Omega Gradient vs SF Compute: Pricing, Specs, Strengths & Weaknesses
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Omega Gradient and SF Compute. Rates sourced from public APIs — verify current availability and billing terms before purchasing.
Overview
Strengths & Best For
Omega Gradient is a GPU cloud provider specialising in high-performance H100 SXM and A100 clusters optimised for large-scale AI training and fine-tuning workloads. On-demand and reserved instances are available with competitive per-GPU pricing and low-latency NVLink interconnects for multi-GPU jobs. A strong option for AI teams that need dedicated cluster access for distributed training without the overhead of hyperscaler pricing or complex procurement.
- Competitive H100 SXM pricing
- NVLink cluster interconnects
- Focused on training workloads
- Simple on-demand access
- Newer provider — smaller track record
- Limited GPU SKU variety
- Fewer regions than established clouds
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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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Key differences
Omega Gradient
High-performance H100 and A100 clusters for AI training
SF Compute
H100 cluster compute — contract and on-demand GPU access from $5.04/hr