MLflow Model Registry vs MLRun

MLflow Model Registry is a model store, API and interface for managing machine-learning models through their lifecycle. It tracks model versions and connects each version to the MLflow run, logged model or notebook that produced it. Teams can organize models with aliases, tags and annotations, register models, add descriptions, and move them between stages such as Staging and Production. On a remote tracking server, MLflow supports basic HTTP authentication and role-based permissions for registered models; basic authentication requires a configured secret key and an admin password of at least 12 characters. When used with Databricks Unity Catalog, the registry supports centralized governance, access controls, cross-workspace access and model lineage. An official Helm chart supports self-hosted Kubernetes deployment. MLflow lists integrations with more than 100 tools, including LangChain, OpenAI and PyTorch, plus support for Python, TypeScript/JavaScript, Java, R and OpenTelemetry. The free Open Source plan uses the Apache 2.0 license. Artifact storage is not supported.

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.