Enterprises are buying AI computing power for speed, but most have no idea what it costs
A survey of 170 companies finds two-thirds are running live AI systems, yet fewer than half can say with any rigour what that computing infrastructure actually costs them.
Key points
- 66% of the 170 surveyed enterprises already run AI workloads in live production, as of July 2026.
- Fewer than half (47%) rigorously track what their AI computing costs or returns.
- Among companies that own their own GPUs (the specialised chips that do the heavy number-crunching AI needs), 69% report those chips run at 50% capacity or less.
- Microsoft Azure leads as the primary AI platform for 26% of enterprises surveyed, with Google Cloud second at 19%.
- 62% of enterprises plan to switch or add an infrastructure provider within 12 months.
Most companies that bet big on AI have already moved past the experiment stage. Two-thirds of the 170 enterprises surveyed in new research first reported by VentureBeat are running AI on live, real-world systems. Nearly a third say they're doing it at meaningful scale. Only 4% haven't started at all.
These decisions, about what hardware to buy and which platforms to use, are being made under real pressure, with real bills arriving every month.
So why can't most of them say what it costs?
Speed took priority, and the financial controls didn't keep up.
When companies were asked what mattered most when choosing AI computing infrastructure, integration with their existing software stack came first (40%). Performance, meaning how fast the system responds and how much it can handle at once, came second at 35%. Access to GPUs came third at 24%. Total cost of ownership, the full long-term price of running a system, came fourth at just 22%.
That ordering makes sense for a team under deadline pressure. What it leaves behind is accounting.
Only 47% of those surveyed can say with rigour what their AI compute spending actually delivers. Even among companies running AI at full production scale, that figure only reaches 56%. Our 7 August story on Rippling's AI cost overruns showed how badly this gap can bite: one engineer was spending $50,000 a month on AI tokens before anyone noticed.
| Metric | Share of enterprises |
|---|---|
| AI in live production | 66% |
| AI at production scale | 29% |
| Rigorously track AI compute costs | 47% |
| GPU utilisation at 50% or below | 69% |
| Plan to switch or add a provider in 12 months | 62% |
Among companies that own and operate their own GPU clusters, 69% report those chips running at half capacity or less. Twelve percent don't measure GPU usage at all. These are expensive pieces of hardware sitting largely idle.
What platforms are companies actually using?
The incumbents dominate, and enterprises aren't picking just one. The average company runs three AI platforms at once. OpenAI appears in 49% of stacks, Google's Gemini in 48%, Microsoft Azure in 47%, and Google Cloud in 42%.
When asked to name a single primary platform, Azure leads at 26% and Google Cloud sits second at 19%. Companies that sell direct access to AI models rather than cloud computing broadly together account for 35% of primary status: OpenAI at 14%, Gemini at 14%, and Anthropic at 8%.
Specialist AI infrastructure providers, sometimes called neoclouds, have attracted enormous investor attention but barely feature in current use. CoreWeave and Lambda each appear in just 3.5% of company stacks. The entire specialist-cloud category is named as a primary platform by just 1% of respondents.
What happens next?
Here's where the numbers get interesting. Despite barely using specialist AI clouds today, 44% of enterprises name them as the top area they plan to evaluate next, and that category carries the strongest forward momentum of any infrastructure type in the survey.
Whether that evaluation converts to real spending is a different question. Sixty-two percent of enterprises say they plan to switch or add a provider within 12 months, but the providers they're actually considering are the same large incumbents they already use.
For ordinary employees and customers, the practical effect is straightforward: the AI tools your company runs are probably live and growing. Whether anyone inside that company has a clear handle on what they cost is, right now, genuinely uncertain. That's the gap worth watching, not how many platforms are in the stack.
Common questions
Does this survey speak for all businesses?
No. The 170 respondents are self-selected organisations actively building AI infrastructure, and 57% have more than 1,000 employees. Smaller businesses and companies not yet investing in AI are not represented.
What does GPU utilisation at 50% mean in plain terms?
It means companies are paying for computing power they aren't using. A chip running at 50% capacity is roughly like renting a warehouse and leaving half of it empty while still paying full rent.
Should employees be worried about their company's AI spending?
Not on safety grounds from this data. The concern is financial efficiency. Companies spending heavily on AI without tracking returns will eventually have to account for that gap, which could shape future investment decisions inside those organisations.
