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Metaflow

Netflix
open source / Overall score: 3.0

Python framework from Netflix for building and operating machine-learning and AI systems. A flow is a class of steps; decorators attach resources, retries and checkpoints to each step, and @batch or @kubernetes send that step to AWS Batch or a Kubernetes cluster to run. Metaflow tracks every run, its artifacts and its models, and can resume a failed flow from the step that failed.

Outerbounds was founded by Metaflow's creators.

Openness

5 high confidence
5.0
license
Apache-2.0(OSI)
source
public(Netflix/metaflow is the framework you run)
core features withheld
no — a 1,255-entry recursive tree carries a root LICENSE and R/LICENSE, every other license file under metaflow/_vendor/, and no path matching enterprise, ee, commercial or proprietary

Metaflow's whole framework is published under Apache-2.0, and a full scan of the repository tree finds no enterprise, ee or commercial path. Outerbounds sells a managed platform beside it as a separate product, not a fuller edition of the open framework.

  • https://api.github.com/repos/Netflix/metaflow/git/trees/master?recursive=1 recorded 2026-09-15

    Full untruncated recursive tree of the default branch, 1,255 entries. Licenses are the root LICENSE and R/LICENSE; every other license file sits under metaflow/_vendor/. No path matches enterprise, ee, commercial or proprietary. A tree lists paths; it is cited for that and for nothing about the vendor's offerings.

  • https://github.com/Netflix/metaflow recorded 2026-09-15

    Repository page for Netflix/metaflow: Apache-2.0 license, public and unarchived, described as 'Build, Manage and Deploy AI/ML Systems'. Establishes the license and that the source is public.

  • https://outerbounds.com/ recorded 2026-09-16

    Outerbounds' homepage, describing its platform as a separate product built around Metaflow and announcing that 'Outerbounds is joining Anaconda.' Establishes a commercial offering beside the open framework rather than a paid edition of it.

Adoption

3 high confidence
3.0

Adoption is measured as PyPI downloads of the metaflow package in the trailing month, the product's declared distribution channel. The figure can be recomputed directly from that feed.

Capability

3 medium confidence
3.0

The @resources(gpu=...) decorator is Metaflow's own field, not a pod spec passed through, and Metaflow decides when a step runs and which queue or cluster it goes to; AWS Batch or the Kubernetes scheduler then chooses the machine. That is the same shape of decision as Flyte, and the two sit together.

Verified 2026-09-15