
Vercel
AvailableBest for Frontend engineering teams building with Next.js or React that require instant global serverless scaling without DevOps overhead.
Locations: Global
Compare 2 cloud providers offering Serverless CPU (varies VRAM). Find real-time pricing, availability, and get matched with verified providers instantly.
Serverless CPU is a high-performance GPU available from multiple cloud providers. It offers strong capabilities for a wide range of AI and HPC workloads.
The spot market for Serverless CPU cloud compute varies widely by provider. On-demand pricing typically ranges from $1.50–$5/hr per GPU for single-instance access. For larger multi-GPU clusters (8x, 16x, or 64x GPU nodes), enterprise pricing with SLAs is negotiated directly with providers. Reserved capacity offers 30–60% discounts vs. on-demand pricing.
When evaluating providers for Serverless CPU GPU cloud, consider:

Best for Frontend engineering teams building with Next.js or React that require instant global serverless scaling without DevOps overhead.
Locations: Global

Best for Web developers and agencies building composable, Jamstack websites requiring automated builds and integrated serverless backend APIs.
Locations: Global
Serverless CPU is commonly used for: AI training, inference, and high-performance computing. Its varies of VRAM makes it suitable for running large models that don't fit in smaller GPU memory.
Serverless CPU cloud pricing varies by provider and region, but typically ranges from $1.50/hr to $8/hr for single-GPU instances. Multi-GPU cluster pricing scales proportionally. Use the filters above to compare current market rates.
ComputeStacker currently lists 2 providers offering Serverless CPU GPU cloud access. These include a mix of hyperscalers, specialist AI cloud providers, and bare-metal GPU hosting services.
Yes — most providers on ComputeStacker offer on-demand hourly pricing for Serverless CPU instances. Reserved and spot pricing options are also available from many providers, offering discounts of 30–70% for committed usage.