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Last Updated: 01-01-2023 12:00 AM (UTC)

MLflow

Trusted by thousands of organizations with over 30 million monthly downloads and 20,000+ GitHub stars, MLflow is the largest open-source AI engineering platform providing end-to-end lifecycle management for traditional ML models, LLMs, and AI agents. The OpenTelemetry-based tracing system captures complete request flows through any LLM provider or agent framework — including OpenAI, LangChain, DSPy, Vercel AI, PydanticAI, and smolagents — with one-line auto-instrumentation that tracks inputs, outputs, token usage, and costs at every intermediate step. MLflow's evaluation engine offers 50+ built-in metrics and LLM judges for systematic quality assessment, detecting issues across correctness, latency, adherence, relevance, and safety dimensions before code reaches production. The Prompt Registry versions, tests, and deploys prompts with full lineage tracking while automated optimization algorithms improve prompt performance using evaluation feedback. The AI Gateway provides a unified API endpoint for all LLM providers, enforcing rate limits, cost controls, and access policies across the organization. MLflow 3.0 introduces the LoggedModel abstraction linking traces, metrics, and prompts to specific model versions across Python, TypeScript, Java, and R SDKs. The model registry manages deployment workflows with automated quality gates, while experiment tracking records parameters, metrics, and artifacts across training runs. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache License 2.0 licensed.

MLflow
MLflow
MLflow
MLflow
MLflow

Benefits

  • Production-Grade AI Observability
  • Capture complete LLM and agent request traces with OpenTelemetry-compatible instrumentation tracking inputs, outputs, token usage, latency, and costs at every execution step.
  • Systematic Quality Evaluation
  • Assess AI application quality using 50+ built-in metrics, customizable LLM judges, and automated detection across correctness, relevance, safety, and adherence dimensions.
  • Unified Model Lifecycle Management
  • Track experiments, version models, manage prompts, and deploy with automated quality gates across traditional ML, deep learning, and generative AI applications.
  • Broad Multi-Framework Integration
  • Connect with 100+ AI tools including OpenAI, LangChain, DSPy, Vercel AI, and PydanticAI using one-line auto-tracing across Python, TypeScript, Java, and R SDKs.

Features

  • OpenTelemetry Tracing
  • Captures complete LLM request flows with one-line auto-instrumentation for OpenAI, LangChain, DSPy, Vercel AI, and PydanticAI frameworks with async production support.
  • AI Gateway
  • Unified API endpoint for all LLM providers enforcing rate limits, cost controls, access policies, and routing rules across the entire organization.
  • Prompt Registry
  • Versions, tests, and deploys prompts with full lineage tracking plus automated optimization algorithms that improve performance using evaluation feedback and labeled datasets.
  • Experiment Tracking
  • Records parameters, metrics, artifacts, and model versions across training runs with comprehensive lineage connecting datasets, code, and evaluation results.
  • LLM Evaluation Engine
  • Provides 50+ built-in metrics and configurable LLM judges for systematic quality assessment detecting issues in correctness, latency, relevance, and safety dimensions.