GPU Cloud Cost Calculator
Compare GPU rental costs across AWS, GCP, Azure, Lambda Labs, and Vast.ai for ML training and inference workloads.
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How to use this calculator
- 1
Select the GPU type that matches your workload requirements.
- 2
Enter the number of GPU-hours you expect to use per month.
- 3
Select your preferred cloud provider.
- 4
Compare the monthly cost and see the cheapest available provider for the same GPU.
Frequently asked questions
Should I use on-demand or spot/preemptible instances?
Spot instances (AWS) or preemptible VMs (GCP) offer 60–80% discounts but can be terminated with little notice. They are ideal for fault-tolerant training jobs with checkpointing. For inference endpoints that need guaranteed uptime, use on-demand or reserved instances.
What is the difference between A100 and H100?
The NVIDIA H100 is approximately 3× faster than the A100 for transformer model training due to the new Transformer Engine and FP8 precision. At roughly 2.5× the price, the H100 typically offers better cost-efficiency for large model training. The A100 remains a solid choice for inference and smaller fine-tuning jobs.
Why is Lambda Labs so much cheaper than AWS?
Lambda Labs operates a smaller, GPU-focused cloud without the breadth of AWS services. Lower overhead, no enterprise SLAs, and a focus on ML workloads allow them to pass savings to customers. However, they offer fewer regions, less storage flexibility, and fewer managed services.
GPU Cloud Cost Calculator — A100, H100, AWS vs Lambda Labs vs Vast.ai
Choosing the right GPU for your workload
GPU selection drives both performance and cost. For inference serving, A10G and T4 GPUs are often the best value — they handle most model sizes efficiently at a fraction of the A100 cost. For large model training (7B+ parameters), A100 or H100 are typically required. V100s remain in service but offer worse performance-per-dollar than newer options.
Reserved vs on-demand GPU pricing
On-demand pricing is what this calculator uses. Committing to 1-year or 3-year reserved instances can save 30–60% on AWS, GCP, and Azure. For production inference endpoints running 24/7, reserved instances often pay back within 3 months. For experimental training runs, on-demand or spot instances are more flexible.
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Results are estimates for informational purposes only and do not constitute professional financial, medical, legal, or technical advice. Read full disclaimer →