You Don’t Always Need The Frontier

Argues, with grounding from recent AI engineering conference content, that the industry overcorrects toward the largest frontier models when smaller, cheaper ones handle the majority of production use cases adequately. The piece tracks a shift in practitioner thinking away from RAG-heavy, GPT-4-class default stacks toward more deliberate model selection. It is not a benchmark post, so do not expect hard numbers — treat it as a framing piece that gives you vocabulary for a conversation with your team about where you are overspending on inference. Reservation: it is a dev.to article, not a technical teardown, so the depth is limited and some points will feel obvious to anyone who has already gone through one model-cost audit. Still a reasonable send to a co-founder or technical PM who is on the fence about re-evaluating the model tier in a specific pipeline. -> Best for: technical PM or SaaS team of 2-5 reviewing inference costs