I read this argument ("async is for I/O-bound applications") often, but it makes no sense to me. If your app is I/O bound, how does reducing the work the (already idling!) CPU has to spend on context switching improve the performance of the system?

IO bound might mean latency but not throughput, so you can up concurrency and add batching, both of which require more concurrent requests in flight to hit your real limit. IO bound might also really mean contention for latches on the database, and different types of requests might hit different tables. Basically, I see people say they're IO bound long before they're at the limit of a single disk, so obviously they are not IO bound. Modern drives are absurdly fast. If everyone were really IO bound, we'd need 1/1000 the hardware we needed 10-15 years ago.

It sounds like you're assuming both pieces are running on the same server, which may not be the case (and if you're bottlenecked on the database it probably shouldn't be, because you'd want to move that work off the struggling database server)

Assuming for the sake of argument that they are together, you're still saving stack memory for every thread that isn't created. In fact you could say it allows the CPU to be idle, by spending less time context switching. On top of that, async/await is a perfect fit for OS overlapped I/O mechanisms for similar reasons, namely not requiring a separate blocking thread for every pending I/O (see e.g. https://en.wikipedia.org/wiki/Overlapped_I/O, https://stackoverflow.com/a/5283082)

The simplest example is that you can easily be wasteful in your use of threads. If you just write blocking code, you will block the thread while waiting on io, and threads are a finite resource.

So avoiding that would mean a server can handle more traffic before running into limits based on thread count.