I’ll be honest, this topic confused me for a long time. Even now, I still have moments where I pause and wonder if I’m making the right choice. But that’s exactly why I’m writing this—not for you (well, not just for you), but for me.

If you’ve ever found yourself stuck deciding between multi-threading and multi-processing, you’re definitely not alone. Let me walk you through the difference in a way that finally made it click for me.

What’s the Real Difference?

At a high level, multi-threading and multi-processing are two ways to run things in parallel, they just go about it in very differently.

Threads run in the same process and share the same memory. Think of them as roommates in the same apartment: they can talk to each other easily (because they share the same space), but they have to be careful not to step on each other’s toes. This is great when your program spends a lot of time waiting, like for a file to finish loading, or for a response from a web server.

Processes, on the other hand, each have their own memory and run in entirely separate spaces. They can be thought of more like neighbors in separate houses. It’s harder for them to communicate, but they won’t mess each other up. This is ideal when your program is crunching numbers, doing math-heavy tasks, or otherwise pushing the CPU to its limits.

Here’s an interesting twist: a single process can actually spin up multiple threads internally. That means if you’re using multi-processing, each process can still have its own little team of threads working together. But if you're only using threads, you can’t easily spawn new processes or manage that kind of nested parallelism. So in that sense, multi-processing gives you more flexibility and control—especially for larger or more complex systems.

So When Should You Use Each?

If your program is waiting on something—network requests, database queries, file reads—multi-threading is usually the way to go. It lets you keep the program responsive while waiting on those slow parts. For example, a web scraper downloading hundreds of pages, or a chatbot handling multiple incoming messages at once, is probably best built with threads.

But if your program is doing a lot of thinking, like analyzing data, training a model, or rendering images, multi-processing will give you better performance. That’s because each process gets its own Python interpreter and can run on a separate CPU core. This avoids Python’s Global Interpreter Lock (GIL), which often limits how much work threads can actually do in parallel.

So Why Not Just Always Use Multi-Processing?

You might be thinking: "If multi-processing gives me more power, isolates memory, avoids the GIL, and still lets me spin up threads inside each process… why wouldn’t I just always use it?"

Fair question. The answer mostly comes down to overhead—and a few other tradeoffs.

Spinning up a new process is significantly heavier than starting a new thread. Processes take more memory, more startup time, and more coordination. If you're launching dozens or hundreds of them, you’ll feel that weight—especially if your tasks are short-lived or lightweight. In those cases, you may actually lose performance by over-engineering with processes when threads would’ve done the job just fine.

There’s also inter-process communication (IPC). Because each process lives in its own memory bubble, passing data between them isn’t as simple as just sharing a variable. You’ll need things like queues, pipes, or shared memory structures, which adds complexity and potential bottlenecks.

Processes also don’t share global state by default. That’s a good thing for safety (less chance of race conditions), but a downside if you need to share a lot of information quickly across workers.

Meanwhile, threads are lightweight, fast to start, and share memory easily. But that shared memory comes at a cost—thread safety. You’ll often need to use locks or thread-safe structures to avoid weird bugs, and if you’re in a language like Python, the GIL can become a performance bottleneck for CPU-heavy work.

So to sum it up:

Multi-processing gives you power and isolation, but at the cost of memory, startup time, and communication complexity.
Multi-threading is fast and lightweight, but can be risky in shared-memory scenarios and limited by the GIL.

A Simple Way to Decide

Here’s the quick gut check I use now:
If the bottleneck is waiting, use threads.
If the bottleneck is computing, use processes.

And if you're building something bigger or more layered, multi-processing gives you more options—you can always add threads inside a process if you need to.

One Last Thought

The biggest mindset shift for me was realizing that the goal isn’t to find the perfect answer—it’s to understand why one approach might work better than the other. And that understanding only comes with experimentation. I’ve made the wrong choice before (plenty of times), and I’ve learned from it each time.

So whether you're reading this to learn, or writing your own version to understand better—welcome to the club. Let’s keep figuring it out.

When to Use Multi-Threading VS Multi-Processing