What is the Difference between API Observability vs API Monitoring
The traditional definition of API monitoring has been around for years from companies like SmartBear, APIMetrics, and Runscope and is useful to check API correctness and performance. An API monitoring tool initiates API calls against your chosen endpoints and then records the response received. Additional checks can be added such as creating a Slack alert on a 500 error or timeout. The test can be started either on a trigger (such as when a new version is deployed by your CI/CD pipeline) or on a recurring schedule (such as every 1 minute). API monitoring tests can be simple, such as basic health and uptime checks, or more elaborate—running through a long sequence of API calls and checking against specific status codes, body fields, and more.
API monitoring and observability are essential for understanding how application programming interfaces function and perform. While API monitoring enables you to check for uptime and take action if your API times out or responds with 500 errors, it is limited by its blackbox nature—tests are predefined and don’t offer visibility into real-world usage patterns. This means monitoring can’t answer ad hoc questions about how your API behaves in production or how specific users interact with it. Tools like Moesif captures real customer API traffic, offering deep insights into usage trends, behavioral patterns, and issues as they occur. As more APIs are exposed to the internet, there are new requirements to explore and uncover unknown unknowns for preventing API threats, troubleshooting customer issues, and understanding API adoption. These needs have driven the rise of API observability.
Unlike blackbox testing and monitoring, API observability is a form of whitebox monitoring that requires an agent or SDK to passively log API traffic to an observability service. This data collection can be done within the application or at a different point, such as with an API gateway like Kong or NGINX. Moesif integrates seamlessly at various layers to collect rich telemetry, enabling product and engineering teams to analyze everything from latency issues to user-specific API behaviors without additional instrumentation.
Understanding API Observability
Definition of API Observability
API observability is the practice of gaining deep insights into the behavior and performance of APIs. It involves collecting, analyzing, and visualizing relevant data to understand how APIs are performing in production environments. Unlike traditional monitoring, which focuses on predefined metrics and alerts, API observability provides a comprehensive view of the entire API ecosystem. This includes tracking API requests, response times, error rates, and usage patterns to ensure the reliability and performance of APIs in complex software systems. By leveraging API observability, organizations can proactively identify and resolve issues, optimize performance, and make data-driven decisions to enhance their API program.
Uses cases for API Observability
Monitor API performance and customer experience
API observability can track every interaction a user or customer has with your API which enables you to profile different end-to-end flows and understand where hot spots may exist. Most teams providing APIs don’t have full control over how their consumers will exercise the API. Those consumers could be from another department or product with a large organization or a different company all together. As developers, we like to assume a customer will use our products in a specific way but API integration work can be messy or buggy. For example, you may have API consumers who ignore your recommendations on pagination and filtering causing an excessive amount of data to be polled. The goal of API observability is to proactively discover these unknown unknowns and remedy them whether it’s something wrong with your own code or the API consumer’s code.
Troubleshoot API errors and issues
Because an API observability tool logs everything happening over the API, you’re able to explore unknown unknowns such as an issue a customer may be having that you’ve never seen before. You don’t need to create a test ahead of time to observe what happened. There are a variety of API issues that pop up, some are easier to investigate such as missing an API key or HTTP header, but others can be far more complex that requires tracking the history of API calls and their timing. Real-time call tracing helps identify and resolve API errors effectively.
Detect and protect from API threats
A well designed API Observability platform not only tracks what is happening over the API, but also who is making those API requests which enables sophisticated User Behavior Analytics (UBA) and User and Entity Behavior Analytics (UEBA). By tracking complex user flows, you can identify suspicious users who may be scraping your API or accessing unauthorized data.
Unlike a web application which can incorporate technology like Capctha and browser fingerprinting, an API exposes raw access to your data. Some APIs may even have customers accessing programmatically which makes it extremely hard to distinguish real API calls from malicious ones. This makes traditional rule-based Web Application Firewalls (WAF) like ModSecurity useless for APIs. Instead, modern API observability solutions that take a user-centric approach can detect unknown unknowns using advanced anomaly detection.
Understand customer API usage
Organizations build APIs to create something of value for the customers or users of the API, not just for the sake of building things. Business-facing teams often overlook API endpoints in their strategies, focusing instead on customer adoption and sales opportunities. Like API security, this requires a mechanism to attribute API calls to individual customers and their revenue and is another use case for User Behavior Analytics (UBA) for APIs. Common teams looking for API analytics include the product org, but also sales and growth teams to track pilot usage, trials, and expansion opportunities. It was only in 2015 that Twilio was valued at $500 million. Now it’s a $30 billion company and a staggering 83% of all HTTP traffic comes from API calls according to Akamai. APIs are no longer just a “backend technology,” but a key business driver.
