Hi, thanks for your response :) Edit: I've been downvoted so I'll add a precision: Usually, it is believed that async shines against other models once you reac

Hi, thanks for your response :) Edit: I've been downvoted so I'll add a precision: Usually, it is believed that async shines against other models once you reach a certain scale (https://en.wikipedia.org/wiki/C10k_problem https://en.wikipedia.org/wiki/C10k_problem). This benchmark shows than async app frameworks are slower than the sync ones when running at a given scale, and since the article doesn't give many details on the incomming traffic, I can only assume that it's low, since it saturates 4 cores. I believe that your conclusion that "Async python is not faster" is an over generalization of your use case. I'm not saying that the configuration in your benchmark is not correct, I am saying that this benchmark may not yield the same results if you try to scale it on bigger hardware. I believe that scheduler overhead can't be ruled out (not for python nor any other program) on a server since we've sometimes observed that the scheduler could be the bottleneck under some circumstances. For instance, some Linux schedulers used to show poor perfs when using nested cgroups with resources quota enabled. Also, I'd like to state my first point again: you need to see how the number of workers will influence the memory usage on your system. Especially with python, if you've got a lot of workers, you can expect some memory fragmentation that can impact the perf of your system.