# Prometheus and OpenTelemetry Are Converging Without Replacing Each Other

- Link: https://thestackobserver.com/prometheus-and-opentelemetry-are-converging-without-replacing-each-other/
- Published: 2026-10-14T12:54:31-04:00
- Author: Stackxx

A new OpenTelemetry community survey offers a useful correction to a familiar observability
narrative: Prometheus and OpenTelemetry are not racing toward a winner-takes-all
finish. They are settling into a shared metrics architecture in which each project
does the job teams already trust it to do.

The survey, published in September 2026, found that using the two projects together
has become measurably easier since 2024. At the same time, adoption patterns remain
deliberately mixed. Prometheus exporters still dominate infrastructure collection,
OpenTelemetry SDKs lead application instrumentation, and the processing layer is
almost evenly split between Prometheus rules and the open source OpenTelemetry Collector.

That combination matters more than a simple adoption ranking. It suggests the practical
endpoint for many Kubernetes and cloud native teams is not a wholesale migration
from Prometheus to OpenTelemetry. It is a stable division of labor: preserve Prometheus-
compatible metrics and PromQL workflows while using OpenTelemetry as the increasingly
common instrumentation and transport layer.

## The headline is coexistence, not replacement

The survey analyzed 81 active OpenTelemetry metrics users running Prometheus or 
a Prometheus-adjacent backend. It excluded observability vendor employees and drew
heavily from mature organizations: 48% described their observability practice as
established, and 42% worked at companies with at least 1,000 employees.

Within that experienced sample, the average rating for ease of use between OpenTelemetry
and Prometheus rose from 3.1 out of five in 2024 to 3.6 in 2026. The share describing
the combination as difficult fell from 29% to 10%, while nobody selected “very difficult.”
The comparison is not perfectly controlled because the earlier survey did not identify
vendor employees, but the authors report that including vendors in the 2026 results
would barely change the ease-of-use distribution.

The improvement is real, but it does not mean the projects have become interchangeable.
The largest group still rated interoperability as neither easy nor difficult. More
importantly, respondents chose different tools at different points in the pipeline.

For infrastructure metrics, 72% use Prometheus exporters and 57% use OpenTelemetry
receivers. Nearly half mix Prometheus-style and OpenTelemetry-style infrastructure
instrumentation. Among teams that use only one style, Prometheus-only remains roughly
twice as common as OpenTelemetry-only.

Application instrumentation reverses the order. OpenTelemetry SDKs are used by 65%
of respondents, compared with 52% for Prometheus SDKs, and 41% rely exclusively 
on OpenTelemetry-style instrumentation for applications. That is the clearest sign
of where the boundary is forming: existing infrastructure integrations favor the
vast Prometheus exporter ecosystem, while new application code increasingly starts
with vendor-neutral OpenTelemetry APIs and SDKs.

## Why the split makes architectural sense

Prometheus built its strength around a pull-based operating model, a huge catalog
of exporters, a durable time-series data model, and PromQL. Kubernetes operators
understand its service discovery, scraping, alerting, and recording-rule workflows.
Replacing that installed base would create risk without necessarily improving the
questions teams can ask of their metrics.

OpenTelemetry addresses a different layer. It standardizes how applications create
telemetry, how agents and collectors receive it, and how signals are processed and
exported. Its value increases when an organization wants consistent instrumentation
across metrics, traces, logs, and profiles, or needs the freedom to route data to
more than one backend.

The two models meet through several paths. The OpenTelemetry Collector can scrape
Prometheus endpoints, and Prometheus can accept metrics pushed over OTLP/HTTP when
its receiver is explicitly enabled. Translation rules cover counters, gauges, histograms,
native histograms, summaries, exemplars, and instrumentation scope. These are not
merely adapters at the edge; they represent a shared compatibility layer that preserves
enough meaning for teams to choose components independently.

This is why the survey’s mixed deployments should not be read as unfinished migrations.
A Kubernetes platform may scrape node and database exporters through a Collector,
accept OTLP from application SDKs, apply selected transformations, and store everything
in a Prometheus-compatible backend. That architecture uses both ecosystems by design.
Removing either one could reduce capability rather than reduce complexity.

## The processing layer is the new decision point

The most revealing result may be what happens between collection and storage. Prometheus
relabeling and recording rules are used by 54% of respondents; the open source OpenTelemetry
Collector is used by 53%. Sixty-five percent run what the survey calls a “vanilla
stack”: Prometheus rules, the standard Collector, or both, without a vendor distribution
or a custom Collector build in the pipeline.

That is a strong signal for platform teams evaluating elaborate telemetry architectures.
The mainstream pattern is not a heavily customized observability mesh. It is a relatively
plain combination of community components, with transformation placed where the 
team already has operational skill.

Prometheus rules remain effective for metric-specific concerns: dropping labels,
rewriting targets, precomputing expensive queries, and keeping cardinality under
control close to the backend. The Collector is better positioned for pipeline concerns:
receiving multiple protocols, enriching signals with resource context, filtering
telemetry, batching exports, and routing data to multiple destinations.

