Why Deterministic Scoring Beats AI Models for Travel Route Ranking

A developer writing on DEV Community argues that using language models to rank travel routes — comparing flights, trains, and buses — breaks down quickly at scale due to inconsistency, high inference costs, and latency. Because LLMs can return different orderings for identical inputs across repeated queries, users experience ranking changes as bugs rather than features. The proposed alternative is a weighted scoring algorithm that normalizes factors like cost, time, comfort, and CO2 emissions, recalculating rankings client-side instantly without any API calls. The approach requires ongoing data maintenance across countries, transport modes, and group-size pricing tiers, but produces fully reproducible, debuggable results. The author concludes that using model inference for structured problems where users expect consistent answers is a false economy.