Why you should never use Python

I know the title sounds outrageous, and, of course, every popular technology has its use cases. However, I want to start by sharing my background and how I arrived at this perspective.

My journey into coding began when I wanted to create a website for the science club I led in elementary school. From that moment on, I was captivated by computers and programming. I was especially mesmerized by what I could achieve with just HTML.

My first steps into programming involved writing Windows batch scripts and experimenting with Visual Basic. However, the first modern programming language I truly learned and understood was Python. I was 16 years old, and I immediately fell in love with it.

During my university years, I continued using Python and even taught introductory courses. However, my primary focus was Java, and I fully embraced the Object-Oriented Programming paradigm. As I transitioned into my professional career, I worked primarily on enterprise Java backend projects.

For the past two years, however, I have been working on a complex service entirely written in Python. This experience has led me to develop strong opinions on why Python is a poor choice for such projects. In this post I want to explain why I consider Python is not suitable for “serious” software.

Questionable Language Design

Python often trades off sound language design in favor of convenience. This is appealing for scripting and small projects, but problematic at scale. For example:

• Dynamic Typing: While it speeds up early development, dynamic typing makes large codebases fragile and difficult to refactor. Type hints help but are optional and inconsistently enforced.

• Late Binding: Python’s default behavior of late binding in closures is counterintuitive and often leads to bugs — a decision that prioritizes flexibility over correctness.

• Magic Methods and Duck Typing: Python heavily relies on conventions like str, repr, and eq, which can make behavior unpredictable, especially when third-party libraries override them in inconsistent ways.

These issues collectively result in software that is harder to reason about, test, and maintain as it grows in complexity.

Inefficient Garbage Collection

Python uses a reference counting approach for memory management, which has notable limitations: • It cannot handle cyclic references without a secondary garbage collector, which adds overhead. • It relies on the Global Interpreter Lock (GIL), a notorious bottleneck that prevents true parallel execution of threads. This severely limits Python’s performance on multi-core systems, making it ill-suited for high-concurrency or CPU-bound workloads.

While workarounds exist (like multiprocessing or using C extensions), these introduce architectural complexity that could be avoided with a language designed for concurrent execution from the ground up.

Multi-paradigm is not an advantage

Python is often praised as a multi-paradigm language — supporting procedural, object-oriented, and functional programming styles. But in practice, this flexibility can be a double-edged sword.

Without strong conventions or compiler-enforced architecture, teams end up mixing paradigms inconsistently. One module is written in a purely procedural style, another adopts functional patterns, while another uses deeply nested classes and inheritance hierarchies. This inconsistency leads to cognitive overload, increased onboarding time, and more bugs.

In contrast, languages like Java (OOP) or Haskell (functional) push developers toward a consistent architectural model, making codebases easier to scale and reason about.

Inconsistent Versions

The infamous split between Python 2 and Python 3 is a testament to the language’s governance problems. For years, the ecosystem was fragmented, with major libraries supporting only one version or the other. Even today, remnants of this split linger in tutorials, documentation, and legacy codebases.

No language that aspires to be used for “serious” software should ever allow such a disruptive and prolonged fork. It demonstrated a lack of foresight and a disregard for backward compatibility, both of which are critical in enterprise environments.

Module mechanism and Dependency Hell

Python’s module and dependency system is one of its weakest points:

• Ambiguous Import Paths: Managing relative vs absolute imports is messy, and circular dependencies often rear their heads unexpectedly.

• Environment Isolation: Tools like virtualenv and pipenv are band-aids on a fundamentally broken packaging system.

• Dependency Resolution: Dependency versioning with pip is primitive compared to modern tools. Conflicts and breakages are common.

Compare this to Rust’s Cargo, which offers seamless dependency resolution, reproducible builds, and workspace-level coordination — or Java’s Maven and Gradle, which provide mature, declarative project structures and lifecycle hooks. These ecosystems support scaling and automation, while Python’s packaging feels like an afterthought.

Conclusion

Python’s strengths lie in its accessibility, rapid prototyping, and the extensive ecosystem that has made it a favorite for teaching, scripting, automation, and data analysis. But as my experience has shown, these same qualities become significant liabilities when building and maintaining complex, mission-critical systems.

From its permissive language design and inconsistent paradigms, to persistent packaging headaches and legacy fragmentation, Python creates friction at scale that more rigorously designed languages address by default. When every design decision must be justified by developer convenience rather than long-term robustness and maintainability, teams end up paying a hidden tax in the form of technical debt, painful refactors, and reliability challenges.

This isn’t to say Python doesn’t have its place—it absolutely does, especially where speed of development is more valuable than architectural purity or performance. But for software that must scale, remain robust, and support large teams over years, we should be clear-eyed about its limitations. Sometimes, choosing the right tool means prioritizing discipline and consistency over flexibility and familiarity. If “serious” software is your goal, it may be time to look beyond Python.