As with most things in software, speed can't be measured only by the running program -- the time required to write the program should be included in nearly all cases.
Python: fast to write, slow to run.
C/C++: slow to write, fast to run.
To put it succinctly, you can write fast programs, and you can write programs fast, but you can't write fast programs fast.
I entirely agree, for most situations the trade off is in favor of high-level languages like Python. The scale tips even further in favor of CPython when you start using C extensions like Numpy. All of the benefits of Python, with speed approaching that of pure C (there's some overhead when calling from Python). For use-cases like web applications, the language is almost never the bottleneck anyways. Network latency and database queries usually eat up far more time than the glue code holding it all together. And this isn't even touching on the security benefits of memory safe languages.
Now I'm genuinely interested in knowing which parts of python are 'slowing down' the application. With the help of Cython[0], one can give hints to the compiler to improve performance for most of the cases.
