MLflow for GenAI: Build Production-Ready AI Applications
MLflow provides a comprehensive platform for developing, evaluating, and deploying Generative AI applications. From LLMs and agents to complex RAG systems, MLflow simplifies the entire GenAI lifecycle with purpose-built tools for observability, quality assurance, and production deployment.
GenAI Getting Started Resources
Whether you're building your first chatbot or scaling enterprise AI systems, these resources will help you leverage MLflow's GenAI capabilities effectively. Each guide focuses on practical, real-world scenarios to get you productive quickly.
- GenAI Quickstart
- Tutorials
- Prompt Engineering
- Tracing Integrations
Start building GenAI applications with MLflow
Quickstarts
Build your first GenAI app in our Getting Started guide
Guides
Master GenAI development with our tracing quickstart
Ensure quality with LLM evaluation
Develop and version control prompts effectively
Guides
Prompt Engineering UI for no-code development
GenAI Development with MLflow
MLflow transforms how teams build, evaluate, and deploy GenAI applications. With comprehensive tracing, automated evaluation, and seamless deployment options, MLflow provides everything you need to move from prototype to production with confidence. Our platform supports the entire GenAI lifecycle while maintaining the flexibility to work with any model provider or framework.
Explore MLflow's GenAI capabilities below to accelerate your AI development!
- Tracing & Observability
- LLM Evaluation
- Prompt Management
- Version Tracking
- Deployment & Serving
- Governance & Security
Debug and monitor GenAI applications with complete visibility
Core Features
MLflow Tracing provides comprehensive observability for GenAI applications, capturing every LLM call, tool interaction, and decision point in your AI workflows.
Key Benefits:
- Complete Visibility: Trace every step from prompt to response
- Framework Integration: Auto-instrumentation for 15+ GenAI libraries
- Interactive Debugging: Native Jupyter notebook support
- Production Monitoring: OpenTelemetry-compatible traces for scalable observability
Guides
Systematically evaluate and improve GenAI quality
Core Features
MLflow Evaluation enables automated quality assessment using LLM judges, custom metrics, and comprehensive test suites for GenAI applications.
Key Benefits:
- LLM-as-Judge: Automated evaluation with configurable judge models
- Custom Metrics: Domain-specific evaluation criteria
- Bulk Testing: Evaluate across datasets with statistical analysis
Engineer, version, and deploy prompts systematically
Core Features
MLflow Prompt Registry provides comprehensive tools for developing, testing, and deploying prompts with full version control and lifecycle management.
Key Benefits:
- Visual Development: No-code UI for prompt iteration
- Version Control: Git-like versioning for prompts
- Lifecycle Management: Manage prompts with aliases (dev, staging, prod)
- Template Management: Reusable prompt components and patterns
Guides
Track and compare GenAI application versions
Core Features
MLflow Version Tracking enables systematic tracking of GenAI application versions, linking traces, evaluations, and production deployments.
Key Benefits:
- Application Versioning: Track complete app configurations
- Evaluation Linkage: Connect evaluation results to specific versions
- Production Tracing: Link production traces to deployed versions
- Version Comparison: Compare performance across versions
Guides
Deploy GenAI applications to production environments
Core Features
MLflow Serving supports deploying GenAI applications with built-in governance through the AI Gateway and custom app serving capabilities.
Key Benefits:
- AI Gateway: Unified interface for LLM providers
- Custom Apps: Deploy complex GenAI applications
- Response Agents: Pre-built serving patterns
- Endpoint Management: Easy deployment configuration
Guides
Secure and govern your GenAI deployments
Core Features
MLflow Governance provides enterprise-grade security and compliance features including the AI Gateway for LLM provider management and Unity Catalog integration.
Key Benefits:
- Unified LLM Access: Single endpoint for multiple providers
- Credential Management: Secure API key storage
- Unity Catalog: Enterprise data governance
- Provider Flexibility: Swap providers without code changes
Guides
Why MLflow for GenAI?
🔍 Complete Observability
See inside every AI decision with comprehensive tracing that captures prompts, retrievals, tool calls, and model responses. Debug complex workflows with confidence.
📊 Automated Quality Assurance
Stop manual testing with LLM judges and custom metrics. Systematically evaluate every change to ensure consistent improvements in your AI applications.
🚀 Production-Ready Platform
Deploy anywhere with confidence. From local servers to cloud platforms, MLflow handles the complexity of GenAI deployment, monitoring, and optimization.
🤝 Framework Freedom
Use any GenAI framework or model provider. With 15+ native integrations and extensible APIs, MLflow adapts to your tech stack, not the other way around.
🔄 End-to-End Lifecycle
Manage the complete GenAI journey from experimentation to production. Track prompts, evaluate quality, deploy models, and monitor performance in one platform.
👥 Open Source Community
Join thousands of teams building GenAI with MLflow. As part of the Linux Foundation, MLflow ensures your AI infrastructure remains open and vendor-neutral.
Running MLflow for GenAI
MLflow can be used in a variety of environments, including your local environment, on-premises clusters, cloud platforms, and managed services. Being an open-source platform, MLflow is vendor-neutral; no matter where you are doing machine learning, you have access to the MLflow's core capabilities sets such as tracking, evaluation, observability, and more.
Run MLflow server locally or use direct access mode (no server required) to run MLflow in your local environment. Click the card to learn more.
Databricks Managed MLflow is a FREE, fully managed solution, seamlessly integrated with Databricks ML/AI ecosystem, such as Unity Catalog, Model Serving, and more.
MLflow on Amazon SageMaker is a fully managed service for MLflow on AWS infrastructure,integrated with SageMaker's core capabilities such as Studio, Model Registry, and Inference.
Azure Machine Learning workspaces are MLflow-compatible, allows you to use an Azure Machine Learning workspace the same way you use an MLflow server.
Nebius, a cutting-edge cloud platform for GenAI explorers, offers a fully managed service for MLflow, streamlining LLM fine-tuning with MLflow's robust experiment tracking capabilities.
You can use MLflow on your on-premise or cloud-managed Kubernetes cluster. Click this card to learn how to host MLflow on your own infrastructure.