sfcompute vs Vercel
Sandbox Benchmark Comparison · Updated October 30, 2026
sfcompute was faster in both shared sandbox benchmarks.
Head to head
Burst TTI
Median time to interactive over time
Median total workload time over time
Cost per DAX run
sfcompute
Per GiB-hour (parkable)Rollout sandboxes at $0.04572/GiB-hr while active (16 GiB); parked/idle drops to ~$0.000139/GiB-hr. Interactive cpu-8 node is $0.4824/hr with a 15-min minimum.
Source pricing pageVercel
Active CPU + provisioned memory$0.128/active-CPU-hr per vCPU + $0.0212/GB-hr provisioned memory (wall clock). CPU upper bound at 100% utilization; real bills for I/O-bound runs are lower.
Source pricing pageMethodology
- One workload, every provider. The same script, payload, and model hit every provider, addressed the way its own API expects. Nobody gets a tuned or special path.
- Repeated on a schedule. Every suite runs many iterations per provider, automated on a recurring schedule, so results reflect steady state rather than one lucky request.
- Raw results published. Every run is committed as JSON to the public benchmarks repo, next to the code that produced it. Nothing is cherry-picked or held back.
- How this comparison is built. Each benchmark compares the two providers' medians; results within 5% count as roughly even. A comparison is published only when both providers completed at least 50% of runs in 2 or more shared benchmarks run in the last 30 days. If a provider’s latest run of a benchmark failed, its most recent successful run from that window is used instead.
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sfcompute was faster in both shared sandbox benchmarks. In Burst TTI (October 30, 2026), sfcompute had a median time to interactive of 322ms versus 423ms for Vercel, 1.31x faster. In DAX (October 9, 2026), sfcompute had a median total workload time of 41.72s versus 83.37s for Vercel, 2.00x faster.