---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: c2fb8ac6314a335754e0028ef3a80269c4ab484695ad95345f0de20a9b420493
  pageDirectory: concepts
  sources:
    - mlflow-on-databricks-databricks-on-aws.md
  confidence: 1
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - open-source-vs-databricks-managed-mlflow
    - OSVDM
    - Open-Source vs Managed MLflow
  citations:
    - file: mlflow-on-databricks-databricks-on-aws.md
title: Open Source vs Databricks-Managed MLflow
description: Key differences between the open-source MLflow and the Databricks-managed version, including exclusive enterprise features, Unity Catalog integration, and differences in telemetry collection defaults.
tags:
  - mlflow
  - comparison
  - enterprise
timestamp: "2026-06-19T19:39:54.859Z"
---

# Open Source vs Databricks-Managed MLflow

**Open Source vs Databricks-Managed MLflow** describes the distinction between the community-driven MLflow project and the enterprise-hosted version provided on [Databricks on AWS](/concepts/databricks-on-aws.md). While both share a common core of APIs and concepts, Databricks-managed MLflow adds infrastructure, governance, and scalability features that are not available in the standalone open source distribution. ^[mlflow-on-databricks-databricks-on-aws.md]

## Shared Foundation

Both open source MLflow and Databricks-managed MLflow are built on the same [MLflow](/concepts/mlflow.md) framework for AI engineering. They share core capabilities such as:

- [Experiment tracking](/concepts/mlflow-experiment-tracking.md) for logging parameters, metrics, and artifacts
- [Model evaluation](/concepts/mlflow-evaluation-ui.md) for assessing model quality
- [Model Registry](/concepts/mlflow-model-registry.md) for versioning and managing model deployments
- MLflow Models as a standardized packaging format
- [MLflow Tracing](/concepts/mlflow-tracing.md) for observability of agent and LLM applications
- [Agent Evaluation](/concepts/mlflow-agent-evaluation.md) for measuring and improving agent quality
- [Prompt management](/concepts/privileges-for-prompt-management.md) for versioning prompt templates

^[mlflow-on-databricks-databricks-on-aws.md]

MLflow provides native Python SDKs, TypeScript/JavaScript SDKs, Java APIs, and R APIs across both distributions. ^[mlflow-on-databricks-databricks-on-aws.md]

## Key Differences

The following table highlights the primary distinctions between open source MLflow and Databricks-managed MLflow:

| Feature Area | Open Source MLflow | Databricks-Managed MLflow |
|---|---|---|
| **Infrastructure** | Self-managed, requires separate setup | Fully hosted, managed, and scalable |
| **Unity Catalog** | Not available | Built-in [Unity Catalog](/concepts/unity-catalog.md) integration |
| **Governance** | User-managed permissions | Enterprise-grade [Unity Catalog](/concepts/unity-catalog.md) governance |
| **Deployment** | Manual configuration | Integrated with [Model Serving](/concepts/model-serving.md) |
| **Observability** | Basic tracing | Rich [Trace data](/concepts/tracedata.md) with annotation |
| **Telemetry** | Collected by default (from 3.2.0) | **Disabled** by default |

^[mlflow-on-databricks-databricks-on-aws.md]

Open source telemetry collection was introduced in MLflow 3.2.0 and is **disabled on Databricks by default**. For more details, refer to MLflow usage tracking documentation. ^[mlflow-on-databricks-databricks-on-aws.md]

## Databricks-Exclusive Features

Databricks-managed MLflow provides several features not available in open source:

- [Logged Models](/concepts/logged-models.md) for tracking model lifecycle across environments
- [Deployment jobs](/concepts/mlflow-deployment-jobs.md) for managing evaluation, approval, and deployment workflows
- Activity log for version governance
- [Serverless Budget Policy](/concepts/serverless-budget-policy.md) integration for cost control
- [Feature Store](/concepts/feature-store.md) automated lookups
- Model version page in Unity Catalog with metrics from multiple runs
- REST API for accessing model metrics and parameters

^[mlflow-on-databricks-databricks-on-aws.md]

## Deployment Integration

Databricks-managed MLflow is tightly integrated with:

- [Model Serving](/concepts/model-serving.md) for REST API deployment
- Model Registry with Unity Catalog for cross-workspace access
- [Model Serving](/concepts/model-serving.md) for automatic request/response capture
- [Trace data](/concepts/tracedata.md) for debugging and monitoring

^[mlflow-on-databricks-databricks-on-aws.md]

## When to Use Each

- **Open source MLflow**: When you need full control over your MLflow instance, prefer self-hosting, or require custom integrations outside Databricks.
- **Databricks-managed MLflow**: When you need enterprise governance, scalability, and seamless integration with Databricks Lakehouse and [Unity Catalog](/concepts/unity-catalog.md).

## Related Concepts

- [MLflow on Databricks](/concepts/mlflow-on-databricks.md)
- [Databricks Serverless GPU](/concepts/databricks-serverless-gpu.md)
- AI engineering platform
- [MLflow Model Registry](/concepts/mlflow-model-registry.md)
- [Unity Catalog](/concepts/unity-catalog.md)

## Sources

- mlflow-on-databricks-databricks-on-aws.md

# Citations

1. [mlflow-on-databricks-databricks-on-aws.md](/references/mlflow-on-databricks-databricks-on-aws-bc75dc1f.md)
