MLRun vs MLtraq
MLRun
mlrun.orgMLtraq
mltraq.comSide by side
| Figure | MLRun | MLtraq |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | Yes | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | Yes |
| API | Yes | — |
| Self-hosted | Yes | — |
| Plans published | 2 | 0 |
| Spec score · Experiment Tracking | 7.9 (#5) | 7.1 (#15) |
| Spec score · ML Experiment Tracking Software | 7.7 (#13) | 7.0 (#28) |
| Collaboration | MLRun provides one technology stack for data engineers, data scientists and machine-learning engineers. | Users can back up, merge, share, and reload experiments with their computation state. |
| Purpose | MLRun is an open-source AI orchestration framework for managing machine-learning and generative-AI applications across their lifecycles. | MLtraq is an open-source Python library for ML and AI developers to design, execute, track, and share experiments. |
| Security | Managed MLRun provides LDAP integration, user and group management, service authentication and authorization, and secured API-gateway authentication. | MLtraq's DATAPAK format uses a subset of safe Python Pickle opcodes for listed basic and container types. |
| 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 Discussions to ask questions and share ideas. |
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.