How Bifrost Reduces GPT Costs and Response Times with Semantic Caching

TL;DR Every GPT API call costs money and takes time. If your app sends the same (or very similar) prompts repeatedly, you are paying full price each time for answers you already have. Bifrost, an open-source LLM gateway, ships with a semantic caching plugin that uses dual-layer caching: exact hash matching plus vector similarity search. Cache hits cost zero. Semantic matches cost only the embedding lookup. This post walks you through how it works and how to set it up. The cost problem with GPT API calls If you are building anything production-grade with GPT-4, GPT-4o, or any OpenAI model, you already know that API costs add up fast. Token-based pricing means every request burns through your budget, whether it is a fresh question or something your system answered three minutes ago. Here is the thing: in most real applications, a significant portion of requests are either identical or semantically similar to previous ones. Think about it. Customer support bots get asked the same question