allocateSelf-hosted vs API break-even
At what volume does renting GPUs beat paying per token? Compare both bills for any open-weight model, with utilization included.
At 35% utilization, self-hosted hardware sits idle 65% of the month and still bills by the hour. Allocate quotes this workload flat: owned weights, metered tokens, no idle cost.
How it works
Common questions
Why does utilization dominate the comparison?
GPUs bill by the hour whether or not traffic flows; APIs bill only per token. A workload that runs hot 8 hours a day leaves rented hardware idle 67% of the time, so its effective cost per token triples. Below roughly 30 to 40% utilization, per-token pricing almost always wins.
What does the self-hosted estimate include?
The GPU fleet your peak throughput requires (at minimum, enough to fit the model at 4-bit with 16K context) at current H100-class rental rates, running all month. It excludes engineering time, orchestration, and failover, which are real costs that land on your team.
When does self-hosting genuinely win?
Sustained high utilization (24/7 batch or steady traffic), strict residency requirements, or token volumes well past the break-even line shown. If your traffic is spiky and follows business hours, it usually doesn't.
Is there a third option?
Yes: managed inference on a pooled fleet. Allocate serves open-weight models token-metered, so you keep what self-hosting offers (your fine-tuned weights, your boundary) with the economics of an API. Idle time costs nothing.
Are these prices exact?
They are estimates. API list prices and GPU rental rates both move. The conclusion, that utilization decides the comparison, holds even as the numbers shift.
More free tools
Allocate gives you both sides of this comparison: open-weight models you fine-tune and own, served token-metered inside your boundary. The control of self-hosting with the economics of an API.