Prefect vs Metaflow — Comparison

Prefect

Orchestrate workflows with Prefect. Build AI applications with Horizon. Open-source foundations, production-ready platforms.

Millions of developers choose our open-source Python frameworks when moving from script to scale. Turn any Python function into a workflow. One decorator. Full observability. No rewrites. Build MCP servers in minutes. Connect AI agents to any system. The standard for context delivery. Same code you wrote. Enterprise scale. SSO, RBAC, governance, and zero infrastructure to manage. Production orchestration without the ops. Autoscaling workers, enterprise auth, and observability that actually helps you debug. Deploy MCP servers with a command. Gateway, registry, and governance for AI agents accessing your business. From fintech to healthcare, teams rely on Prefect to orchestrate their most critical workflows. Faster fraud prevention with enterprise security Tripling production while cutting spend Connecting AI to internal systems Open source to experiment. Cloud and Horizon when you're ready for production. Prefect is used in production at Detailed feature comparison across all tiers Free Hobby tier with full core functionality. Upgrade when you need enterprise features—your workflows always run in your infrastructure. Join an active ecosystem of engineers running Prefect in production who share solutions to common challenges. When failures impact revenue or SLAs, you need more than community support. Enterprise plans include dedicated TAMs and 24/7 coverage. Find step-by-step tutorials, complete API documentation, and troubleshooting guides for every workflow pattern. Every tier includes support options. Begin with free community access and upgrade to SLA-backed coverage when your workflows become mission-critical. Get help from the community. If you're on a paid plan, submit a ticket to our support team. Discuss your requirements, learn about custom pricing, or request a demonstration. Get Prefect news, company information, and media resources. For code related discussions, join our community Slack.

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

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