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A useful SaaS API dashboard starts with three signals: completed request volume, request duration, and failed operations. Track totals with counters, changing in-flight work with a gauge, and latency with a histogram. Define failure by what the operation was supposed to do—not by status code alone—and keep metric dimensions bounded so the dashboard remains useful and affordable to operate.
Which API metrics should a SaaS dashboard track?
Start with completed requests, request duration, and failures. Count an operation when it finishes, so the count, duration, and outcome describe the same completed work. Prometheus recommends this approach because it aligns request totals with latency and error data (Prometheus: Instrumentation).
- Request throughput: the rate of completed operations over a chosen time window.
- Request duration: the distribution of time taken by operations.
- Failed operations: the portion of completed operations that did not meet their expected outcome.
- In-progress requests: a current-state signal that can reveal concurrency or a backlog; this is a gauge, not a request total.
Where practical, observe the service from both client and server sides. If clients report slow or failed calls while the server does not, or vice versa, the difference can help narrow down where to investigate.
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| Metric type | What it represents | API example | How to interpret it |
|---|---|---|---|
| Counter | An accumulated event total that increases over time and may reset when a process restarts. | Completed requests or failed operations. | Apply a rate function over a time window to calculate throughput; the raw total is not a current rate. |
| Gauge | A value that can rise or fall. | Requests currently in progress. | Read it as a current measurement. Do not use a counter rate function on it. |
| Histogram | A collection of observations organized into buckets, with a sum and count. | Request durations. | Use the distribution to understand latency, while the count supplies request-volume context. |
Prometheus gives a practical rule for choosing between a counter and gauge: “if the value can go down, it is a gauge” (Prometheus: Instrumentation). A counter can reset after a process restart; rate calculations account for counter resets in supported Prometheus queries. A gauge such as active requests instead reflects the current state and can move in either direction.
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How should API latency be measured?
Record each request duration as a histogram observation rather than relying only on an average. A mean can hide a long tail: most calls may be quick while a smaller group takes much longer. A histogram retains bucketed observations so you can examine latency distribution and request volume together.
Prometheus histogram choices
Classic Prometheus histograms expose cumulative bucket series ending in _bucket, plus _sum and _count. The count is equivalent to the positive-infinity bucket. Those bucket series can be aggregated across instances for distribution queries; summaries represent quantiles differently and generally do not provide the same aggregatable bucket data (Prometheus: Histograms and summaries).
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Native histograms use composite samples and dynamic buckets. Prometheus describes them as generally more efficient and higher resolution, without requiring explicit bucket boundaries. Whether they are suitable depends on configuration and support across the monitoring backend and the rest of the metric pipeline (Prometheus: Histograms and summaries).
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OpenTelemetry defines HTTP request-duration metrics as histograms. For client requests, the documented metric is http.client.request.duration, measured in seconds. Its conventions specify required method and server address/port dimensions, and conditional error and response-status dimensions. Use the semantic convention appropriate to the instrumentation direction—client or server—and the ecosystem in use; vendor implementations do not necessarily expose identical names.
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For this client duration metric, OpenTelemetry recommends these explicit bucket boundaries, in seconds: [0.005, 0.01, 0.025, 0.05, 0.075, 0.1, 0.25, 0.5, 0.75, 1, 2.5, 5, 7.5, 10]. They are a recommended configuration, not observed latency results or a universal requirement. Choose boundaries that suit the service’s workload and the questions the dashboard needs to answer (OpenTelemetry: HTTP metrics).
How do you calculate an API error rate?
First define what counts as a failed operation for your application. Then calculate the rate of failed completed operations divided by the rate of all completed operations over the same time window. Multiplying by 100 expresses that ratio as a percentage. Use consistent operation boundaries and filtering in both parts of the calculation.
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A response code by itself does not determine whether work failed. OpenTelemetry notes that “The classification of a status code as an error depends on the context.” A 404 can mean a required resource was missing, or it can be the expected result of checking whether a resource exists. Similarly, an attempt that returns an error but is retried or handled successfully should not automatically count as a failed operation if the overall operation completed as intended (OpenTelemetry: Recording errors).
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Record successes and failures consistently
Instrument one duration metric for the operation whether it succeeds or fails. Where applicable, attach an error.type classification to failed observations and omit it on successful ones. OpenTelemetry’s guidance recommends this pattern because it allows throughput and error rates to be derived from the same metric while keeping outcome classification consistent. Include response status where it is useful and available, without creating a separate metric for every outcome class (OpenTelemetry: Recording errors; OpenTelemetry: HTTP metrics).
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Which dimensions belong on API metrics?
Use a small set of dimensions that help answer operational questions, such as HTTP method, route template, and response status. A route template like /accounts/{accountId} groups requests by endpoint instead of creating a separate time series for every account identifier. OpenTelemetry calls for a low-cardinality route template when one is available (OpenTelemetry: HTTP metrics).
Avoid raw URLs, user IDs, request IDs, and other values that can take on an effectively unlimited number of distinct values. In Prometheus, each distinct label set creates another time series, consuming resources. Prometheus recommends labels rather than generating metric names for values, and advises starting without labels when uncertain, then adding them for a concrete use case (Prometheus: Metric and label naming; Prometheus: Instrumentation).
How should you choose latency thresholds and histogram resolution?
There is no universal “good” API latency threshold or SLO target established by these conventions. Set thresholds from product expectations, service objectives, and the workload you actually serve. For example, a duration that is acceptable for a background export may be unacceptable for an interactive request; the dashboard should reflect the promise made to users.
Histogram design involves a trade-off: more useful bucket detail can improve distribution analysis, while classic histogram bucket choices create additional time series. Native histograms offer dynamic buckets but need compatible backend handling. Keep the decision tied to the latency questions you need to answer and the capabilities of your telemetry pipeline.
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