The risk comes from duplicating policy across both. If a label is renamed in the
Collector and then rewritten again in Prometheus, dashboards become harder to reason
about. If aggregation happens at several stages, operators may not know which representation
is canonical. Convergence therefore increases the need for a clear ownership model
even as it reduces protocol friction.

## The hard problems have moved up the stack

Interoperability is no longer primarily about whether one system can ingest the 
other’s wire format. The survey’s open-ended responses point to subtler gaps: differences
between attributes and labels, incomplete handling of resource metadata, and naming
or formatting surprises.

These details determine whether a metric remains useful after translation. OpenTelemetry
models resource attributes separately from individual data-point attributes, while
Prometheus has historically represented identifying context through labels and target
metadata. A technically successful conversion can still produce awkward queries,
unexpected names, or excessive cardinality if teams promote every resource attribute
indiscriminately.

Prometheus documentation reflects that tradeoff. Its OTLP receiver is disabled by
default because a Prometheus server may run without authentication, making an exposed
ingestion endpoint unsafe. Once enabled, operators must choose which resource attributes
become labels and how metric names are translated. Prometheus recommends promoting
a targeted set of attributes such as service identity and deployment environment
rather than copying everything.

Those controls are evidence of maturing interoperability, not signs that translation
is automatic. Platform teams still need conventions for service names, namespaces,
cluster identity, units, histogram behavior, and label budgets. The protocol bridge
can carry data; it cannot decide which organizational semantics are correct.

## What Kubernetes platform teams should do now

The survey supports an incremental strategy rather than a forced migration program.

 * **Keep proven exporters.** If a database, node, or controller exporter is reliable,
   there is little reason to replace it solely to claim an OpenTelemetry-native 
   stack. Receive or scrape it through the component that best fits the current 
   operating model.
 * **Standardize new application instrumentation on OpenTelemetry.** Its APIs and
   SDKs provide a better foundation for correlating metrics with traces and other
   signals while retaining backend choice.
 * **Choose one policy owner for every transformation.** Document whether naming,
   resource promotion, filtering, aggregation, and cardinality controls live in 
   application SDKs, the Collector, Prometheus rules, or the backend. Avoid implementing
   the same policy twice.
 * **Test semantic compatibility, not just delivery.** A successful OTLP request
   does not prove that dashboards, alerts, exemplars, or histogram queries still
   behave correctly. Build golden queries and alerts into migration tests.
 * **Protect ingestion endpoints.** Enabling Prometheus’ OTLP receiver changes its
   network exposure. Put authentication, authorization, and network policy around
   it rather than treating the receiver as a drop-in public endpoint.
 * **Measure the pipeline itself.** Track rejected data points, queue growth, dropped
   attributes, translation failures, and series cardinality. A hybrid pipeline needs
   observability for its boundaries.

Teams should also resist designing around a hypothetical future in which every exporter
disappears. The survey shows a large, active exporter estate coexisting with rising
OpenTelemetry adoption. A platform contract that accepts both Prometheus exposition
and OTLP is more realistic than one that mandates a single ingestion style for every
workload.

## What still needs to converge

The remaining work is concentrated in semantics and defaults. The communities are
discussing closer alignment between attributes and labels, better native metadata,
and more predictable naming. Existing pieces already help: PromQL supports UTF-8
metric names, Prometheus offers configurable translation strategies, and the compatibility
specification defines stable mappings for major metric types. But usable defaults
lag behind the underlying capabilities.

That gap creates an opportunity for the CNCF ecosystem. If common Kubernetes distributions
can ship tested conventions for service identity, resource promotion, histogram 
conversion, and secure OTLP ingestion, teams will spend less time inventing local
rules. The goal should not be to hide every difference between Prometheus and OpenTelemetry.
It should be to make those differences explicit, testable, and boring to operate.

The survey is small and skewed toward mature practitioners, so it should not be 
treated as a census of the entire cloud native market. Its 81 analyzed respondents
are more useful as a directional sample of teams already doing the integration work.
Even with that caveat, the pattern is coherent across instrumentation and processing:
organizations are combining the projects at multiple layers, and the experience 
is improving.

## A durable two-project architecture

The most consequential outcome of better interoperability may be that it removes
pressure to choose a single winner. Prometheus can remain the familiar metrics engine
and query ecosystem. OpenTelemetry can become the common instrumentation and telemetry
pipeline. Cloud native teams can modernize application signals without abandoning
exporters, PromQL, or existing operational knowledge.

That is a more durable architecture than a rip-and-replace migration. It acknowledges
that standards succeed when they connect valuable systems, not only when they displace
them. For Kubernetes operators, the next phase of observability will be less about
choosing Prometheus or OpenTelemetry and more about governing the contract between
them.

## Sources

 * [OpenTelemetry: Prometheus and OpenTelemetry interoperability in 2026 — survey results](https://opentelemetry.io/blog/2026/otel-prometheus-interoperability/)
 * [Prometheus: Using Prometheus as your OpenTelemetry backend](https://prometheus.io/docs/guides/opentelemetry/)
 * [OpenTelemetry specification: Prometheus and OpenMetrics compatibility](https://opentelemetry.io/docs/specs/otel/compatibility/prometheus_and_openmetrics/)