Managed MLflow

What it does

Managed MLflow is a hosted version of open-source MLflow inside the Databricks Data + AI Platform. It takes in training runs, parameters, metrics, artifacts, prompts, and agent execution traces. It produces experiment records, evaluation results, versioned prompts and agents, Model Registry entries, and dashboards for comparing performance across dev, staging, and production.

Teams use it to track classical ML and deep learning experiments, trace GenAI agents step by step (prompts, retrievals, tool calls), score outputs with LLM judges or human feedback in the MLflow UI, and monitor live quality, latency, and PII leakage. The same tracking API runs on a managed server with Unity Catalog governance, role-based approval workflows, and integration with SQL and AI/BI, while the underlying MLflow APIs remain open source.

It suits data science, ML engineering, and AI development teams already working in Databricks who want one lifecycle for models and agents. Access is through a Databricks workspace and API; start with a free trial or a demo, then follow the Quick Start Guide. Pricing is usage-based on Databricks compute, so request a quote or use the Databricks Pricing Calculator for current rates. Advanced features assume familiarity with Databricks and Unity Catalog.

What it can do

• Experiment tracking of parameters, metrics, artifacts and models from any ML or deep learning framework
• Agent tracing with OpenTelemetry-compatible capture of prompts, retrievals and tool calls
• Generative AI evaluation using LLM-as-a-judge and human feedback in the MLflow UI
• Prompt Registry and versioning of prompts, agents and application code
• Generative AI monitoring and alerting for quality, latency and PII leakage
• Model Registry integrated with Unity Catalog for governance and deployment at scale

Works with

• Unity Catalog
• Databricks Model Serving
• Databricks AI/BI and SQL
• Git and CI/CD workflows

Example workflows

Track and compare a classical ML training run (illustrative)

Input: a training script and dataset. Steps: enable MLflow tracking in a Databricks notebook, log parameters and metrics per run, compare runs in the experiment view, then register the chosen model in the Model Registry. Output: an experiment history with a registered, versioned model.

Trace and evaluate a GenAI agent (illustrative)

Input: an agent application and a set of test questions. Steps: instrument the agent so each execution is traced, review traces in the UI, run an LLM-judge scorer over a dataset built from traces, then adjust the prompt and re-evaluate. Output: traces, scored evaluation results, and a versioned prompt.

Before you choose

• Billing is usage-based on Databricks compute; storage and cloud networking costs vary by provider. Request a quote or use the Pricing Calculator for current rates.
• Traces can contain sensitive inputs and outputs, so governance depends on configuring Unity Catalog access controls.
• Advanced evaluation, monitoring, and Unity Catalog governance require a Databricks workspace rather than a standalone install.

Listed under

• Coding
• DevOps and observability
• AI agents
• AI observability
• Experiment tracking
• Genai observability
• LLM evaluation
• MLOps

Get started

Then follow the Quick Start Guide and Managed MLflow GenAI quickstart.

1. Start a free Databricks trial or schedule a demo from the product page.
2. Open the Managed MLflow GenAI quickstart or Quick Start Guide to create an experiment.
3. Instrument your code with MLflow tracing, or run the MLflow setup wizard from your repository, to capture your first trace.

Costs depend on the underlying Databricks services, compute, cloud and region. Use the product’s current SKU rates and calculator; general platform trial credits are temporary.

We couldn’t find published prices for Managed MLflow. See their pricing page ↗

Sources for this overview

Product pages, documentation, and pricing checked 28 Oct 2026. Each source supports part of the overview; availability and prices can change.

• Managed MLflow: AI model and agent management | Databricks ↗www.databricks.com
• Databricks Pricing: Flexible Plans for Data and AI Solutions | Databricks ↗www.databricks.com
• What is agent observability and quality? | Databricks on AWS ↗docs.databricks.com

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Last checked 28 Oct 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.