Why MLflow for GenAI?

MLflow provides the only open-source platform purpose-built for the entire GenAI application lifecycle. From prototype to production, MLflow gives you the tools, integrations, and workflows you need to build reliable, high-quality AI applications.

The MLflow Advantage

🔓 Open Source & Vendor Neutral

No Lock-in, Complete Control

• Free forever: No licensing fees, no usage limits, no surprises
• Deploy anywhere: Your infrastructure, any cloud, or hybrid
• Own your data: Complete control over your AI assets and intellectual property
• Extensible platform: Customize and extend to fit your needs

🚀 Purpose-Built for GenAI

Not retrofitted, designed from the ground up

• Native LLM support: First-class support for prompts, chains, agents, and RAG systems
• AI-powered evaluation: LLM judges that understand language, not just strings
• Conversation management: Track multi-turn interactions and session context with tracing

🌍 Trusted by the Community

Battle-tested at scale

• 20,000+ GitHub stars: Active open-source community
• 25M+ monthly downloads: Proven in production worldwide
• Linux Foundation project: Vendor-neutral governance
• Major contributors: Microsoft, AWS, Databricks, and hundreds of companies

Core Capabilities for GenAI

🔍 Complete Observability with Tracing

See inside every AI decision with comprehensive tracing that captures the full execution flow.

What you get:

• Automatic instrumentation for 20+ frameworks (LangChain, OpenAI, LlamaIndex, etc.)
• Detailed execution logs showing every step, tool call, and decision
• Interactive debugging in Jupyter notebooks and IDEs
• Production monitoring with OpenTelemetry compatibility for exported traces

Why it matters: You can't fix what you can't see. MLflow shows you exactly how your AI makes decisions.

import mlflow

# One line to enable comprehensive tracing
mlflow.langchain.autolog()

# Your app is now fully observable
chain.invoke({"question": "How do I reset my password?"})
# MLflow captures every LLM call, retrieval, and tool use

📊 AI-Powered Quality Evaluation

Move beyond manual testing with automated evaluation using LLM judges and custom metrics.

What you get:

• Pre-built LLM judges for correctness, relevance, safety, and more
• Custom metric creation for domain-specific quality measures
• Bulk evaluation across entire datasets

Why it matters: Systematically measure and improve quality instead of guessing.

note

The GenAI Evaluate feature is only available on Databricks Managed MLflow.

from mlflow.genai.scorers import Correctness, RelevanceToQuery

# Evaluate your app with AI-powered metrics
results = mlflow.genai.evaluate(
    predict_fn=my_app,
    data=eval_dataset,
    scorers=[
        Correctness(),
        RelevanceToQuery(),
    ],
)

📝 Prompt & Version Management

Track every change to your AI application with comprehensive version control.

What you get:

• Prompt registry with Git-like version control
• Visual prompt editor for no-code iteration
• Complete lineage tracking from development to deployment

Why it matters: Know exactly what changed and why, enabling rapid iteration with confidence.

🚀 Flexible Deployment Options

Deploy your AI applications anywhere with consistent APIs and monitoring.

What you get:

• Multiple deployment targets: REST APIs, serverless, containers
• Native integration: Deploy to popular cloud services and serving stacks with ease
• AI Gateway for unified LLM provider management

Why it matters: Focus on building great AI, not deployment infrastructure.

🛡️ Enterprise-Ready Governance

Secure and govern your AI deployments with enterprise-grade features.

What you get:

• Centralized API key management through AI Gateway
• Unity Catalog integration for data governance on Databricks managed MLflow
• Audit trails for all model changes and deployments
• Role-based access control for teams with Unity Catalog integration

Why it matters: Deploy AI responsibly with proper security and compliance.

MLflow vs. Alternatives

vs. Building In-House

Challenge	Building In-House	MLflow Solution
Tracing infrastructure	Months to build, ongoing maintenance	Ready in minutes, maintained by community
LLM evaluation	Complex prompt engineering for judges	Pre-built judges, proven in production
Framework integration	Custom code for each library	20+ integrations out of the box
Production monitoring	Build from scratch	OpenTelemetry-compatible, battle-tested

vs. Closed-Source Platforms

Aspect	Proprietary Platforms	MLflow
Cost	$$$$ per seat/usage	Free forever
Data ownership	Vendor controlled	You own everything
Customization	Limited to vendor features	Fully extensible
Vendor lock-in	High switching costs	Open standards, portable
Community	Vendor support only	Global community + vendors

vs. Point Solutions

Many teams cobble together multiple tools:

• Tracing tool + Evaluation framework + Experiment tracking + Deployment solution = Complexity

MLflow provides everything in one integrated platform:

• Unified experience: One UI, one API, one mental model
• Integrated workflows: Traces → Evaluation → Deployment
• Consistent metrics: Same scorers in dev and production
• Single source of truth: All AI assets in one place

Getting Started is Simple

Install MLflow

pip install --upgrade mlflow

And get to work.

# Start tracing your app
import mlflow
from openai import OpenAI

mlflow.set_experiment("my-genai-app")
# set an active model for grouping traces
mlflow.set_active_model(name="my-app")

# enable tracing for openai
mlflow.openai.autolog()

# Run your application
messages = [
    {
        "role": "user",
        "content": "State that you are responding to a test and that you are alive.",
    }
]

openai_client = OpenAI()
openai_client.chat.completions.create(
    model="gpt-4o",
    messages=messages,
    temperature=0.95,
)
# You're now on the path to production-ready AI!

Join the MLflow Community

🌟 Why Teams Choose MLflow

• Proven at scale: Used by thousands of organizations worldwide
• Rapid innovation: New features added by the community weekly
• Vendor support: Backed by major cloud providers and companies
• Future-proof: Open standards ensure your investment is protected

🤝 Get Involved

• GitHub: Star the repo, contribute code, report issues
• Slack: Join 5,000+ practitioners sharing best practices
• Meetups: Connect with local MLflow users
• Documentation: Learn from comprehensive guides and tutorials

Summary

MLflow is the only platform that provides:

✅ Complete observability for understanding AI behavior ✅ AI-powered evaluation for measuring quality at scale ✅ Human-in-the-loop workflows for real-world alignment ✅ Integrated lifecycle management from development to production ✅ Open-source freedom with enterprise-grade capabilities

Stop struggling with fragmented tools and uncertain quality. Start building reliable GenAI applications with MLflow.