Why Your Database Melts When AI Features Ship: LLM-Aware Connection Pool Design
· 9 min read
Your connection pool was fine until someone shipped the AI feature. Login works, dashboards load, CRUD operations hum along at single-digit millisecond latencies. Then the team deploys a RAG-powered search, an agent-driven workflow, or an LLM-backed summarization endpoint — and within hours, your core product starts timing out. The database didn't get slower. Your pool just got eaten alive by a workload it was never designed to handle.
This is the LLM connection pool problem, and it's hitting teams across the industry as AI features move from prototype to production. The fix isn't "just add more connections." In fact, that usually makes things worse.
