Kulper / Blog
Practical guides to owning and using AI infrastructure. Costs, privacy and the details that matter once your team starts using it.
Local AI makes sense when your team uses AI regularly, wants control over business data, or needs less dependence on a remote provider. The model still has to do the job well at the speed your team needs.
Local inference can keep prompts and model responses inside your own environment. To keep the whole workflow private, you also need to account for document processing, connected tools, stored chats and backups.
Running a downloaded model on your own server avoids a cloud inference charge for those local requests. You still pay for the hardware, electricity and operation of the system. The comparison depends on how much useful work it does.
Your staff can use a shared local AI server through browser chat, while developers connect from a terminal. Open WebUI and OpenCode are two interfaces we can configure to use your local model endpoint.
