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A useful SaaS API dashboard tracks completed-request throughput, request duration, and failures—and defines each consistently. Use counters for accumulated events, gauges for values that rise and fall, and histograms for latency distributions. Classify errors according to whether the operation actually failed, not just by status code.
Which API metrics belong on the dashboard?
Start with three views of the same completed operations: how many finish, how long they take, and how many fail. Counting at completion keeps request totals aligned with the duration and outcome recorded for each operation. Where possible, compare what clients observe with what servers record; a difference between the two can help locate whether a problem lies in the service or along the path to it. Prometheus instrumentation guidance recommends counting requests when they complete.
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- Throughput: completed requests per unit of time, derived from a cumulative request counter.
- Latency: a distribution of request durations, recorded with a histogram.
- Failures: completed operations classified by whether they failed in the application’s context.
These measurements work best when they describe the same operation boundary. For example, count an operation once when it completes, record its duration, and attach a failure classification only if that operation failed.
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| Metric type | What it represents | API example | How to use it |
|---|---|---|---|
| Counter | An accumulated total that increases as events occur; it may reset when a process restarts. | Total completed requests or failed operations. | Apply a rate function over a time window to estimate throughput. A raw total is not a current rate. |
| Gauge | A value that can increase or decrease. | Requests currently in progress. | Read it as a current state. Do not apply a counter-rate calculation to it. |
| Histogram | Recorded observations grouped into buckets, with a sum and count. | Request durations. | Use bucket data to examine latency distributions and preserve the observation count for context. |
Prometheus offers a practical rule: “if the value can go down, it is a gauge.” Its documentation recommends using rate() with counters when measuring change over time, rather than treating a counter’s ever-growing total as a rate. Prometheus metric types and its instrumentation guidance explain these distinctions.
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How should API latency be measured?
Record each request’s duration in a histogram rather than relying only on an average. An average can hide whether most requests are fast while a smaller group is very slow. A histogram retains observations in buckets, along with their sum and count, so dashboards can summarize a distribution while showing how much request volume supports that summary.
Classic and native Prometheus histograms
A classic Prometheus histogram produces cumulative bucket series ending in _bucket, plus _sum and _count. The count is equivalent to the bucket extending to positive infinity. Bucket boundaries determine the resolution available for queries, and each bucket series contributes to the number of time series stored.
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Prometheus native histograms use composite samples and dynamic buckets rather than requiring explicit boundaries. Prometheus describes them as generally more efficient and higher resolution, but using them depends on compatible configuration and support across the monitoring backend and query path. Prometheus metric types covers both representations.
OpenTelemetry HTTP duration conventions
OpenTelemetry defines HTTP request-duration metrics as histograms. Its documented client metric is http.client.request.duration, measured in seconds, with method and server address/port dimensions. Error type and response status dimensions apply conditionally. The convention also gives a recommended explicit bucket-boundary list for this client metric:
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[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] seconds.
This is a recommended configuration, not a latency benchmark or universal requirement. Choose the relevant client- or server-side convention for the instrumentation you use; metric names and supported dimensions can differ between instrumentation ecosystems. See the OpenTelemetry HTTP metric semantic conventions.
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There is no universal “good” latency threshold established by these conventions. Set dashboard thresholds from product expectations, service objectives, and the workload the API is expected to handle.
What counts as an API error?
Define failure around the intended behavior of the operation. An HTTP 404 is an error if the operation expected a resource to exist; it may be a normal result if the operation was checking whether that resource exists. OpenTelemetry states that “the classification of a status code as an error depends on the context.”
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Likewise, a transient error that is retried and ultimately handled successfully should not automatically count as a failed operation. Decide whether the metric represents an individual attempt or the completed operation, then apply that definition consistently. For an operation-level dashboard, record the final outcome rather than counting a recovered attempt as a failed operation. OpenTelemetry’s error-recording guidance discusses these cases.
Derive error rate from classified operations
Record completed operations in a consistent duration metric and classify failures with an error type where applicable. OpenTelemetry recommends using error.type on duration metrics for errors and omitting it on successful operations. With successes and failures represented in the same operation metric, a dashboard can derive throughput and an error rate without creating a separate metric for every outcome class. Include response status when it helps diagnose behavior, while keeping the failure definition grounded in application semantics.
Which labels make the dashboard useful without excessive cardinality?
Use dimensions that answer a real operational question, such as HTTP method, route template, response status, or error type. A route template such as /accounts/{accountId} groups requests to the same route; a raw path such as /accounts/847293 creates values that vary with individual identifiers.
Avoid unbounded or effectively unique values such as raw URLs, user IDs, and request IDs as metric labels. Each distinct label set creates another time series, so high-cardinality dimensions can grow storage and processing costs quickly. OpenTelemetry calls for a low-cardinality route template when available; Prometheus recommends labels rather than generating a separate metric name for every variation and suggests starting without labels if their use is unclear. Add dimensions to answer specific questions, not simply because the data is available. See OpenTelemetry HTTP metric conventions and Prometheus instrumentation guidance.
Quick Recap
How to choose a representation for each dashboard question
- “How many requests completed?” Use a request counter. To see requests per second or another time-window rate, calculate the counter’s rate.
- “How many requests are active now?” Use a gauge, because the value can rise and fall.
- “How long do requests take, and how is latency distributed?” Use a duration histogram. Choose classic bucket boundaries that match the latency ranges you need to distinguish, or use native histograms if the full monitoring stack supports them.
- “How often do operations fail?” Define an operation-level failure consistently and derive the rate from completed successes and failures represented in the same metric.
- “Where are failures concentrated?” Add bounded dimensions such as route template and status where they support diagnosis; avoid identifiers that create a new time series for individual requests or users.
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