ZenML
Python framework for portable ML and agent pipelines with artifact tracking across clouds.
About
Aimed at ML and AI engineers inside companies, this Python framework lets you decorate ordinary functions as steps, chain them into pipelines, and run identical code on whatever infrastructure a configured stack points to. Under the hood it builds container images, versions each artifact with its lineage, records metadata, logs and metrics per run, and caches steps whose inputs are unchanged. Stack components plug in orchestrators such as Kubernetes, Kubeflow, Airflow, SageMaker, Vertex AI, AzureML and Databricks, plus artifact stores, trackers like MLflow or Weights and Biases, and model deployers. Recent releases stretch this to LLM and agent work, with LangGraph examples, pipelines deployable as HTTP endpoints, an MCP server for querying runs from chat clients, and a VS Code extension. Install zenml[server] from PyPI for a local client and dashboard, or host the server with Docker or Helm. The repository is Apache-2.0 and free to self-host; the company sells a Pro control plane, hosted or on your own infrastructure, adding SSO, RBAC and audit logs. Best for teams wanting reproducible pipelines without cloud lock-in.
Should you use ZenML?
Pick it when
Pick ZenML when you want one pipeline definition that moves between local runs and different orchestrators and clouds, with artifacts, tracking, and integrations like MLflow and LangGraph wired in, for classical ML and agent workloads.
Look elsewhere when
Skip it if you will only ever run one orchestrator on Kubernetes, where Flyte runs pipelines directly with less abstraction, or if data scientists want one opinionated framework from laptop to production, where Metaflow fits.
Alternatives to ZenML
- Metaflow
One opinionated framework from laptop to cloud compute, less configuration than ZenML stacks, with fewer swappable backends.
- Flyte
A single Kubernetes-native engine with strong typing and multi-tenancy, more to operate, with no abstraction layer over execution.
- Kubeflow
Full Kubernetes platform including training operators and serving, much heavier, where ZenML can orchestrate on simpler backends.
- MLflow
Covers tracking, registry, and evaluation without pipeline orchestration; ZenML integrates it rather than replacing it.
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Details
- Category
- AI Deployment & MLOps
- Price
- Free
- Platform
- Hybrid
- Difficulty
- Intermediate (3/5)
- License
- Apache-2.0
- Added
- May 7, 2026