MLRun vs Valohai
MLRun
mlrun.org
Valohai
valohai.comSide by side
| Figure | MLRun | Valohai |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | Yes | Yes |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 1 |
| Spec score · Experiment Tracking | 7.9 (#5) | 7.4 (#12) |
| Spec score · Model Registry Software | 7.7 (#11) | 7.4 (#16) |
| Spec score · MLOps Platforms | 7.7 (#9) | 7.4 (#14) |
| Spec score · ML Experiment Tracking Software | 7.7 (#13) | 7.4 (#21) |
| Spec score · Model registries | 7.9 (#14) | 7.6 (#19) |
| Deployment options | The documented deployment options are Kubernetes, an AWS cluster, an Azure AKS cluster and Iguazio's managed service. | The pricing page says Valohai can be deployed as a managed cloud service, on premises, in a private cloud, or in air-gapped environments. |
| Integrations | The ecosystem lists GitHub Actions, GitLab CI/CD, Jenkins and Kubeflow Pipelines for CI/CD integration. | Its ecosystem lists a Hugging Face template and connectors for Snowflake, BigQuery, and Redshift, plus Slurm cluster management integration. |
| Purpose | MLRun is an open-source AI orchestration framework for managing machine-learning and generative-AI applications across their lifecycles. | Valohai describes itself as an MLOps platform for orchestrating machine learning workflows and managing compute, datasets, and pipelines. |
| Support | The open-source offering includes community support, while managed MLRun includes 24/7 enterprise support, service monitoring and log management. | Subscriptions include technical support from the Customer Engineering team, along with training and onboarding resources. |
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.