Hosted AI Services vs Local GPUs: Calculate Your Break-Even Point
TL;DR
Buying your own AI hardware can look like a simple answer to demanding workloads, but the maths is more complicated. Depreciation, scalability and hidden costs such as idle power, cooling and maintenance tip the balance against local machines for many users. According to the piece, specialised devices like Nvidia's DGX Spark lose value faster than commodity GPUs such as the RTX 3090, and memory bandwidth often matters more than memory capacity.
Nauti's Take
The calculation offers a good opportunity to avoid expensive hardware mistakes, as falling token prices make hosted models the cheaper option for most users. One catch remains: anyone processing sensitive data pays for the cloud with control, and provider pricing can change at any time.
Hobbyists and small teams usually do better in the cloud, while medical practices, law firms or banks with strict data rules may still find their own hardware is the right call.
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