The Hidden Cost of Ambiguous Prompts: How Semantic Caching Saved $34K/Month

A 73% Drop in LLM API Costs—But Only If You Solve the Right Problems

Exact-match caching captured only 18% of redundant queries in a mid-sized SaaS company’s system.

By switching to semantic caching with FAISS and Sentence Transformers, the cache hit rate jumped to 67%, reducing LLM API costs by 73%. This shift revealed 47% of queries were semantically similar but phrased differently—FAQs about billing policies, product searches for "wireless headphones," and transactional requests like "cancel my subscription" all masked the same intent.

Threshold tuning became critical. FAQ-style queries required a strict similarity threshold of 0.94 to avoid false positives, while product searches tolerated 0.88 to capture more variations.

Transactional queries demanded 0.97 precision to prevent misrouting. The result? A 0.8% false-positive rate that caused minimal customer complaints, balanced against a 65% latency improvement—despite a 20ms overhead for embedding and vector search.

Cache invalidation strategies adapted to use cases. Time-to-live (TTL) worked for FAQs, event-based triggers handled product catalog updates, and semantic staleness detection flagged outdated transactional data.

Code samples demonstrated FAISS/Pinecone for embeddings and Redis/DynamoDB for storage, showing how open-source tools could replicate enterprise-grade performance at a fraction of the cost.