcast-apis-for-the-right-business-case": {
          "title": "APIs for the Right Business Case",
          "content"	 : " Ep. 14: Erik Wilde, Catalyst at AxwayMoesif’s Podcast Network: Providing actionable insights for API product managers and other API professionals.Joining us is Erik Wilde, Catalyst at Axway, author and standards contributor. He helps organizations on their Digital Transformation journeys, making sure that vision, strategy, and technology are aligned, and that they are complemented by business and organizational initiatives.Lawrence Ebringer, Moesif’s CMO, is your host today.Moesif · 14. APIs for the Right Business CaseListen to the episode on SoundCloud above, watch it on our YouTube Channel or download it on Apple or Google. Table of Contents 1:55Drive Value From Your APIs3:53Focus on a Super Valuable Business Capability8:23BSchool Students Focus on API Biz Cases11:54The Internet of APIs - APIs Under the Hood15:17The Best API Style Is Determined By The APIs Purpose17:13Why Security Is Important to API Health23:30API Observability vs API Monitoring26:28Measuring API Success via Metrics and Conclusions30:15The Future of the API Ecosystem as a WholeLarry: Welcome to Episode 14 from Moesif’s APIs over IPAs Podcast Network. I’m Lawrence Ebringer, your host today and the Chief Marketing Officer of Moesif, The API Observability Platform.Joining me is Eric Wilde, prolific YouTuber, author, standards contributor, Catalyst at Axway and, most recently, guest lecturer at UC Berkeley’s Haas School of Business.Eric’s been working in web technologies and APIs for most of his career.Although his background is in computer science, he believes that technology is rarely the factor holding back organizations, so now he focuses on helping with strategy to ensure companies succeed.Welcome Eric, where in the world do we find you?Erik: Thanks, thanks for the warm welcome Larry and thanks for having me. Right now, you can find me in Berkeley because I was planning on attending an on-campus course here for four days, like the first four days of this week. Well, like so many things, last minute it turned into an online thing but I’m still here. So yes, I’m in the East Bay and I think you were somewhere down the peninsula right.Larry: I’m actually just across the bay bridge in San Francisco.Berkeley’s quite a change from your usual home. Where do you domicile for most of the year?Erik: I live in Zurich now. I used to live in Berkeley for quite a while, when I used to work at Berkeley but we moved to Zurich a couple years ago. We still like to come back here because it’s just such a lovely place.Drive Value From Your APIsWhat should API professionals be focused on today? People no longer need to be convinced why APIs matter, they’re popping up like mushrooms. Rather, the focus in on what makes a good API, how do I get my team to design them and how do I support them with a platform.Larry: Right, that is very true. That is very true.Jumping right in, you and the Three Musketeers recently released a new edition of the seminal work continuous API management. Besides the covers’ color photo, what’s new and how has the API ecosystem changed in the last three years since the first edition?Erik: That’s a good question. What’s new is to some extent, you could say that by now what happens is that we have to less and less tell people why APIs matter, that they are important, and how to design APIs, so to speak. We have to talk more abouthow to do that at scale.Like a good friend of mine, Isabelle Mauny, on some podcast said, “APIs are popping up like mushrooms left and right.”. And I think that to some extent is true. I think as more and more organizations who didn’t see that, of course, they are using APIs, everybody’s using APIs. You cannot use computers nowadays without using a lot of APIs.But managing those and figuring out how do I get to the APIs that drive value for an organization, not just “we have an API so probably good things will happen”.But also understanding what is a good API? What is maybe not such a great API? How do I get my teams to design good APIs? How do I support them with a platform?All of these things, I think we’ll still see momentum picking up in the industry in terms of understanding that APIs are not the only thing you need, but they can be a limiting factor. So it’s important to really manage that, and not just hope for the best.Evaluate and optimize your APIs with Moesif’s powerful API monitoring.Focus on a Super Valuable Business CapabilityAn API is really just an interface. Delivery of the business value is important, but it’s not the most important thing to consider when developing an API offering.Larry: Hmm, very interesting. What a great segway to one of your pins tweets, which reads, “It’s more important to build APIs for the right things than it is to build the right APIs for things”. I mean, not only is that a tongue twister but it’s a little bit difficult to unpack. Could you unpack it for us and tell us what you mean by it.Erik: Sure sure.I think one of the important things about an API is to understand it is really just an interface. An API is the delivery mechanism for a capability and sometimes I think,us in the API space, we overestimate the importance of the delivery, over what is being delivered. What I mean by that is saying that if you have the nicest API in the world, wonderfully documented, perfectly designed, using all the right http methods, anddoing all these things right, but it provides access to a capability that nobody has a need for. That API doesn’t make any sense, it may be a beautiful thing to look ataesthetically, but certainly will not drive any business value.On the other hand, if you have a super valuable business capability that has, maybe not the greatest API, but one that people can somehow live with, that will be much more valuable.In the end I think it’s more important to at least, at the very first step to think about what is it that I’m designing an API for, before you jump right into, “which http method do we use”, and you know all these kinds of things. I think sometimes we really get to focus on the technical part of the design, we don’t really focus enough on what we are actually designing as a product, and not so much just as its delivery.I think the recent books and I think we have a nice wave of books that are published right, so Michael Amundsen just published one.  There was one by James Higginbotham recently, right. There’s one by the API Handyman and in all of these books, I think are now focusing on the whole process, not just “What do you do technically?”, but “How do I design the right thing?”.I think we are understanding that more and more, but it’s still something that I think we have to focus on and make sure that that part of the design is an important step. We are very careful with that before we jump into the technical details.Larry: Very interesting, yeah. It’s interesting that, when we sign up with new customers, classically,  the CEO is the product man and the product, the business case and all of the issues involved in making the complete product are far more important, we see, then actually implementing the API platform in the right way. So your analysis of the product manager having a very important and strategic role in API platforms is something that we see as well, in our customer base.Erik: And it is, I think, in the end, right? It is important to think about what is the value that I’m producing and some of the examples that are used all the time. Like Twilio, Stripe and all these things, I mean I like these examples as well.But in the end, Stripe is not as successful as they are, because their API is good. But Stripe is successful because everybody needs payment services and they provided an API for that right? That’s the main reason.And then, they also did a good job at designing that product right? But without that initial idea of thinking about “Well, really everybody needs payment services and nobody wants to do it themselves”, so why not have a good API for that right?That was the thing that turned them into the company they are today, and then they just executed well on that which is also important right.Learn more about who uses your APIs and how with Moesif.BSchool Students Focus on API Biz CasesUC Berkeley’s Haas students know lots of business is going to APIs.  Teaching scalable and sustainable business models prepare students for the ever-changing API landscape.Larry: Very very true, very true. Yeah I mean, I think there were a lot of examples out there, where you might have a great implementation, but you don’t necessarily have a great business case to justify your company, and even though we’re in a very frothy point in time, in the API space. I think the market and investors still look for revenue and traction in the marketplace, as opposed to a really glossy, well designed API implementation.Erik: Yeah, that’s the thing right? Again, this week for four days, I basically spent the Business School here, and I mean they understand that APIs are important because there’s a lot of business going to APIs. But, when they look at APIs, they don’t get excited about “Well, do they use HTTP Post in the right way?”. They ask questions like “Well, is there a business for that?”. Like what’s your business model? How are you going to build a sustainable business around delivering that thing as a service? And then maybe you should also design it well on a technical level, but that’s really not the main thing. It’s important but it’s not the main thing and it’s not definitely not the only thing that we should focus on.Larry: Right, right. We recently had on our podcast a lead API product person at a large enterprise software company, and her task was to really come up with thewhole plan of implementing some of their functionality over an API. Some of their customers were asking for it, but that wasn’t enough justification. It was interesting talking to her that the most important aspect of getting this right was that primarily it had to be a center of revenue. It had to have something that moved the needle for the executive C suite team to say “Yes, this is worth investing in.”.Erik: Yeah and if you do it well, you can use the API also to understand a little bit what people are more or less interested in. A little while ago, I talked with Tanya Vlahovic, who I think is now at Salesforce, she used to be at eBay back then. And she told the story, where eBay, it was important to have an API but initially they thought “Well the API will be important for people to maybe look for items and do these kinds of things” which they are.But what turned out, which drove much more value, was that the API now drives the most value by large companies putting their excess inventory on eBay. Right? And they do that through an API, and they need an API because, of course, they have to sell, I don’t know, like 100,000 pairs of shoes. You need an API to make sure that this inventory gets from wherever you are into eBay.Having an initial set of APIs and then understanding that “Hey, this seems to be something that really drives value”, and then putting more effort into that right? That is a good way to understand that we have to figure out what is really driving business, and then we can kind of allocate our resources in that direction.Understand your API adoption and scale with confidence with MoesifThe Internet of APIs - APIs Under the HoodAPIs are complex, but understanding the basic building blocks of creating and supporting an API are important to building a great API product.Larry: Right. Very, very salient point, very interesting. So slight non sequitur and really to jump from the business case to more of the underlying technology and understanding technology in general. I really liked your YouTube 12 minute explainer on how the Internet works, where you explain IP, TCP, SSL and UDP.Share with us why you think such an understanding is important for API professionals.Erik: So what I think is important to understand really, at a certain level, at least, how this whole API stuff really works under the hood.Of course you don’t have to understand, like everything down to glass fiber, and I mean at that point, I mean I also would have no idea how those things work. But I think it’s important to really understand the basic mechanics so that you also understand some of the risks, and some of the things that may happen right.For example, you can get delays, you can get, depending on the technologies right, you can get duplications of things, of course, always APIs can kind of cut out right.I think it is important to go a little bit deeper than just understanding http and json, and to understand what is that built on. Show me at least the foundation of that, and the foundation of that then would be TCP IP.I think that also helps, for example, you become more aware of the fact that things might fail right? A lot of things that we still see, I think, is that too much design in the API space are just built on the happy path, assuming that everything will always go right.Now you see a lot of applications that if things don’t go right, they fail miserably.And you can easily say that “Well, that wouldn’t have been necessary” right? By better understanding how things can fail, you would be able to have a more robust application by just taking that into account.I think this is important for somebody who works with that, and takes APIs as the tooling that they work with to assemble pieces. To understand how things can go wrong, I understand that they can go on wrong, how they can go wrong, and then I can better design for those cases.I think designing for failure becomes more and more important.Just recently, we had this article from Gartner predicting that in 2025, I think they said, “Organizations will depend on three times as many external dependencies to APIs as they have today.” And that will mean more and more that you depend on others, which is good, because you can assemble things so that’s nice. But you also have to manage those dependencies responsibly, and maybe build things so that, if that thing fails, I still do at least part of my job right?And I think that understanding sometimes it’s still something where we need a little bit more foundation to really understand what can go wrong, so then we just design a little bit more resilient and robust systems.Get deep visibility into your API, from adoption funnel analytics to latency by endpoint with Moesif.The Best API Style Is Determined By The APIs PurposeThere are many types of API styles to pick from, but the “right” style can be determined by the API project. Why are you building an API is just as important to the process as how.Larry: On that subject, do you have a laundry list of popular API styles that you would choose or you would highlight above others?Erik: I have these five styles that I talk about when I give an overview of styles, where you have the resource oriented style, the hypermedia style, RPC, like the good old function oriented function calls, and then you have query-oriented styles, such as GraphQL, and even event-based APIs.In the end, I think API is just a tool, the tooling that allows us to build new things out of existing building blocks that are connected to a network. Better understanding what kind of tool is a good thing, for which design, for which purpose that I think is a good thing to do. This whole fundamentalism around like rest is always the right thing or GraphQL is always the right thing or event based is always right thing. I think we’re slowly getting away from that a little bit and that’s good.Mostly what I try to tell people is be less fundamentalist and better understand that there are different styles. They are not inherently good or bad, they just have different constraints around them. To better understand how they work, and maybe also better understanding, in which case, which style, maybe a better choice. I think that turns you into a better designer because in the end, designers should not just always say “I’m always doing things this way”. As a designer you should be able to understand that you have to design for a problem, and for people who are faced with that problem. You have to give them a solution that works well for that context.Collaborate with teammates to monitor KPIs with Moesif’s custom dashboards.Why Security Is Important to API HealthGood API security starts with understanding your API landscape. Having a comprehensive understanding of what APIs are actively in use is critical to building a security framework.Larry: Very interesting, yes. There are a multiplicity of styles and implementation procedures that seems more and more difficult as an API platform provider that we have to support, but I suppose that is the nature of the beast.Talking of big changes in API platforms and third party solutions that one has to be aware of. Tell us your perspective on API security, why is it important, how can we facilitate the gold standard for security and also what is your perspective on the recently standardized latest version of fhir for APIs.Erik: Okay let’s get started with security.Security of course is super important. APIs always provide some way to interact with your system landscape, it may be read only, but even then it’s probably some kind of risk that you’re exposing. There’s always the ability that this could be misused in some shape or form.The very starting point for a good API security is to just understand which APIs you even have, and it is amazing to me, like I work with a lot of really big organizations.Whenever we talk to the people in those organizations, they have no idea which APIs they have. There are people in the organization, who build APIs and publish APIs because they just need to get some job done. They may not be aware of the fact that there is a platform where they could secure it, or they just say “It’s good enough, it just talks to my application so how bad could it be”. There’s all these kinds of missing practices sometimes around understanding that every API is a potential security risk.You have to think about what’s the worst thing that can happen and that worst thing often actually could be prepared. It’s not like security is such a super complicated thing, it’s just a set of practices that people need to understand that they have to apply those practices. You can provide them with pretty good tooling to make sure that they don’t have to do that much themselves.But it’s still something within an organization, where I think we’re still oftentimes seeing that it’s not done in a disciplined enough way. Sometimes it’s pretty amazing I’ve seen organizations where at some point, for example, they just closed certain ports in their firewalls. Basically said for a little while let’s not pass through traffic that goes through certain ports, and then they just basically wait for reports coming in, about “Hey, this application stopped working”. Then people understand it’s like “Oh, there seems to be an API there”. Nobody ever told anybody, people just created it and it runs there. It may not be security at all right, and nobody even knew that this was there.It’s very important that you find this application before some hacker. This is like this whole space of observability that we see that is exploding right now. I think that very much, it is also fueled by this, sometimes you have to observe things, just to plainly understand what is even happening in my network.The bigger the organization gets sometimes, the less organizations are actually knowing what is happening. I think this is kind of a rather basic aspect of security that is almost more important than the details of “How do you do it” on a technical level. I think those pieces we have in play pretty well, once you know that you have an API, how to secure it, how to provide tooling for doing that. Just knowing that you have APIs and which ones they are, who should get access to it, and all these kinds of things. I think that sometimes almost the tricky part because there’s so much unknown stuff going on your network.Larry: Right, very true, very true. We had Alissa Knight recently on our podcast and she was talking about man in the middle intercepts and how to avoid that with APIs. Her big takeaway, which really underlines your issue of just knowing which APIs you have out there. Her big takeaway is you can solve 90% of this problem by making sure that when you authenticate a user, that doesn’t necessarily mean they have access to everything. When she hacked people’s APIs, she found that I could be authenticated but then, by changing some of my tokens, just one or two characters, I could now gain access to a whole other set of users API payload material. She said it was amazing, I mean, she’s published this. She did it for medical applications and also banking applications. She said it’s amazing the number of people who because they think “Oh well, this person’s authenticated, now they can have access to anything.”.Erik: Yeah, I think that is number one on the owasp security list. It’s object level access, it’s like people just assume that you have access to something so here’s everything. Just going through the owasp top 10, that already is, in light of organizations, you will find it, “Yeah, you know, we actually have not all of our applications are doing so well.”.Get deep API analytics with confidence with Moesif’s SOC2 compliant, client side encryption.API Observability vs API MonitoringObservability and monitoring may seem synonymous, but they are two sides of the same coin. Observability is about putting mechanisms in place to enable active iteration. Monitoring can be viewed as a technical layer of observation.Larry: Right, very true, very true. Going back to something you touched upon, which was that there is a big movement now for API observability.Why don’t you give me your definition of what is the difference between API observability and API monitoring. Why is observability, more importantly, stays and just pure monitoring.Erik: I will not give you definitions, because I would need to think about that for a little bit longer. But I think, at least in the way that I use it, and I think most people would use it, would be to say that monitoring typically is something that you put in place if you have an API. It’s a rather technical and specific component that you put there and say “Let’s count the number of accesses” or whatever it is. It’s a very specific kind of layer of observation that puts it like this, right, but it’s more specific around, I guess, we need to look at something that we know.I think observability is much more rooted and with the tools that we see that are coming up, it’s much more rooted in this idea of we know there’s stuff going on. But we don’t exactly know what it is, how it behaves, maybe even what we have. So we observe the environment, we observe what’s happening and we actually learn from that. It’s notso much just learning by counting things, such as monitoring. We learned that we had certain APIs that we didn’t really know, we learned that there are certain ways how these APIs are being used that we didn’t think were getting used.I would say it’s much deeper inside, there’s also much fancier technology involved that really tries to not just count bytes but to really get a meaningful understanding of what’s going on. To help you better understand what’s going on, and then you can do all these things, where you can also reverse engineer like Phil Sturgeon, he recently published a piece, where it’s about inferring open API information from just monitoring traffic basics or observing traffic. These kinds of things, which I would say, are much, much more insightful ways of looking at traffic than just the rather simple monitoring that we have in place.Larry: Right, very true. I mean slightly self serving but I work for an API observability company. We’re always pushing the fact yeah, you can countserver uptime, latency and issues on GEO location. But really, you get a whole bunch more business value by actually delving into the nuance of what’s in your payload, and how your customers are actually using it. That seems to have an awful lot more value.Get deep, granular analytics and metrics on your APIs and users with Moesif.Measuring API Success via Metrics and ConclusionsGauging API efficacy is not always as simple as selecting one metric to measure against. Because an API is just a tool, measuring success should start at the business goals for the API as well as any insights found during API usage.Very interesting segway into measuring the value of APIs. How would you gauge whether your API is successful and how if it’s not successful, what metrics do you think are important, or what conclusions can you draw from what you’re monitoring, and observing then turn it into a performance API.Erik: Let me go out on a limb here and say, an API cannot be successful. The thing that can be successful is a capability, a product, or something that you are actually delivering through an API. I have this video, I have to point it out, because it’s one of my favorite ones, where I talk about beer. The analogy I’m giving is that the APIs, the delivery mechanism, so it’s like a bottle or a can, it’s the way you get the beer. What really matters in the end is the beer, but the beer is not the delivery mechanism, there’s a difference, and I think that is something that is still important to really understand thatthe API… Don’t focus too much on the API, just the API is just the mechanics to get things going.In the end, you have to think about what is the business that I’m trying to do with this. What is the actual capability that I’m working with, and that would be more like somebody who does product management right? Who’s really responsible for designing that product, delivering that product, making sure it’s getting used, monitoring how it’s getting used, figuring out that it generates more revenue than it costs us to run it.To that extent, I think pure API metrics are just a relatively small part of that. Where you can see it’s okay here’s maybe an additional way of how we provide access to this capability. In the end, what really matters is “Do we drive business?”, so I think this whole idea of an API having value is maybe interesting, but mostly it’s really about do I push the right things in the right direction.Larry: Very interesting, yes. Yeah, it’s important again going back to Jeannie Hawrysz from SaS, her perspective on creating APIs in existing enterprise software company, your API has to be a central revenue conscious, can’t just be a nice to have or because a product manager wanted to put it out there. The business case and the business metrics are the most important thing you have to get right.Erik: Sorry but because it’s so dear to my heart, I mean there are these cases where I’ve seen companies, where one of the things they did was basically really counting API accesses, and this was not so interesting and almost created bad incentives, because instead of creating better APIs that would be more granular so then you can get more stuff done with one APIs. They were incentivized to basically have “No, we have these 25 API, if you want to get anything done, you have to call all 25 of them”. Then say well it’s good right, you have 25 API calls here so that’s probably not good, but I understand why this is going on and somebody should stop this from happening.Connect your API analytics to your greater business goals and accelerate adoption with Moesif.The Future of the API Ecosystem as a WholeThe API landscape will continue to evolve with better bridges between the technical and non-technical aspects, enabling business success.Larry:  Right, absolutely. Look at the right metrics, don’t just count metrics for the sake of metrics sake, absolutely. So my final question, crystal ball gazing, what does the future hold for the whole APIs ecosystem look like?Erik: So I think what will happen in an API space is that we will see there will be better bridges between the technical aspects of APIs, and the not so technical aspects. I think, right now, we still don’t have enough of an overlap and a smooth transition between the more business minded side of things, really understanding what are the capabilities that we should have, what are the capabilities we should make available.Also thinking about now, do we have APIs for that, like should we invest in building those, and then saying okay someone should design those things, but we also need someone to think about which things should be designed. Going back to building the right things and building them right. I think almost at some point, we have overdone it a little bit with the developers, developers, developers, because of course developers are super important. But they also have to develop the right stuff, and I think building better bridges to make sure that what is getting built is understood by everybody. That I think is important and in part, I found this super interesting this week, that when you talk to companies here in the Bay area, of course they are very technically minded, and there you don’t have to explain that much what APIs are and so forth. But on the other hand,If I go back home to Switzerland and talk to organizations in Germany and Switzerland, who are not so natively digital. The divide between the business side, and the digital side, for the IT side, it’s still much bigger.I think crossing that divide and making sure that everybody understands what’s going on, everybody understands what needs to happen, I think that is something where we still have to develop better tooling, do more education. So that in the end, everybody really understands, what is it that we all should be doing, and how can I contribute. It’s not just developers who are contributing, it’s everybody, and I think that is something where we will hopefully see big change.Larry: Great, great. Yeah, I totally agree that there has to be a team effort. There’s more than just focusing on a great developer experience to get your product out there, and get it successful in the marketplace.So where can our listeners hear more from you,and obviously, please give us an unadulterated plug of your latest excellent publication.Erik: Sure. Sadly, I cannot hold up our book because I don’t have it with me, but of course everybody should check out our latest book “Continuous API Management” second edition, published just a month ago, two months ago now.I hope everybody finds it interesting, it is specifically about APIs and a conference case because we thought when we started the whole initiative around it, this is really where the world is going, and I think continues to go that way so that’s good for us.In terms of where you can find me, of course, you can always find me on Google.But the main outlet that I have these days is my YouTube channel, so just put my name into YouTube, and you’ll find my channel. It’s called “Getting APIs to work”.I have a lot of interesting guests there, who talk about their API practices, their experiences in API space so check that out, and other than that you will find in my usual places such as Twitter and LinkedIn.Larry: Great. Well, it was a pleasure speaking to you today Eric. Thank you very much for your insights, I’m sure our listeners will find them educational and very useful.Erik: Thank you so much for having me Larry, and I hope this is not the last time that we’re doing this. I always like talking about API things and where the space is going.Larry: Certainly. Love to do it again so until next time, thank you very much Eric.                Make Your API Platform Successful With Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /podcasts/developer-marketing/Podcast-APIs-for-the-Right-Business-Case/",
          "author": "Larry",
          "categories": "Podcasts, Developer-Marketing"
        }
      
    ,
  
    
        "api-product-management-api-analytics-keep-a-pulse-on-your-account-health-with-profile-view": {
          "title": "Keep A Pulse on Your Account Health with Profile View",
          "content"	 : "The Moesif product team has been staying very busy as of late! We have been listening to our partners and gathering feedback, and what we have heard is an outpouring of requests for new tooling that provides a 360-degree-vantage-point into your customers’ account health. As a result, we are very excited to announce that we have released our newest feature: Profile Dash View.Our Profile View is designed to empower customer-facing teams with a central source for monitoring and analyzing your customers’ account health. We have created a mini-dashboard that is part of Moesif’s users’ and companies’ section that has a number of our most relevant charts. This tool will allow support teams to quickly get up to speed on where your customers are finding success and what issues they might be running into. The goal is to drive more delightful platform experiences.One of the key reasons we built the Profile View tool is that our partners told us the importance of accessing account metrics in an easy and uniform fashion. They did not want to manage a set of disparate charts spread across the platform. Rather, our partners noted the necessity of having key account metrics under one roof in order to visualize exactly what is happening with every customer. With Moesif’s Profile View you can operate off a single template and your metrics can be applied across your customer base without having the need to make manual adjustments.For instance, imagine you get a call from a customer and they are struggling with 400/500 errors. The support rep has to work with agility to solve the issue quickly and doesn’t have time to build out new reports. To accomplish this, the rep can now have the necessary product metrics already assembled in Profile View, so they can diagnose the issue and get the developer back on track.Moreover, support teams are required to operate from a strategic level by establishing trends showing whether an account is ripe for expansion or whether accounts are at risk of churn. Having access to multiple signals, such as recent log-ins, daily usage, and feature utilization presented in a streamlined fashion in Profile View is critical to future revenue goals and the success of the support team.While support teams have to manage a wide array of priorities, the success metrics themselves can vary dramatically, depending on the business outcomes that you are looking to optimize. This is why our Profile View has the ability to customize the metrics that you’d like to report on. That said, Moesif does provide a number of charts outbox for your team to leverage, which offer a great teeing-off-point for your support team. To name a few:  New Account Sign-Ons  Daily API Growth Rate  Most Active Users  API Error Log  MRR GrowthLearn more about how Moesif can enable collaboration around your analytics.Where to Find Profile View on The Moesif Platform:Log into your Moesif account. Within the left sidebar navigation, under “Saved Dashboards”, you should now see a Dashboard titled “Profile Dashboards”. Click on this row (or the ‘expand’ arrow on its left) to reveal the options for “User Profile” and “Company Profile”. Then click on either of these Dashboards to view. These templates determine which charts are shown in the respective profiles of either customer type—”users” for individual users, and “companies” for entire organizations.In either User View or Company View, you will find Moesif’s out-of-the-box dashboards, but as mentioned earlier, you have the ability to customize the charts based on the needs of your organization. Our Profile View operates in the same framework as our Workspaces, where you can add and delete charts with just a few clicks.Once you’ve finished customizing your Profile View, you can test the new profile out in either the Moesif’s Customer or User page. For example, if you want to check on the account health of a specific customer, you can search for the customer, then click on their orange company ID and from there you can scroll down and see the company dash view with your pre-established charts.ConclusionWith Moesif’s Profile View, support teams can now work at lighting speed to better understand their customers’ account health, as you can see everything you need to know about any customer with just one click. Whether you need to work operationally or from a strategic business angle, Moesif Profile View has your support team covered.                Scale with Confidence              14 day free trial. No credit card required.              Learn More        ",
          "url": " /api-product-management/api-analytics/Keep-A-Pulse-on-Your-Account-Health-with-Profile-View/",
          "author": "Oliver",
          "categories": "api-product-management, api-analytics"
        }
      
    ,
  
    
        "technical-debugging-debugging-a-node-js-express-api-in-vs-code-debugger": {
          "title": "Debugging a Node js Express API in VS Code Debugger",
          "content"	 : "WhyWhen we create software, we rarely do it without errors. API creation isn’t exempt from this fact, so sooner or later we’ll need to debug it. In JavaScript, the first stop for a debugging task is often logging to the console, but using a debugger can give us a more integrated experience.Node js is a cross platform and open source JavaScript runtime environment that allows the JavaScript to be run on the server-side.There are many guides out there for finding the best node js bootcamp, but in this tutorial we’ll learn how to debug an Express-based API with the help of Visual Studio Code,  or VS Code for short.WhatExpress is a “minimalist web framework for Nodejs”. It allows us to link functions directly to API endpoints, which is a quick and simple way to build an API.Visual Studio Code is a “streamlined code editor with support for development operations like debugging, task running and version control.”We will also use cURL to send requests to our API.HowWe will create a simple API with the Express framework and then try to debug it with the help of VS Code’s debugging features instead of the console. You can then go on to easily add API endpoints and make an API call.API SetupFirst, we create a new Node js project and install our dependencies.$ mkdir api$ cd api$ npm init$ npm i express body-parserNext, we create an index.js file that will act as our main server script.const express = require("express");const bodyParser = require("body-parser");const users = [{ id: 0, name: "admin" }];const server = express();server.use(bodyParser.json());server.get("/users", (_, response) => response.send({ users }));server.get("/users/:id", ({ params: { id } }, response) => {  const user = users[id];  response.send({ user });});server.post("/users", ({ body }, response) => {  const { user } = body;  user.id = users.length;  users.push(user);  response.send({ user });});server.listen(9999, () =>  console.log("API running on http://localhost:9999"));We use the users array as our in-memory data store. It gets initialized with an admin user.Next, we create our Express server and use the JSON middleware of the bodyParser package; it allows us to access the values of a JSON string stored in the body of a POST HTTP request.Then, we create three API-endpoints. Two GET endpoints so we can request a list of all users and one specific user by its ID and one POST endpoint to create a new user.Let’s start the API with the following command!$ node .API running on http://localhost:9999Using the APINow that our API is up and running we can try to query it with cURL. For this, we need to open a new terminal window and execute the following commands.Create a user:$ curl -H "Content-Type:application/json" -d '{"user":{"name": "kay"}}' localhost:9999/users{"user":{"id":1,"name":"kay"}}List all users:$ curl localhost:9999/users{"users":[{"id":0,"name":"admin"},{"id":1,"name":"kay"}]}List one user:$ curl localhost:9999/users/1{"user":{"id":1,"name":"kay"}}Create another user:$ curl -H "Content-Type:application/json" -d '{"users":{"name": "xing"}}' localhost:9999/users<!DOCTYPE html><html lang="en"><head><meta charset="utf-8"><title>Error</title>...Oh no! We have a typo in the JSON, users instead of user. Since we didn’t handle this in our POST /users endpoint, Express just responded with an HTML formatted error.This is a simple example of a problem that could be fixed without much hassle, but let’s use it to start VS Code’s debugger so we can investigate what went wrong directly at runtime.Using VS Code’s DebuggerDebugging Node js APIs with VS Code is very easy.We check which endpoint we want to debug and set a breakpoint inside the function that endpoint triggers. This is done with a left-click left to the line number. Let’s to it on line 15, which should be the first line of our POST /users endpoint function.Then we start the debugger by clicking on Debug->Start Debugging at the top menu or by pressing F5.VS Code will start our application and the debugger for us. It will also link the two together via Node.js’ debugging protocol.Then we re-send the request that led to an error with cURL and try to find out what happens.$ curl -H "Content-Type:application/json" -d '{"users":{"name": "xing"}}' localhost:9999/usersThis request will run the function linked to POST /users and halt at the breakpoint in its first line.If we look at the sidebar on the left of our code, we can see a VARIABLES category with various sub-categories like Block and Local. Let’s open Local and see what’s inside.As we can see, we have two local variables, body which is of type Object and response which is of type ServerResponse.Let’s step to the next line with F10 to see what happens.All seems to work as expected.Let’s step to the next line again.BOOM!Somehow we ended up in a whole different place of the codebase?It seems like we created an error by setting the id of our user object, how did this happen?Let’s open our index.js again, move the break-point to line 16 and let the debugger run to the end of the event loop by pressing F5.Then re-send the request with cURL to see what happened before we tried to set user.id.When we look into the side-bar in the VARIABLES/Block category, we can see that our user object is in fact undefined! If we open the VARIABLES/Local category, we can also see why.Our body has a users attribute, but we try to destructure a user variable from it in line 15, which leads to an error when we try to write to user.id in line 16.Now that we now our problem, let’s stop the debugger and fix it.server.post("/users", ({ body }, response) => {  const { user } = body;  if (!(user instanceof Object))    return response.send({ error: '"user" object missing in JSON!' });  user.id = users.length;  users.push(user);  response.send({ user });});Let’s restart our server, so it runs the new code:$ node .API running on http://localhost:9999And resend our problematic request:$ curl -H "Content-Type:application/json" -d '{"users":{"name": "xing"}}' localhost:9999/users{"error":""user" object missing in JSON!"}Finally, we get a useful JSON formatted error message.ConclusionDebugging Node js based APIs with the help of VS Code’s integrated debugger is a straight-forward task. We just need to set a break-point, no additional code involved.It gives us many runtime insights out-of-the-box, including:  Values of current variables  Ability to watch single variables  Current call-stack  Currently loaded scripts                Get User-Centric API Logging with Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /technical/debugging/Debugging-a-Node-JS-Express-API-in-VS-Code-Debugger/",
          "author": "Kay",
          "categories": "Technical, Debugging"
        }
      
    ,
  
    
        "developer-platforms-stripe-how-to-integrate-moesif-and-stripe-to-easily-monetize-your-apis": {
          "title": "How to Integrate Moesif and Stripe to Easily Monetize Your APIs",
          "content"	 : "Once you decide to monetize your app or APIs, the journey begins to find a simple and robust solution for billing. At Moesif, we know that a billing solution is actually really tough to implement. Getting your product from “0-to-monetization” is not always a straightforward path, even if it should be. Our no-code approach to billing is a simple and elegant way to very rapidly gain the capability to bill customers for usage. Easy monetization is the premise for our latest feature for generating revenue from your apps and APIs. Our newest feature can be found under the Billing Meters screen in Moesif.Why use a billing meter?Metering usage of your product, and charging for that usage, is one of the most common ways to monetize technology products. To do this, you’ll need to monitor usage statistics, send the metrics to a billing provider, and then have the billing provider collect the funds.For those who have implemented a usage-based billing solution for their products, you know that the process can be quite complex. It involves gathering a lot of data, getting that data to the right place, collecting the funds owed, and, if the invoice isn’t paid, putting governance in place so that the user can no longer access the service. There’s a lot of coding, integration, testing, and support that goes into creating such billing systems.Moesif makes this easy for you by collecting a vast array of metrics that can be billed upon, and then automatically rounding them up by user and/or company. With Moesif, all of the data you need to accurately bill is already there, which is incidentally why we believed creating a billing meter feature made a lot of sense. We have also done the work for you to create an integration between Moesif and Stripe. This means that with a couple of clicks, you’ll be able to bill customers for usage. You’ll have billing capabilities in a matter of minutes instead of days, or even weeks, depending on the complexity.How to create and use a Billing Meter in MoesifOnce you’ve integrated your APIs with Moesif, monetizing them is very simple. There are a few steps after you’ve integrated with Moesif to get you to the point where you can collect revenue. These steps include:* Setting up products and prices in Stripe* Adding the Moesif webhook to Stripe* Plugging the Stripe API details into Moesif* Configuring the billing parameters in Moesif* Activating the billing meterAll of these steps are very intuitive and take only a matter of minutes.Setting up products and prices in StripeFirst make sure to meet these prerequisites.Then follow these steps to create a product, define its pricestructure, and create a billing meter for the price in Stripe.  Go to your Stripe Dashboard.  Go to Product Catalog and then select Create product.    Select Recurring and then select More pricing options.   Select Recurring and choose Usage-based as the pricing model. You canchoose the usage amount as package, unit, or tier.  Define your price amounts, for example, $0.01 for each 1k API requests.      In the Meter section, select the plus icon + to create a billing meter to associate the price with.    a. Enter the meter name.    b. Enter the event name for which the billing meter reports usage.    c. Select the aggregation method for meter events. Moesif supports the Sum and Last aggregation methods.    d. Expand Advanced Options and make sure that Event Time Window is set to Raw.    In the Advanced section, enter a description for the price.  Select Next and then select Add product to finish.At this point, we now have a product that we can use with Moesif and begin billing for usage.Configuring Stripe in MoesifOnce your products and prices are created, it’s time to begin to integrate Stripe with Moesif. To begin configuring Stripe in Moesif, go to the Billing Meters page and click the Edit Billing Provider dropdown in the top right corner of the screen.This will bring up the Stripe configuration screen walking you through the integration. From this screen, you can get all of the info needed to plug Stripe into Moesif. Each step for configuration is covered within the modal.Adding the Moesif webhook to StripeThe first step in the integration is to add the Moesif webhook into the configuration in Stripe. Adding this allows Stripe to send subscription updates to Moesif.To add the Moesif webhook to Stripe, from the upper right-hand side click on Developers, and then Webhooks in the left-side menu. This will bring you to the Webhooks page where you can view existing webhooks and add new ones. To add a new webhook we will click the Add an endpoint button at the bottom of the screen.From here, we will plug in our Moesif API endpoint URL and configure the events to listen to. You’ll want to copy your Moesif Webhook URL into the Endpoint URL field and then click the + Select Events button.  These details can all be found on the Stripe configuration page in Moesif mentioned in the previous section.You should select the option under Customer for Select all Customer events. After this, click the Add events button at the bottom of the screen.After this, you’ll be returned back to the original screen where you added the endpoint details. Scroll to the bottom of the screen and click Add endpoint to save the endpoint to Stripe.Plugging the Stripe API details into MoesifFor Moesif to add usage quantities to subscriptions in Stripe, we need to add the Stripe API details into Moesif. This is done in the Stripe configuration screen in Moesif, the same screen we’ve been working with previously.Currently, Moesif only supports version 2020-08-27 of the Stripe API so that field defaults for the Stripe API Version field.For the Stripe API Key field, you’ll need to retrieve the API key from Stripe to plug it in. From the Developers screen, the same one we used in the previous step, you’ll click on API Keys. You’ll then be able to see the private key for your API in either the Secret key or a generated Restricted keys field on the screen. Either key can be used.After copying the key from Stripe, you’ll paste this key into the Stripe API Key field back in Moesif. After doing this, back in Moesif you can scroll down to the bottom of the screen and click Save to save the configuration.At this point, your Stripe integration is complete in Moesif and you can begin to use it.  Optionally, you have the ability to customize the Customer ID Source in Moesif as well. The default should work fine for most purposes but if you do need to customize it, it will allow you to specify how to map the Stripe subscription and customer objects to the company ID and user ID in Moesif.Configuring the billing parameters in MoesifOnce the integration with Stripe is added, you can configure your billing parameters in Moesif. If you haven’t done so already, you’ll want to create a new Billing Meter. To do this, from Moesif you’ll need to click on the Billing Meter link in the left side menu as you did when you started the Stripe integration.Once you’re on the Billing Meters screen, you’ll click the + Add Billing Meter button to begin creating a new billing meter.Once on the Add Billing Meter screen, you’ll add in:  Billing meter name  Billing provider info  Add the filter to specify what events to bill uponIn the below example I have set up a billing plan called My Billing Plan which uses Stripe as my billing provider. I’ve also decided to bill on any API call which has a response of 200 OK.Once you have your details dialed in, you’ll see a visual representation of the filter output at the bottom of the screen. Although billing will only happen going forward, you will be able to see historically how your filter is working with existing data. This can help to make sure, especially with more complex filtering, that you have everything configured the way you require it.Activating the billing meterOur final step in monetization is to save and activate the billing meter. To do this we simply need to make sure that billing meter is turned on at the top of the configuration page.And lastly, we need to click Create at the top of the screen.You’ll then be prompted to confirm the billing meters creation in the modal that pops up after clicking on the Create button.  It’s important to note that once a billing meter is created, the criteria for filtering can not be changed nor the billing provider details. Only the name can be changed as well as the status of the billing meter can be toggled on and off. This is for compliance and auditing purposes. Billing meters can also not be deleted but can be archived if no longer in use.We should now see our new billing meter appear back on the Billing Meters home page.Now, we have successfully created a billing meter that will begin to send usage data to Stripe. This integration is possible in a matter of minutes with no code required. For further info, you can also check out our docs.Try it for yourself!If you have an API, or any other part of your product you’d like to monetize, Moesif can help. As noted in the steps above, it is an easy and rapid way to bring in revenue from your products. If you are already monetizing your product and looking for a simpler solution, our billing features are a great way to simplify your setup and reduce support costs around your existing monetization efforts.Moesif also offers many other great features which bundle well with our billing functionality, including funnel metrics and retention analysis, automated user behavior emails, custom metric dashboards, and governance capabilities. Sign up today to get started with billing and much more.                Quickly and Easily Monetize APIs with Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /developer-platforms/stripe/How-To-Integrate-Moesif-And-Stripe-To-Easily-Monetize-Your-APIs/",
          "author": "Matthew",
          "categories": "Developer-Platforms, Stripe"
        }
      
    ,
  
    
        "developer-platforms-stripe-how-to-set-up-usage-based-billing-with-stripe-and-moesif-for-your-api": {
          "title": "How to set up usage-based billing with Stripe and Moesif for your API",
          "content"	 : "A good business model is one that can easily generate revenue. Often, when developers build something it could easily be packaged and used by another organization. This is extremely true when it comes to APIs. If an API product is solving a well-known problem, there is likely a market for it. Being able to expose an API for public consumption can be done in many ways, a popular option being using an API gateway. The real hurdle comes when you decide to start billing for usage. Monetization is one of the biggest challenges faced by an API provider who wants to implement usage-based billing to capitalize on API consumer revenue.API monetization generally takes a lot of integrations, a fair amount of code, customization, and can also lead to a large support burden. This is especially true when things don’t go according to plan, possibly through a bug in the billing setup, and billing issues arise. In short, there are challenges both during implementation, and once the billing system is up and running.What if a simpler approach was possible? At Moesif, we recently introduced a feature for billing meters. This feature allows you to tally up the usage data coming into Moesif from your API products, send this data to a billing provider, and have the users be presented with accurate bills, all with a fraction of the work required to implement a custom solution.To illustrate how it works, let’s assume we have an API that we would like to charge customers to use. As an example, let’s pretend that we have created a new credit score API that companies can use to bring consumers’ credit ratings back to their app. Our API will be /RetrieveCreditScore, and users will be charged for each query/call they send to the endpoint.Our API monetization model will be pretty simple. We will have 3 usage subscription plans that will determine how much the users will be charged:  1-100 queries per month ($5 per query)  101-1000 queries per month ($3 per query)  1001+ queries per month ($2 per query)This tiered pricing scheme is quite common, where the more the service is used, the larger the discount is.Stripe will be the billing provider that we will use to invoice and charge customers via online payment for their usage. Stripe payments are simple to use and will allow us to easily set up products and pricing to adhere to the pricing scheme above. In Stripe, this type of pricing scheme is called Graduated Pricing.  Of course, there are multiple ways to implement pricing schemes in Stripe that will work with Moesif. This is just one example of how it could be done.We will now use Moesif to tally up the usage for the endpoint and send the usage metrics to Stripe. Stripe can then use these metrics to bill the customer accordingly, based on the tier that their metrics coincide with, and collect payment.Integrate your app and APIs with MoesifIn order to use the Billing Meters feature in Moesif, you need to have your APIs integrated with Moesif. This is because Moesif will need the metrics from the API usage to send Stripe the info it requires for accurate direct monetization. Once your APIs are integrated with Moesif, you can also use other features which pair well with our Billing Meters feature, including behavioral emails, governance rules, and alerts.If you are not currently using Moesif to monitor your APIs, integration can be done in a few different ways. If you are using an API gateway or API management platform, you can use one of our many plugins which allow you to quickly move metrics data from your APIs into Moesif. If you are not using a third-party gateway or management platform, or want to do it at the API code level, you can use one of our SDKs. A Moesif SDK will allow you to easily integrate Moesif with your Node, Python, or Java APIs (plus many, many more languages and frameworks) directly from your code. Either way is easy and simple to support.Another Moesif feature that you need to deploy in order to make billing work correctly, is to implement user and company tracking. Generally, this can be set up in a few simple steps. We need this feature enabled so that usage data in Moesif can be tied to specific users and companies. This is how Stripe will map usage to a customer within Stripe, so they can be billed accordingly.Once you have integrated with Moesif, and have user and customer tracking enabled, your next step would be to actually create your products in Stripe,  so they can be used in Moesif for accurate usage based billing.Create plans and add-ons in StripeAfter creating a Stripe account and logging in, you can begin to create products to use for billing. In this example, we will create a product with a graduated pricing scheme.You’ll need to create a product in Stripe that:  Includes a usage-based graduated pricing model  Has a monthly billing period  If using the legacy usage-records in Stripe:          Charges for metered usage by the Sum of usage values during period method        If using Stripe billing meters:          The meter only associates with one price      The meter uses either a Sum or Last aggregation formula to aggregate meter events.        Optionally (but recommended), includes a price descriptionWith all that configured, we will create our pricing scheme. This configuration will look like this in Stripe:Now, at the end of the month, when Stripe creates the invoice, it will do so based on these tiers. Of course, at this point, we only have the plans defined, but no one will be charged since we don’t yet have any usage data being sent to Stripe.Integrate Stripe with MoesifWe still need to actually get the data from Moesif over to Stripe, and vice versa. There are two mechanisms that are used for this: a webhook and the Stripe API. Each has a distinct part in facilitating the data sharing between the platforms.By adding the webhook into Stripe, subscription updates can be sent back to Moesif. By using the Stripe API, Moesif can send usage details to Stripe and can also retrieve details about available products and pricing in Stripe. These two points of contact are all that’s needed for the Stripe and Moesif integration. Thankfully, Moesif walks you through this when you set up Stripe as a billing provider and provides all the needed details. For the specifics, you can also check out our docs on the Stripe integration.Set your billing parametersOnce you’ve integrated Stripe in Moesif, you can set up your billing meter. For our example, it will be very simple. What we will do is send usage metrics to Stripe whenever an API call to /RetrieveCreditScore returns a 200 OK response. This means we will only bill for successful calls and won’t accidentally charge for calls where an error was experienced.Once we create the billing meter, every hour the usage for each customer will be sent to Stripe. At the end of the month, Stripe will create an invoice based on our graduated price structure and bill the user.  You can also use Moesif to automatically send behavioral emails for every successful call, or if they are about to cross into the next discount tier. You could also use Moesif’s Governance Rules to block users with overdue invoices from accessing the API until their invoice is settled.Try it out for yourselfAs you can see, you are just a few steps away from robust API monetization that is simple to implement and support. By using Moesif and Stripe together you’ll be able to charge customers for usage in a matter of minutes, manage subscriptions, and can even use other Moesif features to create the ultimate customer experience. Our billing setup is so simple that it can even be done without having any developer skills.The Billing Meters feature is available for all Moesif users. Sign up for Moesif today to instantly access our Billing Meters feature to begin billing for your customers’ API usage. If you’re already using Moesif, click on Billing Meters in the left-side navigation menu and take a look at our docs to show the exact steps to get you to monetize your APIs.                Easily Monetize Your APIs              14 day free trial. No credit card required.              Learn More        ",
          "url": " /developer-platforms/stripe/How-to-Set-Up-Usage-Based-Billing-with-Stripe-and-Moesif-for-your-API/",
          "author": "Matthew",
          "categories": "Developer-Platforms, Stripe"
        }
      
    ,
  
    
        "technical-elasticsearch-how-to-debug-an-unresponsive-elasticsearch-cluster": {
          "title": "How to Debug an Unresponsive Elasticsearch Cluster",
          "content"	 : "Elasticsearch is an open-source search engine and analytics store used by a variety of applications from search in e-commerce stores, to internal log management tools using the ELK stack (short for “Elasticsearch, Logstash, Kibana”). As a distributed database, your data is partitioned into “shards” which are then allocated to one or more servers.Because of this sharding, a read or write request to an Elasticsearch cluster requires coordinating between multiple nodes as there is no “global view” of your data on a single server. While this makes Elasticsearch highly scalable, it also makes it much more complex to setup and tune than other popular databases like MongoDB or PostgresSQL, which can run on a single server.When reliability issues come up, firefighting can be stressful if your Elasticsearch setup is buggy or unstable. Your incident could be impacting customers which could negatively impact revenue and your business reputation. Fast remediation steps are important, yet spending a large amount of time researching solutions online during an incident or outage is not a luxury most engineers have. This guide is intended to be a cheat sheet for common issues that engineers running that can cause issues with Elasticsearch and what to look for.As a general purpose tool, Elasticsearch has thousands of different configurations which enables it to fit a variety of different workloads. Even if published online, a data model or configuration that worked for one company may not be appropriate for yours. There is no magic bullet getting Elasticsearch to scale, and requires diligent performance testing and trial/error.Unresponsive Elasticsearch cluster issuesCluster stability issues are some of the hardest to debug, especially if nothing changes with your data volume or code base.Check size of cluster stateWhat does it do:  Elasticsearch cluster state tracks the global state of our cluster, and is the heart of controlling traffic and the cluster. Cluster state includes metadata on nodes in your cluster, status of shards and how they are mapped to nodes, index mappings (i.e. the schema), and more.  Cluster state usually doesn’t change often. However, certain operations such as adding a new field to an index mapping can trigger an update.  Because cluster updates broadcast to all nodes in the cluster, it should be small (<100MB).  A large cluster state can quickly make the cluster unstable. A common way this happens is through a mapping explosion (too many keys in an index) or too many indices.What to look for:      Download the cluster state using the below command and look at the size of the JSON returned.    curl -XGET 'http://localhost:9200/_cluster/state'            In particular, look at which indices have the most fields in the cluster state which could be the offending index causing stability issues. If the cluster state is large and increasing. You can also get an idea looking at individual index or match against an index pattern like so:    curl -XGET 'http://localhost:9200/_cluster/state/_all/my_index-*'            You can also see the offending index’s mapping using the following command:    curl -XGET 'http://localhost:9200/my_index/_mapping'      How to fix:  Look at how data is being indexed. A common way mapping explosion occurs is when high-cardinality identifiers are being used as a JSON key. Each time a new key is seen like “4” and”5”, the cluster state is updated. For example, the below JSON will quickly cause stability issues with Elasticsearch as each key is being added to the global state.      { "1": {   "status": "ACTIVE" }, "2": {   "status": "ACTIVE" }, "3": {   "status": "DISABLED" }  }        To fix, flatten your data into something that is Elasticsearch friendly:    {  [    {      "id": "1",      "status": "ACTIVE"    },    {      "id": "2",      "status": "ACTIVE"    },    {      "id": "3",      "status": "DISABLED"    }  ]}      Check Elasticsearch Tasks QueueWhat does it do:  When a request is made against elasticsearch (index operation, query operation, etc), it’s first inserted into the task queue, until a worker thread can pick it up.  Once a worker pool has a thread free, it will pick up a task from the task queue and process it.  These operations are usually made by you via HTTP requests on the :9200 and :9300 ports, but they can also be internal to handle maintenance tasks on an index  At a given time there may be hundreds or thousands of in-flight operations, but should complete very quickly (like microseconds or milliseconds).What to look for:  Run the below command and look for tasks that are stuck running for a long time like minutes or hours.  This means something is starving the cluster and preventing it from making forward progress.      It’s ok for certain long running tasks like moving an index to take a long time. However, normal query and index operations should be quick.    curl -XGET 'http://localhost:9200/_cat/tasks?detailed'        With the ?detailed param, you can get more info on the target index and query.  Look for patterns in which tasks are consistently at the top of the list. Is it the same index? Is it the same node?  If so, maybe something is wrong with that index’s data or the node is overloaded.How to fix:  If the volume of requests is higher than normal, then look at ways to optimize the requests (such as using bulk APIs or more efficient queries/writes)  If not change in volume and looks random, this implies something else is slowing down the cluster. The backup of tasks is just a symptom of a larger issue.  If you don’t know where the requests come from, add the X-Opaque-Id header to your Elasticsearch clients to identify which clients are triggering the queries.Checks Elasticsearch Pending TasksWhat does it do:  Pending tasks are pending updates to the cluster state such as creating a new index or updating its mapping.  Unlike the previous tasks queue, pending updates require a multi step handshake to broadcast the update to all nodes in the cluster, which can take some time.  There should be almost zero in-flight tasks in a given time. Keep in mind, expensive operations like a snapshot restore can cause this to spike temporarily.What to look for:      Run the command and ensure none or few tasks in-flight.    curl curl curl -XGET 'http://localhost:9200/_cat/pending_tasks'        If it looks to be a constant stream of cluster updates that finish quickly, look at what might be triggering them. Is it a mapping explosion or creating too many indices?  If it’s just a few, but they seem stuck, look at the logs and metrics of the master node to see if there are any issues. For example, is the master node running into memory or network issues such that it can’t process cluster updates?Hot ThreadsWhat does it do:  The hot threads API is a valuable built-in profiler to tell you where Elasticseach is spending the most time.  This can provide insights such as whether Elasticsearch is spending too much time on index refresh or performing expensive queries.What to look for:  Make a call to the hot threads API. To improve accuracy, it’s recommended to capture many snapshots using the ?snapshots param    curl -XGET 'http://localhost:9200/_nodes/hot_threads?snapshots=1000'        This will return stack traces seen when the snapshot was taken.  Look for the same stack in many different snapshots. For example, you might see the text 5/10 snapshots sharing following 20 elements. This means a thread spending time in that area of the code during 5 snapshots.  You should also look at the CPU %. If an area of code had both high snapshot sharing and also high CPU %, this is a hot code path.  By looking at the code module, disassemble what Elasticseach is doing.  If you see wait or park state, this is usually ok.How to fix:  If a large amount of CPU time is spent on index refresh, then try increasing the refresh interval beyond the default 1 second.  If you see a large amount in cache, maybe your default caching settings are suboptimal and causing heavy miss.Memory IssuesCheck Elasticsearch Heap / Garbage CollectionWhat does it do:  As a JVM process, the heap is the area of memory where a lot of Elasticsearch data structures are stored and requires garbage collection cycles to prune old objects.  For typical production setups, Elasticsearch locks all memory using mlockall on boot and disables swapping. If you’re not doing this, do it now.  If Heap is consistently above 85% or 90% for a node, this means we are coming close to out of memory.What to look for:  Search for collecting in the last in Elasticsearch logs. If these are present, this means Elasticsearch is spending higher overhead on garbage collection (which takes time away from other productive tasks).  A few of these every now and then ok as long as Elasticsearch is not spending majority of it’s CPU cycles on garbage collection (calculate the percentage of time spent on collecting relative to the overall time provided)  A node that is spending 100% time on garbage collection is stalled and cannot make forward progress.  Nodes that appear to have network issues like timeouts may actually be due to memory issues. This is because a node can’t respond to incoming requests during a garbage collection cycle.How to fix:  The easiest is to add more nodes to increase the heap available for the cluster. However, it takes time for Elasticsearch to rebalance shards to the empty nodes.      If only a small set of nodes have high heap usage, you may need to better balance your customer. For example, if your shards vary in size drastically or have different query/index bandwidths, you may have allocated too many hot shards to the same set of nodes. To move a shard, use the reroute API. Just adjust the shard awareness sensitivity to ensure it doesn’t get moved back.    curl -XPOST -H "Content-Type: application/json" localhost:9200/_cluster/reroute -d '{"commands": [    {      "move": {        "index": "test", "shard": 0,        "from_node": "node1", "to_node": "node2"      }    }  ]}'        If you are sending large bulk requests to Elasticsearch, try reducing the batch size so that each batch is under 100MB. While larger batches help reduce network overhead, they require allocating more memory to buffer the request which cannot be freed until after both the request is complete and the next GC cycle.Check Elasticsearch Old Memory PressureWhat does it do:  The old memory pool contains objects that have survived multiple garbage collection cycles and are long living objects.  If the old memory is over 75%, you might want to pay attention to it. As this fills up beyond 85%, more GC cycles will happen but the objects can’t be cleaned up.What to look for:  Look at the old pool used / old pool max. If this is over 85%, that is concerningHow to fix:  Are you eagerly loading a lot of fielddata. These reside in memory for a long time.  Are you performing many long running analytics tasks? Certain tasks should be offloaded to a distributed computing framework designed for map/reduce operations like Apache Spark.Check Elasticsearch FieldData SizeWhat does it do:  FieldData is used for computing aggregations on a field such as terms aggregation  Usually fielddata for a field is not loaded in memory until the first time an aggregation is performed on it.  However, this can also be precomputed on index refresh if eager_load_ordinals is set.What to look for:      Look at an index or all indices fielddata size like so:    curl -XGET 'http://localhost:9200/index_1/_stats/fielddata'        An index could have very large field data structures if we are using it on the wrong type of data. Are you performing aggregations on very high-cardinality fields like a UUID or trace id? Fielddata is not suited for very high-cardinality fields as they will create massive fielddata structures.  Do you have a lot of fields with eager_load_ordinals set or allocate a large amount to the fielddata cache. This causes the fielddata to be genreated at refresh time vs query time. While it can speed up aggregations, it’s not optimal if you’re computing the fielddata for many fields at index refresh and never consume it in your queeries.How to fix:  Make adjustments to your queries or mapping to not aggregate on very-high cardinality keys.  Audit your mapping to reduce the number that have eager_load_ordinals set to true.Elasticsearch Networking issuesNode left or node disconnectedWhat does it do:  A node will be eventually be removed from the cluster if it does not respond to requests.  This allows shards to be replicated to other nodes to meet the replication factor and ensure high availability even if a node was removed.What to look for:  Look at the master node logs. Even though there are multiple masters, you should look at the master node that is currently elected. You can use the nodes API or a tool like Cerebro to do this.  Look if there is a consistent node that times out or has issues. For example, you can see which nodes are still pending for a cluster update by looking for the phrase pending nodes in the master node’s logs.  If you see the same node keep getting added but then removed, it may imply the node is overloaded or unresponsive.  If you can’t reach the node from your master node, it could imply a networking issue. You could also be running into the NIC or CPU bandwidth limitationsHow to fix:  Test with the setting transport.compression set to true This will compress traffic between nodes (such as from ingestion nodes to data nodes) reducing network bandwidth at the expense of CPU bandwidth.  Note: Earlier versions called this setting transport.tcp.compression  If you also have memory issues, try increasing memory. A node may become unresponsive due to large time spent on garbage collection.Not enough master node issuesWhat does it do:  The master and other nodes need to discover each other to formulate a cluster.  On first boot, you must provide a static set of master nodes so you don’t have a split brain problem.  Other nodes will then discover the cluster automatically as long as the master nodes are present.What to look for:      Enable Trace logging to review discovery related activities.    curl -XPUT -H "Content-Type: application/json" localhost:9200/_cluster/_settings -d '{  "transient": {"logger.discovery.zen":"TRACE"}}'        Review configuration such as minimum_master_nodes (if older than 6.x).  Look at whether all master nodes in your initial master nodes list can ping each other.  Review whether you have quorum, which should be number of master nodes / 2 +1. If you have less than quorum, no updates to cluster state will occur to protect data integrity.How to fix:  Sometimes network or DNS issues can cause the original master nodes to not be reachable.  Review that you have at least number of master nodes / 2 +1  master nodes currently running.Shard allocation errorsElasticsearch in Yellow or Red State (Unassigned shards)What does it do:  When a node reboots or a cluster restore is started, the shards are not immediaty available.  Recovery is throttled to ensure the cluster does not get overwhelmed.  Yellow state means primary indices are allocated, but secondary (replica) shards have not been allocated yet. While yellow indices are both readable and writable, availability is decreased. Yellow state is usually self-healable as the cluster replicates shards.  Red indices means primary shards are not allocated. This could be transient such as during a snapshot restore operation, but can also imply major problems such as missing data.What to look for:      See reason behind why allocation has stopped    curl -XGET 'http://localhost:9200/_cluster/allocation/explain'curl -XGET 'http://localhost:9200/_cat/shards?h=index,shard,prirep,state,unassigned.reason'            Get a list of red indices, to understand which indices are contributing to red state. The cluster state will be in the red state as long as at least one index is red.    curl -XGET 'http:localhost:9200/_cat/indices' | grep red            For more detail on a single index, you can see recovery status for the offending index    curl -XGET 'http:localhost:9200/index_1/_recovery'      How to fix:      If you see a timeout from max_retries (maybe the cluster was busy during allocation), you can temporarily increase the circuit breaker threshold (Default is 5). Once the number is above the circuit breaker, Elasticsearch will start to initialize the unassigned shards.    curl -XPUT -H "Content-Type: application/json" localhost:9200/index1,index2/_settings -d '{  "index.allocation.max_retries": 7}'      Elasticsearch Disk issuesIndex is read-onlyWhat does it do:  Elasticsearch has three disk based watermarks that influences shard allocation. The cluster.routing.allocation.disk.watermark.low watermark prevents new shards from being allocated to a node with disk filling up. By default, this is 85% of the disk used.  The cluster.routing.allocation.disk.watermark.high watermark will force the cluster to start moving shards off of the node to other nodes. By default, this is 90%. This will start to move data around until below the high watermark.If Elasticsearch disk exceeds the flood stage watermark cluster.routing.allocation.disk.watermark.flood_stage, is when the disk is getting so full that moving might not be fast enough before the disk runs out of space. When reached, indices are placed in a read-only state to avoid data corruption.What to look for:  Look at your disk space for each node      Review logs for nodes for a message like below:    high disk watermark [90%] exceeded on XXXXXXXX free: 5.9gb[9.5%], shards will be relocated away from this node            Once the flood stage reached, you’ll see logs like so:    flood stage disk watermark [95%] exceeded on XXXXXXXX free: 1.6gb[2.6%], all indices on this node will be marked read-only        Once this happens, the indices on that node are read-only.      To confirm, see which indices have read_only_allow_delete set to true.    curl -XGET 'http://localhost:9200/_all/_settings?pretty' | grep read_only      How to fix:  First, clean up disk space such as by deleting local logs or tmp files.      To remove this block of read-only, make the command:    curl -XPUT -H "Content-Type: application/json" localhost:9200/_all/_settings -d '{  "index.blocks.read_only_allow_delete": null}'      ConclusionTroubleshooting stability and performance issues can be challenging. The best way to find the root cause is by using the scientific method of hypothesis and proving it correct or incorrect. Using these tools and the Elasticsearch management API, you can gain a lot of insights into how Elasticsearch is performing and where issues may be.",
          "url": " /technical/elasticsearch/How-to-Debug-an-Unresponsive-Elasticsearch-Cluster/",
          "author": "Derric",
          "categories": "Technical, Elasticsearch"
        }
      
    ,
  
    
        "technical-api-analytics-three-reasons-developers-need-turn-key-api-analytics": {
          "title": "3 Reasons Developers Need Turn-Key API Analytics",
          "content"	 : "When your platform runs on APIs, all of those APIs need to run perfectly. Quickly resolving issues in your API isn’t just helpful, it’s mandatory. Latency and error monitoring are only the beginning: a healthy server isn’t the same thing as a healthy product. Resolving error cases and API abuse is easiest with full visibility into your API, which is where API analytics come in.Nowadays, it’s common to use out-of-the-box solutions for authentication, payment processing, and other critical functions. Still, API analytics solutions are often built in-house, eating up months of developer resources. That’s time your team could spend further developing your product itself.Luckily, turn-key solutions do exist. Read on to discover three reasons why API Analytics platforms like Moesif can make API developers’ work easier, faster, and more efficient.Reason #1: De-Mystify API Error Logs  “As a scientist, I’m all about data analytics. Finding, displaying, and sharing API metrics like 400/500 errors, when you have thousands of errors and millions of API calls, is very different”, Rob Dejournett, CTO of pVerifySearching through API event logs is time-intensive and single-value metrics often won’t explain complex error cases.API analytics provide faster queries and deeper insights into a buggy API, supercharging your debugging process. For example, Moesif offers real-time insights into your logs that scales with any volume of API calls. Adding this layer to your API stack allows you to…   Tail and filter HTTP requests in real time    Inspect request and response HTTP payloads    Inspect API logs and replay request in Postman or cURL in seconds    Segment and aggregate volumes of API calls at scale, using a variety of different parametersKnowing how your APIs are behaving is vital for both stability and a great user experience. Enhanced visibility into API calls makes log-searching a much simpler process.Reason #2: Stay In-the-Know with Error Alerts  “As soon as the tool went up and we were able to see the types of transactions happening, we were able to identify security vulnerabilities within our infrastructure. We had transactions from sanctioned countries that were coming through where we weren’t blocking certain countries. That was a win for the tool from the get go, literally day one.”-Marat Asadurian, Sr. Manager of Software Engineering, TruliooInfrastructure monitoring systems aren’t the best tool for uncovering anomalies in API usage. They’re more focused on things like tracking server uptime, latency, and more generic errors. Static and dynamic alerts take some of the guesswork out of error detection. Alerts can be delivered to many different channels, ensuring that technical teams have ample time to fix issues.Static alerts can notify you when a static threshold has been reached. For example, you can create an alert that lets you know when users are facing authorization issues on your web app.Alert if more than 20 HTTP ‘401 - Unauthorized’ status codes are returned in a REST API response within 15 minutesMoesif’s dynamic alerts provide a broader view of novel errors. They automatically compare the error against your historical data, providing the context needed to discover complex errors. You can tailor our dynamic alert filters to the specific issues your product faces. If you are tracking key users, you might set up a dynamic alert like this:Alert if a user’s API usage is abnormally decreasing over the last weekDynamic alerts also provide an additional line of defense against users with malicious intent or an unintentional coding error. For instance, a user may be intentionally or unintentionally hammering an endpoint with traffic. Abnormally large amounts of traffic may overload the server and cause a DDoS outage. To prevent this, you could set up an alert for an abnormal increase in the number of queries against the endpoint. If an endpoint receives an abnormally large amount of requests, this filter alerts engineering and security teams immediately so they can be aware of the situation and be quick to take action.Alert if an allowed user is creating an abnormally large amount of traffic against an endpoint, within the last 24 hoursWhen it comes to errors and threats, additional lines of defense can only enhance the reliability of your product. Alerts give your team the time it needs to react appropriately to anomalies in your API.Reason #3: Streamline Collaboration with Non-DevelopersWhile product and developer teams are on the same side, ever changing requirements and timelines make it seem otherwise. Polished API analytics can help you communicate your ideas to non-developers and help to shape priorities within your product roadmap or bug backlog.Most modern analytics platforms come with powerful data visualization features, and API analytics are no exception. Moesif offers secure, custom dashboards and reports designed to communicate key API insights to non-technical and technical stakeholders alike.For example, let’s say you’re on a team building a mobile platform with in-app purchases. Your product team deprioritized a buggy feature that stores payment information for future use. Using visualizations like Moesif’s funnel report, you can justify why debugging takes priority. A funnel analysis is a common way for non-developers to model the journey of their ideal customer, from discovery of your product to purchase.The funnel report breaks down where your app’s users dropped out of a sales funnel, over the last seven days. Your product team knows that your ideal customer makes at least 100 in-app purchases in that time. So, we they break the funnel into three stages, displayed left to right:   Customers that made an account with your app    Customers that performed at least one API call to a payment platform     Customers that prompted at least 100 API calls to a payment platform We see that customers who make their first purchase did so 6 hours and 5 mins after signing up, on average. We also see that only about half ever make a first purchase after signup. Using this instantly generated visualization, you can assert that fixing the buggy feature could reduce friction between signup and first in-app purchase.When multiple teams join forces to offer a great product, it’s vital that all team members understand each other’s needs. Good communication between developer, designer, product, and customer teams enables easy collaboration, and less interruptions for developers.In Summary…APIs are now the backbone of our interconnected world, but analytics best practices have not kept pace. Currently, developers subsidize much of the inefficiency that comes from an opaque API lifecycle. The solution is not as simple as writing analytics infrastructure in-house. To get the most out of your time as a developer, turn-key solutions are your best option. With an API analytics solution like Moesif, you can…   Quickly and easily debug errors without tedious log-searching and fragile single-metric tests    Uncover deeper insights about your API usage than you can with simple infrastructure monitoring     Automate detection and notification of complex errors, malicious attacks, and latency issues    Enable clear, precise communication about API usage and product health with non-technical collaborators     Win back precious hours for deep focus while coding Want to know exactly how Moesif can save engineering resources? Learn more here.!",
          "url": " /technical/api-analytics/three-reasons-developers-need-turn-key-API-analytics/",
          "author": "Savannah",
          "categories": "Technical, API-Analytics"
        }
      
