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
GPUhub is a Southeast Asian GPU cloud based in Vietnam, offering H100, A100, and RTX instances at affordable APAC pricing for AI and ML workloads across the region. On-demand billing and local support make it one of the most accessible GPU cloud options for Vietnamese and Southeast Asian teams running LLM training, fine-tuning, and inference workloads. A strong regional option for APAC-based developers who want low-latency GPU access at competitive local prices.
- APAC presence
- Affordable pricing
- Local support
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.
GPUhub — specialist provider
GPUhub is a Southeast Asian GPU cloud based in Vietnam, offering H100, A100, and RTX instances at affordable APAC pricing for AI and ML workloads across the region. On-demand billing and local support make it one of the most accessible GPU cloud options for Vietnamese and Southeast Asian teams running LLM training, fine-tuning, and inference workloads. A strong regional option for APAC-based developers who want low-latency GPU access at competitive local prices.
Billing model comparison
AWS uses a On-demand, Reserved (1yr/3yr), Spot billing model with a minimum commitment of None (on-demand). GPUhub 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 GPUhub'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. GPUhub is best suited for: Southeast Asian teams, Cost-sensitive AI workloads, APAC inference. Its key strengths are apac presence, affordable pricing, local support. As a hyperscaler, AWS offers broader ecosystem integration and compliance certifications at a premium price. GPUhub 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). GPUhub offers Standard support across 2 regions (VN, APAC). 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 GPUhub
AWS was founded in 2006 and is headquartered in Seattle, WA. GPUhub was founded in 2021 and is headquartered in Ho Chi Minh City, Vietnam. AWS has 15 years more operational history than GPUhub, 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.