When to use Rust vs. Python

Rust: A brief overview

Rust first emerged in 2006 as a personal project of Mozilla employee Graydon Hoare with the aim of creating a safer alternative to C and C++. Later backed by Mozilla, Rust 1.0 was released in 2015 and has become widely recognized for its emphasis on safety, concurrency and performance without sacrificing low-level control.

Rust made headlines after a 2024 White House report recommended the adoption of memory safe programming languages. Months later, the Defense Advanced Research Projects Agency (DARPA) initiated the program Translating All C to Rust (TRACTOR) to further mitigate the possible cybersecurity risks and vulnerabilities associated with C and C++.

Rust's ownership model is a cornerstone feature of the language, enabling memory safety without relying on garbage collection. This approach and its "fearless concurrency" paradigm sets Rust apart from other programming languages. Rust's strengths lie in its powerful static typing, growing ecosystem of libraries and tools and ability to prevent common programming errors at compile-time -- though compile times can be slower than other languages like C and C++.

For developers new to systems programming, Rust can present a formidable challenge. Its rigorous safety checks and concepts such as ownership, borrowing and lifetimes often require a significant time investment to master.

Python: A brief overview

Guido van Rossum's brainchild, Python, debuted in 1991. Since then, it has become one of the most widely embraced programming languages, currently topping both the TIOBE Programming Community index and the PYPL PopularitY of Programming Language index.

At its core, Python emphasizes readability with clean, straightforward syntax that resembles plain English. While its accessibility makes it an ideal language for beginners, it also offers powerful capabilities for experienced programmers.

Python's strengths lie in its extensive standard library and vast ecosystem of third-party packages, enabling rapid development across various domains. The language's dynamic typing and interpreted nature contribute to its flexibility, allowing for quick prototyping and iterative development.

Despite Python's adaptability, it has slower execution speeds compared to compiled languages such as Rust, making it less suitable for performance-critical applications. Its automatic memory management can also be less efficient than manually managed languages. Furthermore, Python's process lock feature, Global Interpreter Lock (GIL), can be a bottleneck for CPU-intensive tasks. This mechanism can hinder the full potential of multithreading in certain scenarios, such as image/video and big data processing. However, in October 2024, the release of Python 3.13.0 includes an "experimental free-threaded build mode" that disables the GIL.

Rust vs. Python: Key features and differences

Rust and Python are popular with developers for different reasons and stand in contrast on a few fronts. The following categories highlight and compare their strengths and limitations.

Performance and speed

Rust is a systems programming language that provides near-C speeds with its zero-cost abstractions and compile-time optimizations. It excels in CPU-intensive tasks and memory-efficient operations.

Python is generally slower when interpreted but offers rapid development and prototyping capabilities.

Memory management

Rust provides an innovative ownership model for memory management in systems programming. By implementing a system of borrowing and lifetimes, Rust gives developers granular control over memory allocation and deallocation, all while guaranteeing memory safety without the overhead of a garbage collector.

Python uses automatic garbage collection, which simplifies development but can lead to less predictable performance in memory-intensive applications.

Concurrency and parallelism

Rust is well known for its concurrency and allows for safe and efficient parallel programming without data races.

Python's GIL can impede true parallelism in CPU-intensive operations, though it handles I/O-bound concurrency well. While the GIL can be disabled in an experimental free-threaded mode, some have said its removal is a mistake.

Ease of use and learning curve

Python's simple syntax and readability make it exceptionally beginner-friendly and quick to learn. For instance, here's what list comprehensions look like in Python:

squares = [x**2 for x in range(10)]

Rust's syntax is more verbose and has a steeper learning curve, but it offers powerful features for experienced developers. Rust doesn't have list comprehensions. The closest equivalent is the combination of iterator methods used here.

let squares: Vec<i32> = (0..10).map(|x| x * x).collect();

Ecosystem and libraries

Python boasts a vast ecosystem with libraries for almost every conceivable task, particularly in data science -- think third-party libraries like NumPy and Pandas -- and web development frameworks like Django and Flask.

While growing rapidly, Rust's ecosystem is not as extensive but offers high-quality, performance-oriented libraries. For instance, Serde and Polars are data processing options, while Actix Web and Diesel are frameworks for web development, asynchronous programming and database interactions.

Type system

One of Rust's standout features is its robust, static-type system. This powerful mechanism can catch many potential errors during compilation, significantly reducing runtime issues.

Python's dynamic typing offers flexibility but can lead to runtime errors that could be caught earlier when using Rust.

Community and support

Python has a larger, more established community with extensive documentation and learning resources.

Rust's community, while smaller, is highly engaged and supportive, with growing resources and tooling.

Cross-platform development

Both languages offer strong cross-platform capabilities. However, Python's interpreted nature and platform-independent bytecode allow for excellent portability across different operating systems, making it easy to develop applications that run on various platforms with minimal adjustments.

Error handling

Rust's compiler explicitly enforces error handling, which means more robust code.

Python's exception handling is more flexible but can sometimes lead to unexpected runtime errors.

When to use Rust vs. Python

The choice between Rust and Python depends largely on a project's specific requirements. Each language has its strengths and ideal use cases.

Rust offers strong performance and safety guarantees, making it a powerhouse for systems programming, performance-critical applications and embedded systems where every byte and CPU cycle counts.

Python, though less performant, provides faster development cycles and easier maintenance. Its simplicity and vast ecosystem make it ideal for web development, data analysis, machine learning, automation tasks and quick prototyping.

In essence, choose Rust when performance is critical and requires fine-grained control. Opt for Python when the primary concerns are development speed, readability and access to a wide range of libraries.

Twain Taylor is a technical writer, musician and runner. Having started his career at Google in its early days, today, Twain is an accomplished tech influencer. He works closely with startups and journals in the cloud-native space.