Google Cloud vs DataVolt: GPU Compute Price Comparison
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Google Cloud and DataVolt. 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
DataVolt is a European GPU cloud offering H100, H200, A100, and L40S instances with competitive on-demand and spot GPU rental pricing, full EU data residency, and straightforward access for AI and ML workloads. Spot availability makes it a cost-effective option for interruptible LLM training and fine-tuning jobs, while on-demand instances suit production inference. A practical European GPU cloud for teams that need GDPR-compliant infrastructure with flexible billing.
- Competitive H100/H200 pricing
- Spot availability
- EU data residency
- Simple pricing
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.
DataVolt — specialist provider
DataVolt is a European GPU cloud offering H100, H200, A100, and L40S instances with competitive on-demand and spot GPU rental pricing, full EU data residency, and straightforward access for AI and ML workloads. Spot availability makes it a cost-effective option for interruptible LLM training and fine-tuning jobs, while on-demand instances suit production inference. A practical European GPU cloud for teams that need GDPR-compliant infrastructure with flexible billing.
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). DataVolt uses On-demand, Spot billing with a None minimum. Google Cloud's no-commitment on-demand model is more flexible for short-term or experimental workloads, while DataVolt'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. DataVolt is best suited for: EU AI teams, Cost-sensitive H100 workloads, Spot-tolerant training. Its key strengths are competitive h100/h200 pricing, spot availability, eu data residency. As a hyperscaler, Google Cloud offers broader ecosystem integration and compliance certifications at a premium price. DataVolt 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). DataVolt offers Community → Standard support across 1 region (EU). 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 DataVolt
Google Cloud was founded in 2008 and is headquartered in Sunnyvale, CA. DataVolt was founded in 2023 and is headquartered in Europe. Google Cloud has 15 years more operational history than DataVolt, 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.