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
CoreWeave is a purpose-built GPU cloud offering H100 SXM5, H200, and A100 clusters with InfiniBand and NVLink interconnects for large-scale AI training and LLM fine-tuning. On-demand and reserved H100 instances are available across US East, US West, and EU regions, with some of the highest GPU density and lowest latency networking of any specialist cloud. A top choice for AI labs and enterprises running multi-node distributed training at scale.
- Highest GPU density
- InfiniBand networking
- Kubernetes-native
- Fast provisioning
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.
CoreWeave — specialist provider
CoreWeave is a purpose-built GPU cloud offering H100 SXM5, H200, and A100 clusters with InfiniBand and NVLink interconnects for large-scale AI training and LLM fine-tuning. On-demand and reserved H100 instances are available across US East, US West, and EU regions, with some of the highest GPU density and lowest latency networking of any specialist cloud. A top choice for AI labs and enterprises running multi-node distributed training at scale.
Billing model comparison
AWS uses a On-demand, Reserved (1yr/3yr), Spot billing model with a minimum commitment of None (on-demand). CoreWeave uses On-demand, Reserved billing with a None minimum. AWS's no-commitment on-demand model is more flexible for short-term or experimental workloads, while CoreWeave'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. CoreWeave is best suited for: Large-scale AI training, LLM fine-tuning, High-throughput inference. Its key strengths are highest gpu density, infiniband networking, kubernetes-native. As a hyperscaler, AWS offers broader ecosystem integration and compliance certifications at a premium price. CoreWeave 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). CoreWeave offers Standard → Enterprise support across 3 regions (US-East, US-West, EU-West). 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 CoreWeave
AWS was founded in 2006 and is headquartered in Seattle, WA. CoreWeave was founded in 2017 and is headquartered in Roseland, NJ. AWS has 11 years more operational history than CoreWeave, 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.