OpenTelemetry and Prometheus solve different parts of observability, so they are not an either-or choice. OpenTelemetry standardizes how applications generate, collect, process, and export telemetry across traces, metrics, and logs. Prometheus is a metrics monitoring system built around collection, storage, and PromQL queries. Choose OpenTelemetry for cross-signal instrumentation and a flexible telemetry pipeline; choose Prometheus when its metrics workflow fits your needs; use both when you want broader instrumentation alongside Prometheus monitoring.
OpenTelemetry and Prometheus have different jobs
OpenTelemetry is an open-source observability framework for instrumenting, generating, collecting, and exporting telemetry such as traces, metrics, and logs, according to the OpenTelemetry project overview. It provides APIs, SDKs, and collection components that help applications and services produce and route telemetry without tying instrumentation to one backend.
Prometheus is a metrics-focused monitoring system. Its workflow centers on collecting metrics, storing them, and querying them with PromQL. The CNCF comparison is useful for understanding the architectural distinction, while specific current integration behavior is best checked in the projects’ own documentation.
| Question | OpenTelemetry | Prometheus |
|---|---|---|
| Primary role | Instrumentation and telemetry collection, processing, and export | Metrics collection, storage, and querying |
| Signals | Traces, metrics, and logs | Metrics-focused |
| Query workflow | Depends on the receiving backend or system | PromQL |
| Best fit | Common cross-signal instrumentation and configurable routing | A metrics monitoring workflow built around Prometheus |
Which one should you choose?
Choose OpenTelemetry for shared instrumentation and flexibility
Use OpenTelemetry when application teams need a consistent instrumentation approach across traces, metrics, and logs, or when you want to keep backend choice flexible. Its metrics goals include connecting metrics with other signals and working with existing metrics protocols; see the OpenTelemetry metrics specification. OpenTelemetry is vendor- and tool-agnostic, but that does not mean every backend supports every metric feature or mapping identically.
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Choose Prometheus for a metrics-centered workflow
Prometheus is a natural fit when your operational needs are primarily metrics monitoring and your team relies on its scrape, storage, and PromQL workflow. An established Prometheus ecosystem and existing PromQL knowledge can also weigh in its favor. It does not replace the broader role of cross-signal instrumentation that OpenTelemetry provides.
Use both when the roles complement each other
You can instrument applications with OpenTelemetry and continue using Prometheus for metrics. OpenTelemetry documents Prometheus-compatible integration paths, and Prometheus documents OTLP ingestion over HTTP. The OTLP receiver is disabled by default, so it must be configured before that route will work; consult the Prometheus OTLP ingestion guide and the OpenTelemetry Prometheus compatibility guide.
How data gets between them
Prometheus is commonly associated with scraping metrics, while OpenTelemetry can collect and export telemetry through configurable pipelines. That distinction does not make them strictly “pull versus push” systems: the projects support multiple exchange patterns, including Prometheus OTLP ingestion. Pick an integration path that your deployed components support, and verify the exact receiver, exporter, and Prometheus version rather than assuming one protocol or direction applies to every setup.
- Choose the connection path. Decide whether Prometheus will scrape a Prometheus-compatible endpoint or receive OTLP over HTTP.
- Configure the receiving side. If using Prometheus OTLP ingestion, enable and configure its receiver; it is not active by default.
- Check the resulting metrics. Inspect names, labels, resource attributes, temporality, and histogram behavior in the actual output.
What to validate in a Prometheus integration
Labels, scopes, and target identity
Prometheus metrics use a flat metric-name-and-label namespace, whereas OpenTelemetry instruments are associated with named scopes. When metrics are exported, scope labels may be present. Removing those labels is safe only if metric names are not duplicated across scopes. OpenTelemetry resources also differ from Prometheus scrape target identity: resource attributes can map to job and instance, while other attributes may appear through target_info or exporter configuration. Review the OpenTelemetry client-library comparison and confirm the labels you actually query.
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Temporality and metric types
OpenTelemetry supports both cumulative and delta aggregation temporality. The cited Prometheus exporter guide describes Prometheus export as enforcing cumulative temporality, but exact behavior depends on the export path. Compatibility also varies by exposition format and metric type: some formats do not support exemplars or Info/StateSet metrics, and several do not support native histograms. The Prometheus and OpenMetrics compatibility specification says unsupported native histograms should be dropped or may be converted to fixed-bucket histograms.
Quick Recap
- Check the exact exporter, receiver, and Prometheus version used in your deployment.
- Confirm whether the selected format preserves the metric types and exemplars you need.
- Test histogram conversion or dropping before relying on those metrics for alerts or dashboards.
- Review label mapping and naming so existing PromQL queries continue to behave as expected.
A practical decision checklist
- Choose OpenTelemetry first if you need a shared instrumentation standard across logs, traces, and metrics or want to route telemetry to different compatible backends.
- Choose Prometheus first if your problem is metrics monitoring and Prometheus collection, storage, and PromQL already meet the operational need.
- Combine them if you want OpenTelemetry’s broader instrumentation and pipeline capabilities while keeping Prometheus in the metrics architecture.
- Validate before migrating if dashboards, alerts, or downstream systems depend on precise metric names, labels, temporality, or histogram behavior.
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