Google Cloud vs Verda: GPU Compute Price Comparison
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Google Cloud and Verda. 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
Verda (formerly DataCrunch) is a Finnish GPU cloud offering 13 GPU types including H100, A100, RTX A6000, and V100 instances with competitive on-demand pricing and enterprise-grade infrastructure for AI and ML workloads. On-demand and reserved billing options are available from Finnish data centers, providing EU data residency for Nordic and European teams with GDPR requirements. A reliable European GPU cloud for cost-sensitive AI training and inference with broad hardware variety.
- Finnish infrastructure
- Competitive V100/A100 pricing
- 13 GPU types
- Enterprise-grade
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.
Verda — specialist provider
Verda (formerly DataCrunch) is a Finnish GPU cloud offering 13 GPU types including H100, A100, RTX A6000, and V100 instances with competitive on-demand pricing and enterprise-grade infrastructure for AI and ML workloads. On-demand and reserved billing options are available from Finnish data centers, providing EU data residency for Nordic and European teams with GDPR requirements. A reliable European GPU cloud for cost-sensitive AI training and inference with broad hardware variety.
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). Verda uses On-demand, Reserved billing with a None minimum. Google Cloud's no-commitment on-demand model is more flexible for short-term or experimental workloads, while Verda'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. Verda is best suited for: EU AI teams, Cost-sensitive training, Nordic data residency. Its key strengths are finnish infrastructure, competitive v100/a100 pricing, 13 gpu types. As a hyperscaler, Google Cloud offers broader ecosystem integration and compliance certifications at a premium price. Verda 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). Verda offers Standard → Enterprise support across 2 regions (EU-North, EU-West). 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 Verda
Google Cloud was founded in 2008 and is headquartered in Sunnyvale, CA. Verda was founded in 2020 and is headquartered in Helsinki, Finland. Google Cloud has 12 years more operational history than Verda, 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.