Prometheus and OpenTelemetry interoperability in 2026

Interoperability between OpenTelemetry and Prometheus has measurably improved, with the average ease-of-use rating rising from 3.1 to 3.6, and the share of users finding the tools hard to use together decreased from 29% to 10%. In infrastructure instrumentation, Prometheus exporters remain the most common method for collecting metrics, though OpenTelemetry receivers are close behind, with nearly half of respondents using both simultaneously rather than migrating completely. For application instrumentation, OpenTelemetry SDKs are more frequently used than Prometheus SDKs. The most common processing steps involve Prometheus relabeling rules and the OpenTelemetry Collector. While interoperability ease has improved, open-ended feedback highlights friction points in data model unification, handling resource attributes, and naming conventions. A key observation is that mid-size organizations show the highest adoption of OTel-native tooling, and team type influences backend choices, with Platform Engineering and SRE teams favoring OSS Prometheus-compatible backends while Dev teams lean toward plain Prometheus.

The improvement in interoperability scores suggests that the initial friction points were addressable through incremental work, moving from a state where 29% found the integration difficult to a much lower figure. However, the persistence of significant user feedback on data model unification and metadata gaps indicates that functional compatibility is achieved while semantic alignment remains an ongoing challenge. The divergence in adoption patterns across organization sizes—where mid-size entities push OTel tooling—suggests a tension between large-scale platform mandates and practical deployment realities. Furthermore, the distinction based on team type reveals a fundamental split: operational teams favor established tools like Prometheus for direct ownership, whereas platform/SRE teams adopt OSS backends, suggesting that adoption patterns are often dictated by organizational structure and perceived ownership rather than purely technical preference. The call to unify data models and handle resource attributes points toward the next critical phase of evolution: shifting interoperability from mere functional handshake to deep semantic congruence within the underlying specifications. What is the implicit cost of maintaining these separate interpretations when the community is actively pursuing unified standards?