MLRun vs ModelDB
MLRun
mlrun.orgModelDB
github.comSide by side
| Figure | MLRun | ModelDB |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | Yes | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 1 |
| Spec score · Experiment Tracking | 7.9 (#5) | 7.5 (#8) |
| Spec score · ML Experiment Tracking Software | 7.7 (#13) | 7.4 (#19) |
| Deployment | MLRun supports multi-cloud, hybrid and on-premises deployment options with built-in observability. | The project says it works on Docker and Kubernetes and documents Docker Compose and Helm deployment options. |
| Purpose | MLRun is an open-source AI orchestration framework for managing machine-learning and generative-AI applications across their lifecycles. | ModelDB versions machine learning models and tracks ML metadata across the model lifecycle. |
| Support | The open-source offering includes community support, while managed MLRun includes 24/7 enterprise support, service monitoring and log management. | The project directs users to GitHub issues for bug reports and feature requests and to Slack for questions and help. |
Plans
In their own words
MLRun
MLRun is an open-source framework for orchestrating machine-learning and generative-AI applications across their lifecycles. It automates data preparation, model tuning, customization, validation, and optimization for ML models and large language models over elastic resources. Its feature store can collect, transform, store, catalog, serve, and monitor data features. MLRun supports multi-cloud, hybrid, and on-premises deployments with built-in observability; documented options include Kubernetes, AWS, Azure AKS, and Iguazio’s managed service. The ecosystem includes storage and data systems such as S3, Google Cloud Storage, BigQuery, Snowflake, Kafka, Spark, and Dask, alongside PyTorch, TensorFlow, XGBoost, and ONNX. CI/CD integrations include GitHub Actions, GitLab CI/CD, Jenkins, and Kubeflow Pipelines. MLRun also lists model registry, experiment tracking, pipeline orchestration, model serving, data versioning, and managed compute. The open-source plan costs nothing and includes community support. Managed MLRun includes LDAP integration, user and group management, service authentication and authorization, secured API-gateway authentication, service monitoring, log management, and 24/7 enterprise support.