Guide · Observability
Prometheus vs OpenTelemetry: Which and When
Written by
Amber Jain
Published
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4 min read
These two get compared constantly, but they solve overlapping, not identical, problems. Here is how Prometheus and OpenTelemetry relate, and how to use them together.
Prometheus is a metrics monitoring system and time-series database with a pull-based model; OpenTelemetry is a vendor-neutral standard for instrumenting and collecting all three signals, traces, metrics and logs, and exporting them over OTLP. They interoperate rather than compete, and most teams use both.
What each one actually is
Prometheus is a complete metrics stack: it collects (by scraping), stores (its own TSDB), queries (PromQL) and alerts (Alertmanager), all for metrics. OpenTelemetry is not a backend at all; it is instrumentation SDKs plus the Collector and the OTLP protocol, and it is signal-agnostic, covering traces and logs as well as metrics. In short, Prometheus is a monitoring system; OpenTelemetry is how you produce and move telemetry to whatever system stores it.
How they work together
Because their scopes overlap on metrics, they are built to interoperate. The OpenTelemetry Collector can scrape Prometheus endpoints and can export metrics to Prometheus via remote-write, and Prometheus can now ingest OTLP metrics directly. A very common pattern is to instrument applications with OpenTelemetry (so instrumentation is portable across all three signals and not locked to one vendor) while storing and querying metrics in Prometheus or a Prometheus-compatible backend with PromQL.
Which should you choose
It is rarely either/or. Choose OpenTelemetry for instrumentation, because it covers all three signals and avoids lock-in. Choose Prometheus (or a compatible store) for metrics storage and query if you want the open-source standard and PromQL. The teams that get it right use OpenTelemetry to generate telemetry and Prometheus-compatible tooling to work with the metrics, rather than treating them as rivals.
| Dimension | Prometheus | OpenTelemetry |
|---|---|---|
| Scope | Metrics monitoring system + TSDB | Instrumentation and collection standard |
| Signals | Metrics only | Traces, metrics and logs |
| Model | Pull (scrapes endpoints) | Push over OTLP (and can scrape via Collector) |
| Storage | Built-in TSDB | None of its own; sends to a backend |
| Query | PromQL | Backend-dependent |
| Best for | Cloud-native metrics + alerting | Portable, vendor-neutral instrumentation |
A common mistake to avoid
The most frequent error is treating this as a migration, ripping out Prometheus to adopt OpenTelemetry, or refusing OpenTelemetry to protect a Prometheus investment. Both misread how the two relate. OpenTelemetry does not replace your metrics backend; it standardises how telemetry is produced and moved. Prometheus does not block OpenTelemetry; it ingests OTLP metrics and remains an excellent PromQL store. The teams that struggle are usually the ones who framed it as an either-or decision. The teams that succeed instrument with OpenTelemetry for portability and keep Prometheus-compatible tooling for the metrics they already know how to operate.
Three architectural patterns that work
The first pattern is OpenTelemetry SDKs for instrumentation, Collector for shipping, Prometheus for metrics storage and query. Applications produce OTLP; the Collector receives it and forwards traces to a trace backend (Tempo, Jaeger) and metrics to Prometheus via remote-write. Logs travel a similar path. This gives portable instrumentation and keeps the metrics tools your team already knows.
The second pattern is the reverse for migrating estates: Prometheus scrape jobs continue against exporters that have not been re-instrumented yet, and the Collector scrapes those same exporters and normalises the metrics into OTLP for a unified pipeline. This is the safest incremental path for teams that already have hundreds of exporters in production and cannot re-instrument the fleet in one release cycle.
The third pattern is OTLP-native end-to-end, feeding Prometheus's newer OTLP ingest directly. This is where net-new deployments increasingly land: applications produce OTLP metrics, Prometheus accepts them without a translation layer, and PromQL still queries the store. The Collector remains useful for cross-cutting concerns like sampling, batching, redaction and multi-destination routing, but the pipeline is simpler.
How this fits Opstral
Opstral is OpenTelemetry-native and works with Prometheus-compatible metrics, so it sits at the end of exactly this pattern: instrument with OpenTelemetry, keep PromQL where you want it, and let Telemetry Ops and Sentinel AI correlate the signals and resolve incidents autonomously.
The practical benefit of a Prometheus-plus-OpenTelemetry foundation is that the resolution layer above it is not tied to a single vendor's collection stack. Sentinel reads Prometheus metrics and OTLP telemetry with equal fidelity, so an alert firing off a PromQL rule and a trace anomaly detected in OTLP data land in the same investigation. That correlation is what lets ProcBot execute the right fix, and Sherlock verify recovery against the same metrics the team already trusts. Teams that started with Prometheus keep PromQL as the query language of record; teams migrating to OpenTelemetry get a resolution layer that grew up on OTLP.
- Instrument once, resolve anywhereOpenTelemetry SDKs generate portable telemetry; Sentinel consumes it directly and executes fixes through ProcBot as governed Action Tickets. The instrumentation and the resolution layer speak the same protocol.
- PromQL keeps workingExisting recording rules, alerting rules and Grafana dashboards continue to run against Prometheus. Opstral consumes the alerts and metrics as another signal source; no rewrite required.
- One layer for correlated incidentsMetrics, logs and traces flow through the same telemetry pipeline. Sentinel correlates across all three when investigating, instead of asking an engineer to jump between three tools to answer one question.
Frequently asked questions
Is OpenTelemetry replacing Prometheus?
No. OpenTelemetry standardises instrumentation and collection across all three signals; Prometheus remains a leading metrics store and query engine. They interoperate, and many teams use OpenTelemetry to feed Prometheus-compatible backends.
Can Prometheus ingest OpenTelemetry data?
Yes. Prometheus can ingest OTLP metrics, and the OpenTelemetry Collector can export to Prometheus via remote-write, so the two connect in both directions.
Do exemplars still work with the OpenTelemetry pipeline?
Yes. Prometheus supports exemplars on histogram metrics, and OpenTelemetry can carry trace context on the metrics it emits, so a metric spike can link directly to a trace ID for the request that caused it. This is the single most useful bridge between the two worlds for incident investigation.
Which language should new services use for metrics?
For net-new services, the OpenTelemetry SDK is the safer default: it covers traces and logs as well as metrics, and the emitted OTLP is accepted by Prometheus and by every major observability backend. The Prometheus client library remains a fine choice if the service only needs metrics and the team already has years of Prometheus expertise, but portability leans toward OpenTelemetry.
Where does Opstral fit alongside this stack?
As the resolution layer on top. Prometheus and OpenTelemetry produce and store the signals; Sentinel investigates them, ProcBot executes governed fixes, and Sherlock validates recovery. Nothing about the collection or query layer changes; what changes is that most incidents no longer land in an engineer's queue.