Cheaper LLM Routing Saves Money, But Silent Output Failures Can Offset Gains

Routing production LLM traffic to cheaper models can cut inference costs by 70–90%, but teams often fail to verify whether output quality has silently degraded in the process. A successful API response code does not confirm correctness, as errors may only surface several steps downstream in a workflow. The true measure of savings is cost per successfully completed task, factoring in retries and escalations to frontier models, not just the per-call token price. Evaluation harnesses — including prompts, retry logic, and tool schemas — must be versioned alongside models, since unversioned benchmarks produce results that cannot be reproduced or trusted. Teams in Southeast Asia face added pressure from tight budgets and data-sovereignty regulations such as Malaysia's PDPA, making rigorous measurement of routing effectiveness especially critical.