Hyperstack vs GPUhub: GPU Compute Price Comparison
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Hyperstack and GPUhub. Updated July 2026.
Provider Overview
Strengths & Best For
Hyperstack provides NVIDIA-certified H100, A100, and RTX 4090 GPU instances with enterprise-grade support and high availability across US and EU regions. On-demand and reserved billing options are available, making it a reliable on-demand GPU cloud for enterprise AI teams that need certified hardware configurations and responsive support. A strong alternative to hyperscalers for production LLM inference and AI training workloads.
- NVIDIA-certified
- High availability
- EU/US coverage
- Strong support
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
Hyperstack — specialist provider
Hyperstack provides NVIDIA-certified H100, A100, and RTX 4090 GPU instances with enterprise-grade support and high availability across US and EU regions. On-demand and reserved billing options are available, making it a reliable on-demand GPU cloud for enterprise AI teams that need certified hardware configurations and responsive support. A strong alternative to hyperscalers for production LLM inference and AI training workloads.
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
Hyperstack uses a On-demand, Reserved billing model with a minimum commitment of None. GPUhub uses On-demand billing with a None 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
Hyperstack is best suited for: Enterprise AI, NVIDIA ecosystem users, Production inference. Its key strengths are nvidia-certified, high availability, eu/us coverage. GPUhub is best suited for: Southeast Asian teams, Cost-sensitive AI workloads, APAC inference. Its key strengths are apac presence, affordable pricing, local support. 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
Hyperstack offers Standard → Enterprise support across 2 regions (US-East, EU-West). GPUhub offers Standard support across 2 regions (VN, APAC). Both providers have comparable region coverage — choose based on which specific regions overlap with your user base or data residency requirements.
Provider background: Hyperstack vs GPUhub
Hyperstack was founded in 2022 and is headquartered in London, UK. GPUhub was founded in 2021 and is headquartered in Ho Chi Minh City, Vietnam. GPUhub has 1 years more operational history than Hyperstack, 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.