Hopsworks vs Metaflow — Comparison

Hopsworks

Build, deploy, and scale production ML systems with Hopsworks. The Feature Store and MLOps platform for real-time AI, trusted by leading teams.

Founded in 2017, Hopsworks originated from a strong foundation in academia and the open-source community (ex MySQL, Oracle). As a pioneering platform for data and AI, Hopsworks seamlessly integrates the disciplines of data science, data engineering, and machine learning into a cohesive environment. An AI Lakehouse is a modern infrastructure designed to support the unique needs of AI and machine learning workloads. It simplifies the deployment and development of AI models and provides a structured, efficient approach to building and maintaining AI systems, enabling faster model creation and smoother production deployment. Palo Alto 94301, California USA 69 Wilson St EC2A2BB, London United Kingdom Plan 2 116 24, Stockholm Sweden Contact us and learn how Hopsworks can help your organization deploy reliable AI systems.

Metaflow

Build and manage real-life ML, AI, and data science projects with Metaflow.

Open-source Metaflow makes it quick and easy to build and manage real-life ML, AI, and data science projects. Explore with notebooks, develop with Metaflow, and test and debug locally. Results are stored and tracked automatically for easy analysis. Break out from the confines of a laptop or a single notebook. Scale out easily to the cloud, utilizing GPUs, multiple cores, and multiple instances in parallel. Metaflow organizes the work for easy collaboration on the way. Deploy experiments to production with a single click without changing anything in the code. Make flows react to updating data and other events automatically. Get started easily on a laptop. When you are ready to scale, deploy the Metaflow stack on your cloud account or on-premise Kubernetes cluster. Metaflow integrates seamlessly with your existing infrastructure, security, and data governance policies. To get a taste of Metaflow in the cloud, try Metaflow Sandbox in the browser. Deploy on EKS and S3, or AWS Batch & AWS Step Functions. Deploy on AKS and Azure Blob Storage. Deploy on GKE and Google Cloud Storage. For maximum flexibility, deploy on a custom Kubernetes cluster. Metaflow was originally developed at Netflix to address the needs of developers and data scientists who work on demanding real-life ML, AI, and data projects. Netflix open-sourced Metaflow in 2019. Today, Metaflow is used by hundreds of companies across industries, powering diverse projects from state-of-the-art GenAI and compute vision to business-oriented data science, statistics, and operations research. Create flows incrementally step-by-step with the new spin command Build agentic systems with the new recursive and conditional steps Compose flows with reusable custom decorators Use uv to manage dependencies, from dev to cloud Setup the full Metaflow stack on your laptop with one click Checkpoint long-running model training and other tasks with the new @checkpoint decorator Configure flows freely with the new Config object New APIs allow you to run and deploy Metaflow in notebooks and scripts Learn about various patterns of scalable compute with Metaflow. Train and fine-tune large language models and other generative AI models on AWS Trainium. Build observable ML/AI systems with cards that update in real-time. Install dependencies from PyPI as well as Conda in your Metaflow steps. Connect to external services securely using the new @secrets decorator. Metaflow 2.9 allows you to trigger workflows based on real-time events. Apache Arrow and Metaflow.S3 make it easy to process data fast. Learn how to use Metaflow for demanding GPU tasks. Develop with Metaflow, deploy on your existing Apache Airflow servers. Deploy and operate Metaflow on GCP and all other m

Hopsworks

Metaflow

Hopsworks

Metaflow

Hopsworks

Metaflow