    ,
  
    
        "developer-platforms-recurly-how-to-integrate-moesif-and-recurly-to-easily-monetize-your-apis": {
          "title": "How to Integrate Moesif and Recurly to Easily Monetize Your APIs",
          "content"	 : "Building great apps and APIs is not an easy task. Even harder, is trying to monetize and create a sustainable business with them. As part of our mission to help companies create better products, we decided to put a bunch of effort towards helping businesses more easily monetize. Our no-code approach to billing is a simple and elegant way to very rapidly gain the capability to bill customers for usage. Easy monetization is the premise for our latest feature for generating revenue from your APIs. Our newest feature can be found under the Billing Meters screen in Moesif.Why use a billing meter?Metering usage of your product, and charging for that usage, is one of the most common ways to monetize technology products. Tallying-up usage, send the metrics to a billing provider, and then have the billing provider collect the funds.For those who have implemented a usage-based billing scheme for their products, you know that the process can be quite complex. It involves gathering a lot of data, getting that data to the right place, collecting the funds owed, and, if the invoice isn’t paid, putting governance in place so that the user can no longer access the service. There’s a lot of coding, integration, testing, and support that goes into creating such billing systems.Moesif makes this easy for you by collecting a vast array of metrics that can be billed upon, and then automatically rounding them up by user and/or company. With Moesif, all of the data you need to accurately bill is already there, which is incidentally why we believed creating a billing meter feature made a lot of sense. We have also done the work for you to create an integration between Moesif and Recurly. This means that with a couple of clicks, you’ll be able to bill customers for usage. You’ll have billing capabilities in a matter of minutes instead of days, or even weeks, depending on the complexity.How to create and use a Billing Meter in MoesifOnce you’ve integrated your APIs with Moesif, monetizing them is very simple. There are a few steps after you’ve integrated with Moesif to get you to point when you can collect revenue. These steps include:  Setting up plans and add-ons in Recurly  Adding the Moesif webhook to Recurly  Plugging the Recurly API details into Moesif  Configuring the billing parameters in Moesif  Activating the billing meterAll of these steps are very intuitive and take only a matter of minutes.                Integrate Recurly with Moesif Easily              14 day free trial. No credit card required.              Learn More        Setting up plans and add-ons in RecurlyThe first step to monetizing is to actually set up some plans in Recurly for usage to be billed against. To set up a plan in Recurly you’ll need to use the left-side menu to click on Configurations and then Plans.Once you are on the Plans screen, you can click New Plan in the top right of the screen to create a new plan.The plans can contain whatever configuration you need, but you must ensure that at least one Add-on is created. The add-on must be configured as:  Customer to be billed at end of the billing cycle  Charges are based on price per unitConfiguring this in a Recurly plan will look like this:Once your plan is configured, click Create Plan at the bottom of the screen.At this point, we now have a plan that we can integrate with Moesif and begin billing for usage.Configuring Recurly in MoesifOnce your plans and add-ons are created, it’s time to begin to integrate Recurly with Moesif. To begin configuring Recurly in Moesif by going to the Billing Meters page and clicking the Edit Billing Provider dropdown in the top right corner of the screen.This will bring up the Recurly configuration screen walking you through the integration. From this screen, you can get all of the info needed to plug Recurly into Moesif. Each step for configuration is covered within the modal.Adding the Moesif webhook to RecurlyThe first step in the integration is to add the Moesif webhook into the configuration in Recurly. Adding this allows Recurly to send subscription updates to Moesif.To add the Moesif webhook to Recurly, from the left-side menu in Recurly select Integrations, and then Webhooks.This will bring you to the Webhooks page where you can view existing webhooks and add new ones. To add a new webhook we will click the Configure button in the top right corner of the screen.On the next screen, you’ll need to click the New Endpoint button in the top right corner of the screen.From here we will plug in our Moesif API endpoint URL into the ENDPOINT URL field and add our Moesif Application ID into the HTTP AUTH USERNAME field.  Nothing should be entered in the HTTP AUTH PASSWORD field.These details can all be found on the Recurly configuration page in Moesif mentioned in the previous section.Plugging the Recurly API details into MoesifFor Moesif to add usage quantities to subscriptions in Recurly, we need to add the Recurly API details into Moesif. This is done in the Recurly configuration screen in Moesif, the same screen we’ve been working with previously.  Currently, Moesif only supports v3 of the Recurly API so that value is default and uneditable for the Recurly API Version field.For the Recurly API Key field, you’ll need to retrieve the API key from Recurly to plug it in. To do that, in the left side menu click on Integrations and select API Credentials. You’ll then be able to see the private key for your API in the Default API Key field on the screen.After copying the key from Recurly, you’ll paste this key into the Recurly API Key field back in Moesif. After doing this, back in Moesif you can scroll down to the bottom of the screen and click Save to save the configuration.At this point, your Recurly integration is complete in Moesif and you can begin to use it.  Optionally, you have the ability to customize the Customer ID Source in Moesif as well. The default should work fine for most purposes but if you do need to customize it, it will allow you to specify how to map the Recurly subscription and account field to the company ID and user ID in Moesif.                Monetize Your API With Metered Billing              14 day free trial. No credit card required.              Learn More        Configuring the billing parameters in MoesifOnce the integration with Recurly is added, you can configure your billing parameters in Moesif. If you haven’t done so already, you’ll want to create a new Billing Meter. To do this, from Moesif you’ll need to click on the Billing Meter link in the left side menu as you did when you started the Recurly integration.Once you’re on the Billing Meters screen, you’ll click the + Add Billing Meter button to begin creating a new billing meter.Once on the Add Billing Meter screen, you’ll add in:  Billing meter name  Billing provider info  Add the filter to specify what events to bill uponIn the below example I have set up a billing plan called “My Billing Plan” which uses Recurly as my billing provider. I’ve also decided to bill on any API call which has a response of 200 OK.Once you have your details dialed in, you’ll see a visual representation of the filter output at the bottom of the screen. Although billing will only happen going forward, you will be able to see historically how your filter is working with existing data. This can help to make sure, especially with more complex filtering, that you have everything configured the way you require it.Activating the billing meterOur final step in monetization is to save and activate the billing meter. To do this we simply need to make sure that billing meter is turned on at the top of the configuration page.And lastly, we need to click Create at the top of the screen.You’ll then be prompted to confirm the billing meters creation in the modal that pops up after clicking on the Create button.  It’s important to note that once a billing meter is created, the criteria for filtering can not be changed nor the billing provider details. Only the name can be changed as well as the status of the billing meter can be toggled on and off. This is for compliance and auditing purposes. Billing meters can also not be deleted but can be archived if no longer in use.We should now see our new billing meter appear back on the Billing Meters home page.Now, we have successfully created a billing meter that will begin to send usage data to Recurly. This integration is possible in a matter of minutes with no code required. For further info, you can also check out our docs.Try it for yourself!If you have an API, or any other part of your product you’d like to monetize, Moesif can help. As noted in the steps above, it is an easy and rapid way to bring in revenue from your products. If you are already monetizing your product and looking for a simpler solution, our billing features are a great way to simplify your setup and reduce support costs around your existing monetization efforts.Moesif also offers many other great features which bundle well with our billing functionality, including funnel metrics and retention analysis, automated user behavior emails, custom metric dashboards, and governance capabilities. Sign up today to get started with billing and much more.                Quickly and Easily Monetize APIs with Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /developer-platforms/recurly/How-To-Integrate-Moesif-And-Recurly-To-Easily-Monetize-Your-APIs/",
          "author": "Matthew",
          "categories": "Developer-Platforms, Recurly"
        }
      
    ,
  
    
        "developer-platforms-recurly-easily-monetize-your-apis-with-moesif-plus-recurly": {
          "title": "Easily Monetize Your APIs with Moesif Plus Recurly",
          "content"	 : "It’s always great to build something that makes money. The most successful businesses often find the easiest and most efficient ways to make money, while keeping costs and support to a minimum. After all, the best businesses and products are simply the ones that know how to build revenue. Many companies now look to recurring billing for their APIs as part of their overall monetization strategy.API monetization isn’t always easy though. It generally takes a lot of integrations, a fair amount of code and customization, and can also lead to a large support burden. This is especially true when billing issues arise. In short, there are challenges during implementation, subscription managment, and once the billing system is up and running.What if a simpler approach was possible? At Moesif, we recently introduced a new billing platform feature, Billing Meters. This tool is a way to use the data coming into Moesif to monitor usage for a user, send this data to a billing provider for payment processing, and have the users be presented with accurate bills for recurring payments, all with a fraction of the work required to implement a custom solution.Unlocking recurring revenue has never been easier than with a subscription management platform like Moesif for accurate usage based billing.To illustrate how it works, let’s assume we have an API product that we would like to charge customers to use. As an example, let’s pretend that we have created a new credit score API that companies can use to bring consumers’ credit ratings back to their app. Our API will be /RetrieveCreditScore, and users will be charged for each query/call they send to the endpoint via Moesif’s monetization platform.Our API monetization model will be pretty simple. We will have 3 usage tiers that will determine how much the users will be charged:  1-100 queries per month ($5 per query)  101-1000 queries per month ($3 per query)  1001+ queries per month ($2 per query)This tiered pricing schema is quite common, where the more the service is used, the larger the discount is.Recurly will be the billing provider that we will use to invoice and charge customers for their usage. Recurly is simple to use and will allow us to easily set up plans and add-ons to adhere to the pricing scheme above.We will now use Moesif to tally up the usage for the endpoint and send the usage metrics to Recurly. Recurly can then use these metrics to bill the customer accordingly, based on the tier that their metrics coincide with, and collect payment.Integrate your app and APIs with MoesifIn order to use the Billing Meters feature in Moesif, you need to have your APIs integrated with Moesif and your chosen, supported payment gateways. This is because Moesif will use the metrics stored within it and then feed Recurly the info it needs. Once your APIs are integrated with Moesif, you can also use other features which pair well with our Billing Meters feature, including behavioral emails, governance rules, and alerts.If you are not currently using Moesif to monitor your APIs, integration can be done in a few different ways. If you are using an API gateway or API management platform, you can use one of our many plugins which allow you to quickly feed analytics to Moesif. If you are not using a third-party gateway or management platform, or want to do it at the API code level, you can use one of our SDKs. A Moesif SDK will allow you to easily integrate Moesif with your Node, Python, or Java APIs (plus many, many more languages and frameworks) directly from your code. Either way is easy and simple to support your subscription workflow.Another Moesif feature that you need to deploy in order to make billing work correctly, is to implement user and company tracking. Generally, this can be set up in a few simple steps. We need this feature enabled so that usage data in Moesif can be tied to specific users and companies. This is how Recurly will map usage to a customer within Recurly, so they can be billed accordingly.Once you have integrated with Moesif, and have user and customer tracking enabled, your next step would be to actually create your subscription plan in Recurly, so it can be used in Moesif.Create plans and add-ons in RecurlyAfter creating a Recurly account and logging in, you can begin to create your plans. For our purposes, we need to create a plan which includes an add-on that uses tiered, usage-based billing.You’ll need to create a plan in Recurly which is post-paid (payment received at the end of the billing cycle), and the price is per unit. Aside from that, for our pricing scheme, we will create an add-on that has a tiered pricing scheme. It would look like this in Recurly:Now, at the end of the month, when Recurly creates the invoice, it will do so based on these tiers. Of course, at this point, we only have the plans defined, but no one will be charged since we don’t yet have any usage data being sent to Recurly.% include cta_section.html   excerpt=’Integrate Metered Billing Easily’   image=’/images/posts/cta/cta-monitoring.svg’   button_text=’Learn More’   url=’https://www.moesif.com/solutions/metered-api-billing?utm_campaign=Int-site&utm_source=blog&utm_medium=body-cta&utm_term=monetize-with-recurly’%}Integrate Recurly with MoesifWe still need to actually get the data from Moesif over to Recurly, and vice versa. There are two mechanisms that are used for this: a webhook and the Recurly API. Each has a distinct part in facilitating the data sharing between the platforms.By adding the webhook into Recurly, subscription updates can be sent back to Moesif. By using the Recurly API, Moesif can send usage details to Recurly and can also retrieve details about available plans and add-ons in Recurly. These two points of contact are all that’s needed for the Recurly and Moesif integration. Thankfully, Moesif walks you through this when you set up Recurly as a billing provider and provides all the needed details. For the specifics, you can also check out our docs on the Recurly integration.Set your billing parametersOnce you’ve integrated Recurly in Moesif, you can set up your billing meter. For our example, it will be very simple. What we will do is send usage subscription data to Recurly whenever an API call to /RetrieveCreditScore returns a “200 OK” response. This means we will only bill for successful calls and won’t accidentally charge for calls where an error was experienced.Once we create the billing meter, every hour the usage for each customer will be sent to Recurly. At the end of the month, Recurly will create an invoice based on our tiered price structure and bill the user, allowing them to pay with their desired payment method.  You can also use Moesif to automatically send behavioral emails for every successful call, or even if they are about to cross into the next discount tier. You could also use Moesif’s Governance Rules to block users with overdue invoices from accessing the API until their invoice is settled.Try it out for yourselfAs you can see, you are just a few steps away from robust API monetization that is simple to implement and support. By using Moesif and Recurly together you’ll be able to charge customers for usage in a matter of minutes, manage subscriptions, and can even use other Moesif features to create the ultimate customer experience. Our billing setup is so simple that it can even be done without having any developer skills.The Billing Meters feature is available for all Moesif users. Sign up for Moesif today to instantly access our Billing Meters feature to begin billing for your customers’ API usage. If you’re already using Moesif, click on Billing Meters in the left-side navigation menu and take a look at our docs to show the exact steps to get you to monetize your APIs.                Easily Monetize Your APIs              14 day free trial. No credit card required.              Learn More        ",
          "url": " /developer-platforms/recurly/Easily-Monetize-Your-APIs-With-Moesif-Plus-Recurly/",
          "author": "Matthew",
          "categories": "Developer-Platforms, Recurly"
        }
      
    ,
  
    
        "technical-graphql-graphql-in-enterprise-what-it-takes-to-build-deploy-and-monitor-a-new-enterprise-graphql-service": {
          "title": "GraphQL in Enterprise: What It Takes to Build, Deploy, and Monitor a New Enterprise GraphQL Service",
          "content"	 : "New technologies always require some planning, changes, and experimentation before they merge into an enterprise stack. GraphQL adoption has been no exception to this. Companies like Airbnb, Netflix, Shopify, and other industry giants have all taken the leap to use this promising technology.In this blog, I will outline a few key considerations for creating your new service, deploying it, and monitoring the service. This assumes that you have a basic understanding of what GraphQL is, some of the key use cases, and awareness of some of the concerns in regards to adoption. With that in mind, let’s jump in.GraphQL vs. RESTBefore you being to build your GraphQL service, it’s important to consider the reigning approach for building Web APIs: REST. A common misconception about GraphQL is that it is a replacement for REST. Thinking of the technology in this light would be very wrong. Both GraphQL and REST can be very complimentary and most enterprises will use a mixture of both approaches.When enterprises shifted from SOAP to REST, the transition was much easier to see and map out. Both contain strict contracts for request and response which meant that converting SOAP services over to RESTful services made a lot of sense. Some could even just be converted in a “like-for-like” fashion.With GraphQL, it’s a tougher comparison and migration path compared to SOAP and REST. There are no GraphQL request and response contracts since they are defined when a query is executed. The caller of the GraphQL API sends whatever data is required to obtain the result they need and it is returned in the format that the caller has requested. This breaks the traditional pattern established with RESTful API design where there are very clearly defined request and response objects for an endpoint.The greatest benefit of GraphQL over the rigidity of REST is that users can retrieve whatever data they need without having to create a new endpoint for each variation. This can definitely improve developer experience with your APIs and data. With REST, this has actually caused an API problem where it is sometimes easier to create a new endpoint than to modify an existing one and potentially break other applications using it. The result is a whole lot of APIs that are slightly varied and a whole lot more support effort to maintain them.When making the decision of building your new API as GraphQL vs. REST, you’ll want to make sure that you require the flexibility of GraphQL, or maybe you just need the simplicity of a small RESTful API.Do you really need GraphQL?Establishing whether you really need GraphQL or not is a very important question to ask. With the adoption of GraphQL comes a learning curve for engineers and supports teams, as well as complexity.Some examples of a great time to use GraphQL is when:  You have a lot of relational data and you aren’t sure exactly how consumers will query it  You’re currently experiencing issues with REST endpoint duplication and want to reduce the number of individual endpoints you have to support  You have lots of services exposing the data you need and would like an easier way to query it without creating lots of new endpoints  You want to subscribe to changes in data, possibly using GraphQL subscriptions as a solutionREST may be better in situations where:  You’re application only requires simple, pre-defined CRUD operations  You are adding to an existing project that is already efficiently using a RESTful approach  You require firm rate-limiting and quota management  You require heavy caching or need to design for throughputOf course, there are many different arguments for why you should use either technology in lieu of the other. Above are just a few considerations for each approach.If you want to expose your data so that multiple applications can use it exactly as they see fit, GraphQL is a great solution. If you need a simple CRUD interface than others, simpler solutions may be a better choice.Options for building a new GraphQL serviceGraphQL services can essentially fit into the familiar “build vs. buy” dilemma. The original and most flexible way to build a GraphQL service is to select a language and framework, then build “from scratch”. Lots of frameworks exist in almost every language out there. GraphQL.org has a comprehensive list of all the languages and frameworks that support GraphQL.When building from the ground up with a framework you’ll have the most flexibility but also have the steepest learning curve. Your support teams will also need to come up to speed to know the intricacies of the code if an issue arises. The trade-off of high flexibility and customization comes with a price tag that is quite steep in terms of build time, learning the framework, and supporting this custom code once it hits production.An alternative with a seemingly lower learning curve would be to build using a product that allows you to use existing infrastructure to create your GraphQL service. A few examples include:  Hasura          Connect your favorite databases and create a GraphQL API service automatically        Fauna          Create a database and a GraphQL API service from a single schema        Tyk’s Universal Data Graph          Use your existing RESTful APIs to create a new GraphQL API Service with no code required        Mulesoft’s AnyPoint DataGraph          Use the APIs you’ve already built to expose a new GraphQL service      Many of these solutions allow you to use existing infrastructure, services, and data to create a brand new GraphQL service. Almost all require no additional code, aside from customizations you may want to implement, which means the learning curve and build times are heavily decreased. Compared to building a “ground up” service, this approach offers a bit less flexibility but rewards users with very rapid time-to-market.Don’t forget about frontend changesAnother part of bringing GraphQL into the enterprise is to also make changes to your frontend and any clients that will consume the API. In an existing frontend project, likely using RESTful endpoints currently, there can be a significant amount of work to get GraphQL integrated and working. Each RESTful call will need to be mapped into the equivalent GraphQL call and the returned data handled within the application. This may require in-depth knowledge of your GraphQL schema in order to map these operations.The code changes alone can take a significant amount of time and should be budgeted for when planning a GraphQL conversion project. The frontend team will also need to be educated on GraphQL if they are unaware of how to leverage the technology. This will also include the addition of a frontend GraphQL client, such as Apollo Client, and learning how to use it to interact with your new GraphQL service. This obviously is less cumbersome when starting with GraphQL from the inception of the project.A final consideration to be made is the amount of regression testing needed for the product or system once the GraphQL changes are in place. End-to-end testing needs to take place to ensure that the API is working as expected, along with any logic in the services which power the responses. This same testing approach must also be applied to the frontend UI experience to ensure that the app works as it did originally.Managing common GraphQL ConcernsGraphQL, still in its infancy, wrestles with a few concerns which should be considered when choosing to use and implement it. Below we will review a few of the most common concerns to look out for while implementing GraphQL.SecurityA top concern for many adopters of GraphQL is the worries about vulnerabilities built into GraphQL. Although many seasoned GraphQL users are aware of them, it’s very easy to forget about many of the attack vectors that are exposed through a GraphQL service. These include SQL injection, query traversal attacks, and many others which are easy to execute and hard to spot. WunderGraph has a complete breakdown of many of the most important vulnerabilities to look out for in this post.CachingGraphQL is very well-known for its caching intricacies. Unlike REST responses, which are easily cached, GraphQL presents unique challenges. A good amount of the caching issues have been solved by individual solutions but it can be complex to implement and not all solutions holistically solve the issue. A great overview of all the facets of caching in GraphQL is available in this article.ComplexityImplementing GraphQL can sometimes be more complex than solving a problem with a conventional RESTful approach. Sometimes GraphQL services become very complex to build and support even though the data they offer is simple in nature. It may also lead to consumers needing a more complex understanding of the underlying data structure and what fields are available. Having to write complex queries can also add to confusion for developers new to your GraphQL API. At the enterprise level, this could mean a lot of education around the services capabilities for existing consumers.Rate limiting and QuotasWith a RESTful endpoint, it is relatively simple to enforce a rate limit and quota on a specific endpoint. GraphQL services are served through a single endpoint which makes this a bit tougher. You can enforce a rate limit and quota against the GraphQL endpoint but this leaves little flexibility and customization. The best way to enforce rate limiting and quotas is either limiting per field, or more commonly, by doing a GraphQL query cost calculation and allocating a specific amount of points per user. Shopify’s Engineering team did a great write-up on this a few years back. A tool like Moesif’s governance feature can meter and enforce quotas for specific GraphQL fields and operators.Error handlingHandling errors in GraphQL is done a bit differently than it is with a RESTful service. With REST, error codes are returned back when something goes wrong within a call or transaction. It is very much an “all-or-nothing” transaction, either the call is successful or returns back one of a multitude of possible error responses. With GraphQL, even if a response contains errors or if certain fields were not able to be resolved, the response status will still show as successful. This means that error handling must be much more explicit than just looking at the HTTP response status code. The GraphQL spec itself even contains pretty vague guidance on how to format errors in a GraphQL response. Apollo GraphQL has a few tips on how to tackle this frustration in a previous post.Deploying the GraphQL serviceDeploying a GraphQL service, including the GraphQL server and the corresponding frontend GraphQL implementation, works the same way you would deploy a RESTful API service if you’re building it using a framework.When it comes to many of the “bought” solutions like Hasura or Fauna, most of these are offered as a managed SaaS-style deployment. This means that deployments require minimal effort and configuration to get them up and running. Some of these solutions also offer a self-managed, on-premise solution as well, if needed. Obviously managing your own deployment and resources in a self-managed manner is going to take more effort and support resources.The team in charge of deploying the solution should be aware of how to configure it and also, for “bought” GraphQL products, be aware of how to move logic and other dependencies from the development environment through to the production environment.Monitoring the GraphQL serviceOnce your GraphQL service is up and running in production, it’s important to monitor it for errors, usage, and adoption. Traditional APM vendors have trouble monitoring GraphQL since they look at only the URL and status code. However, GraphQL calls are usually to the same /graphql API endpoint and do not necessarily utilize the HTTP status codes. The best GraphQL monitoring tools provide flexibility to analyze specific operations and body fields. A solution like Moesif allows you to get alerted on the presence of specific body fields like response.body.error, which is a common error format for GraphQL APIs.Moesif can also provide product insights into how your GraphQL APIs are accessed compared to your REST APIs. This enables you to track how the usage differs if you’re migrating from REST to GraphQL. This includes the ability for Moesif to track GraphQL operations, such as a GraphQL mutation, and filter based on specific fields so that you can see which are being used and how they are being used.You can also use Moesif to create alerts for your team and automated emails to users based on usage. This can help with onboarding, feature adoption, and customer success efforts in a fully-automated fashion.Considering GraphQL or already using GraphQL in your latest projects? Sign up today to check out Moesif’s GraphQL monitoring capabilities to make the transition to enterprise GraphQL as easy, secure, and successful as possible.",
          "url": " /technical/graphql/GraphQL-In-Enterprise-What-It-Takes-To-Build-Deploy-And-Monitor-A-New-Enterprise-GraphQL-Service/",
          "author": "Matthew",
          "categories": "Technical, GraphQL"
        }
      
    ,
  
    
        "technical-api-analytics-dont-shove-api-data-into-amplitude": {
          "title": "Don't Shove Your API Data Into Amplitude",
          "content"	 : "It’s prudent business practice to only focus on your core features when getting to Minimum Viable Product (MVP). Microservices architectures allow you to outsource non-differentiated pieces of your solution to third-party providers; Use someone else for user management, billing and account management.At first blush, it might seem attractive to develop your own API analytics solution, perhaps by building on top of a web analytics tool like Amplitude, MixPanel or Segment. But once you peel back the onion you’ll soon realize that you’ll be unnecessarily crippling your data analysis through upload limits, de minimis dimensional support and flawed visualization.  Use the right tool for the job and employ a best-in-case analytics solution, one that’s built exclusively for API products, like Moesif API Analytics.Web Analytics is Very Different From API AnalyticsAmplitude is great for tracking user journeys and analyzing real-time user interactions with a simple logEvent API call. When an Amplitude developer on boards onto the platform, some developers will try to use Amplitude as an all-encompassing, holistic platform for their analytics needs. However, during the implementation phase, developers will discover that the Amplitude platform has severe limitations when it comes to upload limits (batches/sec), limits for fields, universal analytics, dimensions, and scale. As a result of these limitations, developers will struggle to build their entire analytics solution on Amplitude and will need to purchase other solutions to fit their analytics needs.Most importantly, Product Managers (PMs) and Product Marketing Managers (PMMs) need to report on KPIs but don’t necessarily have the development knowledge or technical resources to implement complex, self-reporting dashboards themselves. As a result, Amplitude can sometimes go unused in large organizations since KPI dashboards need to be customized on a business unit basis. With Moesif, all of the API/KPI data any PM or PMM could want is available in a simple drop-down menu interface. Understanding your analytics data is easier with a tool meant to visualize API use analytics. In this article, we’re going to cover why you shouldn’t shove your API data into Amplitude, rather consider a solution that’s custom built for APIs’ unique requirements. At the end of the day, website traffic and website analytics produce raw data that is vastly different from a data source like an API, so the tools you use for your API analytics need to be API focused.Track your API Data Easily with MoesifAmplitude’s customers join the platform envisioning that they’re going to have a seamless experience creating charts to track important KPIs and metrics data. However, once Amplitude is implemented, PMs and their development teams quickly discover that setting up charts to track those user behavior metrics is a tedious and manual job.Out of the box Moesif enables your teams to track API metrics that are important to both your infrastructure and platform/marketing teams. Whether you’re in DevOps, Product Management, Application Engineering, or Growth, Moesif is designed to track the product analytics data that you care about. In Amplitude, your API data isn’t initially configured for you and your options for tracking individual user data or overarching company metrics is extremely limited. Moesif allows you to drive insights from detailed analytics with real-time data collection.Here is a shortlist of metrics data that’s trackable out of the box with Moesif, and that would have to be custom created in Amplitude:  API Usage Growth  Unique API Consumers (API DAU and MAU)  Top Customers by API Usage  API retention  Time to First Hello World (TTFHW)  Uptime, CPU, and Memory Usage  Request Per Minute (RPM), Average and Max Latency, Errors Per Minute  Total number of unique visitorsThe full rundown of top API metrics that your team should be tracking is detailed in a companion blog post.With Moesif’s Saved Workspaces feature, teams can save reports and share them across their organization. Are there certain reports that only you want access to? Creating private dashboards is easy with Moesif, just modify your “Edit Sharing” settings on your Workspace and choose whether you want your Workspace to be Private, shared with your Team, or Public. To read more about this feature, click here.Moesif vs Amplitude API Data LimitationsAmplitude has limitations on how many events can be triggered each month as well as how many events you can send per second, depending on the traffic source. Moesif has some limitations such as JSON payload size, however as you’ll be able to see from the table below, Moesif is more cost-effective and doesn’t require you to sign up for an expensive Enterprise plan to access more than 1000 events/sec. With Moesif’s Pay-As-You-Go pricing, you only pay for the events you use and Moesif will not block your requests if you exceed your allocated plan:                   Moesif      Amplitude                  Batch Size      12MB      20MB              Upload Limit      No Limit      100 Batches/Sec and 1000 events/sec, more than 1000 events/sec requires an enterprise plan              Device Limit      No Limit      2000 events per request and under 1MB              Request Limits      No Limit      5 Concurrent Requests Across all REST API Endpoints              Event Type      No Limit      2000 event types per project              Event Properties      No Limit      2000 event properties per project              User Properties      No Limit      1000 user properties per project      With Moesif, there is no upper limit when it comes to API data tracking. Moesif allows developers to collect as many events as a customer wants to send. The infrastructure is designed around clusters and nodes that can be easily scaled on the fly. With Moesif, the load is balanced across multiple clusters/nodes which allows your API data to scale infinitely.API Security and Scale with MoesifMoesif has implemented top-of-the-line security into its platform and is fully GDPR and SOC 2 compliant. All API Tokens are signed using HMAC SHA-256 and Moesif’s SSL implementation received an A+ on the Qualsys’ SSL Labs Analysis, and all of Moesif’s SDKs and agents are configured to use HTTPS by default. With Moesif’s Secure Proxy, HIPAA compliance is seamless with on-prem client-side encryption and Bring Your Own Key (BYOK). These security features will enable you to implement Moesif in any industry or use case that you may need when tracking your API analytics.API Analytics Management with Moesif vs AmplitudeAs you can see, managing your API Analytics with Moesif enables your enterprise to innovate quickly, and enables your developers to build fully scalable and compliant analytics dashboards with ease. Moesif gives organizations the power to create robust dashboards that track common KPIs and metrics data customizable to your business’s needs.  Additionally, Moesif doesn’t set arbitrary limits on your API usage because there’s no reason a single API request should be unaccounted for. Accurate data reporting is the foundation to building a strong, reliable business for your customers, and Moesif is excited to help you achieve those goals through our powerful API Analytics Platform. To learn more about Moesif and see a brief overview of how it works, check our introductory video.                Try Moesif for Sophisticated API Analytics              14 day free trial. No credit card required.              Learn More        ",
          "url": " /technical/api-analytics/Dont-Shove-API-Data-Into-Amplitude/",
          "author": "Larry",
          "categories": "technical, api-analytics"
        }
      
    ,
  
    
        "technical-api-gateways-api-proxy-vs-api-gateway-what-are-the-differences-and-which-should-you-use": {
          "title": "API Proxy vs API Gateway: What Are The Differences And Which Should You Use?",
          "content"	 : "In this article, we will take a high-level look at the differences between an API proxy and an API gateway. When a developer publishes a public API, it’s necessary for that API to have security policies and a way to hide backend logic from API consumers.Decoupling your API from your backend services allows you to shield your apps from backend code changes, and allows users to call your API without worrying about availability. If changes are being made to an endpoint or if a new version is released, users can continue on without interruption. Additionally, an API proxy or an API gateway can help you easily and uniformly secure your API endpoints. This can add another layer of defense and prevent attackers from infiltrating your system.Although an API proxy and gateway have some similarities, their differences are really what set them apart. Let’s jump into the differences between the two solutions and identify which one is going to work best for your use case.API ProxyAn API proxy is an interface that sits between your frontend and backend services. This abstraction helps to further decouple the frontend from the implementation details of your backend. The proxy will expose a URL that your front end will then use to access the API. When the request comes through to the proxy, the proxy then routes this request to the configured API endpoint, the response is returned to the proxy, and the proxy then returns the response back to the caller.An API proxy works similarly to an API gateway in which it can handle data transformations, security, and routing.  When you define a proxy, you can add transport security, monitoring (SLA, performance), quotas, and access levels to different APIs under your proxy.Many businesses have existing services that are exposed by multiple applications, and these applications can be globally distributed which can make API management and reliability difficult. An API proxy may be a good solution for someone constructing a basic API who wants to apply some rudimentary security policies to it. However, at scale, and to meet real enterprise API needs, you are going to need an API gateway.API GatewayAn API gateway can provide developers with much more customization over an API proxy, such as adding end-to-end security to your payload. An API gateway handles authentication, authorization, Denial of Service attacks, SQL injection checks, load balancing, caching, request shaping, orchestration, mediation, and transport security. Some API gateways even allow users to create brand new endpoints from existing services or create virtual endpoints that run solely on the gateway itself without any backend. As you can see, an API gateway provides all the features of an API proxy and much more.As a developer, an API gateway can save you development time since you can focus solely on building application logic. This gives an advantage to developers so they don’t have to worry about building the features, such as security and caching, provided through the API gateway directly into their API code. Being able to leverage an API gateway’s core infrastructure allows you to innovate quickly and rapidly prototype.An API gateway provides a much richer set of capabilities than an API proxy. You can use an API gateway to construct an API by combining multiple existing services together, which is something that can’t be done with an API proxy. A well-designed gateway will automatically optimize its configuration depending upon how it’s used; it should be lightweight and offer exceptional performance. To learn more about reliable API gateways, and about the benefits of API gateways, check out our article on exactly that.API MonitoringWhether you decide on using an API gateway or API proxy, API monitoring is an essential tool that developers need to incorporate to ensure the reliability of their APIs. API monitoring allows developers to view real-time data including errors, exceptions, and other critical events. Additionally, developers can configure logging of API transactional data, and analyze API usage for insights and trends. Additionally, API monitoring can automate report generation, and help your business create holistic KPI and metrics data for both product managers and sales.With Moesif, you can monitor the end-to-end user experience including all your API and frontend events. You can automatically get real-time alerts on your API performance issues or send out automated emails based on a user’s behavior. Moesif is already integrated as a plugin with most API gateways available to developers, which is why you should consider using Moesif for custom API monitoring solutions. If you are using Kong, Moesif is already a top-rated community plugin that you can learn more about and integrate with by visiting this link. You can also plug Moesif directly into your API code by using one of our SDKs.Should you choose an API Proxy or an API Gateway?A well-designed API gateway will act as a proxy and allow you to turn on or off certain capabilities that aren’t relevant to the needs of your application. Every business is different, so a customizable API gateway is necessary to tailor your APIs experience to your desired use case. Once you’ve chosen your API platform, you will want to make sure that your APIs are performing as intended. Implementing a lightweight, analytics solution such as Moesif complements your API gateway by giving you visibility and reporting. We recommend that developers use an API gateway so they can benefit from decoupling, reduce round trips, security, and cross-cutting concerns. Additionally, starting off your development journey with an API gateway will reduce technical debt since your developers won’t need to implement an API gateway later on when their application or customer base scales.Level up with analytics and monitoringRegardless of your choice to use an API proxy or an API gateway, coupling your solution with an analytics solution is a must. Moesif offers end-to-end visibility of your APIs to help you build amazing API products. Deep insights into usage, trends, errors, and adoption can easily be uncovered within Moesif. Use an API gateway or proxy to deliver reliable and secure APIs to your users while using Moesif to ensure you are delivering unrivaled value. Since we support many of the most popular frameworks and API management platforms, integration with Moesif is simple and easy. Sign up for Moesif today to level up your APIs and use analytics and monitoring to augment your gateway or proxy solution.",
          "url": " /technical/api-gateways/API-Proxy-Vs-API-Gateway-What-Are-The-Differences-And-Which-Should-You-Use/",
          "author": "Matthew",
          "categories": "Technical, API-Gateways"
        }
      
    ,
  
    
        "developer-platforms-self-service-best-practices-for-usage-based-billing-to-generate-revenue-from-apis": {
          "title": "Best Practices for Usage-based Billing to Monetize APIs",
          "content"	 : "Why monetize APIs?For many enterprise products, revenue is driven through a subscription to the SaaS, such as number of seats or a yearly license. Subscription-based pricing models are perfect for UI-driven workflows, but they are not suitable for automation and AI-driven workflows.Because of this, APIs are rapidly becoming the de facto interface to use another company’s products. Customers looking to leverage targeted solutions that fit their specific use case, rather than a one-size fits all UI. As an API provider, you likely already have lots of APIs being used to power your internal platform. The next evolutionary step is to expose these APIs to customers and partners for direct revenue generation. Any engineering platform can be very expensive to maintain. In today’s market, efficiency is critical. By directly monetizing your APIs, your highest cost center turns into a revenue center for the company. This fuels further investment into your APIs and products so they are best in class.Challenge of selling APIsHowever, selling an API is not easy. As companies transitioned from on-prem to SaaS, buying power has shifted away from CIO to team managers. With the transition from SaaS to APIs, this trend has accelerated empowering individual developers with tremendous amounts of buying authority. An individual developer can perform research and decide on a set of APIs that fit his/her use case. Yet, no single developer will deploy an app or integration to production in silo. There are many stakeholders that can weigh in or veto a decision to purchase and consume the API product.Even if your proposed economic buyer of your API solution is not part of engineering, APIs are deeply technical in nature and require extensive input from different engineering teams which can create a complex process for selling an API. As part of any procurement process, you’ll need to win over software architects, security engineers, legal teams, and product managers, each who can weigh in or veto a decision to purchase an API product.Land developers firstInstead of trying to win over every stakeholder with a complex, top-down sales process, start by landing developers first with a self-serve onboarding experience. This doesn’t mean you forego the sales team, but what you’re doing is getting developers to start seeing value in the API and advocate to their leadership team before they engage with your sales team. This process is commonly called developer-first adoption or product-led growth. Your goal is to get a developer to pay a token amount on a credit card such as $50/month. They should be able to do this quickly on their own time. Procurement is not involved for this stage so the subscription can simply be placed on a manager’s credit card.Sell through developersOnce your customer is already paying on a self-service plan and meets certain usage criteria, your sales team can become engaged. Because the customer already saw initial value in the API, the sales team takes a consultive approach such that the discussion becomes more of an “upsell” discussion. Sales guides the customer on how to gain more value out of the API or navigate certain business requirements the customer may have. A customer may consider upgrading due to increased usage, new use cases, or have advanced requirements.This means your sales team should understand a customer’s usage to identify when to engage a customer in a sales process. In order to do this, you should set up an API analytics tool which can track the API usage for each customer through an account-based dashboard. This provides a single pane of glass into each customer’s and pilot usage for sales and customer success. It’s recommended to set up automatic notifications such as through Slack, when a customer’s usage trend changes. For example, when an account’s API usage increased by more than 10% week-over-week, then they may be ready for a sales discussion. Other indicators include inviting additional team members to their subscription or utilizing additional APIs that they were not using before.Choosing the consumption metricWith usage-based pricing, it’s important to choose the right metric to charge for and bill on so that it’s correlated with the business value received. Simply charging for every API call is likely a poor choice since some APIs provide no value (such as status probes), they may error out, return no results, etc. Similarly, you might offer both a batch and non-batch API. If you’re building an SMS API offering, then a single API call that sends 100 SMS messages as a batch and then transcribes them to create a larger amount of value for the customer than an API call that sends just a single SMS message. In this case, billing based on the number of SMS sent would be a better metric than billing on the number of API calls.As a secondary example, let’s say your API handles sending marketing emails, but also provides the ability to store email templates in the platform. If you bill based on the number of templates stored, that might be a bad metric as it’s unlikely a customer receives value simply by storing additional templates in the tool. If the customer drafted a massively large number of email templates but never sent a single email through the API, their bill would still be large. On the other hand, customers will do weird behaviors like consistently deleting old email templates after it’s sent even though it would have been better to keep the email template in the tool. In this case, customers get value by automating the process of sending marketing emails. Thus, a better metric aligned to customer value would be a number of unique contacts emailed per month or number of emails sent per month.Common value metrics            Name      Example      When to use      Example Company                  Transaction volume      # of Events ingested, # of Messages Sent      APIs and event-based platforms such as SMS and analytics      Sinch              Revenue/cost share      % of revenue, transaction fee      Platforms focused on money such as payments or expense reporting      Stripe              Data volume      Gigabytes sent, Minutes made      Platforms focused on data such as logging or storage      Datadog              User-centric      Monthly unique users that are active      A modern version of charging per seat or per user      Segment              Resource      Compute Units, active hours      Compute infrastructure such as a database or virtual machine      AWS Redshift      Ensure the API creates businessEven once you’re able to get developers to use the API, this doesn’t necessarily mean the API creates business value. Developers may adopt an API just to experiment with a new technology, create a hobbyist project, pick up new skills, etc. However, businesses don’t care about a simple “Hello World” API. Instead, businesses are purchasing an API because they see it adding value to the organization. When it comes to business value, the three types include:  Reduce cost or time (vs a homegrown solution).  Unlock additional revenue for the organization.  Reduce risk for the organization.Pricing and PackagingIf you’re selling your API, there are many ways to package it depending on your business goals. Due to their transactional nature, many APIs enjoy an usage-based billing model (also called Pay As You Go or consumption-based billing) to unlock revenue growth, but there are many different approaches you can take. Areas that you should consider in your pricing strategy include:  Billing  Packaging  Invoicing1. Billing StrategyBilling strategy impacts adoption and customer activation the most and is usually driven by the product department. Prepaid billing is when a customer pays for a service upfront before it’s consumed. On the other hand, postpaid billing is when a customer pays after the services are already consumed.Prepaid billingPrepaid is a common billing model especially for traditional SaaS and enterprise software companies. Because the API provider gets cash upfront before any services are rendered, prepaid models can drastically increase cash flow for the business as you’re getting customers to commit and help finance your R&D.This is especially important if your acquisition or setup costs are high which allows you to invest further into product and growth. Prepaid is also beneficial for customers as they know what they are committing to upfront which can provide more spend predictability. Because usage amounts are unknown before payment is made, you can handle usage-based billing through a committed spend. This might include volume discounting. However, prepaid might force a customer to do mathematical estimates before they have real world data.Postpaid billingOn the other hand, postpaid is where you’re extending credit to your customers as they use your platform until they pay. Postpaid can reduce onboarding friction since a customer doesn’t need to estimate how much they need. Instead, they can just enter their credit card and deal with the bill later and see how much the damage was (like dining at a restaurant or bar). Postpaid billing has been popularized by consumption-based models like the digital advertising industry, and more recently the cloud industry. A benefit of postpaid is a customer does not need to commit ahead of time before any value is seen.However, there is a downside. Because you’re extending credit, it can be abused by customers depending on your offering (like a dine and dash scenario). Having good safeguards and limits is important to ensure a customer doesn’t accumulate “too much credit”, before they purchase. Postpaid can also have higher cancellation rates. A customer could use much more of an expensive service than expected and then have regrets.                   Prepaid Billing      Postpaid Billing                  Description      Customer purchase credits / quota ahead of time that is then later consumed      Customer only pays for their usage after it’s consumed              Pros      Better cash flowMore familiar with traditional contracts      Less onboarding frictionMakes PAYG easier              Cons      Friction in onboardingHarder with PAYG      Additional credit riskCan be abused      2. Packaging StrategyPackaging strategy impacts initial contract value and expansion revenue the most. Because not every customer will receive the same value and pay for the same amount, packaging refers to how your different SKUs and offerings are presented to a customer. You might use different features or usage components to enable customer segmentation.Tiered pricing is a packaging technique common within SaaS to create a “good”, “better” and “best” plan, each with a predefined set of features and quotas. Pay As You Go (PAYG) is another packaging technique also called usage-based pricing or consumption based-pricing where a customer purchases a quantity or volume such as number of API transactions. PAYG can be a revenue accelerant for developer-first or product-first organizations.Tiered pricingClassic tiered pricing makes it easy for customers to understand their cost and makes pricing more predictable, a plus for large companies purchasing software. It’s common and well understood within the SaaS industry reducing complexities around billing. In addition, it’s super easy to implement. You don’t need any metering or analytics to track usage. You can just implement a subscription billing software.The issue with tiered pricing is the disconnect between price and perceived value. As a customer comes close to the limits of their plan, they naturally should upgrade their plan. However, the price jump to the next plan can be significant which can cause scenarios where the customer “doesn’t feel ready” for the next tier. This can be exaggerated if the tiering utilizes too many variables. It’s uncommon a customer will exceed all limits of a plan and will instead exceed only one quota limit. However, the next plan has “too many extra items” that the customer doesn’t need (such as additional features). Similarly, you don’t want more than three or four tiers. If you have too many, it creates analysis paralysisPay As You Go (PAYG) pricingBecause of these issues with tiered pricing, a more modern approach is being utilized where customers pay for their usage. Consumers have utilized physically metered plans for quite sometime like gas and electric utilities. This concept of metering can be applied to digital products that have a usage-based component. Common things to meter on include transaction volume, gigabytes sent, or unique users.A benefit of usage-based pricing is the price to perceived value gap is significantly reduced as customers only pay for what they need. PAYG is a great accelerant for product-led growth or developer-first businesses. You can design a self-service plan that “hooks in customers”, and then allow them to grow their usage over time. In other words, your pricing model has built-in expansion revenue. However, PAYG can be challenging without knowing what is going on in terms of customer usage levels.                   Tiered      Pay As You Go (PAYG)                  Description      Traditional SaaS tiers with predefined set of features and quota.      Usage-based or consumption-based pricing based on a unit price.              Pros      Enforces a min spendPredictable for customerEasy to implement      More efficient for customerLess friction in expansionCan “appear” cheaper              Cons      Friction in expansionRigid, not aligned to value      Can upset customers with billing surprisesComplex to implement      3. Invoicing StrategyInvoicing strategy impacts cash flow and typically is driven by the financial team the most. Once you decide on a billing model and how your offering is packaged, you’ll want to determine when invoices are triggered and generated. Unlike billing and packaging which have impact on product and expansion revenue, invoicing strategy has a larger impact on unit economics. With recurring invoicing, you invoice the customer on a schedule such as per month or per year. On the other hand, you can also invoice customers once customers reach a threshold such as when they reach a certain quota or outstanding spend. This type of invoicing is called threshold-based invoicing.Recurring invoicingRecurring invoicing is the more popular of the two and easy for customers to understand. You can invoice them in a prepaid way (which is usually a fixed price) or send a bill for what the customer’s usage was for the prior billing period. For buyers of enterprise software, recurring invoicing is usually preferred as it’s predictable and easier to plan for. There are a couple of downsides, which usually come up with extreme Pay As You Go models. If you have some customers with extremely low volumes where they are paying only a few pennies or dollars per month, the transaction fees will exceed the cost of service. Similarly, if a customer can quickly rack up a lot of credit within a billing period or the value received is very transactional, this could create a large accounts receivable balance in between billing periods even though the service has since been rendered. This is common in the digital advertising industry where large spends can accumulate quickly.Threshold-based invoicingIn order to combat the poor unit economics of recurring invoicing, threshold-based invoicing can be leveraged. With threshold-based, the invoice is not generated until a certain threshold is reached. If prepaid, this means a customer is purchasing credits which can then be used (which might be far in the future). If postpaid, the invoice is generated after a threshold is reached such as $1,000 in ad spend. This ensures you only have up to $1,000 outstanding for a customer at a time regardless of their monthly spend. Threshold-based pricing is not without downsides. It can heavily complicate accounting since the spend is not predictable and not exactly aligned to a billing period like quarterly or yearly. The time could be open ended and not well defined.                   Recurring      Threshold                  Description      Customer invoiced on a schedule like each month, quarter, or year      Customer invoiced only after a credit threshold is met (can be prepaid also)              Pros      Easier for revenue recognition/GAAPMore predictable      Reduced transaction costMakes prepaid easier              Cons      Bad unit economics for low cost SaaSComplex if prepaid      Harder for finance to recognize revenueCompany liability      Implementing usage-based pricingImplementing usage-based billing introduces additional complexities beyond typical tiered pricing. You need an accurate mechanism to meter customer usage in a reliable way, and do it at scale. The usage must be auditable and can be relied on to settle disputes. Unlike a logging or monitoring tool, this data must be accurate. You don’t want a scenario where you thought the customer used X, but they have proof stating otherwise.You can build your own data warehousing on a platform like Snowflake or you can use a purpose-built usage-based billing product for APIs like Moesif. This solution connects to your API gateway of choice like Kong or Tyk and then integrate it with a billing provider like Stripe so yoy can easily set up metering rules.  For a detailed tutorial on how to do this with Kong and Stripe, check out the End-to-end API Monetization with Kong, Stripe, and Moesif. The sample project is also available on GitHubPitfalls of usage-based billing after releaseBeyond just implementing a data pipeline to handle usage-based pricing, there are a number of other challenges that come up especially in managing customer expectations. A common problem are customers who blow through the usage far quicker than anticipated creating a large bill they did not expect to receive. This can be especially true in APIs and tools that have a data volume component.It’s important to reach out to customers to keep them informed of this. One way to achieve this is via automated emails that warn customers of their usage once certain predefined thresholds are met. In this case, even though the quota is not a hard limit, you’re letting the customer know how much they used for the month.It’s also helpful to give customers some control around these thresholds. No one wants to receive 10 emails a month on their usage even though it’s a predictable bill. Providing a UI for customers to adjust those thresholds can help ensure they get alerted only when needed.",
          "url": " /developer-platforms/self-service/Best-Practices-For-Usage-Based-Billing-to-Generate-Revenue-from-APIs/",
          "author": "Derric",
          "categories": "developer-platforms, self-service"
        }
      
    ,
  
    
        "developer-platforms-self-service-a-simple-way-to-implement-usage-based-api-billing-is-finally-here": {
          "title": "A Simple Way to Implement Usage-Based API Billing is Finally Here",
          "content"	 : "Metered Billing has finally arrived in Moesif and we are super excited to be rolling out this latest feature. We have worked hard to deliver a smooth and simple way to monetize your APIs by allowing usage that is tracked in Moesif to be metered and billed by your favorite billing providers. Moesif can calculate and send usage data to a billing provider so your customers can be billed accurately, based on their usage.Metered Billing in Moesif is the easiest way to track customer usage of your API and applications and charge for it. The solution requires no-code and with a few lines of configuration, you’ll be up and running. Let’s explore more about our latest feature and why you’ll want to utilize it!What is Metered Billing?Metered Billing is a feature that allows Moesif users to meter their API or web usage and bill users based on that usage. An example may be a pay-as-you-go approach where users are charged a specific amount per API call. Users may also choose to implement a tiered billing approach which allows for specific usage tiers and for users to be charged based on which tier their usage aligns with.  You can also implement custom actions to track any arbitrary  metric like “gigabytes received” or “minutes processed”.At the end of the billing period, the usage is added up and sent to a billing provider so that users can be charged for their usage. Metered Billing is how companies can monetize their APIs in a post-paid fashion. Any API that is being tracked by Moesif can be easily monetized using Metered Billing.  Moesif actually sends usage data to the billing provider hourly. The billing provider will then sum up the usage data and bill for it.Why use Metered Billing in Moesif?Metered Billing is not a new concept and is used quite widely. However, creating a metered billing solution is usually quite complex and includes the need for accurate analytics, data pipelines, and integration with a billing provider. This entire setup may require quite a few applications, data feeds, infrastructure, not to mention engineering and support time.Luckily, Moesif contains all of the data needed to power a metered-billing system and supports direct integration with billing providers. This allows for a seamless “one-stop-shop” for all your metered billing needs. Moesif collects all of your API usage data and keeps track of it by user and by company. With this, Moesif can then easily tally up usage for a given interval and pass the data to a supported billing provider to calculate and charge for the usage.Moesif also allows users to create highly unique filters so that users can be billed for specific  endpoints and how they are used, such as billing based on body or header contents, route, or any other filter criteria that is supported within Moesif. Moesif enables a highly-customizable and flexible approach to bill customers for API usage.Where can I find it in Moesif?Metered Billing in Moesif can be accessed through the left-side navigation menu under the Billing Meters tab. Clicking the menu item will bring users to a screen where you can add your first billing meter or show meters that are currently created.You can create a new billing meter by using the Create New button and selecting Billing Meter. This will bring you to the new billing meter creation screen.How does it work?Getting up and running with Metered Billing in Moesif can be done in a matter of minutes. Here is a high-level overview of the steps required to get it working:Configure the billing providerYour first step will be to configure your billing provider. You will need to have your plans and add-ons set up within your account already. These will then be pulled into Moesif so that you can assign them to a billing meter when you connect your account to Moesif.  Part of this also requires that you set any webhooks or other connectivity that the billing provider and Moesif require to make sure the system is ready to handle communication between the platforms. This will be outlined when you add the billing provider to Moesif.Create a new billing meterAs mentioned above, creating a billing meter can be initiated in a few ways (including using the Create New button in the left-side navigation menu). You will need to name your billing meter and then can proceed to add your billing provider details.  If you have already added your billing provider into Moesif it will show up in the Billing Provider details section on the Create New Billing Meter screen. If not, you’ll be prompted to add your provider from the Billing Provider dropdown on the same screen.In this example, I have set up a billing meter and added billing provider details for the plan this usage should be linked to.Configure your filter and metrics to bill onNext, you’ll configure what usage will be counted towards your billing meter. The first step is to create your filter in the same way you would when creating a chart in Moesif. For instance, you may want to filter on only counting API calls that are sent to a specific API route.The last step is to specify the metric that you want to track. This is the metric that you charge customers on. Generally, Event Count is common to choose from, but other options exist including Unique Users, Unique Companies. You can also formulate your own metric using custom metadata fields such as to track bandwidth consumed or other metrics.Below I have set up a filter and metric to track event count for calls to the /purchase API endpoint.Save your billing meterWith our billing meter set up, we will now have usage based on our filter be linked to our specified billing plan. At the end of the billing cycle, Moesif will tally up the usage and pass this data to the billing provider for them to charge upon.Our final step is to save the billing meter to make it active. After it is saved, usage data will be automatically passed over to the billing system from the usage metrics gathered in Moesif.Give it a go!To try it out, simply sign into Moesif and navigate to the Billing Meters tab to set up your first one. As we expand our capabilities, expect all of your favorite billing providers to be added to Moesif so that you can quickly and easily begin to monetize your APIs. Don’t have an account yet? Simply sign up to get started in a matter of minutes. For more information on Metered Billing and possible configurations, you can also check out our docs pages!                Get the Biggest Business Impact from your APIs with Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /developer-platforms/self-service/A-simple-way-to-implement-usage-based-API-billing-is-finally-here/",
          "author": "Matthew",
          "categories": "developer-platforms, self-service"
        }
      
    ,
  
    
        "developer-relations-developer-marketing-developer-experience-vs-user-experience": {
          "title": "Developer Experience Vs. User Experience",
          "content"	 : "While they may seem similar at first glance, Developer Experience (DX) is not just “User Experience (UX) for developers”. Rather, DX is an extension of UX focused on users who build with technical languages and tooling. DX follows the same core principles of UX but extends it by recognizing that technical details and mechanical processes can be understood and utilized efficiently by a developer.  Great DX happens when developers feel they are being spoken to and having their needs met directly. This means showing code, providing lots of detail, and giving clear instructions for multiple use cases.User experience is a set of general principles most commonly applied to end-users, who may be non-technical and use software for both business needs and personal consumption. Developer experience is focused on enabling developers to use software to build solutions. The distinction between UX and DX comes from the different needs that end-users and developers have. In this article we’ll explore what good UX and DX entail, the key differences between them, and the practical differences when you develop products for developers.Great DX Starts with Principles from UXGood UX has two components: make it easy to start using your product, and ensure it’s enjoyable to continue using it. Developer experience extends these same core ideas, applied to a goal-oriented technical audience.First, make it as easy as possible to start using a new product. In UX generally this means avoiding sign-up flows if possible, or keeping them simple when they are necessary. UX intern can learn from this unique opportunity to delve into the intricacies of user experience design. Products should be intuitive - an end-user or developer should be able to start using your product and achieve their goals with minimal instruction. It is also crucial to remember the value your product is providing. You can use some of the same tools to evaluate UX and DX, like A/B testing, user surveys, and analytics software.The first key difference between UX and DX is the user journey. Good UX keeps the user journey for your product as straightforward as possible. Too many choices can be overwhelming for end-users, which can make it harder to accomplish a task and get value from a product. Good DX needs more flexibility to enable many different possible developer goals. Similarly, while end-users don’t need technical details to successfully use your product, developers need access to detailed information so their requirements can be evaluated.  Good DX makes it easy for developers to understand how a product or service works so they can build with it successfully.While good UX makes a product equally accessible to users with different technical capabilities, DX should take different users’ technical capabilities into account and offer different levels of support. More experienced developers want access to more powerful tools, while less experienced developers may need to start with a simpler workflow that may be easier to configure. It is important to understand what level of technical ability your primary users have in order to make your developer experience better fit your products.Now that we’ve compared the high-level differences between developer experience and user experience, we’ll take a look at an example of an application that shows the difference in practice.UX Vs. DX - A Practical ExampleLet’s walk through what UX and DX would look like in the real world.Most of us have probably been end-users of a consumer shopping app.  Imagine a user on a new shopping app for the first time, looking for a new sweater.  They might enter a search term, “sweater”, in a search box, or click through category navigation.  Then, they would select the sweater they would like and add it to their cart.  To check out, they might have to create an account with billing and shipping information, or they might simply click “Pay with PayPal” and let the site handle the rest. All our user needs to do now is wait for their sweater to arrive.Now, imagine a developer building the same functionality into a new app.  Leaving aside any APIs used for search functionality, they would have at least one API to handle authentication and billing, and likely more. First, they would have to install any required libraries or SDKs and resolve dependency issues. Next the developer would generate an API key and it to the app’s environment variables for reusability. Then the developer would write the API call to the authentication endpoint for the user, and write a listener that confirms the user has been authenticated. The next step is writing a call to the app’s backend that confirms there are still sweaters available, and an API call to process the payment. Finally, if the payment is successful, the developer should write code that listens for a response from the API, updates the backend to remove a sweater from the inventory, sends information to a fulfillment center, and schedules a package for delivery.For a developer to succeed in building this app, they need to understand which API endpoints to use, what methods are allowed at different endpoints, what responses to expect from the API, and how to securely authenticate API calls with an API key. Without good DX, developers may quietly drop your product at any point if they can’t resolve problems quickly, or may never attempt implementation at all if they’re not confident they can succeed.Just as consumers have a lot of choices for sweater shopping, developers have a lot of tooling options.  Making yours a compelling choice starts with good DX.  Developers will choose your product if they feel confident they can use it, and they will keep using it if they feel successful.Great developer experience starts with knowing developers’ unique needs as users.  In the next section, we’ll look at how general UX standards apply to developer-facing products and how you can tailor them to a developer audience.Unlock API Product Value with Good Developer ExperienceDeveloper experience encompasses what happens when a developer interacts with your service or product when they are building. Developers want to feel empowered to create and innovate, and good DX helps them see the possibilities and the steps to achieve their ideas. You need to set up a developer experience that inspires confidence, and then monitor to ensure your users are succeeding and expanding their integrations.Let’s take a more detailed look at the features of good DX and how they are often implemented in practice.Developers need to be able to configure the services they use to fit a variety of workflows. A linear user journey is less than ideal because it doesn’t reflect how developers work or how applications are built.  Great DX provides the tools and information to support flexibility, so your product should support different configurations whenever possible. Good ways of accomplishing this include:  Provide a range of well-scoped API endpoints  Develop SDKs in multiple languages  Split code into smaller reusable methods  Create readable jargon-free code  Document your product extensivelyIt is essential that you give developers enough information to understand the technical details of your product.Your documentation is a developer’s entry point to your product and informs their decision to implement it.  If developers can understand how your product works, they can confidently implement it in their workflow. You need clear, up-to-date, and thorough documentation so that developers can learn how to use your product, troubleshoot problems, and integrate it into their existing workflows. This is particularly important if you build your product for developers with different skill sets and levels of expertise.Ultimately, developers need to feel like you understand their pain points and that your product, tools and documentation address them. With good DX, any developer should be able to set up a basic implementation of your product with minimal instruction. More experienced developers should feel confident extending the functionality of your product to fit unique use cases. You can accomplish this by showing example code, providing detailed explanations in your documentation, using API analytics to understand how developers use your API, and demonstrating clear examples for multiple use cases.Developer Experience: Beyond First ImpressionsRetaining developers requires more than first impressions. Just as good UX needs to be evaluated, refined, and tested over time, good DX is an investment in the long term. You won’t know how well you’ve succeeded without using analytics to evaluate your DX and test changes. Monitoring your API helps you identify users who have not been able to successfully make API calls, find patterns of success and failure for developers, and see how different users are engaging with your product over time. While tracking UX metrics is relatively straightforward for products focused on end-users, DX metrics differ in important ways. You need to develop a good strategy for API analytics so that you track relevant business value metrics while avoiding vanity metrics.While developer experience shares many key aspects of user experience, it is important to remember that developers have specific needs that go beyond standard UX principles. You need to understand DX when you build products for developers so that you can attract developer-users, inspire their confidence and creativity, and support their increasingly complex integrations over time. Building good UX and DX can be challenging, but with Moesif API Analytics you can monitor your API and use metrics to craft the perfect API developer experience.                Understand How Customers Use Your APIs              14 day free trial. No credit card required.              Learn More        ",
          "url": " /developer-relations/developer-marketing/Developer-Experience-Vs-User-Experience/",
          "author": "Adam",
          "categories": "Developer-relations, Developer-marketing"
        }
      
    ,
  
    
        "announcements-features-your-guide-to-moesifs-new-in-app-navigation-upgrade": {
          "title": "Your guide to Moesif’s new in-app experience upgrade",
          "content"	 : "We have launched a redesign of Moesif’s in-app experience and it’s a big one! As part of our journey to give our users the best experience possible, we’ve streamlined our in-app navigation. This update makes it easier to find key features in Moesif and will hopefully allow even existing users to see some features they haven’t used before but may be useful.As with all changes, big and small, there may be a learning curve. That is why we have put together a brief guide to outline how to navigate to all of your favorite features in Moesif.  TL;DR: We added a Create New button which allows user to easily create their favourite charts, analysis, and workflows. All navigation is done through the left-side navigation menu. Dynamic Sampling functionality is now accessible through Settings. Apps within Moesif can be switched by the dropdown located in the top left corner of the Moesif dashboard.The Create New ButtonThe first thing you’ll likely notice is that creating new dashboards, charts, alerts, and workflows can be done by simply clicking the Create New button. This button is located at the top of the left-side navigation.When the button is clicked, you’ll be presented with many different options to easily create new charts, analysis, and workflows. Hovering over each of the options will also give a brief description of what each option can be used for.Picking any of these options will bring you to the corresponding screen for the selected option. Of course, you can still navigate to the screens individually and create from there but this gives a streamlined way to quickly get something new started. This feature enables one-click access to all of Moesif’s most highly used features.FeedbackIf you have feedback on our new design, let us know!Dynamic sampling has moved!  Dynamic Sampling is a cost-savings feature which enables you to control which API calls are logged to Moesif based on customer or API behavior. Because skipped API calls are never received by Moesif, they do not count against your subscription’s event quota. In addition, Moesif automatically extrapolates and normalizes metrics so your reporting is accurate even if different customers and behaviors have different sample rates.Access to configure Dynamic Sampling in Moesif has moved under the Settings menu. From here, you can access all the same features that Dynamic Sampling offered you before.  You can also find our newly released Privacy Rules and Custom Roles features in the Settings menu. Privacy Rules makes it easy to redact fields based on a team member’s role and help keep sensitive data privateLeft SidebarNavigation within the Moesif is now done through the left sidebar menu. We’ve designed it to make Moesif features more accessible and easier to navigate. All of your saved content is also now accessible from the left sidebar. This is where you can find saved dashboards, cohorts, alert rules, and other workflows you or your team has created in Moesif.Below includes some descriptions about where certain features are now located and any improvements to them:Dashboards  Dashboards are able to give you multiple views or charts on a single screen. It’s a customizable way for you to see all the data you want in one place.  So you can get started faster, your Moesif account will have prebuilt dashboards, one for each department:      Product    Customer Success    Engineering    Security    Sales    Marketing    These contain metrics based on what we’ve seen work best. This can provide a good starting point for you to modify or you can start with a blank slate.To access dashboards in Moesif, in the left-side navigation you can select the dashboard you’d like to view under the Saved Dashboards menu item. By default, Moesif has some pre-populated dashboards covering some crucial business areas including Product, Customer Success, Engineering, and a few others. These and any other dashboards you’ve created will be listed under the Saved Dashboards menu item.To create a new Dashboard, simply click on the + button beside the Saved Dashboard menu item.  You also have the ability to move the order of your saved dashboards in the menu and create a sub-dashboard as well. These option appear when you hover over each individual item under Saved Dashboard.We also made some improvements to functionality within a single workspace. Comments for a workspace now display on the right for better accessibility and readability. Now you can easily create and post a comment (including the ability to tage other Moesif users on your team).The comment is then saved for later viewing as well.Another improvement is the relocation of the Restore Search button so you can easily restore searches you’ve saved in the past.Events  The events screen allows users to visualize their data while applying many different filter configurations. Views and charts in the Events screen include Live Event Log, Time Series, Segmentation, Geo Heatmap, and Smart Diff.Creating charts and viewing event logs can be done in the Events screen. To navigate to this screen, in the left-side menu you’ll want to select Events.To choose between different charts and views, like Time Series or Live Event Log, you’ll want to select your desired chart from the dropdown and work with it from there.Users  The Users functionality in Moesif allows you to dive deep into user analytics. This gives the ability for Moesif users to sort and filter user-based metrics. With this data, you can dig into User Funnels, Retention, and User Composition analysis to paint a complete picture of your business and user base.User metrics and workflows are accessible through the left-side navigation as well. In the navigation, you’ll see the Users menu item as well as links to each of your created User Cohorts.When on the Users screen, you can navigate to the different functionalities and workflows through the dropdown in the upper-left of the screen. This where you will navigate to features like Funnel, Retention, and Composition.When a user cohort is created, it will be shown under the User Cohorts accordion menu underneath the Users header in the left-side navigation.Companies  Similar to Users features within Moesif, Companies features allow you to do the same functionality but filter and sort based on a company data.Company metrics and workflows are accessible through the left-side navigation as well. In the navigation, you’ll see the Companies menu item as well as links to each of your created Company Cohorts.When on the Companies screen, you can navigate to the different functionalities and workflows through the dropdown in the upper-left of the screen. This where you will navigate to features like Funnel, Retention, and Composition.When a company cohort is created, it will be shown under the Company Cohorts accordion menu underneath the Companies header in the left-side navigation.Alert Rules  Alert rules enable you to monitor your APIs for issues impacting customers and be more proactive in ensuring a great customer experience. You can also leverage alerting to track metrics on a per-customer level. For example, you may want to get alerted when a new customer has a large drop in traffic or has a large spike in average latency. Alerts can be sent to the channel of your choosing including email, SMS, PagerDuty, Slack, or using a custom WebHook.Alerts screens have also been optimized and easier to navigate. To get to the Alerts screen, you’ll find it under the Alerts menu item on the left-side navigation. Underneath that header, you’ll also see a link to the Alert History screen.The Alert Channels view has also been moved to the right side of the screen to make way for the new navigation menu on the left.Behavioral Emails  The Behavioural Emails feature in Moesif allows users to set up automated emails based on user behaviour within their application. Sending alerts for events such as rate limit warning, onboarding guidance, integration errors, or any flavor of custom email you’d like to send based on customer interaction with your application.Behavioural emails can now also be found in the left-side navigation. Previously they were nested within the Alerts & Governance screen. Now, they are more visible and easily accessible.Embedded Templates  Embedded Templates give users the ability to embed their favourite event logs and charts into their application. This allows these metrics to be viewed by customers without any need for custom code.Embedded Templates have also made their way into the main navigation instead of being nested in the Dashboards screen, like in the previous UI. You’ll still need to create Embedded Templates through the Metrics screen as you did in the past.Once Embedded Templates are created, they can then be viewed through the Embedded Templates screen.Governance Rules  API governance enables you to restrict access to your APIs or add custom headers based on historical usage patterns of your API. Proper API governance enables the foundation for a good API security program. For example, Moesif’s governance rules can block access to users who are maliciously scraping your API or accessing an abnormally large number of items.  Besides API security, you can also leverage governance for business requirements and continuity. For example, you can create a rule to block access to customers with overdue invoices or add deprecation warning headers when customers access an old version of your API.Finally, Governance Rules are also easily accessible through the main navigation as well now. Previously, Governance Rules could be accessed through the old Alerts and Governance screen. Now, in the new left-side nav, the Governance Rules menu item is located at the bottom.Try it out!Our latest updates are now live and ready to be used! Just log into Moesif and experience our latest improvements to the look and feel of the application. We are excited for you to experience our latest iteration of Moesif for yourself!",
          "url": " /announcements/features/Your-guide-to-Moesifs-new-in-app-navigation-upgrade/",
          "author": "Matthew",
          "categories": "Announcements, Features"
        }
      
