GPU Cloud Cost Calculator
Renting a GPU in the cloud is deceptively simple until the monthly bill arrives. The same A100 can cost you $250 or $950 a month depending on the provider, the pricing mode, and how many hours the instance actually runs. This calculator turns hourly list prices into a realistic monthly figure for inference, development, and hosting workloads — and compares the total across six providers so you can see whether a specialist GPU cloud beats the big three for your exact usage.
Configure Your Cloud GPU Setup
The Cloud GPU Cost Formula
Monthly cloud GPU spend is the sum of compute hours, persistent storage, and data movement:
Monthly = (Hourly_Rate × Instances × Hours_per_Day × Days_per_Month) + (Storage_GB × $0.10) + (Egress_GB × $0.09)
The hourly rate is what changes everything: on-demand is full list price, 1-year reserved is typically 45–55% less, and spot is 55–65% less but can be reclaimed at any moment. Storage bills accrue even when instances are shut down, and egress is charged every time data leaves the region. Together these two line items turn a tidy compute quote into a chunky monthly bill — which is exactly why they are included here rather than hidden in the fine print.
Choosing Between On-Demand, Reserved, and Spot
The pricing mode should match how predictable your workload is. On-demand is right for bursts, experiments, and variable traffic you cannot forecast. Reserved instances pay off when you know an instance will run for a year or more — an always-on inference server, a nightly batch job, a development environment. Spot makes sense for anything resumable: if the provider reclaims the machine, you restart from a checkpoint and continue.
- On-demand: maximum flexibility, highest price. Ideal for short jobs and auto-scaling spikes.
- Reserved 1–3 years: up to 55% cheaper. Commit only to base capacity you are confident you will use.
- Spot / preemptible: up to 65% cheaper. Combine with checkpointing and instance-group fallback so interruptions are invisible.
A common hybrid pattern: reserve a small always-on baseline, run burst capacity on-demand, and put resumable batch work on spot. That mix usually lands within 40–60% of a pure on-demand bill.
Worked Example: An Always-On A100 Inference Server
You host an LLM inference endpoint on one A100 80GB, on-demand, 12 hours a day for 30 days. Compute is 1 × $1.55 × 12 × 30 = $558.00. With 200 GB of model weights and logs on persistent storage ($20) and 100 GB of egress to users ($9), the monthly total is about $587 — roughly $7,044 a year.
Switch that same instance to a 1-year reserved rate and compute drops to $0.85/hr, cutting the monthly total to about $335 and saving close to $3,000 a year. Now run the same reservation on RunPod instead of the list-rate provider and the saving grows again. That is the whole point of this calculator: the hardware is fixed, but the price tag is a choice.
Frequently Asked Questions
How much does a cloud GPU cost per month?
An A100 80GB on-demand for 12h/day × 30 days is about $558/month in raw compute; a T4 on the same schedule is about $126/month. Reserved and spot pricing cut those numbers significantly.
Is it cheaper to buy a GPU or rent cloud GPU instances?
At 60%+ sustained utilization, buying and colocating can break even within 12–18 months. For intermittent or variable work, renting (especially reserved or spot) is cheaper and avoids depreciation and maintenance.
Which cloud provider is cheapest for GPUs?
RunPod and Lambda Labs typically undercut AWS, Azure, and GCP by 15–30% for raw compute. The big three charge a premium for enterprise support, quotas, and ecosystem integration.
Why do storage and egress surprise people?
Storage bills monthly even when instances are off, and egress is charged on every byte leaving the region (~$0.09/GB). Together they routinely add 5–20% on top of the compute line.
Is spot pricing safe for production?
For resumable and fault-tolerant workloads, yes — checkpointing plus an autoscaling fallback to on-demand makes interruptions nearly invisible. For a single always-on critical service, reserve it.