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Open-source MLOps · Apache-2.0

The unified layer for ML and AI.

Orchestrate training pipelines and production AI agents on the tools, clouds, and environments you already use — without rewriting your stack.

ZenML is the open-source platform for production ML systems, built by ZenML GmbH and used by ML and AI teams worldwide. Modular, infrastructure-agnostic, no lock-in.

Rows of illuminated server racks in a data center — the cloud infrastructure ZenML orchestrates ML and AI workloads across.

one pipeline · any stack

orchestratorcloudtracker
6,200+
GitHub stars
github.com/zenml-io/zenml
60+
Integrations
orchestrators, clouds & trackers
Apache-2.0
Open source
self-host the full server
AWS · GCP · Azure
Runs anywhere
cloud, hybrid or on-prem

Stack-agnostic by design

Bring your own stack.

ZenML doesn’t replace your tools — it connects them. Choose the pieces you already run and keep the same pipeline code as your stack evolves.

Orchestrator

Cloud

Experiment tracker

your-stack.yaml

orchestrator: Kubeflow

cloud: AWS

tracker: MLflow

Your portable pipeline runs on Kubeflow over AWS, tracked in MLflow — and the same code follows you if any of them changes.

Switch any layer later — your pipeline code stays the same. 60+ integrations and counting.

Browse all integrations

What the platform gives you

Production ML, without rewriting your stack.

Portable, reproducible pipelines

Write a pipeline once and run it on any orchestrator and cloud. ZenML tracks every step, artifact and model so a run from last quarter rebuilds exactly today.

  • Pipelines and stacks across any cloud
  • Model registry, lineage and reproducibility
  • Smart caching and deduplication of steps
01

60+ integrations, no lock-in

Plug into the orchestrators, clouds, trackers and model registries you already use — swap any of them without touching pipeline code.

02

Governance, security and access

Role-based access, secrets and audit trails through ZenML Pro keep teams and regulated workloads in line.

03

Built for agents too

Checkpoint, recover from crashes and replay agent runs — bring the same rigor you expect from training to production AI agents.

Two products · one team

Start open source. Scale when you’re ready.

ZenML meets teams where they are: a free, self-hostable framework to begin, a managed control plane when you grow, and replay tooling for production agents.

01

Apache-2.0 · self-hosted

ZenML (open source)

Star on GitHub

The open-source framework for portable, production-ready ML pipelines. Self-host the server and keep full control of your stack.

  • Pipelines, stacks and artifact tracking
  • Run the server on your own infrastructure
02

Managed control plane

ZenML Pro

Book a demo

A managed control plane for teams: collaboration, pipeline monitoring, role-based access and governance on top of the open-source core.

  • Team collaboration and RBAC
  • Pipeline monitoring and governance
03

Replay for AI agents

Kitaru

Talk to the team

Replay your agent’s real runs with one thing changed, and see what would have happened — debugging and evaluation built for production agents.

  • Checkpoint and replay real runs
  • Crash recovery for agent flows

Questions, answered

Before you book a demo.

Straight answers on licensing, lock-in and what runs where. Still curious? The docs and Slack go deeper.

Read the docs
01

Is ZenML really open source?

Yes. The core framework is licensed under Apache-2.0 and the full server is self-hostable — see the repository at github.com/zenml-io/zenml. ZenML Pro is an optional managed control plane on top of the same open-source core.

02

Do I have to replace my current MLOps tools?

No. ZenML is stack-agnostic: it connects to the orchestrators, clouds, experiment trackers and model registries you already run. There are 60+ integrations including Kubeflow, Airflow, Vertex AI, MLflow and Weights & Biases.

03

Which clouds and orchestrators are supported?

ZenML runs on AWS, Google Cloud and Azure as well as hybrid and on-prem setups, and orchestrates with Kubeflow, Airflow, Kubernetes, Vertex AI and more. You can switch any layer later without rewriting your pipeline code.

04

What is the difference between ZenML and ZenML Pro?

ZenML open source is the free, self-hosted framework. ZenML Pro adds a managed control plane — team collaboration, pipeline monitoring, role-based access and governance. Pricing is published at zenml.io/pricing.

05

Does ZenML work for AI agents, not just training pipelines?

Yes. Alongside training pipelines, ZenML supports production AI agents with checkpointing, crash recovery and replay through Kitaru, so you can debug and evaluate agent runs with the same reproducibility you expect from ML.

06

How do I get started or talk to the team?

Read the docs at docs.zenml.io, star and clone the repo on GitHub, join the community Slack, or book a demo to see ZenML Pro with your own stack in mind.

Get started

Put your ML and AI
on one layer.

Start with the open-source framework today, or book a demo to see ZenML Pro mapped onto the stack you already run.

Book a demo