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DataVolt vs Google Cloud: Pricing, Specs, Strengths & Weaknesses

Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for DataVolt and Google Cloud. Rates sourced from public APIs — verify current availability and billing terms before purchasing.

Overview

Provider type
Specialist
Hyperscaler
Founded
2023
2008
Headquarters
Europe
Sunnyvale, CA
Billing model
On-demand, Spot
On-demand, Committed Use (1yr/3yr), Spot/Preemptible, Flex-start
Min commitment
None
None (on-demand)
Support tier
Community → Standard
Basic → Premium
Regions
1 regions
5 regions

Strengths & Best For

DataVolt

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.

Strengths
  • Competitive H100/H200 pricing
  • Spot availability
  • EU data residency
  • Simple pricing
Weaknesses
  • Smaller provider — limited scale vs hyperscalers
  • Fewer regions than major cloud providers
  • Less mature ecosystem and fewer integrations
Best For
EU AI teamsCost-sensitive H100 workloadsSpot-tolerant training
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Google Cloud

Google Cloud's 2026 GPU lineup spans five accelerator-optimized machine series: A4X (NVIDIA GB200 Grace Blackwell Superchips, 720 GB HBM3e per 4-GPU node), A4 (NVIDIA B200, 1,440 GB HBM3e per 8-GPU node, 3,600 Gbps networking), A3 Ultra (NVIDIA H200, 1,128 GB HBM3e), A3 Mega/High/Edge (NVIDIA H100), and G4 (NVIDIA RTX PRO 6000 Blackwell, 96 GB GDDR7, GPU P2P communication). A2 series (A100 40/80 GB) and G2 (L4) remain available. AI Hypercomputer provides GKE and Slurm-integrated dense clusters. TPU v5 and v5e accelerators are available for TensorFlow/JAX workloads. Vertex AI provides managed ML pipelines. 30+ regions with sustained use and committed use discounts.

Strengths
  • A4X GB200 and A4 B200 — latest Blackwell hardware in cloud
  • G4 RTX PRO 6000 with GPU P2P communication for inference
  • AI Hypercomputer: GKE + Slurm-integrated dense clusters
  • TPU v5/v5e for TensorFlow and JAX workloads
  • Sustained use discounts and committed use contracts
  • Vertex AI managed ML pipelines
Weaknesses
  • A4X and A4 require capacity reservation — not self-serve on-demand
  • Premium pricing vs specialist GPU clouds
  • Spot (preemptible) instances can be interrupted with little notice
Best For
Frontier model training on Blackwell hardwareTensorFlow and JAX workloads on TPUsTeams using GCP services (BigQuery, Vertex AI, GKE)Inference on RTX PRO 6000 with GPU P2P
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Live GPU Pricing

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Region Coverage

Google Cloud — 5 regions
us-central1us-east4europe-west4asia-east1asia-northeast1

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Key differences

DataVolt

European GPU cloud with competitive H100 pricing

Google Cloud

A4X (GB200), A4 (B200), A3 Ultra (H200), G4 (RTX PRO 6000) — plus TPU v5 and AI Hypercomputer

AttributeDataVoltGoogle Cloud
Provider typespecialisthyperscaler
Billing modelOn-demand, SpotOn-demand, Committed Use (1yr/3yr), Spot/Preemptible, Flex-start
Min commitmentNoneNone (on-demand)
Regions1 (EU)5 (us-central1, us-east4, europe-west4, +2)
Support tierCommunity → StandardBasic → Premium
Best forEU AI teams, Cost-sensitive H100 workloadsFrontier model training on Blackwell hardware, TensorFlow and JAX workloads on TPUs
Founded / HQ2023 · Europe2008 · Sunnyvale, CA