Langfuse vs MLflow (2026): LLM-Native or ML Suite for Tracing?

How they actually differ

Langfuse and MLflow both end up in the "tracing for LLM apps" search but they come from opposite directions and the right pick depends on what your team is actually building.

Langfuse was built for LLM applications from day one. The data model (traces, observations, sessions, prompts, evaluations) maps cleanly to how agent and RAG workloads actually run in production. Strong open-source core (MIT-licensed), self-host option, prompt management built in. The community is large and active, the integrations are LLM-shaped (LangChain, LlamaIndex, OpenAI SDK, Anthropic SDK, etc). January 2026 acquisition by ClickHouse brings strong backing.

MLflow is the classical ML platform that added LLM tracing as a feature. The strength is that if your org already runs MLflow for experiment tracking, model registry, and deployment, the LLM tracing piece slots into the same UI. The trade-off is that the LLM data model feels grafted on rather than native. Workflows that are LLM-first (agent traces, prompt management, eval pipelines) tend to feel friction-heavy compared to Langfuse.

Where the trade-off bites: Pick MLflow when your team already runs MLflow for ML workflows and you want one tool for both classical and LLM work. Pick tools like Langfuse when LLM is most of your stack and you want a platform shaped specifically for it.

Where Respan fits. Many teams pick Langfuse for the LLM-native data model but want it bundled with a gateway and evals in one platform. That is where Respan sits: same span-and-trace model as Langfuse, plus a unified LLM gateway across 1,000+ models and built-in evaluator workflows. See our Langfuse comparison article for the head-to-head.

For the operational pattern on top of either tool, RAG observability covers the 4 telemetry layers and the dashboards that matter.