Rust for Backend Engineers: When and Why I Choose It Over Python

Adam Dugan • January 28, 2026

I use both Rust and Python in production. Not because I'm a language zealot, but because they're good at different things.

Python is my go-to for speed of development: data pipelines, AI integrations, prototypes, and anything where developer velocity matters more than runtime performance.

Rust is my choice for performance-critical infrastructure: Lambda functions where cold start times kill UX, high-throughput APIs, and systems where correctness and reliability are non-negotiable.

Here's how I actually make the decision in practice, based on real projects.

Why I Reach for Rust: Lambda Cold Starts

The single biggest reason I write Lambda functions in Rust: cold start times.

AWS Lambda functions have to "wake up" when they haven't been called recently. During this cold start, the runtime initializes, dependencies load, and your code boots up. For user-facing APIs, this delay is visible and frustrating.

Real Numbers: Python vs Rust Cold Starts

From production monitoring across multiple Lambda functions:

Rust is 5-10x faster on cold starts, and often faster on warm execution too.

For APIs serving user-facing requests, that 1.5 second Python cold start is unacceptable. Users perceive anything over 200ms as "slow." If they hit a cold Lambda, they're waiting multiple seconds for a response.

When Cold Starts Actually Matter

Not every Lambda needs to be in Rust. Cold starts only matter when:

• Low traffic: If your API gets 1 request/minute, you're hitting cold starts constantly. High-traffic functions stay warm.
• User-facing: Background jobs, cron tasks, async processing, these can tolerate cold starts. APIs users interact with cannot.
• Cost-sensitive: Faster execution = lower Lambda costs. For high-volume functions, Rust can save 30-50% on compute costs.

Example from BalancingIQ: I have a Rust Lambda that validates OAuth tokens and fetches user permissions. It runs on every API request, but traffic is sporadic. Cold starts were killing UX until I rewrote it in Rust. Now it's consistently under 100ms, cold or warm.

Why I Love Rust: Syntax and Enforced Best Practices

Beyond performance, I genuinely enjoy writing Rust. The language forces you to think about correctness in ways Python doesn't.

Type Safety That Actually Helps

Python has type hints, but they're optional and not enforced at runtime. You can annotate everything perfectly and still get runtime errors from None values, type mismatches, or missing keys.

Rust's type system catches these at compile time:

• Option<T> for nullable values: You can't accidentally use aNone without explicitly handling it
• Result<T, E> for error handling: Errors are values, not exceptions. You can't ignore them without explicitly choosing to.
• Exhaustive pattern matching: The compiler won't let you forget a case in a match statement

This feels restrictive at first, but it catches bugs before they reach production. I've had Rust code that compiled on the first try and ran correctly in production immediately. That almost never happens with Python.

Ownership and Borrowing: Pain That Pays Off

Rust's ownership model is infamous for its learning curve. The borrow checker will yell at you constantly when you're learning.

But here's what you get:

• No data races: The compiler proves your code is thread-safe
• No memory leaks: Memory is automatically freed when it goes out of scope
• No dangling pointers: Can't reference data that's been freed

For infrastructure code, APIs, and concurrent systems, this is a massive win. I don't have to debug race conditions or memory leaks at 2am. If it compiles, it works.

Cargo: The Best Tooling Experience

Rust's package manager and build tool, Cargo, is genuinely excellent:

• Dependency management: No virtual environments, no poetry vs pip vs conda confusion
• Built-in testing: cargo test just works
• Formatting: cargo fmt enforces consistent style
• Linting: cargo clippy catches common mistakes
• Documentation: cargo doc generates docs from your code

Everything you need is standardized and built-in. No bikeshedding about tooling choices.

Why I Still Reach for Python: Speed and Versatility

As much as I love Rust, Python is still my default for most backend work.

Development Speed Matters More Than Runtime Speed

Rust takes longer to write. You're fighting the borrow checker, being explicit about lifetimes, and thinking carefully about ownership. That's great for correctness, but it slows you down.

Python lets you move fast:

• Rapid prototyping: Get an idea working in hours, not days
• Flexible typing: Duck typing and dynamic features when you need them
• REPL-driven development: Test ideas interactively before writing code
• Shorter iteration cycles: No waiting for compilation

For AI integrations, data pipelines, and business logic, I want to iterate fast. Python wins here.

