Beam vs SF Compute: Pricing, Specs, Strengths & Weaknesses
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Beam and SF Compute. Rates sourced from public APIs — verify current availability and billing terms before purchasing.
Overview
Strengths & Best For
Beam is a serverless GPU platform that lets developers deploy AI models and run H100, A100, and T4 compute jobs with automatic scaling and per-second pay-per-use billing — no infrastructure management required. A Python-native SDK (`beam deploy`) and fast cold starts make it easy to build and ship LLM inference APIs, batch ML pipelines, and AI model serving endpoints quickly. Beam handles container builds, GPU provisioning, and scaling automatically. Free tier available. A strong choice for Python-first teams that want serverless GPU infrastructure with predictable, usage-based pricing.
- Python-native SDK — `beam deploy` ships a GPU endpoint in minutes
- Auto-scaling with fast cold starts — no idle cost
- Per-second billing — pay only for actual compute time
- Free tier available for prototyping
- Handles container builds and GPU provisioning automatically
- Less control over underlying infrastructure vs raw VMs
- Smaller GPU catalog than dedicated cluster providers
- Not suitable for multi-node distributed training
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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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Region Coverage
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Key differences
Beam
Serverless GPU infrastructure — Python-native, auto-scaling, per-second billing
SF Compute
H100 cluster compute — contract and on-demand GPU access from $5.04/hr