Why AI infrastructure costs keep surprising IT leaders
IDC projects that AI infrastructure costs at Global 1000 companies will run 30% higher than current budgets by 2027. That gap shows a mismatch between how AI workloads behave in production and how enterprise IT has historically planned for capacity.
The pattern repeats across industries. A pilot project validates an AI model on a controlled dataset, and budgets are created around those economics. When the system moves into production, the bill often outpaces what anyone originally modeled.
The natural instinct is to blame the size of the model or the cost of using tokens, but that’s not where the money goes. The cost lives in the data layer, driven by how often the system reads, how many services it touches, and how continuously those operations run.
What pilots don’t show you
A pilot runs against a narrow dataset, with a handful of concurrent users, on a request-response cadence familiar to...
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