Open Source MLflow vs. Managed MLflow on Databricks

Open source MLflow provides the core data model and SDKs, while Managed MLflow on Databricks adds:

• Scalable for production - High-volume trace ingestion for production workloads
• Advanced eval/monitoring - via Agent Evaluation integration
• Integrated with the Lakehouse - All data available as Delta Tables for use in downstream BI and analytical use cases via Notebooks, Databricks SQL, and Databricks AI/BI dashboards
• Enterprise-ready governance - via integration with Unity Catalog
• Fully managed hosting - Zero infrastructure management

Tip

Your data is always yours - The core data model and tracing capabilities are completely open source. You can export and use your MLflow data anywhere.

Key differences at a glance

Overview comparison

Feature	Open Source MLflow	Managed MLflow on Databricks
Tracing & observability
Tracing data model & APIs	✅	✅
Production-scale trace ingestion	❌	✅
Production monitoring	❌	✅
GenAI evaluation & monitoring
Evaluation data model & APIs	✅	✅
Human feedback UI and APIs	❌	✅
High-quality, research-backed LLM judges	❌	✅
Versioned evaluation datasets	❌	✅
Enterprise readiness
Hosting	Self-managed	Fully managed
Enterprise governance (Unity Catalog)	❌	✅
Data integrated with Lakehouse for AI/BI & SQL	❌	✅
CI/CD deployment jobs	❌	✅
LLM / MLOps
Prompt Management	✅	✅
Experiment Tracking	✅	✅
Model / App Version Management	✅	✅

Why choose Managed MLflow?

Managed MLflow on Databricks extends Open Source MLflow with capabilities designed for production GenAI applications:

Scalable for production

• High-volume trace ingestion designed for production workloads with thousands of requests per second
• Automatic scaling without infrastructure management
• Production monitoring with built-in dashboards and alerts

Advanced evaluation and monitoring

• Agent Evaluation integration provides high-quality LLM judges, human labeling UIs, and versioned evaluation datasets
• Continuous monitoring automatically evaluates production traces
• Quality dashboards visualize trends and identify issues

Integrated with the Lakehouse & Unity Catalog

• Lakehouse integration makes all trace, evaluation, and monitoring data available for downstream workflows using Databricks AI/BI capabilities to create custom dashboards, analytics, and reporting on your GenAI data
• Unity Catalog governance provides enterprise-grade security and access controls

Fully managed hosting

• Zero infrastructure management - Databricks handles all hosting, scaling, and updates
• Enterprise SLAs with high availability and automated backups
• Seamless upgrades to the latest MLflow features

Next steps

1. Create a free trial Databricks account
2. MLflow is pre-installed and configured
3. Follow the quickstarts to get started