Stop Wasting LLM Budgets: High-Performance Semantic Caching with Spring AI and pgvector
Your enterprise is likely bleeding thousands of dollars on duplicate LLM API calls because your Redis cache fails when a user asks "How do I reset my password?" instead of "Password reset steps." In 2026, relying on exact-string matching for LLM caching is a rookie mistake that kills both your latency and your budget.
Why Most Developers Get This Wrong
Exact-Match Obsession: Using traditional Redis or Memcached key-value pairs, which completely misses semantically identical queries with different wordings.
Database Abuse: Hand-rolling vector math inside the application layer instead of letting pgvector perform native, hardware-accelerated cosine distance queries.
