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AI is now as much a utility as any other ongoing business cost, and IT leaders setting out their AI budgets for 2026 need to consider the costs of the underlying resources — the GPUs in modern data centers that are unlocking AI’s potential.
In the three years since ChatGPT arrived, the push for ever more — and better — generative AI tools has continued at a rapid clip. That growth has come at a cost, however: spiraling AI budgets amid low GPU availability and limited energy capacity to run those data centers.
Efforts are now underway to reduce the cost of using GPUs, and the attendant cost of using genAI tools, with smaller data centers, billing tools, software tools, and alternative hardware leading the charge.
Traditional AI budgeting is heavily reliant on GPU pricing, hours, and instance rates. GPU instances are “eye-wateringly expensive” at $30+ per hour for high-end configurations on-demand, said Corey Quinn, chief cloud economist at Duckbill, which provides cost analysis tools for cloud providers.
“For serious AI workloads, GPU costs often become the dominant line item, which is why you’re seeing companies scramble for reserved capacity and spot instances,” he said, adding that AI billing through cloud...