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
Gcore is a global GPU cloud and CDN provider offering H100, A100, L40S, and L4 instances across 40+ points of presence worldwide, with ultra-low latency networking and built-in DDoS protection for edge AI inference workloads. On-demand and reserved billing options are available, making it a versatile GPU cloud for teams that need both compute and network performance at a global scale. A top choice for latency-sensitive AI inference applications that need to serve users across multiple continents.
- 40+ global PoPs
- Ultra-low latency
- DDoS protection
- Edge AI inference
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.
Gcore — specialist provider
Gcore is a global GPU cloud and CDN provider offering H100, A100, L40S, and L4 instances across 40+ points of presence worldwide, with ultra-low latency networking and built-in DDoS protection for edge AI inference workloads. On-demand and reserved billing options are available, making it a versatile GPU cloud for teams that need both compute and network performance at a global scale. A top choice for latency-sensitive AI inference applications that need to serve users across multiple continents.
Billing model comparison
AWS uses a On-demand, Reserved (1yr/3yr), Spot billing model with a minimum commitment of None (on-demand). Gcore 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 Gcore'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. Gcore is best suited for: Global inference deployment, Latency-sensitive AI apps, Teams needing edge compute. Its key strengths are 40+ global pops, ultra-low latency, ddos protection. As a hyperscaler, AWS offers broader ecosystem integration and compliance certifications at a premium price. Gcore 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). Gcore offers Standard → Enterprise support across 5 regions (EU, US, APAC 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 Gcore
AWS was founded in 2006 and is headquartered in Seattle, WA. Gcore was founded in 2014 and is headquartered in Luxembourg. AWS has 8 years more operational history than Gcore, 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.