AI on-premises in enterprises: when to keep computing in-house

What do we mean by local AI, on-premise AI and edge AI?

Local AI, on-premise AI and edge AI are three ways of bringing computing closer to the company or its data, but they do not cover the same scope.

Local AI and on-premise AI

Local AI, also called self-hosted or on-premise AI, is an AI system installed and run on the company's own infrastructure (local server, mini PC or desktop computer), without going through external cloud servers [27]. In practice, it means a language model and its software components deployed on that infrastructure, with the data kept within the company's perimeter [21].

Sources do not, however, use "local" in the same way:

• Some limit it to a single machine: the model runs entirely on a desktop or laptop computer [18].
• Others extend it to a server, either on the company's premises or at a hosting provider [16].

In the second case, "on-site" does not always mean "within your walls."

Edge AI

Edge AI, or AI at the network edge, refers to implementing AI in an edge computing environment [35]. Algorithms and models are deployed directly on devices at the edge [40]. Computing takes place close to where the data is collected, rather than in a centralized cloud or an off-site data center [35][20].

A caveat about the sources

Most of the sources in this file are commercial players (manufacturers, publishers, service providers), whose definitions go hand in hand with an offer. The academic sources in the file deal with other subjects and are not used here.

Keeping data in-house: what real gain for privacy and compliance?

Running AI on-site addresses a scope constraint: the data does not leave the organization. This does not replace compliance work.

What local deployment avoids

When teams send contracts, client files or source code to a US-based AI API, that data leaves the company. This raises legal questions: GDPR, trade secrets, the CLOUD Act [37]. A model run internally removes this step. The manufacturer Lenovo, a commercial hardware player, sums up the benefit this way: keeping control of proprietary data while getting results faster [20][23]. The case is considered particularly useful, if not essential, for confidential information and internal code repositories [33]. A university also offers local models for sensitive data [13].

Less contracting

According to one deployment guide, if no data is sent to an external provider, there is no AI subcontractor to list and no DPA to sign. The same guide considers the CLOUD Act inapplicable, since no US company hosts the data [24]. This argument holds only as long as the hosting chain stays under your control.

What local deployment does not solve

Local deployment addresses the constraint of not letting data leave a defined perimeter, but it does not exempt you from standard GDPR obligations [14]. The regulation's other requirements therefore still apply to processing carried out internally. These include the legal basis, informing individuals and security. This point rests on a single source in the file, which does not detail these obligations.

When do latency and bandwidth justify edge AI?

Processing data close to its source reduces latency and limits the bandwidth consumed, two gains that matter most when data is produced continuously and a rapid response is required. Edge AI consists of performing calculations close to where the data is collected, rather than in a centralized cloud or a distant data center [35]. Doing these calculations locally considerably reduces latency and enables faster decision-making [39].

The typical example is the industrial camera. Instead of sending every image to the cloud for analysis, the images are processed directly on an edge terminal [28]. Only the result of the analysis then needs to travel, which explains the effect on bandwidth. The same reasoning applies to other equipment: some companies move inference from the cloud to workstations to reduce latency and bandwidth costs, while also improving privacy [22].

Edge AI is therefore justified mainly in two cases:

• the decision must be fast, and a round trip to a distant data center is an obstacle;
• the data flow is large and continuous, such as images from a production line, and sending all of it to the cloud would be costly.

A figure circulating about the scale of the trend: according to Renewator, cited by a blog, 55% of AI inference in companies is now done locally or at the edge, up from 12% in 2023 [36]. This figure is reported by a third party; we were unable to verify it, and it should not serve as the basis for an investment decision. Sources that praise edge AI are also often commercial hardware players [39][35].

This is the use case targeted by Nexus One, the Oupi box designed to run AI as close as possible to the data.

Local AI or cloud: what changes in the cost model?

Local AI and cloud have different cost structures: the former aims for an almost fixed cost, while the latter typically bills by usage, per token.

Two billing logics

• Local AI: the model runs on your own server, in your premises or at a hosting provider. Its main advantage is an almost fixed cost [16].
• Cloud AI: costs are generally calculated per token used [25]. A token is the unit of text that the model reads or produces. The bill therefore follows the volume of usage.

These two observations come from sector service providers [16][25]: they describe a trend, not a price.

Reason over a trajectory, not the entry price

The right calculation is made "over a trajectory," not on the "entry price" [21]. A subscription or per-token billing looks light at first, but it grows with adoption across teams. Conversely, local infrastructure requires an initial commitment, followed by more stable spending. So compare usage scenarios over time rather than two starting quotes.

Hardware still has to be financed

"Almost fixed" does not mean zero. Installing AI on your own machine generates no usage cost beyond the hardware [34]: purchase, hosting and operation of the equipment remain your responsibility.

The file provides no quantified break-even threshold, so this article does not offer one. For your case, you need to estimate your foreseeable usage volume, the cost of the hardware and its operation, and the period over which you plan to amortize the investment.

Which hardware to run AI on: workstation, server or box?

The right hardware depends on the size of the model and the number of users: a workstation or mini PC for individual use with a small model, a local server to serve a team.

The options, from lightest to heaviest

• Workstation or mini PC. Local AI can run on the company's infrastructure, whether a local server, a mini PC or a desktop computer, without external cloud servers [27]. On a low-powered device, computing and storage limit what is possible. Small language models (SLMs), such as Phi, are better suited to this. Microsoft, which publishes this recommendation, adds that its Copilot+ PCs come with built-in models [29].
• Local server. A departmental or desktop server, often equipped with dedicated GPUs, can serve a team while keeping data within the internal network [10]. With optimization techniques, a standard enterprise server can already serve capable models for classification or summarization tasks, without an intensive compute cluster [32].
• LLM hosting with a third party. This method is among those listed for hosting an LLM platform [19], and some sources classify "at a hosting provider" as local AI [16]. Definitions therefore do not all match: if the server is no longer within your walls, check where the data is located before calling it on-site AI.

Nexus One

Nexus One is Oupi's box for running AI as close as possible to the data. It complements these options for organizations that want dedicated equipment rather than a workstation or general-purpose server. Details are on the Nexus One page.

What are the limits of local AI, and when does the cloud remain relevant?

Local AI has three main limits: often less powerful models, hardware that must be sized correctly, and security that you must organize yourself. A consumer guide (CNET) states that it requires solid hardware, with at least 8 GB of RAM, and gives access to less powerful models than online services [38]. According to Microsoft, a device with limited computing power or storage may suffer as a result, and small language models (SLMs), such as Phi, are better suited to local use [29].

The cloud keeps the advantage on the largest models. A forum contributor (Reddit) explains that most users go through the cloud because these models require substantial computing power [12]. An expert article also presents the cloud as irreplaceable for certain uses [30]. Finally, a services company (WorkMyWay) points out that the company may remain dependent on its model provider, even if the data stays in-house [25].

Security does not come with the hardware. Local deployment does not exempt you from standard GDPR obligations [14]. A service provider (ID Interactive) describes its own mechanisms: network isolation of the AI server, outbound connections that are controlled and logged, no API key to a third-party service, and monthly audits of access logs [26]. This example comes from a commercial player, but it provides a basis of controls to verify.

Decision grid. On-site computing is mainly justified if:

• the data is sensitive or regulated [14][37];
• latency or bandwidth is critical [22][28];
• usage is intensive, which favors an almost fixed cost [16][25];
• the tasks are compatible with modest models [29][32].

If one of these criteria is missing, or if the task requires a very large model, the cloud remains relevant [12][30]. A hybrid approach is plausible, but the sources in the file describe it only briefly: they do not allow its architecture to be detailed.