Python vs. Rust: Choosing the Right Programming Language in 2025

Selecting the correct programming language is critical for businesses since it impacts efficiency, development processes, scalability, and overall complexity. Elements such as readability and the overall effectiveness of the work done are important considerations. 

Among developers, Python remains one of the most highly desired and admired functional programming languages, but Rust continues to be the most admired, with an 83% score in 2024, according to StackOverflow.

Both Rust and Python are powerful programming languages that serve different purposes in software development.

In this guide, we will compare Rust vs Python concerning their major features, advantages, disadvantages, and use cases. Also, you can find some tips, which are intended to make your choice easier. 

What Is Rust? 

Rust is a systems programming language that many companies appreciate due to its high performance, safety features, and ability to run multiple tasks simultaneously. Rust is designed around the concept of high control of system resources. This makes it very appealing for applications that need high security and efficiency. While doing so, it has some limitations, such as needing to allocate memory through a garbage collector. 

Key features of Rust

• Memory safety. Rust’s ownership model eliminates memory leaks and data races, making it perfect for secure applications. 
• Exceptional efficiency. Rust provides performance on par with C and C++ through cost-free abstractions and effective code compilation. 
• Concurrency. Rust’s rigid ownership and borrowing rules enable safe simultaneous execution. 
• Safety-first method. The compiler imposes stringent safety regulations during compilation, minimizing runtime issues. For example, in languages without strict memory safety, dereferencing a null pointer can cause a program to crash, whereas Rust’s ownership system prevents such issues at compile time.
• Growing ecosystem. Rust’s package manager, Cargo, streamlines dependency handling and enables smooth incorporation of external libraries.

Use cases of Rust

Rust provides reliability, safety, and exceptional performance, making it a strong choice for long-term, scalable solutions. However, companies need to take into account the learning curve and reduced ecosystem, which could affect development speed, recruitment expenses, and time-to-launch. 

What Is Python? 

Python is an interpreted high-level programming language known for its useful, clear, and easy nature. You can find its applications in so many fields like website development, machine learning, artificial intelligence, and even automation, which is why anyone finds it useful. 

Use cases of Python

Syntax and Readability

In comparing Python and Rust, their syntax and readability showcase their design philosophies—Python emphasizes simplicity and clarity, whereas Rust concentrates on safety and efficiency. 

Python: Simplified yet effective through readability 

The structure of Python allows for it to be simple and easily readable which makes onboarding easier. Developers need less time to dive into the project. 

Some of the key benefits include:

• Allowing quick development with simple and clear syntax. 
• The lack of requirement for explicit declaration of variable types makes it dynamically typed. 
• Easier development with the comprehensive libraries, automation tools and data science framework.

For example, in Python development, the factorial of a number can be computed easily with a loop or recursive function.

def factorial(n):
    result = 1
    for i in range(1, n+1):
        result *= i
    return result

print(factorial(5))

Python is dynamically typed, so no explicit types need to be defined, and its loop structure is simple without requiring memory management. Additionally, the code remains concise and readable, which helps developers build and maintain projects quicker.

Rust: Accurate and focused on efficiency 

Conversely, Rust mandates rigorous type safety and memory management, resulting in a syntax that is more organized yet also more intricate. 

• Statically typed, necessitating clear type declarations. 
• Rules regarding ownership and borrowing help avoid memory-related issues. 
• By default, variables are immutable, with mut needed for any changes. 

Now, let’s look at the same factorial computation, but in Rust:

fn factorial(n: u64) -> u64 {
    let mut result: u64 = 1;
    for i in 1..=n {
        result *= i;
    }
    result
}

fn main() {
    println!("{}", factorial(5));
}

Rust is aimed at prioritizing capacity and safety. Its features work together to ensure high-speed, reliable code with minimal memory-related bugs.

Speed & Performance

When analyzing Python and Rust, the one area that stands out is performance. While Python is user-friendly and very easy to work with, Rust is all about getting things done effectively and efficiently.

Python: Slower but flexible

Python is generally a slower language, however it makes up for flexibility. It is an interpreted language, which means it executes code line by line during run time. 

• Dynamic execution. Code is run instantly, resulting in increased computational overhead. 
• Less fast than compiled languages because of interpretation during runtime. 
• Perfect for prototypes and advanced applications, where quickness is not a primary concern. 

Analogy:
Using Python is like reading a book in a foreign language with a translator sitting next to you, translating each sentence as you read. This allows you to start reading immediately, but the process is slower because translation happens on the fly.

With zero effort when it comes to memory management, applications which speed and efficiency run crazy deal with exact memory management.

Rust: Faster and more efficient

Rust is a compiled language, which indicates that the code gets converted into machine code before execution. This leads to considerably quicker efficiency with little runtime overhead. 

• Fast execution is achieved because Rust is compiled directly into machine code, removing delays caused by interpretation.
• High efficiency makes Rust ideal for systems programming, game development, and high-performance computing tasks.
• Minimal runtime overhead is ensured through memory safety without a garbage collector. 

