Why This Python Dev Switched to Rust (and What I Gained!)

You know that feeling, right? That moment when you finally find the one? The programming language that just gets you, speaking your coding love language, you know? For the longest, longest time, that was absolutely Python for me. It was my trusty sidekick, my go-to for pretty much everything-from those super quick little scripts you bash out before breakfast, all the way to huge, sprawling web apps. I even used it for my sneaky automation tools and, like, anything with data, really. Honestly, if there was a job that needed doing, Python was usually the first thing I even thought about, and to be fair, it rarely let me down. Or, well, that’s what I kept telling myself, anyway. 🤔

Like so many developers finding their groove here in November 2025, I was just living my best life in a world where Python felt limitless. Truly. But, you know, under all that comfortable surface, these tiny, tiny little frustrations just started to bubble up. Little by little. Eventually, those small cracks-they just started to show, really. And those little cracks? They actually pushed me to look beyond my cozy comfort zone. I mean, this isn’t some dramatic tale of me ditching Python forever, nope. Not at all. It’s more about stumbling upon this incredibly powerful buddy that kinda filled a gap I didn’t even realize was there. And that buddy? It’s Rust. Seriously. Come on, join me, okay? Let me tell you about my very honest, very personal trip from being a die-hard Pythonista to, like, a total Rust fan. And yeah, what I picked up along the way, too.

Before: My Python Paradise 🐍 My coding story, just like probably a million other folks’, kicked off with Python. And honestly, for a really long time, it was pure bliss. Total paradise, I’d say. Imagine this: a cool new project idea pops into my head, right? And within, oh, maybe five minutes, I’m already spinning up a virtual environment, typing pip install like a maniac for all my favorite libraries, and then, poof! A functional prototype is practically sitting there, grinning at me. Python's readability, man, with its super clean syntax and just, you know, sensible structure, it literally felt like I was just writing stuff in plain English. Like, need to build a web API? Django or FastAPI were always there, ready to hug me. Data crunching? Pandas and NumPy? Total best friends forever. Scripting some boring system tasks? A few lines of Python, a bit of head-scratching, and boom, it's done. Just like that.

It was kinda wonderful, honestly. The sheer volume of its ecosystem meant that almost any coding puzzle I bumped into already had some super battle-tested library just waiting to jump in and help. This whole super-fast development cycle? It let me try out new things, test ideas, and just, like, deliver solutions at a speed that, let’s be real, very few other languages could even dream of. Python just made coding feel fun and, importantly, accessible. For years and years, it was an absolutely essential part of my toolkit. I really truly felt like I could tackle any coding mountain with Python right there beside me. Ah, good times.

Agitation: The Cracks in the Foundation 🚧 But, you know, as my projects kept growing-getting bigger, getting more ambitious-this really annoying, nagging feeling just kinda started to creep into my brain. My little Python paradise, which used to feel so chill, started showing these little cracks. Just little ones at first. I started noticing stuff.

First up, performance. Ugh. For those super simple scripts or, like, basic web services that mostly just waited for stuff, Python just chugged along, no biggie. But when I tried to push it, you know, into those more CPU-intensive jobs, or really tried to squeeze every last drop of speed out of a backend, Python’s Global Interpreter Lock-the famous GIL-it just felt like hitting a brick wall. My multi-threaded apps? They weren’t really parallel. It was kinda like parallel parking, but with one wheel stuck. And honestly, optimizing things just felt like trying to run uphill in quicksand. I’d stare at my code profiles, scratch my head, and realize I was just constantly smacking into these performance ceilings that seemed, like, built right into the language itself. So frustrating.

Then came the memory usage and, oh god, the runtime errors. Python’s dynamic typing, while super neat for getting something working fast, would often just throw curveballs in bigger codebases. Bugs that should have been caught, like, way early? Nope. They’d only pop their ugly heads up in production, leading to these epic debugging sessions. And memory footprints for services that ran for ages could get huge, especially compared to compiled languages. I still vividly remember this one bug-it was super stubborn, mind you-in a data pipeline. It would only show up after, like, hours of running, silently messing up data because of some weird type interaction that the Python interpreter just kinda let slide. Total nightmare fuel. Seriously.

