langfuse vs mlflow

Common questions

What is the difference between langfuse and mlflow?

langfuse: Open source agent evals & observability: Trace, evaluate, and improve LLM applications with one open platform.. mlflow: Open source AI engineering platform for agents, LLMs, and ML models. See the comparison table for live GitHub stats and shared categories.

When should I choose langfuse over mlflow?

Choose langfuse over mlflow when langfuse is primarily TypeScript; mlflow is Python; License: langfuse is Other, mlflow is Apache-2.0; Both Langfuse and MLflow are platforms aimed at managing, evaluating and monitoring ML models and agents; Tags unique to langfuse: analytics, evaluation, llm-observability, monitoring; langfuse ships Docker support for self-hosted deployment; When you need a self-hosted solution for evaluating and monitoring large language model applications, as Langfuse supports local, VM, and Kubernetes environments.

When should I choose mlflow over langfuse?

Choose mlflow over langfuse when mlflow is primarily Python; langfuse is TypeScript; License: mlflow is Apache-2.0, langfuse is Other; Both Langfuse and MLflow are platforms aimed at managing, evaluating and monitoring ML models and agents; Tags unique to mlflow: agentops, agents, ai-governance, langchain; Also covers Developer Tools, Inference & Serving, Model Training; When you need a vendor-neutral solution for managing the lifecycle of AI and ML models, including debugging, evaluation, monitoring, and optimization.

When should I avoid langfuse?

If you are looking for a fully managed service without the need for self-hosting, as Langfuse requires setting up and maintaining the infrastructure yourself. When you need enterprise features that are not covered by the open-source version, as the ee folders are not open-source and may require additional licensing or services.

When should I avoid mlflow?

If you are looking for a platform that is tightly integrated with a specific vendor's ecosystem, as MLflow's vendor-neutral approach may not leverage vendor-specific optimizations. When your project strictly requires proprietary features or services that are not available in open-source platforms like MLflow. If your team prefers a more specialized tool for a specific aspect of AI/ML lifecycle management, as MLflow is a general-purpose platform and may not offer the depth of features in a niche area.

Is langfuse or mlflow more popular on GitHub?

langfuse has more GitHub stars (34,763 vs 28,010). Stars measure visibility, not whether either tool fits your constraints.

Are langfuse and mlflow open source?

Yes - both are open-source projects on GitHub (langfuse: Other, mlflow: Apache-2.0).

Where can I find alternatives to langfuse or mlflow?

GraphCanon lists graph-backed alternatives at langfuse alternatives and mlflow alternatives (langfuse markdown twin, mlflow markdown twin), ranked by typed relationship edges rather than popularity votes.

Is there a machine-readable version of this comparison?

Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

Which is better maintained, langfuse or mlflow?

langfuse: Very active. mlflow: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

Where are the full trust reports for langfuse and mlflow?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: langfuse trust report; mlflow trust report.