    ,
  
    
        "technical-api-product-management-what-is-ttfhw": {
          "title": "What is TTFHW?",
          "content"	 : "TTFHW is not the most common acronym you’ll hear in the tech and product domain. Even though it’s not extremely common, it can be argued that it is one of the most important acronyms for companies trying to scoop up and deliver value to new users.TTFHW, or Time To First Hello World, is likely something you’ve already thought about. Many product-led enthusiasts refer to it as the customers’ “aha!” moment. This is the moment when a customer first derives value from your platform. Something many programming languages and programmer tools refer to as a “Hello World” moment. It’s a make-or-break milestone and one that you obviously want every customer to experience. TTFHW is simply the amount of time it takes to get to that “aha!” moment.What is Hello World?The term “Time To First Hello World” is derived from something that many software developers are aware of: the first time you run a program you’ve built in a new language (and the time it takes to get there). Many languages have Hello World guides and many developers created their first programs with a new language by printing “Hello World” to a console or screen.Without these moments, programming languages and frameworks would not bring us value. A Hello World program tends to be a small and simple program that a developer writes in a specific coding language. This program usually forms the building blocks for later programs they will code.When applying this to a product, the “Hello World” or “aha!” moment might look a little different. It may be very simple, such as a user calling an API for the first time and receiving a response. Once they achieve the Hello World milestone, it’s assumed that they will continue to play around and use the product. The optimal outcome is that they hopefully become a paying customer.In a more complex flow, we may have multiple steps to our Hello World event. For example, if we were running an e-commerce platform, our Hello World moment may occur only after a user has signed up, logged in, and successfully checked out with their first purchase. Of course, other applications and products may have an infinitely complex Hello World moment.Defining Hello World for your productYour product’s Hello World moment can only be defined by you. This is because every product is different. Only you can determine the first moment when a user receives value from your platform or product. There are a few ways to map out your Hello World moment, I’ll give a few more examples:  If you’ve created a new programming language, this could be when the user compiles their code and runs their first program.  If you’ve created an investment application, this could be the moment when a user first deposits funds into their account and makes their first trade.  If you’ve created an API platform (that is monetized, especially), it may be the moment when the user makes their first API call and a charge is added.Your app most likely falls into none of these categories, or maybe it does. Regardless, you may need to really dig in to see what you consider your Hello World event (or events, plural).If you have a multi-step Hello World, it’s important to track each step since you may be able to identify bottlenecks in your flow. For instance, if you have a 3 step process and step 1 is taking 90% of the time for users to accomplish, this is likely where you are also losing many users. By defining all 3 steps instead of just the last one, it gives us a true picture of where our issues are and where to optimize.Time To First Hello World and why it’s importantDefining the Hello world moment for your product is a small part of the TTFHW equation. The largest factor is the time it actually takes to reach that point. There’s no “one size fits all” in terms of how long it should take but based on trends within new products and modern attitudes towards onboarding, it should be quick.The longer it takes for a customer or user to reach the Hello World moment, the higher the likelihood of them dropping off and fully abandoning the product. This means that creating a lean flow to achieve that first Hello World moment is crucial. Especially in regards to a multi-step flow, each input and click should be carefully curated. Anything that can be done after that Hello World moment is achieved should be put off until then.By tracking the Time To First Hello world, you can start to gather real metrics about what parts are taking the longest and where most users are dropping off. You’ll be able to see trends and thresholds, such as when a user “takes more than X number of minutes to complete the Hello World criteria, they usually abandon the product”. This means you can improve the flow and track to see if those improvements are making an impact.There’s also a secondary remedy outside of simply improving the product. This approach may involve notifying your Customer Success team when a customer is exceeding the estimated threshold for TTFHW. Even better, you may do an in-app notification pointing them to further resources or possibly send them an automated email showing them how to move closer to that Hello World moment.Decreasing your TTFHW is important because it’s what gets customers in the door and gets them excited to use the product. Minimizing the time until a user receives value is a win-win for both the user and your product. Optimizing user experience is the easiest to gauge when TTFHW is measured.TTFHW and User Funnels in MoesifSince Moesif is able to track user metrics with a high amount of granularity, it makes it a great platform to define, track, and improve your products TTFHW.By default, Moesif creates a variable on a user’s profile for TTFHW. This high-level TTFHW is calculated as the difference between when the user was first added to Moesif and when they performed their first API call. For some scenarios, this may be all the complexity needed to accurately determine your product’s TTFHW.For more complex flows, our users turn to User Funnels to create multi-step funnels which track each step of the Hello World journey and record the amount of time it takes between each step. This, as you will recall from above, means that we can identify which steps are taking the longest in the entire flow instead of only focusing on a single event. With Moesif, an infinite number of steps can be added to a user funnel. Below is an example of what a user funnel may look like within Moesif:This user funnel reflects a scenario with 3 steps to complete the funnel. The first step is when the user signs up, the second step is when the user makes their first electronic signature, and the final step is when the user completes their 10th esignature. You’ll also notice that you can see the conversion rate for each step as well as the time to convert for each step.An added bonus is that some products may have different Hello World moments depending on what type of user they are if your app supports different profiles and user types. For this, a user funnel can be set up for each of these scenarios so that each flow is accurately tracked.Define, track, and improve your TTFHWUsing Moesif to determine your product TTFHW and improve it is simple. Moesif easily integrates with many platforms including the most common API frameworks and API gateways. Once integrated, your metrics can easily be used to determine all of the factors mentioned above.Starting at a high level, you can easily define some crucial steps in your user funnel. The result of getting to the first Hello World is to later drive realized value for the user and from a business perspective. Here is an example of a funnel that follows a user’s initial visit to the page through to when they (And the business) receive realized value. You can see the average time between each step in the funnel as well as the conversion rate for each step.When tracking these events in Moesif, it’s important to remember that Moesid can track event both on the frontend and backend. What we refer to as “Web Actions” are considered frontend events. “API/Platform Actions” are those events that occur on the backend side of the application. You may want to use one or both types of actions to define your user funnel. Below is an example of how these events might be split up based on the previous example.No matter what the complexity is of getting to that first Hello World or “aha!” moment, tracking each step is an important part of attracting customers and retaining them. TTFHW is about calculating how long it takes to deliver that value. The general consensus is that the faster the TTFHW, the better chance you have of acquiring new customers. This is because the value is delivered upfront ensuring that more customers reach that moment instead of abandoning your product.Want to get on the fast track? Simply sign up for Moesif and check out our guide for an example of how to set up User Funnels to track your TTFHW in a matter of minutes.",
          "url": " /technical/api-product-management/What-is-TTFHW/",
          "author": "Matthew",
          "categories": "technical, API-Product-Management"
        }
      
    ,
  
    
        "technical-api-analytics-building-your-own-in-app-charts-use-moesif-embedded-templates-instead": {
          "title": "Building Your Own In-App Charts? Use Moesif Embedded Templates Instead!",
          "content"	 : "Certain products can benefit from having real-time charts displayed within them. Whether it is an internal or external application, metrics truly come to life when they are displayed nicely, particularly in a dashboard.graphics you would need to implement your own charting tool, map the data or metrics into the tool, and then maintain this implementation. Overall, it was a very inefficient and cumbersome way to display data visually within your applications.With Moesif, adding stunning visualizations to display data is easy and maintenance-free. All of the charts you find useful within Moesif can be easily exported and embedded right within your applications. There are a few ways to do this within Moesif, which include using an embedded template or public link to display a chart within your application.Let’s explore the benefits of using embedded templates, how they work, and some real-world examples of Moesif users leveraging this data visualization feature.Why use an embedded template?There are many applications that can benefit from having a visualization of key metrics. A few examples may be to show:  A historical view of application traffic  New user sign-ups  Errors being experienced within your application  Display live events in real timeNot all users will have access to the Moesif platform at your organization but it doesn’t mean that they should not have access to certain key charts. Embedded templates are a great way to show users relevant data, allow them to customize their experience and visualizations, even giving the option to share with users who don’t have direct access to Moesif.Embedded templates enable users to add dynamic charts to their application without the need to actually code them. This approach saves time, support, and effort while still giving all the benefits of a custom solution to display relevant data in applications and in-app charts.What types of data and charts can be displayed?Depending on what data you need to display, there are 2 ways you could do this in Moesif. You could utilize either a public link or embedded template to display the data.A public link is a static view of your data in Moesif. You set the filter criteria in Moesif and essentially export the corresponding chart. There is no way for viewers to change the criteria or dynamically add a variable to the output of the chart. This is the best way to show users, authenticated and unauthenticated, a specific static dataset.An embedded template is similar to a public link but allows for dynamic parameters to be used to retrieve and display data. Filtering by a specific user ID, date range, or other criteria which are dynamically supplied to the chart rendering. This approach is more feasible for an authenticated user. Data can be filtered or customized depending on what parameters are sent to the query and a corresponding chart can be rendered for ease of gleaning insight.In this post, we will only focus on embedded templates. For more info on  public links, check out our docs.How does it work?Getting an embedded template to work in your application is very simple. It does require that you have access to Moesif, have an API server, and have the ability to add an iframe element to your frontend code.Let’s break down each step a bit further:Create your chart and/or filterThe first step is to actually render a chart in Moesif. For this, you can head over to the Metrics screen, add your filters, and then see the visualization appear on the screen. In this example I am showing all the events for a specific user ID in a Live Event Log.You may also view this same data in a time series chart so I can more easily see the trends over time.At this point, you’ve established your filter, rendered a chart in Moesif, and can proceed to actually generating your embedded template.Create templateCreating the template to render in your UI takes only a few clicks. In the Metrics screen, you can click “Embed” and in the dropdown click “Create Template”.The next screen will ask for some dimensions including a Workspace Name (essentially your template name), your share type (public link, embedded template, etc), and an area to select which fields you’d like to dynamically supply to the template.Once you do this, the template is created and you will see some instructions about how to add the chart into your UI. With a large amount of customizability, Moesif allows you to share data and tables with your team in a meaningful way.Add an endpoint to API serverOnce your template has been created in Moesif, you should create an endpoint on your API server that will fetch the data and return it to your UI. The data returned will contain everything that you need to actually render the chart in the iframe you will embed in your frontend code.Embed the iframe in the UILastly, you’ll need to add an iframe element to your UI code. Your code will call your new API endpoint and return this data to iframe to be rendered. Once the data for the chart is returned from Moesif (through your API), you’ll have a nice rendering of the chart in your UI.  If you want to see a more detailed walkthrough of these steps, check out our guide on embedded templates.A real-world exampleMany Moesif customers rely on embedded templates to give them a simple way to display important metrics to their users and decision makers for purchasing decisions. This saves them time and makes rendering real-time charts in their applications simple and easy to maintain.pVerify, a real time insurance eligibility verification software, uses embedded templates to help customers to see API activity and trends. Instead of creating their own charts and analytics solution, they decided to use Moesif to easily display the required info to their users.pVerify also leverages encryption available within Moesif to make sure that while using embedded templates they are still HIPAA compliant. This was an important part of the solution since pVerify works with highly-confidential health care records.pVerify has a few examples of how they are using embedded templates to help customers monitor and observe their API usage. This includes using Moesif’s Live Event Log display.They also use time-series charts to give a better visual representation of trends.By allowing users to filter and view data in different ways, users can easily view trends and also dive into individual calls to explore in more detail.Using embedded templates has allowed pVerify to easily add charts into their application with minimal development. Allowing their users to securely dive into API analytics, all enabled by Moesif.Try it out for yourself!If you’re already using Moesif, getting started is extremely simple. Embedded templates are available to all Moesif users and easily added into your existing applications. For new users, simply sign up for an account, integrate your APIs, and begin using embedded templates to display your favorite charts in your application.",
          "url": " /technical/api-analytics/Building-your-own-in-app-charts-Use-Moesif-Embedded-Templates-instead/",
          "author": "Matthew",
          "categories": "technical, api-analytics"
        }
      
    ,
  
    
        "api-engineering-api-gateways-open-source-api-gateway-roundup": {
          "title": "Open Source API Gateways: The 2026 Roundup (Kong, Tyk, APISIX, WSO2)",
          "content"	 : "Open-source API gateways are how most teams in 2026 run their API traffic. The proprietary alternatives (AWS API Gateway, Azure APIM, Apigee) are excellent when you are committed to a single cloud, but for multi-cloud, on-prem, or cost-sensitive deployments, the open-source landscape is mature and well-supported.                Rapidly Deploy Moesif Analytics With Our Gateway Partners              14 day free trial. No credit card required.              Try for Free        This is a 2026 roundup of the seven open-source gateways worth evaluating, what each does well, and how to pick between them. The list has shifted since the early-2020s discussions of “the top open-source gateways”: APISIX has grown rapidly, KrakenD has found a niche, and WSO2’s open-source core has become more visible alongside the commercial subscription.What an API gateway doesAn API gateway sits between callers and your backend services. Every external request goes through it, and the gateway handles the cross-cutting concerns that would otherwise be duplicated across every service: authentication, rate limiting, request routing, response transformation, logging, and analytics.The architecture works for the same reason a reverse proxy works: putting one piece of software in front of many services centralizes the operational concerns. In 2026 the category has expanded to include not just REST gateways but also AI gateways (handling LLM call routing and per-token attribution) and event gateways (handling async and WebSocket traffic).Common features of an API gatewayEvery gateway worth evaluating handles five core capabilities:  Authentication. API keys, OAuth 2.0, JWT validation, mTLS. The gateway validates credentials before any backend service sees the request.  Rate limiting and quotas. Per-customer, per-API, per-endpoint limits with the right backoff signals (429 responses with Retry-After headers).  Routing and transformation. Send /users/* to the user service, /orders/* to the order service. Transform headers and bodies on the way through if needed.  Caching. Cache responses to read-heavy endpoints at the gateway to reduce backend load.  Observability. Capture logs and metrics for every request, with enough metadata to debug issues and answer business questions.Beyond these five, the differentiators are usually about deployment model (cloud-native vs. self-hosted), plugin ecosystem (community-driven vs. vendor-curated), and increasingly AI/agent traffic handling.The 7 open-source API gateways worth knowingKongMaintained by Kong Inc. Open-source Kong Gateway plus commercial Kong Konnect / Enterprise.  Built on: NGINX/OpenResty with a Lua plugin runtime.  Strengths: Large plugin catalogue, Kubernetes integration via Kong Ingress Controller.  Trade-offs: Custom plugin development requires Lua expertise. Lifecycle management, governance, monetization, and per-customer analytics are paywalled in Konnect / Enterprise, missing in the open-source core, or assumed to be added by separate tools.  Best for: Kubernetes-heavy environments where Kong Ingress Controller is already in use and the gateway alone is what’s needed.TykMaintained by Tyk Technologies. Open-source Tyk Gateway plus commercial Tyk Dashboard and Tyk Cloud.  Built on: Go, with plugins in Go, JavaScript, Python, and Lua.  Strengths: Self-hostable in air-gapped environments, multi-data-center support, GraphQL gateway included.  Trade-offs: Smaller community than Kong; the analytics dashboard is paywalled in higher-volume deployments.  Best for: Teams that need self-hosted or hybrid deployment, GraphQL-heavy stacks.APISIXMaintained by the Apache Software Foundation. Apache APISIX is Apache 2.0-licensed and community-governed.  Built on: NGINX/OpenResty with a Lua plugin runtime.  Strengths: Hot-reloadable configuration, Apache Foundation governance, multi-protocol coverage (HTTP, gRPC, MQTT, WebSocket), broad built-in plugin set.  Trade-offs: Younger ecosystem than Kong, with documentation that is still maturing. Like Kong, it is gateway-focused; full lifecycle, governance, and monetization typically come from additional tooling.  Best for: Teams that prefer Apache Foundation projects and need a multi-protocol gateway with hot-reloadable config.GraviteeMaintained by Gravitee.io. Open-source Gravitee API Management with commercial Enterprise subscription.  Built on: Java/Vert.x.  Strengths: Event-native gateway (handles async and Kafka alongside REST). Open-source licensing on the management stack, not just the gateway runtime.  Trade-offs: Smaller community and partner ecosystem than Kong, APISIX, or WSO2. Java/Vert.x runtime has a higher memory footprint than Go-based alternatives. AI/MCP support is limited compared to platforms with dedicated AI gateways.  Best for: Event-driven architectures with Kafka or async patterns alongside REST.NGINXMaintained by F5. NGINX is primarily a reverse proxy and web server; it can be configured as an API gateway.  Built on: NGINX core.  Strengths: Long-running, low resource footprint. If NGINX is already in your stack, deploying it as a gateway adds zero new infrastructure.  Trade-offs: No native API management dashboard, developer portal, governance, or monetization. You build those layers yourself or pair with NGINX Plus (commercial) or other tools. No AI/MCP capabilities out of the box.  Best for: Teams already running NGINX who want a thin gateway without adopting a new platform.KrakenDMaintained by KrakenD.io. Open-source under Apache 2.0, with commercial enterprise offering.  Built on: Go, stateless by design.  Strengths: Aggregation pattern specifically for composing multiple backends into single responses. Declarative configuration. Stateless design.  Trade-offs: Narrower scope than Kong, APISIX, or WSO2 (focused on the gateway and aggregation layer), not full API management. No native developer portal, governance, or monetization.  Best for: Aggregation gateways in front of microservices that need response composition, where the rest of the API platform comes from other tools.WSO2 API ManagerMaintained by WSO2. Open-source under Apache 2.0 (the gateway, publisher, and developer portal); commercial WSO2 subscription for support and additional features. Note: Moesif is part of WSO2, so this comparison is not arms-length.  Built on: Java, with Envoy-based and Kubernetes-native runtime options in newer releases.  Strengths: Multi-gateway runtime that deploys across WSO2, Kong, AWS, Azure, and Envoy from a single control plane (a capability no other gateway in this list offers). Native AI Gateway that auto-generates MCP servers from any OpenAPI spec and governs both inbound agent calls and outbound LLM calls. Full-lifecycle coverage including spec-level governance enforced at merge time. Apache 2.0 license across the full management stack, not just the runtime. Named a Leader in The Forrester Wave: API Management Software, Q3 2024.  Trade-offs: Operational footprint is larger than gateway-only deployments like Kong or APISIX. Java runtime has a higher memory footprint than Go-based alternatives.  Best for: Enterprises with multi-cloud or multi-gateway requirements; teams running meaningful AI/agent traffic alongside REST; organizations that want one integrated platform rather than assembling gateway + governance + analytics + monetization from separate vendors.How to choose between themA practical decision matrix:            Need      Recommended                  Cloud-native, Kubernetes, broad plugin ecosystem      Kong or APISIX              Self-hosted, air-gapped, hybrid      Tyk or APISIX              Already running NGINX      NGINX (extended with config)              Event-driven and Kafka alongside REST      Gravitee              High-throughput aggregation      KrakenD              Multi-cloud, multi-gateway, AI-heavy      WSO2 API Manager      The right gateway is the one that fits your existing infrastructure and operational model. Premature optimization on absolute throughput rarely matters; fit-for-purpose almost always does.Open-source API gateways in 2026: AI gateway featuresTwo changes are reshaping the category in 2026.AI gateway plugins or native features. Kong has an AI Gateway plugin set; APISIX has an ai-proxy plugin; WSO2 has a native AI Gateway. The pattern is to handle LLM traffic (routing across providers, caching, cost attribution) alongside REST in the same gateway layer rather than introducing a separate AI proxy.MCP server generation from OpenAPI specs. A few gateways are starting to expose existing REST APIs as MCP servers automatically. WSO2’s AI Gateway is the most mature; others are catching up. If serving AI agents is part of your roadmap, this is a capability worth evaluating.For a deeper look at the developer-portal layer that typically sits alongside the gateway, see our developer portal guide.Extending a gateway with observabilityAPI gateways ship with operational analytics (latency, throughput, error rates). The gap most teams fill with a separate tool is per-customer behavioral analytics and per-customer revenue attribution.Moesif integrates with every gateway in this list as a plugin, middleware, or webhook. The gateway handles the runtime; Moesif captures the analytics layer that the gateway does not produce natively (or produces lightly). For teams already on WSO2, Moesif is the bundled analytics tier.Next stepsThe open-source API gateway landscape in 2026 has more credible options than ever. Pick the one that fits your existing infrastructure and operational model, layer per-customer analytics on top, and revisit annually as the platforms continue to evolve.To see what per-customer analytics looks like on top of your gateway, start a 14-day Moesif free trial. No credit card required.Frequently asked questionsWhat is an open-source API gateway? A gateway whose source code is publicly available under an open-source license (typically Apache 2.0 or MIT). Examples: Kong, Tyk, APISIX, Gravitee, KrakenD, WSO2 API Manager. NGINX is open-source but can be extended commercially.Which open-source API gateway is best? Kong has the largest community and plugin ecosystem; APISIX is the fastest-growing; WSO2 is strongest for multi-cloud and AI-heavy stacks. The right choice depends on your infrastructure and use case.Can I use NGINX as an API gateway? Yes. NGINX is primarily a reverse proxy and web server, but it can be configured to perform API gateway functions (auth, rate limiting, routing). You build the management layer yourself.What is the difference between Kong and APISIX? Both are NGINX-based gateways with plugin ecosystems. Kong has a larger community and ecosystem; APISIX has Apache Foundation backing, hot-reloadable config, and broader native protocol support. APISIX has grown rapidly in 2024-2026.Do open-source API gateways have AI gateway features? Increasingly yes. Kong has an AI Gateway plugin, APISIX has ai-proxy, WSO2 has a native AI Gateway. The category is evolving rapidly as AI traffic becomes a larger share of API consumption.How do I add observability to my open-source API gateway? Most gateways have plugin or middleware hooks for observability tools. Moesif integrates with Kong, Tyk, APISIX, NGINX, and WSO2 through native plugins or middleware.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free        ",
          "url": " /api-engineering/api-gateways/Open-Source-API-Gateway-Roundup/",
          "author": "Adam",
          "categories": "API-Engineering, API-Gateways"
        }
      
    ,
  
    
        "technical-api-analytics-moesif-and-datadog-feature-similarities-differences-and-how-they-can-work-together": {
          "title": "Moesif and Datadog: Feature Similarities, Differences, and How They Can Work Together",
          "content"	 : "Moesif and Datadog are both platforms that provide monitoring and the ability to view metrics. At their core, though, both platforms do very different things for your organization and the teams that use them. Both can complement each other very well and contain features that can aid in making better products that scale seamlessly.Datadog is great for ensuring that infrastructure is working as expected, alerting you of errors, and monitoring downtime and performance degradation. This is all part of a good application performance monitoring solution. Essentially, if you have infrastructure and resources that are crucial to your operation, Datadog is a great way to see a full view of your operations landscape. The platform can help to make sure that all parts of your technical operation are running smoothly and alert you when they are not.Moesif, on the other hand, is more concerned with monitoring and analytics from a user and company perspective. Moesif can ensure that users are returning to use your product, give valuable insights into how they are using it, empower teams who use it to drive adoption, and much more. Moesif allows you to see the end-to-end user journey including how the user interacts with your app or API product and also how they are using it. You can also view how a set of users from a particular company are interacting with your product as well by enabling company tracking.Both have different purposes and goals, but together they can create an end-to-end picture of your business including your users and operations. Building a great business consists of having a great product experience and solid infrastructure monitoring to ensure peak performance.High-cardinality, high-dimension analyticsTools like Datadog excel at single dimension analytics, which means there is a metric name and a value that is changing over time. There may be a small number of tags or dimensions, but usually a handful. It’s important for product-driven teams to not only understand the volume of transactions but also the different flavors of payloads and queries customers send over the API. This can be defined across thousands of different query parameters, HTTP headers, and body keys. In addition, each one of those fields can have a very large number of distinct values (high-cardinality) including the customer identifier itself.This is where Moesif excels, enabling product owners to slice and dice their API usage data by an infinite number of different possibilities. For example, in the below chart, we can track the daily number of unique users who are experiencing OUT OF STOCK vs AVAILABLE in the response payload “label”. While a DevOps engineer is less interested in unique users and whether they saw out of stock, these insights are critical for making good business decisions.With both platforms using different metrics and data for different objectives, it makes sense to use them together. Using the two in unison comes with many advantages and helps to generate a holistic view of the entire business. Covering everything from how customers are using your product all the way down to how the infrastructure it is running on is performing.Now, let’s take a look at what features each platform contains.Breaking Down the FeaturesBoth platforms are filled with features to aid multiple aspects of your business. Below is a breakdown outlining what each platform offers, why it matters, and how it can complement the other platform.Connectors, plugins, and integrationsBoth platforms support many of the most common platforms on the market with a variety of integrations. Since both platforms serve different functions within an organization, their connectors, plugins, and integrations reflect that.Datadog supports infrastructure more heavily so there are many connectors such as those to help monitor security, containers, orchestration, cost-management, and issue tracking (to name a few). Many of the most popular tools and platforms on AWS, Google Cloud Platform, and Azure are supported, just to name a few.Moesif supports most API frameworks. These include server integrations for Node.js, Python, Java, and many more. Moesif also has plugins for popular API gateway and API management tools including Kong, NGINX, Tyk, Envoy, and more.Moesif’s connectors are more focused on product use cases. Some with include Slack, Segment, Pendo, HubSpot, Salesforce, Marketo, and others.  TL;DR - Both platforms have many integrations available. Datadog covers most platforms where infrastructure monitoring would be beneficial. Moesif has integrations for the most popular ways to build and manage APIs including server integration SDKs to plug directly into code or plugins to integrate with API gateways.DashboardsDashboards are a great way to pull together data for a quick overview. Displaying key insights in a single area is something both platforms do well.Datadog allows you to build custom dashboards that show essential metrics. Operations teams can very easily view how each system is running, including performance, usage, and other key values that indicate a smooth operation.Moesif dashboards can be used to distill key customer insights for all of the departments that may care. For instance, a marketing dashboard may show the sources of traffic, website engagement, and customer retention. A product dashboard may show customer activation funnels, average TTFHW (Time to First Hello World), and other metrics which show how customers use the API or platform.  TL;DR - Dashboards in Datadog offer great insights into your operation and ensure that crucial pieces of your business are up and running. These are customizable and are easy to set up and optimize. Moesif also offers easy-to-use and setup dashboards, including some that are added out-of-the-box. Moesif dashboards can show many teams, including marketing and sales, granular metrics to help with performance and adoption.AlertingThe ability for alerts to be sent to specific channels when critical events happen is essential in a monitoring tool. Alerts allow users to be notified on their platform of choice when specific criteria are met. This means that users don’t have to manually watch for events but instead can “set and forget”, knowing that they will be alerted automatically and in real-time.Datadog alerts can be centered around conditions within your infrastructure. Datadog is able to create monitors that actively check metrics, integration availability, network endpoints, and more. When an alert is activated, Datadog can send the alert via email, Jira, PagerDuty, Slack, or WebHooks.Moesif can create alerts when users are experiencing certain conditions. These may be something like a new user sign-up, making a sequence of API calls, an abnormal increase or decrease in API traffic, or if users are experiencing a high number of errors or are having issues integrating. Many of these alerts can help your team to be proactive and ensure a great user experience and to narrow down issues within your product quickly.  TL;DR - Datadog is great for alerting you about infrastructure issues and general tech operations, making sure systems are up and running. Moesif is great at alerting on specific user behaviors and proactively making developers, product managers, and support teams aware of users who are stuck or receiving errors.User and Company trackingA critical feature of user behavior analytics is the ability to analyze the sequences of events made by different users, and not just look at events in isolation. To enable analyzing complex user behaviors, API calls and actions in Moesif can be tied to a user id or company id. From there, you can create reports across multiple API calls. For example, with Moesif you can find users who made at least 10 API calls to GET /widgets/category/:id but made 0 API calls to /purchases.With user behavior analytics, product-led teams can analyze their activation funnels and perform cohort retention analysis to understand the health of their business.These types of analysis can be a huge benefit to product-led organizations where user experience is a top concern.Funnels allow you to see conversion rates between different steps in your products funnel such as the percentage of users who sign up that end up making their first API call. From there, you can slice and dice by user demographics like marketing campaigns to understand where to invest more resources. Retention analysis allows you to see if users are loyal and continue to derive value from the platform. Segmentation allows you to dive into who your users and companies are and which segments they belong to.Because Moesif can pull in customer data via API or through connectors like Segment, you can slice and dice your API usage and API metrics by customer demographics. For example to better understand usage by company size or employment title. This makes it easier to also track usage by plan or other customer information.By analyzing API payloads and UI events (through Moesif’s BrowserJS package or Segment), strategic business insights can be readily gleaned and operated on. An example would be optimizing e-commerce transactions within your application. By tracking checkout non-completions you can identify the exact route of the unsuccessful checkout, troubleshoot the problem with the seller, and proactively notify the purchaser of the problem. This information could also be used within the product team to improve the checkout flow so future transactions avoid the same issues of previously dropped or incomplete checkouts.Moesif’s ability to track each individual user and their company empowers the use of “behavioral metrics”. These types of metrics allow us to look at more than a single event, instead of allowing us to link multiple events to determine a specific behavioral pattern. This is exactly what enables Moesif to do everything mentioned above.With Datadog you are able to also somewhat see actions a user is taking but not with as much granularity as Moesif. However, Datadog does support some very basic user tracking which can help with debugging infrastructure and security issues that are appearing on the platform. Datadog does not support per Company tracking though. There is no support for “behavioral metrics” because of the model that Datadog applies to their analytics model.  TL;DR - Datadog does support tracking user actions and calls, but is more suited for showing overall traffic, API calls, and performance. Datadog’s functionality of user tracking is more in line with Web Analytics. Moesif allows for tracking of users and company usage. This feature empowers Moesif users to look at conversion funnels, retention analysis, and explore user segmentation. Moesif’s functionality is aimed at empowering users and improving your product from a user’s first visit to your application through to their latest conversion.Behavioral emailsOften, certain behaviors a user is exhibiting or actions they take within our products can be tracked. Being able to automatically send a customized and templated email can be a great way to inform users of the next steps or about some condition they may be infringing on, such as a rate limit.With Moesif’s ability to track individual users, we can monitor for specific conditions or behaviors and send emails based on them. Moesif can detect and send emails when a user is having a tough time with onboarding or getting started, is getting close to an API rate limit, or any other criteria where a user might benefit from an email to steer them back onto the correct path.Because Datadog is more concerned with infrastructure and overall performance monitoring, it does not support this functionality. This is another case where Moesif can augment the monitoring you already have in place and help with improving your product and user experience.  TL;DR - Behavioral emails can be used to alert and aid users throughout their onboarding and user journey based on behaviors they display or actions they take. Datadog does not natively support behavioral emails, as it is not as user-centric in terms of metrics. Moesif allows for customized emails to be sent based on when a user fits a specific criterion.Governance RulesGovernance rules are another factor that makes Moesif more focused on user-based analytics. These are requirements such as blocking unauthorized access or blocking access from users with overdue invoices, which are a few examples.This functionality goes beyond just monitoring and alerting and, like behavioral emails, allows you to drive real actions out of the metrics automatically. In Moesif you can create blocking and non-blocking rules. A blocking rule can block access to an API while informing the user why the service interruption is taking place. A non-blocking rule will still let the request continue to your upstream service. Moesif will only override any HTTP response headers but will leave the body and status code alone.Moesif provides this functionality as an easy way to create and enforce governance rules. Since all of the metrics required to determine and enforce such rules are already in the platform, this makes it a great place to create them.Datadog does not support governance in this way. Datadog can be used as a way to explore data that may be of importance to an organization’s governance needs, but it is unable to enforce rules and inform users as Moesif can.  TL;DR - Governance rules, such as blocking unauthorized or possibly malicious access, can be created and enforced by Moesif. These rules could be blocking (stop the request) or non-blocking (still allow the request to pass through). Datadog can’t be used for such functionality but could still be used as part of high-level investigations by governance teams.Infrastructure monitoringServices and products, no matter how great, can only succeed if the infrastructure they rely on is up and performant. Infrastructure monitoring is a key way that organizations ensure that their operations are functioning as they should. Network slowdowns, abnormal increases in traffic, servers experiencing issues, and other events that affect system stability and user experience are things you can monitor.Datadog’s original functionality was heavily focused on this type of monitoring. Arguably, Datadog is one of the best and most popular tools for these needs. A wide array of infrastructure and services can be easily connected to Datadog and monitored. With this being a core use case of Datadog’s platform, it’s no wonder that many of the largest companies in the world leverage Datadog to overlook their operations (including here at Moesif!).Datadog offers some great dashboards which allow you to oversee your entire operation. Each piece of infrastructure in your architecture can be monitored, queried, and optimized using the platform.Moesif does not offer infrastructure monitoring, but many metrics within Moesif - including those signaling API performance issues - can be quickly debugged by also using Datadog. A great example would be when a large influx of issues and alerts start coming into Moesif. We can detect that users are experiencing issues and then flip over to Datadog to narrow it down to an infrastructure issue or if it may be something such as a release issue with some bad code.  TL;DR - Datadog is one of the best platforms available for infrastructure monitoring. Almost every system out there is able to connect to Datadog since they offer a massive amount of integrations. Seeing end-to-end operations and infrastructure states is where Datadog truly excels. Moesif does not offer any type of infrastructure monitoring but, when used with Datadog, can easily help to diagnose and resolve issues affecting users.Wrapping upThe metrics in Moesif can help to shape better user experiences and help to uncover difficulties within your application. This could cover everything from the frontend experience (using Moesif’s BrowserJS or Segment) to backend events such as a successful integration with your APIs. Moesif can also help to enable product-led organizations by tying metrics to workflows that generate automatic emails, notifications, and even governance rules.The metrics in Datadog are great for tracking operational and system health, overall traffic, monitoring infrastructure costs, and many more facets to ensure your application is working at the highest levels.As you can see, both platforms offer many valuable insights to different audiences and roles. Moesif is focused on helping you build better products while Datadog is focused on making sure that your infrastructure and other operational variables are allowing those products to perform as they should. Feature overlap between the platforms is minimal since they have two very distinct roles in how they can enable your business to scale. Using Datadog and Moesif together is one of the best ways to build a complete and comprehensive analytics and monitoring stack.",
          "url": " /technical/api-analytics/Moesif-and-Datadog-Feature-Similarities-Differences-and-How-They-Can-Work-Together/",
          "author": "Matthew",
          "categories": "technical, api-analytics"
        }
      
    ,
  
    
        "technical-api-analytics-creating-the-ultimate-analytics-stack-with-moesif-and-datadog": {
          "title": "Creating the Ultimate Analytics Stack with Moesif and Datadog",
          "content"	 : "When looking at API analytics and monitoring platforms, many seem to be so similar that it’s hard to figure out the differences between them. We often hear this confusion from users and prospects. In a world with so many tools available, how do we figure out which ones are necessary and which are redundant?One of the most common questions we are asked revolves around how Moesif compares to Datadog and how they could work together. We don’t see it as a “Datadog vs Moesif” type of scenario, but instead, see combining the two platforms as a great way to create a top-notch analytics and monitoring stack. Using both can ensure that your systems are running as they should and users are deriving value from the platform.Let’s take a look at each of the platforms, what they do, and how they can work together.What is Datadog?Datadog is one of the most popular infrastructure and application monitoring tools on the market. Datadog has plenty of built-in connectors, many that cover different cloud infrastructure monitoring, cost-management, security monitoring, and much more. Overall, Datadog is a great way to monitor the end-to-end health and operations of all your applications and systems. It also includes great visualization tools to easily see trends in your data.Datadog can help to give end-to-end visibility on:  Infrastructure health and performance  Security  Issue tracking  Cloud Cost Management  And much moreWhat is Moesif?Moesif is an API analytics platform that is mainly focused on how users and companies use your platform. Moesif integrates with your APIs, through a plugin (for many popular API gateway and API management tools) or SDK, and with your applications’ frontend UI. By monitoring both of these angles, Moesif allows users of the platform to see the end-to-end customer journey and derive key insights and trends from that data.Moesif API analytics can be a great asset for companies that are API-first. It can also be a great tool for product-led organizations that are heavily focused on using metrics to improve and monitor their product.Moesif can help to give end-to-end visibility on:  Detailed API usage  Activation funnels  Retention and upsell opportunities  Monetization and quota management  Feature adoptionWho are the platforms for?A major factor in the usage of each platform is who the platforms can serve best.Datadog can service the needs of your DevOps, Site Reliability, and Engineering teams. These teams are concerned about the health of infrastructure and tech operations. Making sure that infrastructure is up and running, efficiently, is of utmost importance.Moesif caters more to the Product, Engineering, and Customer Support teams where the main concern is about understanding usage and customer satisfaction. Ensuring that users are having great experiences, receiving value from the platform, and retaining those users are the key goals.As you can see, the platforms can very much work synergistically together. While one is concerned with infrastructure and operational monitoring to make sure users can access the platform, the other is concerned with monitoring user experiences and usage to keep users happy and to help attract new ones with an improved experience.The metrics: what and why?The differences between the two platforms can really be looked at based on the metrics they gather, aggregate, and help to understand.Key metrics looked at by users of Datadog include:  Uptime and SLA  Memory usage  Latency  Errors per minute  Requests per minuteThese metrics give a good idea of infrastructure health and assist with application performance monitoring. This is where Datadog excels. Being able to track specific metrics within a particular user’s journey, though, can be difficult within the platform.Metrics collected within Moesif have a bit of a different focus than the ones observed in Datadog. The outcomes from the metrics in Moesif can help to support other areas where Datadog can’t or can be complex to implement in Datadog. This is mainly because metrics within Moesif are highly connected and tied to customer behaviour. This means that certain insights can be found because of relationships that can be found within the data for each user and company.These metrics can help in the areas of:  Adoption  Engagement and API usage  RetentionAs you can see, these areas focus on customer success and experience as well as building better products. Put simply, Datadog can help to make sure that your product and services are up and running and Moesif can help to get people using your product and retaining those users.Creating an analytics and monitoring stackAnalytics and monitoring are extremely complex and multi-faceted. Almost every part of the organization, and every team within, can benefit from a solid foundation of analytics. Leveraging analytics and monitoring to keep operations at their peak efficiency and to ensure products are the best they can be.The right analytics and monitoring tool can create a cohesive picture for your entire organization.For your executive, C-suite team analytics can help to show how different efforts throughout the company are being received by users and customers. The ability to tie sales and marketing efforts to specific outcomes that can be easily tracked in the data. This could include sales outreach, improvements in onboarding, or even changes to improve user retention and spending. Leaders within each area can also use concrete numbers to show efforts are working or need to be improved upon.Sales and Marketing teams, including Developer Relations, can quickly see website and application traffic, its sources, and how various efforts tie into attracting and retaining customers. It’s extremely easy to waste time, effort, and money when trying to scale your user base. Metrics are the key to quickly refining strategy so that waste is minimal and impact of individual campaigns is maximized and that outreach from sales teams is targeted and creates opportunities. This expands beyond just new and incoming customers, but also to current customers who could be upsold to improve their happiness with the product and help your company’s bottom line.Product Development teams can see how new features are performing, the impact on the infrastructure where these features have been deployed to, and give hints at future improvements to be made. A/B testing can be scrutinized with precise metrics, and individual customer journeys can be followed. Building a better product is not easy, but the formula is to double-down on what’s working and cut anything that isn’t (if it can’t be improved). End-to-end metrics can power this effort.Operations teams need to know how their infrastructure is performing so they can optimize and keep the customer journey flowing smoothly. A great product needs to be backed up by solid infrastructure. The only way to ensure that things are working as they should is to monitor key metrics and, preferably, put alerts and notifications in place when things are not running optimally.All of the above uses can be encapsulated in a metrics stack which includes Moesif and Datadog. Moesif can tackle all of the user monitoring and metrics, as well as improve onboarding and retention. Datadog can ensure that all of the infrastructure and operations components of your system are working as they should and alert if they aren’t. With Datadog’s ability to check on things outside of just infrastructure health, such as database health and cloud cost management, to name a few, there’s plenty of room for optimization and monitoring.Wrapping upIn short, Datadog is focused on operations and making sure that your infrastructure, security, and performance remain stable. If they don’t, Datadog can quickly make your team aware of these critical issues and help with diagnosing and fixing them. Datadog supplies a massive amount of integrations and aesthetically pleasing charts and dashboards. Overall, it’s no surprise why it is one of the most popular infrastructure and platform monitoring solutions on the market. Developers and technical operations staff benefit greatly from using Datadog and all it’s features.Moesif is focused on making your product and user experience better. The platform has many tools in it that allow you to see a 360 view of who your users are and how they are using your API-driven products. On top of this, Moesif allows you to actually utilize these metrics for automation whether it be alerting your customer success team of issues, enforcing governance policies on endpoints, or even sending emails directly to users based on certain actions or events they are experiencing within your product. Product managers, developers, and even non-technical staff in marketing and sales can leverage Moesif to upgrade their product, customer success, and sales capabilities.Analytics doesn’t have to apply to only one piece of your application or operation. A complete picture can help to improve your product, improve cost savings efforts, and give the ability to holistically manage everything about your operation, without guessing. These platforms give fine-grained control and insights to be pragmatic and practical when striving to build the best product possible.",
          "url": " /technical/api-analytics/Creating-the-Ultimate-Analytics-Stack-with-Moesif-and-Datadog/",
          "author": "Matthew",
          "categories": "technical, api-analytics"
        }
      
    ,
  
    
        "business-soc2-moesif-api-analytics-is-soc2-compliant": {
          "title": "Moesif API Analytics is SOC 2 Compliant",
          "content"	 : "Moesif is pleased to announce that it has successfully completed its SOC 2 Certification. The Moesif API Analytics Platform has now been independently verified to comply with the highest standards of security.Through completion of this comprehensive level of certification, Moesif has demonstrated that it can reliably and securely maintain the confidentiality, availability and integrity of enterprises’ critical data assets.An Accreditation That MattersSOC 2 compliance demonstrates that an organization can maintain a high level of information security. This commitment to handling sensitive information responsibly is especially important in the banking, insurance and government sectors.Indeed, SOC 2’s framework states that compliant organizations can only share data with other organizations who have passed the audit. Moesif’s now able to enter into business relationships with other SOC 2 compliant organizations.Moesif Shines in Data Confidentiality and SecurityThe SOC 2 audit process includes five Trust Services Criteria used to evaluate and report on controls over information and systems: security, availability, confidentiality, processing integrity and privacy.Since Moesif has always handled customer data, we built our solution with security in mind. Our strong processes for handling data securely and confidentially include: encrypting all stored data at rest, internal controls on accessing production data and change control processes for all software modifications that touch customer data.In addition to never storing customer data off of company infrastructure or workstations, all which is remotely monitored, we also implement automated security updates for core services and on-the-spot updates for security issues as they’re identified.Client-Side Encryption for Additional SecurityBeyond server-side encryption and SOC 2, applications with sensitive data can stay private to your organization via client-side encryption  enabling zero-knowledge security. The privacy benefits of on-premises installation are gained, without the complexity of building and scaling your own data infrastructure.Moesif is committed to the security of our customers’ data. Our SOC 2 compliance is the next step in evolving our platform to address more demanding markets.                Moesif Anonymizes Your Data              14 day free trial. No credit card required.              Learn More        ",
          "url": " /business/soc2/Moesif-API-Analytics-is-SOC2-Compliant/",
          "author": "Larry",
          "categories": "Business, SOC2"
        }
      
    ,
  
    
        "api-product-management-api-metrics-vanity-metrics-for-apis-vs-tracking-business-value-from-api-transactions": {
          "title": "Vanity Metrics for APIs vs Tracking Business Value From API Transactions",
          "content"	 : "As an API product manager, you want your API to have a great developer experience. This means that developers can get up and running quickly, they get consistent behavior from your API, it’s easy for them to troubleshoot any errors they encounter, and your API makes it easy for them to address their software engineering and business needs.Tracking your APIs is an important part of understanding how well they perform, which leads most organizations to build out their own internal API tracking systems. While this approach can work out for some organizations, it is much more common for organizations to track process metrics and product metrics incorrectly. Tracking the wrong metrics leads to an incorrect understanding of your API.In this blog post, we’ll look at some of the common pitfalls companies face when they start defining and collecting data for how developers are using their API, and how you and your team can focus on real, actionable project metrics and not just vanity metrics.Why Internal API Analytics Tools Miss the MarkSome organizations prefer to build their own API analytics tools, and track lean metrics internally. When organizations build homegrown analytics tools for API metrics, they encounter certain pitfalls that lead to inaccurate metrics:  Rebuilding website analytics for APIs  Trade-offs between building product and building analytics tools  Pressure to report the most successful-looking metrics  Failing to appropriately measure API transactionsIt’s common for product teams to take the tools they use for website analytics and repurpose them for API analytics. This approach often involves considering API calls equivalent to site views, popular endpoints as a similar metric to popular pages and doesn’t have a method of analyzing developer experience. This means that development teams who use these tools end up with subpar reporting for software engineering metrics.For example, traditional web analytics will lead to reporting at the endpoint or call volume level when that’s rarely the software metric that matters. Sometimes more granular information within the response tells a better story. Or, perhaps aggregating additional request and response meta-data is more important than the usage of a single API endpoint.When you try to fit your API into existing analytics tools, you may misinterpret the results. An organization may falsely believe that they’re receiving many API calls because their API is popular, for example. The real story may be that a software developer accessing it is experiencing frequent errors, or that certain endpoints are unpopular when developers may be having fewer, but longer conversations with your endpoints. If your internal software development team team doesn’t have experience building API metrics tools, they may not be the right team to build your API analytics software.                Lower your API Monitoring Costs with Moesif              14 day free trial. No credit card required.              Learn More        Unless you have unlimited engineering resources, when the question of building API analytics tools comes up internally, it always means diverting developers from developing the product your business is focused on to working on API analytics tools. We’ve already discussed how this isn’t ideal if your development team doesn’t have experience building API analytics tools, there is also the additional downside of less time spent building your product because developers are focused on other projects. Your developers would also most likely prefer to focus on your product instead of building out analytics tools. Building internal analytics tools puts a different type of pressure on the teams that develop them.Don’t Trust Vanity MetricsWhen internal teams have to build API analytics tools, they are also often burdened with the pressure to report good metrics for the API. Nobody wants to be the bearer of bad news, and many organizations will ask their internal metrics teams for good metrics to report to motivate their co-workers, validate their success for investors and share data for marketing purposes. This is what leads highly-skilled developer teams to report vanity metrics. They are faced with unintentional, but quickly compounding pressure to report metrics that look good, which leads them to focus on metrics that look impressive but don’t reflect how successful their API actually is.As tempting as it may be to share a meaningless hockey stick chart, there are more useful metrics than API requests over time, unless you can tie the calls directly to revenue. Instead, consider the overall health and success of the API, such as how your latency improvements have impacted developer experience.Many organizations define the wrong success metrics, which leaves them with the wrong idea of how successfully their API achieves their goals. Vanity metrics are easy to create and they are especially common in products built for developers. Products and services built for developers often focus on the number of hours spent in development or the number of first-time users and API keys provisioned. Metrics like these don’t tell you anything about your API’s developer experience, how much value your API creates for developers, or how well your API aligns with your company’s broader goals.                Track Useful Metrics Like TTFHW with Moseif              14 day free trial. No credit card required.              Learn More        Connect API Transactions to Business ValueNow that we’ve discussed the challenges of internal API analytics tools and how they can lead to vanity metrics, let’s have a look at what metrics to focus on so that you can understand your API’s performance and how well it achieves your business goals. Let’s start by looking at the developer experience for your API users.Your API’s developer experience starts when they sign up to use it. You should track each developer’s journey starting from their initial sign-up, to the first API key they generate, followed by their first API calls and the length and content of their earliest API transactions. This data gives you a lot of information about your API’s developer experience, including:  How easy it is for developers to start using your API  How quickly they integrate it into their software development projects  What endpoint looked most useful at first glance  Where developers are most likely to encounter errors  Which agile teams need supportThis information makes it easy for you to further develop your API and improve your developer experience by providing quick support and quality assurance when they encounter errors while using your API. You can also see whether users from particular channels are more likely to reach a relevant level of API usage.In the medium term, this lets you track how well your API retains developers, which is an important part of understanding how much value your API provides for developer productivity. You will also learn which endpoints have the highest number of transactions and how long a typical transaction lasts which signals where your resources should be focused.The API DORA metrics you track should include unique identifiers to make it easier to track usage data on an individual customer basis. This makes it easy to understand your API performance and makes your customer support lightning-fast. Customer usage is a crucial metric when it comes to tracking business value, it’s important to continuously track API transactions so that your organization clearly understands how well your API addresses your customer’s needs. Quality metrics drive quality insights, which drive an optimized development process, resulting in sustainable growth.Your customers are also interested in metrics about their usage over time. You should provide each customer with their productivity usage information so that they understand when they’re approaching usage quotas so that they don’t hit quota limits unexpectedly. They may also want to know how many network resources they typically use while accessing your API, this makes it easy for them to allocate enough resources to access your API. The endpoints that have the highest deployment frequency or use to transfer the most data are also important test metrics for your customers because this information tells them which endpoints provide the most business value to them. This makes for a great customer experience, which makes them more likely to use your API.                Keep Customers Informed with Embedded Dashboards              14 day free trial. No credit card required.              Learn More        An important aspect of API analytics that you’ll need to consider in the long term is how you’ll scale your analytics. As your customers grow, the number of API calls will grow and the length and content of your API calls will grow as well. While it is already quite challenging to perform API analytics accurately, it becomes much more difficult as time passes and the sheer number of API transactions grows. Your API analytics implementation needs to accurately track business value and handle your API’s growth. This is why many teams turn to software outsourcing rather than custom software development.It’s difficult to build accurate API analytics tools on your own. Even experienced engineering teams need a lot of resources to measure their first analytics, and you’ll need to constantly develop your metrics to get more detailed information for your organization. But, this is no reason to give up. Great API metrics will bring huge value to your business, they make it easier for your product and support teams to improve your customer experience and highlight a relevant key performance indicator to show you what aspects of your software performance you should focus on.Is Tracking API Transactions Effective?So far we’ve covered the most common pitfalls organizations fall into when analyzing APIs, why they occur, and what metrics you should focus on instead. Now we’re going to look at a direct comparison between common vanity software metrics and API transaction metrics for different use cases.API adoption is a metric that is valuable to many organizations, so measuring it correctly is important. The popular approach is to look at the number of API keys that have been provisioned for an API. This suggests that an API is popular and a lot of developers find it useful, but relying on this metric is dangerous. It only tells you that many developers have considered using your API. This won’t tell you if they successfully implemented their first API call, what stage of their onboarding journey they reached, or how many errors they encountered. On the other hand, if you use unique identifiers to look at how well you retain users and track how long it takes to go from creating an API key to “Hello World!” you’ll know if you need to make improvements to your onboarding, documentation, and support.Many organizations have successfully transitioned from building their own API analytics tools internally to Moesif’s API analytics. Symbl.ai is a great example of a data-driven company that faced the tradeoff between focusing on product development and building internal API analytics. Moesif provides deep API analytics that allowed Symbl.ai to fully understand how customers interact with their API.Understanding your API can be challenging, but with the right analytics tools, you can maximize value for your customers and track how successfully you achieve your organization’s goals. With Moesif API Analytics your organization can build great APIs.                Build Great APIs With Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /api-product-management/api-metrics/Vanity-Metrics-for-APIs-vs-Tracking-Business-Value-From-API-Transactions/",
          "author": "Adam",
          "categories": "API-Product-Management, API-Metrics"
        }
      
    ,
  
    
        "customer-success-monitoring-6-ways-moesif-api-alerts-can-help-your-engineering-customer-success-and-sales-teams": {
          "title": "6 ways Moesif API alerts can help your engineering, customer success, and sales teams",
          "content"	 : "The ability to monitor your APIs is an important functionality for any business that is driven by APIs. Beyond API monitoring and observability is the ability to actively create alerts for specific conditions. Moesif is a fantastic platform to utilize alerts with. With our platform, all of your API and user data can be compiled into a single place. it’s a great way to easily configure a wide variety of useful alerts for your REST, GraphQL, and other APIs.With Moesif, when an alert is fired, it can be delivered to many different channels including email, SMS, Slack, PagerDuty, or even a custom Webhook. By having many channels available, users can be sure that the right alert notification is being delivered to the right channel or multiple channels. This flexibility is a key factor in how Moesif enables you to customize for your business.Not all alerts are equal though. In Moesif, we have two types of alert rules that can be configured: static and dynamic. Knowing the difference between the two and where to use them can help to build a robust alerting strategy.Static alerts are quite simple. There is some metric that you’d like to track with a static threshold. For instance, you may want to create an alert that fires when a client receives a certain number of 401 - Unauthorized responses from an API. Your filter may look something along the lines of:    Alert if more than 20 HTTP ‘401 - Unauthorized’ status codes are returned in a REST API response within 15 minutesFor dynamic alerts, the criteria are usually outside of the capabilities of a static alert. Dynamic alerts leverage Moesif’s anomaly detection. This mechanism compares against the trend line from historical data and alerts when the metric looks abnormal. The sensitivity is configurable so that the alerts can be customized based on exactly how much variation is acceptable before an alert occurs. An example of a dynamic alert filter might be something along the lines of:    Alert if a user’s API usage is abnormally decreasing over the last weekBoth types of alerts can be a great addition to your support and alerting strategy. When used in unison, they can help to improve multiple areas of the organization and keep customers happy. Let’s look at a few specific scenarios where implementing alerts could help you improve!To track errors occurring in your APIsThe ability to track and notify teams about errors occurring in their APIs is likely a top use case for alerts. Errors are low-hanging fruit in terms of events that could quickly stall your business and bog down your support team.The easiest way to do this would be through a static alert. An alert notification may be sent when a known endpoint is returning an HTTP 404 Not Found status code more than 10 times in 5 minutes. This may be due to some code configuration issues in the latest API code release or another error that the engineering team will want to know about right away.Dynamic API alerts may also be set up to detect when there is an abnormal amount of errors happening in an API compared to historical data.Having your team be alerted in real-time that an unwanted event has occurred is important. Knowing right away means that teams can diagnose, warn customers about any implications, and get ahead of any negative effects. This is especially important as support SLA times continue to decrease and become an important part of a user’s choice to adopt a platform. With Moesif, you can even use the platform to debug and recreate the issue more easily. Know the moment the event happens and resolve it more quickly and easily.To track potential security issues or API abuseUsing alerts to warn the support and security teams about potential security issues can easily be done. This would help to cover your bases for securing against attacks and intentional misuse. In the form of a static alert, you may send an alert if a blocked user sends a request to a specific endpoint or still logs in somehow. With a dynamic alert, you may inform your support teams of an unexpectedly large spike in API traffic that may resemble a denial of service attack.This is a great line of additional defense in the security battle. The first line of defense would of course be to block these events from happening in the first place. This could be done by ensuring you have proper API security mechanisms in place. This could include only allowing fully authenticated and authorized users to access an API or enforcing proper rate-limiting and quotas to each API call. An API gateway will have all the functionalities to create this first-line defense.If that defense is breached or is for some reason bypassed, Moesif’s alerts are a great way to ensure that your team is still informed of any improper or damaging use of your APIs. Of course, digging deeper may also bring out other great ways to use Moesif to enhance your API security and limit abuse.To reach out to new users who are stuck or strugglingFinding issues and being proactive to help struggling users is hard. When a new user comes on board, a metric we love to track is TTFHW, or “Time to First Hello World”. This metric usually follows users from when they first register until they have their first successful “Hello World” moment. For most technical products this journey includes registration, initial configuration, up until their first successful API call.This metric is extremely important since it heavily defines how easy our product is to use. The easier the product is to use, the higher the likelihood that the user will continue to use the product. The first initial experience with the product is crucial.As we know, this journey takes time to perfect and it will always have snags. Very few companies have perfected their user journey, but good companies strive to come close. What if we could be alerted when a new user is stuck? Then our customer success team could be alert and reach out to that user. With Moesif, we can do exactly this. A great example may be if a user is receiving an HTTP 400 Bad Request response code when sending their API requests. This could obviously be very frustrating and would be a major deterrent for the user.As an example, we could set up a static alert to fire when users appear to be having difficulty. This alert may fire when any new users experience more than 5 HTTP 400’s and their TTFHW > 5 minutes. We could then have this alert go to the customer success team and do a personalized reach out to them to get them unblocked. This alleviates the need for the customer success team to actively monitor these details. It also means that the customer success team can be more effective in reaching out to users before they give up and possibly never return to use the system or application again.From a dynamic alert perspective, we could set up an alert to fire if new users TTFHW becomes abnormally long compared to our historical data. This may help to inform product and engineering teams that the latest onboarding tweaks are impacting users.To improve the user experience with new and updated featuresWhat if we could be alerted when a user is having difficulty using a new feature in our product? This could allow us to do an outreach campaign to ask what the difficulties were. It could also allow us to intervene with the user to de-escalate their frustration if they face an error, help them to use the feature properly, and possibly refine the UI or docs to make usage more streamlined. We may identify API calls that are part of a particular user flow and either put a static alert around them if we know what an acceptable time threshold is to complete it. We may instead create a dynamic alert that checks against historical data to ensure the user flow has not become more difficult to use with our latest release.The ability for customer success, product, and engineering teams to be alerted when features go awry can be a lifesaver. Instead of waiting for feedback to come in through channels or seeing users churn out due to difficulty, we can be alerted to these metrics when they go in the wrong direction.To inform customer success teams about decreased usage for proactive outreachIf a user’s usage is decreasing there could be many reasons. They could be scaling down operations, got stuck with a roadblock on the platform, or may have made some issue in their configuration that they are unaware of. Regardless of the cause, it should be of concern to the customer success team.By using dynamic alerts, the customer success team could be notified when key customers, or all customers depending on how you set up the alert rule, have decreased usage on the platform. The dynamic alert could look for anomalies or trends that would display a significant usage decrease. The customer success team could do an investigation, and even proactively reach out to the users to see if they can help.This type of proactive approach is a great way for the customer success team to show they are invested in making the user’s business a priority. Proactive outreach can help with retention, churn, and customer satisfaction. Some of this can be automated, of course, but having the customer success team aware of issues is still an asset.To inform sales teams about increased usage for upsell opportunitiesWhether your mid-contract or coming up to a user’s renewal date, increased usage can lead to increased revenue. Instead of waiting for the sales team to be reactive, alerts can allow your sales teams to be proactive in bringing forth upgrade opportunities.You could do this in quite a few ways using static or dynamic alerts in Moesif. With a Static alert, you could alert the sales team when any new user has more than a specific quota of API calls in a specific timeframe. This could signal to the sales team that the new users could be a very big account. Instead of browsing through usage data to figure out who to reach out to, let the alerts do the work.You could also use a static alert that responds to a “percent change” metric. An example would be to send an alert when a customer has a greater than 20% increase within the last 7 days. This approach would give sales teams a heads up that a specific company is scaling quickly. This can help sales teams to reach out early for upgrades or help to inform upcoming sales calls.Both of the above use cases can also help the sales team avoid “sticker shock” with customers, especially new ones. “Sticker shock”, in this context, is when the customer gets hit with a surprise bill due to usage. Sometimes this can lead to a loss of trust with the customer. The customer may then look for more platforms or apps that will cost less. In this scenario, possibly upgrading the customer to the next tier or package would help. Tracking metrics like quotas and growth percentage can give an advantage for customer retention and battle against “sticker shock”.Once a history has been established, you could also set up dynamic alert rules. The alert would look at historical data and see if the user’s APIs are experiencing continuous usage growth. If so, the sales team could build a proposal ahead of time and bring the upsell opportunity to the user before they even know they need it.Some products will also have related features. Where if “X” feature is used, then “Y” feature would be beneficial. Alerts could be set up to detect when a specific API is used. This would let the sales team craft their messaging and outreach campaigns to cater to this information and propose a package that involves the other complementary feature.This also shows that your company is invested in a customer’s business and helping them to scale. These types of alerts can help the sales team to nurture new and existing customers proactively.Setting up your first alertsNow that you know about all the benefits, it’s time to try it out! We have some great guides available to show you how to create your first static or dynamic alert. To dig further, you may also want to check out our extensive docs. It’s as simple as creating your alert definition, configuring your channels, and activating the alert. The Moesif dashboard makes configuring alerts seamless and easy. Moesif even has some common alerts set up by default in the alerts dashboard.If you don’t have a Moesif account, sign up today! Once logged in you’ll be able to create custom alerts to augment your API monitoring game. Whether you’re using a REST API or a GraphQL API, Moesif alerts are a great way to increase awareness across all departments.",
          "url": " /customer-success/monitoring/6-ways-moesif-API-alerts-can-help-your-engineering-customer-success-and-sales-teams/",
          "author": "Matthew",
          "categories": "Customer-Success, Monitoring"
        }
      
    ,
  
    
        "developer-relations-monitoring-getting-your-developers-to-see-value-with-a-great-developer-experience": {
          "title": "Getting Your Developers to See Value With a Great Developer Experience",
          "content"	 : "One of the beauties of working with APIs is their convenient and practical ways to share data and applications. APIs have enabled a transformational shift from an interface that relied on custom integrations to now a relatively streamlined process. That said, because of their agile framework, some companies have overlooked the importance of providing a great developer integration experience and are not taking the necessary steps to help drive the Time To First Hello World.Unfortunately, it’s an all too common sentiment surrounding API management that the integration should be left to a hyper-intelligent developer to figure out, and that standard onboarding workflows are unnecessary luxuries. However, this is a misguided notion; developers don’t want to be treated as second-class citizens, they want to be shown how to integrate and would appreciate the convenience of streamlined workflows.That said, there is a growing population of API-driven organizations that are trying to flip this notion on its head. These organizations believe that the integration process is of vital importance to the success of their business, as it sets the stage for the entire length of engagement with their developers. As a result, these organizations are working tirelessly to help their developers reach the First Hello World as soon as possible. Facing the reality that if their onboarding workflows take too long to integrate, developers will pack their bags up and leave in frustration. Conversely, if these organizations are quick to wow and help their developers be successful, it is very likely these organizations will have a lasting and fruitful relationship.To draw a parallel with an entirely different industry but offers a relatable lesson in product adoption is the automotive industry with Tesla. The designers at Tesla have gone to extreme lengths to ensure that the driver’s initial experience is nothing short of perfection. For example, the seamless push to start the engine, the modern interior, a streamlined UI, and an engine that launches you like a rocketship. All these added details culminate into an initial experience that imparts value and helps customers quickly fall in love with their product.Similar to the designers of Tesla, API product and engineering leaders should aim to provide their developers with a world-class integration experience that is streamlined and makes the adoption process painless.To achieve this goal, one of the first steps to take is to create a funnel adoption report. An example of a basic adoption funnel might be: (1) The initial sign-up, (2) First Time to Hello World, and (3) Time to value. Once your funnel report is set up, it will enable you to measure the time it takes to reach each unique stage in your funnel and help you understand what aspects of the API are contributing to developer success. In addition, the report should also identify the percentage of developers that drop off at each stage, which in turn exposes where developers might be running into trouble.While creating a developer adoption funnel is certainly a step in the right direction, it will not alone provide you with the entire picture. To broaden the scope it would be beneficial to add additional perspectives and metrics. Some of the top metrics that you might consider leveraging for your developer adoption funnel are listed below:                Get Deep Insights Into Customers              14 day free trial. No credit card required.              Learn More        Qualified Product Leads - When developers integrate with your API it is important to understand whether they are qualified future users of your services. In many cases, developers who begin the integration process are just kicking the tires and have no actual intent of becoming a subscriber. These folks should be marked with an asterisk, as they don’t provide a true sense of the effectiveness of your adoption funnel. In addition, for the developers that do meet your business requirements, support teams should reach out to them and double down their efforts to help them complete the integration process.Leveraging an extension like Clearbit will help you surface the relevant demographic information, such as role, industry, and company size. This will then allow you to zero in on who is likely to be your next best customer.Marketing Channel -  Marketing teams are working hard to optimize their advertising spend to ensure they are attracting the right type of developer who has a high likelihood of converting. Therefore, it’s critical to track out which advertising channels are yielding the highest percentage of conversion with your APIs, in order to help marketing teams maximize future ad spend and attract the right audience.Type of SDK - Understanding the SDK that your developers are using is a great metric to identify how certain types of integration might contribute to developer success or failure. For instance, does your Python SDK have a much higher churn rate than your GO SDK? If this is the case, it would be a strong indicator that there might be a bug in your Python SDK and that your engineering team should look into diagnosing the problem.Errors - Tracking errors is a great way to expose points of friction that your developers are running into while integrating with your APIs. If you are seeing an uptick in errors at a specific stage in your funnel it enables you to proactively step in and remove future bottlenecks and keep developers from dropping off.The question now falls to you, is your API integration process akin to a Tesla launching your developers to value? If not, to achieve this gold standard, API-driven organizations must look to constantly monitor and analyze their integration funnel in order to improve workflows, remove points of friction, and help drive the Time To Hello World.                Understand How Customers Use Your APIs              14 day free trial. No credit card required.              Learn More        ",
          "url": " /developer-relations/monitoring/Getting-your-Developers-to-See-Value-with-a-Great-Developer-Experience/",
          "author": "Oliver",
          "categories": "Developer-Relations, Monitoring"
        }
      
