OpenTelemetry vs. Prometheus: A 2026 Comparison Guide

Open-source observability runs on two projects more than any others: OpenTelemetry (OTel) for instrumentation across logs, metrics, and traces, and Prometheus for metric storage and querying. The handoff between them decides whether your telemetry pipeline stays easy to operate or turns into daily friction, especially with 77 percent of orgs running Prometheus and 49 percent deploying OpenTelemetry in the same stack.

This guide covers the architectural boundary between OTel and Prometheus, the metric format translation rules that shape any migration, and the production patterns engineers rely on when both systems run side by side. You’ll also get clear criteria for picking one, the other, or both.

What Is OpenTelemetry?

OpenTelemetry (OTel) is a vendor-neutral open-source framework for instrumenting, generating, collecting, and exporting traces, metrics, and logs across more than a dozen programming languages. It’s not a backend, so it doesn’t store, query, or alert on your telemetry. That boundary is the whole reason OTel pairs with Prometheus instead of competing with it: you instrument once, then send the data wherever you want.

OTel splits the work across four pieces that connect in sequence. The application programming interface (API) sits in your code and stays as no-op until you wire in the software development kit (SDK), which handles instrumentation libraries and exporters. The Collector acts as a vendor-agnostic proxy that receives and forwards telemetry, with OpenTelemetry Protocol (OTLP) carrying everything over gRPC or hypertext transfer protocol (HTTP).

What Is Prometheus?

Prometheus is an open-source monitoring and alerting toolkit that ships as a self-contained server combining metric collection, storage, and querying in a single process. SoundCloud built it in 2012, and it joined the CNCF in 2016 as the second hosted project after Kubernetes, with Alertmanager handling routing, silencing, and aggregation as a separate component. Where OTel deliberately stops at the wire, Prometheus owns the full path from scrape to query, which is the architectural inverse of how OTel is built.

The defining property is a pull-based collection model where Prometheus scrapes targets over HTTP at configured intervals, stores time series identified by a metric name and key/value label pairs, and queries that data with the Prometheus Query Language (PromQL). The local time-series database (TSDB) uses a custom on-disk format and runs without native clustering or replication, which keeps single instances simple but pushes high-availability concerns out to remote storage.

Prometheus 3.0 closed the biggest gap with OTel by adding native OTLP ingestion and Remote Write 2.0 with payload reductions of 58 to 88 percent, which is what makes running both side by side feel like one coherent pipeline rather than two glued-together systems.

Key Differences Between OpenTelemetry and Prometheus

OTel and Prometheus sit at different layers of the observability stack, which is why most production setups run both instead of choosing between them. The architectural split shows up across seven axes that shape deployment topology, migration paths, and day-to-day operations.

	OpenTelemetry	Prometheus
Scope	Instrumentation and transport only	Full monitoring stack: collection, TSDB, PromQL, Alertmanager
Signals	Metrics, traces, and logs, with cross-signal correlation via shared trace context	Metrics only
Collection model	Push via OTLP (HTTP/gRPC), with pull supported through the Prometheus receiver	Pull via HTTP scrape, with push via Pushgateway for short-lived jobs
Storage	None, delegated to the backend	Local TSDB, with HA and long-term retention requiring Thanos, Cortex, or Mimir
Instrumentation	Auto-instrumentation agents plus a vendor-neutral SDK for Java, .NET, Go, Python, Ruby, and Node.js	Manual client libraries tied to the Prometheus exposition format
Query language	None, depends on the backend	PromQL, native
Scalability	Stateless Collector fleet with a Target Allocator for sharded scraping	Single-node ceiling, with federation or remote write to Thanos or Cortex required

OTel Handles Generation and Transport, Prometheus Closes the Monitoring Loop

OTel lives on the telemetry generation and transport plane, while Prometheus ships as a single binary that handles collection, storage, querying, and alerting in one process. Choosing OTel means pairing it with a backend and UI, while Prometheus arrives with a TSDB, PromQL, and Alertmanager already wired together. The architectural gap is why a Collector feeding Prometheus shows up as one of the most common production patterns.

