What is the real difference between Multiprocessing and Multithreading in Python?

All answers to this question.

Since your task is making API calls, it is considered "I/O-bound" (Input/Output). For I/O-bound tasks, threading or asyncio is much better. The GIL only prevents multiple threads from executing Python bytecode at the same time, but when a thread is waiting for a network response, it releases the GIL. This allows other threads to start their own requests. Multiprocessing would be overkill because it creates entirely new instances of the Python interpreter for each process, which consumes way more RAM. Use concurrent.futures.ThreadPoolExecutor for a clean, modern way to handle those 500 API calls in parallel without the heavy overhead of separate processes.

   Answered 2025-02-07 by Cynthia Martinez

If you were doing something like image processing or heavy mathematical calculations instead of API calls, would you still suggest threading? I’ve always been told that threading is "fake" parallelism in Python because of the GIL.

   Answered 2025-02-10 by Kevin Douglas

• Great question, Kevin. For CPU-bound tasks like image processing, threading is useless—it will actually be slower due to management overhead. That is exactly where multiprocessing shines. By creating separate processes, you bypass the GIL entirely and can truly utilize every core on your CPU. So: Threading for waiting on the web/disk, Multiprocessing for crunching numbers and heavy logic.

     Commented 2025-02-12 by Tyler Peterson

For API calls specifically, you should really look into asyncio and aiohttp. It’s much more efficient than threading because it uses a single thread to manage thousands of concurrent connections.

   Answered 2025-02-15 by Lawrence King

• Lawrence is spot on. Async is the modern way to go for web tasks, though the learning curve for "async/await" syntax is a bit steeper than just using a ThreadPool.

     Commented 2025-02-17 by Cynthia Martinez