Across 107 enterprises, AI infrastructure investment is accelerating faster than their ability to accurately measure and manage its costs. Most organizations rely on familiar hyperscaler clouds and model-provider APIs for AI workloads but plan to increasingly adopt specialized AI compute resources that few currently use. Many intend to change or add providers within the year to optimize integration and total cost of ownership rather than focusing on headline pricing. Yet GPU utilization rates are low—often 50% or less—and fewer than half of enterprises rigorously track the actual costs of AI compute. This disconnect creates a “compute gap”: rapid infrastructure spending without the visibility needed for control. The report highlights a shift in infrastructure preferences, widespread intent to switch providers, and challenges in cost transparency and efficiency. Enterprises are still early in AI deployment maturity, with only 21% running AI at scale, but their spending ambitions signal a major re-platforming of AI infrastructure ahead. Notably, as inference shifts from compute power to memory bandwidth constraints, many organizations remain unprepared for this next bottleneck. While satisfaction with current infrastructure is moderate, the gap between spending and cost management raises concerns about value for money and operational efficiency as demand for AI computing power grows.
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