GPU Inference Cost Calculator

What This Calculator Measures

If you're hosting your own AI model instead of calling a provider's API, your cost is driven by GPU rental hours, not tokens. This calculator estimates hosting cost using real, currently published on-demand hourly rates from major GPU cloud providers.

Why GPU Prices Vary So Much Between Providers

Specialized GPU clouds built specifically for AI workloads (like RunPod and Lambda Labs) typically price the same hardware at a fraction of what general-purpose hyperscale clouds (AWS, GCP, Azure) charge, because those hyperscalers bundle in broader platform services, support, and networking that many AI workloads don't need. It's common to see the same GPU model priced 2-3x higher on a hyperscaler than on a dedicated GPU cloud.

The Math

total cost = price per GPU-hour × number of GPUs × hours per day × number of days

GPU Hosting vs. API: When Self-Hosting Makes Sense

• Self-hosting tends to win at high, steady utilization — you're paying for the GPU whether you use it or not, so idle time is wasted money.
• API/pay-per-token tends to win at bursty, unpredictable, or low-volume usage, since you only pay for what you actually process.
• Run the numbers both ways: compare this calculator's total against the AI Token & Cost Calculator for your expected token volume on a comparable hosted model.

Common Mistakes

• Only counting compute, not the engineering cost. Self-hosting means you also own model serving, scaling, monitoring, and updates — real costs that don't show up in a raw GPU-hour number.
• Assuming 24/7 utilization when usage is actually spiky. If your real traffic has big idle gaps, a rented GPU sitting mostly idle can end up more expensive than pay-per-token API pricing, even at a lower headline hourly rate.

Frequently Asked Questions