    ,
  
    
        "podcasts-api-security-podcast-api-security-and-fhir-recommendations": {
          "title": "API Security and FHIR Recommendations",
          "content"	 : " Ep. 12: Alissa Knight, recovering Hacker and API Security ExpertMoesif’s Podcast Network: Providing actionable insights for API product managers and other API professionals.Joining us is Alissa Knight, partner at Knight Ink Media, CISO, cybersecurity expert, cinematographer and accomplished content creator for telling brand stories at scale.Larry Ebringer, Moesif’s CMO, is your host today.Moesif · 12. API Security and FHIR RecommendationsListen to the episode on SoundCloud above, watch it on our YouTube Channel or download it on Apple or Google. Table of Contents   2:23Avoid Prison Yellow - Become an Ethical Hacker    5:43Authentication Does Not Equal Authorization    8:14Protect Against BOLA with Scopes    10:06Don’t Use WAFs to Protect Your APIs    13:08Know What Traffic is Going to Your API    14:35Shift Left Security - Shield Right    17:39PHI is Worth 1000X Credit Card Info    21:41APIs are the Weakest Link in Healthcare    24:27APIs Have Multiple Attack Surfaces    27:20Banning Apps From Jail-Broken Phones Doesn’t Help    29:40Use MobSF to Find API Keys    30:44APIs Need to Comply With FHIR    34:55Implement FHIR Correctly    37:11Get FHIR Certified    38:11FHIR Certification Versus HIPAA Compliance    39:59No One Right Solution for API Security    42:59Instrument Your APIs  Larry Ebringer (Moesif): Welcome to Episode 12 from Moesif’s APIs over IPAs Podcast Network. I’m Lawrence Ebringer your host today and the Chief Marketing Officer of Moesif, the API Observability Platform.Joining me is Alissa Knight, CISO (Chief Information Security Officer), cyber security expert, cinematographer and accomplished content creator for telling brand stories at scale. She’s a regular conference speaker, which is incidentally where I first saw her as the keynote on apidays’ Interface Conference. She was giving a fascinating talk on security and healthcare apps, and I thought our audience would love to hear her perspectives on API security.So here we are. Welcome Alissa. Where in the world do we find you?Alissa Knight (Knight Ink Media): So, first of all thanks Larry. That was a great intro. I always feel bad for people that they have to give my bio and introduce me, because it feels like it could just go on forever. So I’m going to start having people just introduce me as, you know, that “security chick,” that “hacker chick.” But no. Thanks for having me on your show. It’s a real honor to be here. Thank you so much.So I’m in Las Vegas. A lot of people don’t know people live here. It’s not just for coming to conferences, but people actually do live here. My wife and I live in an area called Summerlin, which is about 20 minutes from the strip.Larry: Well, it’s great to have you on the show and we’re really glad that you’re going to share your perspectives on security with us.Just as a quick side note, when I mentioned to my family that I was hosting you on our podcast, one of my 12 year old daughters asked if you were white hat or black hat. The language of technology has become so mainstream, that even pre-teens are familiar with it. So on that note, why don’t you share with us your journey from black hat to white hat, starting two cyber-security companies, and then on to today running Knight Inc. Media.Avoid Prison Yellow - Become an Ethical Hacker”Fast forward to when I was 17. I made a very bad decision and hacked a government network. Got caught… and ended up working for the US Intelligence Community in Cyberwarfare.” Even if you start off as a black hat hacker, you could end up an ethical one.Alissa: So, that’s amazing that even a 12 year old knows the idiosyncratic distinctions between black hat and white hat. Great question, and I think a great way to start the show off.I started out hacking when I was around 13 years old. Unfortunately, I had very little guidance. But things were very different back then. You’re talking about a different time when there wasn’t all of these resources available that are available to you today. You know SANS, or even Google, wasn’t a thing back then.I got involved in hacking through IRC. There was this IRC server, Internet Relay Chat, called EFnet, and I was very involved in these EFnet IRC channels. And so, that really was my family. Because, I mean I spent more time online than interacting with my actual family, so they pretty much raised me. I grew up on IRC.And in these channels I learned through other people, and I learned on my own. At the time, there was very little knowledge out there. We have access to so much knowledge these days with Google and with all of these resources, but back then it was really difficult to learn. You really had to learn things on your own.Fast forward to when I was 17. I made a very bad decision and hacked a government network. Got caught, they arrested me at school, believe it or not. After the charges were dropped, ended up working for the US Intelligence Community in Cyberwarfare. So I was forced into a white hat role. I look terrible in orange. Prison wasn’t for me.I went from black hat to white hat outside of my own control and it’s good, because I realized who I was and it was clear to me that I could make a significant amount of money doing what I love most, and that was hacking.And thus was born penetration testing/ethical hacking — actually hacking company networks and explaining to them how we did it in order for them to defend themselves against the real attacker.Larry: I didn’t think anyone looks that good in orange.Alissa: Orange is not the new Alissa Knight.Larry: In one of your great YouTube videos, which by the way I recommend our listeners to tune into, you gave a splendid definition of a hacker, and it was someone who wants to understand how something works and then send a stimulus that the developer didn’t expect or account for. So, really not that nefarious as one would imagine. Also on your YouTube and on your blog content, you really backup all of your claims with great empirical data, which really makes it a lot more valuable.                Protect Your APIs With Moesif              14 day free trial. No credit card required.              Learn More        Authentication Does Not Equal AuthorizationThe most systemic issue developers make when developing APIs is that they’ll remember to authenticate API requests, but they’ll fail to authorize them. Understand the distinction between authorization and authentication: you may have an API key that proves you have legitimate access to send API calls, but you’re not necessarily authorized to receive the data that you’re requesting.Larry: So, as someone who has sat on both sides of the table so to speak, and written extensively about hacking banking & healthtech APIs, and now I just heard a government network. And also someone who presented to Gartner recently about what you thought about API security, what are the most common mistakes that developers make when it comes to API security?Alissa: Good question. I think, for me, the most systemic issue is developers will remember to authenticate the API request, but they’ll fail to authorize it. So, it’s understanding the distinctions between authorization and authentication. Authentication being something that you have or something that you know, versus authorized which is being allowed to actually view the data.I may have an API token or an API key to be able to prove that I have a legitimate user account, have legitimate access to the API to go to send API calls, but I’m not authorized to receive the data that I’m requesting. And this is a problem across all of the APIs that I’ve looked at where there’s just a lack of authorization. Specifically around what are called Broken Object Level Authorization (BOLA) vulnerabilities.For our listeners who don’t really fully understand what that means, the best analogy that I like to use is the whole coat check thing. If you and I went to a cocktail party and I saw you check in your expensive Burberry coat into the coat check, and I wanted to take that home.You were given the number 18 from the coat check person, and I was given 17, and I just take a sharpie and I change that 7 to an 8 and give it back to the coat check and say I want my coat.That’s a great example of a BOLA vulnerability. I’m authenticated, I have a ticket, but I’m not authorized to take home your Burberry coat. That’s basically how authorization vulnerabilities work.Protect Against BOLA with ScopesBOLA vulnerabilities often occur when you haven’t specified what your users are authorized to access. If you use Tokens like OATH, then you can limit access by tying scopes to your Tokens.Larry: You actually preempted one of my last questions about BOLA. So tell us, how can our developers protect against such BOLA attacks.Alissa: So there’s different things you can do. One of the things that I noticed is a lot of developers will implement tokens, but they won’t implement scopes. A really big recommendation is if you’re going to implement tokens like OATH, you want to make sure that you tie scopes to those tokens, which defines the level of access or the records that you’re allowed to see. It basically sets parameters around your token and defines what you’re permitted to be able to request. So if I’m a clinician for example, and I have a login, through those scopes I can only view these specific patients. Or if I’m a patient, my scope for my token should only allow me to see my patient records, not /patient/1,2,3,4,5, all these other patient records. I should only be able to just request my specific patient record.There’s obviously commercial solutions that can implement this as well. But a lot of the vulnerabilities that I’m finding are just basic authorization issues. It’s not like they’re sequel injection, which can be handled by legacy Web Application Firewalls (WAFs). WAFs are great for rules-based/logic-based security control and so can’t really protect against BOLA attacks.                Detect Security Vulnerabilities With Moesif              14 day free trial. No credit card required.              Learn More        Do Not Use WAFs to Protect Your APIsAnalysts have promoted Web Application Firewalls and API Gateways as effective security controls against API attacks. WAFs are good for identifying sequel injection in the payload or cross site scripting, but they aren’t able to to know whether I’m authorized to see the data that I’ve requested.Larry: On the subject of rule-based WAFs like ModSecurity or even API Gateways, I noted that in one of your recent YouTube postings you effectively said that they’re pretty useless for protecting your APIs.Alissa: I’m pretty opinionated!Larry: And funnily enough, I think it was the 2019 report from Gartner What you need to do to protect your APIs, where they espoused the virtues of API Gateways and WAFs. And then you were on Gartner and you basically shot that down.Alissa: I don’t think they like me very much.Larry: Is there anything else that can be done, perhaps like advanced anomaly detection, that can further protect your APIs.Alissa: This is my position on it. I don’t think that security should ever be a feature of a product.For example, when you have an API Gateway they’re adding security in as a feature to their primary responsibilities. For me, that’s a concern.Gartner put a report out on Web Application Firewalls, where they said that WAFs were an effective security control against a business. This is something I could go on all day about — with pay for play and the analyst industry and stuff like that. But I won’t go there. The fact of the matter is, I think it’s creating this false sense of security, because CISOs are buying WAFs and they’re implementing API Gateways and turning on security and it’s creating this false sense of security.Every single one of the APIs that I hacked in my most recent healthcare breaches were protected by WAFs. These CISOs are listening to the information coming out from the analyst industry, who have to print and talk about it because those are their paying clients. But the problem is that they’re not an effective security control against API attacks. How is a WAF going to know whether or not I should be requesting data that doesn’t belong to me? It’s going to be looking for things like sequel injection in the payload, cross site scripting attacks, that sort of thing. But it’s not going to know whether or not I’m authorized to see something or not, that’s outside of its realm of understanding. I think that answered like three fourths of your question, what was the other fourth?                Detect Security Vulnerabilities With Moesif              14 day free trial. No credit card required.              Learn More        Know What Traffic is Going to Your APIThe first step in protecting your APIs is to understand what’s actually going to it. There’s a big difference in legitimate human traffic versus synthetic traffic. You can’t protect what you don’t know you have.Larry: If WAFs and API Gateways don’t swing it, how can you protect your APIs? In fact, I think you touched upon a very cogent point, which I heard in one of your YouTube channels as well, which is that a lot of CISOs and developers view security as an afterthought or a bolt on. And you said, don’t do that, you should security should be an important consideration from the get go.Alissa: I’d like to preface this answer with the fact that any organization that has APIs needs to know what’s going to their APIs. They need to know: is it human traffic — legitimate human traffic, is it synthetic traffic, is it an Account Takeover (ATO) attack using credential stuffing? It’s also a good idea to look for high frequency tools that might be consistently pounding your APIs and stealing very expensive bandwidth and resources from legitimate users. Everyone should know what they’ve got — you can’t protect what you don’t know you have. That’s incredibly important. So I want to preface this with the fact that you should know what’s inside the traffic going to your APIs.                Monitor All API Traffic With Moesif              14 day free trial. No credit card required.              Learn More        Shift Left Security - Shield RightUse a two-pronged approach Implement security while the code’s being written, send devs to secure code training and use tools that alert when writing insecure code. Shield right: secure your protect after its been deployed into production.Alissa: After understanding what types of traffic are going to your API, one of the most important things is shift left security, but also shield right. The concept of shift left security is that when you’re writing the code you should be sending your developers to secure code training, you should be implementing a tool that will watch out for them writing insecure code, and yell at them when they are. Look for a solution that you can compile an SDK with the app, if you’ve got an app-based architecture So, shift left security: implement security while the product’s being created, while the code is being written.Shield right is the idea that okay, not only are you securing it while it’s being written, but after it’s been deployed into production. Because we know that cybersecurity is a very fleeting industry. It moves very quickly. Several new zero day exploits came out just in the period of you and I are talking. There’s new exploits and new vulnerabilities every few minutes, so you need to shield right in anticipation for what’s to come.The unknown unknowns. That’s what’s getting everyone nervous right now, and that’s what’s getting a lot of people breached right now — the unknown unknowns. The knowns are things that, to me, are legacy security controls like network intrusion detection, like the old Snort days when you’re writing Snort signatures for these exploits, you’re basically documenting known knowns. But what about the unknown unknowns? I don’t know what I don’t know yet, and so that is where shield right can take us into the future for the things that we don’t know about.Larry: I just finished a book from Nicole Perlroth, This is how they told me the world ends.Alissa: Oh Nicole, I actually just interviewed her on MoneyFest, Money2020. So if you haven’t seen it yet, I urge you and your audience to go see it. It’s a great book. She’s a brilliant, brilliant journalist from the New York Times.Larry: Right. The book is long and thick. A weighty tome. It reads like a crime thriller, covering the whole exploit industry, starting from how it started off in the early naughts, 2000, 2002. A great perspective on shift left, shield right. I love that graphic analogy.PHI is Worth A Thousand Times Credit Card InfoYour healthcare information is persistent, if it’s released onto the dark web it’s gone, there’s no changing it or getting it back. Contrast that with a bank simply mailing you a replacement credit card after Target is hacked.Larry: Moving on a little. Since Moesif is used in a bunch of healthtech apps and some of our audience works in healthtech, in your experience, is ePHI (electronic Patient Health Care Information) or personal banking information, more valuable on the dark web, and why?Alissa: Oh, I actually did research into that. That’s a great question. So obviously, in the work that  I do I’m browsing Tor sites a lot, dark websites, and one of the things that I can tell you is that electronic health records are worth 1,000 times more than a US credit card number. So when you have a PHI record, EHR record, whatever you want to call it, you have a lot of data. So if I compromise Target and in that compromise, I steal Larry’s credit card number, your bank can very quickly send you out a new card. And it cost a few bucks to the bank. They send you a new card, you’ve got a brand new card and you’re fine.If I compromise your health history and put that up for sale on the dark web, how easy is it for you to get new health history sent to you in the mail? It’s impossible. There’s no such thing. It’s gone. Once it’s out there, it’s done. If I want to figure out how to kill Larry, I find his PHI and I find out he’s allergic to bee stings. So I go after you with some bumblebees. But you can’t undo that once it’s done. That’s one of the reasons why I believe it’s worth so much. The other thing is that, having compromised so many healthcare APIs, I can tell you that there is a treasure trove of data in there.In one instance, not only did I see the admission records for a hospital, but also the family member information of that individual. So when you go into a hospital and they ask for next of kin information and all this other stuff, all of this other data they’re going to need to know, it makes for a very content-rich environment, a very data-rich environment. There’s a lot of data on individuals’ information within PHI records, which is why we need to take this so seriously and which is really the impetus for a lot of my healthcare research.When you’re talking about people’s health, it’s way different than defacing a corporate website. So much has changed over the last two decades. I mean, before when I was hanging out on IRC and doing this, it was all about, world-of-hell mass defacement. And now it’s money. It’s such a lucrative business to be in. When you talk about the dark side and the tens of millions of dollars that these ransomware groups are bringing in, it’s insane. I mean a lot of these ransomware groups are bringing in more money than some countries, more cash on hand than some large companies have. So it’s scary. It’s a huge business and the answer, the long winded answer, to your question is PHI is definitely worth way more than financial data.And that’s not to say, don’t get me wrong Larry, that’s not to say that it’s less important, it just demands a much smaller amount of money than PHI.                Insist On Client-Side Encryption              14 day free trial. No credit card required.              Learn More        APIs are the Weakest Link in HealthcareOne of the biggest problems is chain of custody with PHI, where data goes from a very secure API to a less secure API. Cerner and Epic have very strong security, but once that PHI leaves over an API you have no idea how secure the next system might be. Hackers target the less secure API — rob Paul to pay Peter.Larry: Given that the PHI and the EHR patient records are so valuable, are there unique challenges to locking down healthtech APIs, versus maybe those from say fintech.Alissa: I think probably APIs could be the weakest link in security. Different health care providers will use different HR systems, whether it’s Epic or Cerner or whatever, fill in the blank here. Those systems prior to, and I’m sure we’re going to talk about this, but prior to FHIR, they could not talk to each other. There’s problems where, for example, if I’m targeting something like a Cerner EHR system that may be very, very well protected and very secure, but as soon as those PHI leave that system and go to a less secure API, where do you think I’m going to target as a hacker? I’m going to target the less secure API. I’m going to go after the path of least resistance.I think one of the biggest problems is this chain of custody, or weakest link — PHI can go from a very secure API to a less secure API. It’s less secure because the person who wrote it is just some small business who doesn’t know what they’re doing and didn’t know anything about security. I robbed them. I robbed Paul instead of Peter.Also, I think the second thing is the prevalence, and I would dare to even say it’s very systemic across a lot of the mobile apps that I looked at, where in this new concept of mHealth, or mobile health, apps, where API keys and tokens are being stored in clear text or hard coded in clear text in the  mobile app.  And developers just throw their arms up in and say, “hey, where am I supposed to store them if I can’t store them in the app?” They don’t really know where to put them. So it’s a real problem. I think there’s problems on the app side, where keys and credentials are getting hard coded, and then problems on the back end where the APIs are.APIs Have Multiple Attack SurfacesSince data is everywhere, castle-and-moat security isn’t possible. Attack surfaces range from partner-facing APIs, think supply chain threats, through web APIs, where there’s no SDK to bundle extra security into, through to person-in-the-middle attacks, where certificate hygiene impacts server/client API security.Larry: Right. Can you talk to a little bit more about the different attack surfaces that ePHIs could be compromised over and what could be done to harden them? We covered a little bit about the apps themselves, but what about key stores, the network, the API endpoints, and also then data leaks.Alissa: Sure. There’s just obviously data everywhere. The whole concept of castle-and-moat is completely erased at this point. You can’t control it. So, on the client side the hard coding of keys and tokens, or credentials, that’s the real problem. You have different types of APIs, so let’s talk about the different attack surfaces.You have partner-facing APIs, where supply chain attacks are a real threat today. I don’t even have to go after you from the Internet, I can just find out who you’re doing business with and go after them. Since there’s connectivity between the two of you, through a partner API, a B2B API that’s just facing the two of your companies, and I’m in. Because whoever wrote it felt, well, this is a partner facing API, not facing the Internet, so we don’t have to worry as much about security.You have web APIs, and if you don’t have a mobile app, the security controls on the client side are going to be far different. Whereas you can compile an SDK with the mobile app to add that additional layer of security, what do you do about the web APIs where you can’t necessarily compile a Chrome browser with the SDK? Get Google to distribute that for you?There’s also the concern over women-in-the-middle/man-in-the-middle/person-in-the-middle attacks, where you have a lot of organizations that are not implementing certificate pinning. What I’m able to do as an attacker with a lot of these apps is insert myself in between the communications between the client and the back end API. Then I submit SSL certificates in both directions, telling the API that I’m the client and telling the API client that I’m API server at the API endpoint. They both think they’re talking to each other, but they’re really talking to me. That allows me to decrypt the SSL encrypted traffic and look at it. I can learn how the API works just by intercepting the traffic and decrypting it, and then copying and pasting those API requests into my own API client like Postman. I can then going after the API endpoint myself manually with an API client.                Moesif Keeps Your Data Secure              14 day free trial. No credit card required.              Learn More        Banning Apps From Jail-Broken Phones Does Not HelpHackers don’t care if your mobile app can be run on a jail-broken phone, or not. Downloading the APK, or intercepting the L7 traffic to & from the mobile app, is often enough to figure out how to access all the data.Larry: Got it. I followed one of your tutorials recently on how to actually do that and it was  surprisingly straightforward. And I’m not a super-great coder. In fact, I’m not a coder at all.  But it was a little bit shocking that you could, with Postman and some packages downloaded onto your MAC, pull all of that information out.Alissa: Pacman makes it surprisingly simple. It’s a package manager like Red Hat RPM and really powerful. A lot of these tools are free downloads. For example, I think it’s called Advanced REST Client, that’s a free download. The thing is that when you’re hacking, and that’s what I don’t understand, a lot of developers will implement security and say “oh, don’t worry, we looked to make sure that the mobile app isn’t being run on a jail-broken or routed phone.” I don’t care about that. I don’t need to run it on a jail-broken phone. With a lot of these API attacks I just extract the APK off of my android device. And then load it into my tools on my workstation using Apk Extractor, which you can download from the Google store, ironically enough. I don’t need to execute it. I don’t need to run it in a jail-routed environment. I can just do all this from my laptop or workstation.Alternatively, I can even run it on the android device itself and intercept the traffic with a tool. And then again have access to all the data. Believe it or not, I prefer to do it that way rather than looking at the API documentation. With FHIR there’s a lot of documentation out there on it, because the point of FHIR is that you’ve developed for it. But I prefer to actually intercept the traffic and look at it. I’m a packet monkey. I tend to learn better looking at packets and looking at how the API works at the Layer 3 level, rather than just reading documentation.Use MobSF to Find API KeysTo deconstruct an App simply drag and drop the APK file into MobSF and it just takes it apart, reverses to the original source code and then find hard coded keys/tokens with Grep.Larry: I noticed that you are a Grep proponent.Alissa: Yeah, I’m a Grep girl. Wow, you did watch my videos didn’t you. But thanks for stalking me, I appreciate that. Love my fans.There’s a great tool out there called MobSF, for your audience that may not be familiar with it. It’s called the Mobile Security Framework and it allows you to literally drag and drop the packet APK file into the tool, and then it just deconstructs it. It just takes it apart and reverses it back to the original source code. That’s how I’m able to find all these hard coded keys and tokens.The interesting thing about that, much to your point. is that I don’t like to use the GUI, I’ll actually use that to just have it reverse back to the source code. Then I go into my command shell, into terminal, and use a bunch of Grep strings. That’s my preferred way of finding hard-coded API secrets in apps.APIs Need to Comply With FHIRThe recent Cares Act stipulated that healthtech companies need to make patient data available to those requesting it. FHIR-compliant APIs present a secure way to meet those requirements.Larry: Fascinating stuff. Great segue into the work of HL7 and their soon to be released latest version of the FHIR (Fast Healthcare Interoperability Resources) standard. How important is that work, should developers be building to the latest FHIR standard, and what’s your perspective on how important this will be for the API security industry in healthcare?Alissa: Well it’s not important at all - no, just kidding. This is huge. This is probably, out of all of my vulnerability research, the most important thing that I’ve ever worked on, because you know I’ve had so many people from across the healthcare sector reach out and talk about how important this research is.A lot of people don’t know this, but they automatically assumed us hackers came out of the womb knowing this stuff. I had no idea how to spell FHIR, let alone knew what the hell it was when I walked into this. I had to do my homework. I had to research. I didn’t even know who the heck HL7 was. HL7 is the name of the organization, and it’s the name of the standard. So it’s like the whole Kleenex tissue. There’s all this that I needed to research and understand. It took me months, and I still don’t know all of it, I still don’t fully understand a lot of it. I’m really excited about this research. I’m currently diving into R4 of FHIR, which is Release 4 of the standard.It was created by HL7 International, Health Level Seven International, like you mentioned, and before FHIR, there were all these other different versions of HL7 that predated FHIR. And the ONC (Office of the National Coordinator for Health Information) has basically set these deadlines for healthcare payers and healthcare providers and said “you need to make this patient data available to people requesting it, otherwise you’re in violation of this data-blocking rule, information-blocking rule.” It can mean stiff penalties and fines. It’s a big deal if you are found guilty of this information-blocking rule. There’s a deadline around this, and organizations and healthcare payers need to implement FHIR APIs and make this healthcare data available.The only thing is that I wanted to show what could happen when these APIs aren’t secured properly.That’s the impetus to this research. That’s what I’ve been focused on over the last year. Phase one of this research was targeting mHealth APIs, mobile healthcare APIs, that store medical data. In part two, which we’re calling Playing With FHIR, we’re focused on hacking FHIR APIs. And that’s what this report coming out will detail, it’ll give our findings and what we’re seeing out there. And there’s a lot and I’m really excited to be unveiling that research. And I know you guys are doing a lot in the healthcare space as well. Healthcare is a very target rich environment. There’s so much money there and hackers know it. And there’s just so much data, and that through these vulnerable APIs it’s very easy to get access to.                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It’s going to be based on implementation.One organization may implement a FHIR API, but didn’t follow best practices and have it be completely vulnerable. But, another organization may implement FHIR APIs and have that be rock solid. It may not be immediately evident where vulnerabilities are. Maybe it’ll take much longer than the other organization to find it. I’ll never say that anything is not hackable. If humans are implementing it, it’s going to have vulnerabilities, but they might just be harder to find. That’s the thing about FHIR.  You and I could both implement FHIR APIs to serve our PHI records, but yours may be way more secure than mine, because I don’t know what I’m doing and I implemented it insecurely.So it depends on the implementation. It depends on who’s implementing it and whether or not they know how to secure it properly. So being smart on FHIR brings in OAuth and all these other things like authentication and authorization. So implementing FHIR, of course, is very heavily predicated on the implementer and whether or not they implemented it properly.Get FHIR CertifiedAs an added check it’s worth having your FHIR API certified.Alissa: Now, I do want to throw a wrench in here — there’s actually a certification process that organizations can go through to have their FHIR API certified. So if you’re going to implement a FHIR API and I’m going to implement one, I can actually go and get my FHIR API certified as being implemented according to the standard, with the proper security controls in place. And you can continue to run your FHIR APIs, but not be certified. It’s not compulsory. You’re not required to get yours certified.The EHR vendors that I’ve spoken to in this research, they’re pursuing certification of course. But you can have non-certified and certified APIs. Which means that there’s going to be a very big mix of vulnerable and non-vulnerable APIs. Let me be careful when I say that, because I’m not saying that a certified API is going to be secure and unhackable, I’m just saying that you’re going to have a mix of the two.FHIR Certification Versus HIPAA ComplianceUnlike HIPAA, with FHIR, there are third-party bodies that can certify your APIs.Larry: That sounds a little like HIPAA compliance, where there’s no third-party certification body; there are best practices that you should follow, and there are people who will audit what you’ve built, but HIPAA is basically a bunch of guidelines that you should follow. Sounds like though, that HL7 has gone one step further where there are third-party bodies who can check that you’ve implemented FHIR according to the standard.Alissa: One of the EHR vendors that I spoke to, I won’t name them, but they did say that they were pursuing certification by the end of the year, which would make them the first company to get FHIR. I don’t know who the actual organization is that’s doing the certification, maybe it’s HL7, I don’t know.But you can definitely have the option to go and get it certified or not, and much to your example, you’re not required to.No One Right Solution for API SecurityThere’s lots of great solutions out there for API security. But what the actual best approach is, I don’t know if there will ever be an answer to that.Larry: Got it. Well, we’ve covered a lot on our podcast today. You’ve given a lot of fantastic insights on how to protect your APIs in general, and your mHealth and your healthtech APIs, in particular. As a penultimate takeaway, what’s next for APIs and security that we haven’t seen yet?Alissa: I think #moreplease. This is a great way to close out the show and the interview. First of all if I were to put my fortune-teller hat on, this is definitely the direction the world is headed: we’re moving completely away from monolithic architectures and moving to microservices, everything is moving to the cloud, everything is microservices powered by APIs. I think we’re just going to see more and more of this.The problem is that organizations are going to go from running 1 to 2 APIs, to running 1,000 to 2,000.  I’m working with an organization right now that has over 1,600 APIs. That’s a lot of APIs. I think this is going to create a much bigger marketplace. One of the things that’s coming out of the presentation to Gartner on the state of the APIs, is that the marketplace is going through an identity crisis right now.The API security marketplace doesn’t really know what it is yet. Every company out there with an API security threat management solution believes that they’re doing it the right way. And that’s not necessarily false. Every single company may be doing the right approach to API security, but I think the jury is still out on where that will land. Is it in-line? Is it passive? Do we use SDKs? Do we use distributed tracing? Do we use… There’s all these great amazing approaches, a lot of them are my clients, and they all have great solutions. What the actual best approach is, I don’t know if there will ever be an answer to that.But I know what the wrong approach is. And we’ve talked about that here on the show and that’s these legacy rules-based systems, like Web Application Firewalls. Or, we’ve got an API management solution in there, let’s just have that do security. Through my research in showing that I can hack and breach these APIs and steal all these thousands of patient records by APIs that are protected by WAFs and API Gateways is proving, is that no one should be using these to secure their APIs. They should be looking at solutions like Moesif, looking at solutions like these other threat management solutions like Traceable, and Approve, and lots of other great solutions out there.                Deeply Understand How Your API Is Used With Moesif              14 day free trial. No credit card required.              Learn More        Instrument Your APIsThe most important step in securing your APIs is knowing what’s going over them. Instrument yourself with a tool that’ll give you the ability to delve into your APIs’ traffic.First and foremost, what everyone needs to understand is you can’t protect what you don’t know you have. You need to know how many APIs you have, what kind of data they are serving, are they spacing the Internet (can I reach them from the Internet) or are they partner facing, are we authorizing and authenticating (we’re giving you authenticate access to this, but are we authorizing what data you can request)? All of these things are very important. Knowing what kind of data your APIs are serving. If I have 1,600 APIs, do you think I’m going to know which APIs are serving PII or PHI and which ones are serving PCI data? I should know that, and there’s no way I can memorize that, so I’m going to need a tool to do it. My recommendation to all of you out there is know what data is going to your APIs, interdicting it, looking at it, analyzing it. Know what’s going there, know what is taking the bandwidth from your APIs and what API requests are being sent. Instrument yourself with the tool that will give you the ability to look at it.Larry: And take action on it. What a superb summary and takeaway from this interview. My final question: where can our audience find out more about Alissa and where are you speaking next? I know you’re a very prolific keynote speaker.  What’s coming up for Alissa? And what other resources out there should our audience be following?Alissa: I would say that the primary vehicle for where I distribute my vulnerability research and content as a content creator and filmmaker is YouTube. So definitely subscribe to my YouTube Channel and smash that that bell icon for notifications. But, also follow me on Twitter and connect with me on LinkedIn. I love nerding-out on API security and hacking in general. I have a new book coming out on hacking APIs through Wiley. My second book. I’m in the process of writing a screenplay for a new TV series. There’s a lot going on in my world and I definitely would urge everybody to just follow me on YouTube, LinkedIn, and Twitter because that’s primarily where I’m at. Unless they want to see pictures of my food, I’m on Instagram I post pictures of my food there.I appreciate all of you, and as part of my network and followers and fans keep an eye out because it’s going to be a really exciting year. I’m going to be speaking at HIMSS next, I would say, arguably the world’s largest healthcare conference, I’ll be keynoting there. Alongside some other amazing keynote speakers like A-Rod and Michael Coats.I’ll definitely see what happens if anyone wants to stop by and say hi, and I’m going to be also speaking at DEFCON, I’m speaking at over 30 conferences this year. So a lot of exciting things. I’m also keynoting at the upcoming Money2020 conference. Which is really exciting. And I’m actually posting my event schedule on my website here and on KnightInkMedia.com, so keep an eye out. And again, the best way to hit me up is on social media.Larry: Great. Thank you very much Alissa for your time today. I’m sure our audience will really enjoy this podcast.Alissa: Thank you. Appreciate it.                Make Your API Platform Successful With Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /podcasts/api-security/Podcast-API-Security-and-FHIR-Recommendations/",
          "author": "Larry",
          "categories": "Podcasts, API-Security"
        }
      
    ,
  
    
        "customer-success-monitoring-reduce-feedback-loops-for-customers-integrating-your-api": {
          "title": "Reduce Feedback Loops for Customers Integrating your API",
          "content"	 : "We can all remember a time when we had a negative experience with a platform service. Perhaps we ran into a set of reoccurring errors or the platform was unreliable and continually crashed. The classical example of this might be the early days of Apple Maps. In any case, the common thread of these experiences is the feeling of frustration from valuable time lost, resulting in a tarnished relationship with the product.Thankfully, in the world of APIs, customer-facing teams have taken note. Companies are now investing huge amounts of time and resources to remove platform bottlenecks to ensure that their customer experience is reliable and performant.Yet, for a majority of these API-driven companies when it comes to understanding where their customers’ struggle and the specific types of errors that they are receiving it largely remains a reactive process. Simply put, the process relies on companies’ end-users to notify their support teams when things go awry with their APIs.This reactive process of waiting for your customers to call in platform errors can result in detrimental outcomes, which can negatively impact both internal and external teams.For internal teams this can create a sense of disarray, rushing from one fire drill to the next, barely keeping their heads above water. For example, when an internal team learns of a buggy endpoint that is causing their customer’s application to fail it often requires coordinating with multiple support teams, which have to move at lightning speed to make a quick fix. As you can imagine, this produces internal fatigue and prevents your customer success team from building trust and expanding your customer’s use case.The effects of a reactive support process can certainly be felt at the customer level as well. As mentioned above, when a 3rd party API runs into a performance issue it can cause an entire service to fail. In this scenario, your customers are understandably irate, feeling as though you have not properly invested in the right technical resources to provide a reliable partnership. This could result in your customers looking elsewhere for a similar service or begin the process to build an internal version of your tool.One of the key reasons API-driven organizations suffer from a reactive support process is that they are limited to errors happening on the server-side and not taking into account how their customer API usage is contributing to their platform’s overall reliability and performance.In the world of APIs, errors can happen both on the server as well as on the client-side of the integration, therefore API support teams need a multi-lensed vantage point to drill into both infrastructure metrics as well as customer API usage metrics to remove platform errors. To learn more see the 15 API Metrics Every Platform Team Should Be Tracking.For example, one of your customers could set up a faulty integration, which can unintentionally cause a spike in API usage. This can spell an availability problem that can starve the performance for the rest of your customer base and result in 503 errors. While conversely, a drop in API usage might be the canary in the coal mine of an impending technical issue with one of your endpoints that could eventually affect your entire customer base. In both cases, simply sitting and waiting for the end result won’t buy you any goodwill from your customers.With the knowledge that errors and bugs within your API platform can result in major setbacks, customer-facing teams must look to reduce feedback loops at all costs. The best way to achieve this goal is to implement an API monitoring solution that provides you with automated alerts and tracks out every step of your customer journey, so you can take action before your customers are negatively impacted.Let us know in the comments below what key API metrics you get alerts on or would like to see in the future.Closing thoughts:Companies who are looking to provide a great customer experience and remove platform bottlenecks should look to Moesif to drill into exactly what their user did with your APIs and why their experience suffered, without spending hours in manual log search. In addition, Moesif empowers support teams to scale out their customer success efforts by getting timely automated alerts to prevent future fire drills.                Understand How Customers Use Your APIs              14 day free trial. No credit card required.              Learn More        ",
          "url": " /customer-success/monitoring/Reduce-Feedback-Loops-for-Customers-Integrating-your-API/",
          "author": "Oliver",
          "categories": "Customer-Success, Monitoring"
        }
      
    ,
  
    
        "developer-marketing-market-your-saas-platform-to-developers": {
          "title": "Marketing Your SaaS Platform in Uncertain Times",
          "content"	 : " How to Market Your SaaS Platform to Developers in Uncertain Times Robust APIs and developer platforms are resilient to uncertaintyCreating a cohesive marketing presence across digital platforms is vital to strengthening not only brand awareness but consumer engagement. While it is never fun to plan for the worst case scenario, it is imperative to have structures in place that leverage long term solutions to (hopefully) short term downward trends. Spillover from the coronavirus disease and the subsequent shelter-in-place had drasticconsequences in the startup world. Small brick and mortar businesses that were shuttered due to shelter-in-place rules were no longer spending money on Facebook or Yelp to promote their business nor would theymaintain their SaaS subscriptions. Large enterprises can and will pull back spending insales and marketing in anticipation of a slump in business. While not guaranteed, this type of behavior can cause a reduction in usage of SaaS contracts or seat counts. CFOs and financial controllers will block more purchases than before, which can force SaaS deals into limbo. ..The good news is that many developer platforms and APIs have tricks that make them more resilient to rapidly changing landscapes.However, if you’re not enforcing these ideas  today, now is the time to reconsider to ensure the longevity of your product and/or company.Focus on the self-service businessDuring times of uncertainty, many large enterprises are hesitant to commit financial and human resources to run a paid pilot orsign a large enterprise contract due to their own financial constraints. You could have the best social proof and quantitative numbersvia case studies and strong customer logos, yet VPs may have received a directive to freeze all non-mission-critical spend and hiringleaving them no choice. On the positive side, many senior engineers and managers have a budget for discretionary expenditure forSaaS tools.While the budget may be small and only enable a spend somewhere between $100/month to $1000/month, these expendituresusually require very little to sign off or approve. With the correct usage-based pricing model, you will be able to expand these accountsover time which may even approach the ACV you would normally see with a small enterprise agreement even though the customer is payingmonth-to-month on a credit card. While your competitors watch their sales pipeline dry up and lay off vast numbers of theirsales force, you get to gain self-service customers whose usage may accelerate quickly once their markets start to recover.Fool-proof your pricing strategyWith a self-service business, there are a variety of ways to price. Usually, you want to price using value-metricsrather than cost metrics. However, there may be multiple value metrics you could leverage. Ideally, ensure your pricingenables upsells to be automatic even if there’s a reduction in staff. For example, a sales tool that is priced bythe number of contacts in a CRM or a marketing tool that is priced via MAU (Monthly Active Users) will do better than a toolthat leverages per seat pricing. A large reduction in staff can cause the company to renegotiate a lower seat count. However, few companies will experience a drop off in MAU or number of contacts.Track your adoption funnel religiouslyIf you don’t have any product analytics tools in place, now is the time. Instrument everything you can. Instrument your APIs.Instrument your web apps. Make sure every advert or external link is leveraging UTM parameters.. This enables you to understand whichacquisition channels have the highest ROI and drive product growth. Top of funnel growth is no longer ideal. You should only be investing in channels that lead to direct conversions. For most developer platforms, this means customers have actually integrated and are actively using your API. If you’re only measuring page views and sign ups, take the downtime to re-evaluate what your trueBuild your online presenceWith the conference circuit slowly coming back to life, now is the time to think about your online presence. Create blog posts and webinars that can be posted both to your own blog, but also syndicated out to partners in your space. The key for content is to ensure it’s authentic and relevant. Quality content almost always wins over sheer quantity. Don’t forget to incorporate engaging graphics -resize your images, change backgrounds, add overlays, and make their colors pop. By creating content, you’re increasing your presence in organic search, but you alsogain other benefits. For example, potential customers will share the content via social channels giving you additional exposure for free.You can also leverage the content when following up with a strong prospect. Instead of just consistent nudges on “let’s set up a time to chat”, youcan demonstrate value by sharing the content for them to consume at their own pace.Minimize churn riskIdentifying and minimizing customer churn risk becomes far more important than the top of funnel growth during any period of uncertainty. Many times, we can find additional growth by reprioritizing the potential customers that already signed up and/or are paying. You should have a mechanism to track account health and get alerted when things are not looking good. Things to look out for:  Customers with decreasing API usage week-over-week  Customers that are only accessing only one or two endpoints  Looking into SDKs that have a higher drop off which could indicate bugs or lack of documentation  Customers with a high number of errors or latency such as unauthorized errors  Customers who have not accessed your primary value creation endpointsFocus on customer success and developer advocacyDepending on your company, you may have a more traditional customer success department or maybe a devrel groupwith developer advocates taking over some more traditional customer success responsibilities.Your current customers are your largest advocate during any sort of downturn . Make them successful with your API with an awesomedeveloper experience and they will tell their colleagues and friends. Some small things include following up with developers who integrated with yourAPI already on what their building and how you can help. If you do identify a customer that’s running into integration issues, reach outand offer assistance.Look to partnerships and integrationsWhen paid marketing budgets shrink and cutbacks in sales headcounts occur, achieving the same growth milestones may seem like a moonshot.Yet, there are countless developer platforms that leverage partnerships and integrations for organic, low-cost growth.Take a look at any company that offers a marketplace that you can be listed in such as GitHub, Heroku, AWS, etc. Because some of these may be crowdedreducing your visibility, you should also look to other SaaS tools that your target buyer is also using. If you’re a marketing tool, maybe integration with Hubspot makes sense. If you’re an API monitoring tool, maybe building integrations with PagerDuty and Slack make sense so your customers can get alerts via their existing setup.Concluding thoughtsMarketing an API platform to developers is tough. Usually marketing and sales budgets need to be decreased, enterprises are more reluctant to sign new contracts, etc. By focusing on your adoption funnel and finding growth channels that are cheap such as content and partnerships can work.                Understand Your API Adoption Funnel With Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /developer-marketing/Market-Your-SaaS-Platform-To-Developers/",
          "author": "Derric",
          "categories": "Developer-Marketing"
        }
      
    ,
  
    
        "business-api-observability-apis-are-now-at-the-center-of-digital-transformation": {
          "title": "APIs are Now at the Center of Digital Transformation",
          "content"	 : "As we take stock of how COVID-19 has affected the way we operate, nothing in technology is more apparent than the switch to digital. Although many of us have transitioned from water-cooler conversationalists to reluctant zoom dwellers, the impact on business processes themselves might actually be more profound.According to McKinsey, coronavirus has acted as an accelerant on companies offering digital products and services. Across all business areas digital adoption has accelerated to such a degree that it’s the equivalent of fast-forwarding 6 years so that we’re now operating in 2027, where 60% of all businesses in the US employ digital processes.McKinsey: How coronavirus has transformed business foreverThe evolution that businesses go through when adopting digital processes which simplify, automate and modernize existing systems, is called Digital Transformation. By creating digital experiences for customers, partners and employees, businesses can reap outsized returns on their investment through improved customer engagement, lower operating costs and the opening up of new markets & opportunities.During the pandemic one of the sanest voices in San Francisco was the Head of Medicine at the City’s main teaching hospital, University of California at San Francisco (UCSF). Dr. Bob Wachter manages an organization of 3,000 people, including 800 physicians, and was on the front line of both the local pandemic response and the move to digital as his hospital, like many others, focused on in-person treatment for COVID-19 and farmed-out most everything else to telemedicine. As the pandemic waned, Wachter noticed that the move to digital has become more enduring, in more ways than one could imagine:Perspective on Digital Transformation after COVID-19 from UCSF Chair of MedicineThe companies that have been most successful during the seismic shift that coronavirus represents, are those that have embraced digital transformation and centered their business strategy on APIs, apps and data analytics. Through judicious planning and architecting they’ve managed to streamline engineering development, deliver superb customer experience and offer a low-friction way to integrate with partners.APIs Speed DevelopmentClassically, digital transformation takes time to implement. Modernizing existing systems is all about building skills and confidence by trying things out, learning from them and then moving ahead — analogous to leveling-up at each step in a game.But the pandemic has forced a definite prioritization in what’s business as usual versus what’s change. Organizations have had to respond to change, fund the new priorities and rapidly build capabilities.Operating in the cloud with microservices-based apps, agile teams are able to build smaller functional components and connect them together over APIs. Furthermore, third-party services created by domain-specific experts, can be integrated into apps over APIs, freeing up the business’ dev teams to work on more value-added features instead.Symbl.ai uses services from Moesif to refine product strategy and enhance DevExLike many companies with limited engineering resources, when Symbl.ai was confronted with the option of building a new feature in their reporting dashboard, or adding a new product capability that a customer was asking for, they prioritized the product route. Although reporting and dash-boarding is secondary for product-driven companies, it’s still critical.Symbl.ai were able to add Moesif’s analytics and dashboarding capability into their product with minimum effort over an API, speeding time to market:      “When you’re a small company, it’s very hard to focus 20% of your continued engineering efforts on building a dashboard for yourselves” Surbhi Rathore, CEO & Co-Founder, Symbl.ai                  Speed Development with Moesif              14 day free trial. No credit card required.              Learn More        APIs Present the Unlocked Versions of AppsAnd APIs are becoming a lot easier to use. More people are getting involved in the conversation because web APIs, or RESTful APIs, are a lot less technical — in their simplest form they’re just a URL with a bunch of query parameters, something that almost anyone can use.APIs Support a Consistent User ExperienceDigital transformation is increasingly propelled by changes in customers’ expectations. A consistent 360-degree customer experience is now preferred, one that encompasses computers, phone and physical assets.APIs not only hide complexity in the back end, but, by design, they also enable a single and consistent user interface. Even though your enterprise product might be using 10s to 100s of internal and external services delivered over APIs, you’re still in absolute control of the front end and how your customers experience and interact with your product. APIs avoid the split-horizon problem, where there’s no single point of reference, even when using multiple services.The Best Place to Analyze Performance is at APIsAPIs present natural demarcation points for the services that make up apps. There’s no better place to be for gathering analytics data than where data ingresses or egresses a service.Moesif's Advanced API Analytics drives growthTo make sure your users are getting the most out of your product, Moesif provides deep insights into how customers are using your APIs. We build an advanced API analytics platform that helps everyone at API-driven organizations learn from their API data, and make smarter decisions that drive growth.Thousands of customer-driven teams use Moesif to really understand how their customers and partners use their APIs and to automate the debugging of customer issues.SummaryCOVID-19 has pushed many companies across the digital divide. Some organizations were already in the throes of digital transformation pre-pandemic, whilst some had not yet started. But, as a matter of survival, the majority of US businesses have made the move to digital over the last 18 months.APIs used to be the agents of change between systems, but now they’re really driving business critical use cases. Although we might not appreciate it yet, we’ve entered the golden age of APIs.      “APIs are just everywhere. They’re beneath everything that we touch in our personal and business lives,” Kin Lane, the API Evangelist.                  Make Your API Platform Successful with Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /business/api-observability/APIs-are-Now-at-the-Center-of-Digital-Transformation/",
          "author": "Larry",
          "categories": "Business, API-Observability"
        }
      
    ,
  
    
        "developer-marketing-behavioral-emails-how-to-send-behavioral-emails-with-mailchimp-and-moesif": {
          "title": "How to Send Behavioral Emails with MailChimp and Moesif",
          "content"	 : "In this guide you’ll learn how to send Moesif behavioral emails with Mailchimp.Moesif behavioral emails is a feature that automatically sends emails to customers based on their API usage. This can be used to notify customers about technical issues, such as: hitting rate limits, using deprecated APIs, or trying to run broken integrations. You can even use it to trigger business-related events such as when an item is shipped. If something can be mapped to an API call, then you can send an email from it.In a companion article, we covered how to configure behavioral emails within your Moesif dashboard.Mailchimp is a managed email service. They offer an API to send emails to your users and host SMTP servers for you.PrerequisitesThis how-to guide requires a Moesif account and a Mailchimp account.You also need a Mailchimp Transactional Account to use the Mailchimp API, which is a paid services, but Mailchimp offers a free trial.Setting up an Email ServerThe first step in setting up the email server is getting the SMTP credentials from Mailchimp:  Address: smtp.mandrillapp.com  Port: 25, 587, 2525, or 465 (SSL)  Username: The primary contact email on your Mailchimp account  Password: Any valid Mailchimp Transactional API keyThe Mailchimp Transactional API key can be generated in settings. Simply click the “+New API Key” button and it will generate your key.These credentials have to be entered into the Moesif email server configuration form.To navigate to the form, follow the steps in the next screenshot.You can copy and paste the credentials from the Mailchimp site to the Moesif site.The Moesif form looks like this:                Track End to End Customer Journey with Moesif              14 day free trial. No credit card required.              Learn More        Testing the SetupYou can then send an example email via the freshly configured SMTP server with Moesif.After you’ve configured the mailserver, create a new email template:In the new template, fill in the required fields: template name, subject line and from email address.Once that’s done, click the “Test” button and enter the target email.If everything worked correctly, you’ll see the a new email in your inbox.SummarySetting up Moesif behavioral emails with Mailchimp only takes a few minutes.Using a service like Mailchimp alleviates many common problems that might occur when sending email, such as validation issues or email being incorrectly identified as spam.By combining Moesif’s behavioral email service with Mailchimp, you’re able to keep your API consumers abreast of important issues.                Make Your API Platform Successful with Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /developer-marketing/behavioral-emails/How-to-Send-Behavioral-Emails-with-Mailchimp-and-Moesif/",
          "author": "Kay",
          "categories": "Developer-Marketing, Behavioral-Emails"
        }
      
    ,
  
    
        "technical-dashboards-display-moesif-reports-within-tableau": {
          "title": "Display Moesif Reports Within Tableau",
          "content"	 : "As a product leader, there’s no better way to show the value of your API platform then by graphically displaying key metics. If you’re already working with Tableau, it’s easy to extract key charts, or workspaces, from Moesif’s dashboards and insert them into your visualization platform.The information from Moesif will be inserted into Tableau as a web page object. Web page objects are fully-functional web browser windows based on Microsoft’s IE and, as such, all buttons, links and navigation features within the window will operate just as a regular browser.This guide will take you through the steps required to set up and display Moesif workspaces within your Tableau dashboard: it’s as easy as 1-2-3.Step 1. Moesif’s Workspace Public LinkWithin Moesif’s API Analytics Platform create a workspace of a metric you want to track.Save the workspace as a Public workspace.We’re going to use the Share Link, as shown below, to embed the workspace directly within Tableau.Step 2. Tableau’s Web ObjectOnce you’ve created your Tableau dashboard, you’ll need to insert a new web page object.Click on the Web Page icon under Objects, as shown below, and drag it to the location on your dashboard where you want to insert the Moesif workspace.When you release the icon, a pop up appears. Enter the URL from Share Link in Step 1.The Moesif workspace should appear. Resize the tiles in your Tableau dashboard to show your new metric in its best light.Step 3. Add More WorkspacesRepeat Steps 1 & 2 and populate your Tableau dashboard with key metrics from Moesif to showcase the value of your API platform.The example below shows the existing Tableau dashboard of a product manager, with two Moesif workspaces inserted:  Weekly API usage grouped by company domain  Newly activated customersEasily Visualize Key API Product Metrics in TableauThe data and analytics stack has become a key part of your enterprise solution. Tableau is a popular product to visualize data from your data warehouse. By using web page objects, you’re now able to bring in key API product metrics from Moesif without significant effort.The combined solution offers a single point of reference for your decision making process.                Understand How Customers Use Your APIs              14 day free trial. No credit card required.              Learn More        ",
          "url": " /technical/dashboards/Display-Moesif-Reports-Within-Tableau/",
          "author": "Larry",
          "categories": "Technical, Dashboards"
        }
      
    ,
  
    
        "technical-compliance-secure-proxy-for-hipaa-compliant-api-analytics": {
          "title": "Secure Proxy for HIPAA-Compliant API Analytics",
          "content"	 : "In HeathTech apps, it’s often the case that you’re dealing with private or health-related data. This requires compliance with regulations, such as HIPAA in the United States. These regulations force you to handle sensitive data in a well-defined manner, so only specific people can read it, and if they do, it should be logged for later auditing.To be compliant with HIPAA, technical and administrative safeguards must be implemented both within your company and in your app. The technical safeguards often lead to more complicated software architectures. So, it’s a good idea to make sure the extra engineering development work for HIPAA compliance is necessary before embarking. Alternatively, you could fall under one of the four cases where it’s safe not to comply with HIPAA.In a companion article, we explained how to build HIPAA-compliant APIs. In this article, we’ll dig deeper into how you can use a secure proxy to keep your health data secure and your app HIPAA complaint. Full instructions on how to use Moesif’s patented secure proxy is given in our docs section.Storing Health Data Without HIPAA?If your customer encrypts data before sending it to you, by definition, you don’t know the contents of that data. This leads to plausible deniability. The data you’re storing and processing on your side is worthless to a potential attacker without the encryption key.This can be done with a secure proxy.A secure proxy is an API server between you and your customer — but in your customer’s data center. Data leaving your customer’s data center is encrypted and then sent to your  API in your own data center.For encryption, the proxy uses a key that only your customer knows. If someone steals the encrypted data from you, they can’t read it without the encryption key from your customer.How to Build a Secure Proxy?The first step is to build a server that acts as your own API but can be deployed by your customer on-premise. The clients for your API will then communicate with that proxy instead of sending the requests directly to your API.Next, you encrypt all client data to the right level before sending it to your actual API. So your proxy needs to work with an encryption key supplied by your customer, one that you never have access to.The right level of encryption depends on your API:  For a straightforward key-value-store API. that just stores everything in the request body, it’s good enough to encrypt the whole body before sending  For anything more complicated, your proxy needs to handle encryption in a more involved manner.Take Moesif API Analytics. Moesif’s API receives events and counts them. If this API can receive multiple events per request, you would have to encrypt every event name in the request independently and not the whole request body at once — otherwise, the API wouldn’t understand the request. But with the event names encrypted, Moesif doesn’t know what the event was. Was it health-related? Was it from an online game?Finally, you need to decrypt the data when your customer wants to read it. So the proxy needs a way to show the plaintext data to your customer.Secure Proxy ExampleI’ve created a GitHub repository to illustrate a secure proxy implementation. It contains three parts: an analytics API server, a proxy API server, and a client.Disclaimer: This is just a fundamental example in which I used a crypto library I didn’t actually audit. If you build your own system, you’ll have to do that part of the research independently!With that out of the way, let’s look at the three parts of the system.The Analytics APII extracted the essential parts of the analytics API from the repository. The API is built with Node.js and the Express framework.const eventStore = [];api.post("/event", ({ body }, response) => {  eventStore.push(body.event);  response.status(201).json("201 - Created");});api.get("/results", (request, response) => {  const results = eventStore.reduce(    (results, event) => {      if (!results[event]) results[event] = 0;      results[event]++;      return results;    },    {}  );  response.status(200).json({ results });});api.listen(9000);There are two endpoints, one that will receive an event and store it in an array, and a second endpoint will calculate how often every event was received and send those sums back to the client. Just elementary stuff here, data is received, some processing is done, and results are sent to a client.The Analytics API ClientNext, let’s look at how the client actually uses the analytics API. Again, I extracted the important parts from the repository.async function logEvent(event) {  return axios.post(    "http://localhost:9000/event",    { event }  );}async function getResults() {  const response = await axios.get(    "http://localhost:9000/results"  );  return response.data.results;}await logEvent("plaintext-event-a");await logEvent("plaintext-event-a");await logEvent("plaintext-event-a");await logEvent("plaintext-event-b");await logEvent("plaintext-event-b");const results = await getResults();The client sends JSON objects with event strings to the analytics API, which listens on port 9000. It also fetches the results in the end.Running the ExampleIf we run this example with logging added, we see the following.[Client   ] Sending event: plaintext-event-a[Analytics] Received: plaintext-event-a[Client   ] Sending event: plaintext-event-a[Analytics] Received: plaintext-event-a[Client   ] Sending event: plaintext-event-a[Analytics] Received: plaintext-event-a[Client   ] Sending event: plaintext-event-b[Analytics] Received: plaintext-event-b[Client   ] Sending event: plaintext-event-b[Analytics] Received: plaintext-event-b[Analytics] Sending results:  {  'plaintext-event-a': 3,  'plaintext-event-b': 2}[Client   ] Results:  {  'plaintext-event-a': 3,  'plaintext-event-b': 2}The client sends its events to the analytics API, which saves them in its event store. In the end, the client fetches the results, which is a JSON that contains the sum of each event.We see that the analytics API gets the plaintext names of our events. The analytics API would have to be HIPAA compliant if the events contained health-related data. The events could track how often a person took a new weight loss medication, for example.The Secure Proxy APILet’s see how the secure proxy for this system would look.Note: In the example project, all systems run on localhost, but for the secure proxy to be valid, it must run on-premise at your customer’s data center, and while your own company doesn’t have to be HIPAA compliant, your customer has to. But if they’re handling health-related data to start with, they probably already are.api.post("/event", async ({ body }, response) => {  const event = cryptoStore.encrypt(body.event);  const res = await axios.post(    "http://localhost:9000/event",    { event }  );  response.status(res.status).end(res.data);});api.get("/results", async (request, response) => {  const {    data: { results },    status,  } = await axios.get(    "http://localhost:9000/results"  );  const results = Object.keys(results).reduce(    (results, encryptedEvent) => {      const event = cryptoStore.decrypt(encryptedEvent);      results[event] = results[encryptedEvent];      return results;    },    {}  );  response    .status(status)    .json({ results });});api.listen(9999);Like in the analytics API, there are the same two endpoints in the secure proxy. And for the client, they behave just like the analytics API’s endpoints. The only difference here is, the secure proxy API endpoints will encrypt and decrypt the event names before relaying them to the analytics API.This way, the secure proxy API behaves as a drop-in replacement for the analytics API. The client isn’t aware of its implementation and can simply send events to the proxy and fetch the results as they did before.The level of detail for encryption I chose here was the names of the events. The secure proxy passes along HTTP status codes, sums and keeps the JSON structures as they receive them from either the analytics API or the client.Running the ExampleIf we run the example with logging added and the proxy in between the client and the analytics API, we see the following output:[Client   ] Sending event: secret-event-a[Proxy    ] Received event: secret-event-a[Analytics] Received event: 23c06d46723cd...[Client   ] Sending event: secret-event-a[Proxy    ] Received event: secret-event-a[Analytics] Received event: 23c06d46723cd...[Client   ] Sending event: secret-event-a[Proxy    ] Received event: secret-event-a[Analytics] Received event: 23c06d46723cd...[Client   ] Sending event: secret-event-b[Proxy    ] Received event: secret-event-b[Analytics] Received event: 604c04290177e...[Client   ] Sending event: secret-event-b[Proxy    ] Received event: secret-event-b[Analytics] Received event: 604c04290177e...[Analytics] Sending results:  {  '23c06d46723cd...': 3,  '604c04290177e...': 2}[Client   ] Results:  {  'secret-event-a': 3,  'secret-event-b': 2}In the logs, we see that the secure proxy acts as a middleman. It received the events with the client’s names, but the analytics API receives an encrypted string. It doesn’t know if the event name contains a game score or medication intake info.In the example of API analytics, as with Moesif, the API doesn’t know what kind of APIs are tracked. The only requirement is that all of the events that are of the same type have the same name. That’s enough to correlate them and calculate the result.ConclusionIf your API doesn’t have to identify the type of data it works with, a secure proxy is a handy solution to circumvent HIPAA compliance.Your customers bring their own encryption keys and deploy the secure proxy in their own data centers. HIPAA-related requirements are none of your concern anymore since you now have plausible deniability; you never knew what kind of data you were processing since everything was encrypted before it left the customer’s networks. And you never had access to the encryption key.                Make Your API Platform Successful With Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /technical/compliance/Secure-Proxy-for-HIPAA-Compliant-API-Analytics/",
          "author": "Kay",
          "categories": "Technical, Compliance"
        }
      
    ,
  
    
        "technical-azure-api-management-how-to-monitor-azure-api-management-performance-with-the-moesif-plugin": {
          "title": "How to Monitor Azure API Management Performance with the Moesif Plugin",
          "content"	 : "Azure API Management (APIM) is a powerful platform that enables you to publish and scale APIs while ensuring they are secured. One of the great features of Azure APIM is that you can add plugins and transforms to your APIs within an API Management service instance without any code change or restarts.These capabilities are deployed using XML Policies which are a collection of statements. Moesif API Observability can be added in just a few minutes using an XML policy for APIM which makes it easy get visibility into API calls, even ones that are rejected and never reach your underlying service.What is Azure API Management?Azure API Management is a fully managed service that enables developers to expose their APIs to both external and internal customers. It provides a comprehensive set of tools and services for creating, publishing, and managing APIs, ensuring they are secure, scalable, and easy to use. As a key component of Azure API, it allows developers to build and manage APIs efficiently, leveraging the power of cloud-based services for robust and secure API management.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        Azure API Management ComponentsAzure API Management consists of several integral components that work together to provide a comprehensive API management solution:      API Gateway: This acts as a facade to the backend services, allowing API providers to abstract API implementations and evolve backend architecture without impacting API consumers. It handles all incoming API calls, applies policies, and routes them to the appropriate backend services.        Management Plane: This component provides full access to the API Management service capabilities. It allows API providers to create and manage APIs, configure API policies and security settings, and monitor and analyze API usage and performance.        Developer Portal: An automatically generated, fully customizable website that documents your APIs. It enables API consumers to discover, subscribe to, and learn how to consume APIs in their applications, fostering a community of developers around your APIs.  Integration with Azure ServicesAzure API Management integrates seamlessly with many complementary Azure services to create robust enterprise solutions. Key integrations include:      Azure Active Directory: For robust authentication and authorization mechanisms.        Azure Storage: For efficient storage and management of API data.        Azure Cosmos DB: For handling large-scale API data with high availability and low latency.        Azure Functions: For building serverless API backends, enabling scalable and event-driven API solutions.        Azure Logic Apps: For integrating APIs with other Azure services and external systems, facilitating complex workflows and automation.  These integrations empower developers to build comprehensive API solutions that leverage the full potential of Azure’s cloud services, ensuring scalability, security, and efficiency.API Management Tiers and PricingAzure API Management is offered in a variety of pricing tiers to cater to different customer needs. Each tier provides a distinct combination of features, performance, capacity limits, scalability, SLA, and pricing, suitable for various scenarios:      Developer Tier: Ideal for development and testing environments, this tier offers limited features and capacity, making it cost-effective for non-production use.        Basic Tier: Suitable for small-scale production environments, it provides standard features and capacity, balancing cost and functionality.        Standard Tier: Designed for medium-scale production environments, this tier offers advanced features and capacity, supporting more extensive API management needs.        Premium Tier: Perfect for large-scale production environments, it delivers high-performance features and capacity, ensuring robust and scalable API management.  What is Moesif API Observability for Azure APIM?API Observability enables engineering and business to deeply understand how their APIs are used and identify what needs to be fixed before customers email support and overwhelm your team. API management supports a variety of functionalities as part of a platform-as-a-service model, ensuring compliance and security. Unlike classic API monitoring which usually probes an endpoint for typical “Red, Yellow, Green” status, API Observability enables leaders to observe any behavior happening with the API. Some examples include:  Product owners to understand API usage and what to focus on  Engineering leaders to stay informed of and troubleshoot API issues  Security researchers to investigate API threats and prevent themHow does this solution worksIn order for API Observability to work, a monitoring agent needs to passively log API traffic to an observability service. This can be a custom build on a data warehouse like Azure Synapse Analytics or or a turnkey solution like Moesif. An API Gateway like Azure APIM provides a natural point to centralize API logs. Otherwise, each service would need to have it’s own SDK.This solution is deployed using an Azure Resource Manager Template, which automatically adds a few components to your Azure subscription:            Component      Purpose                  API Management Logger      Captures API logs from your API Management instance and inserts them into the EventHub              Azure EventHub      Buffers the raw API logs until ready to be consumed              Azure WebApp      Runs the ApimEventProcessor app which reads from EventHub and sends logs to Moesif in batches      A diagram is below showing how the solution is deployed in your Azure subscription.Once the API logs are received by Moesif, the rest of the data processing and analytics is handled by the service.Use casesUnderstand Customer API usageA goal for API analytics is to gain an understanding on who is using your APIs and how they use them. To enable this, the integration will automatically associate API calls to specific user profiles. By default, The XML policy will also extract user info like the User Id, First Name, and Email from the context.User object and saved as part of a user profile in Moesif. You can always add additional user properties using Moesif’s user tracking API.A critical report is understanding which customers are using your APIs the most. Because we are tracking name and email, we can open a report in Moesif showing weekly API traffic by company name.Troubleshoot issuesWith high-cardinality, high-dimension API observability, you can slice and dice your API logs by any number of fields such as the URI Route, HTTP headers, and even fields in the payload enabling you to drill into issues impacting customers. One such metric we recommend monitoring is the 90th percentile. Unlike average latency, by looking at 90th percentile latency, you can better see large variations in your latency which is usually worse for customers than an API that’s consistently high latency. An API with seamlessly random latency spikes can wreak havoc in their own services.To do this, go to Events -> Time series and then select the metric P90 Latency. You can also understand this broken down by route or service. To do so, add a group by “Request URI.” Moesif consolidate routes such that /items/1 and /items/2 will show up as /items/:id in the UI which makes it easier for your analysis.Research threatsAs you expose more APIs to the internet used by customers, partners, and single page apps, your security risk goes up. Traditional mechanisms like browser fingerprinting and captchas don’t work so you need to leverage advanced user behavior analytics to find suspicious users.A common API security threat is not having proper protection from data scraping and intentional API abuse. An API provides direct access to your data which a hacker can use to scrape. One way to detect customers abusing your API is to look at the amount of data accessed per user. To create this metric, add a summation of response.headers.Content-Length and then group it by user’s name:How to set up Azure APIM with Moesif API Observability1. Start Azure Resource DeploymentAs an API provider, click the below button to start a Custom deployment with the Moesif Azure Resource Template.2. Configure ParametersWithin the Azure Template Deployment panel, set the following properties:      Set Resource group to the same resource group that contains your exiting Azure APIM instance. This ensures the APIM logger, moesif-log-to-event-hub, is automatically created for you.        Set Moesif Application Id to the one displayed after logging into your Moesif account. You can create a free one on Moesif’s website        Set Existing Api Mgmt Name to the name of your Azure APIM instance. If blank, you will need to manually create the APIM logger.  Once done, click the Review+create button at the bottom and finish the template creation wizard.  Occasionally, Azure reports a failed deployment due to slow propagation of new DNS settings even though everything was deployed successfully. We recommend proceeding with rest of process. If you still have issues after last step, view troubleshooting.3. Add XML PolicyWithin the Azure portal, navigate to your existing Azure API Management instance.Then, add the below XML policies to all products or APIs that you want API logging enabled.  It’s recommended to add the XML policy globally for all APIs. Then, use Moesif dynamic sampling if you want to create rules that selectively sample or suppress data collection. Rules are dynamically enabled based on specific customer behaviors, regex rules, and more.More info on editing APIM policies is available on the Azure docs<policies>    <inbound>        <base />        <set-variable name="moesif-message-id" value="@(Guid.NewGuid())" />        <log-to-eventhub logger-id="moesif-log-to-event-hub" partition-id="0">@{var body = context.Request.Body?.As<string>(true);var MAX_BODY_EH = 145000;var origBodyLen = (null != body) ? body.Length : 0;if (MAX_BODY_EH < origBodyLen){ body = body.Remove(MAX_BODY_EH); }var headers = context.Request.Headers    .Where(h => h.Key != "Ocp-Apim-Subscription-Key")    .Select(h => string.Format("{0}: {1}", h.Key, String.Join(", ", h.Value).Replace(""", """))).ToArray<string>();var jwtToken = context.Request.Headers.GetValueOrDefault("Authorization","").AsJwt();var userId = (context.User != null && context.User.Id != null) ? context.User.Id : (jwtToken != null && jwtToken.Subject != null ? jwtToken.Subject : string.Empty);var cru = new JObject();if (context.User != null) {  cru.Add("Email", context.User.Email);  cru.Add("Id", context.User.Id);  cru.Add("FirstName", context.User.FirstName);  cru.Add("LastName", context.User.LastName);}var crus = System.Convert.ToBase64String(Encoding.UTF8.GetBytes(cru.ToString()));var requestBody = (body != null ? System.Convert.ToBase64String(Encoding.UTF8.GetBytes(body)) : string.Empty);return new JObject(  new JProperty("event_type", "request"),  new JProperty("message-id", context.Variables["moesif-message-id"]),  new JProperty("method", context.Request.Method),  new JProperty("ip_address", context.Request.IpAddress),  new JProperty("uri", context.Request.OriginalUrl.ToString()),  new JProperty("user_id", userId),  new JProperty("contextRequestUser", crus),  new JProperty("company_id", ""),  new JProperty("request_headers", string.Join(";;", headers)),  new JProperty("request_body", requestBody),  new JProperty("contextTimestamp", context.Timestamp.ToString("o")),  new JProperty("metadata", $@"")  ).ToString();}</log-to-eventhub>        <set-variable name="sent-moesif-request" value="@(true)" />    </inbound>    <backend>        <forward-request follow-redirects="true" />    </backend>    <outbound>        <base />        <choose>            <when condition="@(context.Variables.ContainsKey("sent-moesif-request") && !context.Variables.ContainsKey("sent-moesif-response"))">                <log-to-eventhub logger-id="moesif-log-to-event-hub" partition-id="1">@{var body = context.Response.Body?.As<string>(true);var MAX_BODY_EH = 145000;var origBodyLen = (null != body) ? body.Length : 0;if (MAX_BODY_EH < origBodyLen){ body = body.Remove(MAX_BODY_EH);}var headers = context.Response.Headers.Select(h => string.Format("{0}: {1}", h.Key, String.Join(", ", h.Value).Replace(""", """))).ToArray<string>();var responseBody = (body != null ? System.Convert.ToBase64String(Encoding.UTF8.GetBytes(body)) : string.Empty);return new JObject(  new JProperty("event_type", "response"),  new JProperty("orig_body_len", origBodyLen),  new JProperty("message-id", context.Variables["moesif-message-id"]),  new JProperty("status_code", context.Response.StatusCode),  new JProperty("response_headers", string.Join(";;", headers)),  new JProperty("contextTimestamp", context.Timestamp.Add(context.Elapsed).ToString("o")),  new JProperty("response_body", responseBody)  ).ToString();}</log-to-eventhub>                <set-variable name="sent-moesif-response" value="@(true)" />            </when>        </choose>    </outbound>    <on-error>        <base />        <choose>            <when condition="@(context.Variables.ContainsKey("sent-moesif-request") && !context.Variables.ContainsKey("sent-moesif-response"))">                <log-to-eventhub logger-id="moesif-log-to-event-hub" partition-id="1">@{var body = context.Response.Body?.As<string>(true);var MAX_BODY_EH = 145000;var origBodyLen = (null != body) ? body.Length : 0;if (MAX_BODY_EH < origBodyLen){ body = body.Remove(MAX_BODY_EH);}var headers = context.Response.Headers.Select(h => string.Format("{0}: {1}", h.Key, String.Join(", ", h.Value).Replace(""", """))).ToArray<string>();var responseBody = (body != null ? System.Convert.ToBase64String(Encoding.UTF8.GetBytes(body)) : string.Empty);return new JObject(  new JProperty("event_type", "response"),  new JProperty("orig_body_len", origBodyLen),  new JProperty("message-id", context.Variables["moesif-message-id"]),  new JProperty("status_code", context.Response.StatusCode),  new JProperty("response_headers", string.Join(";;", headers)),  new JProperty("contextTimestamp", context.Timestamp.Add(context.Elapsed).ToString("o")),  new JProperty("response_body", responseBody)  ).ToString();}</log-to-eventhub>                <set-variable name="sent-moesif-response" value="@(true)" />            </when>        </choose>    </on-error></policies>4. Success!With the Azure APIM integration done, you should see your API logs show up in Moesif. Make a few calls against your API Gateway domain and see them show up in Moesif’s event log in real-time. You should see the status code, URL, and other HTTP parameters captured like the below screenshot:Identifying usersAPI calls are associated to users using the field user_id. The default XML policy extracts this from the context.User.Id or the JWT Token using the following logic:var jwtToken = context.Request.Headers.GetValueOrDefault("Authorization","").AsJwt();var userId = (context.User != null && context.User.Id != null) ? context.User.Id : (jwtToken != null && jwtToken.Subject != null ? jwtToken.Subject : null);You can modify what the userId is by changing these lines of code.Adding user metadataBy default, the XML policy also saves some helpful user properties like Email and FirstName using the following code in the XML Policy:if (context.User != null) {  cru.Add("Email", context.User.Email);  cru.Add("FirstName", context.User.FirstName);  cru.Add("LastName", context.User.LastName);}var crus = System.Convert.ToBase64String(Encoding.UTF8.GetBytes(cru.ToString()));These will be saved with the user profile in Moesif that matches the defined userId. You can add additional fields from the context.User to meet your requirements by changing these lines of code.Adding Event metadataYou can also save event metadata. Unlike user metadata, event metadata is specific to each API transaction and can contain helpful info not already logged by Moesif such as trace ids or environment variables. The metadata field should be a JSON encoded string.Sampling requestsThis integration also supports dynamic sampling. This enables you to selectively sample API calls based on user behaviorto save on your Moesif subscription cost. Moesif will still extrapolate the original metrics.Advanced User Behavior API AnalyticsYou can leverage your integration beyond just looking at API calls in isolation and stitch your entire customer journey together. This approach makes it easier to see things like funnel reports on “Time to First Hello World” and “Time to Value.”Track user actions in your UI such as “Signed In” or “Viewed Docs” and start tracking user actions in your UI like “Signed In” or “Viewed Docs”. This makes it easier to slice and dice API usage by customer traffics. In order to do so, add the moesif-browser-js to your UI and call the track method:moesif.track('Clicked Sign Up', {  button_label: 'Get Started',  sign_up_method: 'Google SSO'});Once done, the first thing you should do is generate a funnel report. In the below report, we created a funnel analysis composing of three steps.  The first step is a customer signing into your web application (a user action).  The second step is a single payment transaction via the API. Thus moving from step 1 to step 2 shows the conversion rate of sign ups to the first API call.  The third step is over 100 payment transactions. For this example, we consider this the “aha” moment demonstrating customer value. Moving from step 2 to step 3 shows the drop off of customers who made API calls who actually got to see real value.ConclusionAPI observability is critical for engineering and business leaders to make informed decisions on what to focus on and where issues are. While you can roll your own API gateway, data processing pipeline, and a data warehouse, this can create a massive time sink for your engineering team. Using fully managed services like Azure API Management API Gateway and Moesif API Analytics can help you scale without being held back by legacy infrastructure.                Deep AI API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design and Build Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /technical/azure-api-management/How-to-Monitor-Azure-API-Management-Performance-with-the-Moesif-Plugin/",
          "author": "Derric",
          "categories": "Technical, Azure-API-Management"
        }
      
