JFrog ML vs MLRun
JFrog ML
jfrog.com
MLRun
mlrun.orgSide by side
| Figure | JFrog ML | MLRun |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | Yes | Yes |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 2 |
| Spec score · Model Registry Software | 7.9 (#7) | 7.7 (#11) |
| Spec score · Model registries | 7.8 (#16) | 7.9 (#14) |
| Deployment | Models can be served as API endpoints, used for batch inference, and A/B tested. | MLRun supports multi-cloud, hybrid and on-premises deployment options with built-in observability. |
| Deployment options | JFrog ML Cloud offers cloud deployment, while its hybrid architecture allows data and models to remain in a customer's cloud environment. | The documented deployment options are Kubernetes, an AWS cluster, an Azure AKS cluster and Iguazio's managed service. |
| Feature Store | The Feature Store supports batch, streaming, and real-time feature sets, but JFrog ML SaaS supports batch feature sets only; real-time and streaming feature sets require a hybrid deployment. | The MLRun feature store automates collection, transformation, storage, cataloging, serving and monitoring of data features. |
| Integrations | JFrog's integration catalog includes Hugging Face, NVIDIA, Amazon SageMaker, and Azure Machine Learning integrations. | The ecosystem lists GitHub Actions, GitLab CI/CD, Jenkins and Kubeflow Pipelines for CI/CD integration. |
| Purpose | JFrog ML is a unified platform for building, deploying, and monitoring machine learning models in production. | MLRun is an open-source AI orchestration framework for managing machine-learning and generative-AI applications across their lifecycles. |
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.