Introducing the Metaflow-Kubeflow Integration

A tale of two flows: Metaflow and Kubeflow Why Metaflow → Kubeflow Development Scaling Deployment Metaflow → Kubeflow in practice Feedback welcome! A tale of two flows: Metaflow and Kubeflow Why Metaflow → Kubeflow Development Scaling Deployment Development Scaling Deployment Metaflow → Kubeflow in practice Feedback welcome! Metaflow is a Python framework for building and operating ML and AI projects, originally developed and open-sourced by Netflix in 2019. In many ways, Kubeflow and Metaflow are cousins: closely related in spirit, but designed with distinct goals and priorities. Metaflow emerged from Netflix’s need to empower data scientists and ML/AI developers with developer-friendly, Python-native tooling, so that they could easily iterate quickly on ideas, compare modeling approaches, and ship the best solutions to production without heavy engineering or DevOps involvement. On the infrastructure side, Metaflow started with AWS-native services like AWS Batch and Step Functions, later expanding to provide first-class support for the Kubernetes ecosystem and other hyperscaler clouds. In contrast, Kubeflow began as a set of Kubernetes operators for distributed TensorFlow and Jupyter Notebook management. Over time, it has evolved into a comprehensive Cloud Native AI ecosystem, offering a broad set of tools out of the box. These include Trainer, Katib, Spark Operator for orchestrating distributed AI workloads, Workspaces for interactive development environments, Hub for AI catalog and artifacts management, KServe for model serving, and Pipelines to deploy end-to-end ML workflows and stitching Kubeflow components together. Over the years, Metaflow has delighted end users with its intuitive APIs, while Kubeflow has delivered tons of value to infrastructure teams through its robust platform components. This complementary nature of the tools motivated us to build a bridge between the two: you can now author projects in Metaflow and deploy them as Kubeflow Pipelines , side by side with your existing Kubeflow workloads. In the most recent CNCF Technology Radar survey from October 2025, Metaflow got the highest positive scores in the “ likelihood to recommend ” and “ usefulness ” categories, reflecting its success in providing a set of stable, productivity-boosting APIs for ML/AI developers. Metaflow spans the entire development lifecycle—from early experimentation to production deployment and ongoing operations. To give you an idea, the core features below illustrate the breadth of its API surface, grouped by project stage: Straightforward APIs for creating and composing workflows.