Enterprise & Sovereign AI

On-Prem & Private AI: The 2026 Data

Written by Jakub RusinowskiLast updated July 12, 2026Hardware 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.

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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).

Where inference runs: the shift out of public cloud

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.

Sovereignty: vendor origin as a selection criterion

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.

Cost: what the credible comparisons show

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.

The number you should NOT cite

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.

Citing 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.

Frequently asked questions

What percentage of enterprise AI inference runs on-premises?

No credible survey measures "share of inference" directly. The best-sourced adjacent figures: public cloud as the primary environment for production AI inference fell from 56% to 41% of enterprises year-over-year, and 56% now run or plan production inference on private cloud (Broadcom Private Cloud Outlook 2026, n=1,800 senior IT decision-makers). The widely repeated "55% on-prem, up from 12% in 2023" claim has no traceable primary source and should not be cited.

Is the "77% factor country of origin" statistic reliable?

It is among the best-sourced statistics in this space: Deloitte's State of AI in the Enterprise, surveying 3,235 business and IT leaders across 24 countries and six industries in August–September 2025. Deloitte does not sell AI infrastructure, the sample is large and multi-country, and the fieldwork window is disclosed. Cite it with attribution: "per Deloitte's State of AI in the Enterprise survey."

Are enterprises really moving AI workloads out of the public cloud?

Two independent 2026 surveys point the same direction with different strengths: Broadcom/Radius Tech (n=1,800, disclosed methodology) measured public-cloud-primary inference falling 56%→41% year-over-year; Cloudian's vendor-commissioned survey found 93% repatriating, in progress, or evaluating. The honest summary: a significant, measurable shift toward private infrastructure for inference specifically — not a wholesale cloud exodus.

How much cheaper is on-premise AI than cloud APIs?

Above a volume threshold, materially: Deloitte's TMT analysis estimates 50%+ savings over three years, and our own calculator (verified per-token prices, street hardware prices) shows a single-GPU workstation undercutting GPT-4o API spend within two years at ~1M tokens/day. Below a few hundred thousand tokens/day, APIs stay cheaper. The threshold depends on your volumes — compute it, don't assume it.