Enterprise AI compute gap: buying faster than they can measure costs
83% of enterprises run GPUs at ≤50% utilization; 64% plan to switch providers within 12 months.
A new VentureBeat Pulse Research survey of 107 enterprises (100+ employees) exposes a critical compute gap: AI infrastructure spending is racing ahead of the ability to see or steer its economics. Only 21% have AI in production at scale, yet 45% plan to evaluate AI-specialized clouds—a layer almost none currently use. Meanwhile, 83% report GPU utilization at 50% or less, and fewer than half (44%) rigorously track what their compute actually costs. The result: enterprises are buying more infrastructure faster than they can account for what they already own, risking significant waste.
Provider loyalty is also fragile: 64% plan to switch or add an infrastructure provider within 12 months (38% within a quarter). Decision-making favors integration with existing stacks (41%) and total cost of ownership (35%) over headline token price (8%). A looming frontier—the shift from GPU compute to memory bandwidth as inference scales—remains underrecognized, with roughly one in five enterprises either unaware or unaddressed. This churn and lack of visibility signal an urgent need for better cost measurement and strategic infrastructure planning.
- 83% of enterprises report GPU utilization at 50% or less; only 44% rigorously track compute costs.
- 64% plan to switch or add an infrastructure provider within 12 months, with 38% doing so within a quarter.
- Top buying factors: integration with existing stack (41%) and TCO (35%), while token price matters for only 8%.
Why It Matters
Enterprises risk overspending on AI infrastructure without cost visibility—low GPU utilization and high provider churn demand better economic controls.