Best self-hosted LLM observability tool

Combined ranking

1. 1
   Langfuse—15 pts

   GPT #1Claude #1Gemini #1

   Best overall balance of deep agent tracing, sessions, cost/latency analytics, evaluations, prompt management, mature SDKs, OpenTelemetry ingestion, and production Kubernetes deployment; strong default when one private platform must serve many application teams.

   Claude The most mature purpose-built self-hosted option — MIT-licensed core, official Docker Compose and Kubernetes/Helm deployments, ClickHouse-backed architecture proven at production trace volumes, an OTel ingestion endpoint plus SDKs for every major framework, and prompt management, evals, and cost tracking in one place; assumed the team wants an LLM-native UI rather than raw spans, which puts it ahead of OTel-stack approaches

   Gemini It provides the most complete, developer-friendly, and mature open-source (MIT) feature set covering SDK-based tracing, prompt management, evaluations, and datasets, with straightforward Docker/Kubernetes deployment configurations.

   Where it falls short

   per GPT Production self-hosting is operationally heavy, requiring ClickHouse, PostgreSQL, Redis, and object storage, with some enterprise controls commercially licensed.

   per Claude v3 self-hosting is operationally heavy (ClickHouse, Redis, S3, async worker) and some enterprise features (fine-grained RBAC/SSO enforcement, certain eval tooling) sit behind a paid EE license — overkill for a small team wanting a single container

   per Gemini Running it at production scale requires managing and scaling a complex multi-database backend including PostgreSQL, ClickHouse, and Redis.

2. 2
   Arize Phoenix—11 pts

   GPT #2Claude #2Gemini #3

   Excellent OpenTelemetry/OpenInference foundation, broad Python/TypeScript/Java instrumentation, strong trace debugging, RAG analysis, datasets, experiments, and evaluations; near-tied with OpenLIT, ranking higher for its polished investigation and evaluation workflow.

   Claude OTel-native tracing built on the OpenInference conventions, runs as a single container for near-zero-friction on-prem starts, and has the strongest open-source evaluation and dataset/experiment workflow of the group — near-tie with Langfuse for eval-centric teams, ranked second on weaker multi-tenant/production hardening

   Gemini OpenTelemetry-native (using OpenInference standards) and highly optimized for deep RAG analysis, model evaluations, and vector visualization, with a fantastic local notebook execution model.

   Where it falls short

   per GPT Less complete as a turnkey multi-team operations platform for alerting, governance, and long-term fleet management.

   per Claude Auth, RBAC, and multi-team production operation are much thinner than Langfuse; it shines for engineer-driven debugging and evals, not as a hardened shared platform service

   per Gemini Lacks built-in prompt management, API gateways, and multi-tenant access controls in the open-source version, making it harder to use as a shared central enterprise portal.

3. 3
   Helicone—5 pts

   GPT #5Claude —Gemini #2

   Extremely easy to integrate as a drop-in API gateway/proxy, offering instant caching, rate-limiting, threat detection, and cost tracking with minimal code changes under a permissive Apache 2.0 license.

   GPT Fast adoption through an LLM gateway/proxy, with request logging, sessions, cost and latency analytics, caching, rate limits, retries, and a genuinely self-hostable deployment that keeps prompts inside the network.

   Where it falls short

   per GPT Its best experience introduces gateway coupling and is less natural for rich arbitrary spans across complex multi-service agent workflows.

   per Gemini Not built for tracing deep, non-API nested code execution or local model runs that require SDK-level instrumentation rather than HTTP gateway proxying.

4. 4
   OOpik—4 pts

   GPT #4Claude #4Gemini —

   Capable self-hosted tracing for agents and RAG systems, with conversation threads, production dashboards, online evaluations, CI-friendly experiments, cost tracking, OpenTelemetry support, and unusually strong optimization tooling.

   Claude Comet's Apache-2.0 platform with clean self-hosting, first-class tracing plus an eval-first workflow (LLM-judge metrics, regression testing in CI) and rapid development velocity — near-tie with MLflow, ranked below it on operational track record

   Where it falls short

   per GPT The platform is heavier and more application-evaluation-centric than teams wanting a simple, standards-first observability backend may need.

   per Claude Youngest of the group; self-hosted scaling/HA patterns are less battle-tested and the ecosystem of integrations and community answers is still thin compared to Langfuse

5. 5
   MLflowincumbent—3 pts

   GPT —Claude #3Gemini —

   Apache-2.0 with MLflow 3's Tracing giving genuinely capable GenAI trace capture and evals inside a tool platform teams very often already run and know how to operate, backed by Databricks and a huge community — the lowest-new-infrastructure answer for orgs with existing MLflow deployments

   Where it falls short

   per Claude LLM-specific analytics and UX (cost dashboards, prompt diffing, live monitoring views) trail purpose-built tools; if you don't already run MLflow, standing it up just for LLM tracing is the weaker choice

6. 6
   OOpenLIT—3 pts

   GPT #3Claude —Gemini —

   Particularly strong for platform engineers: Apache-2.0, OpenTelemetry-native traces and metrics, Kubernetes deployment, broad automatic instrumentation, cost tracking, GPU/vector-database monitoring, and zero-code controller-based onboarding.

   Where it falls short

   per GPT Its controller, fleet-management experience, and overall product maturity have less real-world seasoning than Langfuse or Phoenix.

7. 7
   LLaminar—2 pts

   GPT —Claude —Gemini #4

   Built in Rust for high-throughput and low-overhead telemetry ingestion, featuring native OpenTelemetry compliance and advanced visualization specifically designed for debugging complex multi-step agentic workflows.

   Where it falls short

   per Gemini As a relatively newer project, it has a smaller ecosystem, fewer legacy integrations, and less mature documentation than established players.

8. 8
   LangSmith—1 pts

   GPT —Claude —Gemini #5

   The absolute gold standard for tracing, debugging, and managing prompts in applications built on the LangChain ecosystem, providing the most polished UI and interactive playground for LLM development.

   Where it falls short

   per Gemini It is closed-source and requires an expensive Enterprise license for self-hosting, presenting a high financial barrier and complex setup for air-gapped platforms.

9. 9
   OOpenLLMetry—1 pts

   GPT —Claude #5Gemini —

   Traceloop's open-source instrumentation emits standard OTel spans for LLM calls into whatever backend you already self-host (Grafana Tempo, Jaeger, SigNoz), meaning zero new stateful services, existing alerting/retention/access controls apply, and no vendor lock-in — the purest platform-engineering answer

   Where it falls short

   per Claude It's instrumentation, not a product — you get spans in a generic trace viewer with no prompt playground, evals, or LLM cost analytics unless you build them yourself

Just missed the top 5

By model

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