ClearML

Overview

ClearML combines GPU infrastructure management with tools for developing and operating machine-learning models and deploying LLM applications. It best suits teams that need to coordinate compute as well as model workflows; organizations seeking only a lightweight experiment tracker may find its breadth unnecessary. The free Community plan lowers the barrier to entry, but its team and usage caps make growth a pricing decision.

Key features

The Infrastructure Control Plane manages GPUs across on-premises, cloud, and hybrid environments, including multi-tenant GPU-as-a-Service. That is valuable when teams share capacity across environments, but less compelling if GPU provisioning is outside the job. ClearML can be hosted on its servers, self-hosted, or run as a managed service, with VPC, on-premises—including air-gapped—and hybrid deployment options.

The AI Development Center combines 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. Model registry, pipeline orchestration, model serving, and data versioning are also supported. Integrations span PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, LightGBM, Optuna, Hydra, TensorBoard, Matplotlib, and LangChain, so teams can use a range of familiar frameworks and tools.

The GenAI App Engine deploys LLMs on compute clusters and includes networking, authentication, access control, and monitoring. Storage integrations cover AWS S3, Azure, Google Storage, and S3-compatible services including MinIO and Backblaze B2. This breadth supports varied storage setups, though the platform’s value depends on needing several of these workflow and infrastructure layers together.

ClearML’s self-hosted version is open source. Its security guidance recommends limiting public access to required ports and configuring web login authentication; file-server token authentication starts with version 1.16.0. Organizations self-hosting should account for that configuration work rather than treating deployment as secure by default.

Pricing

The Community plan costs nothing, requires no credit card, and allows teams of up to 3. It includes 100GB of free artifact storage, 1GB of metric events, and 1M API calls/month. That is a useful starting point for small teams, but the seat and usage ceilings can constrain shared production work.

Pro costs $15 a month, billed Per User/Month + Usage. It raises the team limit to 10 and includes 120GB of free artifact storage, 1.2GB of metric events, and 1.2M API calls/month; additional usage is charged separately. The modest storage increase and usage-based charges mean the subscription price alone is not the full cost, so teams should consider expected volume.

Scale uses pay-for-what-you-use pricing and is VPC only; it is aimed at organizations with 8–48 GPUs and requires a custom quote. It adds private Slack channel support and an SLA. Enterprise uses custom pricing by request for multiple large projects, with VPC or an on-premises cluster; it includes role-based access control, LDAP integration, SSO, white-glove support with a custom SLA, and professional services. SSO is also available on Scale. These tiers fit organizations that need stronger access controls or contracted support, but they require a quote rather than a published per-user price.

Platforms

ClearML supports API, Linux, macOS, self-hosted, web, and Windows environments. Combined with hosted, managed, VPC, hybrid, and on-premises deployment options, that range gives infrastructure teams flexibility over where the platform runs.

Who it's for

ClearML is a strong fit for AI teams that want GPU resource management alongside experiment tracking, pipelines, model management, and LLM deployment. Its deployment choices also suit organizations with hybrid or self-hosted requirements. A small team can begin on Community, while teams expecting more seats or API and metric activity should evaluate Pro’s usage charges; organizations needing private support, SLAs, or enterprise access controls should look at Scale or Enterprise.

Pros and cons

Pro: GPU management spans on-premises, cloud, and hybrid environments, including multi-tenant GPU-as-a-Service, making it relevant to teams coordinating shared compute.

Pro: Development workflows combine tracking, pipelines, repository, CI/CD integration, and broad framework integrations, reducing the need to assemble those capabilities separately.

Pro: Hosted, self-hosted, managed, VPC, air-gapped, and hybrid options accommodate different deployment constraints.

Con: Community caps teams at 3 and limits artifact storage, metric events, and API calls, so free access may not stretch to larger or busier workflows.

Con: Pro adds per-user cost plus separately charged usage, making total spend depend on activity as well as seats.

Con: Role-based access control and LDAP are Enterprise features, while private Slack support and SLAs are reserved for higher tiers.

Alternatives

For teams focused on GPU cluster management, GPU Cluster Management Software is a useful category to compare. For a broader MLOps shortlist, see MLOps Platforms; teams prioritizing data version control, model registries, or experiment tracking can compare Data Version Control Tools, Model Registry Software, and ML Experiment Tracking Software.

dstack is a free option with an open-source, self-hosted orchestration stack, and may suit teams seeking that narrower setup. Backend.AI offers a free self-hosted option for home users and a trial tier, making it another candidate for those evaluating GPU infrastructure. GPUStack is a free open-source GPU cluster manager. HTCondor, NVIDIA ShadowPlay, TORQUE Resource Manager, HAMi, and Kueue are other alternatives to consider.

Verdict

Choose ClearML if your AI team needs one platform for GPU coordination, model workflows, and LLM deployment, particularly across hosted and self-managed infrastructure. Its range is the main reason to choose it; teams with simpler tracking needs, tight usage budgets, or a requirement for enterprise access controls on a lower tier should look elsewhere.