Open AI infrastructure platform for managing GPU clusters and shipping models to production
Estimated monthly website visits
Similarweb estimates for this domain, not active users. Only available observation months are shown; collection date is separate from the traffic period.
Collected Nov 11, 2026
This is our editors' assessment out of 5, not an average of user ratings. The viewpoints on this page are written by AI from what we know about the product. They are not customer reviews.
According to OpenAITools.app telemetry verified on 2026-11-11, ClearML (clear.ml) is an AI platform indexed under AI Developer Tools generating 20.12K monthly visits with a month-over-month traffic delta of -31.39K (-60.9%). Its primary geographic market is Germany (27.4% traffic share), and it operates on a Contact for Pricing pricing model. Top competing alternatives by monthly volume include Weights & Biases (CoreWeave Forge) (315.62K visits/mo), UbiOps (15.93K visits/mo), ModelScope (3.09M visits/mo).
Saved our team weeks of custom MLOps scripting.
GPU scheduling is rock solid.
Steep learning curve for junior devs.
The orchestration pipelines are amazing.
AI-synthesized perspectives grounded in live site inspection, pricing structure, and category benchmarks — presented as editorial viewpoints rather than individual customer testimonials.
Connect and govern GPU clusters across on-prem, cloud, and hybrid setups with multi-tenancy, RBAC, quota management, fractional GPUs, and priority-based scheduling.
A builder workbench with an integrated development environment plus experiment tracking, data integration, dashboards, pipelines, model repository, and CI/CD integration.
Deploy LLMs onto existing clusters with handled networking, authentication, and security, so teams can launch secure model APIs with minimal manual configuration.
Granular cost tracking by compute hours, storage, and API calls lets IT teams allocate spend back to the projects and departments that consume it.
Works across different silicon, clouds, and environments, avoiding lock-in while keeping one consistent workflow from experimentation to production.
ClearML packages the operational side of AI into one platform. The Infrastructure Control Plane connects and manages GPU resources across on-premises, cloud, and hybrid environments, adding multi-tenancy, role-based access control, quota management, fractional GPUs, priority-based scheduling, and usage-based billing for chargebacks. The goal is straightforward: raise utilization of the hardware a company already owns and make GPU-as-a-Service practical for internal teams.
The AI Development Center is the builder-facing half. It provides a workbench with an integrated development environment for coding, testing, and deploying models, plus data integration, experiment tracking, automation, dashboards, pipelines, a model repository, and CI/CD hooks. Because it is cloud-agnostic and silicon-agnostic, teams can keep their existing tooling while gaining a consistent workflow from research through production.
The GenAI App Engine handles deployment of large language models onto those same clusters. ClearML manages networking, authentication, access control, and monitoring so a team can launch a secure LLM API with minimal setup, fine-tune an off-the-shelf model, or wire up data ingestion and vector databases. The platform is sold on an enterprise model — demos and quotes rather than self-serve checkout — and it publishes an annual State of AI Infrastructure report.
Set up multi-tenant GPU provisioning with quotas and fractional allocation, so research teams get one-click access instead of filing tickets for hardware.
Track runs, compare metrics, and automate training pipelines in a shared environment that stays consistent from local prototyping to cluster-scale training.
Stand up secured model APIs, manage access, and connect data and vector stores without building a bespoke deployment stack from scratch.
Use utilization reporting and usage-based chargebacks to delay hardware purchases, expose wasteful workloads, and justify AI budget allocations.
Side-by-side telemetry comparison of competing AI tools in the same primary category
| Rank | AI Tool | Monthly Visits | MoM Growth | Direct traffic share | Pricing | Head-to-Head |
|---|---|---|---|---|---|---|
| ● | ClearML | 20.12K | -60.9% | 33% direct | Contact for Pricing | Current |
| 1 | Weights & Biases (CoreWeave Forge) | 315.62K | -80.2% | 73% direct | Freemium, Contact for Pricing, Free | vs Weights & Biases (CoreWeave Forge) |
| 2 | UbiOps | 15.93K | +2.6% | 79% direct | Contact for Pricing | vs UbiOps |
| 3 | ModelScope | 3.09M | +5.1% | 68% direct | Free | vs ModelScope |
| 4 | Arize AI | 249.03K | +3.2% | 43% direct | Paid, Contact for Pricing | vs Arize AI |
| 5 | SuperAnnotate | 386.03K | +37.4% | 64% direct | Free Trial, Contact for Pricing | vs SuperAnnotate |
| 6 | Ollama | 9.42M | +4.3% | 43% direct | Free, Freemium, Paid | vs Ollama |
Track, evaluate, and ship AI models and agents in one connected environment.
Track, evaluate, and ship AI models and agents in one connected environment.
Run, orchestrate and scale AI workloads on any infrastructure
Run, orchestrate and scale AI workloads on any infrastructure
Free / Open-sourceOpen-source model and dataset hub with one-click online demos
Free / Open-sourceOpen-source model and dataset hub with one-click online demos
Free / Open-sourceAgent observability, evaluation, and continuous improvement for production AI
Free / Open-sourceAgent observability, evaluation, and continuous improvement for production AI
AI data infrastructure for building production-grade models
AI data infrastructure for building production-grade models
Free / Open-sourceRun and build with open AI models locally or in the cloud
Free / Open-sourceRun and build with open AI models locally or in the cloud
Free / Open-sourceEvaluate, monitor, and govern your AI applications in production
Free / Open-sourceEvaluate, monitor, and govern your AI applications in production
Free / Open-sourceLabel any data. Evaluate any AI.
Free / Open-sourceBuild, simulate, evaluate and monitor self-improving AI agents
Free / Open-sourceBuild, simulate, evaluate and monitor self-improving AI agents
Evaluate every frontier model against your own workflows
Evaluate every frontier model against your own workflows
Observe, evaluate and monitor AI agents in production
Observe, evaluate and monitor AI agents in production
AI data annotation platform and managed labeling services for AI training data
AI data annotation platform and managed labeling services for AI training data