    ,
  
    
        "technical-rate-limiting-best-practices-for-api-rate-limits-and-quotas-with-moesif-to-avoid-angry-customers": {
          "title": "Best Practices for API Rate Limits and Quotas with Moesif to Avoid Angry Customers",
          "content"	 : "Like any online service, your API users expect high availability and good performance. This also means one customer should not be able to starve another customer’s access to your API. Adding rate limiting is a defensive measure which can protect your API from being overwhelmed with requests and improve general availability. Certain API requests are rate limited to manage usage effectively. Similarly, adding quota management also ensures customers stay within their contract terms and obligations ensuring you’re able to monetize your API. Software applications are used to monitor API requests and enforce rate limits, ensuring optimal system performance. This is even more important for Data and GenAI APIs where cost of an API can be high and part of your COGS (Cost of Goods Sold). Without quota management, a customer could easily use far more resources than their plan allows even if they stay within your overall server rate limits. Yet, incorrect implementations can cause customers to become angry due to their requests not working as expected. Worst, a bad rate limiting implementation could fail itself causing all requests to be rejected. The 429 error is a common result of such failures, indicating that too many requests have been sent in a given amount of time. This guide walks through different types of rate limits and quotas. Then, it walks through ways to set up rate limiting that protects your API without making customers angry.Introduction to API ManagementAPI management is a crucial aspect of ensuring the smooth operation and security of Application Programming Interfaces (APIs). It involves a set of processes and tools that enable organizations to create, manage, and analyze APIs in a secure and scalable manner. One key component of API management is rate limiting, which controls the number of requests an API can handle within a certain time period. Implementing API rate limiting is essential to prevent excessive usage, denial of service attacks, and to ensure fair usage of API resources. By setting appropriate rate limits, API providers can protect their APIs from abuse, prevent server overload, and maintain reliable performance.Understanding API Rate Limits and QuotasAPI rate limits and quotas are essential mechanisms that servers use to control the number of requests clients can make within a specific time frame. Rate limits set the maximum number of requests allowed in a short period, such as per second or minute, while quotas define the total number of requests permitted over a longer duration, like per hour, day, or month. Rate limits can also be set for individual users to ensure fair access to resources. A fixed number of requests are allowed in specified time intervals to manage request flow. These controls are vital for preventing abuse, maintaining optimal server performance, and ensuring fair resource allocation among all users.How do Rate Limit and Quotas WorkRate limits and quotas are two related concepts in API management that work together to control API usage. A rate limit defines the maximum number of requests an API can handle within a given time period, while a quota specifies the total number of requests allowed within a certain timeframe. When a user exceeds their quota or rate limit, they receive an error message indicating that they have reached their limit. API providers use rate limiting algorithms, such as token bucket or leaky bucket algorithms, to enforce rate limits and prevent excessive usage. By setting quotas and rate limits, API providers can manage API traffic, prevent abuse, and ensure fair usage of API resources.How do rate limit and quotas worksBoth quotas and rate limits work by tracking the number of requests each API user makes within a defined time interval and then taking some action when a user exceeds the limit, which helps in managing traffic to ensure optimal performance. This action could be a variety of things such as rejecting the request with a 429 Too Many Requests status code, sending a warning email, adding a surcharge, among other things. Rate limiting algorithms throttle requests that exceed a set threshold to enforce limits, thereby ensuring system stability. Just like different metrics are needed to measure different goals, different rate limits are used to achieve different goals.Rate limits vs quota managementThere are two different types of rate limiting, each with different use cases. Short term rate limits are focused on protecting servers and infrastructure from being overwhelmed within a short period of time. Whereas, long term quotas are focused on managing the cost and monetization of your API’s resources. Additionally, there are other techniques, such as utilizing client-side JavaScript, that individuals may employ to circumvent restrictions on API usage.Rate limitsShort term rate limits, a type of API rate limit, look at the number of requests per second or per minute within a short period of time and help “even out” spikes and bursty traffic patterns to offer backend protection. Because short term rate limits are calculated in real-time, there is usually little customer-specific context. Instead, these rate limits may be measured using a simple counter per IP address or API key. API rate limits play a crucial role in maintaining the stability and security of servers. By capping the number of requests, servers can mitigate the risk of excessive traffic that could lead to performance degradation or even denial-of-service (DoS) attacks. Preventing unnecessary requests is essential to maintain stability and performance, as it helps in reducing the load on the server and ensures optimal resource utilization. These limits ensure that resources are distributed fairly, preventing any single user from monopolizing the server’s capacity.Example use cases for rate limits:  Protect downstream services from being overloaded by traffic spikes  Increase availability and prevent certain against DDoS attacks from bringing down your API  Provide a time buffer to handle capacity scaling operations  Ensure consistent performance for customers and even out load on databases and other dependent services  Reduce costs due to uneven utilization of downstream compute and storage capacity.IdentifierDue to their time sensitivity, short term rate limits need a mechanism to identify different clients without relying heavily on external context. Some rate limiting mechanisms will use IP addresses, but this can be inaccurate. For example, some customers may call your API from many different servers. A more robust solution may use the API key or the user_id of the customer.ScopeShort term rate limits can be either scoped to the server or a distributed cluster of instances using a cache system like Redis. You can also use information within the request such as the API endpoint for additional scope. This can be helpful to offer different rate limits for different services depending on their capacity. For example, certain services may be very costly to service and can be easily overwhelmed such as launching batch jobs or running complex queries on a database.Short term rate limits can be imperfect given their real-time nature which makes them a poor form for billing and financial terms, but great for backend protection.Quota managementUnlike short term rate limits, the goal of quotas are to enforce business terms such as to monetize your APIs and protect your business from high cost overruns by customers. Quotas can be managed using a fixed rate of requests to ensure consistent performance and adherence to contract terms. They measure customer utilization of your API over longer durations such as per hour, per day, or per month. Managing API call frequency is crucial to avoid exceeding limits, which can lead to added costs and operational issues. Quotas are not designed to prevent a spike from overwhelming your API. Rather, quotas regulate your API’s resources by ensuring a customer stays within their agreed contract terms. Because you may have a variety of different API service tiers, quotas are usually dynamic for each customer, which makes them more complex to handle than short-term rate limiting. Besides quota obligations, historical trends in customer behaviors can be used for spam detection and automatically blocking users who may be violating your API’s terms of service (ToS).Examples use cases for quota limits:  Block intentional abuse such as sending spam messages, scraping, or creating fake reviews  Reduce unintentional abuse while allowing a customer’s usage to burst if needed  Properly monetize your API via metering and usage-based billing  Ensuring a customer does not consume too many resources such as AI tokens or compute which impact your cloud spend.  Enforce contract terms of service and prevent “freeloaders”IdentifierLong term quotas are almost always calculated on a per-tenant or customer level. IP addresses won’t work for these cases because an IP address can change or a single customer may be calling your API from multiple servers circumventing the enforcement.ScopeBecause quotas are usually enforcing the financial and legal terms of a contract, it should be unified across all servers and be accurate. There can’t be any “guesstimation” when it comes to quotas.How to implement rate limitingUsually a gateway server like NGINX or Amazon API Gateway is the ideal spot to integrate rate limiting as most external requests will be routed through your gateway layer. Rate limiting ensures fair use of API resources by preventing any single user from monopolizing the system. For short term rate limit violations, the universal standard is to reject requests with 429 Too Many Requests. It is important to communicate rate limits to API consumers by providing clear documentation and feedback when they exceed request limits. Additional information can be added in the response headers or body instructing the client when the throttle will be cleared or when the request can be retried.For long term quota violations, a number of different actions can be taken. You could either reject the requests similar to short term rate limiting, but you could also handle other ways such as adding an overage fee. An easy way to manage quotas are with Moesif’s API Governance features. A warning message can inform users of rate limit violations, such as ‘API Rate Limit Exceeded,’ indicating that their requests will not be processed until the rate limit resets. For long term quota violations, a number of different actions can be taken. You could either reject the requests similar to short term rate limiting, but you could also handle other ways such as adding an overage fee. An easy way to manage quotas are with Moesif’s API Governance features. This enables you to add rules that enforce quotas and plan limits with just a simple server integration or plugin with an API gateway in few clicks. Setting limits is crucial to ensure fair usage and prevent abuse, balancing system stability and user experience. Instructions on how to do this are below:  Within Moesif, create a user cohort under the User Lookup tab. Add your criteria when a user is considered exceeding their quota. In this example, when a user makes more than 1,000 /purchases or /purchases/:id/decline within an hour period.  Now that we created the cohort, go to API Governance under the Alerting & Governance tab. From here, create a new governance rule as shown below. In this case, we are short circuiting the request with the status code 429 Too Many Requests. We also provide an informational message on why the request is rejected.Informing customers of rate limit and quota violationsLike any fault or error condition, you should have active monitoring and alerting to understand when customers are approaching or exceeding their limits/quotas, which can lead to rate limit errors. Your customer success team should proactively reach out to customers who run into these issues and assist them to optimize their integration. It is crucial to understand the given period for rate limits to prevent exceeding the allowed number of requests within this timeframe. Because manual outreach can be slow and unscalable, you should have a system in place that automatically informs customers when they do run into rate limits as their transactions are getting rejected which can cause issues in their applications.An easy way to keep customers informed of such issues is via Moesif’s behavioral email feature. Instructions on how to do this are below:  Within Moesif, create a user cohort under the User Lookup tab. Add your criteria when to alert customers such as by looking at the number of API calls or when a rate limit header reaches a certain threshold. In this example, we add a filter response.headers.Ratelimit-Remaining < 10  Now that we created the cohort, go to Behavioral Emails under the Alerting & Governance tab. From here, create a new email template and design it to fit your requirements as shown below.Rate limit remaining headersBesides sending emails, it’s also helpful to inform the customer of any rate limit remaining using HTTP response headers. By configuring different limits for various user segments, headers can inform users of their remaining request limits. There is an Internet Draft that specifies the headers RateLimit-LimitRateLimit-Remaining and RateLimit-Reset.By adding these headers, developers can easily set up their HTTP clients to retry once the correct time has passed. Rate limiting is crucial in preventing API abuse, as it helps protect systems from malicious attacks designed to overwhelm resources. Otherwise, you may have unnecessary traffic as a developer won’t know exactly when to retry a rejected request. This can create a bad customer experience.Rate limit implementation errors and 429 too many requestsEven a protection mechanism like rate limiting could have errors itself. Algorithms ensure a consistent rate of requests, allowing for a steady flow of data regardless of input fluctuations. For example, a bad network connection with Redis could cause reading rate limit counters to fail. In such scenarios, it’s important to not artificially reject all requests or lock out users even though your Redis cluster is inaccessible. Your rate limiting implementation should fail open rather than fail closed meaning all requests are allowed even though the rate limit implementation is faulting.Additionally, adjusting limits based on user behavior and data analytics is crucial. Regularly reviewing usage patterns and analytics helps make informed adjustments to rate limiting strategies, ensuring both system protection and user satisfaction. This also means rate limiting is not a workaround to poor capacity planning as you should still have sufficient capacity to handle these requests or even designing your system to scale accordingly to handle a large influx of new requests. This can be done through auto-scale, timeouts, and automatic trips that enable your API to still function.ConclusionQuotas and rate limits are two tools that enable you to better manage and protect your API resources. Enforcing rate limits is crucial for actively managing API requests, ensuring stability and fairness within the system. Yet, rate limits are different from quotas in terms of business use case. Algorithms like the leaky bucket ensure a steady flow of requests, allowing for smooth traffic management and avoiding overloads during bursts of requests. It’s critical to understand the differences and limitations of each. In addition, it’s also important to provide tooling such that customers can stay informed of rate limit issues and a way to audit 4xx errors including 429.",
          "url": " /technical/rate-limiting/Best-Practices-for-API-Rate-Limits-and-Quotas-With-Moesif-to-Avoid-Angry-Customers/",
          "author": "Derric",
          "categories": "Technical, Rate-Limiting"
        }
      
    ,
  
    
        "technical-compliance-implementing-hipaa-technical-safeguards-in-your-api": {
          "title": "Implementing HIPAA Technical Safeguards in your API Platform",
          "content"	 : "The Health Insurance Portability and Accountability Act, or HIPAA for short, is a set of laws around handling health-related data in information systems. It defines safeguards, which are rules you have to follow when handling health data for your customers.There are three safeguard categories:  Administrative safeguards, covering personal and processes in your company  Physical safeguards, which relate to the hardware running the software handling health data  Technical safeguards, requirements your software has to implementAll three categories have to be handled correctly if you want your API to be HIPAA compliant. In a companion article we covered those key requirements and how to build HIPAA complaint API platforms. In this article we’ll down into actually specifics of how to implement the technical safeguards.There are nine technical safeguards, of which four are required, and five are addressable. Required safeguards must be implemented in any case; addressable safeguards should be implemented if it’s reasonable to do so. If you can’t implement an addressable safeguard, you need to document why and explain why you didn’t implement them - essential in case of a data breach.While physical safeguards aren’t the topic of this article, keep in mind that you need to host your infrastructure at a HIPAA-compliant provider. Otherwise it’s possible that certain technical safeguards could be bypassed by physically accessing the server. If you are planning an on-prem deployment, you need to follow the physical safeguards yourself.Required Technical SafeguardsLet’s start with the four required safeguards. These are the minimum requirements you need to follow to be HIPAA compliant from a technical perspective.Unique User IdentificationEvery user of your system needs to have a unique identifier. so that every action on the system can be traced back to the user who did it.An easy way to do this is to use a universally unique identifier (UUID). Every major programming language has a library for creating a UUID. UUID generators follow standardized algorithms that create strings that are globally unique without the need for a centralized authority.HIPAA-compliant managed authentication services, like AWS Cognito, will do this for you. A Cognito user pool will give every new user a unique ID on signup.If you have to implement unique user identification yourself, you can find implementations of UUID here:  Node.js  Java  Python  .NetEmergency Access ProcedureYou will need a way to access protected healthcare data in case of an emergency. Emergency, in this instance means, means something bad has happened to your system or app. For example, your power went down, or you’re under a cyber attack. In this case, the data needs to be accessible through an alternative means.To implement this emergency access, you’ll need to solve two problems. The first one is some type of off-site backup that lets you access the data even if your data-center burned to the ground. The second one is an alerting system that notifies you that an emergency is currently happening.For a managed database, like Amazon DynamoDB, this means you need to activate point-in-time recovery for your tables. Once turned on, the last 35 days of your table data is backed up every five minutes, and can be restored.If you define your table with CloudFormation, you only need to add two attributes for this:Type: 'AWS::DynamoDB::Table'Properties:  TableName: phiTable  PointInTimeRecoverySpecification:    PointInTimeRecoveryEnabled: trueFor other prominent databases, you can find guides on how to set up backups here:  PostgreSQL  MariaDB  SQL Server  MongoDBAudit ControlsAudit controls are all about logging who accesses the healthcare data. It works in tandem with unique user identification. If you get audited, you’ll need to point the auditors to the audit log data to show them that everything’s correctly tracked.An easy way to do this is to have dedicated data storage for healthcare data, and log all access. That way, you can be sure that no developer forgets to implement logging when a new feature is added to your app. Also, you won’t end up with too many audit logs when you store healthcare data alongside the non-healthcare data.Another way is to implement event-sourcing. In this data-architecture pattern, you only save the changes in an immutable list and generate the tables on-the-fly. That way, three updates are actually three records in your event-sourcing database and not just the final result. You can see what happened to your data at every point in time and who caused the changes.You can find information on how to enable access logs for prominent databases here:  PostgreSQL  MariaDB  SQL Server  MongoDBAuthenticationKeep all your users authenticated, so no anonymous access can happen to healthcare data. The best course of action is to use passwords and multi-factor authentication everywhere access to healthcare data can take place. If one of your users’ authentication factors gets stolen, for instance like an unlocked smartphone, they need to be able to prove they haven’t subsequently accessed protected data.Using a managed service like AWS Cognito can save you time because it’s HIPAA compliant out of the box. Auth0 and Okta are very good managed authentication services as well, and they’re also HIPAA compliant.For your own implementation, you find can find Authentication libraries for major API platforms here:  Express  Django  ASP.NET Core  Spring BootAddressable Technical SafeguardsAddressable technical safeguards represent the majority of HIPAA requirements, although a slim majority at 52%. Strictly speaking they’re optional, but rather you should view their “addressable” nature as safeguards that allow some flexibility on implementation.Indeed, you could either implement, find an alternative that accomplishes the same objective, or not implement. If you choose not to implement, you need to document why and probably consult an auditor expert/lawyer before going any further.Automatic LogoffKeep your user sessions as short as needed and log them off automatically. The basic idea is that if somebody leaves their device after logging in, it could be used by an unauthorized person. If you log your users off after they were inactive online for a minute or two, the chances are lower for unauthorized accesses to occur.To get this right, you need to have to lower the session timeout for your API connections. Clients can request new session tokens automatically, so it usually isn’t that big of a deal as compared to a GUI application. With a GUI, the user would have to re-login manually.Encryption and DecryptionAll healthcare data should be encrypted and only be decrypted when needed. This means encryption at rest, like on your servers or your users’ devices. This also means encryption in transit when the data goes over the network. The reason for this is obvious — if the data is encrypted, stealing it won’t help an attacker.While the encryption requirement stated by HIPAA is technology neutral, you should encrypt the data as well as reasonably possible. This means ROT13 isn’t enough, but a one-time pad is overkill. Check what the state-of-the-art encryption mechanisms your technology offers, and stick with that.Again, managed services like AWS DynamoDB encrypt your data at rest automatically.Your API should enforce SSL for all connections to conform. Use the strict-transport security header to inform your clients about using SSL. Don’t deliver any data if the protocol is HTTP, it should only redirect with HTTPS with the location header and a 3XX status code. Maybe, give some information in the body that “HTTP isn’t supported”.If you can’t encrypt the data, but there are other mechanisms in place that prevent access to it, you should document that.Integrity Controls and Mechanisms to Authenticate ePHIYou need a way to be sure that healthcare data wasn’t altered or destroyed in an unauthorized manner.This means you need to keep data integrity in check, for example, with a check-sum or a digital signature. If an authenticated user signs the changed data, any following changes by unauthorized users will be obvious.To prevent unauthorized data destruction, you have to implement a backup that won’t sync changes that an authorized user hasn’t digitally signed. If a malicious user deletes data, it will either show up in the audit logs, or the delete won’t propagate to the backup.Again, event-sourcing can be the solution. Since data is never implicitly deleted and all changes are logged by design.How to Access ePHI Data From Other APIs?If you’re already HIPAA compliant for your own data, third-party APIs aren’t a big issue, but if you’re not HIPAA compliant, it can cause additional costs.When you want to use ePHI data from a third-party API, like EPIC, you also need to apply safeguards. Not all data delivered by an ePHI API falls under the protection of HIPAA, but if the data you request does, you need to implement it. This also includes the physical and administrative safeguards, which aren’t mentioned in this article.How does Moesif Conform to HIPAA?Moesif offers a secure proxy, which you host on your own infrastructure. This proxy will encrypt your data before sending it to Moesif and decrypt it before showing it to you, all done with your own private encryption key. The master encryption keys are never stored on Moesif servers; rather, the secure proxy supports popular key stores and handles key rotation automatically.In the end, Moesif never sees your unencrypted ePHI data.This approach is also something you can keep in mind for your own APIs. If a customer wants to send you their ePHI data, tell them they should encrypt it first, or offer them your own secure proxy they can host on their own infrastructure.SummaryIf you want to implement a HIPAA compliant API your best course of action is to implement all safeguards, even the optional ones. Lower your risks today and avoid uncomfortable technical and possibly legal problems later on.Some of the safeguards are actually layered, for example implementing unique user identifiers will follow from standing-up authentication. The same goes for encryption and decryption, both are counted as separate safeguards but actually work together in practice.Also, backups, authentication, and encryption are good practices that protect your whole business, not just the health-related data you might hold. So the chances are good that your API already has most of the safeguards in place.You should also check out the managed services by cloud providers like AWS, Azure, or GCP. Often these providers already did the hard work to comply and you just have to choose resources with the right configuration.                Make Your API Platform Successful With Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /technical/compliance/Implementing-HIPAA-Technical-Safeguards-in-your-API/",
          "author": "Kay",
          "categories": "Technical, Compliance"
        }
      
    ,
  
    
        "podcasts-developer-marketing-podcast-launching-api-programs-in-non-api-first-companies": {
          "title": "Launching API Programs in Non API-First Companies",
          "content"	 : " Ep. 11: Jeannie Hawrysz, leader of API Programs at SASMoesif’s Podcast Network: Providing actionable insights for API product managers and other API professionals.Joining us is Jeannie Hawrysz, the Lead API Architect at SAS, a 22,000 person business analytics company.  Before that she was an 18-year veteran at IBM and was the Technical Development Manager for IBM’s API Connect Micro Gateway. In our podcast she shares how to successfully launch API programs in non API-first companies.Larry Ebringer, Moesif’s CMO, is your host today.Moesif · 11. Launching API Programs in Non API-First CompaniesListen to the episode on SoundCloud above, or download it on Apple or Google. Table of Contents   1:14Work In API Management    4:45Form a Center of Excellence    8:58Build Bridges Across the Lifecycle    10:30Sweat Dev Personas    12:25Workflows Guide Devs    15:50Biz Reasons for External APIs    18:29Minimum Criteria for Externalizing    22:43Product Management is Accountable    24:28APIs Make Money    27:344 Technical Considerations    30:15Align with a Business Need    31:37Have Patience, a Vision and Empathy  Larry Ebringer (Moesif): Welcome to Episode 11 from Moesif’s APIs over IPAs Podcast Network. I’m Lawrence Ebringer your host today and the Chief Marketing Officer of Moesif, the API Observability Platform. Joining me is Jeannie Hawrysz, the Lead API Architect and Manager at SAS, a 22,000 person business analytics company.  Before that she was an 18-year veteran at IBM and was the Technical Development Manager for IBM’s API Connect Micro Gateway.Jeannie, welcome to our humble podcast network. Where in the world do we find you?”Jeannie Hawrysz (SAS): Thank you. So I’m located in Cary, North Carolina. I work at SAS.  Well actually, I work at home right now with my family.Larry: As we all do.Jeannie: I’m located in North Carolina and love it here. Mountains and beach, you can’t go wrong.Work In API Management“My project’s been canceled … what should I work on … you should try this API-management thing”, as he handed me this O’Reilly book called APIs: A Strategy Guide. I read it overnight. I couldn’t put it down. That’s how I got into APIs and API management.Larry: Excellent. Well, kicking things off, why don’t you share with us your path through the last 18 years at IBM with their API gateway product and then bring us up to speed with you running all APIs at SaaS.Jeannie: So I’m going to try to cover 18 years pretty quick. I’ve been really, really fortunate to have a lot of different opportunities within my career, both at IBM and at SAS. I started out as an engineer working on the mainframe TCP/IP stack, and I got to work in support, performance and development. I got my big first opportunity for technical leadership across divisions to build an SDK that people were going to use to build software and virtual hardware appliances. Unfortunately, that project actually ended up getting canned because of business priority reasons in the summer 2012. Which was a pretty big bummer because it was my big first leadership opportunity. But all the people that were working on it were invited to come work on the data power appliances which were doing pretty well. There were a bunch of different technology areas that needed engineering help. The CTO of data power at the time was actually my mentor when I had been an intern, which was kind of serendipitous. So I immediately went to him and said, “My project’s been canceled and I’m coming to your area, what should I work on?”And he said “You know, I think you should work on this API-management thing.” I had no idea what that was, I had no clue. But he handed me this book called APIs: A Strategy Guide, that was an O’Reilly Book, and I read it overnight. I couldn’t put it down. That’s how I got into APIs and API management.Over the next couple of years I did work as an engineer on the monitoring component of the API management solution for IBM. And then I ended up becoming a software development manager for the team that produced the data power RestAPIs, several of the cloud integration teams and then eventually the IBM API Connect Microgateway.Weirdly one of the reasons that I left IBM and started looking at jobs outside of IBM was that I was actually really getting sick of APIs. I was “APIed out” and was ready to start looking at something else. I had big plans. I was going to become a security expert and totally rebrand myself. But somehow I ended up back at SAS working on APIs again. But, it was different. It was a much, much different experience than I had at IBM. I’ve had to learn, grow and embrace more of the API lifecycle than just on the technical side. Learning a lot of things that I hadn’t learned earlier in my career. So it’s been very interesting.My career is really focused around networking, but all always seems to kind of gravitate back towards APIs. And it’s been really exciting to actually be at the ground floor of a program, the way that I’ve gotten to be at SAS.Form a Center of ExcellenceA center of excellence in API engineering manages standards, guidelines and common patterns, provides a catalog, employs the latest version of OpenAPI, has schema validation and style rules, and reviews each releaseLarry: Well, that’s a ton of experience in the API landscape. I know that O’Reilly book well, that started you on that journey. So that’s very exciting. You’ve built what sounds like a significant number of API programs, both at IBM and at SAS. What, in your estimate, are the key steps in building an API program in an existing enterprise software company that, obviously by definition, is not API-first.Jeannie: So to be clear, at IBM I wasn’t really the puppeteer, the string puller, I was more a piece of the puzzle working as a software development manager. But I took that opportunity to learn as much as possible about the rest of the lifecycle and I’m really grateful for that experience. If I had just gone into trying to lead a program about APIs, without having that technical experience, I would not have been successful I don’t think.When I came to SAS in this role back in 2017, there were actually some good elements of an API program present. They did have a Center of Excellence, which was excellent. They had standards and guidelines, and some common patterns. They were all written down and established and they’d been talked through. They had an internal API catalog, which was great. They were using Swagger V2, which was the choice back in 2017. They had schema validation and style rules, automated at the time. And they were doing API reviews for each release. All of these things were really, really awesome. When I got there I thought that I wasn’t going to have my work cut out for me, because they had already solved it all. But what I found as I started teasing things out is that we had really only focused on the technical aspects of the program. And not really on the business aspects as well and some of the things weren’t working, or weren’t going to scale, or weren’t going to work with CI/CD. At the time I joined SAS we really weren’t doing  CI/CD en mass and we were starting the process towards transforming the company towards that. And reviewing each release using a centralized team wasn’t going to work, so we need to start thinking about how we would decentralize.Once I understood that we were more at the beginning of starting a program than I thought we were, I was definitely scared because I’d never run an API program or any program of this size before. And I had really thought that this job as an architect was going to be more about working on architecture and writing platform support code for all these things. It just ended up that those were things that maybe were in the best shape and they weren’t the priority of the time. We needed to start thinking about how to get alignment from the rest of the business and look at gaps in the lifecycle and fill them. I was asking myself “How do I do this? How do I turn a big ship.” And never having done this before, and still pretty new to the company, I had been at the company for a little over a year when I took this role, I needed to build up some credibility, because who was I to come in and tell people that thought that they were all on the right track, that there were things that needed to change? I didn’t want to be the person coming in from the outside and saying “everything’s terrible”, because things weren’t terrible, but there were definitely things that if we were going to make this a successful program that needed to be looked at.So I’ve been solving this for the last three years. I really believe in APIs and I believe in APIs as a business foundation. Having that background at IBM and in the API economy really inspires me to make this happen. I really believe that it can.  So I think that there’s opportunity there and SAS has such rich analytics capabilities that I know that we can differentiate with our API.Build Bridges Across the LifecycleIdentify believers in other divisions to help build bridges across the entire company, not just within the engineering division, but also within documentation, product management and supportIt did feel slow at first, because the big thing was that I needed to start building understanding of where we really were with the APIs as a program and where we needed to go. We needed to get leadership, not just within the engineering division, but also within other areas like documentation, product management documentation, and support. We needed to get this quorum of people across the entire company that understood the business value and how to move forward. I was very, very fortunate to have some believers in other divisions that helped me establish leadership there and start building bridges across the lifecycle. There were definitely some people that got what we were trying to do from an API program perspective very early.The big thing was that we started getting key roles set up so that they could help me help make this happen, because there was no way I was going to be able to do it all by myself. The first thing we added was a developer advocate, because we needed somebody to be able to help evangelize things both internally and externally around APIs. We added a project manager and who’s actually now more of a program than project manager. A documentation lead and with that we sort of formed what we called our API-First Core Team, a cross-division leadership team responsible for providing the strategy, direction and enablement that is needed across all of SAS to essentially make APIs happened.Sweat Dev PersonasDevelopers should be first-class users of your software, they deserve to have an excellent experienceThere are two things there that I did want to mention that have given us the biggest leaps forward: first of all, as an overall company, we have had a turn towards a more outside-in perspective with regards to technology and moving to be really product lead, And championing these design-first developments, not just API design-first, but design-first development. And the fact that the company was going in that direction anyway really aligned very well with where we needed to go with APIs. Our API-first initiative is totally built on the idea that the developer user experience of interacting with an API is worthy of design thinking and of sweating the personas that are going to use them. The developers are going to be first-class users of our software and they deserve an excellent experience.The second game changer was the support that we’ve gotten from the SAS design team. So they didn’t hesitate at all at the challenge of taking their user-experience design knowledge and helping figure out how we translate user experience to API design workshops with product teams and stakeholders. So that was a big deal when we started doing API design workshops and started getting people across the lifecycle to start thinking about what people are really going to care about in terms of APIs. We could not have made the progress that we’ve made in the last three years without getting that alignment across all the disciplines. And it’s also just sponsorship from our leadership, having our CTO talk about APIs being important in meetings. It’s been pretty amazing the journey that we’ve been on. It’s been really great.                Easily Visualize & Fix API Issues with Moesif              14 day free trial. No credit card required.              Learn More        Workflows Guide DevsTo meet governance expectations, employ guardrails to guide devsLarry: Well, thank you for covering the key elements of what an existing enterprise software company has to go through to, as you said, slowly turn the large ship to attack a new and burgeoning market. Drilling down a little bit into best practices for architecting APIs across different engineering teams, what standards have you found are the best ones to follow?Jeannie: So our ultimate goal is that, through the design-first methodology, teams will be architecting and documenting their APIs to meet standards, as well as performance and security expectations from the beginning. We aren’t all the way there yet, but there are definitely teams further along down this journey and we’re very optimistic and now feel supported in influencing teams to follow our standards as a best practice. We do have a documented set of searchable standards and things like; how to do versioning, what HTTP status response codes are acceptable and common design patterns that are searchable. There are guidelines, recommendations and patterns for how to do different kinds of interactions. We also have a single repository that houses all of our API docs that are now still Swagger V2, but we’re in the process of moving towards OpenAPI 3. We have a decree for people to adopt OpenAPI 3 by the end of the year. And that forms the basis of our API catalog for easy Grepping.But, like I mentioned earlier, it can be to a lot of information for people to be able to parse these standards themselves and interpret them. Because of that we actually wanted to do some more proactive work to help give guidelines for our developers to be able to follow these standards from the start. So, we’ve created something that we call the API-First Workflow, which is essentially a Jenkins Pipeline that takes our OpenAPI documentation, that’s housed in our developers’ repositories, and any supporting markdown that they have written to help guide developers as they’re using the API documentation. And it gives the development teams guardrails to make sure that their APIs are meeting governance expectations. Things like the refs resolving properly, or we have a ton of spectral rules that we have to enforce style and writing guidelines. Things like summaries that don’t have periods at the end of them, that helps us be more consistent across our API docs. We have mock servers set up that are automatically stood up based on our OpenAPI docs as part of this workflow. It’s been successful, we’re still early in adoption and we’re sort of marrying the OpenAPI conversion, the OpenAPI 3 modernization, to adopting this workflow. But so far the feedback that we’ve gotten from the people who have adopted has been really good. We’re continuously taking feedback and learnings and trying to make it easier for developers to stand up their APIs to meet expectations more quickly.Biz Reasons for External APIsAll APIs should be designed with the intent that one day they’ll be externalized, so the product manager’s business rationale has to answer the question “Is this API appropriate for the audience that I’m looking to target?”Larry: That’s fantastic. You’ve certainly built a plethora of supporting infrastructure and documentation to architect your API interfaces. And you’ve sort of usurped my next question, which is what do you think about how important OpenAPI 3.0 is, but you’ve certainly covered that. So why don’t we move on to the next section talking about internal and external APIs. So, we didn’t cover it initially, but your program at SAS has been very successful, you’ve exposed more than 50 API services to the developer community. So, share with us the process for a product manager to release their internal API as an external product.Jeannie: So the big thing is that if we have an existing internal API that a product manager does want to put out into the world, their responsibility is to come up with a business reason for that to happen. That’s because, moving forward what we really want to have happen is for APIs to be architected from the beginning, and designed from the beginning, to be externalized. That hasn’t always been the case with all of our services in the past, so we do have to take a pretty good eye towards “Is this API appropriate for the audience that they’re looking to target”, or does it need a layer of abstraction on it for it to be usable for the audience that they’re targeting.So the sort of authority around that is the product manager who gets to make the ultimate decision on whether or not it gets a release. They could override us, but I have an overall product manager that I work with on the business side, and between she and I we look at the API, we talk it through, we talk to the product manager to really understand what that use case is and we talk about the risks. If you are releasing an internal API and it’s not something that you think is going to be long term you have to think about when’s the end of life going to be. Are you thinking about whether they’re ever going to be breaking changes to this API. We advise them on the risks and in some cases they accept those risks and they publish API anyways for external use. But in other cases, there have been cases where people are like “Oh, well now that I understand this, we really want to build another API that’s the one that needs to be externalized.”Minimum Criteria for ExternalizingMinimum acceptance criteria for releasing an API includes: accurate & tested documentation, a pledge from the PM not to make breaking changes, meeting appropriate security & performance expectations, and a real use case for putting it out thereWe do have criteria for externalizing APIs that we call our minimal acceptance criteria. Our minimal acceptance criteria for the APIs is that they have to meet our SAS API standards and guidelines around basic industry expectations of consistency and usability across the product that they serve. They need to have fully written documentation, and then that documentation needs to be accurate and it needs to meet consumer needs and expectations. We want them to contract test the documentation to make sure that the functionality of the API meets what’s actually documented. We want them to basically pledge to not make breaking changes unless there’s some dire reason to do that in the future, obviously a terrible security issue or something that needs to be addressed. But we want these things to have longevity and we are really thoughtful and mindful about trying not to introduce breaking changes, and advising our product management teams that this matters to people who are using the APIs. We can’t break these out from underneath them, because people get very upset and write snarky things on Twitter about your company if you do that. So we’re very mindful of that. If we do have to deprecate or remove something, we are very thoughtful about deprecation with announcements and cycling around that. We need them to meet all of the product security office’s expectations. They need to meet any performance expectations that are set out for the product. And it needs to have a real use case, it can’t just be like “I wrote this cool API and I want to put it out there and somebody might use it, or nobody might use it like there.” There has to be a real business reason for putting it out there.Then the last thing is a little more esoteric, but it’s that we want to make sure that there’s a feedback loop between the owning product management team, internal consumers and customer facing teams, so that we’re continuously getting input about the API. Because if the APIs not working, then maybe we need to build something else. But that feedback loop between all the teams and getting so the product managers really understand how people want to use the APIs, or are using APIs, that’s really the way that we know that we’re best serving the community that’s going to be using these.Larry: Right, it’s very important to understand what your customer is and isn’t using your API for, and whether it’s being successfully deployed or not. That’s a little bit self serving, because I work for an analytics company and that’s what we do, but I totally appreciate and understand that issue. It’s funny that you brought up one of those criteria for externalizing an internal API, because in a prior podcast with Mike Amundson he said that one of the most important issues you should consider, when creating a new external API, is making sure that it follows all of the compliance and security issues that you would want for an external-facing service from your company. And that the best way of ensuring that, is when you first build your internal API to have the mindset that one day this may well be an externally facing service.Jeannie: Yes. The big thing about that, is that we have a bunch of APIs that already exist and there are going to be cases where we have customers that have an immediate need for them, and I totally get that from a business standpoint, but our goal really is to start designing these to meet these expectations from the beginning. And producing things that can be externalized even if somebody might not want to do that from the first.Product Management is AccountableProduct management should be rigorous in ensuring that all checks are completed, so that a whole product can be deliveredLarry: Right, so you at least have the capability built into the API from day zero in case you do want to deploy it externally. Digging a little bit into what you said earlier about the business case, in your experience who needs to champion an API to get it developed, or externally released? Does it have to have a sales and marketing buy in? Do they need to have a business case that’s ironclad, or is a champion in product management enough?Jeannie: So for externalizing internal IP, you need a product manager. You need somebody to fill the product owner role, somebody that’s going to take ownership of the API from a business perspective and be accountable for it from a business perspective and make sure that it meets all of those life-cycle expectations from a business perspective. They’re definitely, like I said, needs to be a clear business need. You know, we have had engineering teams come forward with APIs that they have written and that they want to externalize. We just need to make sure that all the check marks are done, not just around meeting standards, but stuff around: has the support team been trained so that they’re able to support this API once it’s out, has it been tested, do we have good documentation for it? All of those things matter. So we really need to have the rigor of some sort of product management or somebody in a product management role to be accountable for that.APIs Make MoneyDecision makers should gauge the ability of APIs of be a profit center by showing them use cases from the field and exposing them to leaders in the fieldLarry: Right. Makes a lot of sense. In terms of value creation, making money or losing money from your API - how do you get the decision makers in larger organizations to understand the value potential for APIs, where they could either make money or lose money depending upon if it’s done right?Jeannie: So this has definitely been an uphill battle, battle’s the wrong word, because like anything in business focusing on something that’s brand new to that business that has potential and that hasn’t been proven yet, especially in a 40+ year old company, that’s naturally going to compete with other business priorities. And many of those other priorities that we’ve had over time, those were already making real money. And we’re just theorizing right? And, until it’s real, we’re theorizing that the APIs are going to make money. The tide is definitely turning though. We’ve gotten a lot of use cases from our field. We’ve been connecting with leaders in the field. They’ve been asking for high-value, easy-to-use APIs. We connect those people, that are coming to us and telling us that they have use cases for SAS APIs, with the decision makers, product management and our executive leadership who actually get to prioritize the work and hire the people to create those things. We present our vision and we’ve been very persistent, hopefully not annoying, but we’re pretty persistent in trying to champion the fact that there are a lot of people that are making money off of their APIs. I think that there’s a real opportunity within our business to do that as well.I do think that we are definitely making a difference and I just wanted to share a very cool story that happened just last week actually. One of the coolest things that has happened in my career was that we have a brand new CTO at our company and he asked our API-first program team to come demo our vision of our new reboot. We’re working on a reboot of our developer.sas.com developer portal, which is also very exciting, and he wanted to do an overview of where we wanted to go with providing APIs as a service. And he surprised us by kicking the meeting off with his own research, where he brought a couple industry articles to share with his executive team around companies that were API-first and companies that were very successful being API-first. It was really, really cool to hear him talking about APIs as something that people would pay for. And to know that we’ve got that top-down support to move forward with this program. In addition to that, I’m going to have the opportunity to hire a bunch of new people soon, so be on the lookout on LinkedIn for that.Larry: Well, maybe we could put a link in the transcript from the podcast to your open recs.Jeannie: We can definitely do that.                Moesif: Ship Faster and with More Confidence              14 day free trial. No credit card required.              Learn More        4 Technical ConsiderationsThere are four main technical considerations when establishing an API program in an existing enterprise software company: getting down the design standard & documentation guidelines, being flexible since standards will change, providing guardrails & tools for your devs and insisting on the use of OpenAPILarry: Good. Well, that’s great that you have the support from the top down, from the new CTO, to grow and expand your API program. So, wrapping up, share with us your best practices for establishing and growing an API program in an existing enterprise software company.Jeannie: So, I think there are three different areas that you need to focus on so. The first area is the technical side. Getting that design standard down and documented is essential. Getting the rules of the game down are essential. I was very lucky that we had that Center of Excellence originally, that had really thought that through. I inherited a standard. I don’t love everything about the standard. There are things that I can’t change, because then it would make everything inconsistent, but I love that there is a standard and that there are guidelines to follow. That makes a huge difference to the people that need to consume the APIs, and it really, really matters to them.Being cognizant that standards are going to have to evolve, that’s the second thing. I’m already seeing reasons why I might have to develop a second standard just for low code/no code APIs or APIs as a service. Our standard that we have today is really focused on our microservices RestAPIs and I’m seeing that some of those patterns aren’t really going to work for some of these other areas that we want to move into in the future. So, just knowing that there are things about the standards that I can’t change for the microservices, or will get inconsistent, but those things might not make sense for these other kinds of APIs and that’s okay, it’s okay to move forward, it’s okay to evolve.And then the third thing on the technical side, give guardrails and tools to the makers, the people that are providing the APIs. And make it as easy as possible for them to follow styles, standards and guidelines. I have a very small team and I wish I could do more and give them more tools, but we’re very focused on that. I know it’s hard. The developers and engineers, they have all these things that they need to worry about in terms of quality, as they’re building out these API. The easier that you can make that for people, the better they’re going to do it, adhering to them. And then I just have to plug OpenAPI, because if you’re not using OpenAPI I don’t know what to tell you. You will thank yourself, both for doing design and for doing documentation. And if you’re not doing it, do it.Align with a Business NeedWhen establishing an API program in an existing enterprise software company bring evidence that the API program is worthwhile — customer-facing teams carry a lot of weightOn the business side, especially if you’re at the beginning of forming a program, find allies. And the thing that really started us moving forward on APIs at SAS was once we really started connecting with the other people at SAS, people who believed that APIs could be more than plumbing and that they should not require a 10X developer to perform an integration. It’s one thing to have the API cult, which is what we kind of call ourselves internally, “APIs are important rah, rah, rah”, but when other teams, especially customer-facing teams, started supporting us, that’s when things really started to accelerate.  And that also fits in nicely with my next one, everything you have to do has to be aligned to some sort of business need. You need to understand where you are in the overall priority and do as much as you can within the constraints that you have. Even while you lobby hard to raise the priority and get the help that you need to really make the program take off. You have to be persistent, you have to keep pushing for it. If it’s something that you believe is going to be important to the business, keep making the case. And I know I’m not going to say every company is going to get it, or turn the tide on this, but I will say that if you bring enough evidence, eventually things start to move.Have Patience, a Vision and EmpathyBe patient when building your API program. Be empathetic to others who don’t see things the same way that you do - try to build bridges to help them understand. Have a vision and work towards it.And then on the personal side, I just want to say be patient, I’m not patient at all, I’m not a patient person. When you find yourself at the beginning of something or walking into a situation that’s not as baked as you thought it was going to be, it can be easy to walk away. And you know that was something I thought about early on, I was like “whoa, this is going to take a lot of work, and these are things that I don’t know how to do.” But what I’ve really found was that this is an opportunity to build something amazing. And I still don’t know everything. I’m still figuring out a lot of things. But if you’re in the position that I’m in, where you’re trying to figure out how to build a program, stick with it, because first of all it’s great experience and, second of all, APIs matter. APIs are worth it. And I think that you’ll find that it’s a rewarding experience, even if there might be some early frustrations.The second thing is have a vision. If you don’t have a vision, you’re in trouble. Even if that vision that you have seems really impossible today, or looks like it might take a long time, having a vision kind of gives you a thing to work towards. So I think that’s really important and then the last thing is just have empathy for people that don’t see the things the same way that you do and then try to build bridges to help them understand.The last thing I was going to say about this, is just believe in the APIs. It’s not even a hyperbole to say that APIs are the foundation of our connected existence today. They may take on new protocols and trendy new styles, and widely varied use cases over time, but there will always be a need for integration. And there’s always going to be a need for machines to be able to talk to each other, and for humans to be able to talk to machines and there’s huge value in that ability to communicate in well designed seamless ways. And it’s getting increasingly easier to make this business case around APIs given the emergence of so many API-first businesses. So, if you’re struggling, or frustrated, or you feel like you’re not making progress, hang in there and keep at it and “API the world.” I think you will have success if you keep working on APIs.Larry: Well. That is quite an endorsement for the field. And I’m sure all of our listeners are very happy that you didn’t leave the world of APIs, that you’ve remained in “the cult of APIs”, as you said. Well Jeannie that was a great chat with you today, explaining how a large existing enterprise software company has embraced the burgeoning world of APIs. And how to do it and gain success and profitable services from employing the right procedures and programs. So thank you very much in taking part in our podcast network today.Jeannie: Absolutely, thank you for the opportunity to come talk about thisLarry: Thank you.                Make Your API Platform Successful With Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /podcasts/developer-marketing/Podcast-Launching-API-Programs-in-Non-API-First-Companies/",
          "author": "Larry",
          "categories": "Podcasts, Developer-Marketing"
        }
      
    ,
  
    
        "technical-tyk-api-gateway-how-to-monitor-usage-and-performance-with-tyk-api-gateway-on-aws-ec2-with-moesif": {
          "title": "How to Monitor API Usage and Performance with Tyk API Gateway on EC2 with Moesif",
          "content"	 : "This article provides an introduction to application programming interface (API) Observability, usage, and performance and how it fits within the overall APIOps Cycles. Then, we will walk through an example of how to successfully deploy and leverage Tyk Gateway and Moesif API Observability on Amazon EC2.What is API Observability?API Observability is a key component to properly execute APIOps Cycles and ensure your building something of value for your API users. If you’re not familiar with APIOps Cycles, take a look at this guide which provides an agile framework to quickly build APIs that are business-oriented and serve customer needs.Traditional monitoring focuses on tracking known unknowns. This means you already know what to measure like Request Per Second or Errors Per Second. While the metric value may be unknown beforehand, you already know what to measure or probe such as a counter to track requests into buckets. This makes it possible to report on the health of a system (like Red, Yellow, Green), but is a bad tool for troubleshooting engineering or business issues which usually require asking arbitrary questions.API observability was born out of control systems theory to observe as much as possible the internal workings of a system from its outputs by inferring state and behavior. With a sophisticated analytics tool to analyze all this data, you are able to answer any arbitrary question around your API behavior, not just a few predefined metrics that are directly measurable.API Observability solutions like Moesif are engineered to bridge this gap between monitoring specific predefined metrics vs general observability for the overall health of a product or business. An effective API monitoring strategy is essential for ensuring that your APIs are performing optimally and meeting business objectives. For more info on API observability vs monitoring, see this article.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding API MonitoringAPI monitoring is the process of tracking and analyzing the performance, availability, and functionality of Application Programming Interfaces (APIs). It involves collecting and analyzing data on API requests, responses, and errors to identify issues, optimize performance, and improve the overall user experience. For businesses that rely heavily on APIs to deliver their services, API monitoring is essential as it helps detect and resolve issues quickly, reducing downtime and enhancing customer satisfaction.Effective API monitoring involves tracking various metrics such as API request volume, response time, error rates, and latency. It also includes analyzing API logs, monitoring API endpoints, and tracking API performance across different regions and environments. By keeping a close eye on these metrics, businesses can identify bottlenecks, optimize API performance, and ensure the overall quality of their services. This proactive approach to API monitoring not only helps in maintaining a seamless user experience but also supports the continuous improvement of API strategies.About Moesif and TykAn API gateway like Tyk enables you to have peace of mind scaling and protecting your API portfolio. Written in Go, Tyk’s architecture takes a modular plug-and-play approach enabling you to add the components that fit your requirements, including an API client to simplify interactions with your APIs.. Many of these plugins are provided by Tyk out of the box so you can get up and running with authentication, rate limiting, and cross origin sharing are already handled.Tyk has plugins for a number of analytics backends whether it’s a DIY build on top of open-source technologies like ElasticSearch or a managed service like Moesif API Observability. Moesif is an observability platform designed specifically for API products and has a close integration with Tyk to collect additional context such as the authenticated user (AliasId or OAuthId) so you’re able to drill into customer usage metrics and understand where they drop off in adopting your APIs.With theMoesif Tyk plugin, your API logs are sent to Moesif which are analyzed to provide analytics andreports on usage. Moesif also processes payload information so you’re able to understand utilization of specific JSON keys, etc. For more info on how Moesif and Tyk work together, see Tyk + Moesif: the perfect pairing.  Dynamic Sampling and GovernanceThe Moesif plugin for Tyk pump also supports Moesif’s dynamic sampling and governance features. This makes it easy to sample API traffic based on user behavior rules or regex rules in Moesif so you can save on your Moesif subscription costs. Moesif will still extrapolate the original metrics for accurate reporting. For more info on how this works, see the Moesif infrastructure guide.How to set up Moesif and Tyk on AWSHow to install the Tyk Gateway on EC2In this example, we install Tyk using the Marketplace AMI on EC2. Tyk has three options, depending on your requirements:  PoC  High Availability (2-node)  AutoScaling (3+ node)For this guide, we’ll leverage the high availability (2-node) option, but the process is similar if you want the 3+ Nodes. The PoC version is not recommended for production use.You can also set up Tyk and Moesif on Amazon Elastic Container Service using the docker compose file available on GitHub if the AMI does not fit your requirements.  Go to the Tyk listing on AWS Marketplace and click the yellow subscribe button at the top right. This will launch the setup process for the AMI. You’ll have to wait a few minutes as Amazon processes your request. Once finished, click the yellow Continue to Configuration at the top right.        You’ll want to confirm the Delivery Method is “Tyk Pro HighAvailability API Management”, select your AWS Region and then click the Continue to Launch button at the top right. You’ll need to confirm everything and the select Launch at the bottom.        On the CloudFormation create stack panel, you’ll want to leave the pre populated settings as is and then select Next.          On the stack details panel, you’ll need to fill in all the parameters including the admin username and password for the various components, subnets, and SSH key. You may need to generate this beforehand if you haven’t done so already. For full details on how to fill this out, see the video on Tyk’s website.        Wait for the CloudFormation stack to be created. This might take some time.      Once finished, you should be able to curl the gateway.curl http://TYKElasticLoadBalancerALB-1234567879.us-east-1.elb.amazonaws.com/helloHow to add Moesif pluginNow that you have Tyk API Gateway running. You’ll need to enable the Moesif plugin in Tyk Pump and add your Moesif Application Id.Tyk Pump is an asynchronous component that pushes your raw analytics data to Moesif for analysis. The plugin fully supports Moesif’s dynamic sampling features which makes it easy for you to save cost.  Sign up for a Free Moesif account. On the onboarding installation page, click Tyk Gateway to bring up the set up instructions and your application_id.        Now that you have your Moesif application id, SSH into your EC2 instances that was created in the last step. The Tyk Pump should be already up and running.        In your pump.conf, you’ll need to add the Moesif Application Id and configuration you obtained in the last step such that your config looks like so:  "pumps": {  "moesif": {    "name": "moesif",    "meta": {      "application_id": "Your Moesif Application Id"    }  },}      If you want to log HTTP headers and body, ensure detailed analytics recording is enabled in your tyk.conf file.        With the Tyk Pump configured with your correct Moesif account, you should now restart your Tyk Pump and Tyk Gateway instances.  sudo service tyk-gateway restartsudo service tyk-pump restart  That’s it! Hit the AWS ELB a few times so Moesif can record some API traffic. You should see API logs show up in your Moesif dashboard.Using Moesif and Tyk togetherNow that you have Moesif and Tyk set up, let’s walk through creating some reports.Monitoring API PerformanceOne of the first questions that come to mind is how are my various APIs performing. To bring up this report, you’ll want to log into Moesif, go to Time Series, and then select P90 Latency. This will bring up a metric on 90th percentile latency for all your APIs being tracked via Moesif.A key component of an API observability system like Moesif is to pivot on any property regardless of cardinality. In this case, let’s add a Group By on Request.Route. This will break down our metric by the RESTful path. Since Moesif is designed for APIs, it’ll automatically consolidate URI identifiers like /items/1 and /items/2 to a more analytics friendly /items/:id.`  Understand Customer API UsageA key step in the APIOps Cycles is measuring both business and technical KPIs to reach your goals. This means having a deep understanding of who is using your APIs and how they are used. The Moesif Tyk plugin will map a Tyk Token Aliases to a User Id in Moesif. This makes it easy to store user properties such as email, revenue, and other customer demographics.Install Moesif User TrackingTo do this, install Moesif’s browser-js SDKon your website. Use the same application id for your Tyk Pump configuration:const moesif = require('moesif-browser-js');moesif.init({  applicationId: 'Your Moesif Application Id'  // add other option here.});Then, we can identify the user and save user properties once you have that info:moesif.identifyUser('12345', {  email: 'john@acmeinc.com',  firstName: 'John',  lastName: 'Doe',  title: 'Software Engineer',  salesInfo: {      stage: 'Customer',      lifetimeValue: 24000,      accountOwner: 'mary@contoso.com',  },});While optional, you can also track user actions like “Signed In” and “Viewed Docs” via the moesif.track() method to better understand your customer journey.Create a usage reportNow that you have saved customer demographics, let’s pull up a report showing top customers by usage broken down by the user’s email. Go to the same Time Series view under Events within Moesif. Then select User.Email so we show the API traffic for each customer. Since we want to look at long-term trends, change the interval to 7-days so you have a report like below:  Concluding ThoughtsHaving the right API analytics stack can provide your engineering and business teams with the right visibility to make informed decisions. Moesif and Tyk worked together to provide a turnkey solution for securing and publishing your APIs while gaining a full API observability solution with just a few config changes. A managed API analytics service like Moesif reduces your build and maintenance cost drastically over a custom solution so you can focus on building great APIs. A well-defined API strategy is crucial for aligning API development with business goals and ensuring long-term success.                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design and Build Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /technical/tyk-api-gateway/How-to-Monitor-Usage-And-Performance-with-Tyk-API-Gateway-on-AWS-EC2-with-Moesif/",
          "author": "Derric",
          "categories": "Technical, Tyk-API-Gateway"
        }
      
    ,
  
    
        "customer-success-monitoring-how-customer-success-teams-should-monitor-account-health-and-api-usage": {
          "title": "How Customer Success Teams Should Monitor Account Health and API Usage",
          "content"	 : "Leading customer success for developer-first or API-first businesses is quite different from traditional enterprise software. The best API products are designed to be self-serve and hands-off, meaning customers rarely need to sign into a web portal once implementation is done. If you’re a Stripe or Twilio customer, when’s the last time you signed into their web portal? Hopefully not recently, otherwise that may imply a problem or issue. On the other hand, a traditional enterprise solution may look at the addition or reduction number of active seats for an account as an early signal of expansion or churn.What is customer Success at API-first businessesAPI-first businesses have two different “product experiences” that customer success needs to consider. First, there is the web experience when signing up and logging into your web portal. Then, there is the API experience integrating with and interacting with the API platform.Both are part of “onboarding experience” and should have measurable KPIs. Yet, the API is what drives long-term value. Customers who added many seats or team members but never got integrated with the platform and can churn quickly (if paying already).Below are some comparisons for traditional enterprise vs API platforms. While we use the term “seats”, you can substitute with any pricing unit such as “licenses”, “CPUs”, or “physical locations”.Leading indicators of revenue expansion            Traditional Enterprise Software      API Platforms / Developer Platforms                  Increase in number of active seats/used licenses      Increase in valuable API calls              Deploying to multiple locations or more departments      Deploying to production or more apps      Leading indicators of customer churn            Traditional Enterprise Software      API Platforms / Developer Platforms                  Removal of many seats      Large drop in valuable API calls              Lot’s of inactive seats vs. active seats      Lot’s of low-value API calls vs. valuable API calls      Issues during onboarding            Traditional Enterprise Software      API Platforms / Developer Platforms                  One or small number of seats activated      None or a small number of API calls made              Lot’s of negative support tickets      Lot’s of negative comments on Stack Overflow / Reddit      Bad customer experience            Traditional Enterprise Software      API Platforms / Developer Platforms                  Lot’s of app crashes or UI quirks      Lot’s of 400 or 500 errors on API              Slow page loading      High latency API calls              UI requires many clicks to complete one task      API design requires many API calls to accomplish one transaction      How to create customer success dashboards with MoesifThe first thing you’ll want to do is create two types of dashboards:  An “overview dashboard” of all customer success metrics to identify problematic accounts  A “per account dashboard” to zoom into how a single customer is doingOverview dashboardEach Moesif account has a prebuilt dashboard that enables you to gain the following six customer success reports:            Chart Name      Description              New Users Who Stopped Sending Traffic      Shows all users who signed up recently but haven’t sent any API calls recently which could indicate churn              Large Accounts with 1st API Call      Understand which large accounts got activated today              Top APIs with Errors By User      Monitor users who are experiencing lot’s of errors with the API and may need help              Companies with Most API Usage      It’s good to know who your VIP customers are so you can prioritize support              New User Retention      If new user retention starts sloping downward, this could imply a serious product or customer experience problem              User Retention by SDK Used      Product and customer success teams should keep track of problematic integrations such as buggy SDKs      These metrics provide a good starting point but you can modify or add dashboards that are specific to your API business.Per account dashboardA per account dashboard helps you zoom into how a customer is doing, issues they are experiencing, and provide context on whether you should reach out. Because many API businesses have thousands of customers, it would be tedious work to manually create a dashboard for each account. Instead, we can build a master dashboard and then switch between customers using Moesif’s Filter Override feature.Step 1. Create the master dashboardCreate a new Dashboard in Moesif with a name such as “Single Account View”. Then, create a new chart that is scoped for a single customer. For example, to show a report on API usage for a single account, click on “Events” -> “Time Series”. Add the filters and metrics of interest as shown below.Ensure your chart has a filter for a single account such as by adding a filter on Company Domain.It doesn’t matter what the value is as it’s a placeholder value. In this case, we selected Asana.com as the placeholder value.Continue adding the other charts and metrics until your “Single Account View” dashboard is complete like below:This shows all the metrics related to Asana.comStep 2. Switch between customersNow that we built our master dashboard, navigate to it so we can toggle between accounts.Click the Override Filters button at the top right corner of the dashboard. This will open a popup to select a filter to override as shown below:We can add the filter company.Company Domain and enter a different company value than from Step 1 such as dropbox.com and then click ApplyEven though the original dashboards were filtered on Asana.com, this will be overridden with dropbox.com like below:At the top right of each tile, there will be a blue funnel icon. This icon indicates a filter override was successfully applied to the chart.How to get alerted on changes with an account’s API usageNow that we created some dashboards showing account health, it would be helpful to get alerted when one of these metrics are abnormal so customer success can be more proactive at reaching out to customers.To do this, we can leverage Moesif’s monitoring and alerting features.Step 1. Create a new time series reportA monitor can be added to any time series chart in Moesif. Go to “Events” -> “Time Series” to start creating anew report. In this case, we want to get alerted when an particular account has a large drop off in API traffic which could imply they are about to churn.We should ensure we are tracking a drop of in valuable API Calls, so we can add a filter to only monitor calls where response.Status Code is 200 OK. After all, a drop off in 500 errors is a good thing and doesn’t indicate churn.We also want to group by company.Company Domain as shown in the above blue box. This ensures we are tracking each company separately. For the metric, we’ll just select Event Count. We can later create additional alert rules for other metrics like average latency.Step 2. Create alert ruleClick the orange Alert button, this opens up the Create Alert Rule popup like shown below in the blue box.Because we are grouping by company, Moesif will track eah time series (i.e. each company) separately.Because each company’s API usage varies drastically from one another, dynamic alerts which leverage anomaly detection are more suitable than static alerts.For direction, select “Decrease”, as we only want to get alerted when there is a drop off in traffic, not an increase. Later, we can create a separate alert focused on increases in API traffic for an account.Closing thoughtsMany tools out there are not designed for API businesses or focus only on infrastructure health. If you care about your customer’s success, having the right reporting and alerting in place is critical. Moesif is the number one analytics solution for API product and customer success teams to gain self-serve API metrics without being overburdened by high implementation and maintenance costs.",
          "url": " /customer-success/monitoring/How-Customer-Success-Teams-Should-Monitor-Account-Health-and-API-Usage/",
          "author": "Derric",
          "categories": "Customer-Success, Monitoring"
        }
      
