ZenML Open Source vs Pro
Transform your ML workflows from single-player experiments to multiplayer production systems. ZenML Pro builds on the same open-source foundation you trust: no code rewrites, no metadata migrations required.
ZenML Open Source vs Pro
ZenML Pro is Open Source and More
ZenML Pro extends the beloved open-source foundation with enterprise features designed for collaboration, governance, and scale. Start with OSS, upgrade when ready: your pipelines keep running exactly as they are.
Managed control plane
ZenML Pro offers multi-tenant, fully-managed ZenML deployments. Separate your team into workspaces, and deploy dev, staging, and production servers separately.
Roles and Permissions
ZenML Pro tenants have built-in roles and permissions, as an extension to the open-source product. We connect ZenML with your OIDC provider and offer SSO.
Control and configurability
ZenML Pro control plane allows you to run ZenML pipelines directly from the server, and features enhanced configurability for your pipeline builds.
Enhanced observability
ZenML Pro tenants have an enhanced dashboard with more features including a model control plane to view all your ML models, and the ability to trigger pipelines, do CI/CD and lots more.
Is Your ML Team Ready for the Next Station?
Our subway map framework helps you identify pain signals that indicate it’s time to upgrade your ML infrastructure.
Collaboration
"Who just overwrote my training stack?"
Multiple teams sharing buckets, databases, or GPU quotas without clear boundaries.
Governance
"Who just overwrote my training stack?"
Security teams requiring proof of who changed what, when—especially before production deployments.
Automation
"Can we refresh the model for tomorrow’s demo?"
Non-engineers needing to trigger retrains without CLI knowledge or developer intervention.
Reliability
"The server DB is down again"
Operations teams spending hours on cluster maintenance, upgrades, and backup procedures.
Differences
ZenML Open Source vs Pro Feature Breakdown
A feature by feature comparison between ZenML Open Source vs ZenML Pro
| Feature | OSS | ZenML Pro |
|---|---|---|
| Pipelines ML pipelines are Python workflows that execute a machine learning task | Basic Controls with legacy dashboard | Advanced Controls and modern dashboard |
| Artifact and Model Control Plane See all your models and artifacts in one place | Not available | Accessible in UI |
| Event Triggers External sources | Client can trigger the pipeline only | Webhooks to trigger actions (pipeline run, model promote) etc. |
| Run Templates Create repeatable workflows triggered with one click | Not available | Create run templates with one-click and run templates directly via the dashboard |
| Container management If executed remotely, pipelines run in containers | Basic management | Advanced management with container re-use and optimization |
| Role Based Access Control Roles dictate who has permissions to do what | Not available | Fine-grained permissions |
| User Management A user account in one ZenML server | Basic | Advanced with SSO |
| Infrastructure The infrastructure that supports the central ZenML server | Self-managed | Managed, multi-tenant deployment with database backups, security, compliance, rollbacks, upgrades etc |
| Service Connectors Credentials, authorization, and access control for your ML stack components | CLI only | Modern dashboard |
| Integrations External tools for experiment tracking, model deployment, drift detection, etc. | Community | Purpose-built |
| Support Seeking help when stuck | Community | Dedicated 24/7 |
| Setup of MLOps workflow Setting up of the codebase and infrastructure required to build a successful MLOps platform | Self managed | Specialized onboarding |
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