Python Is Slow, and That’s Exactly Why I Use It

Sometimes speed isn’t everything — and in programming, it can even hold you back.

Python Is Slow, and That’s Exactly Why I Use It

Performance isn’t the only metric that matters in software development. Here’s why I choose Python despite — and even because of — its speed limitations.

Python’s “Slowness” Is Its Superpower

Every few weeks, someone on the internet reminds us that Python is slow.
They’re not wrong.
Run a tight loop in Python, and it will lose embarrassingly to C, Rust, or even JavaScript in some benchmarks.

But here’s the thing: that’s exactly why I keep choosing Python — for real-world projects, for client work, and even for personal experiments.

Let me explain.

Speed Is Overrated in Most Projects

When you’re building software, speed feels like it should be your top concern. But in practice:

• The time you spend writing, debugging, and maintaining code often dwarfs the time the code spends executing.
• For most business applications, shaving milliseconds off a request has negligible business impact.
• Premature optimization often leads to more complex, less maintainable code.

If you’re building:

• An AI-powered data pipeline
• A web API that handles a few hundred requests per second
• An internal automation tool

… then the bottleneck usually isn’t Python’s execution speed — it’s I/O latency, network calls, database queries, or human decision-making.

In other words: your users don’t care if your script runs in 0.05s instead of 0.005s if it took you twice as long to ship it.

Slowness Forces Better Design Decisions

When a language isn’t blazingly fast, it encourages you to:

1. Leverage existing libraries
   Python’s standard library and ecosystem are massive. If Python itself is slower, you naturally lean on C-optimized packages like NumPy, Pandas, or PyTorch, which are lightning fast for heavy lifting.
2. Offload work efficiently
   You think in terms of batching, caching, and asynchronous processing instead of brute-forcing tasks with raw CPU.
3. Focus on clarity and correctness
   You can’t just “throw more CPU cycles” at bad code. You’re nudged toward better algorithms and smarter architectures.

In my experience, these constraints often lead to better software — more maintainable, more scalable, and less prone to subtle bugs.

The Real “Speed” That Matters

Here’s the paradox: while Python runs slower than many languages, I can often ship faster with Python than with any other tool in my kit.

Shipping speed beats execution speed.

Let’s break that down:

1. Faster Prototyping

Python’s syntax is minimal. You can go from an idea to a working prototype in hours instead of days.

# Example: quick data aggregation 
from collections import Counter 
 
words = open("input.txt").read().split() 
word_count = Counter(words) 
 
print(word_count.most_common(10))

A few lines of code. Zero boilerplate. Immediate results.

2. Readable by Everyone

I’ve worked with teams where not everyone was a “Python dev.” Still, Python code is so readable that a data analyst, a backend engineer, and a project manager could all follow along.

3. Endless Libraries

From requests for HTTP calls to sqlalchemy for databases to matplotlib for charts — Python has a library for almost anything, often battle-tested and production-ready.

“But What About Performance-Critical Systems?”

If you’re writing a high-frequency trading engine or a game physics engine, Python is probably not your first choice.
But here’s what many people overlook: you can combine Python with faster languages.

• Use Python for orchestration, glue code, and business logic.
• Use C, C++, or Rust for performance-critical components.
• Call them seamlessly from Python using tools like Cython, PyO3, or even plain-old shared libraries.

This hybrid approach lets you get the best of both worlds: rapid development and high performance where it matters.

The Hidden Cost of “Faster” Languages

I’ve seen developers jump from Python to “faster” languages — only to burn months wrestling with:

• Verbose syntax and boilerplate
• Longer compile times
• Fewer available libraries for quick experimentation
• Steeper learning curves for new hires

And when they finally measure the performance gains, they realize it didn’t actually improve the product’s bottom line.

As Donald Knuth famously said:

“Premature optimization is the root of all evil.

The Confidence Factor

One underrated benefit of Python’s “slowness” is that it makes you more confident in your code:

• You’re not distracted by micro-optimizations until they matter.
• You can rely on battle-tested libraries for the heavy lifting.
• You’re shipping solutions sooner, gathering feedback earlier, and iterating faster.

I’d take a product that’s 90% as fast but ships in half the time over one that’s perfectly optimized but late to market.

My Rule of Thumb

Here’s the heuristic I use in my own work:

1. Build it in Python
2. Measure performance only when real users complain or metrics show a problem.
3. If there’s a bottleneck, profile and optimize — starting with algorithms, then libraries, then lower-level languages.

This approach has saved me countless hours and kept my projects from falling into the “optimization rabbit hole.”

Conclusion: Slow Is a Feature, Not a Bug

Python’s reputation for slowness hides an important truth:
The fastest code is the code you actually ship — and Python gets you there sooner.

If your project is CPU-bound and every nanosecond matters, sure — look elsewhere. But for the vast majority of software we build, Python’s development speed, readability, and ecosystem far outweigh its runtime speed.

Sometimes, slowness is the constraint that forces us to do our best work.

Python may run slower, but it lets me move faster — and in the end, that’s the only speed that matters.