GPU Cloud Rental Cost Calculator

GPU cloud rental cost calculator

✓ Last verified: 2026-08-15· Source: official provider pricing page· Auto-monitored — report change →

Training or self-hosting a model? Cloud GPUs bill by the hour, on or idle. Pick a card — H100, A100, L40S, L4, RTX 4090 and more — set your hours and count to see the monthly bill, the spot vs on-demand saving, and what always-on (730h) would cost. Compare every GPU side by side.

A rented GPU bills every hour it's switched on

The thing that surprises teams moving from APIs to their own GPUs is that the meter never pauses. A per-token API costs nothing when traffic is quiet; a rented H100 costs the same whether it's saturated or sitting at 2% utilization, because you're paying for the hour, not the work. That's why the single biggest lever on your bill isn't the GPU you pick — it's how many hours you keep it running. An H100 you only need eight hours a day costs a third of one left on around the clock. This calculator makes that explicit: set the real hours, not the theoretical maximum, and watch the monthly number move. The quick-set dropdown covers the common patterns — always-on inference, a working-day training box, weekend batch runs.

Tier is the second lever. On-demand capacity is guaranteed for as long as you hold it and priced at a premium; spot, community and interruptible instances are spare capacity sold 20–50% cheaper, with the catch that the provider can take them back. For checkpointed training or fault-tolerant batch jobs, spot is close to free money; for an inference endpoint that can't blink, the on-demand premium buys you stability. The table shows both rates for every card so the trade-off is in front of you. Once you have the rental number, the real question is whether owning GPU-hours actually beats paying per token — work that out with the self-hosted LLM vs API calculator, then price the bandwidth your model serves with the cloud egress calculator and the inference endpoints themselves with the serverless function cost calculator.

How to use it

1. Pick your GPU — its typical on-demand and spot rates load automatically.
2. Choose on-demand or spot, and set how many GPUs you're running.
3. Set the hours per month, or use the quick-set dropdown for a common duty cycle.
4. Read the monthly cost and spot saving, and compare every GPU in the table.

Common mistakes

Assuming always-on. 730 hours is the maximum, not your usage — most workloads need far fewer. Forgetting idle time bills. A GPU left running between jobs still costs full rate; shut it down or use autoscaling. Picking the biggest card. An L4 or 4090 often serves a small model at a fraction of an H100's cost — match the GPU to the model. Ignoring storage and egress. Volumes, snapshots and data transfer are separate line items; add storage above and price egress separately.

Estimate only. GPU rates move constantly and vary by provider and region — verify live pricing before relying on them.

How this calculator works

The GPU Cloud Rental Cost Calculator estimates your monthly bill for renting GPUs from a cloud provider. You pick a specific GPU — H100, A100, L40S, L4, RTX 4090, or another model — then set the number of GPUs, the hours you run per month, and a pricing tier. The main cost drivers are the GPU's hourly rate, how many you run in parallel, and total runtime, so the calculator multiplies rate by GPUs by hours and adds any optional storage or extras. Because it lets you compare pricing tiers such as on-demand versus reserved, you can see how commitment level changes the total for the same workload.

The key trade-off to watch is utilization versus commitment. On-demand rates cost more per hour but only bill for hours you actually use, while reserved or committed tiers are cheaper per hour but charge whether the GPU is busy or idle. Before locking in a lower tier, estimate your realistic monthly hours honestly — a discounted rate saves nothing if the hardware sits unused. Adjust the hours per month and GPU count inputs to model both light and heavy usage, and remember that storage and extras can add a meaningful fixed amount on top of raw compute.

Frequently asked questions

How much does it cost to rent an H100 in the cloud?

As of mid-2026 a single NVIDIA H100 80GB rents for roughly $2 to $3.50 an hour on-demand, depending on the provider, and noticeably less — often $1.80 to $2.50 — on spot or community tiers. Running one flat-out around the clock is about 730 hours a month, so on-demand works out near $1,500 to $2,500 monthly per GPU. This calculator lets you set the exact GPU, hours and count to see your own number, and shows the spot savings beside the on-demand price.

What's the difference between on-demand and spot GPU pricing?

On-demand (or reserved) GPUs are guaranteed for as long as you keep them running — you pay a premium for that stability. Spot, community or interruptible instances are spare capacity sold at a discount, typically 20–50% cheaper, but the provider can reclaim them with little warning. Spot is ideal for fault-tolerant batch jobs and checkpointed training; on-demand is safer for inference endpoints and anything that can't be interrupted. The calculator shows both so you can weigh the saving against the risk.

Is renting a GPU cheaper than using an inference API?

It depends almost entirely on utilization. A rented GPU is billed every hour it's switched on, busy or not, so it only beats a per-token API once you keep it densely loaded — usually well above 30–50% utilization. For spiky or low traffic, a per-token API is cheaper because you pay nothing when idle. For steady high throughput, owning the GPU-hours wins. Use this calculator for the rental side, then compare against per-token pricing with the self-hosted vs API calculator.

Shopping around for the on-demand rate itself? DigitalOcean gives $200 in free credit toward GPU droplets — enough to benchmark real throughput before you commit to a longer contract.

Every GPU at your hours & count