Langfuse vs MLflow (2026): LLM-Native or ML Suite for Tracing?
Updated March 27, 2026
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.
Overview
Langfuse is an open-source LLM engineering platform for tracing, evals, and prompt management, self-hosted or on Langfuse Cloud with a free plan.
MLflow is an open-source AI engineering platform for agents, LLMs, and ML models, with tracing, evals, prompt management, and an AI gateway.
Key features
Core capabilities each platform advertises.
- Tracing for LLM calls, tool runs, and agent steps
- Python and TypeScript SDKs plus OpenTelemetry
- LLM-as-a-judge and custom evaluations
- Datasets and experiments
- Prompt management and playground
- OpenTelemetry-based tracing for LLMs and agents
- 50+ built-in eval metrics and LLM judges
- Prompt versioning and automatic prompt optimization
- AI Gateway with routing, rate limiting, and fallbacks
- Agent server for deploying agents on FastAPI
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- MIT-licensed core, self-hostable with Docker Compose or Kubernetes
- Tracing, evals, and prompt management in one tool
- Unlimited users on paid cloud plans
- Free Hobby plan with 50k units a month
- Native SDKs plus OpenTelemetry for other languages
Cons
- No LLM gateway, so routing, failover, and spend limits need a separate tool
- Hobby plan keeps 30 days of data and caps at 2 users
- Enterprise SSO and RBAC need the $300/month Teams add-on on Pro
- Self-hosting means running and scaling Langfuse's services yourself
Pros
- Apache 2.0 license with Linux Foundation governance
- Tracing, evals, prompts, and a gateway in one open-source project
- Covers classic ML and GenAI in the same platform
- Managed options on Databricks, SageMaker AI, and Azure ML
- Auth and role-based access built into the open-source server
Cons
- Self-hosting means running the tracking server, database, and artifact store yourself
- Auth and role-based access are opt-in and need configuring on self-hosted servers
- Managed versions bill through a cloud platform's compute rather than a standalone plan
- The broader ML scope can mean more to learn for teams that only need LLM tooling
Langfuse or MLflow — which should you choose?
Choose Langfuse if you wantChoose if you want
- Tracing and debugging LLM apps and agents
- Self-hosting LLM observability
- Versioning prompts across environments
- Scoring outputs with LLM judges and human review
Choose MLflow if you wantChoose if you want
- Tracing and evaluating LLM apps and agents
- Versioning and optimizing prompts
- Routing LLM traffic through a self-hosted gateway
- Tracking classic ML experiments alongside GenAI work
- Running AI tooling inside Databricks
Route, trace, and score every LLM call in one place
Respan routes across 1,000+ models, traces every request, and scores output on live traffic. Your existing Langfuse decorators send traces to Respan with no code changes. Start free.
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