No, OpenTelemetry (OTel) will not replace Prometheus. Instead, they have essentially joined forces.
The relationship between the two is one of the most misunderstood topics in DevOps and SRE, but the reality is beautifully collaborative: OpenTelemetry is the industry standard for generating and collecting data, while Prometheus is the industry standard for storing and querying metrics.
The Core Difference
| Feature | Prometheus | OpenTelemetry (OTel) |
| What is it? | A full monitoring system (scraper, database, querying, alerting). | A standardized framework/API for application instrumentation. |
| Data Scope | Metrics only (CPU, memory, request counts). | The “Three Pillars”: Metrics, Traces, and Logs. |
| Storage | Has its own built-in, highly efficient local database (TSDB). | No storage. It only collects and routes data; it cannot store it. |
| Data Flow | Traditionally Pull-based (scrapes apps). | Push or Pull (via the OTel Collector). |
Why OTel Won’t Replace Prometheus (They “Grew Up” Together)
Historically, there was real friction. If you instrumented an application using OTel, it used dot-notation (http.server.request.duration), but Prometheus only accepted underscores (http_server_request_duration). It caused massive formatting headaches.
However, the community completely solved this. With the widespread adoption of Prometheus 3.0, the two systems are perfectly intertwined:
- Prometheus Speaks OTel Natively: Prometheus natively ingests OTLP (OpenTelemetry’s native protocol) and fully supports OTel’s naming conventions (like dots and dashes).
- OTel Lacks a Backend: Because OTel explicitly refuses to build a storage database or a query language, it needs backends. When OTel collects metrics from your applications, it frequently sends them directly into a Prometheus backend.
- Infrastructure vs. Application: * Prometheus remains king for infrastructure monitoring. Thousands of tools (like Kubernetes, Linux Node Exporter, databases) natively output Prometheus metrics.
- OTel is the new king for application monitoring (APM), because it allows developers to write code once and seamlessly correlate metrics with deep distributed distributed traces.
The Winning Architecture
Most modern engineering teams don’t choose between them—they use them together in a hybrid pipeline:
[ App / Code ] ──(Traces & Metrics)──> [ OTel Collector ] │ ┌─────────────────────────┴────────────────────────┐ ▼ ▼ [ Prometheus / Grafana ] [ Jaeger / Tempo ] (Stores & Alerts on Metrics) (Stores & Analyzes Traces)