6 Mistakes Developers Make When Deploying Generative AI on AWS (And How to Fix Them)

Generative AI is everywhere right now. We’re building AI report generators, document summarizers, compliance checkers, risk engines, chatbots — and most of them work perfectly in local development. Until they hit production. Then things start breaking. Timeouts. Retries gone wrong. Users refreshing the page 10 times. S3 buckets accidentally public. No clear job status. Lambda costs increasing silently. I recently built a production-ready serverless Generative AI backend on AWS, and along the way I made (and fixed) almost every mistake in this list. If you’re deploying GenAI workloads on AWS, especially with Lambda, this article will save you time, money, and headaches. Let’s break it down. Mistake #1: Blocking API Calls with LLM Requests The Problem The most common mistake I see: // Inside API handler const result = await callLLM (); return result ; ` Looks simple. But here’s what happens in production: API Gateway has a 29-second timeout LLM calls can take 10–60 seconds External APIs