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ZenML

An open-source framework for writing ML pipelines and AI agents in Python and running them on your own infrastructure.

FreemiumOpen sourceSelf-hostedCommand line
Official websitezenml.ioDocumentationdocs.zenml.io

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About ZenML

ZenML lets you define pipelines and agents in Python and run them on the orchestrator and cloud you already use. You can move between setups, from a laptop to Kubernetes, without rewriting the code.

It is released under Apache 2.0 and can be self-hosted, which keeps data on your own infrastructure. The same company makes Kitaru, which replays agents against recorded production sessions. ZenML suits ML and platform teams that want portable pipelines.

Strengths

  • Pipelines are plain Python
  • Portable across orchestrators and clouds
  • Apache 2.0 licence, and you can self-host it

Limitations

  • Aimed at teams running ML in production, not casual users
  • Needs existing orchestration or cloud infrastructure to get the most from it

Details

Pricing
FreemiumOpen source and free to self-host, with a hosted offering that has a free start.
License
Apache-2.0
Developer
ZenML Labs
Platforms
Self-hosted, Command line
How it runs
Downloadable app, Self-hosted, Hosted service
Best suited for
ML teams that want portable pipelines on their own infrastructure
Last verified
Added
Sources

Alternatives to ZenML

Compare all

Software that can replace ZenML for an important use case, and what changes if you switch.

  • Flyte

    An open-source orchestration platform for durable data, machine learning and AI agent workflows written in Python.

    FreeOpen sourceWebSelf-hostedCommand line

    Flyte is an open-source orchestration platform for Python data, ML and agent workflows that recovers from failures and includes a local devbox with a web UI, though Flyte 1 users must migrate.

  • Kubeflow

    A Kubernetes-native set of open-source projects for running data, ML and AI workloads.

    FreeOpen sourceSelf-hosted

    Kubeflow is an Apache-2.0 set of Kubernetes-native projects for ML training and serving, requiring a Kubernetes cluster instead of ZenML's portability across orchestrators and clouds.

Similar software

Related functionality, not necessarily a direct replacement.

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