Local AI for companies
Cloud AI compared with a fully local setup. The same task, two different paths for your information.
Same example in both diagrams
“Summarise this internal report.”Ready
Provider-hosted
Users + company information
Outside your company network
OpenAI API
AI model hosted by the serviceOne selected destination per request.
Selected information leaves your network.Processed by the selected cloud service.
On your own hardware
Users + company information
Inside your company network
AI model running locally
Local data processingNo external AI calls
The information stays inside your network.Users, server, and model in the same boundary.
Illustrative flow · no real data · no speed comparison
The local setup shown here is fully local. The model, document processing, and search run inside the company network, with external AI calls disabled. Cloud fallbacks, web search, and other connected services need their own data-flow checks. The diagrams show processing locations, without specifying countries or network connection types.
What changes in practice
The processing location changes the data path, who runs the system, and what you need to budget for.
A few useful distinctions
The hosting service determines the destination. For example, Azure OpenAI runs in Microsoft’s environment. A model that can also be installed locally still uses a cloud route when you call its hosted API. Choose the actual service and configuration before deciding where the processing happens.
Microsoft: how Azure processes dataProcessing, storage, and model training are different uses. Policies depend on the product, contract, and settings. For example, OpenAI states that API data is not used for model training by default unless you opt in. Retention rules are a separate consideration.
OpenAI: API data controlsYes, when the model, document processing, search, and required data are available locally and external services are disabled. Setup and updates need a planned way to transfer files. Permissions, backups, and checks on AI answers are still part of operating the system.
A small pilot may fit on a suitable workstation or a single server. Larger models and more simultaneous users need more memory and compute. We size the system around your workload and test it before recommending a purchase.
From idea to everyday use
We start with one useful workflow and measure it with your team before expanding.
Agree on the users, the problem, and what a useful result looks like.
Select current documents, define access, and choose suitable hardware.
Measure answer quality, time saved, response speed, and permission checks.
Assign ownership for updates, monitoring, backups, support, and training.
Your first use case
Tell us about one recurring task and where the relevant information lives. We’ll help define a pilot with a clear scope and a realistic basis for deciding what comes next.