Why AI Teams Should Map Features to the Cheapest Reliable LLM, Not One Default

Many AI product teams default to using a single premium language model across all features, which creates hidden costs, slow responses, and weak margins at scale. A model selection matrix offers a structured alternative, matching each feature to the least expensive model that meets its specific accuracy, latency, and safety requirements. The approach involves defining task-level criteria first, then running small evaluations to compare model tiers on pass rate, cost per success, and latency before scaling traffic. Simple routing logic can then direct requests to the appropriate model based on risk level, task type, and user plan. The method is aimed at solo developers and technical founders who need production-grade AI features without relying on guesswork or over-engineered infrastructure.