Why Google, Microsoft, and Meta are moving AI systems to Rust: an analysis for beginners

What is Rust and why is everyone talking about it?

Rust is a systems programming language that was released in 2010 by Mozilla. If we were to describe it in one phrase: it is a language that gives C/C++ performance, but at the same time protects against the most common errors at the compilation stage.

Imagine: you are writing code, and the compiler is constantly checking whether you are making mistakes with memory, race conditions, or use-after-free. Sounds like a dream? This is Rust.

fn main() {
    let message = String::from("Hello, Rust!");
    println!("{}", message);
    // After this line, the compiler knows exactly
    // that the message is still valid
}

Why did Rust become relevant in the era of AI?

1. Performance matters

AI models are computationally complex systems. GPT-4, DALL-E, Stable Diffusion — they all require huge resources. When you run a model inference (prediction) millions of times a day, every millisecond counts.

🚀 Zero overhead costs

You write high-level code, but you get low-level performance

⚡ No garbage collector

Predictable response time and memory control

💾 Effective memory

Critical for large AI models and high workloads

Case study: Discord rewrote its servers in Rust and reduced memory consumption from 5-10 GB to a stable 2 GB under the same load.

2. Safety is not a luxury, but a necessity.

AI systems often process sensitive data: medical records, financial transactions, personal information. Vulnerabilities in such systems can cost millions of dollars and damage a company's reputation.

C/C++ problem: about 70% of critical vulnerabilities in Chrome, Windows, and Android are related to unsafe memory handling.

Rust solution: the ownership and borrowing system prevents these problems at the compilation stage.

fn process_data(data: &Vec<i32>) {
    // We can read data, but we can't change it
    // The compiler guarantees this!
}

fn main() {
    let my_data = vec![1, 2, 3, 4, 5];
    process_data(&my_data);
    // my_data is still valid here
}

3. Parallelism without a headache

Modern AI applications actively use parallel computing: data batch processing, distributed model training, and asynchronous APIs.

In traditional languages, parallelism = high risk of race conditions. In Rust, the compiler simply won't let you compile code with potential multithreading problems.

use std::thread;

fn main() {
    let data = vec![1, 2, 3, 4, 5];
    
    let handle = thread::spawn(move || {
        println!("Data in the stream: {:?}", data);
    });
    
    handle.join().unwrap();
    // Trying to use data here will cause a compilation error!
}

Real cases: who is already using Rust in AI

1. Microsoft — Azure and AI infrastructure

Microsoft is actively moving parts of Windows and Azure to Rust. In the context of AI, this is especially important for:

• Model inference servers

• Real-time data processing

• Infrastructure for Azure OpenAI Service

2. Hugging Face — Tokenizers library

Hugging Face, the main platform for working with transformers, rewrote its tokenization library in Rust. Result: 10x acceleration compared to the Python version.

// Example of using Tokenizers in Rust
use tokenizers::tokenizer::{Result, Tokenizer};

fn main() -> Result<()> {
    let tokenizer = Tokenizer::from_file("tokenizer.json")?;
    let encoding = tokenizer.encode("Hello, world!", false)?;
    println!("Tokens: {:?}", encoding.get_tokens());
    Ok(())
}

3. Meta — PyTorch and Infrastructure

Meta uses Rust for critical components of its AI infrastructure, including parts of the PyTorch backend and distributed learning systems.

4. Amazon — Firecracker and AWS

AWS built Firecracker (a micro-VM for serverless) on Rust. This technology underlies AWS Lambda and is used to isolate AI workers.

Why critical systems?

When we talk about "critical systems", we mean:

1. Financial systems - where a mistake can cost millions

2. Medical applications - where we are talking about people's health

3. AI Infrastructure - serving millions of users

4. Autonomous systems — drones, robots

Perfect combination: Python for development and experimentation + Rust for production components. Many companies do just that, using PyO3 to link Python and Rust.

// Example of creating a Python module in Rust
use pyo3::prelude::*;

#[pyfunction]
fn fast_computation(n: i64) -> PyResult<i64> {
    // Fast calculations in Rust
    Ok(n * n)
}

#[pymodule]
fn my_rust_module(_py: Python, m: &PyModule) -> PyResult<()> {
    m.add_function(wrap_pyfunction!(fast_computation, m)?)?;
    Ok(())
}

Where to start learning Rust?

Basic concepts:

1. Ownership — each value has one owner

2. Borrowing — you can temporarily take links

3. Lifetimes — the compiler tracks how long the data is valid

4. Traits - an analogue of interfaces, but more powerful

5. Error Handling — explicit error handling via Result<T, E>

Conclusion: Rust is an investment in the future

Switching to Rust is not just a change of programming language. It is:

• Investing in reliability — fewer bugs in production

• Investment in productivity - savings on servers

• Investment in safety — protection against a whole class of vulnerabilities

• Investment in a career — the demand for Rust developers is growing

In the era of AI, when systems are becoming increasingly complex and critical, Rust offers a unique combination: the security of a high-level language with the performance of a low-level one.

In the Codex you will find:

Structured lessons - from basics to advanced concepts

Practical tasks — assign each topic a real code

Step-by-step analysis - understanding through practice, not memorization

And that's just the beginning! Kodika also offers courses in Python, JavaScript, web development, and much more.

💬 Need support? We're here!

2000+ Like-minded people

24/7 Active Community

100% Support for beginners

Join our active Telegram channel, where they discuss code, share experiences, and help each other grow as developers.