Now, as of November 2025, I gotta give credit where it’s due. Python is making some seriously exciting moves! The community? They’ve been hustling! With Python 3.13 and, especially, when it’s fully baked into Python 3.14, we’re finally seeing the truly revolutionary option to, like, disable the GIL. This “no-GIL” build, made possible by something called PEP 703, means that for the very first time, multiple Python threads can actually run side-by-side on different CPU cores. This is huge! We’re talking big performance boosts for those CPU-heavy tasks, especially in AI and data science. Like, wow.

But, you know, this change, while awesome, is still kinda new. It’s an optional build, remember? The whole ecosystem is still figuring things out, and a bunch of existing C extensions will need updates. So, while the GIL’s iron grip is definitely loosening, those old challenges with performance and runtime safety for, well, most Python codebases? They were, and for many, still are, a big source of headache. It really pushed me to look elsewhere. I kept asking myself: I’m coding faster, sure, but am I really coding better? Is my software actually strong and super efficient? Those questions just got louder and louder, especially with how much the industry in 2025 is yelling about needing high-performance and super secure systems. I just felt that friction, that inefficiency, and that little shiver of fear about unexpected production meltdowns. My comfy Python blanket? It started feeling a bit thin in the chilly winds of modern software development. Ugh.

Bridge: The Rust Revelation 💡 It was right during this whole period of mounting frustration that I just kept hearing these whispers, you know, about Rust. “Memory safety without garbage collection!” “Fearless concurrency!” “Blazing fast performance, just like C/C++ but, like, safer!” My first reaction? Definitely skeptical. And, okay, a bit terrified, too. The learning curve looked like a mountain, a sheer cliff face, especially coming from Python’s gentle, rolling hills. Stuff like ownership, borrowing, and lifetimes? They felt so alien, so strict, like a grumpy librarian.

But the promises were just too good to ignore. Too juicy. So, I decided, “Alright, I’ll dip my toes in.” My first few weeks were, totally predictably, a masterclass in humility. The Rust compiler, who I secretly started calling “the borrow checker,” became my really strict, but eventually, super patient teacher. It would constantly, constantly point out my “oopsies,” forcing me to think about memory, how data was accessed, and concurrency in ways I just, honestly, never had before. It felt a bit like learning to code all over again, but this time? With this incredibly powerful safety net. That was kinda cool.

Then, one day, it happened. The aha! moments. ✨ As I slowly started to get Rust’s really unique way of thinking, those compiler errors got less frequent. And the why behind all its strictness? It just became crystal clear. It was beautiful, actually. I began to really appreciate:

• Compile-time guarantees: The sheer, utter relief of knowing that if my Rust code even compiled, it was, like, memory safe. And totally free from those nasty data races. After years of debugging runtime nightmares, this felt like pure magic. Honestly.
• Unmatched Performance: Building a small CLI tool or some data processing bit in Rust and then seeing it just fly, like, orders of magnitude faster than its Python twin? That was genuinely, really thrilling. Made me grin like a kid.
• Fearless Concurrency: Writing multi-threaded code and actually knowing it wouldn’t secretly introduce some sneaky race conditions? That was a complete game-changer for any high-performance app I was dreaming up. No more pulling my hair out!
• Zero-cost abstractions: Rust gives you these super powerful tools and ideas without making your program slow. It’s like having your cake and eating it too-easy to write, but super fast under the hood.

Here’s just a tiny peek at how Rust practically forces you to think clearly and safely, compared to Python. I mean, it’s pretty neat:

1. Python - easy to write, but hey, potential runtime surprises!

data = [1, 2, 3]

1. What if some other part of the code mutates 'data'
2. while 'my_slice' is busy doing its thing?

my_slice = data[0:2]

1. print(data[5]) would be a runtime error, only when it hits that line.

