Interactive AI training costing tool

In a recent report How AI training choices affect infrastructure costs, Uptime Intelligence investigated how the economics of AI model training vary depending on the training approach adopted.

The report shows how AI model training economics vary sharply between complete model training, fine-tuning and parameter-efficient fine-tuning (PEFT) because each consumes a different share of infrastructure capacity. A large foundation model may require substantial time and capacity, while fine-tuning can be completed on modest systems in hours or days. But hardware price alone does not determine cost. Utilization plays a major role: expensive infrastructure that remains idle between training workloads drives up the effective cost of each training run, whereas heavily used systems spread capital cost across many workloads.