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Rust vs. Python: Finding the right balance between speed and simplicity

This blog post was brought to you by Damaso Sanoja, draft.dev.

Deciding whether to use Python or Rust isn’t just a syntax choice; it’s a career bet. According to the StackOverflow Developer Survey, Python dominates in accessibility, with 66.4% of people learning to code choosing it as their entry point. Python usage skyrocketed from 32% in 2017 to 57% in 2024, making it the tool of choice for “more than half of the world’s programmers.” GitHub has also reported that Python overtook JavaScript as their most-used language for the first time in over a decade.

But Rust’s trajectory tells a different story. Despite its complexity, its usage has grown from 7% to 11% since 2020. It’s also been the “Most Admired programming language” for nine straight years, with over 80% of developers wanting to continue using it.

The real question isn’t which language will “win” – it’s which one positions you for the problems you want to solve. This article explores their syntax, performance, learning curves, type systems, memory management, concurrency, ecosystems, and tooling to help you make a decision that aligns with both market realities and your career goals.

TL;DR: Python and Rust serve different goals. Python wins on accessibility, flexibility, and a vast ecosystem. Ideal for rapid development, data science, and automation. Rust shines in performance, safety, and concurrency, making it perfect for systems programming and large-scale, reliable software. In short: choose Python to build fast, iterate faster, and reach users quickly; choose Rust to build software that runs fast, scales safely, and lasts. Many developers now combine both; using Rust for performance-critical parts inside Python apps.

Syntax and Readability

Python’s syntax is praised for its clean, English-like structure that closely resembles written algorithms or planning notes. It prioritizes developer approachability and rapid prototyping through dynamic typing, which means you don’t need to declare variable types explicitly. This allows for very concise code. Python’s PEP 8 style guide establishes consistent formatting conventions that make Python code predictable and easy to scan.

For example, you can define a simple add function, assign a string to a variable, and create a formatted message with just a few lines of code:

# Python: Flexible and concise

def add(a, b):

return a + b

name = "World"

message = f"Hello, {name}!"

Rust uses C-style syntax that requires you to specify types (fn add(a: i32, b: i32) -> i32) and declare when variables can change (let mut message = String::new();). This looks more verbose and adds learning challenges like ownership (like [&str](https://doc.rust-lang.org/book/ch04-03-slices.html) for borrowed string slices), but it’s intentional. Rust’s explicit approach catches more errors at compile time and makes program structure clearer, even though you might write more code. Tools like rustfmt handle code formatting automatically.

Here’s how the same functionality looks in Rust, with explicit type declarations and mutability:

// Rust: Explicit types, mutability, and function signature

// Function definition

fn add(a: i32, b: i32) -> i32 {

a + b

}

fn main() {

let name: &str = "World";

let message: String = format!("Hello, {}!", name);

println!("{}", message);

let mut count: i32 = 0;

count += 1;

println!("Count: {}", count);

// Using the add function

let sum = add(5, 10);

println!("Sum: {}", sum);

}

This core difference – Python’s easy flow versus Rust’s strict structure – is often what developers first notice when choosing between them, and it affects both how you write code and how fast it runs.

Performance and Speed

The differences between Rust and Python become even more apparent when looking at their raw performance.

Rust compiles directly to machine code before you run it, so it’s fast and efficient. Python reads and translates your code line by line as it runs, which is more flexible but slower. Rust’s zero-cost abstractions and lack of a garbage collector also contribute to its speed advantage, especially for CPU-heavy tasks.

You can see the difference in real-world benchmarks where Rust consistently runs faster and uses less memory. However, it’s worth noting that benchmark results are frequently updated and can vary based on the specific tests and implementations used.

These are some commonly observed performance patterns:

• Execution speed: Rust frequently outperforms Python by substantial margins in CPU-intensive tasks, with advantages ranging from modest improvements to dramatic speed-ups depending on the specific computational workload.
• Memory efficiency: Rust’s zero-cost abstractions and lack of garbage collection typically result in significantly lower memory usage compared to Python’s runtime overhead.
• Consistency: These performance characteristics remain relatively stable across different types of computational tasks, from mathematical simulations to algorithmic challenges.

