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The AI compute gap: Enterprises are buying infrastructure faster than they can measure what it costs

A VentureBeat Pulse Research report reveals a significant gap between enterprise AI infrastructure investment and cost visibility. Across 107 enterprises, spending on AI compute is accelerating, but most organizations cannot track unit economics, with GPU utilization at 50% or less and fewer than half rigorously measuring costs. The report highlights that 64% of enterprises plan to switch or add providers within the year, driven by integration and total cost of ownership rather than token price.

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The AI Compute Gap: Enterprise Procurement Outpaces Cost Visibility

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Recent surveys show rapid enterprise AI infrastructure investment, yet most enterprises report GPU utilization below 50%, and only 44% strictly track compute costs. Investment intentions are flowing toward specialized clouds not yet in use, while vendor selection prioritizes integration and total cost of ownership over per-token price.

  • Only 21% of enterprises run AI in production at scale; 38% remain in experimentation.
  • 83% of enterprises report GPU utilization at or below 50%, indicating significant idle compute resources.
  • Only 44% of enterprises strictly track AI compute costs, revealing insufficient cost visibility.
  • 64% of enterprises plan to switch or add infrastructure vendors within 12 months, with 38% planning action within the next quarter.
  • 45% of enterprises plan to evaluate AI-specialized clouds, yet current usage is nearly zero, highlighting a disconnect between investment and current state.
  • Top vendor selection factors are integration with existing stack (41%) and total cost of ownership (35%); per-million-token price influences only 8% of decisions.
Open section navigationDeployment Maturity: Ambition Outpaces Production Scale

Deployment Maturity: Ambition Outpaces Production Scale

The survey shows that only 21% of enterprises run AI in production at scale, while 38% are still in experimentation, 37% have partial production workloads, and 4% run no AI. This means three-quarters of enterprises (76%) have not yet achieved full production deployment, and their infrastructure decisions are largely made by early adopters, with future compute demand and costs set to grow rapidly.

Current Tech Stack: Hyperscale Clouds and Model APIs Dominate

The most commonly used AI platforms today are Google Cloud (48%), followed by Azure (29%), AWS (22%), and Oracle (22%). In terms of model APIs, Google Gemini (41%) and OpenAI (40%) lead, with Anthropic at 12%. Only 6% of enterprises run their own on-premises GPU clusters, and 4% use open-source self-managed stacks, while usage of AI-specialized clouds like CoreWeave and Lambda is nearly zero.

Investment Intentions: Flowing Toward Unused Specialized Clouds

Over the next 12 months, 45% of enterprises plan to evaluate AI-specialized clouds, 32% plan to evaluate non-NVIDIA accelerators, and 28% plan to evaluate NVIDIA Blackwell. This contrasts sharply with current near-zero usage, highlighting a disconnect between investment and current state.

Vendor Switching: High Churn Intentions and Decision Criteria

64% of enterprises plan to switch or add vendors within 12 months, with 38% planning action within the next quarter, indicating very high switching intentions. When deciding, 41% value integration with existing stack, 35% value total cost of ownership, while per-million-token price influences only 8% of decisions.

Cost Visibility: Insufficient Utilization and Tracking

83% of enterprises report GPU utilization at or below 50%, and only 44% strictly track compute costs. This indicates that while enterprises invest rapidly, they lack control over the economics of existing resources, creating a 'compute gap.'

Credibility boundary

This report is based on a survey of 107 enterprises conducted by VentureBeat Pulse Research. The sample is non-probabilistic and skewed toward mid-market and early adopters; results should be viewed as directional signals rather than precise measurements. Data such as GPU utilization are self-reported and may be subject to bias.

Insight takeaway

Enterprise AI infrastructure investment is outpacing their ability to measure and manage costs, leading to low compute utilization, poor cost visibility, and a significant disconnect between investment direction and current actual usage.

Sources for this version

  1. The AI compute gap: Enterprises are buying infrastructure faster than they can measure what it costsVentureBeat AI

Primary report

VentureBeat AI

Primary source