List of ClearML AI Platform Customers
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United States
Since 2010, our global team of researchers has been studying ClearML AI Platform customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased ClearML AI Platform for AI infrastructure from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using ClearML AI Platform for AI infrastructure include: Trax Retail, a Singapore based Professional Services organisation with 699 employees and revenues of $215.0 million, Meraki, a United Kingdom based Professional Services organisation with 10 employees and revenues of $3.5 million, Deepmirror United Kingdom, a United Kingdom based Life Sciences organisation with 10 employees and revenues of $1.0 million and many others.
Contact us if you need a completed and verified list of companies using ClearML AI Platform, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The ClearML AI Platform customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Deepmirror United Kingdom | Life Sciences | 10 | $1M | United Kingdom | ClearML | ClearML AI Platform | AI infrastructure | 2019 | n/a |
In 2019 DeepMirror United Kingdom deployed the ClearML AI Platform as its AI infrastructure to capture experiment tracking, data logging, and model logging for early stage machine learning activities. The ClearML AI Platform runs experiments on DeepMirror’s AWS infrastructure to maintain data control, enabling teams in the UK to reproduce results and iterate on molecule discovery workflows.
The implementation focused on experiment tracking and data logging capabilities, instrumenting metadata capture and artifact storage so experiment inputs and outputs could be reconstituted for reproducibility. ClearML AI Platform was configured to record model artifacts and training metadata alongside dataset provenance, supporting model logging and experiment lineage that are core to AI infrastructure operational practices.
Operational scope centered on DeepMirror’s drug discovery teams, where ClearML AI Platform became the central system for ML experiment lifecycle management, from training runs to result review. Governance emphasized standardized experiment metadata and reproducibility workflows, and the case study states the platform significantly sped up ML driven molecule discovery by helping teams reproduce results and iterate faster.
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Meraki | Professional Services | 10 | $3M | United Kingdom | ClearML | ClearML AI Platform | AI infrastructure | 2025 | n/a |
In 2025, Meraki deployed the ClearML AI Platform, using it as AI infrastructure to standardize experiment tracking, workflow automation, and scalable MLOps across on-prem and cloud projects in the United States. The deployment focused on accelerating computer vision and natural language understanding research and implementing CI/CD pipelines for model production.
ClearML AI Platform implementations centered on experiment tracking, Pipelines for workflow orchestration, and Autoscalers for dynamic compute provisioning. Configuration work emphasized pipeline definition, artifact and metadata capture, and autoscaler policies to manage on-prem and cloud compute elasticity, enabling reproducible experiment metadata and automated training workflows.
Operational coverage included research teams working on computer vision and NLU and model engineering teams responsible for CI/CD for production models, with compute resources spanning on-prem clusters and cloud environments in the United States. Integrations concentrated on orchestrating training jobs and centralized artifact management to improve collaboration and compute utilization.
Governance and process changes established standardized experiment metadata, pipeline-based review checkpoints, and formalized CI/CD workflows that move models from research into production, with ClearML features such as Pipelines and Autoscalers central to those controls. The case study describes improved collaboration and better compute utilization following the ClearML AI Platform deployment.
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Trax Retail | Professional Services | 699 | $215M | Singapore | ClearML | ClearML AI Platform | AI infrastructure | 2025 | n/a |
In 2025, Trax Retail deployed the ClearML AI Platform to centralize development and operational control for its retail computer-vision models. Trax Retail implemented ClearML AI Platform as AI infrastructure to support model lifecycle activities across its headquarters in Singapore and its global operations, aligning the application to retail computer-vision model development and lifecycle management.
Deployment centers on ClearML’s AI Development Center and Infrastructure Control Plane, providing a federated model repository, experiment tracking, and infrastructure orchestration to manage training runs, artifacts, and compute provisioning. The ClearML AI Platform model repository and experiment tracking capture reproducible metadata and versioned artifacts, while the Infrastructure Control Plane orchestrates compute resources to standardize training, validation, and deployment workflows.
Operational scope includes computer-vision engineering teams and ML lifecycle owners, consolidating experiment histories and model artifacts to streamline handoffs between data scientists and production engineers. Governance emphasis moved to standardized experiment tracking, artifact versioning, and pipeline orchestration, improving reproducibility and speeding iteration across Trax Retail’s global operations as described in the ClearML case study.
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