MLflow Model Registry

6.9easy start · #11 of 33
in Model Registry Software

Free to practise onyes
Free trialnot on record
Well documentedyes
Runs where you worknot on record

Runs on api, Linux, self-hosted, Web.

MLflow Model Registry is ranked #11 of 33 in model registry software on The Geeks Club. It runs on API, Linux, Self-hosted, Web. There is a free plan.

MLflow Model Registry plans and pricing

All plans

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

Compared on model registry software

Free plan: Yes
Model versioning: Yes
Approval workflows: Yes
Model lineage: Yes
Deployment tracking: Yes
Model aliases: Yes
Artifact storage: No

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The record

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

Where it ranks on The Geeks Club

Best Model Registry Software in 2026#11 of 33

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Sources

mlflow.org/docs/latest/model-registry/· checked 29 Sept 2026
mlflow.org/docs/latest/self-hosting/security/basic· checked 29 Sept 2026
mlflow.org/docs/latest/self-hosting· checked 29 Sept 2026
mlflow.org· checked 29 Sept 2026
mlflow.org/docs/latest/model· checked 29 Sept 2026