Introduction
AI model training and inference at scale can be expensive. Enterprise buyers often evaluate cloud GPU providers on a cost-per-hour basis, zeroing in on the most visible line item: the GPUs.
However, price per GPU-hour alone can be misleading. In practice, two cloud offerings with identical rates can produce materially different total cost of ownership once the full set of cost drivers behind training and inference is considered.
Downtime, setup and debugging overhead, and required performance tuning of networking and storage can materially affect how much value is extracted from every dollar spent.
Additional non-GPU costs, including storage, CPU compute, networking, and support, are often overlooked. A lower GPU-hour price on paper can ultimately result in a higher overall cost.