ZenML

license

Apache-2.0(OSI)

source

public(zenml-io/zenml is the open-source server and SDK)

core features withheld

yes — ZenML Pro ships as separate container images privately hosted in ZenML's GCP Artifact Registry and AWS ECR repositories, with access granted on request

ZenML's server and SDK are Apache-2.0 and public, but Pro withholds RBAC, workspaces or projects, the Model Control Plane, Artifact Control Plane and run snapshots. Pro ships only as private container images, with access granted on request even for self-hosting, making it a closed piece of the product rather than a platform sold beside a complete open core.

• https://github.com/zenml-io/zenml recorded 2026-09-15

  Repository page for zenml-io/zenml: Apache-2.0 license, public and unarchived. Establishes the license and that the source is public.

• https://raw.githubusercontent.com/zenml-io/zenml/main/docs/book/getting-started/zenml-pro/deploy-prerequisites.md recorded 2026-09-15

  ZenML Pro's deployment prerequisites: 'Container images are privately hosted in the ZenML GCP Artifact Registry and ZenML AWS ECR repositories. Access to these registries is granted upon request', with a contact address. Establishes that the withheld functionality ships as a closed package rather than as a separate hosted product.

• https://raw.githubusercontent.com/zenml-io/zenml/main/docs/book/getting-started/zenml-pro/README.md recorded 2026-09-15

  ZenML's own in-repository OSS versus Pro feature comparison: OSS marked 'No RBAC' and 'No workspaces/projects', Pro adding the Model Control Plane, Artifact Control Plane and Run Snapshots, and offering SaaS, hybrid or self-hosted deployment.

Adoption is measured as PyPI downloads of the zenml package in the trailing month, the product's declared distribution channel.

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

  Trailing-window download counts for the declared PyPI artifact: 125,127 downloads in the trailing month.

ZenML's local and Docker orchestrators are its own code, deciding when each step runs and dispatching it, the same shape of decision as Kubeflow Pipelines. Its remote flavors, including Airflow, Kubeflow, SageMaker, AzureML, Databricks, Tekton and SkyPilot, delegate the whole pipeline to that external system instead, which is the point of using them.

• https://docs.zenml.io/stacks/stack-components/orchestrators recorded 2026-09-15

  ZenML's orchestrator documentation: the orchestrator is 'an essential component in any MLOps stack as it is responsible for running your machine learning pipelines' and 'a mandatory component in the ZenML stack', with flavors listed for local, Docker, Kubernetes, Airflow, Kubeflow, SageMaker, AzureML, Databricks, Tekton, SkyPilot VM and others.