Arize Phoenix vs Langfuse: Best LLM Observability Tool
Quick Comparison
| Arize Phoenix | Langfuse | |
|---|---|---|
| Best For | Developers iterating on RAG pipelines or agents who want code-first evaluation and OTel-native tracing during development. | Teams that need one self-hosted system of record for prompts, traces, and evaluations across a production LLM application. |
| Pricing | Free tier available | Free tier available / custom pricing |
| Winner | Our Pick |
Tool Breakdown
Langfuse
Langfuse is the stronger default for most teams because its entire product -- tracing, prompt management, and evaluations -- is MIT-licensed for self-hosting with no feature caps, making it a complete production system of record rather than a single-purpose toolkit.
- Entire core product -- tracing, prompt management, evals, playground, and annotation queues -- is MIT-licensed with no usage caps or feature gating on self-hosted deployments
- Prompt version management with caching is built directly into the platform, avoiding the latency hit of fetching prompts on every request
- Backed by ClickHouse (which acquired Langfuse in January 2026), giving the storage layer behind traces a clear long-term maintenance path
- Self-hosting requires operating your own Postgres and ClickHouse infrastructure, which adds operational overhead compared to Phoenix's lighter local footprint
- Enterprise controls like SCIM, audit logs, and project-level RBAC sit behind a separate commercial license even for self-hosted deployments
Arize Phoenix
An open-source AI observability toolkit built for experimentation, evaluation, and troubleshooting of LLM and RAG applications, instrumented via the OpenInference/OpenTelemetry standard.
- Built-in LLM-graded evaluators specifically for retrieval relevance, hallucination detection, and QA correctness -- purpose-built for RAG quality work
- OpenInference/OpenTelemetry-native instrumentation makes it vendor- and framework-agnostic and easy to combine with existing OTel pipelines
- Lightweight enough to run locally in a notebook or CI job, not just as a standing server, which suits fast experimentation loops
- Licensed under Elastic License 2.0, not a permissive OSI license like MIT, which restricts using Phoenix itself as the basis for a competing hosted service
- Production-grade prompt management and long-term trace retention are lighter than Langfuse's; teams needing that typically graduate to the paid Arize AX product
Frequently Asked Questions
Is Arize Phoenix or Langfuse more open source, given they're both marketed as open source? +
Both are open source, but under different licenses and with different scope. Langfuse's core product -- tracing, prompt management, evaluations, playground, and annotation queues -- is MIT-licensed with no feature gating for self-hosted deployments; only enterprise add-ons like SCIM and audit logs require a commercial license. Arize Phoenix is licensed under Elastic License 2.0 (ELv2), not MIT, which permits free self-hosted use but restricts offering Phoenix itself as a competing hosted service. In practice this distinction matters most to vendors, not end users: both projects can be self-hosted for free with full product functionality. If license permissiveness for redistribution or embedding into another commercial product matters to your legal team, confirm the exact terms of ELv2 versus MIT before committing.
Which tool is better specifically for evaluating RAG (retrieval-augmented generation) quality? +
Phoenix has the edge here. It ships built-in LLM-graded evaluators for retrieval relevance, hallucination detection, and QA correctness, plus dataset and experiment tracking designed around iterating on a RAG pipeline before deployment -- this is the workflow Phoenix was originally built for. Langfuse supports LLM-as-a-judge evaluations, custom code evaluators, and human annotation queues as part of its broader platform, and these work fine for RAG use cases, but they're one feature among many rather than the product's central focus. Teams doing heavy retrieval-quality iteration -- testing chunking strategies, embedding models, or reranking -- generally find Phoenix's evaluation tooling faster to work with during active development, while Langfuse's evaluations shine more once an application is already in production and you're monitoring live traffic.
Can Arize Phoenix and Langfuse be used together instead of choosing one? +
Yes, and it's a common pattern. Phoenix is lightweight to run locally or in CI, so teams use it during development to evaluate RAG quality, debug retrieval traces, and run experiments before a feature ships. Langfuse (or Arize AX, the hosted version of Phoenix's parent product) then takes over once the application is live, providing persistent tracing, prompt version control, and production monitoring across real user traffic. Because Phoenix instruments via OpenInference/OpenTelemetry, its traces can flow into other OTel-compatible backends, and Langfuse's SDKs are similarly framework-agnostic, so the two don't compete for the same job as much as they cover adjacent stages of the same LLM development lifecycle: pre-production evaluation versus in-production observability.