Google Cloud vs Runcrate: GPU Compute Price Comparison
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Google Cloud and Runcrate. 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
Runcrate is a Berlin-based GPU cloud marketplace aggregating bare-metal and VM instances across 21 GPU types and 11 global regions, with some of the lowest starting prices for on-demand GPU rental available anywhere. The marketplace model gives teams access to H100, A100, and a wide range of other GPU SKUs through a single platform, with flexible spot and on-demand billing. A cost-effective option for EU-based teams needing broad GPU variety and multi-region flexibility.
- 21 GPU types
- 11 global regions
- Very competitive pricing
- Bare metal + VM options
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.
Runcrate — marketplace provider
Runcrate is a Berlin-based GPU cloud marketplace aggregating bare-metal and VM instances across 21 GPU types and 11 global regions, with some of the lowest starting prices for on-demand GPU rental available anywhere. The marketplace model gives teams access to H100, A100, and a wide range of other GPU SKUs through a single platform, with flexible spot and on-demand billing. A cost-effective option for EU-based teams needing broad GPU variety and multi-region flexibility.
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). Runcrate 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 Runcrate'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. Runcrate is best suited for: Cost-sensitive teams, Multi-region deployments, Wide GPU variety needs. Its key strengths are 21 gpu types, 11 global regions, very competitive pricing. Marketplace providers aggregate GPU supply from multiple sources, often offering the lowest spot rates but with more variable availability and less predictable performance compared to dedicated providers.
Support tiers and region coverage
Google Cloud offers Basic → Premium support across 5 regions (us-central1, us-east4, europe-west4 and 2 more). Runcrate offers Community → Standard support across 4 regions (EU-Central, US, APAC and 1 more). 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 Runcrate
Google Cloud was founded in 2008 and is headquartered in Sunnyvale, CA. Runcrate was founded in 2024 and is headquartered in Berlin, Germany. Google Cloud has 16 years more operational history than Runcrate, 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.