MLRun
What it is
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, using elastic resources. Its feature store handles the collection, transformation, storage, cataloging, serving and monitoring of data features. Deployment options include Kubernetes, an AWS cluster, an Azure AKS cluster and Iguazio's managed service, as well as multi-cloud, hybrid and on-premises environments. Built-in observability supports deployments. The ecosystem includes 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 supports 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 adds LDAP integration, service monitoring and 24/7 enterprise support. The browser interface runs on Chrome and Firefox.
For and against
For
- Automates multiple model lifecycle tasks.
- Feature store covers collection through monitoring.
- Supports multi-cloud, hybrid and on-premises deployment.
- Includes model registry, tracking and pipeline orchestration.
- Open-source plan includes community support.
Against
- Open-source support is community-based.
- 24/7 enterprise support is listed for managed MLRun.
- Browser interface support is listed for Chrome and Firefox.
The verdict
MLRun combines lifecycle automation, a feature store and deployment options in an open-source framework.
Who it suits
MLRun provides a shared technology stack for data engineers, data scientists and machine-learning engineers. It suits teams looking to coordinate ML or generative-AI work across deployment environments.
Plans and prices
Open Source MLRun
Community support
Read 30 Sep 2026 · iguazio.com
Managed MLRun on Iguazio
LDAP integration · 24/7 enterprise support · service monitoring
Read 30 Sep 2026 · iguazio.com
On the record
- Automation
- MLRun automates data preparation, model tuning, customization, validation and optimization of ML models and LLMs over elastic resources.iguazio.com · read 30 Sep 2026
- Browser support
- The MLRun browser interface runs on Chrome and Firefox.docs.mlrun.org · read 30 Sep 2026
- Collaboration
- MLRun provides one technology stack for data engineers, data scientists and machine-learning engineers.iguazio.com · read 30 Sep 2026
- Deployment
- MLRun supports multi-cloud, hybrid and on-premises deployment options with built-in observability.mlrun.org · read 30 Sep 2026
- Deployment options
- The documented deployment options are Kubernetes, an AWS cluster, an Azure AKS cluster and Iguazio's managed service.docs.mlrun.org · read 30 Sep 2026
- Feature store
- The MLRun feature store automates collection, transformation, storage, cataloging, serving and monitoring of data features.docs.mlrun.org · read 30 Sep 2026
- Free trial
- Iguazio provides a 14-day free trial for its managed MLRun service.docs.mlrun.org · read 30 Sep 2026
- Integrations
- The ecosystem lists GitHub Actions, GitLab CI/CD, Jenkins and Kubeflow Pipelines for CI/CD integration.docs.mlrun.org · read 30 Sep 2026
- Purpose
- MLRun is an open-source AI orchestration framework for managing machine-learning and generative-AI applications across their lifecycles.mlrun.org · read 30 Sep 2026
- Security
- Managed MLRun provides LDAP integration, user and group management, service authentication and authorization, and secured API-gateway authentication.iguazio.com · read 30 Sep 2026
- Support
- The open-source offering includes community support, while managed MLRun includes 24/7 enterprise support, service monitoring and log management.iguazio.com · read 30 Sep 2026
- Technology ecosystem
- Supported ecosystem components include S3, Google Cloud Storage, Azure, BigQuery, Snowflake, Kafka, Spark, Dask, Horovod, Kubernetes, PyTorch, TensorFlow, XGBoost and ONNX.docs.mlrun.org · read 30 Sep 2026
