Pricing Overview
ZenML follows a freemium pricing model built around an open-source core and a managed Pro platform. The open-source framework, licensed under Apache 2.0, is entirely free and self-hosted, giving teams full control over their MLOps infrastructure. For organizations that want a managed experience with additional collaboration and governance features, ZenML Pro offers four paid tiers: Starter at $399/month, Growth at $999/month, Scale at $2,499/month, and Enterprise with custom pricing.
All paid plans include unlimited team members, which is a notable differentiator in the MLOps space where many platforms charge per seat. The primary scaling dimension is pipeline runs per month, along with the number of projects, snapshots, and workspaces available. Enterprise customers get dedicated support with SLAs, SSO, custom RBAC, audit logs, regional deployment options, and on-premise or hybrid configurations.
Plan Comparison
The table below breaks down everything included in each ZenML Pro tier.
| Feature | Starter ($399/mo) | Growth ($999/mo) | Scale ($2,499/mo) | Enterprise (Custom) |
|---|---|---|---|---|
| Pipeline Runs/mo | 500 | 2,000 | 5,000 | Unlimited |
| Projects | 1 | 3 | 10 | Unlimited |
| Snapshots | 1 | 5 | 20 | Unlimited |
| Workspaces | 1 | 1 | 1 | Custom |
| Team Members | Unlimited | Unlimited | Unlimited | Unlimited |
| Model Control Plane | Yes | Yes | Yes | Yes |
| Artifact Control Plane | Yes | Yes | Yes | Yes |
| RBAC (Standard Roles) | Yes | Yes | Yes | Yes |
| SSO (SAML/OIDC) | No | No | No | Yes |
| RBAC (Custom Roles) | No | No | No | Yes |
| Audit Logs | No | No | No | Yes |
| Regional Deployment | No | No | No | Yes |
| On-prem / Hybrid | No | No | No | Yes |
| SOC2 & GDPR | No | No | No | Yes |
| Support Level | Basic | Priority | Priority | Dedicated + SLA |
All Pro tiers share the same core platform features: Model Control Plane, Artifact Control Plane, and standard RBAC. The tiers differentiate on pipeline volume, project count, and support level. Enterprise adds the full governance stack with SSO, custom roles, audit logging, and compliance certifications.
Hidden Costs
While ZenML Pro pricing is straightforward on the surface, we identified several cost factors that teams should account for before committing.
Infrastructure costs are separate. ZenML is a metadata and orchestration layer, not a compute provider. We pay separately for the underlying cloud infrastructure: Kubernetes clusters, GPU instances, storage, and networking. For teams running on AWS, GCP, or Azure, these costs can easily exceed the ZenML subscription itself.
Pipeline run limits can bite. The Starter plan caps at 500 runs per month. For teams running CI/CD-triggered training pipelines or frequent evaluation loops, this limit can be reached quickly. Overages require upgrading to the next tier, which is a $600/month jump from Starter to Growth.
Single workspace on all non-Enterprise plans. Starter, Growth, and Scale all provide just one workspace. Organizations with multiple business units or strict data isolation requirements will need Enterprise pricing for additional workspaces.
Snapshot constraints on lower tiers. Starter allows only 1 snapshot, which limits rollback and environment versioning capabilities. Teams that rely heavily on reproducibility across experiments should budget for Growth or Scale.
Enterprise-only compliance features. SOC2 and GDPR compliance, audit logs, SSO, and on-premise deployment are locked behind the Enterprise tier. Regulated industries will have no choice but to negotiate custom pricing.
Cost Estimates
Based on the published pricing and typical usage patterns, here is what we estimate teams will spend on ZenML Pro.
Small team (3-5 engineers, early-stage ML): The Starter plan at $399/month works for teams running under 500 pipeline runs monthly on a single project. Annual cost: approximately $4,788. Add $200-500/month in underlying cloud compute for a modest workload.
Mid-size team (10-20 engineers, multiple models): Growth at $999/month covers 2,000 runs across 3 projects. Annual cost: approximately $11,988. Cloud infrastructure for this tier typically runs $1,000-3,000/month depending on GPU usage and data volumes.
Scaling organization (20+ engineers, production ML at scale): Scale at $2,499/month delivers 5,000 runs and 10 projects. Annual cost: approximately $29,988. Infrastructure spend at this level often ranges $5,000-15,000/month.
Enterprise: Custom pricing applies. Based on similar MLOps platforms, we expect Enterprise contracts to start in the $50,000-100,000+ per year range, depending on the number of workspaces, deployment model, and support SLA requirements.
How ZenML Pricing Compares
We compared ZenML against three major competitors in the MLOps category to put its pricing in context.
| Aspect | ZenML | Weights & Biases | Azure Machine Learning | Vertex AI |
|---|---|---|---|---|
| Pricing Model | Freemium (flat tiers) | Freemium (per-seat) | Usage-based (pay-as-you-go) | Usage-based (pay-as-you-go) |
| Free Tier | Open-source (self-hosted) | Free tier available | Studio free tier | Model Registry and Feature Store free |
| Entry Price | $399/mo (Starter) | $60/mo per user (Pro) | From $0.10/hr per instance | From $0.03/pipeline run + compute |
| Team Members | Unlimited on all plans | Per-seat pricing | No seat-based pricing | No seat-based pricing |
| Compute Included | No (bring your own) | Yes (limited) | Yes (pay per use) | Yes (pay per use) |
| Enterprise Option | Custom pricing | Contact sales | Enterprise agreements | Enterprise agreements |
ZenML vs. Weights & Biases: W&B charges $60/month per user on its Pro plan, which means a 10-person team pays $600/month for experiment tracking alone. ZenML's Starter at $399/month with unlimited seats is more cost-effective for larger teams, though it provides orchestration and governance rather than experiment tracking. The two tools often complement each other in practice.
ZenML vs. Azure Machine Learning: Azure ML uses pure consumption-based pricing starting at $0.10/hour per compute instance. For light workloads, Azure ML can be cheaper since we only pay for what we use. For teams running consistent pipeline volumes, ZenML's flat-rate model provides more predictable billing. Azure ML includes built-in compute, whereas ZenML requires separate infrastructure.
ZenML vs. Vertex AI: Google's Vertex AI charges $0.03 per pipeline run plus compute costs. At 500 runs, that is $15 in pipeline fees alone (plus compute), making Vertex AI significantly cheaper for low-volume users. However, Vertex AI locks teams into the GCP ecosystem. ZenML's cloud-agnostic approach and infrastructure abstraction layer justify the premium for multi-cloud or hybrid deployments.