Analogy: Considering our example with a book, a compiled tool like Rust is like receiving a fully translated version of the book. You can’t start reading right away since the entire book needs to be translated first (compiled), but once that’s done, you can read it smoothly without interruptions.  

Rust’s efficiency benefits make it perfect for applications that require rapidity, efficiency, and exact memory management. 

Data Typing 

When deciding between Python and Rust, an important factor to think about is how both languages manage data typing

Python: Dynamic but less efficient

Python employs dynamic typing, which means variable types are established during runtime instead of being declared explicitly. This flexibility streamlines development and reduces the need for boilerplate code, allowing teams to deliver features faster. 

However, the lack of strict type control can introduce unexpected type-related issues during execution. As a result, projects may face increased QA efforts, more debugging time, and potential delays in delivery due to subtle bugs that only appear at runtime. 

Analogy: Using Python’s dynamic typing is like having a personal assistant who figures out what supplies you need while you’re already hosting an event. You don’t spend time planning every detail upfront, so things get moving quickly. But sometimes, the assistant brings the wrong items, and fixing these mistakes on the fly can slow things down or cause disruptions

Rust: Static and optimized

Rust mandates static typing, which requires that variable types be explicitly specified during compilation. This approach helps catch type discrepancies early in the development process, preventing runtime type errors and contributing to more reliable and consistent code. 

By eliminating the need for runtime type verification, Rust enhances efficiency and reduces unnecessary processing. As a result, projects benefit from fewer unexpected issues, improved optimization, and overall higher stability.

Analogy: Using Rust’s static typing is like building a house with detailed blueprints before construction begins. While creating the blueprints requires upfront effort, it ensures that every piece fits perfectly, reducing costly mistakes during the build. Once construction starts, everything runs smoothly, with fewer surprises and a stronger, more reliable structure in the end. 

Memory Management 

Memory management plays a vital role in the performance of software development. Although Python features automatic memory management via garbage collection, Rust grants enhanced control through its ownership system. 

Python: Easy to Use but Possibly Expensive 

Python depends on garbage collection (GC) for memory management, automatically freeing up unused memory to avoid leaks. 

However, this approach comes with several potential drawbacks:

• Performance overhead. Garbage collection interrupts program execution, potentially affecting capacity, particularly in real-time or memory-demanding applications. 
• Uncertain memory consumption. The schedule of garbage collection executions can lead to slight delays in performance-critical applications. 

Analogy: Consider Python’s memory management like a cleaning crew in a bustling office complex. The custodians (waste handlers) regularly inspect and tidy unoccupied areas (memory), making certain spaces accessible. Nonetheless, their actions can briefly interrupt operations, similar to how waste collection may result in minor delays during execution. 

Rust: Predictable and Efficient

Rust employs a distinctive ownership model that removes the necessity for garbage collection, all the while guaranteeing memory safety. 

This model brings several key advantages:

• Ownership and borrowing principles. Rust imposes rigorous memory management regulations during compilation, averting typical memory problems such as leaks and data races. 
• Increased control and efficiency Developers experience enhanced predictability and effectiveness in memory utilization, positioning Rust as perfect for low-latency applications. 

Analogy: Rust’s memory management resembles a meticulously arranged warehouse where every item (memory allocation) is distinctly tagged with an owner (variable), and regulations dictate how items can be lent or moved. 

Concurrency Support

Concurrency is essential for improving application performance by enabling several tasks to operate at the same time. 

Python: Limited by the Global Interpreter Lock 

Concurrency enables the simultaneous execution of multiple tasks; however, Python’s Global Interpreter Lock (GIL) limits genuine parallel execution. 

• GIL limits multi-threading. At any given moment, only a single thread can run Python bytecode, which reduces the advantages of multi-threading. While multi-threading can help with I/O-bound tasks, for CPU-bound tasks, the GIL significantly diminishes its effectiveness. 
• Multiprocessing as a substitute. Python developers often turn to multiprocessing instead, which allows true parallelism by running separate processes that bypass the GIL, enabling better use of multiple cores for computational tasks. 
• Asynchronous programming. The asyncio library in Python enhances concurrency in tasks that are I/O-bound (for instance, web requests or database queries). 

Analogy. Picture a kitchen filled with several chefs, but just one knife (GIL) that they need to share by taking turns. Although they are still able to cook, restricted access to the knife hampers their overall efficiency.

Rust: Native support for high-performance concurrency

Rust is designed for safe and effective concurrency, enabling genuine multi-threading free from the limitations of a GIL. Unlike Python, Rust allows multiple threads to run concurrently, leveraging native parallelism for enhanced capacity. 

This makes Rust an ideal choice for applications that require high performance, particularly in computing, distributed systems, and real-time environments. 

Analogy: Rust’s concurrency framework resembles a well-equipped restaurant kitchen, where every chef possesses a set of tools (resources) to operate autonomously, resulting in quicker and more efficient meal preparation. 

