Python - The Language Everyone Uses (Despite Its Flaws)

Python wasn't supposed to take over the world. Guido van Rossum created it in 1991 because he was tired of writing shell scripts and C programs for simple tasks. The "Zen of Python" sounds nice until you're debugging someone else's "clever" one-liner at 3am that does twelve things in one line.

Why Everyone Uses Python (Despite the Pain)

Python hit the #1 spot on TIOBE in 2025 because it's the least painful option for most tasks. Not the fastest, not the most elegant - just the one that lets you get shit done without fighting the compiler for three hours first.

The 500,000+ packages on PyPI sound impressive until you realize half of them are abandoned projects from 2015, and the other half will break your project during the next pip install. Every major tech company uses Python, but they also have armies of engineers working around its limitations. Instagram has hundreds of people making Python work at scale - your startup probably doesn't.

Check the Computer Language Benchmarks Game to see just how slow Python really is.

The Reality of Python's Design

CPython compiles your code to bytecode that runs slower than basically everything else. The "interpreted nature" marketing speak translates to "your code will run 10-100x slower than compiled languages, but at least you won't spend three hours debugging memory leaks."

The Global Interpreter Lock (GIL) makes Python essentially single-threaded for CPU work. Multiprocessing "works" but good luck debugging race conditions across processes when something inevitably goes wrong at 2am.

Version Upgrade Hell

Python 3.13 added experimental free-threaded mode and a JIT compiler. The 10-15% performance improvements sound great until you realize that's still 10x slower than Go for the same work. Plus, upgrading Python versions is not fun - even minor bumps can break half your dependencies.

Python 3.14 is scheduled for October 7, 2025, with a new tail-call interpreter that was initially claimed to boost performance by 30%. Reality check: those gains were inflated by a Clang compiler bug. The actual improvements are much more modest. Will it break your existing code? Probably something minor. Will you upgrade anyway? Eventually, when pip forces you to.

Where Python Actually Works (And Where It Doesn't)

Python's "versatility" comes from having libraries for everything, even if half of them are poorly maintained. Here's the reality:

Web Development: Django gives you everything out of the box, including terrible performance. Flask is simple until you need to scale, then you're rebuilding Django. FastAPI can theoretically hit 60K requests/sec in synthetic benchmarks - in production with actual business logic and database calls? More like 1K if you're lucky.

Data Science: Python dominates because NumPy and Pandas do the heavy lifting in C while you write Python. Jupyter notebooks are great for exploration, nightmare for reproducible research.

Machine Learning: TensorFlow and PyTorch chose Python as their interface, so now everyone thinks Python is fast at ML. Spoiler: it's not. The actual computation happens in CUDA and C++.

Automation: Python beats bash scripts because it has proper data structures. That's a low bar, but here we are.

Read more about Python web framework tradeoffs in the JetBrains blog.

The Python Community Reality

The Python Software Foundation moves at glacial speed while web frameworks change monthly. PEPs take years to implement, by which time everyone's already using the unofficial solution from GitHub.

The community is friendly, which is nice when you're stuck debugging why pip install destroyed your virtual environment again. PyCon talks are great for learning about libraries you'll never use in production.

Python won because it's good enough for most things and doesn't actively fight you like C++ does. Is it the best language? Hell no. Will you probably end up using it anyway? Yeah, because everyone else already did.

Python in production is like a high-maintenance relationship - it works if you throw enough engineering resources at the problems, but most people don't have those resources.

Why Big Companies Can Use Python (And You Probably Can't)

Instagram runs on Django because they have hundreds of engineers optimizing around Python's performance limitations. When your request takes 100ms instead of 10ms, you hire more engineers to fix caching, not rewrite in Go. Most startups hit the performance wall long before they can afford the optimization army.

Netflix uses Python for recommendation engines and chaos engineering, but their actual video streaming happens in C++ and specialized hardware. They can afford the infrastructure costs of running Python because their revenue per user justifies it. Your AWS bill probably doesn't.

Dropbox migrated 4 million lines of Python from Python 2 to 3, which took them multiple years and a dedicated team. If a company that size struggles with Python upgrades, imagine what it's like for a 5-person startup.

Python uses 3-10x more memory than compiled languages - your infrastructure costs will reflect this reality.

The Reality of Python Performance

"Performance at scale" in Python means throwing hardware at the problem until it works. Discord handles billions of messages by optimizing database queries and caching everything, not because Python is fast.

Here's how companies actually make Python work:

Async Programming: FastAPI hits 60K requests/sec in synthetic benchmarks with no business logic. Add database calls, logging, authentication, and actual work? You're looking at 1-2K req/sec if the database cooperates.

Offload to C: NumPy and Pandas are fast because they're actually C libraries with Python interfaces. The moment you write pure Python loops, performance dies.

Rewrite the Hot Paths: Start with Python for everything, profile to find the slow parts, then rewrite those in Go/Rust/C++. Congrats, you now maintain a polyglot codebase.

See FastAPI benchmarks for the synthetic performance numbers vs reality.

Where Python Actually Succeeds

Financial Services: Banks use Python because financial models change constantly and hiring Python developers is easier than finding COBOL maintainers. The performance hit from Python is acceptable when you're calculating risk models overnight, not executing trades in microseconds.

Scientific Research: NASA uses Python for data analysis because scientists care more about getting results than waiting 10 seconds vs 1 second for code to run. When you're processing telescope data, Python's slow speed matters less than having matplotlib to visualize results quickly.

Healthcare: Pharmaceutical companies use Python because their compute budget is measured in millions while their developer time is measured in months. If RDKit saves six months of development time, nobody cares if it's 10x slower than C++.

Data Pipeline Hell: Companies use Python for ETL because it has libraries for everything and doesn't require a CS degree to understand. Sure, your data pipeline is slow, but it works and the intern can maintain it.

Deployment Reality Check

Docker: Python Docker images are huge compared to Go binaries (500MB+ vs 20MB), but at least containerization solves the "works on my machine" dependency hell. Poetry is better than pip for dependency management, but good luck explaining it to your teammates who just want to pip install everything.

Serverless: AWS Lambda cold starts with Python are painful (2-5 seconds vs 100ms for Go), but serverless works if your functions aren't performance-critical. Just don't expect sub-second response times.

Monitoring: You'll need monitoring because Python applications fail in creative ways. Memory leaks from circular references, GIL contention, and that one library that doesn't handle Unicode properly. DataDog costs money but saves your sanity when your Python app starts eating memory for reasons that make no sense and the error messages are useless as always.

The Bottom Line

Python succeeded because it's good enough for most problems and doesn't actively hate you like C++ does. Is it fast? No. Is it memory-efficient? No. Will you probably use it anyway? Yes, because everyone else did and now you're stuck in the ecosystem.

Python works when development speed matters more than runtime speed, which is surprisingly often. Just don't pretend it's fast - embrace the tradeoff and buy bigger servers.