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MLflow Model Registry

Free plan APILinuxSelf-hostedWeb

Visit MLflow Model Registry Alternatives Pricing

MLflow Model Registry's own site

What MLflow Model Registry is

MLflow Model Registry is a free model store, API, and UI for managing machine-learning models through their lifecycle. It records versions and connects each to the MLflow run, logged model, or notebook that produced it. Teams can organize models with aliases, tags, and annotations, and transition them through stages such as Staging and Production. For registered models on a remote tracking server, MLflow supports basic HTTP authentication and role-based permissions; basic authentication requires a configured secret key and an admin password of at least 12 characters. With Databricks Unity Catalog, the registry supports centralized governance, access controls, cross-workspace access, and model lineage. An official Helm chart is available for Kubernetes self-hosting. MLflow lists integrations with 100+ tools, including LangChain, OpenAI, and PyTorch, and supports Python, TypeScript/JavaScript, Java, R, and OpenTelemetry. The open-source plan is free forever under the Apache 2.0 license. Artifact storage is not supported.

Who it suits

The registry is described as useful for solo data scientists and large machine-learning platform teams. It suits teams managing model versions, lineage, and lifecycle workflows.

What it does well

  • Tracks model versions and lineage
  • Aliases, tags, and annotations
  • Supports Staging and Production transitions
  • Free under Apache 2.0 license
  • Official Helm chart for Kubernetes

What to know first

  • Artifact storage is not supported
  • Basic authentication requires configuration and a 12-character password

Limits

The Open Source plan is free, billed Forever free, and artifact storage is not supported. Basic authentication requires a configured secret key and an admin password of at least 12 characters.

Plans

PlanBilled and limitsPrice
Open SourceForever free · 100% open source · Apache 2.0 licenseFree

As mlflow.org lists them, read 29 Sept 2026. The maker's own page has the final word.

Getting started

  1. Open https://mlflow.org/.
  2. Choose the Open Source plan, which is free under the Apache 2.0 license.
  3. Use the API or web interface on Linux, through the web, or in a self-hosted setup.
  4. For Kubernetes self-hosting, use the official Helm chart.
  5. If enabling basic authentication, configure a secret key and an admin password of at least 12 characters.

Verdict

MLflow Model Registry provides versioning, lineage, and lifecycle tools in a free open-source package. Teams that need artifact storage should note that it is not supported.

Questions

How much does MLflow Model Registry cost?

The Open Source plan is free, billed Forever free.

Is MLflow Model Registry open source?

Yes. The Open Source plan uses the Apache 2.0 license.

Which platforms are listed?

The listed platforms are API, Linux, self-hosted, and web.

Can it track model versions and lineage?

Yes. It tracks versions and links each version to the MLflow run, logged model, or notebook that produced it.

What access controls does it support?

It supports basic HTTP authentication and role-based permissions for registered models on a remote tracking server. With Databricks Unity Catalog, it also supports access controls and cross-workspace access.

Which maker is behind it?

The site identifies the project as the MLflow Project, a Series of LF Projects, LLC.

Facts from MLflow Model Registry's own pages

Access control
MLflow supports basic HTTP authentication and role-based permissions for registered models on a remote tracking server. · mlflow.org, 29 Sept 2026
Authentication setup
Basic authentication requires a configured secret key and admin password; the documentation specifies that passwords must be at least 12 characters. · mlflow.org, 29 Sept 2026
Deployment
MLflow provides an official Helm chart for deploying a self-hosted instance on Kubernetes. · mlflow.org, 29 Sept 2026
Governance
With Databricks Unity Catalog, the registry supports centralized governance, access controls, cross-workspace access, and model lineage. · mlflow.org, 29 Sept 2026
Integrations
MLflow says it integrates with 100+ tools, including LangChain, OpenAI, and PyTorch, and supports Python, TypeScript/JavaScript, Java, R, and OpenTelemetry. · mlflow.org, 29 Sept 2026
Lifecycle workflows
Teams can use aliases, tags, and annotations to organize models and support deployment workflows. · mlflow.org, 29 Sept 2026
Maker
The site identifies the project as the MLflow Project, a Series of LF Projects, LLC. · mlflow.org, 29 Sept 2026
Model library support
The model documentation lists integrations including Keras, PyTorch, scikit-learn, Spark MLlib, TensorFlow, ONNX, XGBoost, and LightGBM. · mlflow.org, 29 Sept 2026
OSS registry
The open-source registry provides a UI and API to register models, track versions, add tags and descriptions, and transition models between stages such as Staging and Production. · mlflow.org, 29 Sept 2026
Purpose
MLflow Model Registry is a centralized model store, API, and UI for managing the lifecycle of machine learning models. · mlflow.org, 29 Sept 2026
Versioning and lineage
The registry tracks model versions and links each version to the MLflow run, logged model, or notebook that produced it. · mlflow.org, 29 Sept 2026
Who it is for
The Model Registry documentation describes it as useful for both solo data scientists and large machine learning platform teams. · mlflow.org, 29 Sept 2026

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