---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: a227fe79e3ca730ed7362ee42d41cb38552f1f8b091c0d7dfbf71d861bfb0a01
  pageDirectory: concepts
  sources:
    - databricks-data-science-and-ml-capabilities-databricks-on-aws.md
    - open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md
  confidence: 0.95
  provenanceState: merged
  freshnessStatus: unverified
  aliases:
    - databricks-managed-mlflow
    - Databricks MLflow
    - Managed MLflow
    - Mlflow vs. Managed MLflow
  citations:
    - file: open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md
    - file: databricks-data-science-and-ml-capabilities-databricks-on-aws.md
title: Databricks-Managed MLflow
description: Databricks' managed version of the open-source MLflow platform providing experiment tracking, model registry, and audit trails integrated with Unity Catalog and Git.
tags:
  - machine-learning
  - mlops
  - experiment-tracking
timestamp: "2026-06-19T18:11:32.340Z"
---

# Databricks-Managed MLflow

**Databricks-Managed MLflow** is a fully hosted, enterprise-grade version of the open-source [MLflow](/concepts/mlflow.md) platform, provided as part of the Databricks Data Intelligence Platform. It uses the same core data model, API, and SDK as open-source MLflow, ensuring data and workload portability, while adding enterprise governance, fully managed hosting, and deep integrations with the broader Databricks platform.^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]

## Overview

Databricks provides a unified platform for the full data science and machine learning lifecycle, from raw data ingestion through feature engineering, model training, deployment, and production monitoring. Databricks integrates with popular open-source ML frameworks while adding enterprise-grade governance, observability, and operational tooling, collectively known as MLOps.^[databricks-data-science-and-ml-capabilities-databricks-on-aws.md]

While open-source MLflow ensures portability of data and workloads, Databricks-managed MLflow adds enterprise-grade governance, fully managed hosting, and integrated tooling for both development and production environments.^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]

## Key Capabilities

### Enterprise Governance with Unity Catalog

Managed MLflow integrates tightly with [Unity Catalog](/concepts/unity-catalog.md) to provide centralized governance for models, feature tables, vector indexes, tools, and other AI assets. When deploying agents, authentication for agent, data, and tool access can be precisely controlled using authentication passthrough and on-behalf-of-user authentication patterns.^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md, databricks-data-science-and-ml-capabilities-databricks-on-aws.md]

MLflow permissions follow the same governance patterns as the broader Databricks platform: workspace objects such as experiments follow workspace permissions, while Unity Catalog objects such as registered models follow Unity Catalog privileges. UI and API authentication match the Databricks platform and REST API. System tables provide usage and audit logs for managed MLflow.^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]

### Experiment Tracking and Model Lineage

[MLflow Experiments](/concepts/mlflow-experiment.md) track work during model development, logging parameters, metrics, artifacts, and code versions. Each model version in the registry links back to the training run, dataset, environment, and git commit that produced it, providing a complete audit trail for any deployed model.^[databricks-data-science-and-ml-capabilities-databricks-on-aws.md]

Databricks notebooks are automatically connected to the MLflow server and support autologging for MLflow tracking. GenAI tracing provides an inline tracing UI for interactive analysis within notebooks.^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]

### Model Registry and Serving

The [MLflow Model Registry](/concepts/mlflow-model-registry.md) provides a centralized repository with a UI and APIs for managing model deployment. [Databricks Model Serving](/concepts/databricks-model-serving.md) is tightly integrated with the Model Registry, providing a unified, scalable interface for deploying, governing, and querying AI models as REST API endpoints.^[databricks-data-science-and-ml-capabilities-databricks-on-aws.md, open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]

Databricks supports both batch inference and real-time serving. Batch inference applies models efficiently to large datasets, whereas real-time serving provides models as low-latency API endpoints. Real-time serving logs to inference tables governed in Unity Catalog.^[databricks-data-science-and-ml-capabilities-databricks-on-aws.md]

### GenAI and Agent Support

Managed MLflow provides a complete platform for developing, evaluating, and monitoring agents and LLM applications:

