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MLflow Model Registry

mlflow.org

Free plan WebLinuxAPISelf-hosted
Visit mlflow.org
Spec score 7.8 #6 of 22 in Experiment Tracking

What it is

MLflow Model Registry is a model store, API and interface for managing machine-learning models through their lifecycle. It tracks model versions and connects each version to the MLflow run, logged model or notebook that produced it. Teams can organize models with aliases, tags and annotations, register models, add descriptions, and move them between stages such as Staging and Production. On a remote tracking server, MLflow supports basic HTTP authentication and role-based permissions for registered models; basic authentication requires a configured secret key and an admin password of at least 12 characters. When used with Databricks Unity Catalog, the registry supports centralized governance, access controls, cross-workspace access and model lineage. An official Helm chart supports self-hosted Kubernetes deployment. MLflow lists integrations with more than 100 tools, including LangChain, OpenAI and PyTorch, plus support for Python, TypeScript/JavaScript, Java, R and OpenTelemetry. The free Open Source plan uses the Apache 2.0 license. Artifact storage is not supported.

Spec sheet

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

Plans and prices

Open Source Free Forever free 100% open source · Apache 2.0 license Read 29 Sep 2026 · mlflow.org

Every MLflow Model Registry plan and price →

Strong points and limits

Strong points

  • Free under the Apache 2.0 license
  • Tracks versions and model lineage
  • Supports aliases and approval workflows
  • Helm chart for Kubernetes deployment
  • Integrates with 100+ tools

Limits

  • Artifact storage is not supported
  • Basic authentication needs a configured secret key
  • Admin password must be at least 12 characters

Who it suits

The registry suits solo data scientists and larger machine-learning platform teams that need model versioning and lifecycle workflows. Teams seeking centralized governance can use it with Databricks Unity Catalog.

Verdict

MLflow Model Registry provides version tracking, lineage and lifecycle workflows, with options for access control and Kubernetes self-hosting. Artifact storage is outside its scope, so plan for that separately.

Where Specifiction places it

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