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What the dashboard should show
For each selected cohort and time window, show the three RED signals: requests, errors, and duration. Grafana’s Tempo documentation describes RED monitoring and examples of dashboards for service read/query and write/ingest paths.
- Request volume: the number of requests represented in the window. Display it beside ratios so readers can judge whether a small cohort has enough traffic for a meaningful comparison.
- Errors: error count and, where useful, error ratio. Make the denominator and time window visible; a percentage without its request volume can be misleading.
- Latency: a distribution rather than only an average. Percentiles or bucketed views help show slow requests that an average can conceal.
Define the window and cohort scope in the response or its surrounding interface. An absent series is not evidence of zero errors or healthy service behavior: it may mean no matching traffic, missing instrumentation, or an incomplete query. Treat empty, partial, warning, and failed results as distinct states.
Use counters and histograms with bounded dimensions
A compact schema can use accumulating counters for requests and errors, plus a histogram for request-duration observations. Prometheus defines counters as cumulative values and histograms as observations grouped into buckets, a fit for quantities such as request duration. See the Prometheus metric types documentation.
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- API Design Patterns
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Give each metric one measured quantity and a consistent unit. For duration, use seconds; Prometheus’s metric and label naming guidance recommends base units and consistent naming. Select only dimensions that support an operational decision, such as cohort, rollout variant, service, and environment. Use controlled values with an owner and review process.
Do not put raw tenant IDs, user IDs, email addresses, arbitrary URL paths, or exception text into metric labels. Each distinct label combination creates a separate time series, and Prometheus explicitly warns against high-cardinality labels such as user IDs and email addresses. Cohort labels should identify a governed group, not encode an effectively unbounded individual identity.
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Make query outcomes part of the API contract
A dashboard API must explain not only what data it returned, but also whether it returned all the requested data. Prometheus’s stable HTTP API is versioned under /api/v1 and documents JSON responses, success and error envelopes, warning and info arrays, and cases where collected data accompanies warnings. Its documented status codes include 400 for bad parameters, 422 for expressions that cannot execute, and 503 for timed-out or aborted queries. See the Prometheus HTTP API documentation.
Use those semantics as a model to assess your own contract, not as a requirement that every dashboard API copy Prometheus’s exact status codes. Document:
- Authentication and authorization, including the trusted source of tenant scope.
- Allowed query ranges, query limits, and timeout behavior.
- The response shape for complete, empty, partial, warning, and failed queries.
- Actionable error details that help users fix invalid requests without exposing sensitive data.
For a customer-facing dashboard, derive tenant scope from authenticated identity or another trusted authorization context. Do not trust a tenant parameter supplied by the browser as the sole access control. Test that changing request parameters cannot expose another tenant’s data.
Keep operational monitoring distinct from product analytics
The operational dashboard answers whether a service responds reliably and quickly for a cohort. Product analytics answers behavioral questions such as conversion, funnels, retention, paths, stickiness, and lifecycle. PostHog documents APIs for product-analytics queries and saved insights, including these query types, in its product analytics API source.
Keep the concepts separate in schemas, access rules, retention decisions, and dashboard labels so users do not confuse service health with user behavior. Separate storage systems may help, but they are an architectural choice—not a universal requirement. The right boundary depends on the system’s architecture and compliance model.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare implementations against requirements
Choose among a managed metrics service, a self-hosted stack, or a product-analytics API by testing the needs of the dashboard, not by assuming a category or vendor is automatically suitable.
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Best Value
| Requirement | What to verify |
|---|---|
| Tenant isolation and authorization | Confirm how identity and tenant scope reach the query layer, then test cross-tenant access. Do not assume a provider’s tenancy model without checking the selected service. |
| Metric and query semantics | Check support for counters, histograms, aggregation, warnings, partial results, and timeouts. Prometheus’s API reference illustrates response behavior worth inspecting. |
| Cardinality and query-cost visibility | Determine whether teams can observe or estimate series growth and query load as cohort dimensions change. Prometheus documents cardinality-related status information, but that does not establish a universal price. |
| Geography and retention | Verify ingestion, storage, query, backups, and support-data boundaries against the required region and retention policy for the specific provider and plan. |
| Product analytics breadth | For behavioral analysis, confirm event capture and query features such as funnels and retention rather than assuming a metrics API includes them. PostHog documents its query capabilities in the product analytics API source. |
| Portability and operations | Check export formats and migration effort directly. These vary by implementation, and there is no basis here for a comparative portability or total-cost claim. |
Grafana’s Tempo documentation also describes a multitenant dashboard for per-tenant ingestion, reads, storage, and metrics generation. That is an example of a tenant-operations view; it does not establish that Tempo is the right backend for every product-analytics use case.
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