Enterprises are aggressively scaling AI infrastructure, yet a widening "compute gap" is emerging between capital expenditure and operational oversight. As organizations rush to secure hardware, they discover their ability to measure unit economics and GPU utilization can’t keep pace with investment velocity.

The Compute Gap: Rapid Investment vs. Low Visibility

A VentureBeat Pulse Research study of 107 enterprises shows a stark disconnect in the AI lifecycle. Companies pour capital into infrastructure but lack the telemetry to manage it. Spending intentions are skyrocketing, yet production maturity stays low; only 21% of surveyed firms run AI in production at scale.

The most critical symptom is inefficiency. Eighty-three percent of enterprises report GPU utilization at 50% or less. Even worse, fewer than half (44%) can rigorously track what their AI compute actually costs. Heavy, fast-moving investments therefore unfold without the visibility needed to steer long-term economics.

Shifting Vendor Dynamics and High Churn

Traditional hyperscalers and model APIs dominate the stack. Google Cloud leads with 48% usage, followed by Microsoft Azure (29%), AWS (22%) and Oracle Cloud (22%). Model consumption clusters around Gemini (41%) and OpenAI (40%). Specialized AI clouds such as CoreWeave, Lambda and Together hold under 2% of the market.

Volatility is high. Sixty-four percent of enterprises plan to switch or add an infrastructure provider within twelve months, and 38% intend to do so within the next quarter. Integration with existing stacks drives 41% of those decisions, while total cost of ownership accounts for 35%. Only 8% cite headline token prices.

The Next Frontier: Specialized Clouds and Memory Bandwidth

Enterprises moving beyond experimentation are eyeing AI-specialized clouds. Forty-five percent plan to evaluate such providers in the coming year—the single largest planned area of assessment.

A new technical constraint is emerging: memory bandwidth, not raw GPU compute, will limit inference at scale. Only about 20% of firms are aware of this bottleneck or taking steps to address it. For developers and CTOs, mastering bandwidth will separate scalable inference from runaway costs.

Key Takeaways

  • Inefficiency is the norm: 83% of enterprises run GPUs at ≤ 50% utilization; less than half can accurately track compute spend.
  • Vendor churn is high: 64% plan to change or add a provider within a year, prioritizing integration (41%) and TCO (35%).
  • Shift to specialization: 45% will evaluate niche AI clouds to close the compute gap.
  • Memory bandwidth looming: Roughly 20% of firms recognize or address this emerging bottleneck.

Article

A VentureBeat Pulse Research survey of 107 firms shows that 83 % run GPUs at half capacity or lower, while only 44 % can pin down the exact cost of each compute hour. The mismatch spurs 64 % of respondents to plan a switch or addition of an infrastructure vendor within the next year.

Why the “compute gap” matters now

The headline numbers paint a stark picture: AI spend climbs, but production maturity lags. Just 21 % of companies say they run AI at scale in production, meaning most investment sits in labs, proof-of-concepts, or idle hardware. Low GPU utilization translates directly into wasted capital—half-filled servers still draw power, need cooling, and occupy rack space. When fewer than half of organizations can track per-GPU cost, budgeting becomes guesswork, and CFOs face a black box that can explode a year’s technology budget.

How we got here

The rush began when hyperscalers rolled out AI-specific instances and model-as-a-service APIs. Google Cloud now hosts 48 % of surveyed workloads, followed by Microsoft Azure (29 %), AWS (22 %) and Oracle Cloud (22 %). Model consumption mirrors that split, with Gemini and OpenAI each capturing roughly 40 % of usage. The appeal was clear: a trusted cloud, ready-to-run GPUs, and a pay-as-you-go model promising transparent costs.

The study uncovers a hidden cost. Integration headaches dominate the decision to move: 41 % cite difficulty weaving the provider’s stack into existing pipelines, while 35 % point to total cost of ownership. Only 8 % say headline token prices push them away, showing raw price tags matter less than ecosystem fit.

The stakes for vendors and buyers

For the big three clouds, dominant market share gives leverage, yet churn intent signals an erosion of that grip.

Buyers face two risks. First, continued low utilization inflates cost per inference or training job, eroding the business case for AI. Second, lack of cost visibility hampers strategic planning; without clear unit economics, it is hard to justify further investment or set pricing for AI-enhanced products.

The rise of specialized AI clouds

Specialized AI clouds—CoreWeave, Lambda and Together—currently hold less than 2 % of the market. Forty-five percent of enterprises say they will evaluate these niche providers in the coming year, making this the single largest planned area of vendor assessment. Their value proposition rests on tighter integration with AI toolchains, more granular billing, and hardware tuned for AI workloads, which can lift GPU utilization well above the 50 % ceiling that haunts most enterprises today.

A technical blind spot: memory bandwidth

The hardware conversation is shifting. As inference workloads scale, memory bandwidth—how quickly data moves in and out of a GPU—becomes a bottleneck, eclipsing raw compute power. Yet only about 20 % of surveyed firms either recognize this constraint or are taking steps to address it. Overlooking bandwidth can mean that even a fully utilized GPU cannot deliver required throughput, leading to inflated instance counts and higher costs.

Counter-point: hyperscalers still dominate

It would be premature to declare the era of hyperscalers over. Their scale, global footprint, and existing enterprise contracts still make them the default choice for many. The survey shows a majority of workloads remain on these platforms, and for organizations with entrenched multi-cloud strategies, inertia may outweigh the lure of higher utilization. Moreover, specialized clouds’ limited market share suggests interest is high but migration will be gradual.

What to watch next

  • Memory bandwidth constraints: As inference workloads grow, bandwidth may become a critical bottleneck, influencing hardware selection and architecture decisions.

Takeaways

  • Inefficiency is the norm: 83 % of enterprises run GPUs at ≤ 50 % utilization; only 44 % can accurately track compute spend.
  • Vendor churn is high: 64 % plan to switch or add an infrastructure provider within a year, driven by integration (41 %) and TCO (35 %).
  • Specialized clouds are on the rise: 45 % will evaluate niche AI clouds in the next twelve months, looking to close the compute gap.
  • Memory bandwidth is a looming constraint: Roughly 20 % of firms are aware of or addressing this emerging bottleneck.

The gap between AI spend and economic visibility is no longer a footnote; it reshapes procurement strategies and prompts migration toward providers that can prove every dollar works harder.