Best GPU provider for AI agents

A coding agent can write a training script. To run it, the agent needs a GPU, and a person clicking through a console defeats the purpose. The provider has to expose its platform in a form the agent can call.

That means a tool interface the agent already understands, a real shell on the machine, and sign-in that does not depend on a secret pasted into a file.

Agents start and stop machines far more often than people do. They launch one to run a test, read the result, and launch another. Each start that is rounded up to an hour multiplies the bill.

Billing by the second fits this pattern. So does a provider that does not bill the minutes a machine spends booting.

An agent with a payment method can spend without limit. A prepaid balance is a natural cap: when it is used up, the spending stops. Check how the provider handles a balance that runs out while a machine is up.

Check too that the agent can list what is running and stop it. An agent that cannot clean up after itself leaves machines billing overnight.

Add the provider to your agent. Ask it to start the smallest GPU, print the name of the card from the machine’s shell, and stop the machine. Watch for any step where it needs you.

If the whole loop runs unattended, the provider works for agents. If it stalls on a login page or a quota form, it does not yet.

Runcrate has a CLI, an SDK and an MCP server. One command adds it to Claude Code, Codex and Cursor, with browser sign-in and no key to paste, so an agent can launch a GPU with a real shell. It bills by the second on most GPUs, and one prepaid balance pays for GPUs, models and storage.

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