Buy vs rent vs API for inference: it all hinges on your sustained GPU utilization
Dizzy_Lingonberry947 · reddit · 2026-10-17
A cost breakdown of the three ways to get inference compute, judged by effective $/GPU-hour at your real utilization:
- Model APIs (OpenAI, Anthropic, Google): cheap at low or spiky volume, but you pay their margin forever at scale and lose control of the serving stack.
- Renting GPUs (AWS/Azure/GCP, CoreWeave, Lambda; RunPod/Vast for spot): H200 on-demand runs $4.59/GPU-hr, reserved lower; you control batching and quantization with vLLM/TGI/SGLang, but hourly costs peak exactly when you're busiest.
- Owning GPUs (Dell, NVIDIA DGX, Supermicro, or B3IQ's model where you keep root access while they handle physical infrastructure and help rent out idle capacity): one upfront spend plus hosting, so effective per-hour cost keeps falling with use.
Verdict: idle or spiky workloads stay on APIs/rentals; busy around-the-clock workloads favor ownership. The author asks owned-H200 operators what sustained utilization they actually hold after nights and weekends.
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