If you are fetching lots (not even 'big data', but a few thousand rows) of data using the Django ORM, you will see a performance difference when using pypy, or

If you are fetching lots (not even 'big data', but a few thousand rows) of data using the Django ORM, you will see a performance difference when using pypy, or at least I did a few years ago. The database can happily return a few thousand rows very quickly, especially if you take care to optimize your queries and have good indexes. Converting a few thousand rows to python/django objects takes _time_. I can't quantify anything, because it's been too long, but I remember it being fairly significant. When I profiled it, the majority of the time was spent calling __setattr__ a few million times. Like you said, it depends on your use case. If your queries are slow, then optimize your database queries. But if your queries are fast and your responses are still slow, then investigating pypy is definitely worth it. You can also play around with .values_list or something in Django, so that you get 'raw values' instead of objects (but there's still a cost to building them up).