The author details the unexpected complexity of efficiently routing LLM calls, which led to building an AI gateway that decides which model to use per request. This system aims to optimize costs and performance by directing simple prompts to cheaper models and using methods like embedding similarity for routing decisions.
This content discusses routing LLM calls in production via OpenRouter, allowing model selection based on price sensitivity and conversation style. It details how to handle `finish_reason=content_filter` edge cases and fallback patterns to ensure continuous replies.
UCCI is an innovative router that uses calibrated uncertainty to optimize the cost of LLM cascades, sending easy queries to smaller models and difficult ones to larger models. It reduces inference cost by 31% on production workloads while maintaining accuracy, by calibrating model confidence.