GPUaaS vs SF Compute: Pricing, Specs, Strengths & Weaknesses
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for GPUaaS and SF Compute. Rates sourced from public APIs — verify current availability and billing terms before purchasing.
Overview
Strengths & Best For
GPUaaS delivers H100 and A100 GPU compute as a fully managed service, enabling European AI teams to access high-performance GPU infrastructure without any infrastructure overhead or operational complexity. On-demand billing and a managed service model make it easy to scale AI training and inference workloads without dedicated DevOps resources. A strong choice for European AI teams that want managed GPU-as-a-service with EU data residency and minimal operational burden.
- Managed service
- Simple onboarding
- European presence
- Small provider — limited GPU catalog
- Fewer regions than larger providers
- Less mature ecosystem vs CoreWeave
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SF Compute offers H100 SXM5 80GB GPU clusters with both short-term on-demand and longer-term contract pricing. The platform targets AI training and research teams that need reliable H100 access at competitive rates. October 2026 on-demand rate: $5.04/GPU-hr. SF Compute provides VMs, bare metal, and managed Slurm cluster options, making it flexible for different workflow types. A practical choice for teams that want H100 compute without the overhead of hyperscaler pricing.
- H100 SXM5 80GB at competitive on-demand rates
- Bare metal and managed Slurm options
- Short and long-term contract flexibility
- US West region — low latency for West Coast teams
- Limited GPU catalog — primarily H100
- Smaller provider — less geographic diversity
- Less mature ecosystem vs established GPU clouds
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Live GPU Pricing
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Key differences
GPUaaS
GPU as a Service
SF Compute
H100 cluster compute — contract and on-demand GPU access from $5.04/hr