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ask about AI that stays inside your own building.
Two things it does. If you are deciding, ask whether private AI fits your business, what it costs and what the rules require. If you are building, it walks you through the setup step by step and reads your errors. It answers from what Altronis has actually done, and it hands you to a person rather than guess.
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What it does
Someone who has done this before, on both sides of the decision.
Straight answers before you commit
Ask whether this suits a firm your size, what it costs to own, and what changes if your data cannot leave the building. No sales call needed to find out.
It shows its working
Answers come from what Altronis has actually built and measured, and it quotes the source so you can check it. It says plainly when it does not know.
A person stays on the hook
For anything unusual or risky it hands you to an Altronis engineer instead of guessing, and it will never suggest loosening your security to make something work.
Free to try, no sign-in, a few questions a day. Want to go further, or talk to a person? Talk to us.
Common questions
Private on-prem AI, in plain terms
What does private, on-premise AI cost?
An entry box with 128GB of unified memory that runs a capable model for a team is roughly S$2,500 to S$9,000 one-time (an AMD Ryzen AI box at the lower end, an NVIDIA DGX Spark higher), plus about S$30 to S$45 a month in electricity. There are no per-seat or per-token fees. Bigger models or heavy concurrency cost more; we size the exact figure with you.
How many people can one AI box serve at once?
A single box comfortably handles a small team's day-to-day use, a handful of people chatting or querying documents at the same time, plus a good volume of automated calls. Because generation speed is shared across concurrent users, dozens of simultaneous users all expecting instant replies needs batching and more hardware. We size it to your real number of users.
Is an on-premise box slower than cloud AI?
For the tasks most firms run, chat, drafting, and question-answering over your own documents, a single modern box is fast enough to feel instant. Speed is capped by memory bandwidth, not by how much memory you buy, so a larger model runs slower on the same box. Very large models are where cloud or bigger hardware wins.
Does my data really stay private with on-premise AI?
Yes. The model runs on your own hardware, in your own building, so your data never leaves the premises. The endpoint is API-key authenticated behind a secure tunnel with no open ports.
On-premise or cloud AI, which is cheaper?
Cloud wins for occasional or unpredictable use and for very large models you would rarely run. On-premise wins for steady daily use and for data that cannot leave the building. Once a monthly cloud or per-seat AI bill would exceed the cost of owning a box, owning usually pays back within months.
What hardware do I need to run private AI?
For most SMEs a single workstation-class box with 128GB of unified memory (an AMD Ryzen AI Max box or an NVIDIA DGX Spark) runs a capable 24 to 35B model for a whole team. The two are within about 7% on memory bandwidth, so they generate text at broadly similar speeds; the difference is prompt-processing and software ecosystem.