    ,
  
    
        "podcasts-developer-marketing-podcast-developer-marketing-essentials": {
          "title": "Developer Marketing Essentials",
          "content"	 : " Ep. 10: Adam DuVander, Principal at EveryDeveloperMoesif’s Podcast Network: Providing actionable insights for API product managers and other API professionals.Joining us is Adam DuVander, Principal at EveryDeveloper and a developer-marketing specialist. In our podcast he examines how to make developers use and love your API product.Derric Gilling, Moesif’s CEO, is your host today.Moesif · 10. Developer Marketing EssentialsListen to the episode on SoundCloud above, watch it on YouTube or download it on Apple or Google. Table of Contents   0:41Tell Stories to Engage    3:38Help Devs Do Their Jobs Better    5:33Show You Understand Devs’ Problems     7:18Use Non-Dev Messaging    13:23Move Beyond Hello World    16:41Measure Success    18:19Pinpoint Your Solution    20:20Talk to Your Users    22:16Allow Devs to Provide Feedback in Your Docs    25:05Docs are Only One Piece of DevEx    28:48For Great DevEx Focus on Getting Started Items    32:00Communicate Over All Channels    35:14Identify Usage Before Deprecating    38:33Streamline Your Guide    41:15Spark Dev Creativity Through Use Cases  Derric Gilling (Moesif): Welcome to Episode 10 of Moesif’s APIs over IPAs podcast network. I’m Derek Gilling your host today and the CEO of Moesif, the API analytics platform.Joining me is Adam DuVander. After being a technical journalist for Wired and editor of Programmable Web, he brought a very interesting perspective to the provider side. He ran developer communications and marketing at Sendgrid and also Zapier. Funnily enough, Moesif itself is a Zapier customer, so love using that product. He also helps out with a lot of different companies and startups working on developer marketing and all things developer platforms. Happy to have you here today, Adam.Adam DuVander: Yeah. Thanks for having me.Tell Stories To EngageAs an accidental marketer, I discovered that the best way to engage a technical audience in your API was to tell a story on why what you have mattersDerric: Well, so just getting into things, would love to hear a little bit about your story, starting off with writing for Programmable Web to how you got into developer-first businesses.Adam: Well, so firstly I’m a developer. I have a computer science degree and worked professionally as a Dev, so I had that background. But really all along I’ve been interested in teaching and sort of that “A-ha” moment that someone gets to when something really clicks for them. I was really doing that all along. The writing for Wired started as tech tutorials for Webmonkey. I was a Dev and thought “Hey, more Devs should know about this cool new technology or tool that I’m using”. Then at some point I realized that that was the bit I wanted to do.I wrote some more for Wired and Webmonkey and then actually my last piece was “Mashups are dead, the web is alive”. This would have been in the late 2000s decade, really where everything was a mash up and I was saying, the future is that all of these APIs are just what the web will be.Of course mobile was pretty nascent. I’m pretty sure that programmable web found me from thatpost and it was sort of perfect timing. I was cocooning writing my first book on mapping APIsand so that was how I started writing for Programmable Web and then eventually joined full time as the first editor.I built a team that found new APIs and reported on APIs. We followed all those trends. Really, part of the story, for me, of developer marketing too, is that I would receive all these PR pitches from companies with new APIs and they were so excited about their API, but weren’t really telling the story of why what they had mattered. And so when I left programmable web I realized there was this opportunity to help tell those stories. And so I call myself an accidental marketer, because really I went back to that sort of teaching and storytelling part that I was doing before just for fun as a Dev, because I realized that’s actually the best way for companies to engage a technical audience. So that’s how I made that transition, or those couple of transitions, from Dev to journalist to technical marketer.Help Devs Do Their Job BetterDev’s view traditional marketing skeptically — something that pulls them in another direction and wastes their time. Instead, give them something that’s going to help them do their job better and communicate like a dev.Derric: Oh definitely. Love hearing that story, especially since it’s super hard to engage with a technical audience. What makes it so hard? Why is it so much harder than just traditional marketing?Adam: You know, you could say that Devs have been burnt by tricky sales tactics before. I think that’s probably true. They have had their time wasted. But really the biggest thing it comes down to is that it’s a developer doing their job, and part of their job is to be skeptical and think about all of the edge cases that you might have. So a lot of sort of traditional marketing they see as gotchas that are going to keep them from reaching their goal, which is to build some great software and not get their time wasted, get pulled in other directions.So I think that a lot of times marketing that is not oriented toward developers can kind of feel like it raises that skepticism and feels like “Oh, this is the stuff that’s wasted my time before, that has pulled me in other directions”. And so they aren’t drawn to it.Contrast that with something that is going to help them do their job better and really comes from that place where they can tell that the one sending that message either has a similar background, or understands where they’re at. That makes all the difference in building that trust.Show You Understand Devs’ ProblemsBuild trust with devs by showing that you’re familiar with their problems: “We know our stuff and here’s what we’ve seen to be successful”Derric: Well that’s a really good point thinking about trust and turning that developer, that skeptic into something that’s more of an evangelist. Is there anything that a company can be doing to build that trust weather it’s around the product, marketing or messaging.Adam: So I think companies who are building tools for developers often are started by developers, for sure have an understanding of those problems, those real developer problems that they are solving. So I think the more that you can share that you understand that, that alone will build trust, because you’re able to say “Look, we were there. We we get it. That’s why this exists.” I mean, companies are started on sort of those opinions, those beliefs, that something is harder than it should be, or the way that other companies are doing this is not the way that they think it should be done. So the more you can kind of share those points of view, you can get developers on board.So part of it is sharing that you understand those problems, but then also being able to show that you know how to solve those problems too is another key piece. That’s why I keep coming back to education, because a developer can’t know everything about everything, as much as they might try, and so, if you’re able to show them “We know our stuff and here’s what we’ve seen to be successful”, you’ll gain a lot of trust and potentially customers.Use Non-Dev MessagingYour product might be used by non-devs: product, marketing, support or customer success, so use non-technical marketing and provide tools that a non-coder could use, such as a visual builderDerric: That’s a really good point to think about educating the market and also it’s better to show than just tell, developers want to see how something works and what makes them do their job better. With that said, there’s a lot of companies out there, Zapier included, that have two different audiences: the developer audience and also you’ve got the business folks that are coming in and trying to figure out what is an API or how do even call it an API. Can you walk me through some of the challenges there and how do you actually speak to both audiences at the same time?Adam: At Zapier there might have even been more audiences than that if you consider that there’s sort of the user side of Zapier. So, those actually building these automations for your own tools, the ones who pay for Zapier, there’s that audience. And that audience definitely had devs, I still have a paid Zapier account that I use quite a bit and I could code up some of the stuff that I do instead in Zaps, but for the most part that audience is not technical and doesn’t know how to make those connections between their tools. They might not even know that APIs are the stuff that’s making it happen.Then you have the platform side, which is where I did most of my work at Zapier. So with the platform side you have a SaaS tool that has an API, you want to connect it to Zapier, integrate it there, to enable all of your users to be able to make those automations. And, my title had developer marketing in it at Zapier, but it took a bit of time there to realize that even that audience was less developery than we might have guessed. There were definitely developers who were building some of those integrations with the Zapier platform, but a lot of those folks were from product or from marketing in some cases, support, customer success, anyone in the company who cared about being able to say “Yes” to more of their customers, “Yes, we do that. Yes, we integrate with that”, was looking to come to Zapier for the platform side, to be able to enable their customers that way. And so there for sure, it was a lot less of the developer message.There were certainly some API best practices, that I tried to share, that spoke more to that technical audience. And sometimes they did kind of go between the business and developer sides, but a lot of the messages were really about wanting to get your product in front of more of your users and expand your product’s ability to do the things that the users want it to do. So, I gave a talk actually about this, I have a post about it, that is “Your portal’s invisible audience”, and I really do believe that more APIs then you realize have an audience that is not what you and I might call a developer, not someone who’s going to write code all day. I think going through ways to be able to have them raise their hands, be able to discover that they’re there.                Easily Visualize & Fix API Issues with Moesif              14 day free trial. No credit card required.              Learn More        In that post I gave an example of how we launched at Zapier a CLI for the platform, based on research from some of the top integrators who wanted a more developer-like experience. But, we realized over several months and changes to the developer platform that they were a minority amongst those who were starting out. That really people wanted a visual builder. So we were able to determine that based on watching the things that they cared about. And slowly making changes to be able to make that case that “Oh, we’ve invested in this really developer-heavy platform and that’s not going away, but we need another interface that speaks to the larger group that’s here”.As much as I love developer audiences I think that there’s room for that in any API, especially in a developer-first sort of infrastructure type of API that maybe doesn’t support a SaaS tool. The differentiator I make there is that developer enabled is often SaaS, where you need a developer to help you use the API, as opposed to something like Twilio or Stripe where it takes a piece of your stack, and that becomes a more developer-first, developer-focused company. But I think for all of those there’s definitely room for that less technical audience if you’re able to look for that audience. But then at some level you also need to have all those things that a developer looks for. So it does put you down a road of looking in two places, so if you’re early enough you might be looking for the ones who are ready to more fully integrate.Move Beyond Hello WorldDetermine that your API is successful by monitoring if it’s being used to make callsDerric: It sounds like you did a lot of measurement to figure out how much are they getting activated with Zapier and what are they doing with the platform. Was there any type of feedback loop back into onboarding or other type of communication they get, and how did it differ depending on if they’re a developer or not a developer audience?Adam: A lot of that was with the platform product team which I did work pretty closely with, but they drove that part. That would absolutely be looking at “do they create an app” (an app being the integration to the Zapier platform) and then how far did they get through that? In that case there are triggers and actions, so you can look and see if they’ve created some triggers and actions to enable their own users. And then, have those been used? Has a true call gone through against that? That was all available in log data and, at least at that point I’m sure they still collect quite a bit, but it was all in a system, that we could as employees, really dig into and put together the case for why we’re being more or less successful. And again a lot of that was with the product team.And then, of course, you might imagine that on the user side there’s a lot of that as well about activation. And I think, even though that’s the user side there’s definitely an API lesson to be had for that, because Zapier, when it works, you don’t have to come back. And an API is sort of the same thing, right? If someone’s returning to your developer portal everyday there might actually be a problem and they haven’t reached the full integration. It’s similar in that way for the user of Zapier. So there it’s looking for the proof in the logs and Zapier even charges that way. So, has someone had a successful run of this integration, this automation that they set up, and how many of those have they had and are they continuing to have those over time? That’s the stuff that you can do also, as you’re looking at “Is someone using my API?”, again looking for those signs that it’s gone beyond that “Hello World”, they have had an initial success and they have moved the code to their machine or to their servers and have calls that are coming regularly enough that you at least know that they’re still testing things out.Measure SuccessDerric: That’s a really good point, especially since you’re not logging into a portal every day. If you’re integrating Stripe why should I? But then that also brings up a different challenge which is how do you actually measure that and create KPIs when you don’t want people to log into your portal? Does that mean the KPIs are different? How do you leverage that API data that you were mentioning earlier at Zapier?”Adam: I think being able to recognize what are the signs of someone who has had success and then making sure that you can measure that. I mean this is great for Moesif, there’s a lot that’s within API logs that can tell a lot of that story for sure. And so I think looking for recent calls and then calls that a certain threshold, calls to particular endpoints with hopefully success. So if you’re seeing a lot of 401s/403s then someone’s still stuck in authentication and, certainly from my perspective as a content person, I immediately think “What’s in that getting started guide”, do we talk enough about what it takes to get authenticated, or do we just sort of hand wave through that. So that would be the sign that I would need to look there if you’re seeing that type of error.Pinpoint Your SolutionThe more specific your platform’s use case, the more success you’ll have with getting the right devs to integrateI think you can see similar things that are going to be specific to your API that you know what it looks like for someone to have success connecting. Even if you’re brand new you can at least figure out what that would look like - to have API calls coming through and then making sure you’re seeing that and looking at it on a per user basis. It’s going to be different for different APIs. I was developer relations at a database API and lots of people came in and kicked the tires once and moved on because in a database you’re not going to rip out your whole database and replace it, you have to be in the right spot, in the right moment. We were an early startup, so we were casting that net probably a little wider than one might when they’re further along and kind of know the use cases that someone would use their API for. But, I would expect any database type of API to have fewer people making it through to success than something that’s much more pinpointed of a solution. I really do think that Twilio and Stripe, some of the things they have going for them, is that they have very specific use cases.Talk To Your UsersTo truly understand your platform user hop on a call or get an email exchange going to get qualitative data about where someone might be strugglingDerric: Definitely, line of business basically, it’s very pointed transactions that you’re providing or business functionality. You spoke of creating more content around how authentication works, when you identified that there might have been some struggles. Were there other ways that maybe you were able to change your marketing strategy based off of looking at activations or looking at how people adopted your platform?Adam: One thing I didn’t mention is actually talking to people. So I know we got right into the sort of the analytics, but another piece was when people would sign up, this was at Zapier but also at Orchestrate which was a database as a service. It was: if someone’s just getting started, how can we hop on a call or get an email exchange going back and forth to get more qualitative data about where someone might be struggling. A lot of that is with the database as a service for sure, how can we get them to that “Hello World”, but at Zapier it was more of understanding what that audience was. A lot of it was internal communication. Many of the people listening here probably use APIs that exists within a company so there’s a lot of internal conversation that has to happen and helping other people see what you have learned. And so that was a big part of the project, to understand that Zapier platform user as well, plotting them on a developer background access along with willingness to write code and being able to make that case that there were a lot more folks who didn’t have that background and weren’t ready to spin up the CLI on their terminal.                Moesif: Ship Faster and with More Confidence              14 day free trial. No credit card required.              Learn More        Allow Feedback in DocsThe capability for users to suggest edits in your docs presents another important way to communicate with your devs. When they make a suggestion, be sure to reply and fix the problem.Derric: Definitely a good point on thinking about both qualitative and quantitative feedback. I know it’s so easy to get very buried in the analytics piece without talking with developers, they’re humans after all. But sometimes they’re not responsive to email or they want to be left alone. Were there any best practices that you found that you can share on the best ways to reach out to developers and have that dialogue go?Adam: Yea, I think being open to multiple ways of communication is important. So we’ve mentioned a couple already here, which is reply to the email: on that list of things that developers get skeptical about is the “I just want to have a few minutes of your time on a video call”. Some are ready to make that leap, but not everyone, so being willing to have email as another one. Then I think something that everyone can do is that within documentation placing opportunities for feedback. Algolia I know has a sort of exclamation point on several places in every page, where when you click it you have an opportunity to fill in ”this isn’t working” sort of thing, at a minimum. At Zapier we had kind of a box at the bottom of every page. Many now have the kind of “edit this”, you can go and fork it in GitHub and put in the edit you think should go here. All of those different levels are a way to be able to get some kind of feedback. I think that the important piece there though, is that you have to actually look at it and then do something about it. At Zapier we won’t be surprised that we had had a Zap that would pipe that into a specific slack channel that all of the platform team was in. Having some way to be able to then reply if that’s attached to a particular user, but at least be able to take action, especially when someone notices a problem because that’s the “we’ve all been there in the documentation that is wrong place” and it’s just developer after developer who you’re upsetting. I mean that’s the quick path to keeping those developers away, is to continue to have inaccuracies in your docs.Docs Are Only One Piece of DevExDevEx includes all interactions someone has with your company and products including docs, so look ofr ways to show up positively as a benefit to the developer experienceDerric: I laughed pretty hard because I know that keeping those docs up to date is always a challenge. In fact, here at Moesif we leverage an edit at GutHub button and our docs are all open source. We use Jekyll with an open source static generator, a great product. But, who actually owns docs in this case? Does that mean it’s a product team, is it marketing? Is there any good process to keep those up to date, because we’re always seeing that as a challenge with developer platforms?Adam: As for who owns it, I often find myself talking to marketing teams and reminding them that they do need to care about the documentation. And many marketing teams do, but very few I would say own it. I think probably most often it is owned in product, but we also know that not every product team is going to see that as the core piece of the product. Larger teams will have documentation teams dedicated to that or individuals, but that of course is not always the case either.I think if you are listening to this, then then you’re probably a good person to champion it within your own company, that at least recognizing that docs spread between multiple teams that might care about it and kind of at the very minimum, putting together an ad hoc group that cares about it. We definitely did that and had multiple calls at Zapier over that time frame of people from support, product, engineering and marketing who had a reason to want more people to have success with the docs.Documentation also is just one piece of that overall developer experience and certainly the product side of the onboarding is a piece of that. I see a lot of the developer content as related to developer experience as well. Because even if it’s the first time that they’ve arrived at a piece of content, they found you because you’re writing about the developer problem that they’re experiencing. That initial experience is an experience and that is part of the whole journey that someone hopefully will go on with your API. It certainly starts before that, it starts with developers chatting in Discord rooms, about which APIs are easier or frustrating them at that moment. That sort of word of mouth is happening too. If you go really broad with what is developer experience, and I tend to go fairly broad in that definition of all interactions someone has with your company and with your products, I’m looking for ways to show up positively is a benefit to the developer experience, when they actually do get to the point of using your API.Focus On Getting Started ItemsTo ensure a dev has a good experience focus on providing excellent getting started types of things, such as SDKs in popular languages and a complete reference thats up to dateDerric: I’m glad you just defined that. I was just about to ask what is developer experience and how you define it. With that said, it’s such an abstract thing, is there a developer experience team, or is that more of a mindset? How do you actually make sure you have a good developer experience and is there a way to measure it or is it not really measurable?Adam: So, in terms of a team, I think that depends on how core the API is to the company. So, if the API is the primary product of a company, and more and more we’re seeing a lot of these, in that case developer experience should probably matter to the whole company on some level. In which case I’m not sure that a team makes sense. I’ve definitely seen developer experience teams. I think most often those teams are either a product team that is in charge of onboarding/dashboard experience or is a documentation team with a different name. I’ve seen those two. I’m sure that some companies may even lump those two together, though I think I’ve seen it more often in separate groups. So, I don’t know whether there needs to be a team with that name or not, but definitely there needs to be a team or multiple teams who are wanting to improve that developer experience, regardless of whether the name is on the group.And so, certainly being able to measure something, we talked a little bit about some of the things you’d want to see to be able to say this is developer success. From a high level I have 13 criteria I look for to be able to say whether someone is in a place that would give a developer a good experience. Certainly there’s more to it, whether they’re actually successful. But things to look for include: do you have SDKs in popular languages, do you have a complete reference that is up to date, and then a lot of the sort of getting started type of things that I look for there. And so I do measure it, but it’s really meant to be high level, and I think the more granular measurement really gets down to the usage that we’ve already talked about, because that’s really where it answers whether it’s a good developer experience or not - “Did the developers have success?”. Did they get from finding you to not only that “Hello World”, but something that’s actually useful and pushes them forward towards solving their problem.Communication Over All ChannelsWhen changing an API use all channels to communicate the change including: email, dashboard notifications, sunset headers and even flicking the lights on and offDerric: And then what’s next after they’re fully up and running? Sounds like they’re not kicking the tires anymore. Any good practices when it comes to developer communication?Adam: Certainly if things are changing, all of your docs should be up to date and wonderful to get to developer success. But then if your API is changing a bunch and breaking things for the developer, even if you’re updating your docs every single time that happens, every time you make a change it’s always up to date, but what they built on before, if that’s breaking, then that’s certainly going to create a worse developer experience. So that sort of product change is something that has to be happening continually, but that means that you need to have those open lines of communication. That’s tough when a skeptical dev might sign up with a different email address that they don’t check very often, those sorts of things. But email can be just one of many ways to be able to get a hold of them. Certainly if someone’s logging into a dashboard you have that as a way to get a hold of them. We, even at Zapier, Ben Peter who’s the API master, would always be the one in charge of figuring out when there were deprecations or problems across what is now 3,000 APIs. I sure hope Ben has some help now, not just Ben working on it. But he wrote about some sort of deprecation best practices, even right down to putting some stuff in the header.I know that Eric Wilde has a sunset header. Going through the steps of trying to communicate, then putting things into the header and even so much as doing a kind of flick the lights on and off kind of test, where you give them a chance to catch it actually going down, potentially breaking something, but then put it back up so that it’s not a complete firefight on their side. Ben wrote a couple of posts about those best practices. But certainly if you can start out communicating with a dev and set that expectation that you’re going to send helpful things via email and posting on your blog, then they’ll be more likely to want to have that as an ongoing communication channel. Which would then mean that you’d be more likely to be able to reach them when you do have to make changes like that.Identify Usage Before DeprecatingIf you have to deprecate, then analyze usage and success using Moesif to understand what would be impacted even down to a per-call basisDerric: Yeah funnily enough, we just came across a few of those blog posts. We actually implement a lot of that functionality at Moesif itself, flicking the lights on and off or a deprecation header. How do you actually identify what to deprecate and is this something that you write about or how do you actually decide on this?Adam: So from the complete ideal developer standpoint, you would never deprecate anything. I think if you look out there, Twilio might not have ever deprecated anything. I think they do a versioning based on the date, at least the last I had looked. So if you integrated at a particular time you have this endpoint and you can always upgrade to the latest, but they keep it going. That’s definitely very developer friendly, but you and I know that’s not always possible. There might be a system that you want to take down that is expensive in some way, in maintenance or in actual server costs, and so there’s a lot of reasons why you might want to make that choice. But definitely erring on the side of not updating is good. I think it’s the typical sort of product decisions where it’s “how much is this used?”. Understanding who would be impacted, and even understanding who would be impacted on a per-call basis is actually possible if you really have access to your logs. You know what endpoints people are calling. You know what data they’re receiving. So if you’re going to change the shape of data, you do have ways to be able to get at that if you have set things up to be able to access that.Derric: You’ve just got to make sure it’s set up ahead of time right.Adam: I think OpenAPIs as a data format for APIs is one way that we have now to at least have some expectations around what is within an API, in a way to talk about it and even programmatically test against. There’s a company called the Optic that will actually look against your live calls and do some of that stuff that I mentioned of sort of “hey the shape of this API call changed or the shape is going to change in your next version and here are the users that that impacts”. And then certainly being able to have a platform like Moesif to be able to look at endpoints that people call and be able to get that sort of analytics on usage and success would be another thing to help.Streamline Your GuideHave only a single Getting Started Guide and make it streamlined, so that it clearly shows how to make a callDerric: Definitely. My last topic is really around startups who are just starting to think about a developer platform. Any best practices that you able to share for someone just starting out in the developer marketing space.Adam: Well, if you’re just starting out, for sure to have those conversations that we talked about. We can maybe assume that someone is having those: talking to developers about what they actually want. Then I think kind of going and looking for the basics to get someone to “Hello World”, because as much as it’s important what happens after that, they won’t ever get to that if they don’t get to that “Hello World”. So, is there a Getting Started Guide that you can make sure really focuses on that. Some of the problems I see with Getting Started Guides is often someone wants to share all of the context you would ever need to know about using your product and that’s the wrong place for it. If you have to study up on concepts, before you can even get started, that’s a non starter for most developers. Most developers will skip that part and get to the part where I actually make a call. And so helping someone to be able to get where they want to go, so streamline that process in the Getting Started Guide. You can always layer in a little bit of concept as it’s needed and then you always can link out to the other spots where you cover that more in depth. So that’s definitely a problem I see after the bigger problem of not having that sort of content at all to get started.Sometimes there will be multiple Getting Started Guides, so that’s also confusing. Being able to have that single place to send them through. And then certainly looking for ways to make sure that your docs will stay up to date. I mentioned OpenAPI. Can you generate from your OpenAPI at least your API reference, so that you know that that is up to date. It requires that you also keep your OpenAPI document up to date, but there must be some way to be able to do that. So that way you’re putting yourself forward with at least the basics of documentation that someone expects.Spark Creativity Thru Use CasesShowing devs what you can do though a use case will often spark their creativityAnd then from a startup perspective you’re going to want as quickly as you can to understand how someone wants to use your product. Hopefully, you have some ideas around that already, put those out there in the form of use cases and sample apps. Validate that those are the problems that someone wants to solve. I’m seeing this less but within the last week I’ve had a conversation about this, where it’s “we’re this platform that can do anything and we wouldn’t want to hold back developer creativity by telling them what we do”, and I think that telling them what you can do is the spark that starts that creativity. So, being able to give some of those use cases is what will make someone realize “Oh well, this is sort of like this other thing that they didn’t have in mind”, then they will be creative. But just providing something and saying “do something with this”, doesn’t inspire that creativity in the way that people think it does.Derric: That’s a really good lesson, which is it’s really good to show than tell — show the different use cases and provide that very concise and specific onboarding or getting started guide. But don’t have 10 of them, just have one. Cool, well, thank you very much for having us here Adam. It was a pleasure talking about developing marketing and developer experience.Adam: Thanks for having me.                Make Your API Platform Successful With Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /podcasts/developer-marketing/Podcast-Developer-Marketing-Essentials/",
          "author": "Larry",
          "categories": "Podcasts, Developer-Marketing"
        }
      
    ,
  
    
        "api-product-management-developer-experience-api-analytics-across-the-developer-journey": {
          "title": "API Analytics Across the Developer Journey",
          "content"	 : "Every API product manager wants as many developers as possible adopting and using their APIs. They want them to get to Hello World quickly and have a great developer experience (DX) along the way. Of course, the bigger goal is to be able to tie API success into the larger objectives of the company. For many, despite the best intentions, their metrics are too simplistic, narrow, and based on outdated models of engagement.With complete API analytics, you can guide API users throughout the entire journey—from signup and education to Hello World and app deployment. Often, it’s not enough for someone to get started with your API. Instead, you want them to make complete use of it. And raw usage of the API does not tell the full story of your collective customer experience. You can use analytics to carefully plan the design of every API, improve the experience for every developer, and maximize the outcomes for your product.Drive More Developers from API Signup to SuccessDevelopers typically have choices of APIs to integrate, or whether to integrate at all. When you’ve managed to attract a signup to your API, you want to do what’s needed to get the developer to the next step. You need to simultaneously keep this micro success in mind while also aiming for macro success across the entire API journey.As an API provider, you want to ensure that every developer who creates an account successfully integrates your API with their application. API analytics can enable you to:  Improve API Onboarding  Help Developers Get to Hello World Faster  Encourage the Use of More API Features and EndpointsSome of these may be familiar to you. You’re likely attempting them without much data. And the final success of deep integration is something most API product managers don’t track—likely because it’s invisible.Improve API OnboardingA lot can happen from the time a user generates an API key to when they’ve integrated your API with their application. With API analytics and user interface data (such as through Moesif’s Pendo integration), you can track developers as they go through onboarding, collecting data from different points of the journey, such as:  When a user signs up for an API account  When a user generates an API key  Each step or page included in your onboarding flow  When a user makes their first API callYou should look closely at API and user data to determine what onboarding flow works best for most developers. The fewer decisions users have to make during onboarding, the better. Try offering limited onboarding options to start with and then later expand the onboarding paths based on the data presented.Personalize onboarding by analyzing API and user data and then sending well-timed, customized emails—which is easy to do with apps like HubSpot or SendGrid and Moesif’s behavioral emails.Initial onboarding can engage a developer that’s kicking the tires. The next step with your API analytics should be to gain insights to help those developers get to Hello World faster.Help Developers Get to Hello World FasterLooking at your API data, you could find that some developers are having trouble moving from the pre-integration to sandbox stage- the Time to First Hello World (TTFHW). The TTFHW is one of the most crucial API metrics you need to track, and reducing this time should always be a priority. A range of issues could increase the time it takes a developer to reach their first Hello World.Among those potential issues are:  API errors  Bugs in your SDKs  Flaws in how users have implemented your APIWith API analytics, you can track API usage and look at developer behavior as they use your API.  You can find the places where users get tripped up and help them work through any roadblocks. For example, are some developers visiting pages of your documentation after using specific API endpoints? Use this data to improve your documentation and make it more relevant for them. You may need to help some developers get to the first Hello World, so provide them plenty of information about your API via relevant content and assistance.Some users may never get to the first Hello World. This is another reason TTFHW is such a crucial metric to track because it can signal eventual developer churn. You need to understand which aspects of your API users struggle with and then help them stay on the right track. Create a funnel with your API analytics and tag the actions to indicate not only a successful Hello World, but the signs of more than a single call.Many API product managers stop at Hello World and don’t explore more deeper success. Or, sometimes those next signals are more manual, such as sales conversations. By exploring usage across multiple endpoints, you can encourage developers to adopt additional API features.Encourage the Use of More API Features and EndpointsDevelopers usually start with a few core endpoints, often unaware of everything an API has to offer. The long-term success of an application usually requires integrations beyond a single area of an API. Use API analytics to track which endpoints haven’t gained much traction with developers. Then use triggered emails and content marketing to introduce developers to your entire API.For example, you could send some users an email with a link to product pages, documentation, or a short video using Veed. It’s a powerful tool that can be helpful in creating professional-looking videos with an inbuilt video trimmer function and convert files into different formats. Provide additional content that explains the value applications would gain from that endpoint functionality. Periodically introduce new and unused endpoints to developers through automated emails, encouraging them to use more API features.Another way to identify deeper integrations is through SDKs. You can pull out API headers to determine how much each is used. Further slice that by day of first API call to determine whether your product has been integrated into an active project.Once you have your API analytics data available, there are many ways to interpret it. In addition to following the developer journey, you can depend upon data to make better API product decisions.Make Better API Product Decisions with DataWhen you have visibility into the entire developer journey, you can make product decisions driven by real API usage. You can understand the “hot spots” and lesser-used parts of your API, then plan future updates accordingly. With API product management a newer field, you need a tool that understands the unique questions you want to answer.For example, you may want to know:  Top customers by API usage  Endpoints used by each customer  Implications of endpoint deprecation  Which marketing channels drive actual API usageArmed with this data (among others you might seek), you can explore current results and make product updates with confidence. When you consider the full developer journey, you’ll revisit metrics to help more developers not only take the important first steps, but also more fully integrate your API into their systems.Get started building great APIs with Moesif API Analytics. Learn more.                Deeply understand your developer journey with Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /api-product-management/developer-experience/API-Analytics-Across-the-Developer-Journey/",
          "author": "Adam",
          "categories": "API-Product-Management, Developer-Experience"
        }
      
    ,
  
    
        "technical-aws-api-gateway-how-to-monitor-api-usage-and-performance-with-the-moesif-plugin-for-aws-api-gateway": {
          "title": "How to Monitor API Usage and Performance with the Moesif Plugin for AWS API Gateway",
          "content"	 : "API gateways provide a central point to govern and control access to your APIs, enabling customers and partners to quickly create new experiences. Amazon API Gateway has native support for a variety of compute resources like AWS Lambda or Amazon Elastic Compute Cloud (Amazon EC2).API ObservabilityAPI observability can provide your business and engineering teams with deep insights into how your APIs are used, including key API metrics that help monitor and analyze performance. API observability is leveraged by a variety of teams including:  Product teams to understand API usage and business value  Engineering teams to monitor and troubleshoot API issues  Security teams to detect and protect from API threatsMoesif API Analytics is an API observability solution that you can leverage to better understand API usage. There is a native integration with Amazon API Gateway which makes deployment just a matter of a few clicks and does not require any code change or restarts. As the gateway to the rest of your infrastructure, API gateways are also the natural place to provide API observability to your various business and engineering teams.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        Understanding APIs and API PerformanceWhat is an API? - Application Programming Interfaces ExplainedAn API, or Application Programming Interface, is a set of defined rules that enable different software systems to communicate with each other. It acts as a bridge, allowing various applications to exchange data and functionality seamlessly. In essence, APIs define how requests and responses should be structured, enabling different systems to interact efficiently.APIs are integral to modern software development, facilitating communication between disparate systems. They are widely used across various industries, including finance, healthcare, and e-commerce, to enable interoperability and data exchange. By providing a standardized way for applications to interact, APIs help streamline processes and enhance the functionality of software systems.Solution overview and use casesMoesif can add API analytics to your APIs hosted behind AWS API Gateway. This can help you optimize your API through detailed performance analysis. It works by forwarding structured API access logs from your Amazon API Gateway instance to Moesif via an Amazon Kinesis Data Firehose. Deployment of the solution can be done in a few clicks using the included CloudFormation template and doesn’t require any downtime. Once done, you can achieve a few objectives:Understanding customer API usageA key objective for API analytics is understanding who is using your APIs and how they use them. By default, Moesif ties your API calls back to a user identifier through parsing the request context with $context.authorizer.principalId or $context.identity.cognitoIdentityId so you can understand user behavior.A critical report is understanding which customers are using your APIs the most. APIs logs traditionally don’t include customer demographic information, but an API analytics system like Moesif can automatically join with other data sets containing customer attributes like user email or company domain.Users can be tracked with client integrations like moesif-browser-js or Segment using the same user id. Then we can bring up a usage report showing API traffic by company.Troubleshoot API performance issuesWith high-cardinality, high-dimension API observability, you can slice and dice your API logs by any number of fields including HTTP headers or response time. This makes it easy to quickly troubleshoot API errors without manual log search. A core engineering metric for APIs is latency percentiles such as the 90th percentile. The best practice is to look at 90th percentile latency over the average. This practice helps uncover large variations in your latency that can be masked by low averages. Your API users are looking for consistently low latency not the lowest average as spikes can wreak havoc in their own services.To do this, go to Events -> Time series and Select the P90 Latency Metric. It’s a good idea to also understand this broken down by route or service. To do so, add a group by “Request URI.” Moesif will automatically consolidate routes such that /items/1 and /items/2 will show up as /items/:id in the UI:Find API security threatsAs you expose more APIs to the internet used by customers, partners, and single page apps, your security risk goes up. Traditional mechanisms like browser fingerprinting and captchas don’t work so you need to leverage advanced user behavior analytics to find suspicious users.A common API security threat is not limiting access to your proprietary data. A hacker can then download all this data via a pagination attack. A method to detect customers abusing your API this way is to look at the amount of data downloaded per customer. To create this metric, add a summation of response.headers.Content-Length and then group it by customer name:How to set upThe integration works by adding an Amazon Kinesis Data Firehose which receives API Access logs from your Amazon API Gateway and sends them to Moesif. There are two types of logs from API Gateway logs: API Access Logs and CloudWatch Execution Logs. While execution logs are unstructured but human readable, API access logs are structured and more machine-parsable. In addition, the logs contain the user identity which makes them perfect for user behavior analytics tools like Moesif.1. Launch CloudFormation StackUse the Amazon Web Services CloudFormation template from Moesif to automatically create a Kinesis Data Firehose and configure it to send API access logs to Moesif. To get started, click the launch stack button below.This will open the Quick create stack within the AWS Console. You will need to enter your real Moesif Application Id under the Parameters section. Your Application Id can be found by signing into your Moesif account which can be done on AWS Marketplace.2. Enable API Gateway access loggingYou will need to enable API Access Logs in Amazon API Gateway and send it to the Kinesis Data Firehose from Step 1.  Go to your AWS API Gateway instance within the AWS Console.  Select Stages on the left menu and then select the Logs/Tracing tab  Toggle on Enable Access Logging.  Add your Firehose ARN from Step 1 under Access Log Destination ARN.3. Add the JSON log formatNow that you enabled access logs, you need to add the below JSON log format so the output is compatible with Moesif. Moesif will safely ignore any extra keys.{   "apiId": "$context.apiId",    "requestId": "$context.requestId",    "requestTime": "$context.requestTime",    "protocol": "$context.protocol",    "httpMethod": "$context.httpMethod",    "resourcePath": "$context.resourcePath",    "requestHostHeader": "$context.domainName",    "requestUserAgentHeader": "$context.identity.userAgent",    "ip": "$context.identity.sourceIp",    "status": "$context.status",    "responseLength":"$context.responseLength",    "durationMs": "$context.responseLatency",    "caller": "$context.identity.caller",    "user": "$context.identity.user",    "principalId": "$context.authorizer.principalId",    "cognitoIdentityId": "$context.identity.cognitoIdentityId",    "userArn": "$context.identity.userArn",    "apiKey": "$context.identity.apiKey"}4. Success!With the API Gateway integration done, you should see your API logs show up in Moesif. Make a few calls against your API Gateway domain and see them show up in Moesif’s event log in real-time. You should see the status code, URL, and other HTTP parameters captured like the below screenshot:API Call Monitoring and AnalyticsTracking and Analyzing API CallsAPI call monitoring and analytics are pivotal for gaining a comprehensive understanding of how your APIs are utilized, identifying performance bottlenecks, and optimizing overall API performance. By meticulously tracking and analyzing API calls, you can uncover valuable insights into usage patterns, pinpoint areas for improvement, and enhance the user experience.API call monitoring involves real-time tracking of API requests, responses, and errors. Utilizing robust API monitoring tools, you can gather detailed analytics and insights into various aspects of API performance. These tools enable you to track essential API metrics such as response time, throughput, error rate, and latency.Analyzing API calls allows you to identify trends and patterns in API usage. For instance, you can determine peak usage times, the most frequently accessed API endpoints, and common error messages. This information is invaluable for optimizing API performance, bolstering API security, and ultimately delivering a superior user experience. By leveraging these insights, you can ensure that your APIs are running efficiently and meeting the needs of your users.API Performance OptimizationImproving API PerformanceEnhancing API performance is crucial for delivering a smooth and efficient user experience. Several strategies can be employed to optimize API performance, including reducing the number of API calls, minimizing data transfer, and implementing caching and load balancing.Optimization techniques such as reducing the number of API calls can significantly improve performance. By consolidating multiple requests into a single call, you can reduce the overhead and latency associated with multiple requests. Additionally, minimizing the amount of data transferred in each call can further enhance performance by reducing the load on the network and backend services.Caching is another powerful technique for improving API performance. By storing frequently accessed data in a cache, you can reduce the need for repeated API calls, thereby decreasing latency and improving response times. Load balancing, on the other hand, helps distribute API traffic across multiple servers, ensuring that no single server becomes a bottleneck. This not only improves performance but also enhances the reliability and scalability of your API.Advanced User Behavior AnalyticsYou can leverage your integration beyond just looking at API calls in isolation and stitch your entire customer journey together. This approach makes it easier to see things like funnel reports on “Time to First Hello World” and “Time to Value.”Track user actions in your UI such as “Signed In” or “Viewed Docs” and start tracking user actions in your UI like “Signed In” or “Viewed Docs”. This makes it easier to slice and dice API usage by customer traffics. In order to do so, add the moesif-browser-js to your UI and call the track method:moesif.track('Clicked Sign Up', {  button_label: 'Get Started',  sign_up_method: 'Google SSO'});Once done, the first thing you should do is generate a funnel report. In the below report, we created a funnel analysis composing of three steps.  The first step is a customer signing into your web application (a user action).  The second step is a single payment transaction via the API. Thus moving from step 1 to step 2 shows the conversion rate of sign ups to the first API call.  The third step is over 100 payment transactions. For this example, we consider this the “aha” moment demonstrating customer value. Moving from step 2 to step 3 shows the drop off of customers who made API calls who actually got to see real value.API Usage and Customer InsightsUnderstanding Top Customers and API Usage PatternsGaining a deep understanding of API usage patterns and customer behavior is crucial for driving business growth and enhancing the user experience. By analyzing API usage data, you can uncover insights into your top customers, their usage patterns, and key customer demographics.API analytics tools provide detailed insights into how your APIs are being used. By examining this data, you can identify your top customers, the most frequently accessed API endpoints, and common error messages. This information helps you understand which aspects of your API are most valuable to your users and where there may be opportunities for improvement.Additionally, customer insights can be derived by analyzing demographic data such as location, industry, and company size. This information allows you to tailor your API development and deployment strategies to better meet the needs of your top customers, thereby improving the overall user experience. By understanding who your customers are and how they use your APIs, you can make more informed decisions that drive business growth and customer satisfaction.API Metrics and ReportingAPI Metrics for DevOps and Business GrowthTracking API metrics and generating comprehensive reports are essential for measuring API performance and driving business growth. By monitoring key API metrics, you can gain valuable insights into performance, identify bottlenecks, and optimize your APIs for better efficiency.API metrics can be tracked using advanced API monitoring tools that provide detailed analytics and insights. Key metrics to monitor include response time, throughput, error rate, latency, and overall API traffic. These metrics offer a clear picture of how your APIs are performing and where improvements may be needed.API reporting involves generating detailed reports on these metrics to provide a comprehensive view of API performance. These reports can help identify trends and patterns, allowing you to make informed decisions about API development, deployment, and optimization. By leveraging these insights, you can ensure that your APIs are running smoothly, meeting user expectations, and contributing to business growth.API Security and AuthenticationSecuring API Keys and AuthenticationSecuring API keys and authentication mechanisms is paramount to prevent unauthorized access to your APIs. API keys are used to authenticate and authorize API requests, but they can be vulnerable to theft and misuse if not properly secured. To protect your API keys, it is essential to use encryption and secure storage.Authentication tokens provide an additional layer of security by validating each API request. These tokens can be generated using various algorithms and should be securely stored and transmitted. It is crucial to keep API keys and authentication tokens confidential and not share them with unauthorized parties. Implementing robust security measures ensures that your APIs remain protected from unauthorized access and potential threats.ConclusionHaving the right API observability solution can provide your team with the right visibility to make informed decisions. While you can roll your own API gateway, data processing pipeline, and a data warehouse, this can create a massive time sink for your engineering team. Using fully managed services like Amazon API Gateway and Moesif API Analytics can help you scale without being held back by high maintenance costs or outdated data infrastructure. To get started, you can sign up for a free Moesif account right on AWS Marketplace.                Deep AI API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design and Build Better APIs!            Use Moesif to up your API game with powerful analytics and more.            Try for Free            No credit card required            ",
          "url": " /technical/aws-api-gateway/How-to-Monitor-API-Usage-and-Performance-with-the-Moesif-Plugin-for-AWS-API-Gateway/",
          "author": "Derric",
          "categories": "Technical, AWS-API-Gateway"
        }
      
    ,
  
    
        "developer-platforms-self-service-a-playbook-to-properly-implement-pay-as-you-go-pricing": {
          "title": "A Playbook to Properly Implement Pay As You Go Pricing",
          "content"	 : "Usage-based pricing, consumption-based pricing, and PAYG (Pay As You Go) are relatively new SaaS pricing models that enable you to drive both top of line growth while also increasing net revenue retention over more traditional subscription pricing models such as license or seat-based pricing. With Pay As You Go, a customer only needs to pay for what they consume suchas hours of a VM or number of messages sent. APIs naturally are transaction based which make them suitable for new consumption based pricing.In fact, all three cloud providers (AWS, Google, and Azure) leverage consumption based billing so their customers can optimize their infrastructure.Prepaid vs postpaid billingFirst, you’ll want to decide whether you want prepaid or a postpaid billing. Prepaid billing requires the customer to purchase credits or pre-negotiated quota ahead of time creating a positive balance that is then “burned down”. If they are credits, a customer will need to periodically top off their account by purchasing additional credits before they run out. Some systems enable “automatic top off” once a balance falls below a defined threshold. This can improve your business cash flow since you’re able to leverage the spent capital even before any costs are incurred to deliver their service. Irrespective of Pay As You Go, most enterprise companies require enterprise contracts to be prepaid for the term as prepaid enables more payment options such as a bank wire or ACH. Customers also benefit since they can set hard budgets for your service and disable any “auto top off” functionality.The downside to prepaid billing is the additional friction added when purchasing a plan when usage is not predictable, especially if it’s not a bundled amount.Prepaid billing requires the customer to estimate how much to purchase even before even using the service which can also cause more refund requests if they don’t end up using the credits. Unless a card is on file, collecting additional fees due to overage or term violations may be harder. Customers may be opposed to prepaid billing since the capital is tied up. This can be especially true for growth oriented spend such as pay per click advertising or credit card processing. In these cases, a customer may be willing to pay a higher price since they already collected the revenue from their users.A third option is to leverage a hybrid of both. For example, a bundled amount could be prepaid on a credit card, but then overage fees are post paid.Billing frequencyYou will also need to decide how often a customer is billed which is related to whether you leverage prepay vs postpay billing. One option is to invoice periodically such as the nth day of the month. This makes it easier on your customers’ billing department as your service more predictable while minimizing extra work to pay invoices. This is also easier to understand as most SaaS subscriptions are already billed annually or monthly.It’s not recommended to have a common billing day for all customers like the start or end of a month as this can cause your support team to deal with a flood of billing requests on that day.A second option is threshold based. If prepaid billing, a customer needs to purchase credits which in turn needs to be topped off once the balance drops below a threshold. Many systems allow this to charge a card automatically such as When the balance drops below $10, add $100 worth of credits. If you implement postpaid billing, the threshold is simply the reverse in that when the negative balance goes above a threshold such as $500, an invoice is generated and credit card charged.This is increasingly common in self-serve advertising. The downside is a lack of predictability since some months could have many more charges than other months.If not careful, this can generate an unnecessary number of invoices with small charges. Similarly, this can cause a nightmare for your accounting team and harder to predict future revenue.Billing measurementBased on your billing time and frequency, you will need to implement a system that can accurately measure and report on transaction volume for each customer account. This usually means having an analytics system on API calls or transactions. Because each customer may have a different billing period, your analytics solution should have a way to handle this. If you leverage periodic billing, then you can query a customer’s usage when you invoice them which is usually on a predefined billing day.For threshold billing, measurement may be a little more tricky since an invoice can be generated at any time, not just on the customer’s billing day. This means you need to keep track and trigger invoices once the threshold is reached. Leveraging a ready-made API analytics tool like Moesif can reduce your development and maintenance time drastically vs a custom in-house billing.Quota emailsWhen it comes to pricing, customers don’t want surprises. Yet, with consumption-based billing, invoice amounts can vary depending on their usage. By sending automated quota and usage emails, your customers can stay informed when their cost increases or they approach a quota limit.If you leverage a hybrid model such as a bundled fee and an overage fee, you may want to remind customers once they used the majority of their quota or when they exceeded it. A behavioral email platform like Moesif can be configured to silence notifications for a period (like 10 days), one a customer gets alerted to avoid spamming your customer’s inboxes. Then after 10 days, if a customer is still exceeding the limits, you can send a second email.Self-serve billing dashboardsBecause you are billing based on usage, it’s imperative to provide your customers with visibility into these usage and success metrics in a self-serve way. Rather than force your customers to ask for support just to understand how much quota they used, you can embed metrics right inside your developer portal with a tool like Moesif’s embedded charts. Your customers will want to slice and dice this data so that they can understand where their usage is going and where they can save money.Requirements for usage reports include:  Ability to see usage by different intervals like hourly, daily, or monthly.  Shows number of business transactions, even if your API can handle requests in batch  A way to break usage down by user demographics or clients.Beyond usage reports, you can also embed other metrics like SLA, API logs, and more to improve your developer experience and reduce barriers to adopting your API platform.ConclusionPay As You Go pricing can drive both revenue and retention for your business with implemented properly. Yet, consumption-based billing is also more challenging to implement than more traditional subscription pricing. You need accurate measurement of usage and also workflows to ensure your custoemers are not surprised once they receive an invoice. Tools like embedded charts and quota emails can go a long ways to keeping your customers informed.",
          "url": " /developer-platforms/self-service/A-Playbook-To-Properly-Implement-Pay-As-You-Go-Pricing/",
          "author": "Derric",
          "categories": "Developer-Platforms, Self-service"
        }
      
    ,
  
    
        "podcasts-api-product-management-podcast-successful-api-product-management-in-large-enterprises": {
          "title": "Successful API Product Management in Large Enterprises",
          "content"	 : " Ep. 9: Claire Barrett, Director at APIsFirstMoesif’s Podcast Network: Providing actionable insights for API product managers and other API professionals.Joining us is Claire Barrett, Director at APIsFirst and a seasoned consultant in strategy and technology change. In our podcast she identifies the key issues to ensure digital transformation success in larger, more mature organizations, and examines why digital savviness is key, how you should always aim for shared business goals, and why balanced teams are preferable.Larry Ebringer, Moesif’s CMO, is your host today.Moesif · 9. Successful API Product Management in Large EnterprisesListen to the episode on SoundCloud above or download it on Apple or Google. Table of Contents   1:00IT-Enabled Polyglot    4:40Project to Product Mindset Differences    8:24Digital Transformation Takes Time    11:45Digital Savviness is Key    14:5360-40 Rule for Project Size    18:24Half your Resources are in Consultants    21:36Find Stakeholder Shared Goals    26:24Balanced Teams Perform Better    28:53Build Skills with Women in APIs  Larry Ebringer (Moesif): Welcome to episode nine from Moesif’s APIs over IPAs podcast network. I’m Larry Ebringer, your host today, and the Chief Marketing Officer of Moesif, the API Observability Platform.Joining me is Claire Barrett, a seasoned consultant in strategy and technology change, with long tenures at international consulting companies and financial institutions, and currently the director of APIsFirst with Mehdi Mejowie. Claire’s also an active champion for women in technology and the leader of [Global Women In APIs]. Claire, welcome to our humble podcast network. Where in the world do we find you?Claire Barrett (APIsFirst): Thank you Lawrence for inviting me. I’m joining you today from the UK.Larry:: Great, I know it well.IT-Enabled PolyglotIT-enabled change polyglots speak the language of strategy, IT for business, business architecture, project management, scaled agile practices, digital transformation, organizational change and comms. And all of these come together in the API space.Larry:: Kicking things off, why don’t you share with us your journey to become director at APIsFirst and the leader of Global Women in APIs, and perhaps illuminate on what were your drivers along the way.Claire:: So my professional background has been in IT leadership and management consulting, almost always with, and around, large and more complex mature organizations going through change.I grew up as a child in a military family, so I got to travel quite a lot. I changed schools, neighborhoods, countries during my, if you like, formative years, and it means that I’ve always been comfortable in new situations. I see it more as an adrenaline rush, than a fear. So I’ve always been drawn to helping people and organizations manage and thrive through change, and build the kind of skills, resilience and cultures that allow them to respond.I’ve also got a real curiosity about the world. I describe myself as a sort of IT-enabled change polyglot, in that I speak the language of strategy, the language of IT for business, business architecture, project management, scaled agile practices, digital transformation, organizational change and comms. So to me, these all come together in the API space, and my role at APIsFirst was aligned with a move back to Europe from Australia, where I’ve been for most of my professional career.We help people and organizations realize the full potential of APIs, generally as part of a wider organizational transformation. Joining APIsFirst also coincided with getting to work with the team around the API Days Conferences and their Global Women in APIs initiative. And I’ve had the privilege of leading that group, which is about building peoples’ confidence and skills with speaking and writing in public.Project to Product Mindset DifferencesProject Management is about chunking things down into small manageable pieces, iterating continuously, learning and moving towards a goal, whereas product management is ownership of the product lifecycle wrt customers, championing the vision for the product and finding new markets, channels and business opportunities for creating business value.Larry:: Great. A very diverse background and again, thank you for joining us on our podcast network today. So let’s talk a little about product management in large organizations. How do you define product management in higher complexity mature organizations, and how is it different from project management?Claire:: So an excellent question that a lot of people are wrestling with or thinking about deeply at the moment. Perhaps I’ll start with how I define project management, or what is it about, its essence.Project management are about three key things. It’s about achieving a particular sponsor’s vision and goal, and project managers typically have quite a close emotional connection with the sponsor’s dream and vision. It’s secondly about ownership of deliverables and about running processes to execute that change across technology, people and skill sets. Good project managers can own deliverables and run the process to execute, often regardless of which industry or underpinning technologies or types of teams and problems they’re solving. Good project leaders and the more experienced they are, and the wider the scope of the change that they’re used to driving, the more able they are to be dropped in to solve complex problems, regardless of what the project’s trying to achieve. And the third thing is really about how they organize and do their work: planning the work and working the plan is a typical mantra. These days, this is about chunking work down into small manageable pieces, iterating continuously, learning and moving towards a goal. So those are some of the themes around project management.Product management would have a different set of things that are important that they would rank. Since they have ownership of the product lifecycle, they’re looking much longer term than necessarily getting to the end of a project and a scope. They’re championing the vision for the product in the API sense. It’s about finding new markets, new channels, new business opportunities for creating business value.Secondly, they’re very customer centric. Now those customers in an API sense could be other developers in other organizations who may be looking to create models, or they could be focused on products that are being digitized and automated through APIs. There, the emotional connection of a product manager is more strongly to its customers, to the users of the product. For APIs, this is the potential that other systems will use it to fire up those teams. And the third thing that is important for product management is a very data fact-based evidence element to the decisions that they’re making.The project manager and the product manager both share strong commercial acumen, they need to be able to manage and drive change, they need to be able to move things forward and drive action, they need to influence and communicate across very large and diverse groups of stakeholders.  They both have roles in bringing people together, who may not necessarily work for them directly, so they’ve got to be able to operate through influence.But I do think they have a different emphasis and connection. I don’t think you can just rebadge a role that looks under the covers a bit like a project manager, because they’re just good at organizing things and getting stuff done, and re-label it as a product manager. I think you need to be thinking of product managers as deeply connected to understanding the business subject — they’re deep business subject matter experts in the capability of what’s being offered, and their level of understanding in an API sense can’t be necessarily agnostic about technology in the same sense that perhaps project managers are able to operate across more domains.Digital Transformation Takes TimeEspecially in larger, more mature organizations, modernizing existing systems is about building skills and confidence with trying things out, learning from them and then moving ahead — analogous to leveling-up each step in a gameLarry:: I think you hit the nail on the head. I always love to slow down in these podcasts when there’s an especially cogent moment, analysis or insight. How does product thinking manage the challenge of meeting short term priorities, whilst transforming more broadly? Or, put another way, how do you re-engineer a whole plane while it’s flying?Claire:: So I think striking these balances is such a real problem for people, particularly in larger, mature organizations that are striving to simplify, automate and modernize their existing systems, at the same time as trying to find new opportunities which potentially, if not well managed, are at risk of adding complexity into the environment. So how do they do the “walk and chew gum” thing?The organizations that are getting the most success in this space are able to right-size the levels, their architects are able to focus on particular domains or particular areas of business and technology, and have the right organizational structure and ownership for it. But it’s also a lot about how they fund and manage the ability to prioritize. So, as one moves from project-based change to more product-based thinking, going along with that is the ability to think of long build and run capability as being long running through the life cycle of a product, as distinct from being time-bound and scope-bound in a traditional project tense. And this certainly plays out in things like what’s OpEx versus CapEx, it plays out in things about what’s business as usual versus what’s change. So, it can go quite heavily to the core of how organizations have traditionally thought of change. In order to be able to respond. But they can do experiments and build capability if you’ve got the right kind of attitude and they can chunk things down to be small enough, rather than try and overthink it too quickly.It’s a lot about building skills and confidence with trying things out and learning from them and then moving ahead, it’s like leveling-up each step in a game. As you’ve leveled-up into the teens and the 20s levels, you can deal with an awful lot more complexity and stuff coming at you all the time from different places. And the prize is bigger and you can collaborate with different people than you thought of in the past. Early on you need to build skills and muscles at managing change.Digital Savviness is KeyOffering external APIs into new markets often feels very different for people and so digital savviness for the sponsoring teams and product ownership is key to successful product developmentLarry:: Great. Love that definition. It’s a lot about moving slowly initially and then building upon the, as you said, muscles and the skills that you’ve built up over time to really formulate large-scale change. What cultural, behavioral levers need to be pulled and where should you invest to make a product successful across large organizations?Claire:: So if we focus in on API products in particular, they do call for a different way of thinking than APIs as integration components. A lot of people can quickly understand that it’s not a big stretch in thinking of APIs as a better and simpler way of providing hooks between systems and hooks between external parties and organizations.What’s a next step of thinking is how might an API externalized be offered as a product into new channels that don’t yet exist and into new markets, as opposed to re-engineering, or offering a new experience, this is offering a product in a new sense to a new market which feels very different for people. And so a digital savviness is for the sponsoring teams and product ownership is key. Having the right fit-for-purpose governance in place that underpins and supports those API products and teams around them. So the very clear and easy to understand minimum standards and expectations around security, around usability, around data management.The other, as we were talking about before, this project to product mindset, as well as being understood by multiple different stakeholders in the organization is key. So it is about building that capability, finding the places that early on will resonate and that will tell a story that can be bought into by people.So what is the particular opportunity, which early on might not be as far reaching as once you’ve leveled up a number of times, then you can kind of be exploring more progressive ways of working. But, by then you’ve sort of built a momentum and you’ve got the engineering confidence, you’ve got the scalability confidence, the performance confidence, the security, the finance, funding arrangements, those capabilities that need to be in place.60-40 Rule for Project SizeIn digital transformation it’s been identified that at 60% of the effort in higher-complexity organizations goes into projects that have a big long-term impact versus 40% into low-hanging fruit pieces of change, whereas in lower-complexity organizations that split is the other way aroundLarry:: Yes, I totally see that in our customer base. What do you mean by when you say achieving change stickiness and what are some of the things people can take away from your research findings into it? And I should probably say that I got the idea for this question from your excellent LinkedIn article on this topic.Claire:: Thank you Lawrence. Transformation change stickiness has been an interest of mine in terms of what IT-enabled change leaders, so these are tech-savvy business leaders, CTOs, CIOs, consultants supporting organizations through transformational change and seasoned technologists, what are some of the trade offs they are encouraging or making that allow them to do this building and repairing the plane while flying it at the same time? And the findings have been interesting and they are actually different depending on the complexity and size of the organization.So, there’s an appreciation that if you’re driving large change and you’re looking at it through an IT-enabled change lens, there are ways in which you can be more or less effective, depending on where you want to put your efforts, because there’s always more things that you can do than you have enough resources, teams, skills, funding, to go after. So how do you choose what to do, how do you choose what to do later, or what to not do?At higher complexity organizations, for example, they would put 60% of their effort in getting attention for the change that’s going on, they put 60% of their effort towards large symbols of new things that are being done. Versus 40% of their effort towards what I call the kind of low hanging fruit pieces of change, so the things that will show that there’s some change happening but they may not be the really, really big long term impact. Whereas in lower complexity organizations that split would be the other way around — it’s actually more like they would put 65% of their effort towards things that make a meaningful difference, low hanging fruit or problems that have been around for a while, that by addressing them, they will get trust and confidence from employees and customers close to the coalface of their operation and then support some of the bigger symbols of change.So big symbol of change could be a major reorganization around perhaps a different kind of scaled agile arrangement. It could be a change in language, I’ve heard of changing an international language for a non-English speaking organization into English, for example. So something that’s kind of gets people sitting up and listening.Half your Resources are in ConsultantsGiven the war for talent in APIs, almost 50% of companies’ talent in digital transformation comes from external consultantsClaire:: Another piece that I ask people about, which I think is very relevant in the API space, where there’s a big, if you like, war for talent at the moment in terms of finding for example API product managers, is where people would put their efforts and investment in building skills and capability in house, taking perhaps a bit longer to recruit and to retain and build new capability that will will drive their transformation, versus what effort would they put into getting externals into help them with a with a fast start. And, regardless of the size of organization or industry, it was more than 50%, about 55%, of their efforts would go to building capability internally, versus nearly half, 45%, on external support. So again, both options are reasonable things to choose, but there was a recognition both by external consultants on the importance of organizations building internal capability, as much as they was the recognition for people trying to build skills in house, about the need to be able to get support from externals.Larry:: That’s fascinating. I would have never thought that it’s 50/50 for building internal capabilities, versus bringing it in from outside.Claire:: Yes, just to qualify, it’s 55% of the effort that they would put towards building internal capability, versus they would put 45% of their effort towards getting help from outside, in driving a digital change. So people recognize that they need that long term capability in house, it’s just they don’t necessarily have the time, they can’t afford at the moment to take the amount of time, it will take to build that capability in house, either re-skill existing people with new digital capabilities, or with finding and recruiting those people, they need the external support to give them that head start. With a net strong expectation that that role is to coach and support the development of that capability, as opposed to having a long term third-party reliance, on something as core and fundamental as the API space. For example, if you’ve got API products that are building out an embedded business model for you and you see that as part of your future, and the people that are supporting that as having the capabilities that you want for the long term, then you don’t see that as something that you need to outsource.                Moesif: Ship Faster And With More Confidence              14 day free trial. No credit card required.              Learn More        Find Stakeholder Shared GoalsTo ensure lasting change identify shared goals between the different API stakeholders: sponsors, transformers and technologistsLarry:: Right. In fact that’s very topical. We have a number of new customers in HealthTech and it’s fascinating that the earlier on the customer is, the more likely they are to have external consultants helping them with HIPAA and HITECH compliance. But then when dealing with larger organizations in HealthTech, that have an established product and are further down the path, practically all of their expertise is internally built and developed. So we certainly see that in our market today. How does your proposed model of API stakeholders work where sponsors, transformers and technologists interact to execute lasting change?Claire:: So the description that we use is that these three different communities, if you like, within a lot of organizations today going through transformational change, is not an organizational hierarchical split. It’s more of a role between these kind of elders, the sponsors of the organization that are looking to realize the strategies that they’ve committed to and want to see realized as quickly as they can. Inevitably while APIs may not be called out in those strategies, they certainly are inherent in the ability to be able to move faster, be able to explore and participate in new ecosystems and new kinds of models and marketplaces. So that’s the elders. The transformers or the explorers are the people who are out there looking for new opportunities. They may already be at a kind of API as product base, or they may be focused on perhaps digitizing existing experiences or re-imagining customer journeys. Then the third are the technologists, the kind of navigators of the IT landscape and the tooling and the architectures and the enablers, of which APIs are certainly a core.One of the challenges for these groups is not to have them standing with their backs to each other, trying to shout into the wind, but singing from a shared song sheet and finding the synergies in the white space between the teams. They do often have different starting points, all correctly and appropriately intentioned towards driving this broader change, but are not necessarily aligned around the same priorities. So all focused on the things that are closest to where their function will make the highest impact on the organization more broadly. But that can create tension between which things to work on first. So finding the shared API goals is really critical. At the lower levels this may be relatively small, in terms of its ambition and scoping opportunity, but as organizations level up it may be across all three.So early on, maybe just two of those teams would be working and focusing on something such as the APIs that are going to enable processes to be digitized faster. The IT team working on the tooling for that, making sure that those can be secure and self-served internally. That may lead on to some other impacts more broadly for other sponsors. But then the transformers may be looking to actually trigger more change in the elders, the sponsors community, to start thinking differently. The IT teams may actually be able to offer new ways of thinking about that, that hadn’t existed before. And creating the right constructs for those three parties to operate together could be around a product, or it could be around a program in order to inform the product, is for me part of the secret sauce for getting fast impact at each level.Balanced Teams Perform BetterTeams that are more balanced when it comes to gender, race, culture, age, orientation and ability, are more creative and successful than homogeneous teamsLarry:: Fascinating distinction between those three different, very important, groups. So changing gears a little and moving on to a topic close to my heart, since I have two daughters. Share with us your experiences of being a leader of women in the API community. What are the benefits of greater workplace balance and inclusion for product teams?Claire:: So product teams are naturally expected to be creative, to be responsive, to be thinking not just about technical product opportunities, but empathy and the feelings and sentiments of their communities and user bases. And so greater balance in a team will naturally create a more diverse way of looking at problems. And you know balance is not just gender, it’s race and culture and age and orientation and ability. All of those elements will allow people to think up products that are more creative and that are more responsive.Another key thing is that people are more engaged if they feel included and will bring their whole selves to work and be more connected. So inclusion pays up very heavily in increasing staff engagement. Personally I think that diverse teams are more resilient to change because people are more open to others and to others’ ideas, and therefore they tend to be more sensitive to what’s going on in the external environment. Certainly, research bears out that organizations with greater balance in their leadership teams and greater balance in their board of directors, will respond more successfully to change. That couldn’t be more real than what’s happening in the world today.Build Skills with Women in APIsThe programs run by Global Women in APIs initiative helps people build confidence through enhancing skills in public speaking and writingLarry:: Right, that is so true and so cogently put, especially here in Silicon Valley where you need a whole lot more inclusion and diversity in the tech workspace. So on that subject, as the leader of Women in the API community, what do you suggest that under representative people listening to this podcast can do to help further their careers, and specifically what programs might be available.Claire:: We run programs to help people build their confidence and skills with speaking and writing in the public domain. So we feel strongly that people who can showcase the talents of their teams, their colleagues and by association themselves are building the opportunity to be recognized not just externally, but internally as well. And we feel that as people build confidence with speaking about what they and their teams are doing, and how that’s making a difference, that that encourages perhaps less represented other team members to step up to contribute.We support people in their careers by running programs that allow them to practice in a safe space. Applying to speak at conferences, for example, we provide them with coaching and support before, during and after conference participation, and increasingly now in writing in the public domain. We provide networking events and we support profiles of women in the APIs community, which is, I should say, pretty broad. Last year we increased our membership by 150%. We offered five programs. Our Members are now from over 23 countries and more than 85 different organizations and the membership’s growing literally by the day.The Members come from large organizations with API architecture roles, they come from API management system vendors, they come fromstartups, they come from freelancers in the API space, technical writers, UX. It’s a really fantastically broad group with men, women and people identifying as non binary, in our membership. It’s been a fantastic way, particularly for people who are perhaps a little less connected in this pandemic world that we’re in, to provide connection through a virtual format. For people in all corners of the world this has provided a really important feeling of being part of something that’s bigger than one is.Larry:: Brilliant. Where online should people go to find out more information about your excellent programs?Claire:: So women in APIs is at the initiatives page of APIDays. You can join there and find out about the programs. You can contact me on LinkedIn,{:target=”_blank” rel=”noopener”} if you want to contact me directly. On the subject of API Product Management, as part of a group of global API experts at the API Collective, we’re running a API bootcamp program from the 23rd of March, with more details on the API Collective’s training page.Larry:: Excellent. Well, we finished on a very instructive and helpful note providing a couple of great links for furthering one’s career. Thank you very much Claire for taking time today to cover such interesting and important topics. We wish you the best and stay safe.Claire:: Thank you Lawrence. It has been an absolute delight to have you here and I look forward to following the continued success of Moesif.Larry:: Great.Thank you very much.Claire:: Thanks                Make Your API Platform Successful With Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /podcasts/api-product-management/Podcast-Successful-API-Product-Management-In-Large-Enterprises/",
          "author": "Larry",
          "categories": "Podcasts, API-Product-Management"
        }
      
    ,
  
    
        "financial-procurement-how-to-be-an-outstanding-engineering-manager-by-investing-in-the-right-tools": {
          "title": "How to Be An Effective Engineering Manager By Investing In The Right Tools",
          "content"	 : "After working with a diverse set of software engineering teams, we at Moesif have gained a unique perspective on what traits enableengineers to take on leadership positions and become outstanding managers vs others who have a harder time rising through the ranks.Gaining an advanced title and responsibility is not an easy task. After all, there are far more software engineers than executives at acompany regardless of size. So how do you stand out to land that awesome VP or C-suite role?To understand what makes a great leader, it’s best to understand from the perspective of the person driving the promotion.The mindset of leadershipEngineering naturally applies logical reasoning to a set of problems or challenges. Whether it’s to build and scale a new data platform, orhow to gain a leadership title. Engineers love to “grade” a system on metrics such as performance or defect rate. This can even manifest intoorchestrated mass testing activities such as the familiar Hackathon. Yet, the CEO or CTO probably doesn’t care how fast you cancode an Uber clone or hook up some API. Even the usual textbook items of soft skills matter less. While it’s handy to have great communication andspeaking skills, there are many executives who dread going up on a stage to present some eye candy. So what is it?Becoming a great engineering leader is not really about soft skills or a grade, rather it’s about a mindset of finding ways to put capital to work andcreate business ROI. Every company (unless you’re nonprofit) is in the business to make money. This means the work that you create has to create positive ROI for this business. If you create negative ROI for the business such as not delivering your work, then that is a quick path to being let go. Yet, businesses hire and invest into junior engineers all time through training, career mentorship, and other activities. This is because even though the short term ROI of that hire may be negative, the long term ROI is expected to exceed any initial investment cost.Good engineers already understand this concept very well and will strive to create more value which can appear as “proving oneself”. Let’s say your managertasks you with building an internal API analytics tool so you can understand customer usage. A naive way would be to claim you can build this in a day and spend all weekend coding an ad hoc solution that works but will be costly to scale and maintain. Sure, you coded it quickly, but does that accomplish the business goal in this most efficient way?This way of thinking can turn oneself into more of a “code monkey” vs. an engineering leader. In fact when taken to the extreme, friendly competition and “leveling up” can turn unfriendly and create office politics.Leverage capital efficientlyOutstanding leaders repeatable demonstrate their ability to leverage capital efficiently to get work done.CapitalCapital in this context can be human capital, technical capital, financial capital, or other resources available. A business by definition is theallocation and use of capital to create more capital usually in the form of increasing cash reserves, headcount, or patent portfolio.LeverageLeverage enables business leaders to utilize all available capital at the business to create the largest outcome that’s beyond what one could do without any resources. A single person can’t build a search engine overnight, but great leaders can lead a 100 person engineering org to accomplish a lot.As example, an engineering leader may leverage human capital (i.e. engineers) to create technical capital (a sellable product or service). Then, business leaders may leverage that product to create financial capital (i.e. revenue). At each stage, more value is created. In this case, the financial capital (revenue) must be higher than the initial investment (engineering salary). The larger it is, the better the outcome for your boss.Managers who can prove that they repeatedly create positive returns on investment are allocated more capital to leverage which can be seen as growing your responsibility and ability to magnify your business outcomes.EfficiencyA good leader not only leverages capital to create positive business incomes, they do so efficiently to have the largestinvestment multipliers. Efficiency is poorly understood by inexperienced managers and can cause situationswhere employees end up doing “busy work” that doesn’t maximize business outcomes.The cost of consuming business capital such as engineering time is larger than the intrinsic value.There is an opportunity cost which a manager needs to consider which means you should use capital efficiently.If you’re able to generate positive returns but they are single digit (i.e. the returns are small relative to the capital spent), thenyour use of capital is inefficient and could have been spent in alternative ways. Money tracking is also essential for engineering managers who want to make the most of their team’s resources. By tracking how money is being spent, managers can identify areas where they can save money or invest in more important areas. Money tracking can also help managers identify potential problems early on, such as budget overruns or unexpected expenses.Why experienced managers purchase softwareEngineering and business leaders commonly invest in enterprise software like API analytics and monitoring to make the best decisions that maximize returns. This is because such tools can make your employees more efficient which enables you to have greater leverage.Yet one’s approach to procuring software and tools varies greatly based his or her leadership experience which causes debates onbuild vs buy. Deciding whether to commit engineering resources to build an internal solution or commit financial resources to purchase a turnkeya solution from a third party vendor can be daunting. Inexperienced managers commonly resists purchasing software and wouldcampaign to build an internal solution whereas experienced leaders usually lean towards purchasing from external vendors.There are usually three motivations why inexperienced managers want to build:  The inexperienced manager believes building something will raise his/her “profile” at the company.  The inexperienced manager has never navigated procurement and financial controllers so don’t know where to start.  The inexperienced manager has a belief that purchasing is corporate waste.Yet, experienced managers almost always prefer purchasing from a third party sense they have a better understanding of the true cost and risk to build internally vs the leverage that can be gained by choosing to go with a third party.When you purchase enterprise software, you’re also leveraging the vendor’s investment into years of research and development tobuild a far superior product. A typical SaaS vendor may have 100’s of millions of dollars in annual revenue and years of expertisewhich they can leverage to further develop their product. It would be a poor business decision to commit 100’s of millions of dollars in human capital to build an equivalent level of functionality and performance that a third party vendor has. This means an internal build is almost always inferior to the third party solution or is buggy.Understanding short term and long term business costLet’s say you did make a decision to build, then you can impact the business negatively both in the short term and the long term which can cause your boss to question your decision making.In the short term, developing in house can cost a business a lot of money due to engineering salary in addition to multiple opportunity cost including:  You can’t utilize the tool until it’s completed while your competitors gain market share.  Your engineers are busy building and maintaining this tool instead of revenue-generating features.  Increased company risk that the tool doesn’t work or is buggy.In the long term, the maintenance costs and technical debt increases as employees move to other projects or leave the company.According to SAP, 78% of homegrown enterprise apps are abandoned after first use. Feature requests and bug fixes remain unresolved after the initial delivery due to lack of engineering resources.Leveraging a vendor’s leverageBy purchasing software, you’re also able to leverage the vendor’s available leverage and resources.Most vendors have thousands of customers which they leverage to learn from and create more innovative solutions that beat competition.You are then able to leverage these innovations while the cost is split across many different companies.No single customer has to bear the entire R&D cost.Even costs like cloud compute are split among many customers enabling their platform to be more elastic and handle peaks in demand better.Whereas your internal build will always have infrastructure running even when no employees are accessing it.Not Invented Here Syndrome (NIHS)There is a name for this tenancy which is called _Not Invented Here Syndrome (NIHS). Companies with large not-invented here cultureshave stifled growth and lack innovation due an unwillingness to adopt new ideas or products from diverse perspectives. Experienced managersthat have seen the consequences of building are usually the most vocal on purchasing something that’s already tested. Leave your emotion and ego at the door andmake decisions based on data and overall business sense is the surest way to gain that next promotion.Make your boss look like a starA different way to think about maximizing output is by placing yourself in your boss’s shoes.He or she also wants to leverage the outcomes you and your team generates to execute on his or her own agenda.This could mean fighting for more revenue to sponsor their project or gain a promotion. The moreyour work can impact business goals, the better your boss will look in the eyes of the CEO or board.This is where the saying make your boss look like a star has true meaning.After all, the CEO or department head is also evaluated on business goals such as revenue andcustomer success.",
          "url": " /financial/procurement/How-To-Be-An-Outstanding-Engineering-Manager-By-Investing-In-The-Right-Tools/",
          "author": "Derric",
          "categories": "Financial, Procurement"
        }
      
