Overview

ClearML is an AI platform that combines GPU infrastructure management with model development and LLM deployment. It suits teams that need those workflows to span cloud, on-premises, or hybrid compute. Its breadth is compelling, but teams should weigh the Community caps and Pro usage charges against the custom-priced options for larger deployments.

Key features

The Infrastructure Control Plane manages GPU resources across on-premises, cloud, and hybrid environments, including multi-tenant GPU-as-a-Service. That makes ClearML a stronger fit for organizations coordinating shared compute than for teams seeking only an experiment log.

The AI Development Center covers model development, training, testing, and deployment, with monitoring, pipelines, a model repository, and CI/CD integration. Experiment tracking includes comparisons, artifacts, metrics, plots, and Git version control integration. Support for PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, LightGBM, Optuna, Hydra, TensorBoard, Matplotlib, and LangChain gives teams room to keep familiar tools in their workflow.

The GenAI App Engine deploys LLMs on compute clusters and provides networking, authentication, access control, and monitoring. Storage integrations include AWS S3, Azure, Google Storage, and S3-compatible services such as MinIO and Backblaze B2. Model registry, pipeline orchestration, model serving, and data versioning round out the platform.

ClearML can run on its servers, as a managed service, or self-hosted, with VPC, on-premises—including air-gapped—and hybrid options. Its self-hosted version is open source. Self-hosting brings control over deployment, but the security guide calls for limiting public access to required ports and configuring web login authentication; file server token authentication starts with version 1.16.0.

Pricing

Community costs 0.00 USD per free, requires no credit card, and allows teams of up to 3. It includes 100GB free artifact storage, 1GB metric events, and 1M API calls/month. That is a meaningful no-cost entry point, but the team and usage ceilings constrain shared or growing workloads.

Pro costs 15.00 USD per month, billed Per User/Month + Usage. Teams can have up to 10, with 120GB free artifact storage, 1.2GB metric events, and 1.2M API calls/month; additional usage is charged separately. It fits teams that need more headroom than Community, but the per-user price and usage charges make total cost dependent on both team size and activity.

Scale uses custom pricing, billed Pay for What You Use | VPC only, for organizations with 8–48 GPUs. It includes private Slack channel support and an SLA. Enterprise is custom pricing by request for multiple large projects, with a VPC or on-prem cluster; it adds white-glove support with a custom SLA and professional services. Scale and Enterprise list SSO integration, while Enterprise also lists role-based access control and LDAP integration—useful distinctions for organizations with specific access-control requirements.

Platforms

ClearML supports API, Linux, macOS, Windows, web, and self-hosted use. The choice of hosted, managed, VPC, on-premises, air-gapped, or hybrid deployment makes it adaptable to different infrastructure boundaries, though self-hosted operators take on configuration and access-control work.

Who it's for

ClearML is best suited to ML teams that want experiment tracking and development tools alongside GPU management, model operations, or LLM deployment. It is less compelling for a team that needs only lightweight tracking, or that does not need to manage compute and would rather avoid a platform with multiple usage dimensions.

Pros and cons

Pros: Combines GPU management, experiment workflows, and LLM deployment, reducing the need to assemble those capabilities separately.
Pros: Broad framework and storage integrations let teams build around established tools and storage providers.
Pros: Open-source self-hosting and VPC, on-premises, hybrid, and managed deployment choices support varied infrastructure requirements.
Cons: Community limits teams to 3 and caps storage, metric events, and API calls, so it may not carry a growing team far.
Cons: Pro adds usage charges on top of its per-user monthly price, making costs less predictable when activity increases.
Cons: Scale and Enterprise require custom pricing, and self-hosting requires deliberate security configuration.

Alternatives

For a focused experiment-tracking or MLOps comparison, start with MLOps Platforms and ML Experiment Tracking Software. Teams evaluating the infrastructure layer can compare GPU Cluster Management Software; teams prioritizing registry or version-control workflows can browse Model registries and Data Version Control Tools.

Amazon SageMaker Autopilot is a paid, API- and web-based alternative with a free trial and pay-as-you-go pricing, suited to readers considering an AWS-linked managed route. Opik offers a free plan and open-source observability and evaluation code that can be run locally, making it a better comparison for teams focused on those workflows. Outerbounds is built around open-source Metaflow and has a free plan, so it is worth considering for teams already oriented around that framework.

Weights & Biases has a free plan with 5 model seats, 5 GB/mo storage, and 1 GB/mo Weave data ingestion; those caps provide a concrete comparison for teams weighing a smaller free starting point. ZenML offers an open-source self-hosted plan with unlimited executions and projects, which may suit teams prioritizing those limits. Metaflow is free and open source, but production scaling requires infrastructure in the user's cloud account or Kubernetes cluster. MLRun offers a free open-source option with community support. Polyaxon has an Apache 2.0 core and a Community Edition for a single cluster.

Verdict

Choose ClearML if your team needs GPU resource management, model-development workflows, and LLM deployment under one platform, especially across mixed infrastructure. Its open-source and deployment flexibility are strong reasons to shortlist it. Look elsewhere if experiment tracking alone is the goal, or if strict cost predictability matters more than the platform's combined scope: Community has tight caps, Pro adds usage charges, and larger tiers use custom pricing.

	ClearML	Radiant AI Cloud Model Registry
Free plan	Yes	No
Free trial	No	No
Paid from	$15/mo	—
Open source	No	No
Platforms	api, Linux, macOS, self-hosted, Web, Windows	api, Linux, macOS, Web, Windows

ClearML vs Radiant AI Cloud Model Registry

ClearML

Radiant AI Cloud Model Registry