Performance differences will depend on your specific setup and code, but Rust consistently wins for heavy computational work.

The speed gap means Rust works better for large, time-critical systems, while Python’s speed is often good enough when you want to build and test ideas quickly rather than squeeze out maximum performance.

Ease of Use and Learning Curve

Python is famous for its gentle learning curve; its simple syntax and flexibility make it great for beginners who want to build things quickly. For example, reading user input and displaying a personalized greeting takes just a few straightforward lines:

# Python: Reading and printing a name

name = input("Enter your name: ")

print(f"Hello, {name}!")

Rust’s strict compiler and ownership system require much more upfront learning. The same greeting task in Rust immediately throws several complex concepts at you. This code shows how you need explicit imports for basic input/output, function signatures that handle errors (io::Result<()>), mutable string creation (String::new()), explicit error checking (the ? operator), and details like flushing output and trimming input:

// Rust: Reading and printing a name with error handling

use std::io::{self, Write}; // Import necessary modules

fn main() -> io::Result<()> { // main function returns a Result for I/O errors

print!("Enter your name: ");

io::stdout().flush()?; // Ensure the prompt is displayed before input

let mut name = String::new(); // Declare a mutable string to store input

io::stdin().read_line(&mut name)?; // Read line, handle potential error

println!("Hello, {}!", name.trim()); // Trim whitespace (like newline) and print

Ok(()) // Indicate success

}

This heavy upfront learning makes Rust harder to start with, but it’s part of Rust’s design. The language acts like a strict teacher, forcing you to write safe, efficient code from the beginning. You get more control and fewer runtime crashes in exchange for the initial difficulty.

Python gets you started immediately, while Rust makes you work harder upfront for more reliable code.

Typing System (Static vs. Dynamic)

Python uses dynamic typing, where types are checked at runtime, providing great flexibility and accelerating initial development as you rarely need explicit type declarations. This freedom, however, means type-related errors (like TypeError) may only surface during execution, which is a potential risk in larger systems. To address this, Python developers often use tools like mypy, a static type checker that analyzes code before runtime to catch type-related errors early and improve code safety in larger projects.

Rust uses static typing, which means it checks all your types before your code even runs. While this demands more upfront effort to satisfy the compiler’s rigor, it’s a powerful early warning system and significantly reduces type-related runtime errors and improves code maintainability. Rust’s type system includes enums like Option<T> and Result<T, E> to handle the absence of values and recoverable errors explicitly. You need to weigh Python’s development speed against Rust’s early error detection and type safety checks that happen before the program runs.

Memory Management

Python uses automatic garbage collection with reference counting and a cycle detector to clean up memory automatically. This frees you from the burden of memory management, which simplifies coding. But it can cause unpredictable pauses and performance overhead, which can be a problem for time-critical applications like financial trading systems or robotics, where consistent timing is crucial.

Rust takes a completely different approach with its ownership system, which handles memory management while your code compiles. Through ownership rules, borrowing (utilizing & for immutable and &mut for mutable references), and lifetimes, Rust prevents common memory errors like null pointer dereferences, dangling pointers, and use-after-free errors in safe code – all without a runtime garbage collector. While unsafe blocks allow you to bypass these protections when needed, most Rust code benefits from these safety guarantees. This results in highly predictable performance and fine-grained control. However, mastering the borrow checker and its concepts can be challenging if you’re used to garbage-collected languages.

The choice here is stark: Python offers easy memory management, while Rust demands more work upfront for memory safety and performance.

Concurrency and Parallelism

The threading module in Python’s common CPython implementation allows you to easily create threads:

# Python: Basic thread creation

import threading

def python_task():

print("Hello from a Python thread!")

# To run it:

# thread = threading.Thread(target=python_task)

# thread.start()

# thread.join()

However, Python’s Global Interpreter Lock (GIL) restricts multiple native threads from executing Python bytecode simultaneously for CPU-bound tasks, which limits true parallelism on multicore processors. This means that even on a system with multiple CPU cores, only one Python thread can actually execute at any given time – threads must take turns rather than running simultaneously on different cores. This makes Python’s threading module most effective for I/O-bound operations, where threads can release the GIL while waiting for external operations.

