Originally published on AI Tech Connect.
Twenty customers, twenty adapters, one GPU budget The trap is pleasant on the way in. A customer asks for output in their house format, you tune a small adapter, it beats prompting, everyone is happy. A second customer asks the same. By the eighth you have a training pipeline, an artefact store and a habit. By the twentieth someone in finance asks why the infrastructure line grew twentyfold, and the honest answer is that you designed a training story and let serving follow it. This guide is about the serving story. It assumes you can already produce an adapter — our eval-driven LoRA and QLoRA recipe covers that — and that you know the generic levers for cheaper inference, handled in our guide to cutting self-hosted serving costs. Neither answers the question here: given a fleet of…
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