MLflow vs. Langfuse — two layers, one audit trail
MLflow vs Langfuse is an observability decision. MLflow covers the ML lifecycle with experiment tracking, model registry and deployment; Langfuse specialises in LLM tracing, evaluation and production monitoring. Teams building RAG or agent systems use Langfuse for prompt-level tracing and MLflow for model lifecycle. Pexon wires both into the gateway for a complete audit trail.
The comparison is usually framed as a choice. It is not one.
MLflow and Langfuse are the two most common answers to two different questions. MLflow answers the model lifecycle question: which experiment produced this model, what were its metrics, where is it registered, how is it packaged and deployed. Langfuse answers the production question: what did the LLM actually do, trace by trace, prompt by prompt, with evaluation scores attached.
A team that picks one and skips the other usually discovers the gap at an audit — either the model registry has no production trace, or the traces have no model version attached. The practical answer is both, in different layers.
MLflow owns the model lifecycle. Langfuse owns the production trace. They answer different questions and both feed the same audit trail. The choice is a layering decision, not a winner.
MLflow versus Langfuse, side by side
| Dimension | MLflow | Langfuse |
|---|---|---|
| Primary job | ML lifecycle: experiments, registry, deploy | LLM observability: traces, evals, production |
| Experiment tracking | Core strength | Limited |
| Model registry | Yes | No |
| Prompt-level tracing | Basic | Core strength |
| Evaluation scores | Via custom code | First-class, per trace |
| Production monitoring | Secondary | Core strength |
| Open source | Yes (Linux Foundation) | Yes (core) |
Both are open source and self-hostable; Langfuse also offers a hosted tier. The split above is about where each is strongest, not a feature-complete inventory.
The failure mode is choosing before the audit trail is designed. A model registry without production traces cannot answer 'what did this version do in production', and traces without a registry cannot answer 'which model was this'.
What the wiring actually looks like
The integration point is the gateway. Every request already carries the model, the prompt, the trace and the workload label — that is the raw material both tools need. From there, MLflow receives the model version and its evaluation metrics at release time, and Langfuse receives the live traces with their evaluation scores at runtime.
The two join on the model version: MLflow registers v12 of the model, Langfuse records every production trace against that version, and the audit trail reconstructs any answer from either direction.
The join key: model version in every trace
Langfuse trace metadata carries the MLflow model version, so the audit trail joins in both directions.
# Langfuse trace with the MLflow model version attached
from langfuse import Langfuse
langfuse = Langfuse()
trace = langfuse.trace(
name="customer-support-answer",
metadata={
"mlflow_model_version": "12", # the join key
"workload": "support-triage",
"model": "llama-3.3-70b-instruct",
},
)For a regulated deployment, the two-layer split is not an optimisation — it is what makes 'which model answered this, with what evaluation score' answerable after the fact. That is the question every audit starts with.
Three decisions that shape the stack
The tool choice is settled in five minutes; the stack design is the work.
- Where the join key lives — the model version must be attached to every production trace, which means the gateway has to carry it as metadata from day one.
- What the evaluation scores mean — a score without a defined rubric is decoration. The eval harness and the rubric come before the dashboard, not after.
- Who owns the audit trail — a named owner, a retention policy and a documented answer to 'how far back can we reconstruct' are part of the deliverable, not an afterthought.
The registry without traces is a shelf. The traces without a registry are a firehose. The join key is what makes either of them evidence.
Continue on observability and cost
- LiteLLM enterprise setup — The gateway that produces the trace data both tools consume.
- Claude Code cost per developer — The eight OpenTelemetry metrics worth instrumenting, MLflow-compatible.
Observability questions
What is the difference between MLflow and Langfuse?
MLflow is the general ML lifecycle platform: experiment tracking, model registry, packaging and deployment. Langfuse is purpose-built for LLM applications: prompt and trace-level observability, evaluation scores, and production monitoring. For a pure LLM application Langfuse is the more specialised fit; for the surrounding ML lifecycle MLflow has the broader scope.
Do we need both MLflow and Langfuse?
Often yes, in different layers. MLflow owns the model lifecycle — which model version is registered, what its metrics were, how it is packaged. Langfuse owns what happens in production — every prompt, trace and evaluation score. The two answer different questions, and both feed the same audit trail.
Can Langfuse replace MLflow?
Not for the model lifecycle. Langfuse does not manage model registries or packaging. It replaces the observability layer of an MLflow deployment, not the lifecycle layer. The reverse is also true: MLflow's tracing is not Langfuse's, and teams that need production LLM observability usually end up adding Langfuse.
Next step
Get the observability stack wired before production
Two-week readiness blueprint at €4,900: we map what your ML stack currently tracks, wire MLflow and Langfuse into the gateway, and hand back the tracing, evaluation and model-registry setup your audit trail needs. The plan is yours to keep whoever builds it.