// Rust - the compiler is your strict but loving parent, ensuring safety. fn process_data(data_slice: &[i32]) {

   // data_slice is like a library book you borrowed, you can't scribble in it!
   println!("{:?}", data_slice);

} fn main() {

   let mut data = vec![1, 2, 3];
   // Here, we can borrow an immutable slice. Think of it as looking at a photo.
   process_data(&data[0..2]);
   // Or, a mutable slice. BUT, and this is a BIG BUT,
   // only ONE mutable reference can exist at a time. Seriously.
   let mutable_slice = &mut data[0..1];
   mutable_slice[0] = 100;
   // If I tried this now: process_data(&data[0..2]); // ERROR!
   // The compiler would scream, "Hey! You can't look at `data` here
   // because it's already being changed somewhere else! Pick one!"
   println!("{:?}", data);

}

Okay, so the Rust code might look a bit more verbose at first glance, I’ll give you that. But honestly, it stops a whole, entire class of errors that Python would just, like, casually let happen until your program blew up at runtime.

This whole new level of confidence, all thanks to the compiler’s super strict checks, was just… so liberating. Truly. It felt like I’d been programming with a blindfold on for years, and Rust just, you know, gently but firmly, took it off. “Surprise!”

After: My Rust-Powered Future ✨ So, zooming forward to November 2025, my toolkit? It’s way richer. My whole approach to programming? Much more, like, nuanced. Rust hasn’t replaced Python entirely-and, I mean, it absolutely shouldn’t! Python is still, in my humble opinion, completely unbeatable for getting prototypes out the door fast, for data science magic, and for those times when lightning-fast development and a massive, super mature ecosystem are the absolute top priorities. It still shines so bright in things like AI/ML research and whipping up web stuff with frameworks like Django and Flask. Seriously, Python’s still got it.

But for certain kinds of jobs, Rust has become my absolute champion. It’s just my go-to now when:

• I’m whipping up high-performance backend services where throughput and super low latency are, like, critically important. Think of those apps where every millisecond counts!
• I need to build some robust and super-efficient command-line tools that just compile down to one tiny, fast binary. No fuss, just pure speed.
• I’m messing around with embedded systems or IoT gadgets where resources are tight and memory safety is, like, a life-or-death situation. Heard that Tesla and SpaceX are even playing around with Rust for their firmware these days! Pretty cool, right?
• I’m diving deep into WebAssembly (WASM), pushing seriously fast code right into the browser. Imagine a Rust-powered ray tracer zipping along on your GPU, right there in your web browser. Mind. Blown.
• I’m trying to sprinkle some performance fairy dust into my existing Python apps using FFI (Foreign Function Interface). It’s like getting the best of both worlds, truly.
• I’m tackling the big, hairy beast of AI and Machine Learning infrastructure. We’re talking where execution speed for moving data around and making those models think fast is super, super crucial. Projects like DataFusion and Polars are literally reshaping how we do AI pipelines, all thanks to Rust.

Major tech companies, you know, the big players, they’re seriously, seriously investing in Rust in 2025. Google, for instance, saw a massive drop in Android memory bugs just by bringing Rust into the mix. Microsoft? They’re rewriting parts of the Windows kernel and even their Azure cloud stuff in Rust, all for that sweet, sweet security. Amazon’s little Firecracker micro-VMs, which power huge chunks of AWS Lambda and Fargate, are pure Rust-for speed, security, and, well, saving energy. Meta has actually officially adopted Rust as a backend language right alongside Python and C++, and I heard a little birdie whisper they’re even planning to rewrite their mobile messaging server from C to Rust by, like, the end of 2025. And get this: the Linux kernel, which was practically a C-only club forever, now actually accepts Rust modules-that’s a monumental shift, people! Plus, the Debian APT package manager? It’s set to start integrating Rust dependencies for some really critical bits starting May 2026. This just cements Rust’s spot in those core system utilities.

Honestly, embracing Rust has just made me a much better programmer. Full stop. It kinda forces me to really think hard about how I design systems, how I manage memory, and how I handle errors, right from the get-go. While that initial learning hump is, yeah, pretty significant, the payoffs in terms of rock-solid, super fast, and easily maintainable software are just enormous. It’s not about picking either Python or Rust. Nah. It’s about figuring out when each one really shines and, even better, how they can actually team up in a modern tech stack. Like, a superhero duo! So, if you’re a Pythonista out there, and you’re feeling those same old frustrations about performance or those sneaky runtime bugs in the really important parts of your system, maybe, just maybe, it’s time to take a little peek over the fence. You might just stumble into a whole new world of coding possibilities, just like, well, I totally did.