Prometheus Carries Metrics Only, OTel Carries Three Signals

Prometheus speaks metrics and nothing else, while OTel moves metrics, traces, and logs across one wire format with shared trace context tying them together. If your incident workflow depends on pivoting from a metric spike to the trace that caused it, Prometheus on its own cannot bridge that gap. OTel collapses three instrumentation paths into one, so you avoid running separate SDKs to debug a single outage.

Pull-Based Scraping Versus Push-Based OTLP

Prometheus scrapes targets on a fixed interval and emits an up metric per target, which doubles as free liveness monitoring. OTel pushes over OTLP, so ephemeral workloads like Lambdas, batch jobs, and short-lived pods can ship telemetry before the process exits. If your fleet is mostly batch jobs or short-lived containers, plan for Pushgateway on the Prometheus side or OTel push on the OTLP side, because pull alone loses telemetry from any process that dies before the next scrape.

OTel Delegates Storage, Prometheus Bundles a Local TSDB

Storage is where the two systems diverge hardest. Prometheus writes to a local TSDB that ships in the same binary, while OTel carries no storage of its own and hands the data off to whatever backend you point the Collector at. Backends like Coralogix write telemetry to your own cloud bucket in open Parquet format, which keeps long-term retention independent of any single vendor’s storage tier.

Auto-Instrumentation Across Runtimes Versus Manual Client Libraries

OTel ships auto-instrumentation agents that attach without code changes, plus a manual SDK for Java, .NET, Go, Python, Ruby, and Node.js. Prometheus expects you to import a client library and expose /metrics in its exposition format, which works for greenfield Go services and turns painful across a fleet of mixed runtimes. OTel instrumentation can also target any OTLP backend without rework, so swapping vendors does not mean re-instrumenting every service.

PromQL Is Native, OTel Inherits Its Query Layer

PromQL is a first-class part of Prometheus, with a built-in web UI for ad-hoc queries and recording rules baked into the same binary. OTel defines no query language of its own, so what you can ask of the data depends on the backend sitting downstream of the Collector. Routing OTel metrics into Prometheus is a common compromise: PromQL on the query side, OTLP on the ingest side.

OTel Scales Horizontally, Prometheus Hits a Single-Node Ceiling

The Collector is stateless, so scaling is a matter of adding replicas behind a load balancer, with the Target Allocator sharding Prometheus scrape targets across them. Prometheus itself runs as one process per instance, which caps how much a single server can scrape and store. Pushing past that ceiling pulls in federation or remote write into Thanos, Cortex, or Mimir, each of which adds a second tier of infrastructure to operate.

Metric Format Compatibility Between OpenTelemetry and Prometheus

Translation rules between the two formats quietly break dashboards and alerts during migration, and the OTel Prometheus compatibility spec still carries “Experimental” status, so behavior can shift between releases. Three rules cover most of what your pipeline runs into: naming conventions, type mapping, and aggregation temporality.

Dots Become Underscores, Creating Collision Risks

Name collisions are the first migration trap, since OTel and Prometheus disagree on legal characters. OTel uses dots as namespace delimiters (for example, http.response.status_code), while Prometheus metric names must match [a-zA-Z_:][a-zA-Z0-9_:]*. The spec replaces discouraged characters with underscores and collapses consecutive underscores into one, so an OTel counter named hvac.on with unit s exports to Prometheus as hvac_on_seconds_total. Two structurally distinct OTel names can resolve to the same Prometheus name once both . and _ collapse together, and that collision is a documented risk you have to design around in your naming policy.

How OpenTelemetry Types Map to Prometheus Counters, Gauges, and Histograms

Type mapping mostly works, with one gap worth planning for. An OTel monotonic Sum maps to a Prometheus Counter, while an OTel Gauge and a non-monotonic Sum both map to a Prometheus Gauge. OTel Histograms with cumulative temporality convert to three series: {name}_count, {name}_sum (emitted only when sum is enabled), and {name}_bucket with an le label per bucket. Prometheus Summary has no direct OTel equivalent, so any dashboard that depends on Summary quantiles needs rebuilding against histogram buckets.

Delta vs. Cumulative Aggregation Temporality

Temporality is where stateless Collector deployments quietly fall over. Prometheus is always cumulative, and OTel supports both delta and cumulative, with the exporter spec requiring cumulative for all instrument kinds. Delta-to-cumulative conversion needs a stateful Collector that remembers prior values per series, which means any delta stream has to route to a single Collector instance rather than load-balance across stateless replicas. If you skip that routing rule, every counter resets on each scrape and your dashboards quietly go wrong, so pin delta streams to one Collector replica before you turn the pipeline on.

