MLflow Model Registry vs ModelDB
MLflow Model Registry
mlflow.orgModelDB
github.comSide by side
| Figure | MLflow Model Registry | ModelDB |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Linux | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · Experiment Tracking | 7.8 (#6) | 7.5 (#8) |
| Deployment | MLflow provides an official Helm chart for deploying a self-hosted instance on Kubernetes. | The project says it works on Docker and Kubernetes and documents Docker Compose and Helm deployment options. |
| Purpose | MLflow Model Registry is a centralized model store, API, and UI for managing the lifecycle of machine learning models. | ModelDB versions machine learning models and tracks ML metadata across the model lifecycle. |
Plans
In their own words
MLflow Model Registry
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.