Provider Overview
Strengths & Best For
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
Zettabyte provides scalable H100, H200, and A100 GPU infrastructure for large-scale AI training and inference, with enterprise reliability and on-demand and reserved billing options for organizations that need consistent GPU cluster access. High-capacity configurations and a focus on enterprise-grade uptime make it a strong choice for AI labs and enterprises running production LLM workloads at scale. A reliable on-demand GPU cloud for teams that need scalable infrastructure with enterprise-level reliability.
- Scalable infrastructure
- Enterprise reliability
- Large-scale training
Live GPU Pricing
Region Coverage
Popular Comparisons
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.
Zettabyte — specialist provider
Zettabyte provides scalable H100, H200, and A100 GPU infrastructure for large-scale AI training and inference, with enterprise reliability and on-demand and reserved billing options for organizations that need consistent GPU cluster access. High-capacity configurations and a focus on enterprise-grade uptime make it a strong choice for AI labs and enterprises running production LLM workloads at scale. A reliable on-demand GPU cloud for teams that need scalable infrastructure with enterprise-level reliability.
Billing model comparison
Wafer uses a On-demand billing model with a minimum commitment of None. Zettabyte uses On-demand / Reserved 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
Wafer is best suited for: AI startups, Short training runs, Inference. Its key strengths are simple pricing, flexible instances, fast setup. Zettabyte is best suited for: Enterprise AI, Large training runs, Production inference. Its key strengths are scalable infrastructure, enterprise reliability, large-scale training. 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
Wafer offers Standard support across 1 region (US). Zettabyte offers Standard support across 1 region (US). Both providers have comparable region coverage — choose based on which specific regions overlap with your user base or data residency requirements.
Provider background: Wafer vs Zettabyte
Wafer was founded in 2023 and is headquartered in United States. Zettabyte was founded in 2022 and is headquartered in United States. Zettabyte has 1 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.