    ,
  
    
        "business-compliance-building-hipaa-compliant-apis": {
          "title": "Building HIPAA Compliant APIs",
          "content"	 : "Legal disclaimer: Nothing stated herein is legal advice. It is provided for informational purposes only. You should work closely with legal advisors to determine exactly how HIPAA may affect your business.Health care represents 17% of US GDP, around $4 trillion in 2020. COVID has normalized the use of remote medicine and accelerated the dispersion of health care away from doctors’ offices and hospitals, to services being delivered on smartphones and online apps.In the midst of this sea change, more patient records are being digitalized, transmitted, stored and utilized electronically. APIs stand at the vanguard of swiftly enabling new services in this burgeoning market. However, with the advent of these electronic records and the ease at which information can be obtained from an API, now more than ever there’s an existential need to protect patients’ data.HIPAA/HITECH Laws and Your APIsAny information that can be used to identify a patient is considered protected health information, or ePHI for electronic Protected Health Information, and its usage is controlled by the federal laws HIPAA & HITECH.Unlike the PCI standard in FinTech, no one can certify that your app is HIPAA compliant — the Office for Civil Rights (OCR) (part of the Department of Health and Human Services) as the governing body, doesn’t recognize certifications. Rather, the onus is left to you, the API provider, to perform periodic technical and non-technical evaluations to make sure that security policies and procedures meet the letter of the law.In the past, OCR has vigorously pursued judgments against companies’ HIPAA non-compliance, employing both civil and criminal penalties.  The Health Insurance Portability and Accountability Act (HIPAA) is a Federal Law from 1996 and was significantly amended and expanded by the Health Information Technology for Economic and Clinical Health Act (HITECH) in 2009. The purpose of HIPAA/HITECH is to improve the efficiency and effectiveness of the healthcare system by standardizing and protecting the communication of health information, with particular regard to privacy, security and electronic data interchange.API Developers Touching ePHI Need to Comply with HIPAA and BAAsIn the simplest form, if your API comes into contact with ePHI then you have to adhere to HIPAA.There’s another twist for compliance — if you touch ePHI then you also need to sign legal agreements, Business Associates Agreements (BAAs) with the upstream provider of that ePHI, and downstream partners who you might share that ePHI with.The ultimate upstream providers are the US health care and health insurance providers, they produce the bulk of ePHI and are called Covered Entities. Business Associates are companies that perform work or other functions involving the use of that ePHI, on behalf of the Covered Entity or other Business Associate. BAAs between developers and their vendors/partners must be in place before ePHI is exchanged, otherwise the exchange becomes a HIPAA violation.In the majority of cases, API products will be developed outside the Covered Entity itself, so this blog post will focus exclusively on Business Associates, those API development companies creating programs/apps using ePHI from Covered Entities and/or other Business Associates.Let’s look at the illustrative example above. An API company wants to build a product that schedules doctors appointments. The API company needs to request information from a medical practice such as name, nature of visit, speciality of doctor, etc. Before ePHI is released, the holder of the patient information (the medical practice itself or an electronic healthcare records provider) needs to sign a BAA with the API company. If the scheduling company wants to utilize a product from another vendor, such as an API mapping service which shows doctor office locations, then they need to sign another BAA with that downstream partner. The original API company and its partners are now legally bound to follow both the HIPAA requirements and also those regulations outlined in the BAAs.When It’s Safe to Not Comply with HIPAAThere are four instances when you don’t have to comply with HIPAA:1. Don’t Transmit, Store or Process ePHIOne way of protecting your company from HIPAA would be to not transmit, store or process any ePHI. So you can rest easy, here are the 18 unique identifiers (Personally Identifiable Information) that HIPAA specifies become ePHI when used in conjunction with health information:  Name  Address  Dates related to an individual, like birthday or visit date  Telephone/Fax numbers  email  SSN  Medical record/Health plan/Account number  Certificate or license number  Car VIN/Plate number  Device ID  Web URL  IP address  Finger or voice print  Names of relatives  Photos of patient  Any other characteristic that could uniquely identify the individual2. School or Employment RecordsHealth information residing in employment or school records is not considered ePHI.3. Self-Collected Health DataData collected by a user that’s not shared with a physician is not considered to fall under the jurisdiction of HIPAA:  Steps counted on a FitBit  Calorie counters  Blood sugar readings  Heart rate readings4. ePHI Stripped of IdentifiersHealth data that has been stripped of the above identifiers, also called de-identified or anonymized data, cannot be used to identify an individual and thus does’t have to comply with HIPAA.There are many data providers out there who can anonymize health care data and provide insights into general population trends and value care-based programs.  If your API only uses de-identified healthcare data then it doesn’t have to comply with HIPAAKey HIPAA Regulations for API DevelopersTo comply with HIPAA, API developers need to adopt administrative, physical and technical safeguards to protect the privacy and security of ePHI. This doesn’t just stop at encrypting data at rest, in transit and in use, it also refers to how encryption keys are distributed, how PHI is referenced when being discussed by members of your team and the establishment of regular internal audits of your systems. Everyone handling health information needs to fully understand their responsibilities under HIPAA to safeguard ePHI, avoid liability and protect your company.HIPAA has 3 main regulations:  Privacy Rule: Defines the standards and requirements for the protection, use and disclosure of ePHI held or transmitted in any form including electronically and orally  Security Rule: Establishes the standards for securing ePHI when it is at rest or in transit  Breach Notification Rule: Dictates what and how notifications of breached ePHI should be made                Log All Your Events With Moesif              14 day free trial. No credit card required.              Learn More        HIPAA’s Privacy Rule Means Keeping Detailed API LogsThe Privacy Rule defines and limits circumstances in which your company may use and disclose ePHI, it establishes individuals’ rights regarding ePHI and it requires that you adopt administrative, physical, and technical safeguards to protect the privacy of ePHI. Basically, if you implement privacy best practices just like we suggested in our CCPA/FDPR blog post, then you’ll be able to comply with HIPAA’s privacy requirements.As a Business Associate you must grant to individuals the same rights that HIPAA specifies for Covered Entities. You’re only allowed to use or disclose ePHI as defined in the service agreement & BAA with your Covered Entity, or as permitted by your agreement with applicable individuals.At the outset, you must provide a Notice of Privacy Practices to individuals (a document often sourced or developed in collaboration with your Covered Entity) which outlines how ePHI may and may not be used, and what the rights and obligations individuals have with their ePHI. After that you’ll need to have protocols in place to honor the following rights:Right to Request Privacy ProtectionIf an individual requests it, you must:  Restrict using or disclosing ePHI (with a few carve outs)  Deliver ePHI to them in a secure and confidential manner  Communicate ePHI to them using alternate means  Not disclose ePHI to a health plan if the individual has already paid for the serviceRight to Logs of ePHI DisclosuresLogging API transactions is crucial to fulfill the HIPAA requirement of individuals requesting an account of the disclosures of their ePHI. If an individual requests it, you must provide an account of each ePHI disclosure including:  Date of disclosure  Name of entity or person who received ePHI  Brief description of ePHI disclosed  Brief statement of purpose of the disclosureRight to Access and Amend ePHI  This is analogous to the Right to Access/Amend/Erase as defined in GPDR and CCPA              Request      What’s To Be Provided                  Right to Access      Copy of their ePHI or form in a specific format              Right to Amend      The ability to amend ePHI              Right to Send      Transmission of a copy of ePHI to another person      Administrative RequirementsBest practices calls for adopting various administrative issues to ensure compliance:  Designate a Privacy Officer  Train employees  Implement safeguards to prevent intentional and accidental disclosures  Establish a complaint system  Sanction employees who violate your HIPAA policies and proceduresHIPAA’s Security Rule Means Encryption and MoreBusiness Associates are required to ensure the confidentiality of ePHI in transit, at rest & in use, to protect against reasonably anticipated threats or hazards to the security of ePHI, and to protect against uses or disclosures that are not permitted by the Privacy Rule.Fundamentally as an API developer, security falls into three areas: administrative, physical and technical safeguards.Many of these are generic security best practices like: keeping audit logs on what’s been assessed/changed, choosing appropriate app/computer passwords, not sharing passwords, and encrypting portable devices that contain ePHI.But some are specific to HeathTech, such as: signing off apps that contain ePHI after use, avoiding using individuals’ names, medical record numbers or account numbers in verbal communication, electronic chat or unencrypted email, promptly reporting any loss or theft of ePHI and informing the Privacy Officer of improper uses of ePHI.Technical Safeguards for HIPAA SecurityTechnical safeguards are where API developers will spend the majority of their time when building compliant apps. To ensure the confidentiality of ePHI in transit, at rest & in use, ePHI should be encrypted and special care should be taken with key distribution. There’s been much discussion on what constitutes appropriate encryption, with HIPAA merely requiring a mechanism to encrypt and decrypt e-PHI, but adopting the latest recommendations from NIST would be a safe bet.            Safeguard      What’s Required      How to Implement in an API Environment                  Access Control      Policy to allow only authorized access to ePHI      Authenticate users  Assign unique ID for each user  Devise access control rules  Implement auto logoff  Support emergency access to ePHI              Audit Control      Formal procedure that records & examines access to systems containing ePHI      Keep API logs of all accesses  Regularly run risk assessments to check proper functioning              Integrity Control      Policy that ensures ePHI is not improperly altered or destroyed      Track any modification to ePHI              Transmission Security      Technical security measures should guard against unauthorized access to ePHI in transit      Use client-side encryption when possible  Verify HTTPS is used      As an example of a completely secure API deployment that could support ePHI, the architecture below shows Moesif’s Secure Proxy enabling zero-knowledge security with on-premises client-side encryption and Bring Your Own Key (BYOK). The privacy benefits of on-premises installation are gained, without the complexity of building and scaling your own data infrastructure. The master encryption keys are never stored on Moesif servers, rather the secure proxy supports popular key stores and handles key rotation automatically. Data is encrypted when in transit, at rest and in use.HIPAA’s Breach Notification Rule Makes All Breaches PublicA breach is defined as the acquisition, access, use, or disclosure of ePHI in a manner not permitted by HIPAA’s Privacy Rule, which compromises the security or privacy of the ePHI. A breach is presumed unless you can demonstrate a low probability that the ePHI was compromised based on your risk assessment.As a Business Associate, in event of a breach, you’re required to notify the Covered Entity and their customers within 60 days.Penalties for Non-ComplianceIn 2018 there were over 63K individual breaches of ePHI, including 302 affecting 500 or more individuals, resulting in OCR imposing fines totaling $27M.HIPAA/HITECH defines a tiered penalty structure with scalable penalties based on the nature and circumstances of the violation, including knowledge and willfulness. Breaches are made public and the Business Associate can be subject to Civil and Criminal penalties.Civil penalties are in the range $100 - $50,000 per violation, depending on the level of knowledge or intent associated with violation. Employees of a Business Associate who knowingly disclose ePHI in violation of HIPAA can be fined up to to $250,000 and imprisoned for 10 years depending on level of intent behind disclosure.Health Care is Ripe for Disruption and APIs are Center StageCOVID has acted as an accelerant of existing trends, with perhaps the most profound dislocation being in health care. The new normal will enable HealthTech vertical-based APIs that, behind the scenes, simplify and automate legacy business processes.                Moesif Anonymizes Your Data              14 day free trial. No credit card required.              Learn More        ",
          "url": " /business/compliance/Building-HIPAA-Compliant-APIs/",
          "author": "Larry",
          "categories": "Business, Compliance"
        }
      
    ,
  
    
        "podcasts-api-product-management-podcast-vc-perspective-on-developer-first-companies": {
          "title": "VC Perspective on Developer-First Companies",
          "content"	 : " Ep. 8: Tyler Jewell, Managing Director at Dell Technologies CapitalMoesif’s Podcast Network: Providing actionable insights for API product managers and other API professionals.Joining us is Tyler Jewell, a Managing Director at Dell Technologies Capital. Before that he was the CEO of API Management platform company WS02. As an investor he’s placed almost $150M in DevOps companies and is a self-confessed geek in dev tools, devops, infrastructure and dev platforms.Tyler gives his perspectives on investing in developer-first companies, what single trait most successful companies share, why developer joy is important and many more.Derric Gilling, Moesif’s CEO, is your host today.Moesif · 8. VC Perspective on Developer-First CompaniesListen to the episode on SoundCloud above, download it on Apple or Google, or watch it on YouTube. Table of Contents   0:37From BEA to Dell Capital    3:17A Trillion Dollar Ecosystem    5:35Open Source Project Experience is Key    7:05Different Metrics for Cloud vs On-Prem    8:00Continuously Build Relationships    9:51Authentic Developer Experience    12:00Aim For Emotional Dev Responses    13:30Dev Tools Cycle Every 15 Years    16:50APIs Disrupt Brick & Mortars Like GameStop    20:43Plaid Wanted Out    23:13Contracts Beats Pay As You Go  Derric Gilling (Moesif): Welcome to episode eight of Moesif’s APIs over IPAs podcast network. I’m Derric Gilling your host today and the CEO of Moesif, the API analytics platform. Joining me is Tyler Jewel, the Managing Director at Dell Technology Capital. Before he was the CEO of API Management Platform company WS02. As an investor he’s placed almost $150 million in DevOps companies and is very focused on developer-first companies out here. Happy to have you here today.Tyler Jewell (Dell Capital): Good afternoon Derric.From BEA to Dell CapitalTyler is a 25 year veteran with expertise in enterprise infrastructure, operations and investment experience, and  joined Dell Capital to focus on investing in developer-first companiesDerric: Cool. Well just to kick things off, would love to hear your story from starting off as CEO of WS02 to managing director at Dell Capital.Tyler: You know, I started in the software industry about 25 years ago as an engineer and then moved into product at some large publicly traded companies, BEA and Quest Software. Along the way I got opportunities to run companies as well. You do the big company leadership thing and then you have some ideas and you go off and do some startups. I was CEO of a small media company that had been started by Ed Roman. And then eventually got bought by TechTarget, that was in 2004. And then, after my second stint a large publicly traded company, I started my own company in 2011 called CodeEnvy. CodeEnvy was a cloud IDE, cloud developer workspace, at a time when it was not really a well regarding category. We grew that business over five years, and that was acquired by Red Hat.One of the investments I had made in 2009 was WS02. I had been on the board for a number of years and that company had grown up pretty nicely, and the founders asked me to come and become their CEO. So I took over the reins and was CEO for a few years. I took that company to scale and made it profitable, with over 600 employees now around the world, it’s one of the largest open source companies by revenue. All along the way I’d been doing investments either as an angel or as a VC, been invited to participate and made about a dozen investments all in developer related businesses - Sauce Labs, WS02, CodeEnvy, InfoQ, CloudInt, APPHarbor, ZeroTurnaround and bunch of others as well. And  then Dell came knocking. They were a really well regarded VC unit, who had been looking to expand the partnership. They were looking for somebody who had expertise in Enterprise Infrastructure, had been an operator and had investment experience. And so it was just a great combination and then a super group of people, and so I decided to make the leap.A Trillion Dollar EcosystemToday there’s 900 developer-led or developer-focused companies generating revenueDerric: Super awesome to see having that operator experience, along with an investing site, even from your personal angel investing. We’ve been noticing that pushed out this new developer-lead landscape back in 2020, what was the motivation behind that and can share some of those insights?Tyler: So, I think early on in my career 20 years ago, when I was starting to work on developer tools and in and around developer businesses, the common refrain was that there’s no money to be made in in developer-owning businesses. I still think that that’s probably a common attitude that a lot of people have about it. And so, I’ve always been on this march towards getting the broader investment business and technology ecosystem to have a stronger appreciation for the value that developers, and developer based businesses, can bring to the table. And in order to do that I think that a lot of people just don’t recognize how how big that market space is. And so, as part of my just kind of daily reading routine I’ve been slowly collecting all the products, at least commercial-based products that I think fits somewhere into this landscape, that were developer led or developer influenced, and I had been building that up for over a decade. And I finally said “hey, might as well publish it”, and I bet you people would get a lot of value in seeing just the raw number of companies and products that covered this ecosystem.It turns out it’s about 1,200 commercial-based products that generate revenue in some form or fashion. Most of those are probably across 900 or so companies, all of which have raised venture capital, or you know, in some cases bootstrap themselves. And it’s generating almost across all those products together about $40 billion in annualized recurring revenues. And so, if you think about all those businesses that are either started by a developer or they make products bought by developers or heavily used by developers, just the raw influence of the developer ecosystem is really significant. It may now account for well over a trillion dollars of value out in the marketplace. So it was just an exercise to get the rest of the ecosystem to see the world, and have a positive affiliation to it, the way that I have.                Moesif: Ship Faster And With More Confidence              14 day free trial. No credit card required.              Learn More        Open Source Project Experience is KeyVenture capitalists often look for open source experience in the founding team, i.e. maintaining and growing a community in and around an open source project, since it’s comparable to having commercialization or classical go-to-market experienceDerric: Oh, over a half a trillion dollars in value that’s a quite a bit. When we look at Twilio itself that’s half a billion dollars. When it comes to looking at founders, is there anything that you notice different in terms of developer-first companies versus more traditional sales company?Tyler: Well, you know develop-led businesses are almost always started by engineers. That engineer may or may not have commercialization experience, go-to-market experience. So, you have to work a little bit more to you help them build a team that’s going to be able to balance out the strengths that they bring. Now what’s interesting is that a lot of developers that are starting businesses today, they may not have commercialization experience, but they do have open source maintenance experience. A lot of the process of maintaining and growing a community in and around an open source project, is very similar to the skills necessary to building out a commercial organization as well on that front. So, the balance is increasingly there on that front. And you know that’s kind of the biggest components of what you see.”Different Metrics for Cloud vs On-PremFocus on the business metrics pertinent to your type of business: if you’re building a cloud-based product, focus on SaaS metrics, or if you’re building an on-prem product, focus on classic ARR, quota coverage, CACDerric: Does that mean you think about acquisition and go-to-market strategy differently when you look at these different companies, or is it roughly the same metrics like ARR and sales funnel?Tyler: The metrics that are appropriate for a business depends upon the type of business that they’re building, not all businesses are the same. So if the developers are building a cloud-based business, yeah you might work with SaaS metrics on that front. But if it’s an Open Source business or Open Core business, there might be other metrics that aren’t so much SaaS metrics but related to classic ARR, quota coverage, cost of customer acquisition, things of that nature that are a little bit more oriented to that on-prem sale. Along with recognizing that both of the open sources is the top of the funnel and so there’s a whole series of DevRel, developer relationship, metrics that you’ve got to incorporate as well.Continuously Build RelationshipsIf you want your company to be successful really invest in networking: for engineering talent, to acquire early customers, or for business development opportunities, and don’t obsess over inward issues like product or hiring.Derric: When we look at the consumer world, the Hotmail hack where something’s sent from Hotmail or something’s sent from my iPhone, have you seen any go-to-market hacks that have worked in the developer landscape?Tyler: You know I don’t really think there’s anything you can do to hack. What I will say is that, regardless of whether it’s a developer-focused business or anything else in the enterprise-software space, a lot of the difference between the companies that are successful and those that aren’t, is the ability to network and hustle on behalf of the founders — whether it’s networking with amazing engineering talent, or networking to potentially acquire early customers, or networking on a business development front. Those founders who really invest into continuously building relationships have a tendency to perform better than those that obsess about just product or just obsess about hiring their people and kind of have a little bit of an inwardly view.Derric: That’s a really good point. It’s easy to get buried into product itself and think about developer experience, but people are still people, even if you’re a developer. So how you make those relationships and push forward is always important.Tyler: Yeah, I think that it’s very rare for somebody to get inbound interest for something. Innovation and interest in what you’re doing tends to come from people just knocking on a lot of doors and sharing what they’re doing, and then finding people who share the same values. And people who share the same values have a way of gravitating towards each other. But then you’ve got to nurture that and then grow that, and grow that, and next thing you know you’ve actually got a legitimate business there. But, that basic exercise is something that founders have to learn if they don’t have.Authentic Developer ExperienceProvide an authentic developer experience by transparently blending your service into their existing workflow, without forcing them to learn something newDerric: Definitely. And when you think about things like developer experience and being empathetic towards developers, how do you actually get it right. Sometimes we see companies that did just get it somehow and then there’s another company that’s not really developer first.Tyler: The thing about developer experience is that it all comes down to engagement and loyalty from the developers, and developers will give you engagement and loyalty if you give them an authentic experience. So everybody goes “okay, well what’s an authentic developer experience”, and the thing is there’s no way to measure that, it’s not scientific, it’s part art it’s part science. But, what I can tell you is that developers know right away when something isn’t authentic. I’ve tried to do my best guess at kind of describing what makes something authentic and it’s any sort of you solution that blends itself into an existing developer workflow. So if you have a developer and there’s something that they’re doing on a regular basis, while they’re writing code or another part of a task that they’ve got to do, if there’s a way for you to just transparently blend in to that and then still deliver your solution through that without forcing them to learn something else, that tends to be a great authentic experience and then that drives a lot of value.We saw that with Heroku, you know Heroku was just to Git push, so were you already using Git, so you just blended into that. We saw it with Snyk, you still do a pull request, you don’t have to change that, but Snyk started adding a lot of static analysis and upset testing capabilities. Even to a lesser degree VS Code, VS Code made the plugin model just so transparent that you don’t even have to know that you’re installing plugins, it’s just kind of starts adding these values as you start writing code because it can infer what you want from that. So those are really great examples of how engineers were really thoughtful about not not bending the existing experience too much and adding value at the same time.Aim For Emotional Dev ResponsesLook for developer joy, where developers talk about your product emotionally, as a measure of your DevEx. More contributions or downloads of your project are usually just an early indicator of good DevEx.Derric: Funnily enough, I was just going to ask how do you actually measure this developer experience and, you’re right, it’s hard to measure. Is there any high level things that you can be looking at to just get a sense or a pulse of developer experience?Tyler: Look, there’s the obvious things of which projects are getting more contributors, which projects are getting more downloads and stuff like that. I think that’s a proxy, maybe an early indicator, that there’s a great developer experience there. But, frankly, the thing that I look for is a certain amount of joy. If you spend 20 minutes with a developer talking about a certain technology, there are those things that developers talk about scientifically and then there’s other things that they talk about emotionally. You’re looking for the emotional ones.Derric: The Vim versus Emacs?Tyler: That’s a whole different discussion. They’re both authentic experiences that’s just moredeveloper geek, because there’s certain trade offs that developers are going to argue to the end of time and Emacs and Vim are the personification of those trade offs. You know, frankly, those trade offs that they advocate, can show up in distributed computing platforms, or blockchain architecture, pick a domain, it doesn’t matter. those same sort of problems materialized in other ways. I think Vim and Emacs are just an easy way for developers to get those arguments out on the table.Derric: I guess we’re seeing it in everything from REST to GraphQL out, no SQL to SQL, you always have to have a debate, right?Tyler: They always have to have a debate, yeah. Wouldn’t be fun otherwise.Dev Tools Cycle Every 15 YearsDev tools go through massive waves of bundling and unbundling every 15 years, where Git in 2005 turned everything into distributed version control, now GitLab, a highly opinionated single platform, is increasingly expanding to provide a one stop shop with tool setsDerric: So where does this take us in a couple years with all of the No-Code/Lo-Code solutions out there? Does that mean developer experience is different, is it changing, will look the same as today?Tyler: I think that we’re going to see a re-platforming of developer tools over the next decade or two for a couple of fundamental reasons, but that’s not driven by a Lo-Code. I think the Lo-Code/No-Code enthusiasm is really just solutions unlocking the ability for both developers and non-developers to build a class of applications that they couldn’t build before. If you need to actually sign and write code and go through a whole lifecycle tooling process, the application has to be something really special for you to invest all that time and energy to build it that way. So there was just over the past 30 or 40 years a whole category of applications that people really wish they could build, they just couldn’t afford to do it because it was too costly to do with a code structure on that. And you just don’t need the power and control that writing it with code is. So I think now Lo-Code systems enable you to unlock those costs of applications and as a result, it allows more people to do application development, because they don’t have to have the same skill set. I think that we’re able to see those systems because there’s now so many APIs, and those API is a pluggable and so the reusable, and so these systems can really make it easy to wire those things together.In terms of the developer experience, over the past 50 or 60 years the DevOps community has gone through massive waves of bundling and unbundling, and it takes about roughly 15 years per wave. The previous 15 years was a massive unbundling effort and the unbundling happened on a paradigm shift, and the paradigm shift here was the introduction of Git in 2005. Git turned everything into distributed version control and so really the whole tool chain has been reinvented along those concepts and we’ve gotten lots of individual silos. We’re now starting to see evidence that there’s going to be a great re-bundling of all that. GitLab is a highly opinionated single platform that really tries to offer a consistent workflow. GitHub is increasingly expanding from the left to the right to provide a one stop shop with tool sets. And even vendors like JFrog and Docker, because they are systems of record for all the dependencies that you need to work with, either they can build really tightly integrated broad reaching workflows that bundle things together. So I think we’re on this path of this massive bundling. And then we’ll go through another unbundling exercise and maybe 5 to 10 years, probably driven by edge. Where the idea of building applications on the edge is just all developers are going to have to understand a form of eventual consistency that they just don’t have to worry about in cloud-computing architectures. And I think most of the tools and systems that we have today are really not well suited for that, and so there will probably be a rethink again on that front.”APIs Disrupt Brick and Mortars Like GameStopThe new investors in GameStop are designing a set of APIs for the distribution of games, they’re digitally transforming a brick and mortar that’s losing money hand over fist into software companyDerric: Really interesting to see what happens in the next few years here. We’re already seeing quite a few different folks leverage stuff like Moesif in the edge with CloudFlare workers and other stuff like that. But it’s still just the beginning days. Speaking of APIs a little more and the unbundling of things, there’s a lot of tooling right now in the API space from a focus on developers, to security, to analytics, to the deployment of APIs. How do you see APIs and the way you work with them change over the next five years?Tyler: Well, the nice thing is that on a technical level APIs are supporting more protocols. A lot of APIs are building in realtime streaming mechanisms, to get away from just the old request/response mechanism. They’re all going to be asynchronous. This will allow APIs to perform better, there’s going to be a lot more richer data that you can extract when you’re interacting with those APIs. And that’ll enable a much richer type of consumer-based application that can be built with them. That’s one of the technical trends, something that we’re going to see.The second is that we’re seeing a lot of businesses becoming API-first businesses, which is that they’re disrupting brick-and-mortar systems and they’re doing it by building a really rich API. While we’re talking about this, something like GameStop is up 400% today. It’s all the WallStreetBets crowd. It’s really phenomenal because this company is losing money hand over fist. It’s a brick and mortar that is selling games that you don’t need to go and physically buy anymore, you can go to websites and download these things. So, by all means you know there’s this massive short experience. But all that’s happened is that there’s been new investors that have come in and said “well, you know we’re going to turn GameStop into a global one stop shopping video game digital delivery”. So it’s been digitally transformed now and it’s a software company, it’s not a brick and mortar. Sales up and and lo and behold the stock is going through the roof over the mania of that.  But I mention all that because what they’re talking about doing is designing a set of APIs for interacting with games, the distribution of games, and then they’re going to retool the business in and around that. So I think we’ll see more APIs that are just disrupting these brick-and-mortar businesses.I worry about whether startups can do a great job. A startup is unfortunately going to have to find such a niche area of APIs to deliver and they’re gonna have to deliver such a phenomenal experience that enables an app developer to just be way better. In communications, financials and content we’ve now got some mega vendors in the API space and the mega vendors have worked really hard to simplify the API and do the integrations and because they’re buying in bulk they give you relatively cheap rates. And so I think it’s going to be hard for certain types of small startups to compete in similar areas. They’re going  to have to find new areas where the API innovation hasn’t happened what.                Understand Your Audience With The Moesif API Observability Tool              14 day free trial. No credit card required.              Learn More        Derric: Does this mean like things like vertical SaaS, we’re going to have these vertical APIs or where do you see the next generation API for startups going?Tyler: I think almost all of them are gonna be some sort of vertical based APIs, because it’s that’s where the disruptions need to take places - in old, legacy business processes that haven’t been automated and that can be automated.  And the API is just a simplification, behind the scenes it’s doing the automation, but the API interface is just a simplification of the process that’s there.Derric: I’m just waiting to hear the jingle “there’s an API for that.”Tyler: Yeah, we’re probably not far for from that.Plaid Wanted OutThe public face of regulators stopping the Plaid acquisition, was a story of convenience. Frankly, the deal took so long that Plaid execs and investors wanted out, since with the growth that they’ve had, the deal was just too cheapDerric: Speaking of these API-first startups though, there was some huge news that came out last week around the Plaid acquisition and the regulatory side. How’s that going to impact the fintech APIs and the overall API ecosystem?Tyler: We’re back to the WallStreetBets scenario here. I think that the public face of what we heard, that it was a regulatory issue, was a story of convenience. Frankly, it had taken so long, six to nine months, that the $5B deal, with the growth that they’ve had, makes it just look too cheap at this point in time. So I think all things being equal, I think that the Plaid investors and the Plaid founders were looking for a way to get out of that deal. The company may very well be worth $10 to $15 billion at this point. So I think that their value hasn’t been diminished at all, I think it just reinforces how important these things are. It’s just going to keep fueling the fire on others.Derric: That’s a really good point around just how that valuation changes so quickly, for these API-first companies.Tyler: Yeah, they grew 50% or 60% and, in this day and age with multiple expansion, they probably doubled their value. So you might have had a lot of buyer’s remorse sitting around the table at the time thinking $5 billion was great, when we were going to be worth a billion dollars. But then you realize, jeez we’re actually worth 10. What do you do with that?Derric: The Plaid CEO actually released a really interesting article around building an API-first company, how sometimes it might take a little while to get things going because of the integration component, but once you are integrated it’s just exponential growth right after. Does that mean founders should be looking at different things in terms of growth as an API-first company, versus a more traditional enterprise sales company.Tyler: I think the nice thing about building an API business is that you can measure engagement really clearly. If people are using your API and they use it more often, then you’ve done something right. But if they use it and abandon it, then something’s not right, and you’ve got more work to do: either the interface is not there, they didn’t understand how to use it, or you’re not providing enough value to the integration that you’re doing behind the scenes.Derric: What if there’s no interface? What do you measure then, just an API?Tyler: You’re obviously metering the API. Your goal is to monetize that probably and so the number of applications that are making use of that, and ultimately paying you something, is a great barometer for how how effective the API is on that front.Contracts Beat Pay As You GoIn billing APIs, pay as you go is a great way to get developers started and for people to find affinity with your service, but once you reach scale you often turn it over into contractualized relationshipsDerric: That’s a really good point around metering the API. And then when it comes to billing  we’ve seen this huge trend over the last couple years towards the pay as you go model. A lot of it was driven by AWS Lambda, we’ve seen Algolia move towards it. Where do you think billing is going in the future when it comes to these API-first companies?Tyler: I think there’s a lot of talk about the pay as you go model. But, I think that at scale, when you look at the businesses who are really generating a lot of money over APIs, none of their large customers are actually paying as they go, they work in contracts. They forced them to buy a minimum amount of consumption, but also prevents burst billing and unplanned consumption and whatnot, so it kind of bounds it. And that’s in the API companies’ interest, because they want to have predictability on the revenue that they’re going to get and the buyer doesn’t want to you end up overpaying because they had some rogue developer go to town on something. So I think the pay as you go is a great way to get developers started, it’s a great way for people to find affinity for the service that you’ve got,but once you start to have a real community and it’s really starting to grow, you have a tendency to turn over a lot of those things into contractualized relationships. And that’s the natural evolution of things.Derric: That’s a really good point, because when we think about these developer-first companies, in some ways you’re selling to developers first trying to get them integrated, we still have an enterprise sales process that you have to run. It’s about two companies at the same time.Tyler: At the same time yeah. You know, you can get a lot of traction and then when it’s time for you to scale your business then you can think about doing those sorts of things that are appropriate for scaling your business.Derric:Definitely. Well cool. Really appreciate having you here today Tyler. Anything else you’d like to add, for our listeners?Tyler:No. Thank you for having me. This was great cool.Derric:Well, thank you very much.                Make Your API Platform Successful With Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /podcasts/api-product-management/Podcast-VC-Perspective-on-Developer-First-Companies/",
          "author": "Larry",
          "categories": "Podcasts, API-Product-Management"
        }
      
    ,
  
    
        "business-dashboards-how-to-show-the-business-value-of-your-apis-with-embedded-metrics": {
          "title": "How to Show the Business Value of Your APIs with Embedded Metrics",
          "content"	 : "When you’re providing APIs to your customers, you want to ensure they are getting value from them. At the same time, the best APIs are designed to be fully automated without requiring human intervention. This can leave your customers in the dark on whether your API is even being used by the organization and if you’re meeting any SLA obligations in your enterprise contracts.Types of metrics to surfaceMost API first companies have some sort of developer portal for customers to log into, manage API keys, and customize features. This area is a great way to also expose key metrics to your customers demonstrating how much value they are getting from your API. This can be as simple as a counter showing number of API transactions made within a billingperiod or provide additional metrics around what those transactions are. Each customer has different metrics they want to look at. Developers will want to look at access logs where as product and engineering leadership are more interested in usage and performance metrics. Finally, the finance department may need to look at billing usage for capacity and financial planning.Providing API LogsA quick and easy way to demonstrate your empathy towards your developers is by providing API logs. Engineers are now integrating countless APIs to ship their applications. They don’t have time to understand every response or error code that your API creates. Yet, having detailed and structured logs is super helpful for debugging integration issues. Instead of relying on your customers to instrument your integration, you can easily provide API logs directly in your developer portal. If you take this approach, ensure your access logs are useful by including the following features:  Structured data which enables developers to drill down quickly without slow search  Included context beyond request parameters such as client location or API version  Advanced filters which enable creating compound queries including body fields  Replay functionality in tools like Curl, Swagger UI, and Postman  Event Ids that can be shared with developer supportDisplaying API usageWhile business and engineering leadership may need to inspect the API logs themselves, they are keen on having the right metrics in place to make informed decisions. If they’re sponsoring a new project, they want to ensure they’re getting their money’s worth. Providing metrics on usage of your platform helps demonstrate the value business leaders receive.API calls themselves don’t show value, but they represent some sort of business transactions, whether making a payment or sending a text message to remind an end user that a delivery is present. Because of this, ensure your usage reports can show the historical number of transactions in a given period. Requirements for usage reports include:  Ability to see usage by different intervals like hour/day/month  Shows number of business transactions, even if your API can handle requests in batch  A way to break usage down by user demographicsDisplaying Performance MetricsWhile some APIs are used by only internal applications, many are integrated within customer-facing apps. If your API has performance or reliability issues, this can impact their end users. This can tarnish their reputation or cause lost revenue. If you have an obligation to meet certain SLAs (Service Level Agreements), show that to your customers. This can provide peace of mind knowing that your APIs are reliable and performant.Providing Billing DashboardsMost APIs leverage consumption based billing. If you’re providing a payments API, this may be the number of transactions per month or percentage of dollar volume. Whereas if you’re providing a messaging API, you may bill based on the number of messages sent within a billing period. Your customers will want to optimize their usage to maximize the value of your API, which means you should show where their money is being spent. Keep in mind you should have a consistent source of truth for billing, otherwise your support team will be bombarded with requests due to a billing discrepancy.Outside of billing, you should also show any rate limits or quotas that your customers are bound by. In fact, this can be an easy way to upsell to larger plans by showing how close they are to current limits. Because many business teams are unaware of or don’t log into your developer portal, you should also send out notifications letting your customers know once quotas are breached.How to get started with embedded chartsWith Moesif, you can embed API logs and usage reports in a few minutes. The integration has three parts  Create an embed template within the Moesif UI with the correct view and filters. You should also select your dynamic variable names like user_id or company_id which will be set in the second step  An API on your backend that calls the Moesif management API to get a time-limited workspace URL. This is returned to your frontend  Embed the chart using an iFrame with the generated workspace URL.A working React app using Moesif embed template is available here.To embed a chart or API log:Step 1. Create a new embed template in Moesif  Go to Events from the top menu and select the view you want to embed.  Add any filters and other chart settings that you want applied to the embedded chart.  Click on the Share button at the top right and then select the Embed Template tab.  Add any dynamic variables such as a User Id or a Company Id.  Click Get Embed Code, You will be presented with the code snippets to add to your application (also shown below).Step 2. Generates the workspace URLCreate a new endpoint that calls the Moesif API to generate workspace URLs. When you call the Moesif API, set the dynamic variables you added to your template in step 1. In this example, user_id is a dynamic field that needs to be populated with the correct value.curl -X POST -H 'Authorization: Bearer YOUR_MANAGEMENT_API_KEY' -H 'Content-Type: application/json' -i 'https://api.moesif.com/v1/portal/~/workspaces/{WORKSPACE_ID}/access_token  -d '{  "template": {    "values": {      "user_id": "1234"    }  }}'Moesif returns a workspaces URL. This URL can only access the data defined by step 1. In this example, the data is scoped touser id 1234.{  access_token: '{WORKSPACE_ACCESS_TOKEN}',  url: 'https://www.moesif.com/public/{WORKSPACE_ACCESS_TOKEN}/ws/{WORKSPACE_ID}?embed=true}Step 3. Add the iFrameAdd an iFrame to your frontend using the workspace URL generated in step 2. A user with the workspace URL can only access the data scoped based on your dynamic variables set in step 2.<iframe  id="preview-frame"  src="https://www.moesif.com/public/{WORKSPACE_ACCESS_TOKEN}/ws/{WORKSPACE_ID}?embed=true"  name="preview-frame"  frameborder="0"  noresize="noresize"></iframe>ConclusionDemonstrating the value your customers get can go a long way to making your customers successful. With Moesif, it’s easy to embed API metrics and logs into your application with just a few lines of code.",
          "url": " /business/dashboards/How-To-Show-The-Business-Value-Of-Your-APIs-With-Embedded-Metrics/",
          "author": "Derric",
          "categories": "Business, Dashboards"
        }
      
    ,
  
    
        "api-engineering-api-observability-convert-api-log-data-into-actionable-information": {
          "title": "Convert API Log Data into Actionable Information",
          "content"	 : "You’ve built an API to solve technical problems, but you know that’s just the beginning. In addition to helping developers use it, you need to understand how they use it. You want to measure its performance and popularity, and make adjustments based on what you discover.Maybe some developers are seeing a lot of errors when making API calls or it’s taking too long for them to get to the first “Hello World.” Perhaps the number of developers converting to paying customers is below your expectations. You want to understand the usage of your API and ensure that customers use your API in the long term.At this point, you might reasonably look to your API logs. You’re seeking deep insights into your API and its users, but by default what you’ll find is raw usage. How can you visualize not just the usage, but also insights that help you take action? This post will cover how you might approach this on your own, as well as how Moesif can help you move more quickly in your API gateways.Your API Logs Contain a Wealth of Actionable InformationThe amount of valuable information you can obtain from your API logs may amaze you. For example, you can use API log data to answer a multitude of questions about your API, such as:  What are the API request and response payloads being sent?  Which API version is used the most?  Where is the API experiencing higher latency?  What is the sequence of transactions leading to errors on POST /*/count/events?  Which SDKs are used to access the API the most?You can also use API log data to answer questions about the users of your API, like:  What is the top API endpoint used by each of my customers?  Who are my top customers by API usage?  Which users are scraping large amounts of data from the API?  Which customers are running into 401 unauthorized errors?  What is the API activity for a specific customer?Your API logs contain valuable information that you can use to the benefit of your customers and your business. But how do you access and analyze that log data? This is where an API management platform comes into play.The first tool many API product development teams turn to for API logging and metrics is Kibana, a useful tool for looking at open source APIs.                Track the end to end customer journey with Moesif              14 day free trial. No credit card required.              Learn More        You Probably Pipe Your Logs into KibanaIf you want to gain insights from your API log data, then you’re probably piping those logs into Kibana. If you’re not familiar, Kibana is one of the most popular, if not the most popular, tools for log visualization. It’s part of the Elastic Stack (formerly ELK Stack), which consists of three open source projects: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search and analytics engine built on Apache Lucene. Logstash is a server-side data processing pipeline. And Kibana lets you explore and visualize Elasticsearch data.Like any stack, you’ll need to put the microservices architecture pieces together. While they work well together, these are three separate tools also used apart. You’ll need to stitch together the customer journey data, from UI to API. These traces, as they’re called in Kibana terms, are important for turning data into something actionable. It’s not as simple as connecting logs and being done—you need to determine what’s needed, handle collisions, and assume the ongoing cost of growing indices.Kibana is a log visualization tool, which makes it an appropriate place to start when thinking about API analytics. However, the primary use case for Kibana is infrastructure monitoring and metrics. It’s not designed specifically for API products, so there are some drawbacks to using Kibana for API logs and metrics.Pros and Cons of Kibana for API Logs and MetricsIf you’ve built an API product, you may already use Kibana to gain insights into its performance or how developers use it. Perhaps you’re considering using Kibana because of its popularity when it comes to log visualization. Either way, it helps to know some of the pros and cons of using Kibana for API logs and analytics.Kibana ProsThe pros of Kibana include:  Open source and free to use.  Great at visualizing API logs.  You can explore massive volumes of log data.  Many useful features. Although it should be noted that some features are available separately and some are currently experimental or in beta.  Layered on top of Elasticsearch (also a con), making it ideal for use on high-cardinality, high-dimension log data- a must-have feature for API logs.And now onto the cons.Kibana ConsKibana’s cons include:  Compatible with Elasticsearch and Logstash only. If you want to use Kibana with other databases, you’re out of luck  Designed for infrastructure metrics and not specifically for API products. You have to customize Kibana for API log use cases.  To connect user behavior and API activity into a single journey is incredibly manual and prone to errors over time.You need to use the Kibana Query Language (KQL) or Lucene query syntax (Kibana legacy query language) for queries, so there is a moderate to high learning curve.  Maintaining the Elastic Stack takes a lot of effort. For example, you must periodically upgrade each part of the stack and make sure upgrades won’t break any plugins you’re using or require that you rewrite any of your visualizations.Visualization is only a starting point when it comes to gaining insights from your API logs. And while Kibana is great at visualizing logs, it often leaves you wondering, “what’s next?” What action should I take based on this data?Move Beyond Kibana and Get Actionable Insights from Your API Logs with MoesifMoesif is more than  log visualization, we  extract actionable information from your API logs and we guide you as to what to do with it. Moesif is a user behavior API analytics and monitoring service that features a fast query engine and structured event-based data. We designed our platform for API products, so we support a wide variety of API protocols including REST, GraphQL, JSON-RPC, Hypermedia (HATEOAS), and SOAP.With Moesif, you can filter and aggregate billions of API calls and user actions on pretty much any field, even high cardinality fields like a session token or user id. Capturing data from multiple API gateways and nurturing API deployment of open APIs is simplified via Moesif.The Moesif platform includes many features and capabilities that you won’t find in Kibana and Elastic Stack, such as:  High-cardinality, high-dimension API metrics that are compatible with any database, not just Elasticsearch.  Automatic analysis of REST and GraphQL APIs.  Automatic insights on query parameters and HTTP text payloads like JSON and XML.  Track API calls, user actions and behavior,traffic, and website activity.  Embeddable API logs and charts.For more details, see the complete comparison of Moesif vs. Kibana.Moesif lets you to obtain valuable information from your API logs and helps you understand what your data is telling you through custom and pre-built dashboards.Understand What Your API Log Data is Telling YouExtracting data from your API logs won’t help much if you don’t understand what that data is telling you. Both Kibana and Moesif provide custom dashboards that help you make sense of the data obtained from API logs. However, unlike Kibana, Moesif features pre-built dashboards designed specifically for API products as well as embed templates and public share links. You can use our pre-built dashboards or build your own custom dashboards to gain a better understanding of what your API log data is telling you.For example, a fintech company (and Moesif customer) created a dashboard that lets them track key API and user metrics, which includes:  Recent API errors  HTTP status requests  Product usage  Daily active users (DAU)  Most active usersThrough the dashboard, the fintech API product team sees who their customers are, how they integrated the API with applications, and what users of their API are experiencing. The team also gains insights into how well their API is performing and how many developers moved from a trial or sandbox version of the API to a production version.Don’t Miss Out on Valuable InsightsYou need to move beyond log visualizations and towards user behavior API analytics so that you can gain actionable insights from your API log data.Want to learn more about how Moesif can help you gain deep insights from your API logs?Get started building great APIs with Moesif API Analytics. Learn more.                Lower your API monitoring cost with Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /api-engineering/api-observability/Convert-API-Log-Data-into-Actionable-Information/",
          "author": "Adam",
          "categories": "API-Engineering, API-Observability"
        }
      
    ,
  
    
        "podcasts-api-product-management-podcast-on-architectural-best-practices-with-loungebuddy-vp-engineering": {
          "title": "Podcast on Architectural Best Practices with LoungeBuddy VP Engineering",
          "content"	 : " Ep. 7: Jessica Lam, ex-VP Engineering of LoungeBuddy/AmExMoesif’s Podcast Network: Providing actionable insights for API product managers and other API professionals.Joining Moesif is Jesscia Lam, currently a technical advisor and angel investor in startups, and the original Chief Architect and VP Engineering at LoungeBuddy, which was acquired by American Express. At LoungeBuddy she designed their APIs, many of which continue to be in use today.As a CTO, architect and engineering lead at multiple companies, Jessica shares her experience on how to build products to be more resilient, why error handling is so important, how to treat internal APIs vs. external APIs, and many more.Derric Gilling, Moesif’s CEO is your host today.Moesif · Architectural Best Practices with LoungeBuddyListen to the episode on SoundCloud above, download it on Apple or Google, or watch it on YouTube. Table of Contents   0:30APIs are Concise Units of Value    4:44Don’t Treat Internal APIs Differently    6:55Simplify New APIs with a Traffic Controller    9:55Build in Good Error Handling    13:40Follow the 80-20 Rule for Testing    15:53Minimize Technical Debt and Move Quickly  Derric Gilling (Moesif): Welcome to another episode from Moesif’s APIs over IPAs podcast network. I’m Derric Gilling your host today and the CEO and Moesif, the API Observability platform. Joining me is Jessica Lam who was previously the Chief Architect and VP Engineering at LoungeBuddy. They got acquired by American Express and she designed many of the APIs there, that continue to be used today. Love to hear a little bit more on that, like how did you architect it and what were some of the design decisions that went into that planning.APIs are Concise Units of ValueThink of your APIs as Concise Units of Value, providing features or tasks that others can reuse and derive value from.Jessica Lam (LoungeBuddy): Cool. So the evolution of those APIs were in my mind like a natural progression of the company, as well as the product. Initially, the product was just a mobile app, with the usual app backing behind that, there wasn’t any sort of API in addition to that. After that mobile app took off I joined at pre-seed stage. And so, as the first engineer hire, and also the chief architect, one of the first major projects that we were thinking of was how do you enable purchasing for lounges. So some context on LoungeBuddy, it was initially a lounge finder and then later on, it became a lounge management platform.At the point where we decided to add purchasing, there was sort of the idea that purchasing lounges shouldn’t conceptually be just restricted to the app. And so at that point the insight was that this is a concise unit of value that anyone can derive value from. So in the future, if we’re building a web app, or if we’re building a website for external partners, this concise unit of value can be kind of reused. And so, going back to my calculator programming days, where I offset repetitive tasks, and really thinking about what is the thing that has been repeatedly used that other people can derive value from, that’s where the API idea came from. So let’s not do this as part of the app backend, it would make more sense that we start an API service.So we thought about what is the centralized way to manage all of those things. Breaking it down into what are the essential steps for making a purchase, first we needed to check availability to see if there’s inventory for a particular lounge. And so, just looking at that sentence you realize that there are certain variables, like which lounge, so you know that would be a parameter you pass to the API. After going through availability you move on to the next thing that you’re interested in: what is the pricing on it? And then, after that, figuring out how to make the purchase. So initially, thinking about conceptually, what are the essential steps, and then just putting it out there, and iterating on it.One of my core engineering philosophies is that making something easy to change is better than trying to predict the future. So, after the purchasing API was constructed, we first started to test it using our mobile app. And so in the mobile app we used the purchasing flow which helped us work out the communication, as in: whether or not the API makes sense, whether it’s concise enough, or was it overly complex. After that’s worked out, then we started developing the website to use the same API.Derric: Really great to hear especially thinking about concise units of value. Whenever you’re looking at an API understanding how do you provide more value to your customers, whether it’s something that can be repeatable. How did you measure that? Was there a way to understand each value that each API brought to the business?Jessica: I would say that this is something that was lacking on the platform and I really wish that you guys were around when we started many, many years ago. We didn’t really have a way of measuring that. We were focused on error handling and making sure that it was robust and that people weren’t experiencing errors. We had some very preliminary surface-level metrics around how often do APIs have calls, but not necessarily anything more than that. And obviously we would know how many people made purchases through the API. But apart from those surface metrics, we didn’t really have anything else beyond that.Do Not Treat Internal APIs DifferentlyTreat every client as if they were an external client, so that if you do open up an internal API it’ll just work out the box.Derric: I guess we should have started a few years earlier. Speaking more towards your APIs, some of these were used by your mobile app and some were used by your website, how did you actually organize your internal APIs versus external facing ones? Were they the same or were they different?Jessica: That’s a really good question and my principal on that is that just because you’re internal, it doesn’t mean that you get special treatment. One of the ways which was really helpful in using that principle for developing the API, was that when we did open it up to external partners, it literally just worked. Because of the way that we were developing, where we assumed every internal application that uses the API is actually an “external partner”, we didn’t let the internal API have some special back end thing to just optimize some small thing in order to do whatever they wanted. And that’s always the case in negotiation between front end and app back end. For anyone that has worked with different developers, people would say “oh, if the API just returned all of this it would be so much easier for the front end” or something like that. So resisting the urge to have those sort of workarounds will make the API pretty clean and also the separation of concerns and responsibility very clean across the system. So I think my recommendation for developing APIs internally is to treat every client as if it were an external client.Derric: That’s a really good point, especially when we go to larger companies where you have so many different teams accessing the API, where each each team is effectively a customer. This also helps with security, in terms of making sure the same security that you apply externally, you also apply to other teams, so you don’t ever have a case where there’s maybe a vulnerability in one area of your network.                Moesif: Ship Faster And With More Confidence              14 day free trial. No credit card required.              Learn More        Simplify New APIs With a Traffic ControllerWhen standing up new API functionality you don’t want to have to refactor your app. Use a gateway-like service, a traffic controller, for access to your APIs, so that migration is simply supported through the controller.Derric: Speaking of these APIs a little bit more, did you design the APIs up front, or was it more like you start with the service and then organize it around an API? How do you think about API first?Jessica: So that’s a really good question. I’ve actually done it two different ways. For the purchasing API it was very obvious from the onset that it was something that was going to be reused across the platform, across all of our different services, and externally. But there were other things that kind of sneaked up on us. For example, there was this one core utility on the application, which is looking at lounges based on access rules and access items. An example of an access item would be your credit card, and then an access rule would be something like: if you have this credit card and you’re inbound to a particular airport then you have access to this particular lounge, otherwise if you’re outbound then you don’t have access. That was initially all inside the iOS app. So what we realized later on when we did integration with American Express, was that there was a need to be able to access that functionality. At that point, figuring out how to effectively abstract that out is an interesting process.There are people who would say “okay let’s just stop everything, completely refactor that application, take out all of those services and put it in a different service”. In my mind that’s actually a very high-risk endeavor and it doesn’t necessarily give you as much benefit as you think it would. One of the ways that typically I do this type of migration, or changing a paradigm within a system, is to have it use not really an API gateway but a service called traffic control. Essentially the expectation is that if you’re accessing any API you would use the traffic control service. Behind the traffic control service sometimes the API is implemented, but other times it’s actually rerouting to the back end of another service that might have what you’re looking for. So to actually facilitate migration, and if you want to move the code from an app backend into the actual service, you can optionally do that with traffic control.By setting the expectation that if you’re accessing APIs, use traffic control. You don’t care how traffic control gets that information as long as that contract is with traffic control. And so I think that’s also a really good way to preserve velocity, because with a startup velocity is make our break. I feel like that way of operating was really helpful - to not have to predict the future. We didn’t know that we’re going to be using those externally, but there’s a way to do it, in a very low risk way.Build in Good Error HandlingWhen Amex’s iPad app was launched an integration partner of AmEx didn’t have error handling, so when multiple 500 errors appeared, it was up to LoungeBuddy to identify and let the partner know of their issue.Derric: That’s a really good point, the ability to iterate as quick as possible, especially for early stage startups. But you also brought up an interesting point around migration. What process did you have in place to make sure you didn’t break your integration with AmEx or other customers? Was there a process in place?Jessica: So startup and process that’s kind of an oxymoron. There was a couple points of integration that we did with American Express. One was that their app used our purchase API. So right now if you log into the American Express app and then go into lounges, the QR code that’s generated at the end is from our purchase flow. The other integration was actually when the iPad was introduced to the AmEx lounges. There was the integration behind the scenes with the process flow, but also integration with their external partner. So the way that it worked was that when someone swiped the card, the card number was actually sent to their service. So there wasn’t a risk in terms of us getting certification and having all of that security stuff in place. After that the external partner returned an access item, which then we did something within our service to figure that out.We had a lot of error handling in place. The initial integration with that third party actually had a lot of 500 errors. The interesting thing is that they didn’t have error handling, so that they didn’t actually know what errors were happening. But because we had error handling, we were like “we’re getting 500 actually from you” and here are the error messages. And so for the initial launch, not sure I’m supposed to be talking about this, but it was sort of like “so on the front end of the app they will see the 500 errors”, but because their service didn’t have error handling it was up to us. There was a lot of back and forth from us and them. It’s like when this happened we’re getting an error from your service, even though the user is seeing an error on our app. And so that’s kind of the importance of having error handling and very good error handling. Anytime there is a potential for some sort of error, log it, definitely.Derric: Definitely. Have that error handling and monitoring in place, so you can ship with confidence. Without it then you’re almost flying blind. How did you actually set this up and what were some of the challenges that you ran into.Jessica: Right. I wonder how much of that has changed since I left about maybe a year or two ago. But the way that we had at that time was that we were using the Heroku backend and they had some tooling just right off the bat, that if you had over a certain percentage of errors, then you could set up an alert to track that. And so we had all that in place and the other thing that calls you, PagerDuty. We had alerting setup with PagerDuty so that over a certain percentage of errors on different services, especially the poor external facing services, someone would get a call. So, have all the error handling in place and then make sure you’re not going to get a call at 3am.Follow the 80-20 Rule for TestingInstead of aiming for 100% code coverage test the specific API calls that you’re exposing externally, like the GETs and POSTs, run Artillery for load testing and then get something out there and make it more robust over time.Derric: No definitely, we see a lot of customers using PagerDuty and so far the service is pretty good. Moesif itself has an integration with PagerDuty. When it comes to testing, did you have any best practices or process there, what were they like and what were some of the challenges for testing the APIs?Jessica: Yes, that’s a really great question. When it comes to startup and optimizing we try to go for the 80/20 rule. There are obviously theoretical best practices like “oh, we want 100% code coverage”, or “we want to make sure everything is tested”. I feel like I’m a bit of a contrarian on that. I guess my principle was that test the most important things and then, if there’s additional bugs that over time have surfaced, then you add test that way. So, doing the minimum essential thing and then looking at what kind of air you have and then build on top of that - sort of the anti-fragile approach to development.So some of the things that we did was obviously having really robust tests on the actual specific API calls themselves, like the GETs and POSTs and all of that that you’re exposing externally. And then after those are completed then sometimes, depending on how often a particular service is called on, we also use Artillery to do load testing just before things get deployed as a sanity test to make sure that things aren’t airing out randomly because of high load and things like that. Those tests are phased in after initial launch. So it goes back to what I was talking about before, on getting something out there first and then seeing what kind of errors that you’re getting back and then making it more robust over time, instead of trying to over engineer right off the bat. So when we started to see load issues, then we started to do load testing just to see how many requests per second we were getting. We ran load testing on 2x the amount just to make sure that we had enough padding there.                Understand Your Audience With The Moesif API Observability Tool              14 day free trial. No credit card required.              Learn More        Minimize Technical Debt and Move QuicklyAlways leave the campsite cleaner than how you found it: when fixing a bug or creating a new feature, you’re going to be touching existing code, and when you touch it, if something is inefficient, have the will to think through it and realign the code base.Derric: Definitely, making sure you don’t over engineer is always important for startups. In engineering leadership a lot of times you’re balancing customer needs versus engineering needs, and you have technical debt that gets built up. What are some ways that you’re able to iterate quickly and balance customer needs without overburdening your engineering team?Jessica: That’s a really good question. From all of the different startup that I’m an advisor with, or have worked on in the past, that’s always a problem. As in, things are never moving fast enough and there’s always technical debt. The way to kind of risk mitigate that sort of refactoring is to actually always leave the campsite cleaner than how you found it. For example, instead of taking a sprint or two out to refactor something, because you know it started to have technical debt, the ideal way to go about it is that in the midst of fixing a bug, or in the midst of creating a new feature, you’re going to be touching existing code. And when you touch it, if something is inefficient, have the will to think through it and realign the code base.The primary reason why technical debt is accumulated is that when you’re developing the system for the first time there’s certain business assumptions around the relationship, but over time, perhaps because of different features, those relationships might change. So even if you might notice that change on a design level, or on a feature level, the missing part is that sometimes you don’t do the work to change it on the code level. Again, this goes back to thinking about the system in a holistic way - it’s not that you have design and then you have front end, back end and system, it’s actually all the same thing. Like I said, there should be a consistent conceptual thread that runs through all of it, and design it just like a visual representation of that relationship. And so understanding that relationship deeply, and making sure that that’s reflected in code, in my opinion, is kind of the best we can do to minimize technical debt, and that’s the way to make sure that you can move quickly. If those concepts are properly represented, then to reuse them in the right way is super easy. And so there’s this ideal that when you’re not even designing for a particular method to take on a particular way of being used, it should just work, it’s kind of like a framework. This is actually one of the reasons I really love Apple’s framework - it usually just works if you’re using it in the right way. Whereas a lot of time I think developers don’t really think about the intention of the specific methods or APIs and just try to do it the way that they want to. And so I think it’s important to just also really take the time to understand and then refactor as you develop, as opposed to refactor in a vacuum.Derric: Really great insights, especially around technical debt and how to think about developing. Really great to have you here Jessica, on our podcast, and hopefully we will visit you in LA sometime soon.Jessica: Yeah, definitely.                Make Your API Platform Successful With Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /podcasts/api-product-management/Podcast-On-Architectural-Best-Practices-with-LoungeBuddy-VP-Engineering/",
          "author": "Larry",
          "categories": "Podcasts, API-Product-Management"
        }
      
    ,
  
    
        "api-product-management-docusaurus-how-to-track-developer-experience-with-docusaurus-and-moesif-api-analytics": {
          "title": "How to Track Developer Experience with Docusaurus and Moesif API Analytic",
          "content"	 : "If your product is an API, your customers are typically developers. While many developers aren’t fond of writing documentation for their own applications, they certainly appreciate well written docs for APIs they use. Well written docs can help developers ship their integrations and apps faster instead of getting buried in integration issues and errors.Good documentation can also be a powerful marketing tool as it go beyond typical reference like pages and show your developer customers the potential use cases of your platform. Developers may find your documentation from search engines when they’re looking for solutions to your customers’ most pressing problems. Aim to have your solutions rank first for SEO.Good docs are written iteratively by constantly tweaking the developer experience. This required having the right tooling to measure metrics like Time to First Hello World and pinpoint where customers struggle the most. When a customer runs into a 400 error with your API, take a look at what doc pages they were browsing. If they kept running into 400 errors without resolve, maybe those doc pages need to be updatedThe Docusaurus Plugin for Moesif API AnalyticsDocusaurus is a popular framework for writing and publishing documentation developed by Facebook’s developer team and has a variety of plugins for analytics vendors, search vendors, and more. [Moesif has a plugin for Docusaurus](https://www.moesif.com/docs/client-integration/docusaurus which enables you to track what doc pages are being browsed and track the customer journey. Moesif is designed for API products and thus can track both web activity along with monitor API traffic. This enables you to build advanced funnel reports such as time to first API call.The SDK automatically collects useful context from a user’s device including any marketing attribution, device type, and location information and stores in the user and/or company profile in Moesif. You can add additional customer properties such as user email and company domain via the identifyUser() and identifyCompany() methods.This allows you to integrate Moesif into your docusaurus.config.js with just a few lines of code.Install it with this command:$ npm i docusaurus-plugin-moesifAnd update your docusaurus.config.js like this:module.exports = {  plugins: ["docusaurus-plugin-moesif"],  themeConfig: {    moesif: {      applicationId: "Your Moesif Application Id",      // Add other Moesif options here.    },  },};This now sends all views to your docs to Moesif, so you can see which of your docs are popular.If you want to get deeper insights you can also identify which user is currently browsing which doc, and then link what they viewed to their API request.window.moesif.identifyUser("12345", {  email: "john@example.com",  firstName: "John",  lastName: "Doe",  title: "Software Engineer",  salesInfo: {    stage: "Customer",    lifetimeValue: 24000,    accountOwner: "ceo@example.com",  },});Tracking doc visits and API trafficOnce you have Moesif set up, you can leverage it to track your conversion funnel such as how many developers who viewed your docs actually converted to an active user. Then you can understand your best and worst docs by looking at each conversion rate based on what they browsed.Using analytics on your docs can also be an excellent way to determine which features potential customers are interested in. This also means you can solve two of your problems at once: delivering solutions to your existing customers and using those solutions to acquire new customers.The other way to get value from running analytics on your docs is to link up the viewing of a documentation page with your API’s behavior. If you see that a customer read a specific API doc and then later gets errors from your API because they sent the wrong request, it could be that your docs imparted the wrong information. Conversely, if a customer got error responses that vanished after reading the docs, you could conclude that the docs helped them.In order to do so, you should install a Moesif server integration. An example of how to do this for Node.js is below:1. Install the SDKnpm install --save moesif-nodejs2. Initialize the SDK// 1. Import Modulesconst express = require('express');const app = express();const moesif = require('moesif-nodejs');// 2. Set the options, the only required field is applicationIdconst moesifMiddleware = moesif({  applicationId: 'Your Moesif Application Id',  // This function should return the same id used in the above window.moesif.identifyUser  identifyUser: function (req, res) {    return req.user ? req.user.id : undefined;  },});// 3. Enable the Moesif middleware to start logging incoming API Callsapp.use(moesifMiddleware);  The user is used for the function window.moesif.identifyUser in Docusaurus should match the id used in the server integration’s identifyUser method. This ensures Moesif can stitch together correctly.Keeping track of your docs’ search terms is one of the most important issues, since you can then identify which pieces are missing from your docs and even from your API.                Lower your API monitoring cost with Moesif              14 day free trial. No credit card required.              Learn More        SummaryAPI docs are table stakes in an API SaaS company. Even a mediocre API implementation can be a pleasure to with when the docs are detailed and complete.If you run analytics on your API docs’ usage, you can get significant insight into your customers’ mindset: what do they want to do with your API, is it possible, are the docs lacking, and is your API lacking. Analytics can extract all of these things from analyzing your docs’ usage alone.With Moesif you can run analytics on your API and its docs in one place, even linking actions to requests and thus  gathering even more insights. Do your customers find the docs related to their errors, and do the docs solve their errors? With the integrated solution you can be sure to always be on top of how your customers use your API product and how you can improve it moving forward.",
          "url": " /api-product-management/docusaurus/How-to-Track-Developer-Experience-with-Docusaurus-and-Moesif-API-Analytics/",
          "author": "Kay",
          "categories": "API-Product-Management, Docusaurus"
        }
      
    ,
  
    
        "developer-marketing-behavioral-emails-an-email-marketing-campaign-that-drives-api-integration": {
          "title": "An Email Marketing Campaign that Drives API Integration",
          "content"	 : "One of the most effective marketing strategies is to send emails based on the behavior of the recipient. By triggering on how your customers interact with your product, you’re able to share content that’s actually aligned with what they’re doing and thus more likely to resonate.By using automated email workflows it’s possible to share über-relevant content at scale with large cohorts of customers. And through segmentation based on behavioral criteria, as opposed to demographic/firmographic criteria, you can move away from inflexible static drip campaigns and take account based marketing to the next level. While these strategies can be highly effective in the digital realm, don’t forget that you can also optimize an Instagram account to further complement your overall marketing efforts and engage with your audience in a more visual and interactive way. Additionally, consider leveraging a reel maker tool to create captivating video content for your Instagram account, enhancing your marketing strategy even further.Within our own customer base we’ve been using sophisticated email workflows for some time. We’ve found that those who moved through the integration funnel from the sandbox stage to production engaged with targeted email content twice as much, as those who didn’t progress. Furthermore, by optimizing content we were able to get a bump in engagement of 3x, thus improving the adoption funnel.Middle of Funnel OptimizationIn a companion blog post, How To Accelerate API Integration with Behavioral Emails and Developer Segmentation we examined how behavioral emails can drive customers at every stage of the integration funnel. In this blog post, we exclusively focus on the Middle Of Funnel (MOF) stage - where customers have signed up and made their first API request, but something’s holding them back from taking their app to production. They remain in the sandbox stage.The time from initial sign up to rolling out to production is sometimes called Time to First Working App or Time to First Paid App. API platform providers look to optimize that metric and also maximize the number of customers moved into production.Focus on Behavioral SegmentsAt the MOF stage, there’s often a lot of different stakeholders that have to be won over before an enterprise sale can be completed. Now’s the time to demonstrate the value your product brings and help remove any roadblocks that might be holding up the decision makers. When in stage of sending emails, highlight the value of your promotions, and consider using an SPF record checker or take other email security steps to increase email deliverability.Behavioral emails should provide the right content, at the right time and to the right audience to be most effective. Key audience members include Product Managers (PMs play a key role in determining features, roadmap, cross-discipline management, documentation, etc), General Council (GCs focus on legal compliance), CSO (security review), CFO (finance analysis) and CEO (strategic fit). Additionally, developers need collateral and tools to help them with integration issues, as well as support with functional & performance testing.Real World Results: Moving Apps from Sandbox to ProductionMoesif has deployed MOF email workflows to thousands of customers. By tracking the interaction of users with behavioral emails and correlating that with data from our own analytics platform, we’ve been able to accurately determine the effectiveness of our marketing efforts, as shown in the table below.            Email Area      Topic      Audience      Open Rate      Assistance in Conversion                  Feature Highlight      Dashboards      PM      ——+      ——+              Assistance      Design Review      Dev      ——+      ——+              Case Study      Driving Adoption      PM      +——      ——+              Feature Highlight      Scalability      PM      ——+      —+—              Feature Highlight      Customer Comms      PM      ——+      —+—              ROI Calc      Build vs Buy      CFO      —+—      +——              Case Study      Corp Insights      CEO      +——      +——              Security*      Data Integrity      CSO      ——+      +——              Compliance*      Dev Adoption      GC      —+—      +——      * Estimate since not enough dataWe found that the best way of demonstrating value of our API product and thus driving conversion is through case studies and feature highlights. By showing how our API platform addressed other customers’ needs and what advanced features were available, those in the integration funnel saw what they could achieved with our product and how we could solve their pain points.If we look at the universe of all customers who went through the workflow, we find that those who took their apps to production opened and interacted with emails twice as much as those who remained in the sandbox stage. Customers who developed production apps had an email open rate of 59%, approximately double that of those who remained in the sandbox. Engagement is proportional to conversion.Increase Funnel Conversion with Behavioral EmailsThe efficacy of behavioral email workflows has been shown in a real-world example. By using targeted emails in drip sequences we’ve been able to increase our conversion rate and decrease our customers’ Time to First Working App.                Save Time And Resources With Behavioral Emails Workflows              14 day free trial. No credit card required.              Learn More        ",
          "url": " /developer-marketing/behavioral-emails/An-Email-Marketing-Campaign-that-Drives-API-Integration/",
          "author": "Larry",
          "categories": "Developer-Marketing, Behavioral-Emails"
        }
      
