ZenML
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
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What is ZenML?
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.
How ZenML works
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.
Watch ZenML in action
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What makes it worth shortlisting
The capabilities that matter most for teams evaluating ZenML.
Stack configuration
Decouples pipeline logic from infrastructure by defining which orchestrator, tracker, artefact store, and model registry to use — changing infrastructure without touching pipeline code.
Portable pipelines
Same pipeline code runs locally during development and remotely on cloud ML infrastructure by changing Stack configuration, eliminating environment-specific pipeline rewrites.
Integration library
30+ pre-built integrations with ML infrastructure tools (Kubeflow, Airflow, MLflow, Weights & Biases, BentoML) reducing custom glue code for standard ML stacks.
Best use cases
Who should use it
Pros
- Pipeline portability across local, cloud, and ML infrastructure without code changes is a unique positioning
- Integration library covering 30+ ML tools reduces custom glue code for common ML stacks
- Open source Apache 2.0 licence provides full infrastructure independence
- Stack abstraction makes switching ML infrastructure (e.g. from Kubeflow to Vertex AI) straightforward
Cons
- Smaller community than Airflow or Kubeflow for problem-solving resources
- ZenML Pro $59/month is competitive but adds ongoing cost to an otherwise free tool
- Less maturity than Airflow for complex DAG orchestration beyond ML-specific workflows
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Is it worth the price?
Open source and free for self-hosted. ZenML Pro managed from $59/month. Enterprise custom.
Model
Open Source
Starting price
Free
Free trial
No
Tools like ZenML
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.
ZenML vs Hugging Face
A side-by-side look at the closest alternative in this category.
ZenML
ZenML
Hugging Face
Hugging Face
Open source and free for self-hosted. ZenML Pro managed from $59/month. Enterprise custom.
Free for most models and Spaces. Pro $9/month for private models, priority inference, and ZeroGPU. Enterprise Hub from $20/user/month for team collaboration and security.
Open source self-hosted keeps all pipeline data on customer infrastructure. ZenML Pro subject to ZenML's data handling. Enterprise agreements available.
Enterprise Hub includes SSO, access controls, audit logs, and private infrastructure. SOC 2 compliant.
Self-hosted ZenML keeps pipeline metadata and artefacts on customer-controlled storage. ZenML Pro transmits pipeline metadata to ZenML's managed infrastructure.
Public models and datasets are openly accessible. Enterprise Hub provides private model storage with data handling agreements. Review individual model licences before commercial deployment.
- Pipeline portability across local, cloud, and ML infrastructure without code changes is a unique positioning
- Integration library covering 30+ ML tools reduces custom glue code for common ML stacks
- Open source Apache 2.0 licence provides full infrastructure independence
- Stack abstraction makes switching ML infrastructure (e.g. from Kubeflow to Vertex AI) straightforward
- De facto standard hub for open source AI models with 500,000+ available
- Transformers library provides consistent API for diverse model families
- Spaces enable easy demo deployment without web infrastructure
- Free for public models and strong community support
- Smaller community than Airflow or Kubeflow for problem-solving resources
- ZenML Pro $59/month is competitive but adds ongoing cost to an otherwise free tool
- Less maturity than Airflow for complex DAG orchestration beyond ML-specific workflows
- Technical platform primarily for developers and researchers
- Inference API quality varies for very large or complex models
- Navigation complexity increases with scale of the platform
Technical & deployment info
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
Security & privacy
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.
What users are saying
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Common questions about ZenML
Editorial Verdict
Should you use ZenML?
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.