Efficient Clustering with Provable Guardrails for LLM Inference at Scale
Making AI chatbots cheaper by grouping similar questions safely
When companies run large language models for millions of users, the cost becomes crushing. This paper shows how to cluster similar user requests together, answer just one representative request per cluster, and safely give the same answer to everyone in that group—cutting costs and speed by 50 times. The key innovation is mathematical guarantees that each user's question is actually similar enough to its group's representative that they can share an answer without losing quality.
Running an AI chatbot for 38 million customers costs enormous amounts in computing power and causes frustrating delays. This method slashed those costs in half and made responses 50 times faster while keeping personalization intact—enabling companies to actually deploy AI services that would otherwise be too expensive to run at scale. The technique works because it mathematically proves each user gets grouped with genuinely similar requests, not arbitrary bucketing.