Making AI an asset, not an expense
TL;DR
When customers talk about AI costs, the conversation usually starts with token prices and ends with access to the latest, most capable model in the cloud. Do they always need that level of capability? Not necessarily. But that is often where the conversation goes.
Nauti's Take
Small teams should start with a real workflow and a basic cost-benefit measurement: which tasks truly require the top model, and which are reliable enough with a smaller one? Test quality, latency, token use, and human rework across representative cases before committing to one cloud provider or an expensive default model.
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