What is on-premise AI?
On-premise AI (also called local AI, self-hosted AI or on-site AI) is a generative AI model running on a server in the company’s own premises or data centre. No data is sent to an outside provider.
In practice, a server fitted with graphics cards (GPUs) runs a large language model (LLM) whose weights are published by their developer — so-called open-weight models. An application, such as a conversational assistant, sits on top and is connected to the company’s documents.
The technique that lets the AI answer from your own files is called RAG (Retrieval-Augmented Generation): the AI finds the relevant passages in your documents, then writes an answer that cites its sources. With on-premise AI, the document index is stored internally too.
Sovereign AI means the company keeps full control of the infrastructure, the data and the model, without depending on a provider subject to foreign legislation.
On-premise AI, cloud API or SaaS assistant: what is the difference?
The difference is where your data is processed. With on-premise AI, everything stays on your servers; with a cloud API or a SaaS assistant, every question and every document passes through the vendor’s servers.
| On-premise AI | Cloud AI API | Consumer SaaS assistant | |
|---|---|---|---|
| Where data is processed | Your servers, your network | Vendor’s servers | Vendor’s servers |
| Exposure to foreign law (Cloud Act) | No | Yes if the vendor is American | Yes if the vendor is American |
| Cost model | Flat rate per server, unlimited use | Billed per token | Subscription per user |
| Answers from your internal documents | Yes, with your directory’s permissions | To be built yourself | Limited to uploaded files |
| Dependence on the internet and the vendor | None for everyday use | Total | Total |
| Control over usage (shadow AI) | Centralised in the company | Per project | Difficult |
Why is local AI more secure?
Because the data never leaves the company. There is no transfer to a third party, no storage at a vendor and no exposure to foreign legislation: the risk surface shrinks to your own network.
- Confidentiality: contracts, HR data, accounts and trade secrets stay in-house.
- Cloud Act: this 2018 US law lets US authorities demand data held by an American provider, even when stored in Europe. AI installed on your premises is not subject to it.
- GDPR: with no transfer of personal data to a processor or outside the European Union, compliance is much easier to demonstrate.
- Access rights: each user only sees the documents they are already entitled to, based on your Active Directory.
- An end to shadow AI: an internal AI as capable as consumer tools stops teams pasting sensitive documents into them.
Is local AI as good as ChatGPT?
On everyday business tasks, the gap has become small. Across more than 360 business trials, the model Artemia deploys locally scores 94.7 %, against 99.3 % for the best cloud model on the market.
Open-weight models improve every quarter. For searching documents, summarising, writing, translating, extracting data or analysing a spreadsheet, the difference is not noticeable in use.
On-premise AI also has an advantage the cloud lacks: it knows your internal documents, vocabulary and procedures, which makes its answers more useful than a general-purpose assistant’s.
What can professional on-premise AI do?
The same things as a cloud generative AI assistant, but on your own data: search, summarise, write, translate, analyse, produce documents and automate tasks.
- Document search with sourced answers across your contracts, procedures and standards
- Writing, rewording and translation in more than 180 languages
- Spreadsheet analysis, calculations and charts
- Formatted PDF and Word report generation
- Information extraction from scans and images (OCR)
- Code generation and review
- Meeting minutes
- Automations and agents connected to your business tools through an OpenAI-compatible API
What do you need to deploy AI locally?
A GPU server, on site or in your data centre, and a solution that installs it and connects it to your tools. One server serves 150 to 250 users, and installation takes a single day.
- Hardware: your own server if it meets the requirements, or a dedicated server sized and installed by the provider.
- Capacity: 150 to 250 users per server, up to 1,000 by combining several units.
- Connections: SharePoint, Google Drive, OneDrive, Confluence, file servers, SSO and Active Directory.
- Timeline: installation, network connection and team training in one day.
- In-house skills: no AI expertise is needed; updates and new models are installed by the provider.
How much does on-premise AI cost?
A one-off set-up fee, then a flat rate per server, never per user, covering unlimited use with no per-token billing. The cost stays fixed whatever the usage.
The cost depends neither on the number of questions nor on the number of documents: the more your teams use it, the lower the cost per use. It is the opposite of cloud APIs billed per token and per-user subscriptions, whose bill tracks your adoption curve.
How do you choose an on-premise AI solution?
First check that the data really stays on your servers, then that the solution connects to your tools, respects your access rights, gets updated and has a predictable cost.
- Data and index stay on your infrastructure — not on a “private cloud” hosted by a third party.
- The model is replaced when a better one comes out, at no extra cost.
- Permissions follow your directory (Active Directory, SSO).
- The solution connects to your existing document spaces, with no migration.
- Pricing is a flat rate: no per-token or per-seat billing.
- An OpenAI-compatible API lets you plug in your other tools.
- Support and team training are included.
On-premise AI, at your site, in one day
Artemia installs MIA, its generative AI assistant, on your servers: sourced answers from your documents, assistants shared across teams, unlimited flat rate. An OpenAI-compatible API and Qualiopi-certified training complete the offer.