CyberFreezeDev
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Every reply an AI system gives is inference, and you pay for it either per token to an API or in hardware, power and people on your own server. Here is how each is billed, what really drives the cost of a server, and a method to estimate both for your workload.
Keyword search fails when staff remember what a document says but not the words it uses. Here is how to go from a shared drive of PDFs, contracts and SOPs to a search box that finds by meaning, then to a chatbot that answers with citations, with every part able to run on your own servers.
You can run several AI agents on your own hardware with open-weight models, and none of your documents has to leave the building. Here is the architecture, which frameworks and servers work fully local, what to switch off, and why small local models drift on long plans and how to stop it.
Running an LLM on your own server is worth it when client data cannot leave your control and the work keeps a GPU busy. This guide covers which open-weight models you can use commercially, how to size GPU memory, which hardware tier fits, which serving software to run and what the DPDP Rules ask of you.