For efficient single-threaded concurrency in I/O tasks, Python offers asyncio. For true CPU parallelism, you need the multiprocessing module, which creates separate processes to bypass the GIL but uses more memory and requires complex communication between processes. While experimental efforts like PEP 703 aim to make the GIL optional, Python’s concurrency requires navigating these trade-offs.

Rust is known for its fearless concurrency, thanks to its ownership and type systems that prevent data races at compile time. Creating a basic thread is straightforward using std::thread:

// Rust: Basic thread creation

use std::thread;

fn rust_task() {

println!("Hello from a Rust thread!");

}

// To run (e.g., in main function):

// let handle = thread::spawn(rust_task);

// handle.join().unwrap();

Critically, threads like this in Rust can run in true parallel on different CPU cores because Rust does not have a GIL. Compile-time checks involving Send and Sync traits ensure that data shared between threads is handled safely. For more complex scenarios, Rust offers powerful tools like [async/await](https://rust-lang.github.io/async-book/01_getting_started/04_async_await_primer.html)for asynchronous programming and types like Arc and Mutex for safe shared-state concurrency.

Summing up, Rust excels at building highly concurrent, multithreaded applications that demand maximum hardware efficiency.

Ecosystem and Libraries

Python has an extensive, mature ecosystem; PyPI (Python Package Index) offers an unparalleled array of libraries for nearly every domain, especially data science (eg NumPy, pandas, and TensorFlow), web development (eg Django, Flask, and FastAPI), and automation, complemented by a batteries-included standard library.

Rust’s ecosystem, managed via the Cargo build tool and package manager sourcing from crates.io, is younger but expanding. It’s particularly good for systems programming, WebAssembly, networking, and command line tools, with libraries engineered for performance and safety. Interestingly, Rust is increasingly used to build high-performance tools for the Python world (eg Polars, Ruff, and uv), showing how the two languages can also work together rather than just compete.

The ecosystem choice often depends on project requirements: Python’s mature, extensive libraries make it ideal for rapid development and established domains like data science and web development, while Rust’s performance-focused ecosystem shines when you need maximum efficiency and safety or are building foundational tools that others will depend on.

IDE and Tooling Support

Python’s great tooling matches its popularity; it’s been the #2 programming language since early 2020. Comprehensive IDEs like PyCharm and interactive tools like Jupyter Notebooks give you everything you need: debuggers, linters, test runners, and more. All of which makes Python even easier to use.

Rust’s tooling evolution reflects its remarkable developer satisfaction story. How does a systems language achieve 83% developer retention and “Most Admired” status for nine consecutive years? Partly through exceptional tooling that makes complex concepts manageable. Cargo revolutionized build management, rust-analyzer provides precise code intelligence, and Clippy offers intelligent linting. As more companies adopt Rust, IDEs are also becoming more mature, with tools like JetBrains RustRover providing specialized support for Rust’s ownership model.

Color-coded inlay error descriptions

It’s a classic feedback loop: Python’s popularity drives better tooling, which makes Python even more popular. Rust’s exceptional tooling experience explains why developers who try it stick with it. Both languages benefit from this virtuous cycle, serving different developer needs and project requirements.

Comparison Table: Rust vs. Python

Here’s an overview of the key differences between Rust and Python:

