Kubeflow Pipelines

license

Apache-2.0(OSI)

source

public(kubeflow/pipelines carries the backend, the frontend and the SDK)

core features withheld

no — a 5,632-entry recursive tree carries a root LICENSE, every other license file under a third_party/ path, and no path matching enterprise, ee, commercial or proprietary

Kubeflow Pipelines is Apache-2.0 with no paid edition. The repository tree carries one root LICENSE, and every other license file is a vendored third-party dependency.

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

  Full untruncated recursive tree of the default branch, 5,632 entries. Licenses are the root LICENSE; every other license file sits under third_party/ or frontend/third_party/. 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/kubeflow/pipelines recorded 2026-09-15

  Repository page for kubeflow/pipelines: Apache-2.0 license, public and unarchived, described as 'Machine Learning Pipelines for Kubeflow'. Establishes the license and that the source is public.

• https://raw.githubusercontent.com/kubeflow/community/master/committee-steering/charter.md recorded 2026-10-07

  The Kubeflow Steering Committee charter: 'The Kubeflow Steering Committee (KSC) is the governing body of the Kubeflow project', and 'No more than one (1) seats may be held by employees of the same organization'.

Adoption is measured by PyPI downloads of kfp, the pipeline SDK and the product's declared distribution channel.

• https://pypistats.org/api/packages/kfp/recent recorded 2026-09-15

  Trailing-window download counts for the declared PyPI artifact kfp: 11,622,421 downloads in the trailing month, above the >10M level-5 floor.

KFP treats a pipeline run as a first-class object it tracks end to end: it decides the order in which steps run and which cluster executes them. It leaves placement of each container step to the Kubernetes scheduler rather than deciding placement itself.

• https://raw.githubusercontent.com/kubeflow/pipelines/master/sdk/python/kfp/dsl/types/artifact_types.py recorded 2026-09-15

  The KFP SDK's artifact type definitions: Model with schema_title system.Model, plus Dataset, Metrics and ClassificationMetrics. Establishes the typed ML artifact system that distinguishes KFP from the general workflow engine it runs on.

• https://www.kubeflow.org/docs/components/pipelines/overview/ recorded 2026-09-15

  Kubeflow Pipelines overview, describing pipelines as containerized components with typed inputs and outputs run on Kubernetes.