The Hidden Cost of Calling AI Too Early I stopped calling AI on every request — and everything got better. The Problem In one of my projects, I was generating AI-based insights from user activity. The initial design was simple: Every request for today’s insight → call the AI model → return a fresh response. GET /api/insights/today Enter fullscreen mode Exit fullscreen mode At first, this felt clean and correct. But in practice, it created serious problems: 429 rate limit errors within hours Daily quota exhausted before noon Random failures affecting users Costs scaling linearly with traffic The system was working — but it wasn’t sustainable. The Real Issue The problem wasn’t the AI provider. It was the trigger model . The system never asked basic questions before making an expensive call: Has anything actually changed? Did I already generate a response recently? Is the user even active today? Without these checks, every request was treated as: “Generate a new insight now.” That assumption was the real bug.…