I didn't try to hit that because that's harder to call, especially at a small startup. If what is probably "the guy doing all this" happens to be more comfortable with k8s than the AWS stack you can end up winning by going with a nominally more complicated k8s stack that doesn't force you to spend dozens of hours learning more new things and instead just using what you already know. For a small startup those training costs are proportionally huge compared to a more established larger going concern already making money. Startups should generally always go with whatever their engineers already know unless there is a damned good reason not to. (And "I just wanted to learn it" is not a good reason for the startup.)

But monetarily, even for a startup, $400/month savings is something you shouldn't be pouring the equivalent of $5000 (or more, just picking a reasonable concrete number to anchor the point) into. You really need to solve a $400/month problem by putting your time into something, anything that will promote revenue growth sooner and faster rather than optimizing that particular cost.

If your parent company sysadmins invest heavily into automation each sysadmin could be managing thousands of servers.

Also, 6000 CPU "cores" on the cloud is more like 3000 CPU cores. Which you can get in just 20-50 servers. This is in the range of something that could be taken care of as a part time job.

Exactly this. I know people don't like to use this term (because it comes from traditional IT), but this is effectively known as TCO (total cost of ownership). The whole "bare metal" versus the well known hyperscalers debate often misses this with a hand-wavy "just get better devops people and its cheaper".