Written by Jakub Rusinowski · Last updated 2026-07-12 · Hardware figures computed by our VRAM engine
The measurable 2026 picture: enterprises using public cloud as the primary environment for production AI inference fell from 56% to 41% in a year, while 56% now run or plan production inference on private cloud (Broadcom, Private Cloud Outlook 2026); 77% of organizations factor an AI vendor's country of origin into selection decisions (Deloitte, n=3,235). Every figure on this page carries its source and methodology inline — and one widely repeated number that has no source is flagged as such.
Enterprise AI statistics have a laundering problem: a number appears in a vendor deck, gets repeated without its methodology, mutates in transit, and two blog-generations later is "well known." This page is the antidote for the on-prem/sovereign corner of the field. Each statistic below states who measured it, how, and when. Where a figure is vendor-commissioned, that's labeled — commissioned research isn't worthless, but you should know who paid. And where a widely repeated number turns out to have no traceable source, we say so instead of repeating it.
Figures are current as of July 2026; this page is revised as new survey waves publish (see the last-updated date above).
Public cloud as the primary environment for production AI inference: 56% → 41% in one year. The most load-bearing datapoint in this space. Source: Broadcom, *Private Cloud Outlook 2026* (published June 2026); survey conducted by Radius Tech in February–March 2026 among 1,800 senior IT decision-makers at enterprises with 1,000+ employees across eight countries in North America, Europe, and Asia-Pacific. The same survey found 56% of enterprises now run or plan to run production AI inference on private cloud. Caveats: Broadcom sells private-cloud software (VMware), so the topic choice is not neutral — but the methodology is disclosed, the sample is large, and the year-over-year framing uses the survey's own prior wave.
93% of enterprises have repatriated some AI workloads from public cloud, are in the process, or are actively evaluating it — 79% have already moved some. Source: Cloudian, *Enterprise AI Infrastructure Survey* (March 2026) — vendor-commissioned by a storage company that benefits from the conclusion; full methodology was not published alongside the headline figures. Treat as directional support for the Broadcom finding, not as a standalone anchor. The survey's stated drivers are worth noting even so: 55% of respondents cited public-cloud latency for AI inference, 52% cited keeping training data on-premises for security or compliance.
Inference has become the dominant AI workload. Industry analyses through 2025–2026 consistently put inference at roughly two-thirds of all AI compute, up from about one-third in 2023, with Gartner projecting 55% of AI-optimized IaaS spending supporting inference in 2026. This matters for the on-prem question because inference — unlike training — is a steady-state, predictable workload: precisely the profile where owned hardware amortizes well.
77% of organizations factor an AI vendor's country of origin into selection decisions. Source: Deloitte, *The State of AI in the Enterprise* (2026 edition); survey fielded August–September 2025 among 3,235 business and IT leaders across 24 countries and six industries. The companion finding from the same wave: nearly three in five organizations build their AI stacks primarily with local vendors. This is the strongest-methodology statistic in the sovereignty conversation — large multi-country sample, disclosed fieldwork window, non-vendor publisher — and it's the one we cite on the sovereign AI explainer.
Regulatory timeline, for context rather than survey data: the EU AI Act's main obligations for high-risk AI systems apply from August 2, 2026, with penalties for prohibited-practice violations reaching up to €35M or 7% of global annual turnover (the Act's own text, Regulation (EU) 2024/1689). Regulation is a driver of the survey numbers above, not a statistic itself — we list it here because it anchors the "why 2026" question.
At sustained volume, on-premises inference undercuts metered APIs. Deloitte's TMT analysis puts on-premises savings at 50% or more over three years versus running the same workload in the public cloud, above a token-volume threshold. Our own break-even calculator — which uses verified current per-token prices and street hardware prices, and shows its math — lands in the same territory: at ~1M tokens/day, a single-GPU workstation build undercuts GPT-4o API spend within the first two years, and the gap widens with scale. Run your own volumes rather than trusting either headline; the threshold is the whole question.
Deployment cost brackets observed across 2026 practitioner guides (and consistent with our build recommender): roughly $1,500–4,000 for a department-scale single-GPU workstation (24 GB class), $10,000–15,000 for a mid-size setup serving a 70B-class model, and $40,000–190,000 for production multi-GPU server deployments of the largest open models. Wide brackets are honest brackets — concurrency and context-length requirements move these numbers more than list prices do.
A claim circulates that "55% of enterprise AI inference is now performed on-premises or at the edge, up from 12% in 2023." We attempted to verify it for this page and could not find any primary source — no survey, no analyst report, no methodology. The credible measurements above tell a different (and more modest) story, and IDC-cited figures still put roughly two-thirds of enterprise AI compute in the cloud. The likely origin is a garbled version of a Gartner prediction about edge analytics — "by 2025, 55% of all data analysis by deep neural networks will occur at the point of capture in an edge system, up from less than 10% in 2021" — which measures something else entirely (data analysis at point of capture, not enterprise inference share).
If you've used the 55% figure in a deck: the defensible replacement is the Broadcom 56→41% shift, which is sourced, recent, and directionally similar. If you've seen other uncited on-prem AI numbers you'd like run down, tell us — corrections and verifications are added to this page.
Every statistic above should be cited to its original publisher (Broadcom/Radius Tech, Deloitte, Cloudian, Gartner), not to us — this page exists to make those chains easy to follow. If you link this roundup as a secondary source, it lives at a stable URL and each revision updates the last-updated date above. For dated, citable snapshots of the local-AI hardware and model landscape more broadly, see our biweekly reports.