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It's a different situation, but if you're talking about fixed cost subscriptions, this is often easier to manage when you have fixed income than when you don't; less risk. So I'd stick with whatever is affordable.
For my example above, I was getting a bit restless. So I said "okay Fable... propose a fix." (in expansive, precise instructions.) It burned through my 5h allocation + $19 in extra credits. I get the result, read it, it feels wrong. Fire up GLM and GPT concurrently to do an assessment. Both find the same error I thought I found, and extras. No clean solution. $30 down the drain plus a 4.5h pause on my subscription, no progress and thus no billables because no results. No offset to this cost. I just have to bear it, find another solution. Plan a refactor instead. Which is even more expensive and carries heightened risk, but it has to be done. So I'm doing that now.
Still, the bots found bugs that otherwise would be on other people to fix. It is still better this way. The quality of the final result will have to make up for it and at least I'll be able to sleep well, not worried about what bugs I may have introduced. Bottom line, I'll not make a loss on the token cost, but just worked to get rather inefficient, yet hopefully really high quality, results
Billable hours -- that probably makes a big difference between you and me.
As a researcher on a fixed wage without productivity incentives, other than my personal drive and/or passion, my employer is not really pushing me to be more productive. If/when I eventually become a civil servant, the carrot that is the permanent academic post will disappear as well.
Still, I want to be the best at what I am doing now, advance the field, publish impactful work, etc, so incorporating AI in my workflow is non-negotiable. So, that's what rationalizes me to pay out-of-pocket, but I do hope that at some point I can use some of my grants to pay for it. Especially when I get back to heavy development work, it was painful when I was reaching my limits more often than not.