Hopsworks AI Lakehouse vs MLRun

Hopsworks AI Lakehouse is a platform for building batch, real-time, and LLM AI systems using open standards. It supports Apache Iceberg, Delta Lake, and Apache Hudi, with streaming feature pipelines that feed a low-latency serving database and provide sub-second feature freshness. Its Query Service transfers data to Python clients using Arrow and supports temporal joins, filter pushdown, credential vending, and reproducible training data. For LLM workflows, teams can query lakehouse data from prompts, integrate vector search for retrieval-augmented generation, and create fine-tuning instruction datasets while maintaining data governance. Feature, training, and inference pipelines cover batch, streaming, real-time, and LLM work, with trained models sent to a model registry. Integrations include Airflow, Apache Flink, Databricks, Snowflake, AWS SageMaker, Spark, Vertex AI, and Weights & Biases. Deployment options include SaaS, managed Kubernetes on AWS, Azure, or GCP, and air-gapped data centres. The Free plan is free and includes one project, Feature Store, Model Registry, and community support; SaaS uses usage-based pricing, while Enterprise is custom-priced.

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.