Infrastructure and compute: Enterprises are buying AI compute for speed while flying blind on what it costs

AI Summary

Enterprises are increasingly deploying AI infrastructure in production, prioritizing performance and GPU availability over cost. Despite widespread AI use, many organizations struggle to track compute costs and GPU utilization remains low, with next investments focused on specialized AI clouds.

Across 170 enterprises, AI infrastructure has moved decisively into production — two-thirds now run AI workloads live and three in 10 run them at scale — while the ability to account for what that infrastructure costs has not kept pace. Enterprises have quietly demoted cost in the buying decision: performance and GPU availability now outrank total cost of ownership, and reliability outranks price as the measure of success. That reordering is rational for teams under production pressure, but it lands on an uncomfortable fact — fewer than half can rigorously track what their AI compute costs, most GPUs still run at half capacity or less, and the next dollar is aimed at specialized clouds that fewer than one in twenty of them actually use. This wave of VentureBeat Pulse Research examines enterprise AI infrastructure and compute: where organizations are in their deployment journey, what they run AI on today, how they buy and measure it, where the next investment is aimed, and — most revealingly — how well they can see the economics of the compute underneath it all. This is an operational cohort. Two-thirds of enterprises (66%) have AI workloads running in production, and 29% describe AI in production at scale, with only 4% not yet running AI workloads at all. That maturity shows in the stack: the average enterprise runs three infrastructure platforms, with OpenAI (49%), Google Gemini (48%), Microsoft Azure (47%), and Google Cloud (42%) all present in roughly half of them. Asked to name one primary platform, Azure leads at 26%. The most consequential shift is in how enterprises decide. Integration with the existing cloud and data stack remains the top selection factor at 40%, but performance — latency and throughput — has climbed to second at 35%, and access to GPU availability to third at 24%, both ahead of total cost of ownership at 22%. The same ordering governs measurement: uptime and reliability is the primary success metric for 51% of enterprises and developer productivity for 39%, ahead of cost per million tokens at 31%. Enterprises under production pressure are buying and measuring for speed and availability, and have moved cost down the list. That would be unremarkable if the economics were under control, but they're not. Among the 155 enterprises that operate their own GPUs, 69% report utilization of 50% or less and only 23% clear the halfway mark; 12% do not measure utilization at all. Fewer than half (47%) rigorously track what their AI compute costs and returns, and even among enterprises running AI in production at scale that figure only reaches 56%. Value for money is the weakest of three satisfaction scores at 3.87, against 4.14 for overall satisfaction — the softness landing precisely on the dimension hardest to judge without measurement. The next round of spending points away from the current stack. AI-specialized clouds are the top planned evaluation area at 44% and carry the strongest net momentum of any infrastructure approach (+36), yet CoreWeave and Lambda each registers at 3.5% of current usage and the rest of the neocloud field sits below 3%. Non-Nvidia accelerators draw 39%. And 62% of enterprises intend to switch or add a provider within 12 months — though the consideration set is dominated by the same incumbents they already run. Methodology VentureBeat fielded this survey as part of its ongoing Pulse Research series, this one focused on enterprise AI infrastructure, compute, and inference economics. Responses are filtered to organizations with more than 100 employees (n=170; the survey’s smallest size band, 1–100 employees, is excluded), drawn from a single July 2026 wave. Because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends; all figures are drawn from the July fielding only. Several questions were multiple-select, so those shares can sum to more than 100%. By organization size this wave reaches further up-market than the mid-market skew this series usually carries: 251–1,000 employees (28%) and 1,001–5,000 (25%) lead, with 10,001+ (19%), 101–250 (15%), and 5,001–10,000 (12%) filling out the rest — meaning 57% of respondents sit above 1,000 employees. By role it spans managers (48%), individual contributors (27%), the C-suite (12%), and VPs and directors (9%); on purchasing authority it is buyer-credible, with 39% final decision-makers and another 43% recommenders or influencers for AI solutions. Technology/Software is the largest industry at 35%, followed by Manufacturing (14%), Financial Services (12%), and Healthcare/Life Sciences (9%). At 170 respondents the sample is large enough to read directionally with reasonable confidence, but it should still be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It is best read as the view from organizations actively building and operating AI infrastructure rather than from the larg