
ClearML
Open-source MLOps platform for tracking experiments, storing artifacts and managing AI infrastructure.
How the 7.6 was reached
- Quality of output7/10
6,895 GitHub stars, active project with a recent push, Apache-2.0, and 2,100+ organizations claimed; adoption is solid but not tens of thousands of stars. github.com
- Ease of use7/10
Vendor says sign-up takes under 2 minutes and a free tier needs no card; docs exist, but there are no independent usability reports. github.com
- Pricing value9/10
Free Community tier with 100GB artifact storage and Apache-2.0 open-source code; Pro is $15 per user per month. clear.ml
- API and integration quality8/10
Open-source Python library with a 2-line integration and a documented docs site. clear.ml
- Solves the problem it claims to solve7/10
Active open-source MLOps platform with 6,895 stars and 802 forks supports the infrastructure claim; there are no independent reviews of the enterprise-scale claim. github.com
7.6/10 is the mean of the 5 criteria that apply. Scored on the five-criteria rubric v1, 29 September 2026. How scoring works
Pricing
- 100GB artifact storage
- 1GB metric events
- 1M API calls/month
- No credit card required
- 120GB artifact storage
- 2M API calls/month
- $0.1/1GB artifact storage
- $0.01/1MB metric events
- $1/100K API calls
- $0.04/hr per Application
Key Features
- Open-source Python library with 2-line integration
- Infrastructure control plane
- AI development center
- GenAI app engine
- GPU-as-a-Service for enterprises
Pros & Cons
Pros
- Free Community tier and Apache-2.0 open-source code
- Pro is $15 per user/month
- 6,895 GitHub stars and an active project
- Documented docs site
Cons
- Adoption is solid but far below the biggest ML projects
- No independent usability reports
- Enterprise-scale claim has no independent reviews
- Usage add-ons make costs harder to predict
ClearML suits ML teams that want an open-source, self-hostable tracking and orchestration stack with a cheap paid tier. Teams wanting proof of enterprise scale should ask for references. A good 7.6.
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