- **MLflow Tracing**: Records inputs, outputs, and metadata for each intermediate step of a request.^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]
- **Human Feedback Tools**: Includes a Review App with a Chat UI for vibe checks and an expert feedback UI for labeling traces.^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]
- **Production Monitoring**: Continuously evaluates production traffic using LLM judges and scorers, powered by production-scale trace ingestion that stores traces to Unity Catalog tables.^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]

### Infrastructure and CI/CD Integration

Managed MLflow integrates with Databricks infrastructure-as-code tools:

- [Declarative Automation Bundles](/concepts/declarative-automation-bundles.md): Manage experiments, models, and other resources.^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]
- [MLOps Stacks](/concepts/mlops-stacks.md): Templates for automated, repeatable promotion from development to production using infrastructure-as-code.^[databricks-data-science-and-ml-capabilities-databricks-on-aws.md]
- [MLflow 3 Deployment Jobs](/concepts/mlflow-3-deployment-jobs.md): Integrate Databricks Workflows with Unity Catalog to automate staged deployment of ML models.^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]

## Differences from Open Source MLflow

| Feature | Open Source MLflow | Databricks Managed MLflow |
|---------|-------------------|---------------------------|
| Governance | Self-managed | Enterprise-grade with Unity Catalog |
| Hosting | Self-hosted | Fully managed, production-ready servers |
| Security | Platform-agnostic | Databricks platform permissions and auditing |
| Feature Store | Not integrated | Native [Feature Store](/concepts/feature-store.md) integration |
| GenAI Monitoring | Open-source tools | Production monitoring with LLM judges |
| Human Feedback | Manual processes | Integrated Review App and Chat UI |
| Telemetry | Open source tracking (3.2.0+) | Disabled by default |

^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]

## Benefits

- **Unified platform**: Manage all data and AI assets — data, features, models, and endpoints — in one governed platform with [Unity Catalog](/concepts/unity-catalog.md).^[databricks-data-science-and-ml-capabilities-databricks-on-aws.md]
- **Portability**: The core data model and APIs are completely open source. You can export and use your MLflow data anywhere.^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]
- **Scalability**: Databricks provides MLflow servers with automatic updates, designed for scalability and production. Managed MLflow is used by thousands of customers across the globe.^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]
- **Open ecosystem**: Full support for open-source ML frameworks including scikit-learn, XGBoost, PyTorch, TensorFlow, Hugging Face Transformers, and Ray. MLflow artifacts are stored in open formats that can be exported and run outside Databricks.^[databricks-data-science-and-ml-capabilities-databricks-on-aws.md]

## Related Concepts

- [Unity Catalog](/concepts/unity-catalog.md) — Centralized governance for models and AI assets
- [MLflow Model Registry](/concepts/mlflow-model-registry.md) — Repository for managing model versions and deployment
- [Databricks Model Serving](/concepts/databricks-model-serving.md) — Real-time and batch inference endpoints
- [MLOps Stacks](/concepts/mlops-stacks.md) — CI/CD templates for ML promotion workflows
- [Declarative Automation Bundles](/concepts/declarative-automation-bundles.md) — Infrastructure-as-code for MLflow resources
- [MLflow 3 Deployment Jobs](/concepts/mlflow-3-deployment-jobs.md) — Automated staged model deployment
- [Feature Store](/concepts/feature-store.md) — Governed feature management for ML training
- [Autologging](/concepts/mlflow-autologging.md) — Automatic MLflow tracking in Databricks notebooks
- [MLflow Tracing](/concepts/mlflow-tracing.md) — Input/output tracking for GenAI applications
- [Agent Evaluation](/concepts/mlflow-agent-evaluation.md) — Quality measurement for LLM-based agents

## Sources

- databricks-data-science-and-ml-capabilities-databricks-on-aws.md
- open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md

# Citations

1. [open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md](/references/open-source-vs-managed-mlflow-on-databricks-databricks-on-aws-ce848b0f.md)
2. [databricks-data-science-and-ml-capabilities-databricks-on-aws.md](/references/databricks-data-science-and-ml-capabilities-databricks-on-aws-8cf8bf4a.md)
