An API can return successful HTTP responses and still be slow, overloaded, or fail to complete the task a user intended. Monitoring won’t fix the service, but it can show whether trouble lies in the API, a dependency, limited capacity, or the way reliability is being measured. Start with the outcomes users need, then monitor latency, traffic, errors, and saturation alongside traces, logs, and carefully chosen synthetic checks.
What should you monitor for your API?
Start with the user-visible outcome, not a dashboard full of infrastructure metrics. Identify the API operations that support critical tasks, such as signing in, placing an order, or retrieving an account balance. For each operation, ask what a user would consider successful and how quickly it needs to complete.
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Turn those answers into service level indicators (SLIs): measurable signals that represent service behavior. Useful examples include the proportion of requests that complete the intended operation, the distribution of response times, and request volume. A route returning HTTP 200 is not sufficient evidence of success if the response contains the wrong result or a downstream action did not happen.
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OpenTelemetry’s observability primer illustrates the distinction: a system can be up continuously yet unreliable if “Add to Cart” does not consistently add the selected item. Define success in terms of the operation’s outcome, not just the server’s availability. OpenTelemetry’s observability primer explains this user-centered view.
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How do you know if an API is healthy?
Google’s Site Reliability Engineering guidance recommends four golden signals: latency, traffic, errors, and saturation. Together, they help distinguish a slow service from a sudden demand increase, failed work, or a capacity constraint. Google SRE’s monitoring guidance describes the signals and why each matters.
- Latency: How long requests take. Use a distribution or threshold-based view rather than relying only on an average; a healthy average can conceal a slow tail affecting some users.
- Traffic: The demand reaching the API, measured in a way that fits the service, such as requests per second. A sudden shift can explain load-related symptoms or reveal that an operation has become unusually popular.
- Errors: Failed requests and unsuccessful operations. Track outcomes as well as HTTP status codes where needed, because a technically successful response can still fail the user’s task.
- Saturation: How close the service or a constrained dependency is to its usable capacity. The right indicators depend on the application; they might include resource pressure, queueing, or exhausted connection capacity.
Measure failed-request latency too
Keep latency for successful requests distinguishable from latency for failed ones. A request that fails quickly can pull down an aggregate response-time figure and make the API look faster even as users see more failures. Google SRE explicitly warns that it is important to track error latency rather than simply filtering errors out.
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Set an SLO that reflects the service
A service level objective (SLO) is a target for an SLI over a defined period. It makes “reliable enough” specific: the indicator, the target, and the window all matter. Google Cloud’s SLO API documentation gives illustrative examples—not universal recommendations—of 99% of requests in each rolling week below 200 milliseconds, and 99.5% of requests in each calendar month succeeding. Google Cloud’s SLO API reference shows these examples.
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How do metrics, traces, and logs work together?
Metrics, traces, and logs answer different questions. Metrics summarize behavior over time and make trends or alert conditions visible. Traces follow an individual request through its lifecycle and can show which service or dependency contributed delay or failure. Logs record detailed events that help explain what happened in a component.
Use them together: a metric can reveal that latency rose, a trace can locate the slow span, and related logs can add event-level context. OpenTelemetry describes these signals and provides a vendor-neutral framework for instrumenting, generating, collecting, and exporting telemetry. It is not, by itself, a complete monitoring backend or alerting service. Telemetry can be sent to an OpenTelemetry Collector, standard output during development, an open-source backend, or a vendor service. See OpenTelemetry’s signals overview and observability primer.
Keep metric dimensions bounded
Metric labels or attributes with too many unique values can create high cardinality, increasing storage and processing demands or hitting system limits. Avoid unbounded values such as raw user IDs and request IDs as metric dimensions. Use bounded attributes for aggregate metrics; when you need per-request detail, traces and logs are generally more appropriate places to look.
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When should you use synthetic checks?
Synthetic checks run scripted requests or workflows on a schedule, letting a team test selected routes or critical journeys even when ordinary user traffic is absent. They can catch regressions, unexpected responses, and failures in important paths.
They are only a sample of the behavior they exercise. A successful probe does not establish that every real request is healthy, and a failed probe may not reveal the full cause. Pair synthetic checks with telemetry from actual requests for broader operating evidence and better diagnosis. Google’s API monitoring guidance cautions that synthetic monitoring alone is insufficient for precise diagnosis at high request volumes. Google Cloud’s API monitoring documentation describes its monitoring approach.
How should dashboards and alerts guide troubleshooting?
A useful dashboard helps answer two questions: what is wrong, and where might the cause be? Organize views around critical API operations and their SLIs, then make it possible to inspect latency, traffic, errors, and relevant saturation indicators together. Correlating a symptom with traces and logs can help distinguish an API issue from a dependency or capacity problem.
Alerts should identify conditions that need action, not every metric fluctuation. Tie them to user-impacting symptoms or meaningful SLO risk, and make sure the alert gives responders enough context to investigate. Google SRE recommends pairing basic metric collection and aggregation with alerting and dashboards rather than treating a pile of raw measurements as a monitoring strategy.
Which monitoring approach fits your API?
There is no universally best vendor or configuration. Compare approaches by what they cover, how they help diagnose problems, and the operational work they add.
Quick Recap
| Choice | What it provides | Trade-off to consider |
|---|---|---|
| Synthetic probes | Repeatable tests of selected routes or workflows. | They cover only the requests and conditions scripted; pair them with real-request telemetry. |
| Metrics | Aggregated behavior and a practical basis for dashboards and alerts. | They show patterns, not the full sequence of events in an individual request; keep dimensions bounded. |
| Traces and logs | Request-level component context and detailed event information. | They add data volume and require useful correlation and instrumentation to support diagnosis. |
| User-outcome SLIs | Reliability measurements tied to what API operations are meant to accomplish. | They require an explicit definition of successful work, beyond whether a server responded. |
| OpenTelemetry-compatible instrumentation | A vendor-neutral way to generate and export telemetry to a chosen destination. | It is instrumentation and telemetry plumbing, not a complete backend or alerting service. |
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