AWS vs Google Cloud: GPU Compute Price Comparison
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for AWS and Google Cloud. Updated July 2026.
Provider Overview
Strengths & Best For
AWS offers on-demand, reserved, and spot GPU instances across EC2 P4d (A100), P5 (H100), and G6 (L40S) families, spanning 30+ global regions with enterprise SLAs and deep ML tooling via SageMaker. H100 and A100 clusters are available with InfiniBand networking for distributed LLM training and large-scale AI inference. The broadest ecosystem of any GPU cloud provider, making it the default choice for enterprises already invested in the AWS stack.
- Widest global region coverage
- Deep ecosystem integrations
- Enterprise SLAs
- Reserved instance discounts
Google Cloud provides A100 and H100 GPU instances via Compute Engine and Vertex AI, with sustained use discounts and committed use contracts that can significantly cut hourly GPU rental costs. TPU v4 and v5 accelerators are also available for TensorFlow and JAX workloads, giving teams a unique alternative to NVIDIA hardware. Spanning 30+ regions, it is the top choice for ML pipelines deeply integrated with the TensorFlow and Google ecosystem.
- Sustained use discounts
- Vertex AI integration
- TPU availability
- Strong networking
Live GPU Pricing
Region Coverage
Popular Comparisons
AWS — hyperscaler provider
AWS offers on-demand, reserved, and spot GPU instances across EC2 P4d (A100), P5 (H100), and G6 (L40S) families, spanning 30+ global regions with enterprise SLAs and deep ML tooling via SageMaker. H100 and A100 clusters are available with InfiniBand networking for distributed LLM training and large-scale AI inference. The broadest ecosystem of any GPU cloud provider, making it the default choice for enterprises already invested in the AWS stack.
Google Cloud — hyperscaler provider
Google Cloud provides A100 and H100 GPU instances via Compute Engine and Vertex AI, with sustained use discounts and committed use contracts that can significantly cut hourly GPU rental costs. TPU v4 and v5 accelerators are also available for TensorFlow and JAX workloads, giving teams a unique alternative to NVIDIA hardware. Spanning 30+ regions, it is the top choice for ML pipelines deeply integrated with the TensorFlow and Google ecosystem.
Billing model comparison
AWS uses a On-demand, Reserved (1yr/3yr), Spot billing model with a minimum commitment of None (on-demand). Google Cloud uses On-demand, Committed Use (1yr/3yr), Spot/Preemptible billing with a None (on-demand) minimum. Both providers offer flexible billing options — compare the live pricing table above to find the best rate for your specific GPU model and workload duration.
Which workloads each provider suits best
AWS is best suited for: Enterprise workloads, Production ML inference, Teams already on AWS. Its key strengths are widest global region coverage, deep ecosystem integrations, enterprise slas. Google Cloud is best suited for: ML training pipelines, TensorFlow workloads, Teams using GCP services. Its key strengths are sustained use discounts, vertex ai integration, tpu availability. Both providers target similar workload profiles — the live pricing table above is the most reliable way to determine which offers better value for your specific GPU model and region requirements.
Support tiers and region coverage
AWS offers Basic → Enterprise support across 5 regions (us-east-1, us-west-2, eu-west-1 and 2 more). Google Cloud offers Basic → Premium support across 5 regions (us-central1, us-east4, europe-west4 and 2 more). Both providers have comparable region coverage — choose based on which specific regions overlap with your user base or data residency requirements.
Provider background: AWS vs Google Cloud
AWS was founded in 2006 and is headquartered in Seattle, WA. Google Cloud was founded in 2008 and is headquartered in Sunnyvale, CA. AWS has 2 years more operational history than Google Cloud, which may matter for teams evaluating provider stability and long-term contract risk. Use the live pricing table above to compare current on-demand and spot rates for specific GPU models, and the region map to verify coverage in your target geography.