Feature	Rust	Python
Syntax & Readability	Explicit, C-family syntax; can be verbose but aims for clarity and may offer lower cognitive complexity; rustfmt for style.	Minimalist, near-pseudocode style; generally beginner-friendly and concise; PEP 8 for style conventions.
Performance & Speed	Compiled to optimized machine code; very high raw performance and no GC pauses; ideal for CPU-bound and time-sensitive systems.	Primarily interpreted; slower execution speed for CPU-bound tasks; potential GC pauses; often adequate for I/O-bound tasks and rapid development.
Ease of Use & Learning Curve	Steeper learning curve due to its strict compiler, ownership model, and lifetimes; rewards effort with safe and efficient code.	Gentle learning curve with simple syntax and dynamic typing; facilitates rapid development and prototyping; very accessible to beginners.
Typing System	Static, strong compile-time type enforcement; significantly reduces runtime type errors and enhances maintainability; expressive type system.	Dynamic typing with runtime type checking; offers flexibility and reduces boilerplate, but type errors may surface at runtime; optional static type hints available.
Memory Management	Compile-time enforcement via ownership, borrowing, and lifetimes; no garbage collector, leading to predictable performance and control.	Automatic garbage collection (primarily reference counting with a cycle detector); simplifies memory management for developers but can introduce overhead and pauses.
Concurrency & Parallelism	“Fearless concurrency” due to compile-time data-race prevention; no GIL, enabling true multicore parallelism for CPU-bound tasks.	GIL in CPython limits true CPU-bound parallelism in threads; asyncio is effective for I/O-bound concurrency; multiprocessing for CPU-bound tasks.
Ecosystem & Libraries	Rapidly growing ecosystem via Cargo and crates.io; strong in systems programming, WebAssembly, networking, and CLI tools; performance-focused libraries.	Vast, mature ecosystem via PyPI; dominant in data science, machine learning, web development, and automation; extensive “batteries-included” standard library.
IDE & Tooling Support	Significantly improved and maturing; strong support from rust-analyzer, Cargo, and Clippy; dedicated IDEs like RustRover by JetBrains enhance productivity.	Excellent and mature tooling; widely supported by IDEs like PyCharm and VS Code, Jupyter Notebooks for data science, and a rich collection of linters and debuggers.

Career Insights

Python’s career appeal lies in its versatility across high-growth sectors. The language powers data science, machine learning, web backends, enterprise applications, and automation, which are all in high demand.

Rust presents a different but compelling career trajectory. While job openings are fewer, the compensation could be higher: some Rust developers earn up to $198,375 annually. However, Rust’s lower market share also means more volatile job opportunities, with Rust developer wages ranging dramatically from $40.38 to $64.66 per hour and an average of $53 per hour. This wage variation suggests that Rust’s job market is less predictable than Python’s, which enjoys a consistent demand for developers.

Overall, Rust’s career advantage lies in specialization. 43% of organizations are currently using Rust for non-trivial production use cases like server backends, web3 services, and cloud technologies. These infrastructure roles have the potential to command premium salaries due to their critical nature and the specialized expertise required.

Your career choice comes down to whether you want Python’s broad job market or Rust’s growing specialization in high-performance, reliable systems.

Hybrid Development Strategies: Rust-Enhanced Python Development

The choice between Python and Rust isn’t always black and white. Many developers are going for a third option: use both languages strategically. Keep Python for your main application logic where readability matters, but drop in Rust for the parts that need serious speed.

This hybrid approach is gaining real momentum. Major Python tools are being rewritten in Rust with dramatic results. Pydantic-core rewrote its validation logic in Rust and got 17x faster performance. Ruff provides Python linting that’s 10–100x faster than existing tools while installing with a simple pip install ruff.

The PyO3 ecosystem makes this integration surprisingly easy. You can write performance-critical functions in Rust and call them directly from Python. Start by prototyping in Python’s friendly syntax, then optimize the slow parts with Rust. Tools like pyo3_bindgen even automatically generate the connections between your Python and Rust code.

This co-usage model is appearing in production across major projects, including FastAPI, Jupyter, Pydantic, and Polars, showing its viability at scale. This approach offers immediate performance gains without requiring complete codebase rewrites, making it an attractive middle ground between Python’s accessibility and Rust’s performance characteristics.

Conclusion: Choosing Your Language – Rust and Python

This comparison shows that Rust and Python are built for different needs and approaches. Modern software development increasingly reveals that their strengths work together rather than compete.

Python remains excellent for rapid application development, data analysis, machine learning, and scripting, propelled by its accessible syntax, flexibility, and mature ecosystem. On the other hand, Rust is the best choice for performance-critical applications, systems programming, embedded development, and projects where memory safety and efficient concurrency are essential.

If you’re trying to build reliable, high-performance systems with Rust or exploring the language professionally, JetBrains RustRover offers a minimally configured environment with deep language support. With a non-commercial license available for learners and hobbyists, it’s a superb tool to grow with as you delve into Rust.

Curious about how Rust can benefit from other programming languages? Check out our previous blog post comparing Rust with Java and Go.

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