Domino Data Lab vs MLflow Model Registry
Domino Data Lab
domino.aiMLflow Model Registry
mlflow.orgSide by side
| Figure | Domino Data Lab | MLflow Model Registry |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 3 | 1 |
| Spec score · Experiment Tracking | 7.4 (#9) | 7.8 (#6) |
| Deployment | Domino supports deployment of apps such as Streamlit, Dash, and Shiny, which can scale with user demand and use discovery and approval workflows. | MLflow provides an official Helm chart for deploying a self-hosted instance on Kubernetes. |
| Governance | Domino tracks experiments and model lineage, and provides a governed model registry and built-in audit trails. | With Databricks Unity Catalog, the registry supports centralized governance, access controls, cross-workspace access, and model lineage. |
| Purpose | Domino’s Enterprise AI Platform brings together model development, MLOps, collaboration, and governance for enterprises building and operating AI at scale. | MLflow Model Registry is a centralized model store, API, and UI for managing the lifecycle of machine learning models. |
Plans
Domino Data Lab
- Domino CloudPriced on request
- EnterprisePriced on request
- PremiumPriced on request
In their own words
Domino Data Lab
Domino Data Lab has no write-up on Specifiction yet; its figures are above.
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.