A routing layer can slash AI inference costs while quietly lowering answer quality, user trust, and retention. Here’s why LLM routing becomes a Pareto trap and how to catch harmful tradeoffs early with better evaluation and monitoring. #ai #llmops #costoptimization #mlops #productengineering #datascience

For teams building AI products, cutting inference cost feels like an obvious win. Model bills grow fast, usage is unpredictable, and pricing pressure arrives long before the product is fully stable. When a routing layer promises to send simple requests to a cheaper model and reserve premium models for harder tasks, the idea sounds efficient, modern, and financially responsible.

On paper, it often works. The dashboard shows lower average cost per request. Finance is happy. Engineering has a clever system to celebrate. The product appears to be doing the same work for much less money.

Then the slow damage begins. Customer satisfaction softens. Users retry prompts more often. Support teams notice that outputs feel less reliable, but the problem is hard to prove because the product has not