I would use it in production. It just depends on what I'm producing. "Production" can range from everything like a banking website serving millions (nope) to a kids toy (yup).
There are a ton of uses where performance doesn't matter, and some where data is even ephemeral or non-critical. These sorts of simple tools are also really nice for test cases, development environments, and a ton of other uses.
I'm developing a tool designed for large-scale data processing, and I have a dummy back-end very similar to this which I use for development.
My computer has close to 4GHz and multiple cores. Essentially anything which ran on an 80486 back in the day will be fast enough in interpreted Python. That's actually a lot of stuff.
And yes, I'm not disagreeing, but agreeing and expanding on your easily-missed disclaimer ("production *that has scaled up*").
There are a ton of uses where performance doesn't matter, and some where data is even ephemeral or non-critical. These sorts of simple tools are also really nice for test cases, development environments, and a ton of other uses.
I'm developing a tool designed for large-scale data processing, and I have a dummy back-end very similar to this which I use for development.
My computer has close to 4GHz and multiple cores. Essentially anything which ran on an 80486 back in the day will be fast enough in interpreted Python. That's actually a lot of stuff.
And yes, I'm not disagreeing, but agreeing and expanding on your easily-missed disclaimer ("production *that has scaled up*").