Migrating from Python to Rust

Python is an incredibly versatile language that powers everything from web applications to data science pipelines. However, as organizations scale, they often encounter limitations around performance, type safety, and robustness. While Rust isn’t a direct replacement for Python, it has some answers to these challenges.

But is Rust the right choice for your team?

In this article, you’ll learn:

• How to evaluate whether Rust is the right choice for your Python codebase
• Practical strategies for Python-to-Rust migration
• Common pitfalls and how to avoid them
• Ways to maintain productivity during the transition
• How to leverage Python’s strengths alongside Rust

Key Differences Between Python and Rust

Aspect	Python	Rust
Type System	Dynamic, optional type hints	Static, strong type system
Memory Management	Garbage collected	No GC, ownership and borrowing
Performance	Moderate	High performance, low-level control
Deployment	Runtime required	Single binary, minimal runtime
Package Manager	Multiple (pip, conda, uv)	cargo (built-in, consistent)
Error Handling	Exceptions	Result type
Concurrency	Limited by GIL	zero-cost abstractions, no GIL
Learning Curve	Gentle	Steep
Ecosystem Size	Vast (500,000+ packages)	Growing (160,000+ crates)

Why Python Teams Consider Rust

Python excels at readability and rapid development, but teams often hit scaling challenges as their applications grow. Common pain points include:

Performance Bottlenecks

Python’s Global Interpreter Lock (GIL) limits true parallelism, making it challenging to fully utilize modern multi-core processors. There is a version of Python without the GIL, but it doesn’t solve the performance issues.

While tools like asyncio help with I/O-bound tasks, CPU-intensive operations remain constrained. Teams often resort to complex workarounds involving multiple processes or C extensions.

Type Safety Concerns

Despite Python’s type hints, runtime type errors still occur. Types are optional in Python. Developers need the discipline to add and maintain type hints consistently, which can be challenging in large codebases. Furthermore, adoption is inconsistent across the Python ecosyste (e.g., third-party libraries). Large Python applications can become difficult to maintain and refactor confidently.

From experience, there is a breaking point around the 10-100k lines of code mark where the lack of type safety becomes a significant liability.^[1]

Deployment Complexity

Python applications require managing runtime environments, dependencies, and potential version conflicts. A lot of the issues can be mitigated with containerization. However, bundling Python applications for deployment is not an easy task, especially when targeting platforms with different architectures.

Resource Usage

Python has a relatively manageable memory profile, but it can be inefficient for certain workloads. For example, Python’s memory overhead can be significant for large-scale data processing or long-running services. CPU-bound tasks often get offloaded to C extensions or to worker processes, adding complexity and overhead.

Why Python Developers Transition to Rust

Python developers often want to transition to Rust for several reasons:

1. Developers interested in Rust are likely willing to understand systems programming concepts. They grew out of Python’s limitations and are looking for more control over performance and memory management.

2. Python developers often long for stronger type guarantees. They appreciate Rust’s static type system and the safety it provides.

3. Developers with a Python background often work on data processing or web applications. These are areas where Rust’s performance benefits are most pronounced.

Key Challenges in Transitioning to Rust

The transition from Python to Rust presents unique challenges:

Syntax

Python is very syntax-light, which makes it easy to read and write. By comparison, Rust is full of symbols and keywords that can be intimidating at first. Developers need to see through the syntax and understand the underlying semantics. This is a critical step in the transition process.

# A list of bands
bands = [
    "Metallica",
    "Iron Maiden",
    "AC/DC",
    "Judas Priest",
    "Megadeth"
]

# A list comprehension to filter for bands that start with "M"
m_bands = [band for band in bands if band.startswith("M")]

# A list comprehension to uppercase the bands
uppercased = [band.upper() for band in m_bands]

# We get ["METALLICA", "MEGADETH"] 

let bands = vec![
    "Metallica",
    "Iron Maiden",
    "AC/DC",
    "Judas Priest",
    "Megadeth",
];

let uppercased: Vec<_> = bands.iter()
                    .filter(|band| band.starts_with("M"))
                    .map(|band| band.to_uppercase())
                    .collect();

// uppercased = vec!["METALLICA", "MEGADETH"]

Ownership and Borrowing

Python developers need to adjust to Rust’s ownership model. This is often the biggest hurdle, as memory management in Python is handled automatically. Plan for a 3-4 month learning period where developers will need to understand concepts like:

• Stack vs heap allocation
• Borrowing and references
• Lifetimes
• Move semantics
• Pointer handling and boxing
• Trait-based generics

Type System Adaptation

Moving from Python’s dynamic typing to Rust’s static typing requires a mindset shift. Developers need to:

• Think about types upfront
• Understand generics and traits
• Lean into Option<T> and Result<T, E> for error handling
• Use enums for modeling complex states

The pattern matching syntax in Rust can be a game-changer for developers coming from Python and is often cited as one of the most enjoyable features of Rust.

