Buying snapshot
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| Decision point | What ClearML states |
|---|---|
Deployment | ClearML's hosted servers, self-hosted, or a managed service in a VPC, on-prem (including air-gapped) or hybrid. |
License | The clearml repository is under the Apache-2.0 license. The pricing page says you can run self-hosted ClearML "100% Open Source on GitHub". |
Self-hosted server | ClearML Server (web UI, API server and file server) ships as AWS EC2 AMIs, Google Cloud Platform custom images, Docker images for Linux, macOS and Windows 10, or Kubernetes using Helm. |
Getting started | pip install clearml, run clearml-init with server credentials, then add two lines (Task.init) to a script. |
Support | The docs point to a Slack channel, Stack Overflow (clearml tag) and support@clearml.ai. Scale lists a dedicated Slack channel and a standard SLA; Enterprise lists white-glove support with a custom SLA. |
Our take
Choose ClearML if you want one system that links experiment records, dataset versions, pipelines and compute queues, and you are ready to own it as a platform. If your team only needs a place to log metrics and parameters, most of what ClearML includes will go unused.
Evaluate ClearML
Comparisons
Where it stands out
Tracking with little code. Two lines in a script log the full experiment setup: source control info including uncommitted local changes, installed packages and versions, hyperparameters, console output and resource use. Teams keep the code state that a metrics-only log loses.
Data, pipelines and serving in the same place. The open-source project covers experiment management, data management on top of S3, GS, Azure or NAS, pipelines, orchestration and model serving. Dataset versions store only the change set from their parent versions, and comparisons span experiments, datasets and models, so results stay tied to the data that produced them.
Budget-bound cloud autoscaling. The AWS and GCP autoscaler apps spin instances up when tasks enter a queue, up to the maximum set in the budget, and spin idle machines down. Agents can also run through Kubernetes using a Helm chart.
Shared GPU control. The Infrastructure Control Plane manages GPU clusters on-premises, in the cloud or both. Fractional GPUs are basic in the open-source version and dynamic on Enterprise. On the pricing page, secure multi-tenancy support and granular usage reporting per user are Enterprise only.
Product screenshot
Trade-offs to weigh
Self-hosting is a server to run. ClearML Server bundles a web server, an API server and a file server for media and models. Your team deploys it, configures web login, subdomains and load balancers, and upgrades it over time.
Larger teams move to private servers. Under "Need Private Servers?", ClearML points larger teams with security and compliance needs to Scale and Enterprise. Decide early where ClearML will run for your team.
Access control sits in the top tier. Role-based access control and LDAP integration are in the Enterprise plan list. Scale lists SSO integration. Check the plan before you plan multi-team access rules.
Some production features are beta. The pricing page marks model serving engines, ClearML Deploy and task monitoring as beta, and model genealogy as coming soon. Test them against your own needs before you rely on them.
Pricing and plan conditions
Community and Pro run on ClearML's hosted servers. Community is free with included usage. Pro charges per user and adds metered usage: storage and activity above the included amounts are billed at the rates in the table. For needs outside the listed plans, ClearML offers to build a custom license with your team. The open-source server can also be self-hosted.
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| Plan | Published price or basis | What it adds |
|---|---|---|
Community (teams up to 3) | $0, no credit card required | Dataset versioning, model training, experiment management, model repository, artifacts, pipelines, agent orchestration, CI/CD automation and reports. Includes 100GB artifact storage, 1GB metric events and 1M API calls/month. |
Pro (teams up to 10) | $15 per user/month + usage | Community features, plus cloud auto scaling (AWS, GCP, Azure), hyperparameter optimization, pipeline triggers and automations, and dashboards. Includes 120GB artifact storage, 1.2GB metric events and 1.2M API calls/month. Additional usage: $0.1/1GB artifact storage, $0.01/1MB metric events, $1/100K API calls, $0.04/hr per application. |
Scale (organizations with 8-48 GPUs) | Pay for What You Use; VPC only | Pro features, plus Hyper-Datasets, fine-tuning, IDE launcher, vector database integration, Kubernetes integration, task scheduling and triggering, alerts, SSO integration, private Slack channel support and an SLA. Control plane: hardware- and cloud-agnostic orchestration, job scheduling and fractional GPUs. |
Enterprise (organizations with multiple large projects) | For VPC or on-prem cluster | Scale features, plus ClearML custom apps, configuration vault, Slurm/PBS integration, LDAP integration, role-based access control, white-glove support with custom SLA and professional services. Control plane: advanced scheduling and quota management, dynamic fractional GPUs, resource allocation policy management, and on-premise, air-gapped, multi-cloud or hybrid setups. |
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
48 GitHub commits 90d6.9k GitHub stars0 vulnerabilities across 1 package
See all signals from 6 sources
PyPI · clearml@2.2.0
Alternatives to ClearML
The reviewed substitutes for ClearML among the ML platforms, and what would make each one the better answer.
Direct alternatives
Reviewed substitutes: products bought for the same job, where a team picks one.
- Domino Data Lab
- ClearML and Domino Data Lab are enterprise MLOps platforms for running experiments, deploying models and managing compute. Domino Data Lab emphasizes governance, audit and environment management for regulated organizations, while ClearML is open source and emphasizes GPU cluster management and pipeline orchestration that teams can self-host.Applies to: Choosing between these two for the managed ml platforms decision.
- Azure Machine Learning
- ClearML and Azure Machine Learning both cover the ML lifecycle, from experiment tracking and training to deployment and model management. Azure Machine Learning is Microsoft's managed cloud service with automated ML and responsible AI tooling, while ClearML is an open-source platform that teams can host on any cloud or on their own GPU clusters.Applies to: Choosing between these two for the managed ml platforms decision.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- Comet ML
- ClearML and Comet ML both track experiments, datasets, code changes and models for ML teams. Comet ML adds evaluation for LLM applications and monitoring of production models, while ClearML is open source and wraps tracking into a wider platform with pipelines, model deployment and GPU compute management.Applies to: Choosing between these two for the experiment tracking decision.
- Weights & Biases
- Weights & Biases and ClearML both log training runs, hyperparameters and models so ML teams can track and reproduce experiments. W&B is a commercial platform known for its visualizations and sweeps, while ClearML is open source, can be self-hosted, and adds pipeline orchestration, dataset versioning and GPU cluster management for teams that want one broader platform.Applies to: Deciding how the stack is shaped, where both products can be part of the answer.
- MLflow
- MLflow and ClearML are open-source MLOps tools that track experiments and manage models. ClearML goes further, adding pipeline orchestration, dataset versioning and GPU compute management in one platform, while MLflow stays focused on tracking, a model registry and deployment, which fits a team that wants a smaller core to build around.Applies to: Deciding how the stack is shaped, where both products can be part of the answer.
- Gemini Enterprise Agent Platform
- ClearML is used rather than Vertex AI for self-hosted MLOps workloads requiring integrated experiment, dataset, and compute orchestration. **ClearML** is an open-source MLOps platform combining experiment tracking, pipeline orchestration, dataset versioning, model deployment, and compute orchestration.
- Amazon SageMaker
- Amazon SageMaker and ClearML each give ML teams one platform to build, train and deploy models. SageMaker is a fully managed AWS service that fits teams already on Amazon's cloud, while ClearML is open source with self-hosted and managed options and puts extra weight on managing GPU clusters and workflow orchestration.Applies to: For ML teams that run on AWS and could either use a managed service or host an open-source platform themselves.