Google Cloud vs GPUhub: GPU Compute Price Comparison
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Google Cloud and GPUhub. Updated July 2026.
Provider Overview
Strengths & Best For
Google Cloud provides A100 and H100 GPU instances via Compute Engine and Vertex AI, with sustained use discounts and committed use contracts that can significantly cut hourly GPU rental costs. TPU v4 and v5 accelerators are also available for TensorFlow and JAX workloads, giving teams a unique alternative to NVIDIA hardware. Spanning 30+ regions, it is the top choice for ML pipelines deeply integrated with the TensorFlow and Google ecosystem.
- Sustained use discounts
- Vertex AI integration
- TPU availability
- Strong networking
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
Google Cloud — hyperscaler provider
Google Cloud provides A100 and H100 GPU instances via Compute Engine and Vertex AI, with sustained use discounts and committed use contracts that can significantly cut hourly GPU rental costs. TPU v4 and v5 accelerators are also available for TensorFlow and JAX workloads, giving teams a unique alternative to NVIDIA hardware. Spanning 30+ regions, it is the top choice for ML pipelines deeply integrated with the TensorFlow and Google ecosystem.
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
Google Cloud uses a On-demand, Committed Use (1yr/3yr), Spot/Preemptible billing model with a minimum commitment of None (on-demand). GPUhub uses On-demand billing with a None minimum. Google Cloud'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
Google Cloud is best suited for: ML training pipelines, TensorFlow workloads, Teams using GCP services. Its key strengths are sustained use discounts, vertex ai integration, tpu availability. 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, Google Cloud 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
Google Cloud offers Basic → Premium support across 5 regions (us-central1, us-east4, europe-west4 and 2 more). GPUhub offers Standard support across 2 regions (VN, APAC). Google Cloud's broader region footprint gives it an advantage for latency-sensitive workloads or teams with data residency requirements in specific geographies.
Provider background: Google Cloud vs GPUhub
Google Cloud was founded in 2008 and is headquartered in Sunnyvale, CA. GPUhub was founded in 2021 and is headquartered in Ho Chi Minh City, Vietnam. Google Cloud has 13 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.