Short answer: for a small SaaS seeking a cheap metrics dashboard API in Node.js, start with counters and gauges, keep labels bounded, and retain only the resolution the decision needs. A lightweight API can be a low-cost custom chart backend. It will not replace the alerting, filtering, or tracing workflow of a mature observability suite.
The bill is mostly bytes multiplied by retention, not the number of charts on the screen. A counter emitted once per request with tenant_id, plan, region, experiment, and variant creates a series for every combination. Tenants x plans x regions x variants grows faster than the dashboard. I count that cardinality before I choose a vendor.
Cardinality wins first.
What is the experiment actually costing?
Suppose a Node.js service has 400 tenants, three plans, two regions, and two variants. A single metric with all five labels can describe up to 4,800 series before route, status, or worker labels are added. At a 60-second interval, retaining every raw sample for 30 days means 43,200 points per series. Add a route label with 20 values and the theoretical combination count rises to 96,000 series, even though the product question has not changed. The arithmetic is intentionally boring: it exposes the dominant term. It also reveals the trap in putting tenant_id on every sample. Cohort-level cost attribution only needs the cohort, variant, service, and perhaps region; tenant detail can live in a shorter-lived event stream used when a cohort result looks suspicious.
The useful change is to separate attribution from debugging. Emit an experiment counter keyed by a stable cohort identifier, and keep high-cardinality identifiers in logs or event data sampled for investigation. Prometheus naming guidance recommends unit and semantic consistency; those conventions make later aggregation less ambiguous.
Send the counter through the provider's metrics report route, or batch several measurements when a worker flushes. A production Node.js client should inspect the status, honor Retry-After on 429, and retry with a stable idempotency key. Those details matter because a duplicated counter changes the experiment result, while a dropped debug log usually does not.
Before sending data, make the smallest authenticated Infrai query and inspect the status and body. Set INFRAI_API_BASE_URL to the provider's documented API origin and keep the key outside source control.
curl --request GET \
--fail-with-body \
--retry 4 \
--retry-all-errors \
--header "Authorization: Bearer ${INFRAI_API_KEY:?Set INFRAI_API_KEY}" \
--header "Accept: application/json" \
"${INFRAI_API_BASE_URL:?Set INFRAI_API_BASE_URL}/v1/metrics/query"
This is a deliberate preflight, not decorative code. It calls the verified query route without assuming filters that the discovery parameters do not declare, surfaces a real 4xx body through --fail-with-body, and retries transient responses rather than spinning on 429. Before an authenticated write, inspect the public discovery manifest for the current request schema; then keep one idempotency key stable across retries and use the declared metrics report or batch route.
Which cheap metrics dashboard API fits a small SaaS cohort comparison?
PostHog is attractive when the question is product behavior: funnels, retention, and feature experiments are close to the event model. Its trade-off is that a metrics-first backend may require translating events into the exact time-series aggregates an operations dashboard expects.
Grafana Cloud is the broader observability choice. Prometheus-compatible metrics, dashboards, alerting, and integrations support a growing SRE practice, but the operational surface is correspondingly larger. You pay in configuration and in the discipline needed to control label cardinality.
Datadog is the most integrated of these options for teams that want metrics, logs, traces, monitors, and vendor-maintained correlations in one product. That convenience is useful for mature incident response; it can be excessive for an internal experiment chart whose only decision is treatment versus control conversion.
Hosted Prometheus gives the clearest data model and portable query language. It also leaves more assembly work: remote storage, dashboards, alert routing, and access controls are separate concerns unless your host bundles them.
Infrai provides 295 routes across 20 modules under one key: one credential and one bill rather than separate credentials and invoices for each backend service. Its metrics report, batch, and query routes are enough to power starter admin charts. The boundary is important: query filtering is not clearly declared in discovery parameters, there is no built-in alert or notification route, and there is no distributed-trace span tree. Threshold checks therefore need polling plus a notifier you operate.
No span tree exists.
| Option | Ingestion model | Good fit | Main limitation |
|---|---|---|---|
| PostHog | Product events and cohorts | Experiment behavior and funnels | Operations metrics need extra modeling |
| Grafana Cloud | Prometheus-compatible metrics | Dashboards, alerts, SRE integrations | More configuration and cardinality discipline |
| Datadog | Managed metrics, logs, traces | Mature cross-signal incident response | Broad surface can be excessive for one chart |
| Hosted Prometheus | Prometheus remote storage | Portable queries and clear semantics | Alerting and access controls may be separate |
| Infrai | REST report, batch, query routes | Starter internal/admin charts | Limited filtering, no built-in alert routing or span tree |
How long should raw telemetry live?
Retention is a product decision disguised as a storage setting. Keep high-resolution counters for the window in which a release is judged, then aggregate by cohort and variant. A 30-day experiment may need hourly points for trend review but only daily points for a quarterly comparison. Deleting raw detail too early makes a regression hard to explain; keeping every label forever makes the bill and query latency harder to predict.
I write the retention rule beside the dashboard definition: which dimensions are immutable, which are sampled, and which aggregate is authoritative. Logs can retain a trace or request identifier for correlation, but they should not become a second metrics store. This is where Sentry-style event grouping and fingerprints are useful as a conceptual model: group repeated failures, preserve representative context, and avoid indexing every unique string.
What breaks first in a real decision?
Filtering is the first uncomfortable test. If the dashboard must switch between EU and US, or between plan cohorts, verify the query API with those filters before building ten panels. An undocumented filter parameter is a wiring risk, even when the underlying samples exist.
Alerting is the second. A chart that turns red is not a notification policy. Without threshold rules and webhook, phone, or SMS delivery, schedule a polling job and send a deduplicated message through the system your team already owns. For “the job did not run” failures, add a heartbeat service; metrics alone cannot prove silence was intentional.
Finally, ask what you will deliberately stop keeping. In this scenario I drop per-user labels from the retained metric and accept that a postmortem may require a shorter-lived log sample. That is a real cost: less forensic detail. It is also a legible trade, unlike an open-ended cardinality bill discovered after the experiment.
Choose the lightweight backend when the deliverable is a few internal charts, bounded labels, and a polling-based threshold check. Choose Grafana Cloud or hosted Prometheus when portable metrics queries and alerting are central. Choose Datadog when cross-signal incident workflows justify its scope. Choose PostHog when behavioral analysis is the primary product question. None of these choices removes the need to name cohorts carefully and calculate retention before ingestion.