Rust outperforms Python in terms of concurrency speed, making it the top option for applications that require multi-threading and parallel processing. This includes use cases such as high-performance computing (e.g., simulations and scientific calculations), real-time systems (e.g., video streaming or gaming engines), and distributed systems (e.g., web servers and cloud infrastructure). 

Ecosystem and Resources 

The ecosystem of a programming language involving libraries, frameworks, tools, and community support plays an integral role in improving development productivity.

Python: From web development to machine learning 

Python enjoys a large and diverse ecosystem making it a favorable choice for many tasks including:

• Data Science and Machine Learning: NumPy, Pandas, TensorFlow, Scikit-learn. 
• Web Development: Django, Flask, FastAPI.
• Automation & Scripting: Selenium, PyAutoGUI.
• Scientific Computing: SciPy, SymPy.

Python’s vast array of well-documented libraries streamlines development and makes it a preferred choice for quick prototyping, research, and production-level applications. 

Rust: From asynchronous to system programming

The Rust ecosystem is still developing, yet it is swiftly gaining momentum, particularly in areas where performance is crucial. Prominent Rust libraries include: 

• Asynchronous Programming: Tokio (for async I/O).
• Web Development: Rocket, Actix-web.
• Data Serialization: Serde (for parsing JSON, YAML, etc.).
• Systems Programming: Rayon (parallel computing), Warp (networking).

As the variety of libraries expands in different areas, Rust is becoming a more attractive option for developers looking for both safety and capacity. 

Python vs Rust Comparison Table

For your convenience, here is a summarised comparison of Rust and Python:

Python and Rust are both robust programming languages, with unique strengths and applications. Python is recognized for its straightforwardness and user-friendliness, which makes it a favored option for quick development. 

Conversely, Rust focuses on performance and safety, providing enhanced memory management control but presenting a more challenging learning curve. 

The choice between them depends on the needs of the project—simplicity and flexibility vs. speed and safety.

Rust vs Python: Selecting the Appropriate Programming Language for Your Project 

Below you can find several tips on choosing the right language for your project. The choice always depends on the project requirements and business needs. 

#1. Applications that require high performance 

For projects requiring exceptional performance, memory safety, and low-level management, it’s better to pick Rust. For example, it’s typically used in these cases: 

• Programming systems 
• Integrated systems 
• Backend services with high-performance 
• Game creation 

#2. Quick prototyping & data analysis 

For projects needing rapid iteration, simplicity in development, and extensive library support, Python is the preferred language. It stands out in: 

• Analytics and artificial intelligence 
• Website creation 
• Scripting and automation 
• Computational science 

#3. Concurrency & parallelism 

If your project requires multi-threading or high-concurrency tasks, such as real-time data processing or high-performance computing, Rust offers a safer and more efficient solution than Python, due to its ownership model and absence of GIL limitations. 

#4 Safety in memory & efficiency of resources 

For initiatives that need high efficiency in resource-constrained settings, or require dependable and robust memory handling, Rust is the perfect option. Its ownership framework ensures memory safety at compile time, making it ideal for use in embedded systems, IoT gadgets, or other resource-limited settings.

How Mobilunity Can Help You Expand Your Business 

Mobilunity’s core aim is to provide highly skilled developers to International companies through dedicated development teams. Our vast experience in recruiting developers skilled in both Rust and Python enables our clients to select the ideal talent for their unique project requirements. 

We hire developers to focus solely on your project, fully collaborating with your in-house teams to enhance productivity and achieve outstanding results.

Mobilunity clients know they can rely on us for our wear transparency, bespoke solutions, and successful business outcomes. Because of Mobilunity’s vast experience and knowledge in many industries, we have prepared dependable and scalable teams that specialize in many tools and technologies.

Below, you’ll find some insights on how we supported the Canadian FinTech company. 

Dedicated Development Team for a FinTech Company

Client: FinExpert, a leader in premium financial solutions, lacked an experienced CTO who could design their technology strategy and lead product development efforts.

Our solution:

We organized a dedicated development team for the client and hired a Ukrainian CTO with a wealth of experience in Python. The CTO was instrumental in crafting the product’s design, scalability, and architecture in resonance with the client’s strategy.

Technologies:

• Python, Django, and Celery for scalable server-side software development
• Vue.js for building a rich responsive user interface
• PostgreSQL, Webpack, and Sass to improve database management performance and users’ interface and experience design

Results Achieved:

• Improved functionality of the product by applying advanced Python techniques.
• Company growth through a comprehensive technical structure.
• Strong cooperative relationship and partnership over the years.

For more information on this partnership, check out our case study.

Instead of the Conclusion: Which Language Is the Best Choice for Your Project – Rust or Python?

Both Rust and Python are popular programming languages that have advantages and disadvantages. 

When deciding between Rust and Python, it’s crucial to clearly define your project’s needs and priorities. Every language has its strengths in various domains, and recognizing your particular requirements – be it performance, adaptability, or simplicity in development – will assist you in choosing the appropriate tool for the task.

Written by:

Kateryna Oriekhova

Market Researcher