{"slug":"best-self-hosted-llm-observability","title":"Best self-hosted LLM observability tool","question":"What are the best self-hosted / open-source LLM observability and tracing tools for platform engineering teams that can't send prompts to a third-party cloud?","verdict":"As of 2026-07-14, ChatGPT, Claude, Gemini and Grok collectively rank Langfuse #1 for self-hosted llm observability tool on ModelsAgree — a unanimous pick. The models' case: Best overall balance of deep agent tracing, sessions, cost/latency analytics, evaluations, prompt management, mature SDKs, OpenTelemetry ingestion, and production. The models' main caveat: Production self-hosting is operationally heavy, requiring ClickHouse, PostgreSQL, Redis, and object storage, with some enterprise controls. The strongest alternative is Arize Phoenix — Excellent OpenTelemetry/OpenInference foundation, broad Python/TypeScript/Java instrumentation, strong trace debugging, RAG analysis, datasets. Source: https://modelsagree.com/best/best-self-hosted-llm-observability (modelsagree.com, CC BY 4.0).","category":"AI Infra","url":"https://modelsagree.com/best/best-self-hosted-llm-observability","updated":"2026-07-14","models":["ChatGPT","Claude","Gemini","Grok"],"consensus":"All 4 models rank Langfuse the top pick","disagreement":null,"combined":[{"rank":1,"product":"Langfuse","domain":"langfuse.com","score":20,"appearances":4,"modelRanks":{"ChatGPT":1,"Claude":1,"Gemini":1,"Grok":1},"reason":"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."},{"rank":2,"product":"Arize Phoenix","domain":"arize.com","score":15,"appearances":4,"modelRanks":{"ChatGPT":2,"Claude":2,"Gemini":3,"Grok":2},"reason":"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."},{"rank":3,"product":"Helicone","domain":"helicone.ai","score":5,"appearances":2,"modelRanks":{"ChatGPT":5,"Gemini":2},"reason":"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."},{"rank":4,"product":"OpenLLMetry","domain":"traceloop.com","score":4,"appearances":2,"modelRanks":{"Claude":5,"Grok":3},"reason":"Lightweight Apache 2.0 OTel-native instrumentation for LLM apps (providers, vector DBs, frameworks); zero-config tracing of prompts, latencies, costs that pipes into existing self-hosted OTel backends (e.g., SigNoz, Tempo); ideal for platform teams extending current observability without new full-stack tool."},{"rank":5,"product":"Opik","domain":"comet.com","score":4,"appearances":2,"modelRanks":{"ChatGPT":4,"Claude":4},"reason":"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."},{"rank":6,"product":"MLflow","domain":"mlflow.org","score":3,"appearances":1,"modelRanks":{"Claude":3},"reason":"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"},{"rank":7,"product":"OpenLIT","domain":"openlit.io","score":3,"appearances":1,"modelRanks":{"ChatGPT":3},"reason":"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."},{"rank":8,"product":"Laminar","domain":"lmnr.ai","score":2,"appearances":1,"modelRanks":{"Gemini":4},"reason":"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."},{"rank":9,"product":"LangSmith","domain":"langchain.com","score":1,"appearances":1,"modelRanks":{"Gemini":5},"reason":"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."}],"perModel":{"ChatGPT":[{"rank":1,"product":"Langfuse","reason":"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.","fix":"Production self-hosting is operationally heavy, requiring ClickHouse, PostgreSQL, Redis, and object storage, with some enterprise controls commercially licensed."},{"rank":2,"product":"Arize Phoenix","reason":"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.","fix":"Less complete as a turnkey multi-team operations platform for alerting, governance, and long-term fleet management."},{"rank":3,"product":"OpenLIT","reason":"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.","fix":"Its controller, fleet-management experience, and overall product maturity have less real-world seasoning than Langfuse or Phoenix."},{"rank":4,"product":"Opik","reason":"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.","fix":"The platform is heavier and more application-evaluation-centric than teams wanting a simple, standards-first observability backend may need."},{"rank":5,"product":"Helicone","reason":"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.","fix":"Its best experience introduces gateway coupling and is less natural for rich arbitrary spans across complex multi-service agent workflows."