Yet, the API calls only tell half the story (engagement), a good API observability tool also provides the ability to store customer demographics and revenue (for monetization and segmentation) along with tracking what happens outside of the API such as sign ups and plan purchases.
Real-world Examples of API Observability
API observability offers numerous benefits, including enhanced reliability, performance, and security. For instance, an e-commerce company can use API observability to monitor the performance of its checkout API. By analyzing API traffic and identifying bottlenecks, the company can optimize the checkout process, reducing cart abandonment rates and improving customer satisfaction. Similarly, a social media platform can use API observability to monitor the performance of its authentication API. By tracking API requests and response times, the platform can ensure that users can log in seamlessly, providing a smooth user experience. These real-world examples demonstrate how API observability can drive significant improvements in API performance and user satisfaction.
API Monitoring vs API Observability
Key Differences between API Monitoring and API Observability
API monitoring and API observability are related but distinct concepts, each with its own focus and approach. API monitoring is primarily concerned with real-time tracking of key metrics to ensure API functionality. It involves setting up alerts for specific thresholds, such as response times or error rates, and checking for anomalies. For example, if an API endpoint returns a 500 error or experiences a timeout, an alert is triggered, allowing teams to respond quickly.
On the other hand, API observability provides a holistic view of API performance in production. It involves gathering data from various sources, such as application logs, network traffic, and user requests, to gain deep insights into API behavior. Unlike API monitoring, which focuses on detecting and responding to issues in real-time, API observability aims to understand the underlying causes of performance issues and optimize the overall API program.
In summary, API monitoring is a subset of API observability. While both share some similarities, they have distinct goals and approaches. API monitoring focuses on real-time detection and response, ensuring that APIs are functioning correctly. In contrast, API observability provides a comprehensive understanding of API behavior and performance, enabling organizations to optimize performance, scale effectively, and make informed business decisions.
Pillars of API Observability
API Metrics
API metrics enable you to monitor both engineering and business KPIs such as performance and customer usage. Monitoring CPU and memory usage is crucial for understanding an API’s saturation and overall health. Some metrics tracking tools can only monitor single-value metrics like requests per minute. However, a key theme for observability is the ability to answer any question around your API and services without planning ahead for which metrics need to be monitored. This is where high-cardinality, high-dimension metrics and data stores can become handy to explore and answer unknown unknowns with the ability to slice and dice instantly.
API Logs
Like API metrics, an API observability platform also has the ability to inspect and export log data for debugging and auditing purposes. API logs show you the exact calls in an instant in time. Because an API is by definition structured unlike traditional logging, API logs can also be used for generating aggregations and metrics while maintaining context. However, API calls can have a large number of HTTP headers, body keys, and attributes. This means your API observability tool should be capable of filtering and aggregating by them without relying on a full scan of your data store (which can take forever).
GDPR and CCPA require new procedures for things like API logs which are used across the company from security teams to business teams. You can no longer simply dump API logs into a log management solution. Instead, API businesses need a way to export and delete (or anonymize) all the associated logs and events specific to a single customer. This means API logs have to be tied to some permanent user identifier (and not just API keys which are rotated).
API Analytics
A third pillar of API observability is API analytics. API analytics provides valuable data insights that enrich the understanding of application design and performance. Unlike API metrics and logs which focus on answering engineering specific questions, API analytics is focused on answering product and business related questions. API analytics in a nutshell is the convergence of API metrics with data on who the customers are and how they are using your APIs. Many times API analytics tools can pull data from other systems like your CRM or marketing automation tools so you’re able to map out the customer journey. API analytics can include funnel metrics like how many people activate and use your API to understand which endpoints generate the most revenue or growth.
API Traces
Being transactional in nature, APIs contain timing information on when the request was initiated, how long it took the service to respond among other times. Different timing can create race conditions and bugs that are hard to reproduce. API traces make it easy to understand how long different services take to respond and where timing errors can occur. Traces enable you to further explore which logs to look at for a particular session or related set of API calls.
Closing thoughts
Anyone looking to ship APIs with speed and confidence should be looking for an API observability system. APIs provide the window to the rest of your companies infrastructure and enable entirely new digital experiences, but traditional monitoring can only answer already known questions like “Is my API down”, but cannot answer arbitrary questions required for data-driven engineering and business teams, nor report on actual customer API usage needed for security and product analytics.
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