Metaflow

Metaflow is an open-source framework for building and managing machine learning, AI, and data science projects.

Metaflow is an open-source framework designed for machine learning engineers and data scientists to build, manage, and deploy real-life ML, AI, and data science projects. It allows users to explore with notebooks, develop locally, and scale computations out to the cloud utilizing GPUs, multiple cores, and parallel instances. The platform integrates with existing infrastructure, security, and data governance policies, supporting deployments on AWS, Azure, Google Cloud, and Kubernetes. It provides features for automated versioning, orchestration, compute management, and production deployment. Originally developed at Netflix, it is used by hundreds of companies to power diverse projects ranging from generative AI to statistics.

Metaflow at a glance

Best for: Software engineer, Researcher
Runs on: CLI, Web, self-hosted, open source
Pricing: Free
Released: September 2019
Category: AI coding tool
Website: metaflow.org

Key features

Automatic result tracking and storage
Cloud scaling on AWS, Azure, Google Cloud, and Kubernetes
One-click production deployment
Support for recursive and conditional steps
Dependency management with uv
Real-time, dynamic cards for observability
Support for AWS Trainium

Use it for

Building machine learning and AI models
Orchestrating multi-stage data science workflows
Scaling compute tasks to the cloud
Deploying experiments to production

Alternatives to Metaflow

MLflow — Complete observability and evaluation for production AI apps.
DataFlow — Automates and scales data preparation through reusable, operator-based pipelines.
Replicate — Run and fine-tune thousands of open-source and custom AI models via a single cloud API.
OpenMetadata — Unifies technical context and business semantics into one searchable graph.
Apache Hamilton — Creates modular, testable, and self-documenting data pipelines using Python code.
Ray — It orchestrates distributed infrastructure for any AI workload at any scale.
LMFlow — It offers memory-efficient training and full Accelerate support for large language models.
cascadeflow — Reduces agent inference costs by up to 90% with sub-5ms overhead.
Genkit — Provides unified APIs and local developer tools for AI application workflows.
Cube Studio — An open source, end-to-end platform for MLOps and distributed model training.
Cohere — Private, secure enterprise AI deployment with customizable models.
Flyte — Resumes failed tasks automatically from the exact point of interruption.

All Metaflow alternatives