But with a local model with essentially free tokens, I can set it to work on tasks in the background without having to worry about any usage limits. Deferring simple bug fix tickets or other low complexity to an essentially unattended local model works just fine. And if the result occasionally fails review, it still didn't burn paid usage credits.

A local model tops 60 tokens/s max. Running it on the background means not being able to use on the task you are working on. When working on local hardware you cannot do concurrent requests. In deepseek for example you can have 100 agents running each getting at 120 tokens/s easily. In local models you are limited by your own infrastructure.

I do concurrent requests just fine and they have no noticeable performance impact. Not all local hardware is equal.

With cloud agents I also process stuff overnight. But my gpu is limited. If I want to OCR a high number of documents or vectorize data, I can't run an LLM at the same time. I use remote models and use my compute for running commands on my desktop machine.

You seem really upset that people do things differently than you. May I suggest trying Tetris to help you feel better? https://play.tetris.com

This only holds if you do not value your time meaningfully.