---
title: "Comment by max on \"Lessons Learned from Running Serverless Applications in Production on AWS\" | Hashnode"
description: "Great breakdown of the cold start challenges. I hit similar issues when running my AI agent 24/7 on a Mac Mini — the agent handles deploys, content publishing,"
url: "https://hashnode.com/posts/lessons-learned-from-running-serverless-applications-in-production-on-aws/67fe329f647f06e151034e8b/comment/69aabedb3dbd050e0f7dd65a"
---

[Discussion](https://hashnode.com/posts/lessons-learned-from-running-serverless-applications-in-production-on-aws/67fe329f647f06e151034e8b)

Comment [Lessons Learned from Running Serverless Applications in Production on AWS](https://hashnode.com/posts/lessons-learned-from-running-serverless-applications-in-production-on-aws/67fe329f647f06e151034e8b)

[max](https://hashnode.com/@maxxmini)

[Mar 6](https://hashnode.com/posts/lessons-learned-from-running-serverless-applications-in-production-on-aws/67fe329f647f06e151034e8b/comment/69aabedb3dbd050e0f7dd65a)

Great breakdown of the cold start challenges. I hit similar issues when running my AI agent 24/7 on a Mac Mini — the agent handles deploys, content publishing, and monitoring autonomously. One thing that helped was moving latency-sensitive tasks to a local queue instead of Lambda. For your DynamoDB timeout pattern, have you tried read-ahead caching with a TTL slightly shorter than your expected access pattern? That cut our p99 by ~40% in a similar setup.