Most AI product teams do not have a model problem. They have a matching problem.
A chat rewrite, a support answer, a SQL assistant, and an autonomous workflow should not all use the same large model just because it is the default in your SDK. That habit feels safe in a prototype, then quietly turns into slow responses, messy invoices, weak margins, and confusing quality bugs in production.
The better path is boring in the best way: build a model selection matrix. Map each feature to the cheapest model that reliably meets its accuracy, latency, safety, and product requirements. Then prove it with small evals before traffic scales.
This guide shows a practical workflow for solo SaaS developers, AI SaaS builders, micro SaaS builders, and technical founders who need production AI features without guessing.
Why one default model becomes expensive fast
