GPU Cloud Cost Calculator
Estimate GPU compute costs for AI training and inference. Compare on-demand, spot, and reserved pricing — and find the break-even point for reserved commitments.
94+
Providers
3
Pricing modes
Workload presets
Total GPUs in your training cluster
Wall-clock hours for the full run
Effective compute utilization (MFU)
$454.18
182 GPU-hours
$198.82
Save $255.36 (56%)
$344.74
Save $109.44 (24%)
$2.490
at stated utilization
Cost comparison
Cost optimization tips
- Use spot instances with checkpointing to save 56% on this run
- Mixed-precision (BF16/FP8) can reduce training time 30–50% on H100 80GB
- Gradient checkpointing trades ~30% speed for lower VRAM, enabling larger batch sizes
How these estimates work
Training cost
GPU-hours × rate × utilization factor. GPU-hours = GPU count × wall-clock hours. Utilization accounts for idle time during data loading, checkpointing, and communication overhead.
Inference cost
GPU-hours are fixed (GPUs run 24/7 for serving). Cost per request = daily GPU cost ÷ daily requests. Concurrency = requests/sec × latency in seconds.
Break-even
Reserved pricing is billed for all hours in the commitment period regardless of usage. Break-even is the daily utilization at which reserved total cost equals on-demand total cost.