How to Slash LLM API Costs by 73% with Semantic Caching 2026

Is your AI infrastructure burning a hole in your pocket? Lead software engineer Sreenivasa Reddy noticed a 30% month-over-month increase in LLM API bills, even when traffic growth was moderate. The culprit? Users asking the same questions in slightly different ways, forcing the LLM to re-generate identical answers at full cost.

Semantic Caching for LLM Cost Reduction: Intent over Text

Traditional exact-match caching only captured 18% of redundant calls. By implementing Semantic Caching, which uses embeddings to find similar queries, the cache hit rate skyrocketed to 67%. This single architectural change reduced API costs by 73%—from $47,000 to $12,700 per month.

Mastering Thresholds and Cache Freshness

The secret to production-grade semantic caching lies in the similarity threshold. A global threshold is a recipe for disaster. Reddy discovered that FAQ queries require high precision (0.94) to avoid wrong answers, while product searches can tolerate more flexibility (0.88).

To prevent stale data, a hybrid invalidation strategy is necessary. This includes time-based TTLs, event-driven triggers when products update, and periodic 'freshness checks' that compare cached embeddings with new LLM outputs. This multi-layered approach kept the false-positive rate at a negligible 0.8%.