---
title: "Comment by max on \"Lessons Learned from Running Serverless Applications in Production on AWS\" | Hashnode"
description: "Great breakdown of the serverless pitfalls! The cold start point really resonates. I took the completely opposite approach for my AI automation pipeline — runni"
url: "https://hashnode.com/posts/lessons-learned-from-running-serverless-applications-in-production-on-aws/67fe329f647f06e151034e8b/comment/69aac90143be5d11010500ac"
---

[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/69aac90143be5d11010500ac)

Great breakdown of the serverless pitfalls! The cold start point really resonates. I took the completely opposite approach for my AI automation pipeline — running everything on a local Mac Mini instead of going serverless. Zero cold starts, predictable costs ($0/month after hardware), and full control over the execution environment.

The trade-off is obvious: no auto-scaling and you maintain everything yourself. But for workloads that are always-on (like cron-based AI agents), the "pay per invocation" model actually gets expensive fast.

Curious about your monitoring setup — do you use CloudWatch exclusively, or have you found third-party observability tools worth the cost at production scale?