From Solo Science to Team Engineering

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. Seperate your team into workspaces, and deploy dev, staging, and production servers seperately.

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.
ZenML Cloud toolbox emerging from a box, representing MLOps solutions and model deployment.
A metro line map showing Collaboration, Governance, Automation and Reliability stations in the ZenML OSS line

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
Scheduling
Run pipelines on a schedule
Orchestrator-dependant, not available for all orchestrators
Orchestrator-independant managed by ZenML Pro
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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