The original headline is "I slashed AI API costs by 80% with a cache that actually works"

Short summary

Developer reduced AI API costs by 80% using semantic caching with embeddings instead of string-matching. By converting prompts to vectors and using cosine similarity (0.92 threshold), he served 8,200 of 10,000 daily queries from cache, dropping costs from $400 to $80/week with ~30ms latency vs. 1.2s API calls. Trade-offs include embedding computation time, memory growth, and threshold tuning; for repetitive queries it's a significant win.

• •Semantic caching reduced API calls by 82% (8,200 of 10,000 queries daily) and costs from $400 to $80/week
• •Uses embeddings (all-MiniLM-L6-v2) + cosine similarity search instead of string matching to find semantically similar cached prompts
• •Requires threshold tuning (0.92 worked best); trade-offs include embedding latency and memory growth; best for repetitive query patterns

Generated with AI, which can make mistakes.