The Ecosystem: Python Has Everything

Python's ecosystem is unmatched:

• AI/ML: OpenAI, LangChain, HuggingFace, TensorFlow, PyTorch
• Data: Pandas, NumPy, Polars, SQLAlchemy
• Web: FastAPI, Flask, Django
• Cloud SDKs: boto3 (AWS), Azure SDK, Google Cloud Client
• Third-party APIs: Every SaaS tool has a Python SDK

Rust's ecosystem is growing, but it's nowhere near Python's maturity. If I need to integrate with Xero, QuickBooks, Twilio, Stripe, OpenAI, Python has battle-tested libraries. Rust often requires writing bindings yourself or using immature crates.

Example: BalancingIQ's Architecture

In BalancingIQ, my AI-powered financial advisory platform, I use both:

• Rust Lambdas: Auth validation, API gateway, high-frequency endpoints
• Python Lambdas: Xero/QBO integrations, AI analysis, KPI generation, data transformations

The Rust functions handle performance-critical paths. Python handles everything involving external APIs, LLMs, and complex business logic. This hybrid approach works beautifully.

The Hidden Cost: Hiring and Long-Term Maintenance

Here's the most important factor that doesn't get talked about enough: who will maintain this code in two years?

Python Developers Are Abundant and Affordable

Python is one of the most popular languages in the world. The developer pool is massive:

• Junior developers: Learn Python in bootcamps, CS programs, online courses
• Mid-level developers: Plentiful supply across web, data, ML specializations
• Senior developers: 10+ years of Python experience is common

This means hiring is easier and more affordable. If I need to bring on another engineer to help scale BalancingIQ, I can find high-quality Python developers within my budget.

Rust Developers Are Rare and Expensive

Rust developers, by contrast, are scarce:

• Smaller talent pool: Far fewer developers have production Rust experience
• Higher salaries: Rust developers command premium rates because they're in demand
• Steeper onboarding: New hires take longer to become productive in Rust

If your entire backend is Rust, you've narrowed your hiring pipeline significantly. For startups and small teams, this is a business risk.

The Strategic Decision

As a founder, I have to think long-term:

• Can I afford to hire Rust developers if I need to scale the team?
• Will I be able to find contractors for quick fixes and features?
• If I get hit by a bus, can someone else maintain this codebase?

For most of my backend, Python is the pragmatic choice. I use Rust strategically where performance truly matters, but keep the majority of the codebase in Python so I can hire and scale affordably.

My Decision Framework: When to Use Rust vs Python

Here's how I actually make the decision in practice:

Use Rust When:

• Cold starts matter: Low-traffic, user-facing Lambda functions
• Performance is critical: High-throughput APIs, real-time systems
• Correctness is non-negotiable: Security, auth, payment processing
• You need concurrency: Rust's fearless concurrency makes parallel work easy
• Long-running processes: Services that stay up 24/7 and need zero memory leaks

Use Python When:

• Speed of development matters: Prototypes, MVPs, rapid iteration
• AI/ML integration: OpenAI, LangChain, HuggingFace, etc.
• Data processing: ETL pipelines, analytics, reporting
• Third-party integrations: SaaS APIs, cloud SDKs, webhooks
• Team scalability: Easier to hire and onboard new developers
• Background jobs: Async processing where cold starts don't matter

Real-World Example: A Hybrid Architecture

Here's what my production architecture looks like in (BalancingIQ, SOA Assist Pro):

This gives me the best of both worlds:

• Fast, reliable user-facing APIs (Rust)
• Rapid development and easy maintenance (Python)
• Cost-effective at scale (Rust for hot paths, Python for everything else)
• Flexible hiring (mostly Python, occasional Rust specialists)

Getting Started with Rust for Backend

If you're a Python developer curious about Rust, here's how to start:

1. Learn the Basics

• The Rust Book: Free, comprehensive, official guide
• Rustlings: Small exercises to learn syntax and patterns
• Rust by Example: Practical examples of common patterns

2. Build Something

Start simple: rewrite a small Python function in Rust. Compare cold start times and execution speed. You'll be shocked at the difference.

3. Don't Fight the Borrow Checker

Early on, you'll get frustrated with lifetime errors and ownership issues. This is normal. Use .clone() liberally at first. Optimize later once you understand borrowing and Rust patterns.

LLMs were used to help with research and article structure.