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An API can return successful HTTP responses and still be slow, overloaded, or fail the task a customer came to complete. Effective monitoring measures those user-visible outcomes, then uses request metrics, traces, logs, and synthetic checks to locate the cause. Monitoring won’t repair an API; it helps you tell whether the trouble lies in the API, a dependency, capacity, or the way reliability is being measured.
What should you monitor for your API?
Start with the operation a user or client depends on, not a dashboard full of infrastructure graphs. Identify critical API operations and the outcomes they support. Then choose service level indicators (SLIs) that reflect whether those outcomes happen: successful completion, response-time performance, and request volume appropriate to the service.
An endpoint can be technically available while producing the wrong result. OpenTelemetry’s observability primer illustrates the distinction with an online store: a system may be up, but unreliable if adding black shoes to a cart does not consistently add black shoes. Monitor the task’s outcome, not only whether the endpoint answered. OpenTelemetry’s observability primer
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Use the four golden signals as a diagnostic frame
Google SRE identifies latency, traffic, errors, and saturation as the four golden signals. Each helps answer a different question: Google SRE’s monitoring guidance
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- Latency: How long do requests take? Look at distributions or the share of requests meeting a defined threshold, rather than relying on a single average.
- Traffic: How much demand is reaching the service? A change in volume can explain why other signals shifted.
- Errors: How often does work fail? Define failure in terms that matter to the API’s consumers, not only server errors.
- Saturation: How close are relevant resources or components to their limits? Choose measures that suit your application; there is no single saturation metric that applies to every API.
Track duration alongside request outcome. If failed requests return immediately, excluding them can make latency look better precisely when users are seeing errors. Google SRE advises tracking error latency rather than filtering errors out of latency measurements.
How do you know if an API is healthy?
Define an SLO—a service level objective—as a target for an SLI over a specified period. A useful SLO makes the desired user experience measurable: for example, the proportion of requests that succeed or meet a latency threshold during the period.
Google Cloud’s SLO API documentation gives illustrative examples: 99% of requests in each rolling week below 200 milliseconds, or 99.5% of requests in each calendar month succeeding. These are documentation examples, not recommended targets or guarantees for every API. Choose a goal and time window that fit the operation, user expectations, and service context. Google Cloud’s SLO API reference
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A generic “three nines” target is not a substitute for deciding what users need. A low-traffic API, a checkout operation, and a background endpoint may warrant different indicators and targets. Tie the SLO to a critical operation and an observable user outcome.
How do metrics, traces, and logs work together?
These telemetry signals answer complementary questions. Metrics summarize behavior over time and help reveal changes or trigger alerts. Traces show a request’s path through services, exposing where time was spent or where a failure occurred. Logs record detailed events that can help explain a particular symptom. Correlating them gives more diagnostic context than any one signal alone. OpenTelemetry signals and its observability primer
OpenTelemetry is 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. The destination is a separate choice from how the application is instrumented. What is OpenTelemetry?
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Where do synthetic checks fit?
Synthetic checks run scripted probes against selected routes or workflows. They are useful for catching regressions and unexpected responses, including when ordinary traffic is low or absent. A probe can test a critical journey repeatedly, but it covers only the scenarios it performs.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you choose a monitoring approach?
Compare approaches by what they observe and what work they create, rather than assuming one product or configuration is right for every service.
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| Decision area | What to consider |
|---|---|
| Coverage | Synthetic probes repeatedly test selected routes and journeys; telemetry from real requests covers actual operating behavior. |
| Diagnostic depth | Metrics expose aggregate changes; traces add request and component context; logs add event detail. Correlation helps connect the symptom to a cause. |
| User relevance | Infrastructure health is useful context, but SLIs tied to API operations and user outcomes show whether the service is doing the intended work. |
| Operational overhead | Account for instrumentation impact, data volume, metric cardinality, and the effort required to maintain useful alerts. |
| Portability and integration | OpenTelemetry-compatible instrumentation and export can separate telemetry generation from a particular backend; provider-specific stacks may offer tighter integration. |
How do you keep monitoring useful and safe?
Keep metric dimensions bounded
Metrics become costly and difficult to manage when every distinct attribute combination creates another time series. Avoid high-cardinality metric labels such as raw user IDs or request IDs. Use traces or logs for per-request detail, and keep metric dimensions to bounded categories that support aggregation. OpenTelemetry guidance also cautions against instrumentation that blocks end-user applications by default or consumes unbounded memory. OpenTelemetry specification overview and OpenTelemetry metrics concepts
Make dashboards and alerts actionable
A dashboard should help answer what is wrong and where. An alert should identify a condition that requires action, not fire on every metric fluctuation. Google SRE guidance recommends basic metric collection and aggregation paired with alerting and dashboards. Start with signals tied to important user outcomes, then add diagnostic detail that helps responders isolate a problem.
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