ClearML is an AI infrastructure platform that centralizes GPU resource provisioning, model development, and GenAI deployment across on‑premise, cloud, and hybrid environments. Its control plane offers multi‑tenant GPU‑as‑a‑Service with quota management, priority scheduling, and built‑in security, while the integrated IDE provides experiment tracking, data versioning, and automated hyper‑parameter optimization. The platform also includes a GenAI App Engine for rapid LLM and RAG workload serving with role‑based access control and detailed usage billing.
Funding
$13.8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Enterprises and AI teams often struggle to efficiently provision, manage, and scale GPU resources across on‑premise, cloud, and hybrid environments, leading to underutilized hardware, high operational costs, and fragmented workflows from model development to deployment.
Solution
ClearML provides an end‑to‑end AI infrastructure platform that unifies resource provisioning, model development, and GenAI deployment on a single control plane. The Infrastructure Control Plane abstracts GPU clusters as a GPU‑as‑a‑Service, offering multi‑tenant access, quota management, and priority‑based scheduling to maximize utilization and reduce waste. The AI Development Center supplies an integrated IDE with experiment tracking, data versioning, distributed training, and automated hyper‑parameter optimization, streamlining the entire model lifecycle. The GenAI App Engine enables rapid deployment of large language models and Retrieval‑Augmented Generation workloads with built‑in networking, authentication, and role‑based access control. Together, these layers deliver consistent performance, cost optimization, and secure, scalable AI operations across any compute environment.
Target Audience
ClearML targets AI/ML engineering teams, data science groups, and DevOps/IT departments in medium to large enterprises that need centralized, scalable GPU management and end‑to‑end tooling for model development and GenAI deployment.
Features
- Multi‑tenant GPUaaS with one‑click provisioning for on‑premise, cloud, or hybrid clusters
- Dynamic quota and fractional GPU allocation to improve compute utilization and enforce cost controls
- Priority‑based job scheduling and autoscaling of GPU resources based on workload demand
- Integrated IDE offering experiment tracking, data versioning, distributed training, and hyper‑parameter optimization
- Built‑in monitoring, logging, and detailed usage dashboards for real‑time performance insight
- Secure networking, authentication, and role‑based access control for deployed GenAI services
- Seamless deployment of LLMs and RAG workloads with automated model serving and API generation
- Granular billing and chargeback based on compute hours, storage, and API usage