Integration Strategies

There are several ways to integrate Rust into your Python codebase:

1. PyO3 Integration

PyO3 lets you write Python extensions in Rust or call Rust functions from Python. This is ideal for:

• Optimizing performance-critical components
• Gradually introducing Rust while maintaining Python interfaces
• Creating Python packages with Rust internals

2. Microservices Migration

For distributed systems, you can:

• Build new services in Rust
• Migrate existing services one at a time
• Use REST or gRPC for inter-service communication

3. CLI Tools and Utilities

Command-line tools are excellent candidates for initial Rust projects:

• Self-contained scope
• Easy deployment (single binary)
• Clear performance benefits

4. Worker Processes

For CPU-bound tasks, consider:

• Offloading work to Rust worker processes
• Using message queues for communication Kafka, RabbitMQ, or Redis are popular choices

Planning Your Migration

A successful migration requires careful planning:

1. Start Small

   • Choose non-critical components first
   • Focus on areas where Rust’s benefits are most valuable
   • Build team confidence through early wins

2. Invest in Training

   • Allocate time for learning Rust fundamentals
   • Consider bringing in external expertise for guidance
   • Set realistic expectations for the learning curve

3. Measure Success

   • Define clear metrics (performance, resource usage, development velocity)
   • Document improvements and challenges
   • Adjust strategy based on results

Ecosystem Considerations

Rust’s ecosystem is smaller than Python’s.

On top of that, Rust has a much smaller standard library compared to Python. This means you’ll commonly rely on third-party crates for functionality that’s built into Python.

Depending on your use case, here are some key differences to consider:

Data Science

Python dominates data science with libraries like NumPy and Pandas. However, Rust is making inroads with libraries like Polars and Apache’s Arrow.

You don’t have to migrate your entire data science stack to Rust. Chances are, you have experts on your team who are comfortable with doing data analysis in Python. You can start by using the Rust libraries for data preprocessing and ETL tasks. They have Python bindings, so you get a lot of the benefits of Rust without having to do a full migration.

Backend Services

Rust wasn’t initially planned to be a strong contender in the web development space. This has changed in recent years with the rise of frameworks like Axum and Actix-web. Now, Rust is a viable option for building high-performance APIs and web applications. It is one key are the Rust team is investing in, and the ecosystem is maturing rapidly.

In combination with sqlx for database access and serde for serialization, Rust is a very effective choice for web backends. What surprises many Python developers is how similar it is to working with other web frameworks like Flask or FastAPI. Another surprise is how robust the final product is, as it catches many bugs at compile time and scales extremely well. Production Rust web applications are extremely robust. This lifts a lot of the burden from the operations team.

Keeping Python’s Strengths

Not everything needs to be migrated! Python excels at:

• Rapid prototyping (even though I love to prototype in Rust)
• Data analysis and visualization
• Machine learning workflows
• Admin interfaces and tools

Consider a hybrid approach where each language handles what it does best.

One winning strategy is to explore a space in Python and moving production-workloads to Rust once the requirements are clear and the project can benefit from performance and scale. Speaking of which…

Expected Performance Improvements

Actual performance numbers vary significantly based on workload and implementation, so take these numbers with a grain of salt.

Based on my experience helping teams migrate, here are typical improvements:

• CPU Usage: 2-10x reduction in CPU utilization
• Memory Usage: 30-70% reduction in memory footprint
• Latency: 50-90% improvement in response times
• Throughput: 2-5x increase in requests/second

Especially the P90 latency improvements are often surprising to teams. There are very few outliers and things tend to run smoothly, no matter the load. In Python, this is a common source of frustration, where a single request can take significantly longer than the rest.

Conclusion

Migrating from Python to Rust is a significant undertaking that requires careful planning and execution. While the learning curve is steep, the benefits in terms of performance, reliability, and maintainability can be substantial.

Success depends on:

• Clear understanding of migration goals
• Realistic timeline expectations
• Strong team support and training
• Pragmatic approach to choosing what to migrate

Remember that this isn’t an all-or-nothing decision. Many organizations successfully use Python and Rust together, leveraging each language’s strengths. Write down your reasoning for the migration. Many issues can be solved in Python, and the migration might not be necessary. However, if you’re hitting the limits of Python, Rust is a strong contender.

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1. See discussions on large-scale Python applications on Reddit and HN. ↩