A single high-end GPU can cost more than a used car.

A cluster to actually train a serious model? That’s a capital commitment most startups and research teams simply can’t justify, especially when the workload is temporary.

This is the core inefficiency in AI infrastructure: teams are buying permanent hardware for temporary compute needs.

Usage-based GPU cloud flips that math. You pay for the hours you actually train, not for hardware sitting idle the other 340 days a year.

The teams winning right now aren’t the ones with the biggest GPU budgets. They’re the ones spending compute dollars only when compute is actually working for them.