Enterprise AI Compute Gap: Spending Outpaces Visibility by a Wide Margin

A VentureBeat Pulse Research survey of 107 enterprises, conducted in June 2026, found that AI infrastructure spending is accelerating well ahead of the ability to measure or control it. Most organizations run AI on hyperscalers and model APIs, yet GPUs sit at half utilization or less for 83% of them, and fewer than half can rigorously track compute costs. Despite this visibility gap, 64% plan to switch or add an infrastructure provider within the next twelve months, and the top planned investment area is AI-specialized clouds that almost none of them currently use.

What happened

Metric	Finding
Enterprises surveyed	107 (all 100+ employees, Q2 2026)
Running AI in production at scale	21%
GPU utilization at 50% or less	83%
Can rigorously track compute costs	44%
Plan to switch or add provider within 12 months	64%
Plan to switch or add provider within 3 months	38%
Top planned evaluation area (AI-specialized clouds)	45%
Primary switching factor: stack integration	41%
Primary switching factor: total cost of ownership	35%
Primary switching factor: cost per million tokens	8%

VentureBeat published this research as part of its Pulse Research series, drawing on a single wave of responses from organizations with more than 100 employees. The sample skews toward the mid-market: 36% have 101 to 250 employees and 27% have 251 to 1,000. Roles span managers (38%), individual contributors (28%), VPs and directors (19%), and the C-suite (13%). On purchasing authority, 45% are final decision-makers and 30% are recommenders or influencers. Technology and software is the largest industry at 26%, followed by healthcare and life sciences (15%), financial services (13%), and retail and e-commerce (12%).

The report’s central term is the “compute gap”: the distance between how aggressively enterprises are buying AI infrastructure and how little they understand its economics. Most are still early in their journey. Three-quarters (76%) are either experimenting or running only some workloads in production. That means the spending intentions captured here are the leading edge of a build-out, not the settled preferences of mature operators.

What the current stack looks like

Right now, enterprises run AI on the vendors they already pay for. Google Cloud leads at 48%, and general-purpose hyperscalers (Google, Microsoft, AWS, Oracle) together with major model APIs (Gemini, OpenAI, Anthropic) account for nearly all current deployment. The specialized “neocloud” GPU providers that generate most AI infrastructure headlines, such as CoreWeave, Lambda, Crusoe, and Nebius, register at or near zero. Only 6% run their own on-premises GPU clusters and 4% use a custom open-source stack.

That makes the switching intentions notable. Almost none of these organizations use AI-specialized clouds today, yet 45% name them as the top area they plan to evaluate over the next twelve months. That is a big jump from near-zero to top priority.

Why is GPU utilization so low?

The survey does not give a single cause, but the pattern is consistent with organizations that are still building toward production. When 76% of enterprises are still in experimentation or partial production, the compute they have provisioned is sized for anticipated future workloads, not current ones. The result: most GPUs sit at half capacity or below, and cost visibility is poor. Fewer than 44% of respondents can rigorously track what that idle capacity is costing them.

There is also a technical shift that few have addressed. The report notes that as inference scales, the bottleneck moves from raw GPU compute to memory bandwidth. Roughly one in five enterprises is either unaware of this shift or has yet to address it, which means future performance planning may hit a wall that current purchasing decisions do not account for.

Why it matters

The combination of high churn intent, poor cost visibility, and underutilized hardware is a recipe for wasted budget. Enterprises are committing to new infrastructure categories before they have measured the return on what they already own. The 38% who plan a provider change within the next quarter are making foundational decisions without a clear unit-economics baseline.

For teams thinking about AI integration in their own stack, this is a useful warning: procurement is moving faster than measurement. Organizations that skip the step of tracking cost per workload before adding more compute will likely repeat the same waste at larger scale.

The vendor-switching criteria are also telling. Integration with the existing stack (41%) and total cost of ownership (35%) are the top reasons enterprises would switch providers. Headline token price is the deciding factor for only 8%. That means the cheapest API option rarely wins on its own. Fit with existing tools and predictable long-term cost matter far more to buyers.

Our earlier coverage of enterprise AI agent deployment found a similar pattern: organizations rushing deployment before the operational fundamentals are solid. This compute survey reinforces that picture.

Our take

The compute gap is real, and it is not surprising. Most enterprises expanded AI budgets because leadership demanded it, not because a measured pilot proved ROI. The result is a fleet of underused GPUs and a finance team that cannot tell you what each model inference costs per customer interaction or per transaction.

The shift toward AI-specialized clouds is also worth watching carefully. Moving from a Google Cloud bucket with an OpenAI API key to a CoreWeave cluster requires real operational maturity: networking, orchestration, billing instrumentation. The organizations planning that jump in Q3 2026 without a cost-tracking baseline are about to spend more money they cannot measure.

The one genuinely useful signal from the switching-criteria data: buyers weight integration and TCO over token price. If you are evaluating AI infrastructure for a client or your own business, build the integration and total-cost case first. A lower API rate that requires six months of glue code is not actually cheaper.

What to do about it

1. Audit your current GPU or API utilization before signing any new compute contracts. If you cannot report utilization rate, you are not ready to expand.
2. Instrument cost per workload now, even roughly. Track spend by use case (customer support bot, internal search, code generation) so you have a baseline before the next procurement cycle.
3. Map your existing stack’s integration points before evaluating neocloud providers. The 41% who prioritize stack integration are right to do so.
4. Brief your infrastructure team on the GPU-to-memory-bandwidth shift in inference. If one in five enterprises has not addressed it yet, it is worth a two-hour technical review before your next capacity plan.
5. If you need outside help structuring these decisions, talk to the Lumien team about what a practical AI infrastructure audit looks like for a mid-market business.

The best infrastructure decision you can make right now is to measure what you have before buying more of it.

Source: VentureBeat · AI