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ClearML costs Free to $15 per month as of July 2026, with 4 plans available including a free tier. Plans: Community (free), and Pro at $15/month. Enterprise pricing is available on request. Pricing depends on your chosen tier, contract length, and negotiated discounts.

Use the interactive pricing calculator to estimate your exact cost based on team size and requirements.

  • Free tier: Yes

ClearML offers 4 pricing tiers: Community, Pro, Scale, Enterprise. A free plan is available. Paid plans include Pro at $15/per user/month. The Pro plan is ml teams that need managed cloud autoscaling and pipeline automation beyond the free tier.

Last verified: | Sources: 1 pricing pages and reviews
+70% above list price
Know Before You Buy — Try ClearML Free

ClearML true cost runs 70% above the listed $0-$15/month price as of July 2026. For a 25-person team, expect ~$7,650 in year-one costs vs the $4,500 base license. Key hidden costs: infrastructure costs, usage-based overages, human capital costs. Verified from 1 sources by CostBench.

Hidden Costs Breakdown

1

Infrastructure Costs

high implementation

Underlying cloud infrastructure costs for compute, storage, and data transfer apply even with managed services, and are direct for self-hosted deployments.

industry

Community Plan: This is a free, open-source tier that can be self-hosted (unlimited users) or used as a free hosted SaaS tier (limited to 3 users, 100GB artifact storage, 1M API calls/month).

2

Usage-Based Overages

high overage

Exceeding quotas for artifact storage, metric events, and API calls on the Pro plan incurs additional charges that can accumulate rapidly.

industry

For instance, overage rates are approximately $0.10 per GB for artifact storage, $0.01 per MB for metric events, $1 per 100K API calls, and $0.04 per application hour.

3

Human Capital Costs

critical implementation

Implementing and maintaining an MLOps platform requires specialized talent, and underestimating this investment leads to significant, unexpected operational costs.

industry

Human Capital Costs: Implementing and maintaining an MLOps platform requires specialized talent, including ML platform leads and MLOps engineers.

4

Migration Complexity

high migration

Moving from existing workflows and infrastructure to a new platform like ClearML can involve significant migration effort and may require vendor support.

industry

Migration Complexity: Moving from existing workflows, experiment tracking, and model deployment infrastructure to a new platform like ClearML can involve significant migration effort and may require vendor support.

5

Skill Gaps

medium training

Teams may face skill gaps in platform-specific concepts, which can extend onboarding timelines and increase implementation effort.

industry

Skill Gaps: Teams may face skill gaps in platform-specific concepts (e.g., Kubernetes, infrastructure-as-code, MLOps patterns), which can extend onboarding timelines and increase implementation effort.

6

Integration Delays

medium implementation

Integrating with legacy data infrastructure, proprietary ML frameworks, or complex multi-cloud environments can cause delays.

industry

Integration Delays: Integrating with legacy data infrastructure, proprietary ML frameworks, or complex multi-cloud environments can cause delays.

7

Opportunity Cost

critical implementation

The cost of not having a mature MLOps practice can manifest in unbudgeted, high-stress events, eroded morale, delayed business impact, and lost revenue.

industry

Opportunity Cost: The cost of not having a mature MLOps practice can manifest in unbudgeted, high-stress events, such as debugging corrupt model states, eroded morale, delayed business impact, and lost revenue due to models degrading silently in production.

8

Specialized Talent

high implementation

Implementing and maintaining an MLOps platform requires specialized ML engineering talent, and assigning these tasks to generalist software engineers can lead to inefficient troubleshooting and suboptimal solutions, increasing operational costs.

industry

Assigning these tasks to generalist software engineers without MLOps experience can lead to inefficient troubleshooting and suboptimal solutions, increasing operational costs.

9

Data Management and Governance

medium implementation

Costs associated with data versioning, lineage, quality checks, and storage are often underestimated, directly impacting model reliability and reproducibility.

industry

These costs can escalate with data growth and model complexity.

10

Professional Services

high implementation

For ClearML's Scale and Enterprise tiers, implementation and professional services fees are not publicly disclosed and should be anticipated.

industry

Professional Services: For Scale and Enterprise tiers, implementation and professional services fees are not publicly disclosed and should be anticipated.

11

Technical Debt

critical implementation

The cost of not having a mature MLOps practice can manifest in unbudgeted, high-stress events like debugging corrupt model states, leading to lost engineering hours, eroded morale, and delayed business impact.

industry

Technical Debt: The cost of not having a mature MLOps practice can manifest in unbudgeted, high-stress events like debugging corrupt model states, leading to lost engineering hours, eroded morale, and delayed business impact.

12

Operational Costs for Production Models

high support

Significant ongoing expenses include data governance, model retraining, infrastructure scaling, continuous monitoring, and specialized talent, often exceeding upfront development budgets.

industry

Model retraining can also cost millions of dollars.

13

On-Premise/VPC Setup

medium implementation

While ClearML offers self-hosting, the initial setup and on-premise configuration can be time-consuming and may involve a learning curve.

industry

Initial Setup for On-Premise/VPC Deployments: While ClearML offers self-hosting, which can eliminate subscription fees, the initial setup and on-premise configuration can be time-consuming and may involve a learning curve.

Example: True Cost for 25 Users

License (25 × $15 × 12) $4,500/yr
Usage-Based Overages +$0.10 per GB for artifact storage, $0.01 per MB for metric events, $1 per 100K API calls, and $0.04 per application hour
Operational Costs for Production Models +idle AI endpoints can cost between $500 and $23,000 monthly; Model retraining can also cost millions of dollars
Estimated Year 1 Total ~$7,650
That's roughly 1.7× the advertised license price.

Frequently Asked Questions

01 What hidden costs should I budget for with ClearML?

Beyond the license fee, budget for: Usage-Based Overages ($0.10 per GB for artifact storage, $0.01 per MB for metric events, $1 per 100K API calls, and $0.04 per application hour); Operational Costs for Production Models (idle AI endpoints can cost between $500 and $23,000 monthly; Model retraining can also cost millions of dollars). Total ownership typically runs 70% higher than the listed price.

02 Does ClearML charge for implementation?

ClearML implementation is not included in the license cost. Underlying cloud infrastructure costs for compute, storage, and data transfer apply even with managed services, and are direct for self-hosted deployments..

03 How much does ClearML support cost?

Significant ongoing expenses include data governance, model retraining, infrastructure scaling, continuous monitoring, and specialized talent, often exceeding upfront development budgets.. Estimated impact: idle AI endpoints can cost between $500 and $23,000 monthly; Model retraining can also cost millions of dollars.

04 Are there overage or storage costs with ClearML?

Exceeding quotas for artifact storage, metric events, and API calls on the Pro plan incurs additional charges that can accumulate rapidly.. Estimated impact: $0.10 per GB for artifact storage, $0.01 per MB for metric events, $1 per 100K API calls, and $0.04 per application hour.

05 What add-ons cost extra with ClearML?

Add-on pricing for ClearML varies by feature. The sourced cost breakdown above lists any verified add-on costs we have.

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