ZenML / zenml.io
Open source MLOps framework for building portable, reproducible ML pipelines that run consistently across local machines, cloud platforms, and ML infrastructure without pipeline code changes.
Pricing
Free
Free plan
Yes
Category
Developer Tools
Platforms
3
Free plan
Yes
API access
Yes
Open source
Yes
Platforms
3
ZenML is an open source MLOps framework that solves the portability problem in ML pipelines — the same pipeline code runs locally during development, on cloud compute during training, and with any combination of ML infrastructure (MLflow tracking, Kubeflow orchestration, Vertex AI serving) without modification.
The core abstraction is the ZenML Step — a Python function decorated with @step that becomes a reusable, tracked pipeline component. Steps are composed into @pipeline functions that ZenML orchestrates, tracking each step's inputs, outputs, and metadata for reproducibility. The same pipeline runs locally with `pipeline.run()` and remotely with configuration changes, not code changes.
Stack configuration separates pipeline logic from infrastructure — a Stack defines which orchestrator (local, Kubeflow, Airflow, Vertex AI), experiment tracker (MLflow, Weights & Biases), artifact store (S3, GCS, Azure Blob), and model registry (MLflow, BentoML) to use. Changing the Stack changes the infrastructure without touching pipeline code.
The ZenML dashboard (local or ZenML Pro) provides a visual view of pipeline runs, step lineage, artifact versions, and model metadata — enabling teams to track what ran, when, with what data, and what it produced.
At open source with self-hosting, ZenML is accessible to any ML team. ZenML Pro adds team collaboration, remote dashboards, and managed infrastructure at $59/month.
ZenML runs as ml platform software built around code and data workflows. Users typically start with a prompt, upload, or connected data source, and the underlying model handles the heavy lifting before returning a result you can refine or export. It's available on python, cli, and web, with API access for teams that want to embed it into their own products.
The capabilities that matter most for teams evaluating ZenML.
Decouples pipeline logic from infrastructure by defining which orchestrator, tracker, artefact store, and model registry to use — changing infrastructure without touching pipeline code.
Same pipeline code runs locally during development and remotely on cloud ML infrastructure by changing Stack configuration, eliminating environment-specific pipeline rewrites.
30+ pre-built integrations with ML infrastructure tools (Kubeflow, Airflow, MLflow, Weights & Biases, BentoML) reducing custom glue code for standard ML stacks.
Open source and free for self-hosted. ZenML Pro managed from $59/month. Enterprise custom.
Model
Open Source
Starting price
Free
Free trial
No
Kubeflow Pipelines is the Kubernetes-native alternative. MLflow provides experiment tracking alongside a simpler pipeline concept. Prefect and Airflow handle general data pipelines. ClearML (covered) provides a more complete integrated MLOps platform.
A side-by-side look at the closest alternative in this category.
Key facts about model providers, platforms, and team support.
Model Provider
ZenML
Platforms
Python, CLI, Web
Deployment
Open Source, SaaS, Self-hosted
Integrations
MLflow, Weights & Biases, Kubeflow, Airflow, Vertex AI, SageMaker, AWS, GCP, Azure, BentoML, API
Team Collaboration
Yes
Launch Year
2023
Compliance signals and data-handling notes as reported by the vendor.
Open source self-hosted keeps all pipeline data on customer infrastructure. ZenML Pro subject to ZenML's data handling. Enterprise agreements available.
Self-hosted ZenML keeps pipeline metadata and artefacts on customer-controlled storage. ZenML Pro transmits pipeline metadata to ZenML's managed infrastructure.
Editorial Verdict
ZenML is the best open source MLOps framework for ML teams who want portable, reproducible pipelines that run consistently from local development to any cloud ML infrastructure without code changes.
Last verified July 24, 2026.
Open source and free for self-hosted. ZenML Pro managed from $59/month. Enterprise custom.
Open source self-hosted is free. ClearML Cloud managed from $13/month. Enterprise custom.
Open source self-hosted keeps all pipeline data on customer infrastructure. ZenML Pro subject to ZenML's data handling. Enterprise agreements available.
Open source self-hosted keeps all data on customer infrastructure. ClearML Cloud subject to ClearML's data handling policy. Enterprise includes data handling agreements.
Self-hosted ZenML keeps pipeline metadata and artefacts on customer-controlled storage. ZenML Pro transmits pipeline metadata to ZenML's managed infrastructure.
Self-hosted deployment keeps all ML experiment data on customer infrastructure. ClearML Cloud transmits experiment data to ClearML's servers — review privacy policy.
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