Best OpenTelemetry backends for self-hosted observability

Combined ranking

1. 1
   Grafana LGTM Stackgrafana.com ↗incumbent—9 pts

   Claude #1Gemini #2

   The most battle-tested self-hosted answer for all three signals with native OTLP ingest, huge community, mature Helm charts, and object-storage-backed components that scale from a single binary to very large clusters; the assumption shaping its #1 rank is a practitioner willing to operate 3-4 components in exchange for best-in-class flexibility and ecosystem depth

   + model takes & fixes− hide details

   Claude The most battle-tested self-hosted answer for all three signals with native OTLP ingest, huge community, mature Helm charts, and object-storage-backed components that scale from a single binary to very large clusters; the assumption shaping its #1 rank is a practitioner willing to operate 3-4 components in exchange for best-in-class flexibility and ecosystem depth

   Gemini Offers unmatched visualization flexibility, enterprise-grade multi-tenancy, and modular composability. It utilizes cheap cloud object storage for long-term retention of massive data scales and is backed by the largest community and ecosystem in observability.

   Where it falls short

   per Claude It is several systems, not one — you stitch together Tempo, Loki, and Mimir with separate configs and query languages (TraceQL, LogQL, PromQL), which is real operational overhead for a small team that just wants one box

   per Gemini High operational complexity and resource overhead; managing four separate distributed microservice components (Mimir, Loki, Tempo, Grafana), each with its own query language, requires significant dedicated engineering resources.

2. 2
   SigNozsignoz.io ↗—9 pts

   Claude #2Gemini #1

   Provides a unified, Datadog-like UI and native OpenTelemetry support out-of-the-box, storing metrics, traces, and logs in a single, highly performant ClickHouse database. This dramatically reduces self-hosting complexity and resource usage compared to composable stacks, making it the most cost-effective and operationally simple complete solution for small-to-medium teams.

   + model takes & fixes− hide details

   Gemini Provides a unified, Datadog-like UI and native OpenTelemetry support out-of-the-box, storing metrics, traces, and logs in a single, highly performant ClickHouse database. This dramatically reduces self-hosting complexity and resource usage compared to composable stacks, making it the most cost-effective and operationally simple complete solution for small-to-medium teams.

   Claude OTel-native from day one and the strongest single-application experience — traces, metrics, and logs in one ClickHouse-backed system with APM-style service views, alerting, and dashboards out of the box, making it the best value for a small-to-mid team that wants Datadog-like ergonomics without SaaS bills; near-tie with the Grafana stack for teams prioritizing simplicity over ecosystem

   Where it falls short

   per Claude Smaller ecosystem and fewer integrations/plugins than Grafana, and at very large scale you are operating and tuning ClickHouse yourself

   per Gemini It is less customizable than modular best-of-breed stacks, has a smaller community plugin ecosystem, and is not suitable for organizations where ClickHouse is not a supported or viable database engine.

3. 3
   VictoriaMetricsvictoriametrics.com ↗—4 pts

   Claude #5Gemini #3

   Outstanding CPU and disk storage efficiency for time-series metrics combined with simple single-binary operations. Its native OTLP ingestion support allows it to ingest OpenTelemetry metrics at a fraction of the hardware cost of Prometheus/Mimir, scaling effortlessly with minimal operational overhead.

   + model takes & fixes− hide details

   Gemini Outstanding CPU and disk storage efficiency for time-series metrics combined with simple single-binary operations. Its native OTLP ingestion support allows it to ingest OpenTelemetry metrics at a fraction of the hardware cost of Prometheus/Mimir, scaling effortlessly with minimal operational overhead.

   Claude Extraordinary resource efficiency and operational simplicity for the metrics-heavy shop — single small binaries that ingest OTLP and routinely replace Prometheus/Mimir at a fraction of the RAM and disk; ranked on the assumption metrics dominate your workload

   Where it falls short

   per Claude The traces and logs pieces are much newer than the metrics core and it has no bundled visualization — you still front it with Grafana, so it's a backend component more than a complete platform

   per Gemini It relies primarily on persistent block storage rather than cheap cloud object storage for primary performance, making long-term storage of massive volume datasets expensive, and its unified features for logs and traces are still far less mature than its metrics capabilities.

4. 4
   CClickStack—3 pts

   Claude #3Gemini —

   ClickHouse's official observability stack gives exceptional query speed and cost efficiency on wide events, with HyperDX's fast search-first UX over traces/logs/metrics/session replay — the strongest choice for high-cardinality, high-volume telemetry where SQL access to raw data matters

   + model takes & fixes− hide details

   Claude ClickHouse's official observability stack gives exceptional query speed and cost efficiency on wide events, with HyperDX's fast search-first UX over traces/logs/metrics/session replay — the strongest choice for high-cardinality, high-volume telemetry where SQL access to raw data matters

   Where it falls short

   per Claude Younger product with a less mature metrics/alerting story than SigNoz or Grafana, and it assumes comfort operating ClickHouse

5. 5
   EElastic Stack—2 pts

   Claude #4Gemini —

   First-class OTLP support in modern versions, unmatched full-text search over logs, mature APM UI, and deep security/SIEM crossover — the right pick when observability and log search/security investigations share one platform

   + model takes & fixes− hide details

   Claude First-class OTLP support in modern versions, unmatched full-text search over logs, mature APM UI, and deep security/SIEM crossover — the right pick when observability and log search/security investigations share one platform

   Where it falls short

   per Claude Heaviest resource footprint and operational cost of the list (JVM heap, shard management), and licensing/feature gating pushes real deployments toward paid tiers

6. 6
   OpenObserveopenobserve.ai ↗—2 pts

   Claude —Gemini #4

   A unified Rust-based single-binary backend that handles logs, metrics, and traces with extreme storage efficiency. By writing directly to object storage and indexing metadata with high compression, it achieves up to 10-15x lower storage cost compared to Elasticsearch-based setups, providing an extremely easy turnkey setup.

   + model takes & fixes− hide details

   Gemini A unified Rust-based single-binary backend that handles logs, metrics, and traces with extreme storage efficiency. By writing directly to object storage and indexing metadata with high compression, it achieves up to 10-15x lower storage cost compared to Elasticsearch-based setups, providing an extremely easy turnkey setup.

   Where it falls short

   per Gemini The visualization dashboards and alerting capabilities are less mature and customizable than Grafana, and it is not suited for teams that require deep, highly custom APM correlations or complex multi-tenant controls.

7. 7
   QQuickwit—1 pts

   Claude —Gemini #5

   A specialized search-engine backend written in Rust that decouples compute and storage, allowing direct querying of OpenTelemetry logs and traces on object storage. It is incredibly cost-efficient and lightweight for massive trace/log search workloads, scaling sub-second queries on petabytes of data without high RAM or CPU costs.

   + model takes & fixes− hide details

   Gemini A specialized search-engine backend written in Rust that decouples compute and storage, allowing direct querying of OpenTelemetry logs and traces on object storage. It is incredibly cost-efficient and lightweight for massive trace/log search workloads, scaling sub-second queries on petabytes of data without high RAM or CPU costs.

   Where it falls short

   per Gemini It is strictly a search and storage engine, not a complete visualization/APM platform; it lacks an out-of-the-box unified UI for metrics and requires pairing with tools like Grafana or Jaeger UI to be useful to end users.

Just missed the top 5

By model

Common questions

Tracked by ModelsAgree · rank 1 = 5 pts … rank 5 = 1 pt · re-polled weekly