AWS vs Google Cloud GPU Pricing
Side-by-side GPU cloud pricing comparison with live data and real-time availability.
AWS is cheaper on 6 of 9 shared GPU models
No dataGPU pricing comparison
- AWS $3.36/GPU/hrGoogle Cloud $1.67/GPU/hrΔ $1.70 (50%)Google Cloud cheaper
Egress Cost Comparison
AWS
$0.09/GB
Google Cloud
$0.12/GB
Exclusive GPUs
Only on AWS (10)
- A10G
- B300
- Inferentia
- Inferentia2
- L40S
- Radeon Pro V520
- RTX PRO 4500
- T4g
- Trainium
- Virtex UltraScale+ (VU47P)
Only on Google Cloud (7)
- P100
- P4
- TPU v2
- TPU v3
- TPU v5 Lite
- TPU v5p
- TPU v6e
Which Should You Choose?
Choose AWS if...
- You need the widest region coverage and the deepest service ecosystem (S3, SageMaker, EKS, Bedrock)
- You run large multi-node training and want EFA plus Capacity Blocks to reserve H100/H200 clusters for a fixed window
- Your data, IAM, and compliance stack already lives in AWS
Choose Google Cloud if...
- You want TPUs (v5e / v5p / v6e Trillium) alongside NVIDIA GPUs — Google is the only one of the two that rents them
- You can commit to 1-3 year CUDs (committed-use discounts) for the steepest hyperscaler savings
- You're building on Vertex AI or GKE and want native GPU autoscaling
Frequently Asked Questions
Is Google Cloud cheaper than AWS for GPUs?
Roughly comparable on H100/H200 on-demand — small differences swing by region and instance generation. Both are premium; check the pricing table above for live per-GPU rates. The bigger lever is commitment: GCP committed-use discounts and AWS reserved/savings plans both cut 30-50% off on-demand, but we don't track commit pricing — verify on each provider's own calculator.
Does AWS or Google charge less for egress?
Both charge tiered per-GB egress with no meaningful free tier for GPU traffic at scale, which adds up on data-heavy training. See the egress comparison section above. If egress dominates your bill, a free-egress neocloud like Lambda or Runpod can beat both on total cost.
Can I get H100s on demand from either?
Both gate top GPUs behind quota requests, and on-demand H100/H200 capacity is tight. AWS offers Capacity Blocks for ML to reserve GPU clusters for a set window; GCP offers reservations and the Dynamic Workload Scheduler. GPU Finder tracks catalog pricing, but live hyperscaler stock still requires a quota check in-console.
What's the biggest difference for ML workloads?
TPUs and networking. Google Cloud is the only place here to rent TPUs, which can be cheaper per token for large training/inference if your stack runs on JAX/XLA. AWS's EFA is the more mature high-bandwidth fabric for NVIDIA multi-node NCCL jobs. Pick GCP for TPU flexibility, AWS for GPU multi-node maturity.