Automatically routes each launch to the lowest cost available GPU
GPU.ai is a compute aggregator. Rather than running its own datacenters, it scans live capacity and pricing across upstream GPU clouds and places each launch on the lowest cost host matching the requested spec.
Capacity spans North America, Europe, and India, and is exposed through a CLI, a REST API, and a web console. Researchers and ML teams can run on-demand GPUs without holding accounts at each upstream provider.
GPU.ai Homepage
Billed per second, from the moment an instance reaches SSH-ready until it is stopped, with no minimums and no idle reservation fees. On-demand rates vary by region and upstream provider, and the platform routes each launch to the cheapest one with stock. Reserved capacity is quote-only and arranged with sales.
Here are some of the GPU configurations offered by GPU.ai:
10 configs 1x-8x
2 configs 1x-8x
2 configs 1x-8x
4 configs 1x-8x
4 configs 1x-8x
6 configs 1x-16x
8 configs 1x-8x
4 configs 1x-8x
No GPU models match .
Heads up: A provider's own page may quote a different figure, for example on tax, region or a promotion. A monthly-only plan shows a derived hourly rate. Verify before provisioning. More on how we price. See GPU.ai's pricing.
Here are some of the services that GPU.ai offers:
Compare GPU.ai against other cloud providers:
Shadeform is a GPU marketplace that launches instances across 30+ clouds from a single console and API.
RunPod offers on-demand and serverless GPUs with a container-first workflow.
Thunder Compute provides low-cost virtualized GPU instances aimed at prototyping and development.
Sesterce is a European GPU cloud offering on-demand instances, reserved clusters, and bare metal.
Hyperstack is a GPU cloud running its own renewable-powered European datacenters.
Our data for GPU.ai was last updated on Sept. 10, 2026.
