Owning vs renting vs API for inference comes down to GPU utilization
Milan_Slov26 · reddit · 2026-10-19
A breakdown of the economics of the three ways to get inference compute, with the core takeaway that utilization decides everything:
- API (OpenAI/Anthropic/Google): cheapest to start, pay per token — best for low or spiky traffic, but bills scale forever and you don't control the serving stack or data location
- Renting GPUs: H200 runs $4-5/hr on demand; compare reserved rates if always-on. You get your own stack (vLLM/TGI, batching, quantization) without capital outlay, but the meter never stops
- Owning hardware: upfront spend plus hosting; effective per-hour cost keeps dropping the more you run it — wins when pinned all day
Work out your real utilization first, nights and weekends included. Get it right and the box pays for itself; get it wrong and you bought the smartest space heater money can buy.
Related event: Buy, Rent, or API? GPU Utilization Decides Your Inference Economics(2 posts)→
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