What is on-premise AI, and is self-hosted AI worth it?
On-premise AI means the AI runs on computers your company controls. The language model, the search index over your documents and the conversation logs all live on your servers, in your office, your data centre or your own private cloud account. Nothing you type is sent to an outside AI company.
The cloud alternative works the other way round: you send your question, and often your documents, to a provider’s servers over the internet, and their model sends an answer back.
How a local LLM works
A large language model (LLM) is a file of numbers, called weights, plus software that runs it. Open models such as Llama, Qwen, Mistral and Gemma can be downloaded and run on your own hardware. A typical private setup has four parts:
- The model, running on one or more servers with GPUs.
- A search index of your documents (Confluence, SharePoint, shared drives), so answers come from your own material with the source shown.
- Permissions copied from your directory, so each person only gets answers from files they can already open.
- An outbound block: the servers are not allowed to call the internet, which you can check yourself.
When self-hosted AI is worth it
It tends to pay off when at least one of these is true:
- Your data is sensitive. Legal contracts, customer records, salaries, patient or financial data.
- Rules say where data may live. For example India’s DPDP Act, the GDPR, HIPAA in the US, or a bank’s own vendor policy.
- Use is steady. A team asking hundreds of questions a day gets better value from its own servers than from per-seat cloud licences.
It is usually not worth it for occasional, low-risk tasks, or if nobody can host a server. In that case a managed private environment, where a vendor runs a dedicated instance only for you, sits in between.
What it costs
Two parts: the servers, and the software and setup. One GPU server covers most teams of a few hundred people. Compared with per-seat cloud AI, the maths often favours self-hosting for steady use. Microsoft 365 Copilot lists at $30 per user a month, about $108,000 a year for 300 staff, before Microsoft 365 licences. Our own pricing for a 300-person company using three products comes to about $47,300 in the first year, setup included, and about $31,500 a year after that, with your servers on top. See the full breakdown.
How to tell if a vendor really keeps data inside
Ask them to prove it, not promise it:
- Can the system run with all outbound traffic blocked?
- Do answers cite the source document?
- Does it respect existing permissions?
- Will they sign your data-processing agreement and answer your security questionnaire?
KeepInside is built around those four checks. Book a 30-minute demo and we will run it with the internet switched off.
Questions people ask
What is on-premise AI?
AI that runs on servers your company controls, in your office, data centre or private cloud. The language model, the search index and every document stay inside your network, so no prompt or file is sent to an outside AI provider.
What is a local LLM?
A large language model whose weights are downloaded and run on your own hardware, for example an open model from the Llama, Qwen, Mistral or Gemma families, instead of being called over the internet.
Is self-hosted AI worth it?
It is worth it when your data is sensitive (contracts, customer records, health or financial data), when you have steady daily use across a team, or when regulations require data to stay in a set place. For occasional, low-risk tasks, a cloud tool is usually simpler.
Is a local model as good as ChatGPT?
For searching your own documents, drafting, summarising and analysis, current open models do the job well. For the hardest open-ended reasoning, the largest cloud models still lead. A good vendor tells you where that line is for your use cases.