Developer cuts AI API costs by 80% using semantic similarity caching

A developer building an AI-powered product description generator saw their API bill reach $400 within two weeks, prompting a search for a cost-cutting solution. Basic prompt caching failed because users phrased similar queries differently, and simpler normalization or TF-IDF approaches could not capture true semantic meaning. The developer built a semantic cache using the sentence-transformers library, converting prompts into vector embeddings and reusing stored responses when a new query exceeded a cosine similarity threshold of 0.92. After deployment, 8,200 out of 10,000 daily prompts were served from cache, cutting the weekly bill from $400 to under $80 while reducing response latency from 1.2 seconds to around 30 milliseconds. The developer noted that tuning the similarity threshold is critical and plans to migrate from in-memory storage to a vector database like pgvector for larger-scale use.