How this calculator works
The GPU Rental Cost Calculator estimates what it costs to run inference on a rented GPU, converting an hourly rental rate into a monthly cost and a cost per inference. You enter the GPU's price per hour, how many hours per day you actually run it, and how many inferences that GPU completes each hour. From those three inputs it multiplies the hourly rate by daily hours and by roughly 30 days to get the monthly bill, then divides that total by the number of inferences served over the same period to reveal the unit economics. The main drivers are the hourly rate and, just as importantly, utilization—how many hours the GPU is billed versus idle.
The key trade-off to watch is utilization versus throughput. A cheaper GPU can still cost more per inference if it processes fewer requests per hour, and a GPU billed 24 hours a day but used only a few of them buries your cost per inference in idle time. Before committing to an instance, compare candidates on cost per inference rather than sticker rate alone, and pack workloads or scale down during quiet periods so you pay mainly for hours that actually produce results.
Frequently asked questions
How is GPU rental cost calculated?
Hourly rate × hours running. Cost per inference = hourly rate ÷ inferences per hour, so utilization is everything — a GPU billed 24/7 but used 10% of the time has a 10× inflated per-inference cost.
When does renting a GPU beat an API?
Only at high, steady volume where you keep the GPU busy. Below that, per-token APIs win because you pay nothing when idle. Bursty workloads favor serverless GPU or APIs.
Comparing rental rates before committing?
DigitalOcean gives $200 in free credit toward GPU droplets — enough to run your utilization numbers on real hardware before signing a longer contract.