For a Node.js SaaS dashboard that needs counts, rates, and duration trends, instrument defined metrics and send them to a metrics backend. Keep logs available to investigate individual events and their context. The choice is not simply “API or logs”: an endpoint is an ingestion interface, not necessarily the dashboard database or the whole observability system. Choose the operating model after you define what the dashboard must answer.
1. Decide what question the dashboard must answer
Use metrics for questions that can be expressed as measurements over time: how many requests succeeded, what fraction failed, or how request durations are distributed. A counter or histogram gives a stable basis for aggregations, trends, and alerts.
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Use logs when a person needs to inspect a particular event and its surrounding details, such as an error message or request context. Logs complement metrics: they help explain an anomaly, while a metric can show when and how broadly it occurred.
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2. Define the metrics and attributes before choosing storage
Give each metric a stable name, unit, and description, then add only bounded attributes that answer known questions. Attributes add context to measurements; OpenTelemetry recommends using its semantic conventions where applicable. It also recognizes that privacy needs can justify omitting an attribute or using a custom one. See the OpenTelemetry JavaScript metrics guidance.
- Prefer dimensions such as a known operation or outcome when they are useful to the dashboard.
- Do not use arbitrary identifiers or exception text as metric dimensions. Decide what context belongs in logs instead.
- Check that names and attribute choices are consistent across services, so dashboards compare like with like.
Designing dimensions is an engineering concern, not a reason to avoid metrics. Keep detailed, unpredictable event context in logs rather than turning every detail into a metric attribute.
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3. Choose an ingestion and backend model
OpenTelemetry JavaScript documents a Prometheus exporter and OTLP exporters as ways to export metrics. Its metrics guide shows manual counters and histograms in a Node.js SDK setup, including graceful SDK shutdown. The exporter is the route out of the application; select storage and visualization separately.
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These common operating models differ chiefly in who runs the components and which signals they cover:
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| Option | Fits when | Boundary to consider |
|---|---|---|
| Prometheus plus Grafana | Your team wants a metrics-focused path and already operates, or can operate, the stack. | In Google’s described architecture, Prometheus covers monitoring while logging is configured separately. Grafana is a visualization layer; it should not be mistaken for the metrics database. The deployment and integration choices remain part of the operating model. See Google Cloud’s hybrid monitoring and logging patterns. |
| Managed observability such as Grafana Cloud | You want hosted telemetry ingestion and storage capabilities documented for metrics and other signals. | Review data governance, network access, required signals, integrations, and commercial terms. Grafana Cloud documents Prometheus and OpenTelemetry support, managed signal backends, and an OTLP-compliant endpoint; these are vendor-described capabilities, not independent performance validation. See Grafana Cloud documentation. |
| An existing third-party monitoring service such as Datadog | Your organization already uses a partner service and values integration with its cloud architecture. | Google Cloud names Datadog as an example of a third-party monitoring service that can connect to Cloud Monitoring API. That example does not establish cost, feature parity, or performance. See Google Cloud Monitoring overview. |
| Logs-oriented backend | The primary requirement is searching individual events and investigating their context. | For dashboard KPIs, define stable measurements rather than relying only on arbitrary log text; account for parsing and data lifecycle as part of the design. |
A hosted platform can reduce the amount of stack infrastructure your team operates, but it adds hosted-service, network, data-governance, and vendor considerations. Likewise, a Prometheus-based setup is not inherently a complete logging system: Google’s cited pattern configures logging separately, though that architecture guidance should not be generalized into a rule for every Prometheus deployment.
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Check the constraints that determine whether an otherwise suitable backend will work for your service:
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- Node.js support: OpenTelemetry JavaScript says it supports active or maintenance LTS Node.js versions. Older versions may work but are not tested.
- Signal maturity: At the time of the OpenTelemetry JavaScript documentation check on October 5, 2026, traces and metrics were listed as Stable, while logs were listed as Development. Treat that distinction as relevant when choosing an SDK path. See OpenTelemetry JavaScript documentation.
- Dashboard and alert needs: Confirm the backend can query the measurements and dimensions you defined and support the dashboards and alerts you actually need.
- Privacy and retention: Review which attributes leave the service, network access requirements, governance rules, and retention requirements against the chosen service’s terms and configuration.
- Operational ownership: Decide who maintains exporters, collection, storage, dashboards, and any separate logging path.
- Total cost: Compare the complete cost model for your expected telemetry and operating approach. The documentation cited here does not establish a cost comparison between these choices.
Before committing, exercise the actual metric shapes and dashboard queries with your workload. No comparative benchmark or implementation test is established here, so backend performance and cost should not be inferred from product descriptions alone.
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