[throwaway894345]

> Memory is not cheap when dealing the real world cost of deploying a production system.

What? What makes you say that? What did you think I was talking about if not a production system? To be clear, we're talking about the overhead of single-digit additional python interpreters unless I'm misunderstanding something...

[Grimm1]

Observed costs from companies running the pre fork worker model vs alternative deployment methods and just in the benchmark they're running double digit interpreters which I've seen as more common and expensive.

[throwaway894345]

Double-digit interpreters per host? Where is the expense? Interpreters have a relatively small memory overhead (<10mb). If you're running 100 interpreters per host (you shouldn't be), that's an extra $50/host/year. But you should be running <10/host, so an extra $5/host/year. Not ideal, but not "expensive", and if you care about costs your biggest mistake was using Python in the first place.

[Grimm1]

I don't know where you're seeing the < 10mb from the situation I saw they were easily consuming 30mb per interpreter. Even my cursory search around now shows them at roughly 15-20mb so assuming the 30mb Gunicorn was just misconfigured that's still an extra $100 per host using your estimate and what I'm looking at Googling around and across a situation where there are multiple public apis that's adding up pretty quickly.

Another google search shows me Gunicorn, for instance, using high memory on fork isn't exactly uncommon either.

Edit: I reworded some stuff up there and tried to make my point more clear.

[throwaway894345]

The interpreter overhead on macos is 7.7mb. I can't speak to gunicorn configuration but it's far from the only game in town.

[Grimm1]

Totally fair point, my experience with fork type deploys has only been Gunicorn so I'll take this as a challenge to try some others out.