    ,
  
    
        "technical-kong-kubernetes-how-to-add-moesif-api-analytics-and-monitoring-to-kong-ingress-controller": {
          "title": "How to add Moesif API Analytics and Monitoring to Kong Ingress Controller",
          "content"	 : "Kong is a popular open-source API gateway to help manage your APIs. With Kong, you can handle authentication, rate limiting, data transformation, among other things from a centralized location even though you have multiple microservices.Kong is built on NGINX at it’s core, one of the most popular HTTP servers. Being open-source, Kong is very easy to deploy on-premises usually in just a few minutes without requiring the installation of many components other than a Postgres or Cassandra store.Kong Ingress Controller implements authentication, transformations, and other functionalities across Kubernetes clusters with zero downtime. Kong Gateway connects Kubernetes clusters with services running across any environment or platform. By integrating with the Kubernetes Ingress Controller, Kong ties directly to the Kubernetes lifecycle. As applications are deployed and new services are created, Kong will automatically live configure itself to serve traffic to these services. Kong and the Kong Ingress Controller has a full management layer and API, live configuration of targets and upstreams, and a durable, scalable state storage using either Postgres or Cassandra that ensures every Kong instance is synced without delay or downtime.Moesif provides a plugin for Kong that makes getting started with API observability in just a few minutes so you can stay focused on shipping features customers love rather than deal with the maintenance cost of building your own data infrastructure. With Moesif, you’re able to understand how your API is used and by what customers, identify which customers are running into integration issues, and monitor for endpoints that need optimization. In this article, we’ll talk about the insights Moesif gives you, and how to integrate it with Kong Ingress Controller. Then, we will talk about how to best leverage API observability using Moesif and Kong.OverviewMoesif Kong plugin is an agent that collects metrics and sends to the Moesif collection network. This enables you to get a complete picture of your API usage even across different Kong instances and data center regions. Moesif provides deep insights for engineering teams to understand how their APIs are used and quickly troubleshoot complex issues. Because Moesif also tracks who is calling your API and how they are accessed, product-driven teams can understand the entire customer journey and where to invest more resources. With API observability, forward-thinking engineering leaders can empowers customer-facing teams with self-service analytics on activation funnels, retention reports, and more. Moesif also analyzes your API payloads for troubleshooting and business insights so you’re able to understand utilization of specific payload keys, etc.Setting up the Kong Ingress ControllerPLEASE NOTE that you need to perform this step only if you don’t have ingress controller setup and running. If you’ve already have ingress controller running, refer to the step on how to load the moesif plugin with existing kong ingress controller running.We’ll perform following steps to deploy the Ingress Controller and load the Moesif plugin.Create a ConfigMap with the Moesif plugin code:You’ll need to clone the kong-moesif-plugin and navigate to the kong/plugins directory to create a configMap usingkubectl create configmap kong-plugin-moesif --from-file=moesif -n kongPlease ensure that this is created in the same namespace as the one in which Kong is going to be installed.Create namespaceIf you’ve the namespace that you’d like to use is created, you’d skip this step. But please make sure to use the same naespace in which Kong is installed.{  "apiVersion": "v1",  "kind": "Namespace",  "metadata": {    "name": "kong",    "labels": {      "name": "kong"    }  }}Create the namespace usingkubectl apply -f namespace.jsonAdd Kong ChartYou’ll need to add the kong chart and update the repo via Helm.# Add kong chart helm repo add kong https://charts.konghq.com# Update helm repohelm repo updateDeploy the Kubernetes Ingress Controller & Load Moesif plugin:With Helm, you could load the Moesif plugin by adding the following values to your values.yaml file:# values.yamlplugins:  configMaps:  - name: kong-plugin-moesif    pluginName: moesifSetting up Kubernetes Ingress controller and loading the Moesif plugin using Helm chart is as simple as:helm install kong kong/kong --namespace kong --set ingressController.installCRDs=false --values values.yamlSet up an environment variableYou’ll set up an environment variable with the IP address at which Kong is accessible. This will be used to actually send requests into the Kubernetes cluster. You will create a tunnel for kong-proxy and get the url.If you’re deploying Kong Ingress on Amazon Elastic Kubernetes Service (EKS), you’ll need to set an environment variable usingexport PROXY_IP=$(kubectl get -o jsonpath="{.status.loadBalancer.ingress[0].hostname}" service -n kong kong-proxy)Please note that if your kong-proxy service is named differently, you’ve to change the command accordingly.Please note that if you’re deploying Kong Ingress via a different method, please refer to the docs on how to set an environment variable.You could echo the environment variable using -echo $PROXY_IP# http://192.168.99.100:32728Testing connectivity to KongThis assumes that PROXY_IP environment variable is set to contain the IP address or URL pointing to Kong. If you’ve not done so, please follow one of the deployment guides to configure this environment variable.If everything is set up correctly, making a request to Kong should return back a HTTP 404 Not Found.curl -i $PROXY_IPHTTP/1.1 404 Not FoundDate: Fri, 21 Jun 2019 17:01:07 GMTContent-Type: application/json; charset=utf-8Connection: keep-aliveContent-Length: 48Server: kong/1.1.2{"message":"no Route matched with those values"}This is expected since Kong doesn’t know how to proxy the request yet. Please note that if you’re using minikube, make sure that the services of type LoadBalancer is exposed via the minikube tunnel command. It must be run in a separate terminal window to keep the LoadBalancer running. Ctrl-C in the terminal can be used to terminate the process at which time the network routes will be cleaned up.Next, we’ll enable the Moesif plugin globally.Load Moesif Plugin with existing Kong Ingress Controller runningPLEASE NOTE that you need to perform this step only if you haven’t performed the previous step. You should perform this step when you’ve Kong Ingress controller already running and wants to enable Moesif plugin.This section assumes that you’ve kong ingress controller already running and want to load Moesif plugin. This also assumes that you’ve helm kong chart available, if not please add Helm kong chart.Create a ConfigMap with the Moesif plugin code:You’ll need to clone the kong-moesif-plugin and navigate to the kong/plugins directory to create a configMap usingkubectl create configmap kong-plugin-moesif --from-file=moesif -n kongPlease ensure that this is created in the same namespace as the one in which Kong is going to be installed.Deploy the Kubernetes Ingress Controller & Load Moesif plugin:With Helm, you could load the Moesif plugin by adding the following values to your values.yaml file:# values.yamlplugins:  configMaps:  - name: kong-plugin-moesif    pluginName: moesifRollout the deploymentYou’ll need to patch the kong ingress controller deployment with the Moesif plugin and rollout the deployment.helm upgrade kong kong/kong --namespace kong --values values.yamlNext, we’ll enable the Moesif plugin globally.Enable Moesif plugin globallyPLEASE NOTE that you need to perform this step, irrespective of which of the above step you performed. This is a critical piece which would enable Moesif plugin globally and start logging data to Moesif.To set up KongClusterPlugin resource, you will need your Moesif Application Id. You can get one by signing up for a free Moesif account, then select Kong during the onboarding. Moesif recommends using of a single application Id for all Kong instances and data-center regions. This ensures you have a unified view of your API usage data regardless of physical topology. You can still break down by any number of attributes using Moesif’s high-cardinality, high-dimension analytics engine.Moesif still recommends creating separate Application Ids for each environment such as Production, Staging, and Development to keep data isolated.Create a global-plugin.yaml fileapiVersion: configuration.konghq.com/v1kind: KongClusterPluginmetadata:  name: moesif  annotations:    kubernetes.io/ingress.class: kong  labels:    global: "true"config:  application_id: Your Moesif Application Id  debug: falseplugin: moesifand then apply the plugin globally.kubectl  apply -f global-plugin.yamlPlease note that setting the label global to “true” will apply the plugin globally in Kong, meaning it will be executed for every request that is proxied via Kong.Please ensure that this is created in the same namespace as the one in which Kong is installed. If your namespace is different from kong, you should change this command accordingly.See Moesif plugin in actionAt this point, if you already have Kong Resources (services) configured, all the api calls proxied via Kong will get logged in Moesif. You don’t have to do the next steps as that is just an example to create service/resource for someone who wants to create sample service.Set up an echo-serviceSet up an echo-service application to demonstrate how to use the Kubernetes Ingress Controller:kubectl apply -f https://bit.ly/echo-service -n kongPlease note that echo-service file is not being changed, it’s listening to port 80.Please ensure that this is created in the same namespace as the one in which Kong is installed. If your namespace is different from kong, you should change this command accordingly.Create a new Ingress resource which uses Moesif pluginYou can annotate an Ingress or Service resource to instruct Kong on when to execute the plugin. You could configure plugins globally, on Service resource, or for a specific consumer. This will create an ingress rule to proxy the echo-server created previously.echo "apiVersion: extensions/v1beta1kind: Ingressmetadata:  name: demo-example-com  annotations:    kubernetes.io/ingress.class: kongspec:  rules:  - host: example.com    http:      paths:      - path: /bar        backend:          serviceName: echo          servicePort: 80" | kubectl apply -f - -n kongPlease note that the servicePort is set to 80 because in the echo-service we created earlier, port 80 was exposed. If you’ve the service exposed at different port, please change servicePort accordingly.Please ensure that this is created in the same namespace as the one in which Kong is installed. If your namespace is different from kong, you should change this command accordingly.Send a RequestOnce you have the Moesif plugin installed, your API traffic should start showing up in the live event stream within Moesif. You could send traffic to the resource configured usingcurl -i -H "Host: example.com" $PROXY_IP/bar/sampleIf everything is set up correctly, you’d see the 200 response. Below is what sample response would look like -HTTP/1.1 200 OKContent-Type: text/plain; charset=UTF-8Transfer-Encoding: chunkedConnection: keep-aliveDate: Wed, 17 Nov 2021 21:31:04 GMTServer: echoserverX-Moesif-Transaction-Id: f8a8660e-1843-4844-89a7-67fbf2ac15aeX-Kong-Upstream-Latency: 1X-Kong-Proxy-Latency: 0Via: kong/2.5.1Hostname: echo-5fc5b5bc84-5s772Pod Information:node name:ip-192-148-47-105.us-west-2.compute.internalpod name:echo-5fc5b5bc84-5s772pod namespace:kongpod IP:192.118.36.8Server values:server_version=nginx: 1.12.2 - lua: 10010Request Information:client_address=192.368.25.12method=GETreal path=/bar/samplequery=request_version=1.1request_scheme=httprequest_uri=http://example.com:8080/bar/sampleRequest Headers:accept=*/*connection=keep-alivehost=example.comuser-agent=curl/7.68.0x-forwarded-for=192.178.50.41x-forwarded-host=example.comx-forwarded-path=/bar/samplex-forwarded-port=80x-forwarded-proto=httpx-moesif-transaction-id=f8a8660e-1843-4844-89a7-67fbf2ac15aex-real-ip=192.178.50.41Request Body:-no body in request-How to use API observabilityEngineering metricsThe first thing you’ll probably interested in is engineering metrics like how is my API performing. This type of metric can be pulled up by going to Events -> Time Series view within Moesif. Then you can select 90th percentile latency as the metric to plot. You can then group by the URI route to understand which endpoints have the worst performance. Here, you can filter your traffic by API attributes like route, verb, along with HTTP headers and body fields.Handling sensitive dataIf your application consists of sensitive data such as healthcare or financial data, you can become data compliant with one of two options:1. Zero-knowledge securityClient-side encryption has the benefits of low-maintenance SaaS while still putting you in control of your data. Because you control and encrypt the data on-premises before being sent to Moesif, Moesif physically cannot access your data. This requires only running a small appliance within your infrastructure called secure proxy to handle encryption/decryption of your data on the fly.2. Data maskingIf you don’t want client-side encryption, you can also mask data directly using the plugin. This is handled via the config.request_body_masks and config.response_body_masks configurations option. For example, you could mask a field called password that might be present in the payload. Additionally if you want to remove logging request and response body all together, you could set the config.disable_capture_request_body or config.disable_capture_response_body configuration option to true.Closing thoughtsIn this way, Moesif plugin will capture API requests and responses and log to Moesif for deep API observability and real-time monitoring of your API traffic via Kong and Kong handling all the other services around the application.                Get Deep API Observability for Kong Ingress Controller for Kubernetes              14 day free trial. No credit card required.              Learn More        ",
          "url": " /technical/kong/kubernetes/How-to-Add-Moesif-API-Analytics-and-Monitoring-to-Kong-Ingress-Controller/",
          "author": "Keyur",
          "categories": "Technical, Kong, Kubernetes"
        }
      
    ,
  
    
        "podcasts-api-product-management-podcast-on-how-to-build-an-api-first-company-with-nick-patrick-radar-ceo": {
          "title": "Podcast on How to Build an API-First Company with Nick Patrick, Radar CEO",
          "content"	 : " Ep. 6: Nick Patrick, CEO of RadarMoesif’s Podcast Network: Providing actionable insights for API product managers and other API professionals.On today’s episode we have Nick Patrick, the CEO of API-first location platform company Radar. Nick cut his teeth in PM roles at Microsoft, Foursquare and Handy, before starting Radar in 2016.As cofounder and leader of Radar, Nick shares his experience on how to fuel growth, choose your partners, ship products faster & with confidence, and many more invaluable perspectives for professionals in the API platform ecosystem.Derric Gilling, Moesif’s CEO is your host today.Moesif · 6. Nick Patrick, CEO at RadarListen to the episode on SoundCloud above, download it on Apple or Google, or watch it on YouTube. Table of Contents   0:39PMs are Mini-CEOs    2:04Startups Run at a Million MPH    3:20Fuel Growth with Partnerships    4:41Overlapping Customers & Use Cases Are Best    6:41Show ROI Inside Your Platform    8:20Use Moesif to Ship Faster with Confidence    10:19Use Consistent Names With New Endpoints    12:39Package Your API to Solve a Pain Point and Move a Metric    14:20Focus on the Economic Buyer    16:05Docs Should be Simple Yet Complete    18:30Use a Forcing Function to Keep Docs Current  20:46Do More Localization Post-COVID    22:42Find a Cofounder That Complements You  Derric Gilling (Moesif): Welcome to Episode six of Moesif’s  APIs over IPAs podcast network. I’m Derric Gilling your host today and CEO of Moesif, the API Analytics Platform.Joining me is Nick Patrick, the CEO of API-first company Radar. Nick has worked in product roles at Microsoft, Foursquare and Handy, in fact it was at Handy where he met his cofounder, Colby Berman, and they came up with the idea of an API-first location platform. Super happy to have you here today, Nick.Nick Patrick (CEO of Radar): Thanks for having me, Derric. I forgot this was APIs over IPAs, maybe I should have poured myself an IPA first, to make for a more interesting podcast. But I forgot to do that.PMs are Mini-CEOsIf you like to build things and are interesting in business, then product management is a good first step to starting your own company — PMs are CEOs of their product.Derric: No worries. We’d love to hear about your journey, starting off in product and then launching Radar. What were your drivers along the way?Nick: Yeah, I’ve always been into coding and building things. I built a computer as a kid and learned to code in middle school and got really into science in high school. So when I went to college I was a double major in biology and computer science and thought I wanted to get a Ph.D. in computational biology. Realized I hated research and just wanted to build stuff, and was also interested in business and starting a company someday.So product seemed like a good mix of technical and business. It’s sort of cliche, but you get to sort of be the mini-CEO of a product. And so, took a PM role Microsoft, went to business school, ended up working at Foursquare, which is where I met Coby, my cofounder, and also Tim our CTO, who joined a little bit later. And then went to Handy, which was kind of the earliest stage company. I saw the company go from Series A to Series C or D stage, and was it there that I got the idea of building a developer-first location platform. So my journey from always liking to build things, always into tech and coding, was just a progression. Product management seemed like a good first step, and then ultimately found my way here to Radar.Startups Run at a Million MPHA big difference in a startup versus any other company is that you have to find your right speed or level of process, and it’s often a million miles an hour.Derric: Oh really glad to hear that and especially that in the product sense you are, in a way, a mini CEO. How is it different building a developer-first platform than what you saw at Microsoft and at some of your other roles?Nick: I think it’s different in a couple ways. Microsoft is obviously a huge company that moves pretty slow. I think I joined in the third year of a three year shift cycle and this was when they were working on their CRM product, a Salesforce competitor. At the time they mostly sold it on shrink-wrapped DVDs —  Microsoft was just embracing the cloud. So I think one way that it’s changed is that I’ve gone progressively smaller and obviously when you’re a two person startup, just getting off the ground, you’re running at a million miles an hour. So I think one difference is the speed. I think building an API company and building a SAS company is very different than building a consumer company, for example. And very different from Handy, where we were building a marketplace. So I think a lot of the same principles apply about understanding your customer, building a great product and finding the right speed or level of process. But certainly there’s some things you need to build an API-first business, which which we can talk all about.Fuel Growth with PartnershipsAPI-first companies like Radar often send data to other tools, so it makes sense to integrate with those tool companies, forming partnerships that can fuel growth.Derric: Would love to drill into some of those unique challenges. What’s unique about APIs for the CEOs out there that might be thinking about a new API-developer platform?Nick: I would say the most effective growth channels at Radar were different than what our most effective growth channels were at Handy, for example. I think when you’re working at a B2C company virality, referrals and coupons are great ways to drive growth.  Obviously having a great product or service drives growth too. One thing about Radar is, as an API, we can collect data through our APIs and then send that to other tools, many of which have APIs themselves. And so, there’s lots of opportunities for integrations and partnerships. That’s actually been a key part of our growth strategy at Radar — we plug into tools like Segment, Braise, Amplitude or MixPanel. And location data is a key input into understanding your customer, so it makes us to be collecting location data and using that to power great experiences. I think the biggest learning for us was just the opportunities for integrations and a partner strategy that fueled growth.Overlapping Customers & Use Cases Are BestThe key to making partnerships work is to find companies with overlapping customers and use cases, or where the combined solution makes the developer’s life easier.Derric: Awesome. Speaking to those Segment integrations, or some of those other ones, can you walk me through where to invest in terms of new integrations, or the ones that you already have? How do you know if they’re working or not working?Nick: We think about a couple things. One is we obviously like to partner with somebody when there’s overlapping use cases, there’s overlap in target customers and buyer personas. And so, the main consumer is say a product manager that wants to power a product experience, or a developer that wants to build a best-in-class tech stack for collecting customer data and activating it, or a marketer that wants to activate on this data and do location-based targeting or trigger campaigns. So, I think we look at where is their overlapping customers and use cases, where Radar can add value. In the same way that the platform that we’re partnering with is adding value.The other way that we think about it is that we obviously want to make it as easy as possible for developers and startups and enterprises to build great location-based experiences — to collect location data and do it in the right way. And so we think about integrations that just make spinning up those experiences, make collecting and acting on that data, easier. If we can save somebody needing to set up a Webhook themselves, send some data manually to another tool or forward data to a data warehouse and put in work to crunch all that data, by just replacing that with a turnkey integration where you copy and paste an API key, that’s tremendously valuable for our customers and helps them get to value faster. So where there’s overlapping use cases and target customers, and then where do we just see opportunities to make it easier to get started collecting location data and building these types of experiences.Show ROI Inside Your PlatformOne of the problems with API platforms is that customers may recognize and measure the value your product brings only outside of your platform. Try to bring product activation details and ROI visibility inside of your platform, so you can see what they’re using and show what they’re getting.Derric: Oh, definitely. You know, the easier it is for someone to adopt a new API platform, the better it is for your customers, and really for anyone involved. On the flip side, we also hear that customer success can be a challenge with developer platforms. How do you scale that out, especially if you keep growing a developer community, and ensure that everyone gets the right help that they need without overburdening your internal teams?Nick: It’s a great question. You know, one of the challenges of having all these integrations where you’re sending data outside of Radar is oftentimes the activation, or where the value is being recognize and measured outside of Radar. And so we’re thinking about ways that we can help you activate your data and sort of understand your ROI inside of Radar as well. We look a lot at is API usage growing over time, are you expanding your usage of the platform over time. Maybe that’s uploading more geo fences, maybe that’s collecting more location data, if you’re using our place detection offering maybe that’s turning on new chains or categories, or maybe it’s creating new Webhooks or turning on new integrations. And depending on how the product is priced, you can potentially charge more and grow those contracts over time. So that’s kind of how we think about it. And we think about ways that we can obviously deliver value and clear ROI with the platform, help our customers measure it, have visibility into it inside of Radar and then just continue to build new experiences based on location and consume more parts of the platform over time.Use Moesif to Ship Faster with ConfidenceWhen introducing new API endpoints you want to have visibility into how they’re being used and hoe they’re performing: what programs are using them, what’s their response time, whether they’re returning 200s, 400s or 500s, etc. In a time when product release speed is really important, Moesif can help you ship faster and with more confidence.Derric: Definitely. And speaking to visibility and analytics, super happy to have Radar as a Moesif customer. Love to hear what your current use cases for Moesif are and where you have found most success?Nick: We started as a geo-fencing platform and we realized an opportunity, as I said before, to expand the different use cases and building blocks that are our platform offered. An existing customer that were using Radar for geo-fencing, for example, maybe they’re now sending a push notification when a customer walks into a store or you open the app inside of a store and they’re using geo-fences to show an in-store mode. Maybe you’re showing loyalty or scan-and-pay features when you’re in store and shopping features when you’re sitting at home on your couch. But they also said, “Hey we’re paying Google Maps an arm and a leg for geo-coding or autocomplete or places search, can you just let us search our geo-fences or maybe power store locator or power an address autocomplete use case.” So we launched geo-coding APIs, we launched search APIs, we launched routing APIs, so that we can tell you not just “hey you’re 600 meters away from this geo-fence”, but  also “hey you’re four minutes walking from the geo-fence” as well. And as we spun up all these new  APIs we needed to very quickly instrument them and have visibility into them. For our earliest customers we wanted to know how are they using them, what parameters are they passing, what are their response times, are we returning a lot of 200s or are there a lot for 400s or even 500s, and Moesif is a great tool for us to basically spin up all these API endpoints quickly, give them to customers, but also do so with confidence knowing that we had visibility into how they were using them and how they were performing. That’s been super useful and just helped us ship faster, and move faster this year, in a year when speed was really important.                Moesif: Ship Faster and with More Confidence              14 day free trial. No credit card required.              Learn More        Use Consistent Names with New EndpointsWhen developing new APIs or endpoints make sure that they are consistent with your existing parameter and entity names. Also check if new endpoints could be spun up as infrastructure to make your existing offering better.Derric: Sounds like you’ve been shipping a lot of new APIs and endpoints over the last year. Can you walk me through any processes or things that you’ve found to make that delivery of a new API easier and make sure that that’s consistent with the rest of your platform?Nick: A couple things come to mind. One is Radar is, even before we launched these APIs, kind of collecting and storing a bunch of location data, might be: lat longs, might be the locations of devices or users’ geo-fences, we have a bunch of out-of-the-box POI data, address data, or admin boundary data. So one of the things we want to be really thoughtful about is how do these APIs fit with our existing APIs and what parameters are called, what entities are called, are we making sure that we’re naming things consistently. If you’re using Radar for geo-fencing, is it easy to get started with the geo-fence search API, for example, or the place to search API, or do things sort of feel different and disconnected. So we tried to be really thoughtful about what stuff was called and how everything fits together.I think the other thing that we thought about was that there were things that could be shipped or productized as an API that could also be spun up as infrastructure to make our geo-fencing offering better. For example, you can offer a geo-coding API to turn an address into a lat long, that’s maybe powering address autocomplete or store locator. We can also use that to make it easier to import a geo fence — maybe you can give us an address for the center pin of the geo fence instead of a lat long. And so we thought a lot about how we can spin up some of this infrastructure to power these APIs, but also to make our existing product better and kill two birds with one stone.There were other kind of tactical things, like how is this exposed in the SDKs, should we ship this as a beta first or just kind of open it up to everyone. When do we feel comfortable opening up to everyone, is there a quality bar, or some scalability milestone that we need to reach first. So all these considerations went into making sure these are easy for our customers to use and also scalable and smart for us from an operational perspective as well.Solve a Pain Point and Move a MetricYour API should solve a pain point, that moves a metric. So be clear about what you’re offering and what primitives are exposed to developers, but also how it’s packaged so that it’s really compelling and easy to understand for the business buyer.Derric: When it comes to these different building blocks, how do you actually service that to your customers to say “hey, not only can you do this, we can also do this, this and this”?Nick: If you’re a developer you kind of just want to know what are the API endpoints that are there, what functions can I call on your SDK, so on and so forth. Whereas, if you’re a business leader, for example, maybe you’re a Chief Digital Officer at a big retailer or you’re a Chief Information Officer at a restaurant chain or QSR, you care about the building blocks and obviously you want to pick the best tool for your technical team to implement this and move quickly. We also want to know how the building blocks fit together. In a way, you want to buy a solution that solves a pain point, that moves a metric. So the way we square that is we say, “let’s be very thoughtful and clear about what our building blocks are, what primitives are we exposing to developers, but also how are they packaged up in really compelling and easy to understand ways for the business buyer, the economic buyer, the executive that’s buying this tool. And this is actually something that we’re going to be spending a lot of time on next year. How do we package up our use cases and package up all the things that we can solve in a way that feels as simple and navigable, but that’s also really powerful and compelling. And that’s how we think about squaring the two.                Understand Your Audience With the Moesif API Observability Tool              14 day free trial. No credit card required.              Learn More        Focus on the Economic BuyerSplit up messaging based on buyer personas: for engineer decision makers it’s a straight line from self-serve, bottoms-up, DevEx-optimizing onboarding to sale, whereas if the economic buyer is a business leader then tell a compelling story about solving business problems.Derric: And this brings up a really interesting point around developer platforms. We hear this challenge a lot which is, you are always talking to different audiences at the same time, you’ve got the developer, then as you mentioned, you have the economic buyer. What are some tips for CEOs of developer platforms to think about that duality?Nick: I think if the economic buyer is an engineer, who is also the technical leader, there’s a little bit more of a straight line from self-serve, bottoms-up strategy and everything kind of works together. It doesn’t necessarily mean that the blog posts that a fortune 500 CTO or CIO wants to consume are the same as individual developer hackathons or something like that. But there’s more alignment there. Whereas for us, I think what we found over time is that at least with respect to our geo-fencing offering, it’s not to say that the economic buyer is always non technical, that’s definitely not true, but we just sort of split the two and say “hey, let’s be really thoughtful about developer experience and make sure that technical folks are using the product, or evaluating the product, or integrating the product, have a great experience. But, we’re also speaking to the business layer, the economic buyer, as well. And at the end of the day, if the economic buyer is a business leader that means you need to tell a compelling story there and maybe your growth strategy should be focused on that and not necessarily on developer signups coming from HackerNews or some channel like that. That’s okay if that’s what works for you.Docs Should be Simple Yet CompleteThorough docs can be great, but they can also feel overwhelming sometimes, and so you need to strike a balance of simplicity and brevity with completeness and thoroughness.Derric: When it comes to developer experience, how do you actually organize that and set that up as a process? Is there a single owner that owns developer experience, is that more of a way of thinking across Radar itself? Sometimes we hear it’s part of the product team, sometimes part of developer relations, it’s such an ambiguous role.Nick: Right now at Radar our product team is the primary owner. But, I think you’re right. What do your landing pages look like, are they speaking to technical personas — that’s a marketing thing. Do you have a presence at hackathons, for example — maybe that’s a developer advocacy or developer evangelism thing. Right now the product team primarily owns it.We think about a couple different things. We think about developer signups, obviously. We also think about activations, once you sign up are you making your first API call. Are you continuing to make API calls, which is an indication that you’ve gone to production or integrated this into your app. It’s also NPS. Once you start using the product is it great and delivering value and easy to use and you want to tell your friends? And we also think about net revenue retention, like I said. Are you expanding your usage and calling different APIs over time? So I think our primary focuses is on making the integration as simple as possible, importing geo-fences, installing the SDK — how do you make that as simple as possible? And it really comes down to documentation, onboarding and is the dashboard easy to use or navigate. And a balance that we’re working on striking is that thorough docs can be great, but they can also feel overwhelming sometimes, and so how do you strike that balance of simplicity and brevity with completeness and thoroughness. That’s that’s really how we think about it and who owns it today.And also, I would say, we built out a really great engineering team. We just started to build out our product team and, even though as a product person, I think our product team is better product people than I was and that’s kind of fun. And so transitioning some of this off of my plate and and onto our product teams plate is a focus as well.Use a Forcing Function to Keep Docs CurrentImplement a forcing function to keep docs up to date. For example, instigate a policy of only answering customer emails when you’re able to refer them to a doc, landing page, case study or guide.Derric: Referring to the developer documentation that you have, how to keep it up to date? A big challenge, we hear all the time, is that it just gets out of date or the wrong endpoints are there, or there’s misspellings. And who owns it?Nick: Historically, I’ve owned it, actually. And you know, I was talking to a developer growth leader at Twilio and I think in a lot of API-first companies the founders own it for a while. I think you raise a good point, which is we’ve talked about consistency and quality, and you know this is true of anything, not just maintaining docs, but if you have five people touching something or 10 people touching something instead of one, it’s easier for stuff to get out of sorts or get disjointed. That being said, I have a lot of other stuff to do, so it’s easy for me to be a bottleneck. So what we’re working towards is a system where anybody, and really in practice it’s our customer success team, solutions engineering team, and engineers and product teams as they ship new features, can input and say this is missing, or this should be added. We group it and prioritize it. In the future we want to get to a place where anybody can propose an edit to the docs and there’s some sort of approval or review process.I spoke to a very early product person at Segment, and it sounds like this is actually true, but they imposed a rule pretty early on where you couldn’t answer a customer question unless you send them a link to the docs. And so it sort of forced you to add it to the docs, or refactor the docs as appropriate, before you went back to somebody. And there’s definitely been customer questions or troubleshooting or whatever, where I’ve answered the same question like 50 different times over the last three years, I probably should have added this to the docs. So I think some forcing functions like that can be can be helpful as well.Derric: That’s a really good point. We’ve been actually following that quite heavily, where there has to be a link to some landing page, whether it’s docs or a case study or anything else, rather than just copy and paste that snippet of email.Nick: That’s great.Do More Localization Post-COVIDIn 2020 the physical world has become more varied than ever before. Taylor your product experience and messaging to take into account the different situations on the ground.Derric: What’s next for APIs? There’s a lot of new API-first companies coming online, especially with COVID, where do you see the future going?Nick: Yeah, I think we’re still just getting started. We look at Twilio’s acquisition of Segment as something that’s really exciting. You know, we were part of a marketing campaign with Segment called The Platform of Independents, where the thesis or the argument was that the best customer experiences are powered by best-in-class platforms, best-in-class APIs tightly integrated together, as opposed to monolithic clunky platforms. Obviously we see location data as a critical input to that. There’s all sorts of different inputs to understanding your customer and building great experiences, but we see location data as a critical input and it’s just not being leveraged enough yet.2020 was certainly an interesting year. If you think about it, the physical world is kind of more varied from place to place than ever before and how we interact with it is more critical than ever before. So thinking about product experiences that you’re delivering for your customers, or you think about messaging that you’re sending to your customers, the situation on the ground is pretty different from state to state, city to city, country to country, and not enough folks are taking advantage yet.So, I think we’re going to continue to see integrations and synergies that help you do more with your data and understand your customers, understand your users better. And I mentioned integration and partner strategy was very important for us, we’re really excited about the partnerships we have in place today but thinking about ways to do more, to go deeper, to expand those partnerships, to just make it even easier for folks to collect location data in a thoughtful, privacy sensitive way and then build product experiences off of that.Find a Cofounder That Complements YouIt’s difficult to start a company by yourself. You need to be able to build your API product and also sell it. Make sure you have the DNA to do both.Derric: So my last question is really around new grads and other folks who want to move into a product management role in an API team. Any tips for them or in terms of reading material or what they should be looking at?Nick: You know what one thing that we kind of keep coming back to as a product team on our side is (obviously we have amazing product folks and amazing engineers), they’re all used to using amazing developer tools and they know where the bar is — you’ve used a Stripe, you’ve used a Twilio. We should be holding ourselves to the same standard and know what that standard is. And to really understand how developers work, how they operate and what they need to be successful. Or their non-technical counterparts that are consuming the experiences with the datasets you have built off of those building blocks. You have to use the tools and you have to know where the bar is and hold yourself to that same standard. So I think developing a first-hand knowledge of the great developer tools (there are patterns you can follow) is super important. I think that’s true if you’re a product manager.I think if you’re talking about being a founder, one thing that I would recommend is I worked on a couple of failed startup ideas before Radar and I did it as a solo founder. It’s really hard, some say it can’t be done. Unless you get lucky and your open source library goes goes viral, which is possible, you’ve got to be able to sell and distribute as well. And so, find a partner or a cofounder thatcomplements you. I was the technical cofounder, product and engineering, and my cofounder Coby, who I met at Foursquare, is in sales and BD. He’s really good at just grinding, booking meetings, selling SaaS, and I’ve learned a ton from him, and that gives him energy. The product and engineering and kind of business stuff gives me energy. And so find a cofounder that compliments you so that you can understand what it takes to build a great developer tool and actually build it, but also distribute it and sell it. At least for me personally, being a PM, that was the way that I built a network and made these connections that made it possible for me to start Radar a couple of years later.Derric: That’s a really good point, you have to both build and sell. If you build only, then that’s a full company, and if you only sell but don’t build, that’s not a full company either.Nick: Sometimes you build it and they will come, but I would say more often than not, that’s not true. So you gotta figure it out as well.Derric: Well. Cool. Well thank you very much Nick for being on our podcast today and I look forward to seeing what next year looks like for Radar.Nick: Thanks, Derric. I think it’s about time for an IPA. So I will, as soon as this call ends.Derric: Thanks. Have a good one and have a happy holiday.Nick: Right. Likewise.”                Make Your API Platform Successful with Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /podcasts/api-product-management/Podcast-On-How-To-Build-An-API-First-Company-With-Nick-Patrick-Radar-CEO/",
          "author": "Larry",
          "categories": "Podcasts, API-Product-Management"
        }
      
    ,
  
    
        "podcasts-api-product-management-podcast-with-charles-miller-documentation": {
          "title": "Podcast with Charles Miller on Documentation Best Practices",
          "content"	 : " Ep. 5: Charles Miller, documentation strategist.Moesif’s Podcast Network: Providing actionable insights for API product managers and other API professionals.To help you fend off documentation from being the Achilles heel of your API product, we have a thirty-year veteran of technical writing on our podcast today. Charles Miller is currently the lead content strategist for APIs at Medidata Solutions, a Dassault Systems company.Charles fills us in on how to create outstanding technical documentation, what is the plain language principle and why it’s so important, what standards and guidelines you should follow, when to use the active voice and many more.Derric Gilling, Moesif’s CEO, is your host.Moesif · 5. Charles Miller, Documentation StrategistListen to the episode on SoundCloud above, download it on Apple or Google, or watch it on YouTube.Derric Gilling (Moesif): Welcome to another episode from Moesif’s API’s over IPAs podcast network. I’m Derric Gilling, your host today, and the CEO of Moesif, the API Observability platform.Joining me is Charles Miller, the lead content strategist for APIs at Medidata Solutions, a Dassault Systems company. Charles is a multiple award-winning technical writer and leads document creation for APIs at his company. Really happy to have you here today.Charles Miller (Medidata Solutions): Thank you Derric.Derric: Definitely. So just getting right into things, I’m curious what’s your path to writing documentation for APIs, and as a follow up to that, how did you transition from hanging out with Allen Ginsberg to writing award-winning technical documentation?Charles: Well, that incident was definitely in the past. I hung out with them once, so don’t want to give necessarily a wrong impression. But, you know, it was fun. You know it was a long road that led me from writing the Great American Epic poem, if you will, to realizing that I needed to meet my family’s needs and not just my own.So my first step was to find a job where I could meld my interest in writing and technology. And so I had to go back to school for technical writing and editing classes. After school, I spent a lot of time at the beginning of my technical communication career working as a consultant. Actually for about 10 years I was a consultant on and off. And as many people know who’ve done consultancy, that can be pretty stressful. Where you have a job that maybe goes on for a year or maybe half a year, and then it ends and then you have to look for further work. But I was lucky, I had pretty steady work. So even though it was stressful, a little bit of digging taught me many avenues to learn different approaches to documentation, the experience taught me how to collaborate with many different audiences and kept me up to speed on the technologies. So every job I went to I was able to accumulate new knowledge about technology, and fortunately it was all new technology to me anyway. So, by the way, I didn’t create the Great American Epic poem, but I was able to have some of my Poems published in two anthologies.Derric: So speaking about your approach and all the knowledge that you’ve accumulated over the years, what is the number one takeaway for any of our listeners on how to create outstanding technical documentation?Charles: Well, as everyone knows, I’m sure, it’s all about time. Time is our enemy and sometimes perhaps our friend. But often it’s a deadline crunch. So the more you have of it, the better your text will be, in my experience. Your time to investigate and understand the context of the information that you’re presenting and that you’re writing about. You have time to do interviews, and you also have time to write, rewrite and test your documentation. The main test, of course, is always to get documentation in front of the users, so you can gauge its usability. Then if you can, you can perform user surveys and usability testing to gain user input feedback.Derric: And then when it comes to the changes in API documentation, I know a lot has happened in terms of OpenAPI spec and Swagger and such, how do like auto generation and how do the new technologies play in the major advancements for documentation?Charles: Well, having been in the business for about 30 years, I can say there’s been huge amounts of change. As people will probably have heard, or maybe even some know, it’s evolved from manually creating technical content in Microsoft Word, to now using content management systems for online consumers. We use Confluence for our major means of deployment. I’ve actually created documentation in HTML, with the text editor, and then uploaded it to the portal via file transfer protocol. That shows you how far back I go. Moving to a content management system like Confluence was major, because it helped us move towards more automated ways to generate documentation, as you said. And then that was consistent with format and brand. We can actually create a consistent message across all of our services, all of our products, because we’re creating a single brand. But, it also allows us to impose and adhere to our writing standards, as well as our programming standards. So, that’s kind of a little bit of history where I’ve been.Derric: And then can you tell me more where it’s going? What’s next for API documentation?Charles: I think that what’s next is digital assistance, with AI and the ability to do natural language processing and natural language understanding. I think we’ll begin to see systems (I’ve already seen some at conferences), which are able to take a user’s question and come back with an answer. The answer today might be pretty-much canned, because the system has been set up for it. But in the far, far future, we’re going to be talking about having AI digital assistants skating the database, getting the information and being able to come to semantic conclusions, and presenting answers based on that analysis. I should be able to do analyses of the information and get conclusions based on what it’s been able to identify and scan.Derric: Sounds like documentation is a lot more than just the text on an HTML site. So what does documentation encompass?Charles: Sometimes it’s getting out what a system or computer would need to know. So there’s computer-readable documentation, then there’s human-readable documentation. So documentation from my end, even though we do some computer-readable documentation, most of it is human-readable. I have to be able to create text and information that is understandable and usable. Users need to be able to understand the information almost automatically.They shouldn’t have to scan and parse information to figure out what it means. And then usable — if they’re working on a task they should be able to take that information and finish the task that they have set up, or that they have in front of them.Derric: In terms of making it usable, is there a set of guidelines that you use, or follow, in terms of starting a new project?Charles: Well, I’m fortunate to work with an organization and in a department where we’ve defined writing standards, and writing standards are key. There are the industry standards: there’s the API manual style, there’s the Chicago manual style, there’s the Microsoft manual style and Apple manual style. So those are good places to start. Anyone coming into the field should obviously be aware of those and know those almost like the back of your hand. But then an organization should take those and develop its own standards. So that’s what really begins the process. And then there are standards on top of that, such as the plain language principles, plain language standards, which really emphasize usability and understandability. So, we incorporate those plain language standards and principles into our own standards. That’s a good place to start.Of course, you can extend that into usability testing, where some companies have the luxury of doing that. It does take time to do, but it’s invaluable being able to get user feedback. And of course, you’re always opening up your documentation to comments, so users can provide comments about how useful the information is and how usable it is. And then user surveys as well and take that input and modify your information as needed.Derric: One thing we’ve seen when it comes to documentation is sometimes you have so many different product owners or product teams, your documentation might not be consistent in tone across the whole company. And as you’re speaking to standards, what are some ways to make sure that you have a consistent tone across the entire company?Charles: Yeah, that is challenging. Certainly you can’t go wrong having those consistent standards. But often you’ll have, for example, business writing standards and we have our technical writing standards. Sometimes people think the business writing standards are different than the technical writing standards. I tend to disagree. I’ve taught business writing and I’ve also taught technical writing. I don’t think they’re that different, but you will find some organizations and perhaps departments that don’t agree. So you have to find the baseline which is again, for me, it’s plain language. Use the plain language principles and stick to those principles where you’re talking about making the information usable, informative and communicate a sense of friendliness, a sense of transparency that you’re giving them the information as clearly and succinctly as you can. I think that in the bigger picture that will begin to migrate to other departments as well. You have to create a consistency within the organization, so your internal documentation should be the same, or should try to emulate what you’re doing for your external audiences. You know it’s a challenge. I’m not going to say it’s not, but standards are definitely the beginning.Derric: Does this mean there’s like a central team that owns documentation, like a developer experience team? Or should it be up to the various product owners themselves?Charles: Well, we have a user experience department that maintains their own standards. We work very closely with them and we try to correlate our standards with theirs. And then we have our own department, which is  technical communication services, which is devoted strictly to providing the technical information to our users. So it’s user-facing documentation, and I work with within that department. It’s a partnership: you have a partnership with the developers, you have a partnership with product management, you have a partnership with testing, and each one of those should be able to provide input into the documentation. And, as you say, sometimes maintaining the tone with all those voices coming in is difficult. But, it’s not something that you can’t overcome — it’s a challenge, it’s an obstacle, but maintaining your standards, writing to those principles where you use active voice (for me active voice is crucial), that will allow your tone to come across as often being very direct and transparent.Derric: That’s really good to know the active voice gets your message across a lot better than the passive one. When it comes to keeping these docs up to date, sometimes it could be an afterthought. How do you keep them up to date — you’ve got old screenshots, API specs that are out of date, maybe the version doesn’t exist anymore. And tooling has gone a long ways in terms of stuff like OpenAPI and Swagger, but still there’s a lot of docs out there that are out of date. Do you have any best practices for that?Charles: Well, the best practices that we use, for API documentation specifically, we use OpenAPI. So that, like you said, really does help us to maintain the documentation, because nothing’s going to change unless the engineers change it and they’re the ones responsible for OpenAPI. We, as technical writers, act as technical editors making sure that the writing, limited to places that there are within the OpenAPI spec, is consistent with our writing standards. So, it’s always a matter of working with your partners. It’s a matter of making sure that you have partners who are willing to create really, really good documentation that communicates with end users.Derric: And how do you make sure that you’re speaking to the right audience? Sometimes you have developers that are more novice, who are looking for just a Getting Started Guide, and you have other developers that are super experienced and they want to get right to the point and get started? There’s so many different types of developers and flavors of developers out there. Is there any way to structure documentation or practices on that side?Charles: Yeah, we structured our API portal for example, and I’m going to stick strictly the API documentation for the moment, we structured it around, like you said, being able to provide information to our various audiences. We had newbies (or new users), developers, and really, really experienced developers. We also have managers coming in to look at the documentation saying “Hey, what is this? What is this API going to do? How come I should be interested in this? What am I going to get out of it?”. Testers need to be able to read it and understand it.So, for example, in the Getting Started Guide, getting started is something where we provide enough information for all developer levels to get started immediately. So we provide things like authentication, authorization information, how to register your API app, so developers can get up and running as quickly as possible. And then of course we provide code snippets. We provide samples of making API calls. I think OpenAPI itself, the specification, is really good about that because it provides the baseline information almost any developer would require to make an API call, to make a request. So, that has also really helped us to be able to reach all of those levels of users that we have.Then understanding your audience, in terms of how they process information, is important — not all developers process information the same way. So we have some developers look at it from the really big global view, and want to have an entire comprehension of the system as a whole, so they can work their way down to the bottom level and  piece everything together and have a clear view of what they’re doing, for whatever reason. Some developers just want to get started right away. In fact, most of your developers are those who want to get started right away and have a specific task at hand. They want to be just able to accomplish that task, get it done and then move onto their next task. So we do provide things like workflows, we call them recipes: how to make a certain call within a specific instance or context — like many developers have to perform certain tasks within our platform. So we identify those tasks and then we provide the steps with links to the OpenAPI documentation which they can get the details from, for making that call to accomplish a series of tasks in order to do, let’s say for example, gets a username or something like that, that’s very basic. But we have more complex tasks that they might perform, where still each one of those steps in accomplishing that task links to a specific type of documentation within the specification.Derric: Definitely, we’ve seen a lot more of playbooks and recipes, we totally think that’s a great way to improve the developer experience. In fact, I think Postman’s doing a lot of stuff with Collections nowadays, so you can start off pretty quickly. Anything else that technical writers and developer experience experts should be doing in terms of assisting those developers getting started with your platform?Charles: Well, we would like to begin to get a playground where users can actually make calls inside the documentation itself. OpenAPI does provide that, so we’re working towards that as well. We want to begin implementing a forum so that developers, either customers or internal developers, can ask questions and receive almost realtime answers to. And then we can collect that information and keep it in a database, so that your later users can come in and question the database and receive an answer back. As we move towards digital assistance that type of database will be really, really useful.Derric: Do you have some best practices on how to start a forum, especially if you’re brand new and you don’t have enough of a community. How do you make sure it doesn’t look like it’s empty.Charles: Well, since we haven’t developed one, that’s going to be a real question. But like everyone else we’re going to go to Stack Overflow and then steal from them. Just kidding. We have a lot of really good developers who are interested in the customer’s point of view. So I think that we could easily populate a forum once we get it up and running with questions that users will want to know, and will need to know, even. And, in fact, we’re working on that now. We’re moving forward with this idea of recipes of workflows. Identifying those standardized tests, objectives and goals, that most users would need to do to work within our system, and to navigate throughout the platform.Derric: What about the questions that the developer doesn’t know they need to ask? We talked previously around forums where they have a specific question in mind, or when they’re getting started, but what about things like a new feature that they didn’t know existed, or a new use case? How do you think about documentation from that perspective?Charles: You know that’s a really interesting question and it’s something that I addressed in my presentation to API:World, which was about context — you have to understand the context. So, a writer has to understand how to provide the information in such a way that a user is going to come away from reading your documentation and be able to ask new questions and come up with their own answers. So, that’s certainly not something that’s easy to do, but it has to be done at least in a more mature user documentation setting. You have to be able to understand the platform, in our case it’s a platform. You have to understand the product, and you have to able to provide that type of information that those users will most obviously, most always, ask. So that they can make use of your products.Derric: So sounds like having context is really important to know what the person is looking for. Does this mean like static documentation is on its way out and are we moving towards more personalized or, dynamic, docs? What will that look like in the future?Charles: I think dynamic docs is definitely it, but the context is always going to be there. To me, some people might think of it as reference information, as I presented in API:World. The different types of documentation that developers find include reference information, recipes, code snippets, but this reference information is overview information and I’m not sure that digital assistants could do that at the start. That information has to be provided by someone who understands how all of this fits together and how all of it works. Which is really an encapsulation about what context does and what context doesn’t mean. So there would have to be a human being at the beginning writing all of this, and providing it in a format that a digital assistant could somehow scan through and formulate answers to. So to answer your question, I think written documentation is going to have to at least be there until, well I mean it’s just going to have to be there even for digital assistants. That’s just how I see it.Derric: Oh, definitely. I mean, you’ve got to have your API reference somewhere, right? I mean, you can’t just have it all through voice or digital assistants.Charles: Unless they develop AI systems which can begin to actually create information, create context, and that’s something I don’t see happening for a while. I mean 50 years maybe, but who knows, natural language understanding is pretty impressive.Derric: It will be interesting to see what Intercom and Drip does when it comes to documentation and helping with those digital assistants. Any other tools that you recommend for our listeners besides using OpenAPI and that type of stuff?Charles: Well, we use Confluence. It’s a really powerful tool. It provides us the ability to guide users and to categorize information. It provides the writers templates that they can use to begin generating documentation. So, for example, we can provide for our developers templates that they can use to begin writing their own recipes. At this point it’s me as a technical writer working with the developers, working with product management, with testers perhaps, to come up with the steps that are required to accomplish a specific goal. What we’re looking at now is to create a template that the developers can take and begin to fill in the steps that are required. And then I, as a writer, would go back through it and make sure that it obviously follows some writing standards and make sure that it fills in all of the holes for what we’re talking about with audiences — “well, wait a minute, maybe this level of audience needs to know this”, “what’s the answer to that question”, or maybe “another audience type needs to answer that question”. Those are the types of things that we call curating information, so we, as technical content specialists, would not be the generator of information, but we would be the curator. We would take the information and make sure that abides by the standards that we’ve set up and then also fills in everything that the customers would need to know.Derric: Totally makes sense. It sounds like that’s a good way to have the standard and have a good process in terms of developers filling in all the information. But you already have the standard of what it should look like, and look and feel like, right?Charles: Oh, absolutely. And that’s what Confluence is. It gives us a chance to brand our information and to make it consistent across all of our products.Derric: Where do other formats like audio, video fit in? We see a lot of folks trying to add different things like Gifs and stuff inside the documentation. Is that important too, or do you think that’s just like a fad?Charles: I definitely don’t think it’s a fad. I think it’s the future. The future is going to be where you have digital assistants and then that’s going to be melded with either Gifs and/or videos that provide information based on the user’s needs and their context — what context are you working on, what kind of information can I provide that will answer your questions. So video is going to be very important. And then getting it down to the Gif level, where you can go through a tiny piece of a process, will be really, really important. We do have videos currently in our end-user documentation. We do have plans providing videos around integration and how to integrate different products with our product using APIs. So that’s going to be important and I see that as something which is part of the future. Videos are very labor intensive, at least now. We have to get to the level to take away so much of the labor intensity and maybe put it into the hands of developers. There are some of our developers who are really adept at creating videos, as are our testing team. So somehow being able to single source that information, from let’s say testing, and being able to get it out to our external audiences, would be an interesting way to go. But, we have to fit our standards, you can’t just put anything out. To be professional, it has to look professional, has to be branded, etc.Derric: Makes sense. And then my last question is if you look at all the different companies out there doing some great stuff with documentation, what’s your favorite and what makes it such a great company in terms of documentation?Charles: Well, my favorite is still probably GitHub. I think it’s so comprehensive and they really do get into the nitty gritty, they answer the questions everyone should know. Some say “everyone should know this, so we won’t document it”, but you can’t take that attitude, even though that’s an attitude that we often still encounter. But GitHub takes the baseline approach — what would a person who’s never worked in our platform need to know to get up and running and to perform specific tasks. And so that’s why I like their documentation. They’ve really thought through all the ramifications of their information, and they’ve also thought through what their different audiences require to make use of their platform and their product.Derric: Definitely agree. I like what Github has done with their changelog stuff. I think it’s pretty cool to see all the updates to the GitHub platform over the years.Charles: Well, the last time I checked, and I haven’t checked in a while, but we based our changelog on what they do. I mean, it’s very stripped down, very minimalistic — we change this, we added this, we deprecated that. And then we give links, of course, to any additions in the resources that were changed. Our customers require detailed information and our release notes are pretty extensive for our end-user documentation. It even gets worse because we work in a regulated industry, we have to provide as detailed information as we can, and then our customers are very fastidious about how much information they want. You know, they’re very sophisticated. So we do provide all of that information, we have templates setup. But that’s, again, our end-user information for API documentation. We’ve taken the standard of providing what’s been added, what’s been changed, what’s been deprecated, and then we give maybe a line or two, a sentence or two, of what the change included.Derric: Funnily enough, we actually based our change log off of Github also, they do just such a good job, like you mentioned, getting right to the point and not adding too much fluff to it.Well, thank you very much Charles. I appreciate hosting you here on the Moesif podcast. Anything else that you want to add before we end?Charles: Thank you Derric. Thanks for giving me the opportunity to spend time with you and to talk about something I love very much, which is documentation.Derric: Well, definitely. Documentation is always super important to help developers get started and make sure they’re successful with your platform.Charles: Always. We’re customer focused and we try to stay customer focused.Derric: Well, thank you very much Charles. Have a good one.Charles: Thank you Derric. You too.                Make Your API Platform Successful with Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /podcasts/api-product-management/Podcast-With-Charles-Miller-Documentation/",
          "author": "Larry",
          "categories": "Podcasts, API-Product-Management"
        }
      
    ,
  
    
        "developer-marketing-behavioral-emails-using-sendgrid-with-moesif-api-analytics-to-send-behavioral-emails": {
          "title": "Using Sendgrid with Moesif API Analytics to Send Behavioral Emails",
          "content"	 : "In this guide you’ll learn how to send Moesif behavioral emails with Sendgrid.Moesif behavioral emails is a feature that automatically sends emails to customers based on their API usage. This can be used to notify customers about technical issues, such as hitting rate limits, using deprecated APIs, or broken integrations. You can even use it to trigger business-related events such as when an item is shipped. If something can be mapped to an API call, then you can send an email from it.In a companion article, we covered how to configure behavioral emails within your Moesif dashboard.Sendgrid is a managed email service. They offer an API to send emails to your users and host SMTP servers for you.PrerequisitesThis how-to guide requires a Moesif account and a Sendgrid account.Setting up an Email ServerThe first step in setting up the email server is getting the SMTP credentials from Sendgrid.For this, you have to log into the Sendgrid dashboard.The following screenshot illustrates how you navigate to the credentials.This will start a guide that will lead you through the Sendgrid API key creation, give you the SMTP credentials, and also test that everything works as expected.The API key needs a name to be created. The generated API key will be your SMTP password.These credentials have to be entered into the Moesif email server configuration form.To navigate to the form, follow the steps in the next screenshot.You can copy and paste the credentials from the Sendgrid site to the Moesif site.If you have problems with the SSL port, you can used port 587.The Moesif form looks like this:                Track the end to end customer journey with Moesif              14 day free trial. No credit card required.              Learn More        Testing the SetupYou can then send an example email via the freshly configured SMTP server with Moesif.After you’ve configured the mailserver, you have to create a new email template:For the new template, you have to fill out the required fields: template name, subject line and from email address.Press verify integration in the Sendgrid console and Sendgrid will wait for you to send a test email.After that click the “Test” button in the Moesif site and enter the target email.The Sendgrid console will then confirm that the SMTP connection worked.SummarySetting up Moesif behavioral emails with Sendgrid only takes a few minutes.Using a service like Sendgrid alleviates many common problems that might occur when sending email, such as validation issues and email being incorrectly identified as spam.By combining Moesif’s behavioral email service with Sendgrid, you’re able to keep your API consumers abreast of important issues.                Make Your API Platform Successful with Moesif              14 day free trial. No credit card required.              Learn More        ",
          "url": " /developer-marketing/behavioral-emails/Using-Sendgrid-with-Moesif-API-Analytics-to-Send-Behavioral-Emails/",
          "author": "Kay",
          "categories": "Developer-Marketing, Behavioral-Emails"
        }
      
    ,
  
    
        "technical-envoy-how-to-log-api-traffic-from-envoy-proxy-and-monitor-metrics-with-moesif": {
          "title": "How to Log API Traffic from Envoy Proxy and Monitor Metrics with Moesif",
          "content"	 : "Envoy is a high-performance C++ distributed proxy designed for microservices and service-oriented architecture, as well as a scalable communication bus and “universal data plane” designed for large  scale service meshes. Envoy runs alongside every application and abstracts the network by providing common features in a platform-agnostic manner. When all service traffic in an infrastructure flows via an Envoy mesh, it becomes easy to centralize cross-cutting concerns like observability, security, in addition to adding substrate features in a single place. Envoy, supports a static configuration model, also allows configuration via gRPC/protobuf APIs simplifying management at scale. Envoy also has a variety of filters to add support for gRPC, rate limiting, shadowing, canary routing, and API observability. API observability is a crucial part of the API lifecycle, ensuring that APIs are monitored and optimized throughout their development and deployment stages.API observability is a new trend that is an evolution of legacy monitoring that empowers business and engineering teams with the ability to answer arbitrary questions. Traditional monitoring can answer known unknowns like Errors Per Minute or status like Red, Yellow Green, because monitoring works by probing for an already known metric and thus you would add performance counters or other instrumentation to monitor that metric. API observability on the other hand enables you to answer unknown unknowns whicharise from complex engineering and business challenges.                Monitor and Analyze APIs with Moesif              14 day free trial. No credit card required.              Try for Free        Moesif provides a plugin for Envoy that makes getting started with API observability take just a few minutes so you can stay focused on shipping features customers love rather than deal with the maintenance cost of building your own data infrastructure. In this article, we’ll talk about the insights Moesif gives you, and how to integrate it into your Envoy proxy. Then, we will talk about how to best leverage API observability using Moesif and Envoy.Introduction to API Logging and MonitoringAPI logging and monitoring are essential components of modern software infrastructure, playing a critical role in ensuring the performance, security, and reliability of Application Programming Interfaces (APIs). In this section, we will delve into the world of API logging and monitoring, exploring their importance, benefits, and key concepts.What is API logging and why is it important?API logging refers to the process of capturing and recording API interactions, including requests, responses, and errors. This information is crucial for troubleshooting, debugging, and optimizing API performance. API logging provides a detailed audit trail of API interactions, enabling developers and system administrators to identify security breaches, analyze user behavior, and make informed decisions about infrastructure scaling.API logging is important because it:      Provides insights into API performance, aiding in debugging and optimization        Ensures adherence to security standards and compliance regulations        Offers a comprehensive audit trail of API interactions, crucial for identifying security breaches and analyzing user behavior        Enables informed decisions about infrastructure scaling and resource allocation  By maintaining detailed API logs, organizations can ensure their APIs are running efficiently, securely, and in compliance with relevant standards.Benefits of monitoring API performance and analyticsMonitoring API performance and analytics is vital for ensuring the reliability, security, and efficiency of APIs. The benefits of API monitoring include:      Improved API performance and responsiveness        Enhanced security and compliance        Increased visibility into API usage and analytics        Better decision-making through data-driven insights        Reduced downtime and improved overall reliability  By monitoring API performance and analytics, organizations can identify potential issues before they escalate, optimize API performance, and ensure a smooth and reliable user experience. This proactive approach to API monitoring helps maintain high standards of service and user satisfaction.Envoy BackgroundIf you’re a microservice practitioner concerned about networking and observability issues when moving to a distributed architecture for your organization’s API portfolio, your most likely solution is including a service proxy platform in your tech stack. In today’s cloud-centric world, business logic is commonly distributed into microservices. Each service has other consumers that depends on that service, whether other teams, partners, or even revenue-generating customers. While most of these are RESTful APIs, they can also be SOAP, gRPC, GraphQL, Thrift, or other protocols.” (Technically gRPC still uses http/2 under the hood, but not like our typical JSON/RESTful APIs). Managing and observing L7 is crucial to any cloud application, since a large part of application semantics and resiliency are dependent on L7 traffic.Among the proxies available HAProxy, NGINX, and Envoy are reliable, proven proxies, with Envoy being the latest inclusion to the list. While HAProxy is a very reliable, fast, and proven proxy, there are concerns with the minimum set of features available for microservices. NGINX was designed initially as a web server, and over time has evolved to support additional use cases like proxy servers and API gateways. NGINX is a high-performance web server that does support hitless reloads but NGINX open source has a number of limitations, including limited observability and health checks. Envoy was designed from the ground up for microservices, with features such as hitless reloads (called hot restart), observability, resilience, and advanced load balancing. Envoy also embraced distributed architectures, adopting eventual consistency as a core design principle and exposing dynamic APIs for configuration. Monitoring API endpoints is essential for maintaining optimal performance and quickly addressing any issues that may impact end-users.Understanding API LogsAPI logs are a critical component of API logging and monitoring. In this section, we will explore the structure and components of API logs, providing a deeper understanding of their importance and relevance.Structure and components of API logsAPI logs typically consist of several key components, including:      Timestamp: The exact date and time when the API request was made or an event occurred        HTTP Method and Endpoint: The HTTP method (GET, POST, PUT, DELETE, etc.) and the specific endpoint that was accessed        Request and Response Details: The headers, query parameters, and body of the request, as well as the response sent by the server        Client Information: The IP address of the client making the request and, in some cases, additional details like the device type or browser used        Latency: The time taken to process each request, essential for performance monitoringA well-structured API log provides a holistic view of API interactions, combining these components to offer a detailed narrative of each request and response. By analyzing API logs, organizations can gain valuable insights into API performance, security, and usage, enabling informed decision-making and optimization. This comprehensive understanding of API logs is crucial for maintaining the health and efficiency of your API ecosystem.  How to set up Envoy API monitoringWith the Moesif Envoy filter, your API traffic is logged to Moesif for analytics and reporting. Moesif provides deep insights for engineering teams to understand how their APIs are used and quickly troubleshoot complex issues. Because Moesif also tracks who is calling your API and how they are accessed, product-driven teams can understand the entire customer journey and where to invest more resources. With API observability, forward-thinking engineering leaders can empowers customer-facing teams with self-service analytics on activation funnels, retention reports, and more. Moesif also analyzes your API payloads for troubleshooting and business insights so you’re able to understand utilization of specific payload keys, etc. Effective log management is crucial for collecting, analyzing, and visualizing API logs to enhance troubleshooting and performance monitoring.Envoy exposes various APIs that lets you dynamically configure the proxy. By configuring a Listener which allows Envoy to listen to network traffic at configurable address, you can enable the flow of traffic through the proxy, and enhance the data flow using several Filters. The API for HTTP level filters allows the filters to operate without knowledge of the underlying protocol.Moesif uses the HTTP Lua filter to capture API request/response for API analytics and monitoring. When Envoy loads the script in the configuration, it looks for two global functions that the script defines: envoy_on_request and envoy_on_response. During the request path, Envoy will run envoy_on_request passing a handle to the request API, while during the response path, Envoy will run envoy_on_response passing handle to the response API.Moesif captures request method, headers, and body information when envoy_on_request function is called and response status code, headers, and body when envoy_on_response function is called for every request. It adds that event to the queue and periodically flush the queue to send the events to Moesif. Moesif ensures that no latency is added by batching events even during high traffic.Add the Moesif Envoy pluginTo add the Moesif plugin for Envoy, you will need your Moesif Application Id. You can get one by signing up for a free Moesif account, then select Envoy during the onboarding. Moesif recommends using of a single application Id for all Envoy proxy instances and data-center regions. This ensures you have a unified view of your API usage data regardless of physical topology. You can still break down by any number of attributes using Moesif’s high-cardinality, high-dimension analytics engine.Moesif still recommends creating separate Application Ids for each environment such as Production, Staging, and Development to keep data isolated.The envoy.yaml file should look something like this: http_filters:    - name: envoy.filters.http.lua    typed_config:        "@type": type.googleapis.com/envoy.extensions.filters.http.lua.v3.Lua        inline_code: |        local log = require("moesif.plugins.log")        -- Moesif configs        log.set_application_id("Your Moesif Application Id")        function envoy_on_request(request_handle)            -- Log Event Request to Moesif            log.log_request(request_handle)        end        function envoy_on_response(response_handle)            -- Log Event Response to Moesif            log.log_response(response_handle)        endOnce you have the Moesif Envoy plugin installed, your API traffic should start showing up in the live event stream within Moesif.More information on how to configure your Envoy proxy to capture traffic and logs to Moesif.How to use API observabilityEngineering metricsThe first thing you’ll probably be interested in is metrics related to your API’s performance. This type of metric can be pulled up by going to Events -> Time Series view within Moesif. Then you can select 90th percentile latency as the metric to plot. You can then group by the URI route to understand which endpoints have the worst performance. Here, you can filter your traffic by API attributes like route, verb, along with HTTP headers and body fields.Business metricsTo associate API calls to individual customers, configure Envoy with the set_user_id_header() or set_company_id_header() config option.Once done, you can then store additional customer attributes like Company Domain or User Email via Moesif’s user tracking SDK. This provides clarity for customer-facing teams to understand who is using your APIs along with how they are using them.Moesif supports analyzing common payload content-types including JSON and XML. As an example, you can group API calls by response.body.label and then group by Company Domain as shown in the below chart. This shows which customers received a bad experience due to running into Out of Stock issues even though no 500 error was thrown.Funnel reportsThere are a variety of actions a customer can take outside of your API such as within your UI. To track this, you can add moesif-browser-js to track user actions. User actions are goals that a customer did within your UI such as Signed In or Purchased Plan. Once done, you can track your end-to-end customer journey from both the UI to the API. A user-friendly interface in monitoring tools can significantly enhance the ease of tracking and analyzing user actions.In the above report, we created a funnel analysis composing of three steps.  The first step is a customer signing into your web app.  Moving from the first to second step shows the drop off for customers who actually made their first payment transaction through your platform. The time it takes to get to this second step is called “Time to First Hello World” or TTFHW.  Moving from the second to third step shows the percentage of user who end up making over 100 payment transactions. This could be your “aha” moment or when someone receives full value out of your API.Handling sensitive dataIf your application consists of sensitive data such as healthcare or financial data, you can become data compliant with one of two options:1. Zero-knowledge securityClient-side encryption has the benefits of low-maintenance SaaS while still putting you in control of your data. Because you control and encrypt the data on-premises before being sent to Moesif, Moesif physically cannot access your data. This requires only running a small appliance within your infrastructure called secure proxy to handle encryption/decryption of your data on the fly.2. Data maskingIf you don’t want client-side encryption, you can also mask data directly using the plugin. This is handled via the set_request_body_masks() and set_response_body_masks() configurations option. For example, you could mask a field called password that might be present in the payload. Additionally if you want to remove logging request and response body all together, you could set the set_disable_capture_request_body() or set_disable_capture_response_body() configuration option to false.Closing thoughtsWith the Moesif Envoy plugin, your engineering and business teams are empowered with deep API observability without the high cost of building and maintaining your own homegrown tooling. Monitoring APIs is essential for maintaining high standards of service and ensuring a smooth user experience.To see the Envoy integration with Moesif in action, you can git clone and run this example appfrom GitHub                Deep API Observability with Moesif              14 day free trial. No credit card required.              Try for Free                    ×                                                                            Design and Build Better APIs!            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          "url": " /technical/envoy/How-to-Log-API-Traffic-from-Envoy-Proxy-and-Monitor-Metrics-with-Moesif/",
          "author": "Keyur",
          "categories": "Technical, Envoy"
        }
      
    ,
  
    
        "podcasts-api-product-management-podcast-with-mike-amundsen": {
          "title": "Podcast with Mike Amundsen",
          "content"	 : " Ep. 4: Mike Amundsen, author and speakerMoesif’s Podcast Network: Providing actionable insights for API product managers and other API professionals.Joining us this week we have the well known author and speaker Mike Amundsen. He’s a prolific writer on all things APIs and recently released his latest book entitled Design and Build Great Web APIs: Robust, Reliable, and Resilient. When he’s not writing, Mike helps companies capitalize on opportunities in APIs, Microservices, and Digital Transformation.Mike shares his perspectives on why organizations think about APIs in three levels, how AWS’s Werner Vogel does deprecation, what the future holds for API automation tools, and many more knowledgeable insights .Derric Gilling, Moesif’s CEO is your host today.Moesif · Mike Amundsen, author and speakerListen to the episode on SoundCloud above, download it on Apple or Google, or watch it on YouTube. Table of Contents   0:32What Roy Fielding's PhD solves    3:00API First Equals KYC    5:15How Best to Build API First    8:16How to Construct your API Team    12:25What to Focus on in your MVP    14:36Reduce Custom Builds with APIs    16:52What Does Low Code No Code Bring    19:00Is API Automation Ready    21:20What Metrics Should I Measure    25:48Observability Versus Monitoring    29:56Designing for the Unpredictable    35:54Internal APIs Often Lack Resiliency    39:04Deprecating APIs with Werner Vogel    46:09COVID Sped Up API Automation  Derric Gilling (Moesif): Welcome to another episode from Moesif’s APIs over IPAs podcast network. I’m Derric Gilling, your host today and the CEO of Moesif, the API Observability Platform.Joining me is Mike Amundsen, well known author and speaker. He started the API Academy and was one of the first to be in APIs 20 years ago. He currently consults with companies to help them capitalize on APIs, microservices and digital transformation opportunities. Happy to have you here today”Mike Amundsen (Author & Speaker): It’s great to be with you Derric. It’s great to talk with you.What Roy Fieldings PhD SolvesRoy Fielding’s PhD dissertation from 2000 is most well known for introducing REST. But, it also helps solve scaling and customization problems when building SaaS products.Derric: Awesome. You’ve been doing APIs for quite some time since discovering Roy Fielding’s PhD dissertation  introducing REST. What’s the biggest changes in the last 20 years and how did you get into APIs.Mike: Well, there’s a lot packed in the first question. I should say that actually I didn’t by myself start the API Academy. I want to make sure that I give a shout out to Matt McLarty and Ronnie Mitra. The three of us were the initial sort of founders of that group, and they had a lot to do with its initial design and success. So I want to shout out to them. The Academy’s just one of the steps along the way that I’ve experienced in this whole API world, and a reference to some of the earliest stuff that I had done.I had been working on creating systems that were based on the web. I had been doing it beforehand using Microsoft tools, using SOAP services and remote procedures and all these other things. Things were working great, things were going fine, but I was having scaling problems. I was literally working on an early Software as a Service type implementation, but just wasn’t able to scale it the way I wanted. So I was looking around and searching desperately to find something that could work and I stumbled on Fielding’s dissertation. He had written it in 2000. He initially had written an article and showed some stuff to Microsoft in 1998, but I didn’t come to it until about 2003 or 2004. It helped me solve my immediate problems, which had to do with scale at first, and then eventually had to do with customization. So the way I found Fielding was out of desperation