I was developing an app on gae using python 2.7, an ajax call requests some data from an API, a single request could take ~200 ms, however when I open two browsers and make
David Beazley gave a talk about this issue at PyCon 2010. As others have already stated, for some tasks, using threading especially with multiple cores can lead to slower performance than the same task performed by a single thread. The problem, Beazley found, had to do with multiple cores having a "GIL battle":

To avoid GIL contention, you may get better results having the tasks run in separate processes instead of separate threads. The multiprocessing module provides a convenient way to do that especially since multiprocessing API is very similar to the threading API.
import multiprocessing as mp
import datetime as dt
def work():
t = dt.datetime.now()
print mp.current_process().name, t
i = 0
while i < 100000000:
i+=1
t2 = dt.datetime.now()
print mp.current_process().name, t2, t2-t
if __name__ == '__main__':
print "single process:"
t1 = mp.Process(target=work)
t1.start()
t1.join()
print "multi process:"
t1 = mp.Process(target=work)
t1.start()
t2 = mp.Process(target=work)
t2.start()
t1.join()
t2.join()
yields
single process:
Process-1 2011-12-06 12:34:20.611526
Process-1 2011-12-06 12:34:28.494831 0:00:07.883305
multi process:
Process-3 2011-12-06 12:34:28.497895
Process-2 2011-12-06 12:34:28.503433
Process-2 2011-12-06 12:34:36.458354 0:00:07.954921
Process-3 2011-12-06 12:34:36.546656 0:00:08.048761
PS. As zeekay pointed out in the comments, The GIL battle is only severe for CPU-bound tasks. It should not be a problem for IO-bound tasks.