MLflow

open_source

MLflow is the largest open source AI engineering platform. Debug, evaluate, monitor, and deploy AI agents, LLMs, and ML models with 30M+ monthly downloads.

Data & Analytics

AI Models & Infrastructure

LLM Developer Tools

About

MLflow is the most widely adopted open source AIOps and MLOps platform, downloaded over 30 million times per month. Built on OpenTelemetry and backed by the Linux Foundation, it supports the full AI and machine learning lifecycle—from experimentation to production deployment. For LLM and agent workflows, MLflow offers production-grade observability with complete trace capture across any LLM provider or agent framework. Teams can run systematic evaluations using 50+ built-in metrics and LLM judges, or define custom ones with flexible APIs. A built-in Prompt Registry enables versioning, testing, and deployment of prompts with full lineage tracking, while automatic optimization algorithms improve performance over time. MLflow's AI Gateway provides a unified, OpenAI-compatible interface for routing requests across LLM providers, managing rate limits, handling fallbacks, and controlling costs. The Agent Server enables one-command production deployments with FastAPI-based hosting, streaming support, and built-in tracing. For traditional ML, MLflow covers experiment tracking, hyperparameter tuning, model evaluation, and a Model Registry for versioned deployment. It integrates with 100+ frameworks and supports Python, TypeScript/JavaScript, Java, and R. MLflow is purpose-built for engineering teams of all sizes who want to ship high-quality AI products faster without managing complex infrastructure.

Key Features

Production-Grade Observability: Capture complete traces of LLM applications and agents built on OpenTelemetry, supporting any LLM provider or agent framework. Monitor quality, cost, and safety in production.
Systematic Evaluations: Run structured evaluations with 50+ built-in metrics and LLM judges or define custom metrics via flexible APIs. Track quality over time and catch regressions before they reach production.
Prompt Registry & Optimization: Version, test, and deploy prompts with full lineage tracking. Automatically optimize prompts using state-of-the-art algorithms to improve model performance.
AI Gateway: Unified, OpenAI-compatible API gateway for all LLM providers. Manage routing, rate limits, fallbacks, and cost control through a single interface.
ML Lifecycle Management: Full experiment tracking, hyperparameter tuning, model evaluation, and a versioned Model Registry for deployment across traditional ML and deep learning workflows.

Use Cases

Tracking and comparing ML experiments across hyperparameter configurations and model architectures to find the best-performing model.
Monitoring LLM applications and AI agents in production for quality degradation, cost overruns, and safety issues.
Running automated evaluations of prompt changes before deploying them to production, using built-in LLM judges and custom metrics.
Managing a centralized prompt library with versioning and lineage tracking across multiple AI products and teams.
Deploying AI agents to production via the MLflow Agent Server with built-in streaming and tracing support.

Pros

Truly Open Source: 100% open source under the Linux Foundation with no vendor lock-in, 30M+ monthly downloads, and a large, active community.
Broad Ecosystem Compatibility: Integrates with 100+ frameworks and supports Python, TypeScript/JavaScript, Java, and R—fitting into nearly any existing AI or ML stack.
End-to-End Coverage: Covers the entire AI lifecycle from experimentation and evaluation to production deployment and monitoring, eliminating the need for multiple disparate tools.
OpenTelemetry Native: Built on the OpenTelemetry standard, enabling seamless integration with existing observability infrastructure and industry-standard tooling.

Cons

Self-Hosting Complexity: Running MLflow at scale requires significant infrastructure knowledge and DevOps effort for self-hosted deployments without a managed offering.
Steep Learning Curve: The breadth of features and configuration options can be overwhelming for teams new to MLOps or LLMOps workflows.
UI Can Feel Dated: The default web UI, while functional, lacks the polish and modern UX of some commercial alternatives.

Frequently Asked Questions

Yes, MLflow is fully open source and free under the Apache 2.0 license, backed by the Linux Foundation. You can self-host it at no cost.

MLflow integrates with 100+ tools across the AI ecosystem, including OpenAI, Anthropic, LangChain, LlamaIndex, Hugging Face, and many more. It supports Python, TypeScript/JavaScript, Java, and R.

MLflow is the most widely adopted open source option, offering both traditional ML lifecycle management and modern LLMOps capabilities—observability, evaluations, prompt management, and agent deployment—in a single unified platform.

Yes. The MLflow Agent Server provides a FastAPI-based hosting solution with automatic request validation, streaming support, and built-in tracing, enabling deployment from prototype to production endpoint with a single command.

The AI Gateway is a unified, OpenAI-compatible API proxy for all LLM providers. It handles routing, rate limiting, fallbacks, and cost management from a single interface, simplifying multi-provider LLM usage.