When to Use OpenTelemetry vs. Prometheus

No single configuration fits every organization, and the answer turns on three inputs: your signal requirements, your existing investment, and your operational capacity to run extra components. The decision is rarely binary, and most production estates land on a mix.

When Prometheus Is the Right Choice

Prometheus is the right choice when all four of these scenarios describe your team:

• Metrics-only workloads: Your incident workflow doesn’t need trace or log correlation, so the single-binary Prometheus stack covers everything you query.
• Heavy PromQL investment: Dashboards, recording rules, and alert routes already encode years of institutional memory that a rewrite would discard for no gain.
• Lean operations team: You don’t have headcount to run a Collector fleet, manage SDK upgrades, and own a separate config surface.
• Pull-friendly topology: Your services live in stable network locations where Prometheus scraping works without proxies or push gateways.

If all four match your environment, stay on Prometheus until one of them changes, because adding OTel mostly adds operational surface without expanding what you can answer during an incident.

When OpenTelemetry Is the Right Choice

OTel is worth the lift when any of these describes your environment:

• Greenfield instrumentation: You’re starting fresh and want write-once SDKs that survive backend swaps without touching application code.
• Three-signal correlation: Traces, logs, and metrics have to share context from day one, which Prometheus alone can’t deliver.
• Vendor portability mandate: Procurement or architecture has set a hard requirement to avoid lock-in to any single observability vendor.
• Dedicated platform group: You have a team that can own the Collector fleet, configuration, and SDK upgrades as a product.

If any one of those holds, lead with OTel and pick a backend that supports OTLP natively, because the write-once instrumentation pays back across every backend change you’ll make over the next decade.

When to Run Both Together

The hybrid pattern is the most common production reality, and it’s often the right one. Running both makes sense when you have years of PromQL dashboards and alert rules you can’t flag-day rewrite, alongside new services that need distributed tracing or OTel-native pipelines. Prometheus keeps scraping legacy targets while OTel SDKs instrument new code, with the Collector translating between formats at the edge. The seams need careful planning, since the cost of running both shows up in duplicated metric storage, two query languages on call, and the routing rules that decide which signal goes where.

How to Use OpenTelemetry and Prometheus Together

Three integration patterns cover most production starting points: existing Prometheus, greenfield OTel, or mixed environments. The right pattern is the one that matches where your telemetry already lives today, and the migration plan flows from there.

Scraping Prometheus Endpoints With the OpenTelemetry Collector

The OTel Collector’s Prometheus receiver supports the full scrape_config surface and converts scraped metrics to OTLP internally. Existing scrape_configs blocks transfer directly into the receiver’s config key, so your target discovery keeps working while metrics route to any OTLP-compatible backend.

receivers:

  prometheus:

    config:

      scrape_configs:

        – job_name: ‘app’

          static_configs:

            – targets: [‘app:8080’]

exporters:

  prometheusremotewrite:

    endpoint: <remote-write-url>

service:

  pipelines:

    metrics:

      receivers: [prometheus]

      exporters: [prometheusremotewrite]

The alert_config.alertmanagers and rule_files directives must come out of scrape configs before migrating, since the receiver doesn’t support them. Running multiple Collector replicas with identical configs produces duplicate metrics because every replica scrapes every target, which the Target Allocator resolves by sharding scrape jobs across replicas.

Sending OpenTelemetry Data to Prometheus via OTLP

Prometheus 3.0 accepts OTLP natively through the –web.enable-otlp-receiver flag, exposing an HTTP/protobuf endpoint at /api/v1/otlp for metrics only, disabled by default. The OTel project recommends an intermediary Collector for production deployments rather than wiring SDKs straight to Prometheus. That extra hop keeps your pipeline flexible and gives you one place to process or reroute telemetry before it hits storage. It also lets you fan out to a second backend during validation without touching application code.

