Metaflow Pricing 2026

Open-source

• Use any Python libraries for models and business logic
• Manage library dependencies, locally and in the cloud
• Deploy workflows to production with a single command
• Integrate with other systems through events
• Track and store variables inside the flow automatically for easy experiment tracking and debugging
• Create robust workflows in plain Python
• Develop and debug locally, deploy to production without changes
• Leverage the cloud to execute functions at scale
• Use GPUs, multiple cores, and large amounts of memory as needed
• Access data from data warehouses
• Flows data across steps, versioning everything on the way
• Explore with notebooks
• Test and debug locally
• Results are stored and tracked automatically for easy analysis
• Scale out easily to the cloud
• Utilizing GPUs, multiple cores, and multiple instances in parallel
• Organizes the work for easy collaboration
• 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
• Deploy the Metaflow stack on your cloud account or on-premise Kubernetes cluster
• Integrates seamlessly with your existing infrastructure, security, and data governance policies
• Deploy on EKS and S3, or AWS Batch & AWS Step Functions (AWS)
• Deploy on AKS and Azure Blob Storage (Azure)
• Deploy on GKE and Google Cloud Storage (Google Cloud)
• Deploy on a custom Kubernetes cluster (Kubernetes)