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

note

Open source telemetry collection was introduced in MLflow 3.2.0, and is disabled on Databricks by default. For more details, refer to the MLflow usage tracking documentation.

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 quickstart to get started