Langfuse vs MLflow: Observability, Prompts & Evals Comparison
Compare Langfuse and MLflow side by side. Both are tools in the Observability, Prompts & Evals category.
Updated March 27, 2026
The short answer
Choose Langfuse if fully open-source with MIT license and free for commercial use with no usage limits.
Choose MLflow if truly open source with Linux Foundation governance — no vendor lock-in, Apache 2.0 license.
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 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 250+ 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.
Want to compare Langfuse and MLflow on your own traffic?
Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 250+ models through one gateway. Free tier covers 10K traces per month. Setup in 5 minutes, no credit card.
Quick Comparison
| Category | Observability, Prompts & Evals | Observability, Prompts & Evals |
| Pricing | Open Source | Open Source |
| Best For | Teams who want open-source LLM observability they can self-host and customize | ML engineers and AI teams, especially those in the Databricks ecosystem |
| Website | langfuse.com | mlflow.org |
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Pros & Cons: Langfuse vs MLflow
- +Fully open-source with MIT license and free for commercial use with no usage limits
- +Acquired by ClickHouse in January 2026, providing strong backing and future investment
- +Active development with strong community support and comprehensive documentation
- +Transparent pricing model based on units rather than complex metric calculations
- +Deep integrations with major frameworks like LangChain, OpenAI, and LlamaIndex
- −Recent acquisition by ClickHouse may lead to platform changes and strategic shifts
- −Smaller team of 7 employees may impact support capacity
- −Enterprise pricing at $3,450-$5,700/month can be expensive for mid-sized teams
- +Truly open source with Linux Foundation governance — no vendor lock-in, Apache 2.0 license
- +Massive ecosystem with 900+ contributors and integrations with 100+ AI frameworks across Python, TypeScript, Java, and R
- +Comprehensive GenAI platform with OpenTelemetry tracing, 50+ eval metrics, prompt management, and built-in AI Gateway
- +Unmatched adoption at 60M+ monthly downloads and 19,000+ companies globally
- +Unique combination of traditional MLOps and modern GenAI observability in a single platform
- −No built-in user management or RBAC in the open-source version — teams need Databricks or custom solutions for access control
- −Steep setup complexity for shared team deployments requiring proper storage backends, auth, and networking
- −Best features like Unity Catalog integration and serverless deployment require Databricks, creating soft vendor lock-in
- −GenAI-specific UI and developer experience less polished than LLM-native tools like Langfuse or LangSmith
Pricing: Langfuse vs MLflow
- · Full platform access
- · Self-hosted
- · Apache 2.0 license
- · All GenAI features included
- · Managed MLflow
- · Cloud-hosted tracking server
- · Integrated with Databricks
- · Everything in Standard
- · Serverless compute
- · Unity Catalog integration
- · Everything in Premium
- · Advanced security
- · Compliance controls
When to Choose Langfuse vs MLflow
- Self-hosted LLM monitoring
- Open-source tracing for AI applications
- Prompt versioning and management
- LLM observability & tracing
- Automated evaluation
- Prompt optimization
About Langfuse
Langfuse is an open-source LLM engineering platform that provides comprehensive tools for traces, evaluations, prompt management, and metrics to debug and improve LLM applications. Founded in Berlin, Germany in 2022, Langfuse quickly became a leading platform in the LLM observability space. The platform features MIT-licensed open-source core with no usage limits for commercial use, making it highly accessible to teams of all sizes. Langfuse offers deep integration with popular frameworks including LangChain, OpenAI, LlamaIndex, and LiteLLM. The platform provides detailed tracing capabilities, evaluation tools, comprehensive prompt management, and rich metrics tracking. In January 2026, Langfuse was acquired by ClickHouse, Inc., marking a significant transatlantic venture exit and validating the platform's technology and market position. The acquisition demonstrates the value of Langfuse's approach to LLM observability, evaluations, and prompt management.
About MLflow
MLflow is the leading open-source platform for managing the end-to-end machine learning lifecycle, now expanded into a comprehensive GenAI engineering platform. Created by Matei Zaharia (also the creator of Apache Spark) at Databricks in 2018 and donated to the Linux Foundation in 2020, MLflow has grown to over 20,000 GitHub stars and 60 million monthly downloads, making it one of the most widely adopted ML tools in the world.
With the release of MLflow 3.0 in June 2025, the platform underwent a major pivot to become a unified AI engineering platform for agents, LLMs, and ML models. The GenAI capabilities include OpenTelemetry-compatible tracing for LLM observability, 50+ built-in evaluation metrics with LLM-as-judge support, prompt versioning and optimization, and a built-in AI Gateway providing unified API access to all major LLM providers with rate limiting and cost control. The platform auto-traces 50+ AI frameworks including OpenAI, Anthropic, LangChain, LlamaIndex, and DSPy.
MLflow is used by over 19,000 companies globally, including Fortune 500 organizations like Amazon, Microsoft, Google, and BNP Paribas. While it is 100% free and open source under the Apache 2.0 license, Databricks offers a fully managed MLflow experience integrated into their cloud data platform. MLflow's unique strength is combining traditional MLOps capabilities (experiment tracking, model registry, deployment) with modern GenAI observability — something no other tool in the category offers.
What is Observability, Prompts & Evals?
Tools for monitoring LLM applications in production, managing and versioning prompts, and evaluating model outputs. Includes tracing, logging, cost tracking, prompt engineering platforms, automated evaluation frameworks, and human annotation workflows.
Browse all Observability, Prompts & Evalstools →Other Observability, Prompts & Evals Tools
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