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
Wafer offers H100 and A100 GPU cloud compute for AI and ML teams with straightforward on-demand pricing and flexible instance options that make it easy to scale training and inference workloads without complex billing structures. Simple setup and transparent pricing lower the barrier to entry for startups and small teams exploring GPU compute for LLM fine-tuning and model deployment. A no-frills on-demand GPU cloud for AI teams that want clear pricing and flexible instance configurations.
- Simple pricing
- Flexible instances
- Fast setup
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.
Wafer — specialist provider
Wafer offers H100 and A100 GPU cloud compute for AI and ML teams with straightforward on-demand pricing and flexible instance options that make it easy to scale training and inference workloads without complex billing structures. Simple setup and transparent pricing lower the barrier to entry for startups and small teams exploring GPU compute for LLM fine-tuning and model deployment. A no-frills on-demand GPU cloud for AI teams that want clear pricing and flexible instance configurations.
Billing model comparison
AWS uses a On-demand, Reserved (1yr/3yr), Spot billing model with a minimum commitment of None (on-demand). Wafer uses On-demand billing with a None minimum. AWS's no-commitment on-demand model is more flexible for short-term or experimental workloads, while Wafer's commitment requirement suits teams with predictable long-running jobs.
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. Wafer is best suited for: AI startups, Short training runs, Inference. Its key strengths are simple pricing, flexible instances, fast setup. As a hyperscaler, AWS offers broader ecosystem integration and compliance certifications at a premium price. Wafer as a specialist provider typically offers lower per-GPU rates for teams that don't need the full hyperscaler ecosystem.
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). Wafer offers Standard support across 1 region (US). AWS's broader region footprint gives it an advantage for latency-sensitive workloads or teams with data residency requirements in specific geographies.
Provider background: AWS vs Wafer
AWS was founded in 2006 and is headquartered in Seattle, WA. Wafer was founded in 2023 and is headquartered in United States. AWS has 17 years more operational history than Wafer, 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.