Running a Hybrid Pipeline in Production

Operating OTel and Prometheus side by side adds operational surface area that stays manageable with a few guardrails. Most production failures show up in scrape behavior, label mapping, and metric name changes, all of which are catchable before cutover. A safe hybrid rollout follows these five steps in order, from version pinning through to traffic cutover:

• Pin component versions: Lock OTel Operator, Collector, and Target Allocator to versions from the same compatibility matrix before any rollout. Version drift across the three is one of the most common causes of silent scrape failures and missing targets after upgrades.
• Enable resource-to-telemetry conversion: Set resource_to_telemetry_conversion: enabled: true on the prometheusremotewrite exporter so OTel resource attributes like service.name and k8s.pod.name arrive as Prometheus labels. Without it, every PromQL alert keyed on those labels returns no series and pages silently disappear.
• Audit metric naming before cutover: OTel’s dot-separated metric names become underscore-separated in Prometheus, so http.server.duration lands as http_server_duration. Grep dashboards, recording rules, and alert expressions for the old names, and stage the rename in version control before the switch.
• Stage and validate parity: Run the OTel pipeline alongside Prometheus in a staging cluster that mirrors production scrape targets, then diff series counts, label sets, and alert firing patterns for at least one full business cycle. Dashboard-level parity catches mismatches that unit tests will not.
• Cut over with a rollback path: Promote OTel as primary, keep Prometheus writing to its local TSDB for a week as a fallback, and only decommission once dashboards and on-call runbooks have lived against the new path through an incident or two.

Platform teams running fleets of Collectors across clusters usually centralize this work through tooling like Coralogix Fleet Management, which gives one place to push configuration changes and version pins across every agent. That single control point keeps a hybrid rollout from turning into a per-cluster archaeology project six months in.

Unify OpenTelemetry and Prometheus With Coralogix

For most production teams, the answer is to run both OTel and Prometheus, and the long-term operational question is which backend captures the combined stream your team lives inside during incidents. Coralogix accepts both as first-class ingestion paths, with OpenTelemetry-native ingestion for traces, metrics, and logs through the Collector, and Prometheus Remote Write for scraped metrics. Streama, the in-stream processing engine, analyzes telemetry and alerts on patterns before any of it lands in storage, with pricing per gigabyte ingested and no per-host or per-series charges. DataPrime queries logs, metrics, and traces in one language while PromQL stays available for existing dashboards, so cross-signal investigation works without leaving the workspace.

Start a free 14-day Coralogix trial and run OTel and Prometheus side by side into your own Parquet bucket against real production data. The trial includes full feature access with no credit card, and both pipelines can be reporting inside an afternoon.

Frequently Asked Questions About OpenTelemetry and Prometheus

Can OpenTelemetry replace Prometheus entirely?

OTel can replace Prometheus’s scraping function for many use cases, since the Collector’s Prometheus receiver handles the same scrape_config syntax your existing jobs already use. Storage, PromQL, Alertmanager, and the automatic up metric the pull model generates per scrape target stay outside OTel’s scope. The dominant production pattern is OTel running alongside Prometheus rather than replacing it, with both feeding a backend like Coralogix that accepts both pipelines without forcing a choice.

Does Prometheus accept OpenTelemetry Protocol (OTLP) data?

Yes. Native OTLP ingestion became stable in Prometheus 3.0 via the –web.enable-otlp-receiver flag at /api/v1/otlp, with metrics only and disabled by default. Prometheus 3.2 still supports only cumulative temporality for scraped metrics, with delta temporality absent for OTLP.

Which is better for Kubernetes monitoring, OpenTelemetry or Prometheus?

Neither wins universally. Prometheus ships a single server binary with built-in storage and PromQL, a separate Alertmanager, and mature exporters like kube-state-metrics, while OTel ships the Kubernetes Cluster Receiver and Kubelet Stats Receiver plus shared context propagation that correlates traces, logs, and metrics across services. Coralogix accepts both feeds in the same workspace through Prometheus Remote Write and OTLP, so the choice is about instrumentation strategy rather than backend lock-in.

How do I migrate from Prometheus to OpenTelemetry gradually?

The safest path runs an OTel Collector alongside your existing Prometheus instance, with the Prometheus receiver scraping the same targets and exporting to your new backend while Prometheus keeps running unchanged. Once dashboards and alerts validate against the OTel path, you cut Prometheus over to OTLP ingestion or retire it entirely. Dual-writing from one Collector to two destinations during validation shortens the cutover window considerably.