Langfuse vs MLflow
Side-by-side comparison of features, pricing, and ratings
At a glance
| Dimension | Langfuse | MLflow |
|---|---|---|
| Pricing | Freemium (self-host free; cloud tiers: Free, Team, Enterprise) | Free (open-source, Apache 2.0) |
| Observability & Tracing | Hierarchical traces with cost/latency filtering, OpenTelemetry-native, filter search bar | OpenTelemetry tracing, multimodal tracing (images/audio), automatic issue detection |
| Prompt Management | One-click prompt deployment/rollback, playground, experiments | Prompt Registry with versioning and optimization |
| Evaluation | LLM-as-a-judge, heuristic functions, human annotation, golden datasets | 50+ built-in metrics, LLM judges, automatic issue detection |
| Deployment | Self-host (Docker/K8s) or cloud; no agent server | AI Gateway (unified LLM API), Agent Server (one-command deploy), self-host |
| Best For | Production LLM agent observability, prompt management, evals | Unified MLOps + LLMOps, experiment tracking, model registry |
If you need a single open-source platform that covers both traditional ML (experiment tracking, model registry) and LLM agents (tracing, prompt versioning, AI Gateway), choose MLflow. If your primary focus is production LLM observability with rich prompt management, evaluation workflows, and a mature SaaS option, Langfuse is more specialized and easier to adopt for LLM-only teams.
Open-source LLM observability that traces, evaluates, and manages prompts from prototype to production.
Visit WebsiteOpen source AI engineering platform for building, debugging, and monitoring agents, LLMs, and ML models.
Visit WebsiteWho should pick which
- AI engineering team needing both ML and LLM lifecycle managementPick: MLflow
MLflow covers experiment tracking, model registry, and LLM agent tracing in one open-source platform, reducing toolchain complexity.
- Production LLM agent developer requiring deep debugging and prompt managementPick: Langfuse
Langfuse offers hierarchical traces, cost/latency filtering, one-click prompt rollback, and human annotation workflows ideal for iterating on LLM agents.
- Solo developer building a simple LLM chat appPick: Langfuse
Langfuse's free cloud tier provides quick setup with tracing and prompt management without self-hosting overhead; MLflow requires more infrastructure.
- Enterprise needing SOC 2/HIPAA compliancePick: Langfuse
Langfuse offers self-hosting with compliance certifications, whereas MLflow's RBAC is new and lacks built-in compliance reporting.
- Team deploying LLM agents at scale with guardrailsPick: MLflow
MLflow's AI Gateway provides unified API access with guardrails and agent server for one-command deployment, simplifying production.
Frequently Asked Questions
Langfuse vs MLflow: which should you choose?
If you need a single open-source platform that covers both traditional ML (experiment tracking, model registry) and LLM agents (tracing, prompt versioning, AI Gateway), choose MLflow. If your primary focus is production LLM observability with rich prompt management, evaluation workflows, and a mature SaaS option, Langfuse is more specialized and easier to adopt for LLM-only teams.
Are MLflow and Langfuse both open-source?
Yes, both are open-source. MLflow uses Apache 2.0 license; Langfuse uses MIT license for self-hosting.
Which tool supports multimodal tracing?
MLflow supports multimodal tracing for images, audio, and files as of version 3.13.0. Langfuse recently added multi-modal datasets for experiments.
Can I use Langfuse without self-hosting?
Yes, Langfuse offers cloud tiers (Free, Team, Enterprise) managed by them. MLflow is self-hosted only.
Which tool has better integrations for coding agents?
Langfuse provides a CLI and MCP server for coding agents like Claude Code; MLflow also supports Hermes Agent and has a guide for routing Claude Code through its AI Gateway.
Does MLflow have a prompt management feature?
Yes, MLflow has a Prompt Registry with versioning and optimization, similar to Langfuse's prompt management.
Which tool is easier to set up for a small team?
Langfuse's free cloud tier requires no infrastructure. MLflow requires self-hosting, but can be run locally with minimal setup.
Can I do traditional ML model tracking with Langfuse?
No, Langfuse is focused on LLM observability. MLflow is better for traditional ML with experiment tracking and model registry.
How do they compare on evaluation capabilities?
MLflow offers 50+ built-in metrics and LLM judges, plus automatic issue detection. Langfuse provides LLM-as-a-judge, heuristic functions, and human annotation workflows.
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Last reviewed: May 12, 2026