Online Learning for Cost-Efficient LLM Routing

Ramp engineers developed a Thompson-sampling-based router that dynamically selects the most cost-effective large language model for each query, reducing inference costs by up to 40% while maintaining response quality. The system, detailed in a blog post on Ramp's engineering site, continuously learns from online feedback to balance exploration and exploitation across multiple LLM providers.