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What a hosted metrics dashboard API does
The phrase “hosted metrics dashboard API” can describe several connected parts rather than one standardized product: an application metrics API for recording measurements, an exporter or collector that sends them, a hosted service that stores and queries them, and a dashboard interface that presents the results. The exact integration depends on the chosen backend.
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For a small Node.js SaaS, OpenTelemetry JavaScript is a practical instrumentation layer. Its documentation marks traces and metrics stable and logs as in development, and describes support for actively maintained and maintenance LTS versions of Node.js. Project status and supported runtime versions can change, so check the current OpenTelemetry documentation when selecting versions.
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There are two common export patterns. In a scrape setup, the service exposes a metrics endpoint and a collector retrieves it. With OTLP, the service or a collector sends metrics to a backend endpoint. The receiving service then stores and makes the data available for queries, dashboards, and alerts.
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| Pattern | How data moves | What to verify |
|---|---|---|
| Prometheus scrape | A Prometheus-compatible collector periodically fetches an endpoint such as /metrics exposed by the application. |
Confirm the endpoint is reachable only where intended, the backend can scrape it, and the required metric names and labels are present. |
| OTLP export | An OpenTelemetry exporter sends measurements to an OTLP-compatible receiver, directly or through a collector. | Match the backend’s required protocol, endpoint, transport, and authentication configuration. |
OpenTelemetry’s JavaScript metrics guide demonstrates both a Prometheus exporter and an OTLP metrics exporter. Its Prometheus example exposes a local endpoint using port 9464 and the path /metrics; that is an example configuration, not a universal port or requirement. Its OTLP example uses a periodic exporting reader. Use the endpoint and protocol specified for the backend you choose.
Initialize the SDK before expecting data
Calling a metrics API alone does not guarantee that measurements will reach a dashboard. The SDK must be initialized and connected to a metric reader and exporter. In the OpenTelemetry JavaScript guide’s pattern, the application configures a NodeSDK with an exporter as its metric reader and starts the SDK before starting the HTTP service.
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- Add instrumentation. Use suitable automatic instrumentation for supported libraries and add manual instruments for application-specific measurements, such as a request counter.
- Configure export. Choose a Prometheus exporter for a scrape endpoint or an OTLP exporter and periodic reader for push-based delivery.
- Start telemetry early. Initialize and start the SDK before the application begins handling requests so the intended instrumentation can be registered.
- Check the receiving end. Confirm the endpoint is reachable or that OTLP export is configured for the backend, then verify that expected measurements arrive before building dashboards around them.
The OpenTelemetry guide includes a Fastify quick start. The same general SDK-and-exporter pattern can be used in HTTP services built with frameworks such as Express, but framework instrumentation and configuration details vary.
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What Postgres instrumentation can show
For applications using the Node pg driver, the OpenTelemetry instrumentation-pg package documents metrics for database operation duration, connection counts, maximum connections, and pending requests. Those measurements can help distinguish slow database work from a pool under pressure.
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Do not assume that the package provides automatic table-level attribution: its documentation says the driver does not expose table names separately and does not collect a collection or table attribute. Exact metric names, available attributes, and backend mapping depend on the instrumentation and its version.
Query text may appear among reported attributes. Before exporting or retaining it, review what the application can place in that field, including parameter values, and decide on redaction, access controls, and retention. Do not treat query text as safe to store by default.
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Choose dashboards that answer operational questions
A useful first dashboard should help answer whether the service is receiving traffic, returning errors, slowing down, or exhausting database capacity. Start with a small number of views and confirm that the relevant instruments are actually being emitted.
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- Errors: whether failed requests are increasing, with dimensions that help isolate the affected service or operation.
- Latency: how request duration is distributed, rather than relying only on an average that can conceal slow requests.
- Database operations: operation duration and the database signals available from the chosen instrumentation.
- Connection pool pressure: active and maximum connections, plus pending requests where emitted.
- Service health: a compact view of whether the application and its dependencies are behaving within your operational expectations.
Metric availability is not guaranteed merely because a dashboard is created. Instrumentation, SDK initialization, export configuration, and backend interpretation all affect what appears. Avoid high-cardinality labels such as unbounded customer identifiers unless you have deliberately assessed their cost and operational consequences.
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Hosted and self-managed destinations
These are examples of distinct operating models, not a complete market comparison. Decide based on export compatibility, operational ownership, query and dashboard portability, retention and ingestion costs, alerting, and data-region or security requirements.
| Option | Documented fit | Operational consideration |
|---|---|---|
| Grafana Cloud | Posit Connect documentation describes managed Grafana with built-in Prometheus-compatible storage. It identifies an OpenTelemetry Collector or Grafana Alloy agent as needed, without additional local infrastructure in that documented setup. | Check the current ingestion path and configuration for your application; the Posit Connect setup context should not be assumed to cover every deployment. |
| Datadog | The same Posit documentation describes a commercial APM platform with native OTLP ingestion. | That source calls for the Datadog Agent on the Connect host in its particular product context. Treat it as a setup requirement for that context, not a universal requirement for all deployments. |
| AWS CloudWatch OpenTelemetry Metrics | AWS documents OTLP ingestion and PromQL querying. | AWS documentation states a limit of up to 150 labels per data point and 15 months of storage, and says pricing is per GB ingested. Verify current regional prices and the scope of those limits before budgeting. |
| Google Cloud Managed Prometheus | Google documents a PostgreSQL exporter integration and an included PostgreSQL Prometheus Overview dashboard. | The integration page says verification may take one or two minutes; that is a setup note, not a service-level guarantee. The page was last updated 2026-09-16 UTC. |
| Self-hosted Prometheus and Grafana | The Posit guide describes Prometheus scraping a /metrics endpoint or receiving OTLP, with Grafana for visualization. |
You retain control but take responsibility for operating components, upgrades, retention, and alerting maintenance. |
Plan prices and service-level comparisons are not established here. Review the selected provider’s current terms, regional availability, security controls, retention options, and ingestion pricing before committing.
Keep metrics separate from business records
Metrics are designed to summarize behavior over time and support alerting; they are not a replacement for the individual records that explain what happened to a particular customer or transaction. Keep event-level and business-context data in PostgreSQL when the application needs to retrieve or join it. Use metrics to identify a trend or incident, then use an appropriate application workflow and access-controlled records to investigate individual cases.
This division is a useful design approach, not a rule that every system must follow. Avoid sending sensitive or identifying business details into metric labels simply to make dashboards convenient: labels can multiply the number of distinct time series, and metric systems are not a substitute for access-controlled transactional storage.
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