}],"Claude":[{"rank":1,"product":"Langfuse","reason":"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","fix":"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"},{"rank":2,"product":"Arize Phoenix","reason":"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","fix":"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"},{"rank":3,"product":"MLflow","reason":"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","fix":"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"},{"rank":4,"product":"Opik","reason":"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","fix":"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"},{"rank":5,"product":"OpenLLMetry","reason":"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","fix":"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"}],"Gemini":[{"rank":1,"product":"Langfuse","reason":"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.","fix":"Running it at production scale requires managing and scaling a complex multi-database backend including PostgreSQL, ClickHouse, and Redis."},{"rank":2,"product":"Helicone","reason":"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.","fix":"Not built for tracing deep, non-API nested code execution or local model runs that require SDK-level instrumentation rather than HTTP gateway proxying."},{"rank":3,"product":"Arize Phoenix","reason":"OpenTelemetry-native (using OpenInference standards) and highly optimized for deep RAG analysis, model evaluations, and vector visualization, with a fantastic local notebook execution model.","fix":"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."},{"rank":4,"product":"Laminar","reason":"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.","fix":"As a relatively newer project, it has a smaller ecosystem, fewer legacy integrations, and less mature documentation than established players."},{"rank":5,"product":"LangSmith","reason":"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.","fix":"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."}],"Grok":[{"rank":1,"product":"Langfuse","reason":"Leading open-source (MIT) LLM observability platform with comprehensive tracing, evaluations, prompt management, datasets, and dashboards; full self-hosting via Docker/K8s with feature parity to cloud (Postgres/ClickHouse/Redis stack); framework-agnostic with strong integrations (LangChain, OpenAI SDK, OTel); battle-tested at scale for production RAG/agents with data sovereignty focus. Assumes typical platform team needs end-to-end visibility without SaaS data exfil.","fix":"Requires managing multi-component stack (DBs, etc.) for production scale; some advanced EE features (RBAC, audit) need license."},{"rank":2,"product":"Arize Phoenix","reason":"Strong open-source/self-hostable (ELv2) observability & evaluation tool focused on LLM traces, RAG/agent debugging, evals (LLM-as-judge), and OpenInference/OTel standards; simple Docker/Python deployment keeps data fully on-prem; excels for experimentation-to-prod workflows in privacy-sensitive environments. Near-tie with Langfuse on core merits but edges on lighter ML-native eval depth for some teams.","fix":"Phoenix OSS is more dev/experimentation-oriented than full enterprise production platform (lacks some scaling/polish of Langfuse's ClickHouse backend)."},{"rank":3,"product":"OpenLLMetry","reason":"Lightweight Apache 2.0 OTel-native instrumentation for LLM apps (providers, vector DBs, frameworks); zero-config tracing of prompts, latencies, costs that pipes into existing self-hosted OTel backends (e.g., SigNoz, Tempo); ideal for platform teams extending current observability without new full-stack tool.","fix":"Instrumentation-focused SDK more than a standalone UI/dashboard platform; needs backend for full visualization/experience."}]},"missedByModel":{"ChatGPT":[{"product":"SigNoz","reason":"excellent self-hosted OpenTelemetry traces, metrics, logs, dashboards, and alerting, but lacks the purpose-built evaluation and prompt-debugging depth of the top five"},{"product":"OpenLLMetry","reason":"valuable vendor-neutral instrumentation, but it is an instrumentation layer rather than a complete observability product"}],"Claude":[{"product":"LangSmith","reason":"excellent tracing/eval product with a self-hosted Kubernetes option, but self-hosting is gated behind an enterprise contract, putting it out of reach of the typical practitioner this category serves"},{"product":"OpenLIT","reason":"OTel-native with a self-hosted ClickHouse-backed UI and GPU/cost monitoring, but a much smaller community and less mature product than the top five"}],"Gemini":[{"product":"Traceloop","reason":"excellent SDK-level instrumentation via OpenLLMetry, but its full dashboard platform is heavily commercialized and less frequently self-hosted than pure open-source alternatives"},{"product":"LangWatch","reason":"provides strong tracing and guardrails but has lower community adoption and fewer integration options compared to Langfuse"}],"Grok":[{"product":"MLflow","reason":"strong open-source tracing/evals with simple self-host but broader ML focus dilutes LLM-specific depth for pure platform observability teams"}]}}