Crusoe Cloud vs Wafer: GPU Compute Price Comparison
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Crusoe Cloud and Wafer. Updated July 2026.
Provider Overview
Strengths & Best For
Crusoe Cloud powers H100 SXM5 and B200 GPU instances using stranded and renewable energy sources, making it one of the few carbon-negative on-demand GPU cloud providers in the US. Competitive pricing on H100 clusters for AI training and LLM fine-tuning, with a mission to reduce the carbon footprint of large-scale compute. An ideal choice for ESG-conscious enterprises and AI labs that want high-performance hardware without the environmental cost.
- Clean energy compute
- Competitive H100 pricing
- B200 availability
- Carbon-negative mission
Wafer offers H100 and A100 GPU cloud compute for AI and ML teams with straightforward on-demand pricing and flexible instance options that make it easy to scale training and inference workloads without complex billing structures. Simple setup and transparent pricing lower the barrier to entry for startups and small teams exploring GPU compute for LLM fine-tuning and model deployment. A no-frills on-demand GPU cloud for AI teams that want clear pricing and flexible instance configurations.
- Simple pricing
- Flexible instances
- Fast setup
Live GPU Pricing
Region Coverage
Popular Comparisons
Crusoe Cloud — specialist provider
Crusoe Cloud powers H100 SXM5 and B200 GPU instances using stranded and renewable energy sources, making it one of the few carbon-negative on-demand GPU cloud providers in the US. Competitive pricing on H100 clusters for AI training and LLM fine-tuning, with a mission to reduce the carbon footprint of large-scale compute. An ideal choice for ESG-conscious enterprises and AI labs that want high-performance hardware without the environmental cost.
Wafer — specialist provider
Wafer offers H100 and A100 GPU cloud compute for AI and ML teams with straightforward on-demand pricing and flexible instance options that make it easy to scale training and inference workloads without complex billing structures. Simple setup and transparent pricing lower the barrier to entry for startups and small teams exploring GPU compute for LLM fine-tuning and model deployment. A no-frills on-demand GPU cloud for AI teams that want clear pricing and flexible instance configurations.
Billing model comparison
Crusoe Cloud uses a On-demand, Reserved billing model with a minimum commitment of None. Wafer uses On-demand billing with a None minimum. Both providers offer flexible billing options — compare the live pricing table above to find the best rate for your specific GPU model and workload duration.
Which workloads each provider suits best
Crusoe Cloud is best suited for: Sustainability-focused teams, Large-scale AI training, ESG-conscious enterprises. Its key strengths are clean energy compute, competitive h100 pricing, b200 availability. Wafer is best suited for: AI startups, Short training runs, Inference. Its key strengths are simple pricing, flexible instances, fast setup. Both providers target similar workload profiles — the live pricing table above is the most reliable way to determine which offers better value for your specific GPU model and region requirements.
Support tiers and region coverage
Crusoe Cloud offers Standard → Enterprise support across 2 regions (US-West, US-Central). Wafer offers Standard support across 1 region (US). Crusoe Cloud's broader region footprint gives it an advantage for latency-sensitive workloads or teams with data residency requirements in specific geographies.
Provider background: Crusoe Cloud vs Wafer
Crusoe Cloud was founded in 2018 and is headquartered in San Francisco, CA. Wafer was founded in 2023 and is headquartered in United States. Crusoe Cloud has 5 years more operational history than Wafer, 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.