Why Python’s Speed Problem Is So Tough to Solve
Say “Python is slow” in a room full of Python developers and you’ll probably get a mix of groans, eye rolls, and resigned nods. It’s an old criticism, and one that—painful as it is to admit—has truth to it. Python, in its “pure” form, isn’t built for raw speed. Compared to C, C++, Rust, or even Java, Python lags significantly when it comes to execution time, especially for computation-heavy or low-level tasks.
Developers have long found clever ways to work around this limitation. Libraries like NumPy and Numba bring lightning-fast numerical capabilities by leaning on optimized C code under the hood. Cython allows developers to compile Python to C, squeezing out more performance. These tools work incredibly well—but they all require stepping outside the comfort zone of standard Python, and they introduce trade-offs in code complexity, expressiveness, or language compatibility.
The dream, of course, is native speed: a faster Python that doesn’t need extra tooling to compete with compiled languages. But the road to that dream is filled with obstacles. The issue isn’t that Python is interpreted rather than compiled—it’s that Python is dynamic to its core. Variable types are fluid. Functions can be redefined at runtime. This flexibility is part of what makes Python so friendly and powerful—but it also makes it incredibly hard to optimize.
You might think that type hints, which were added to Python relatively recently, could be the solution. Unfortunately, they weren’t designed to improve performance. Type hints in Python are strictly for tooling and static analysis; the interpreter doesn’t enforce or even use them at runtime. And while tools like Cython and other Python dialects can take advantage of types to speed things up, they only help when you’re working with primitive data. The moment you dip back into Python’s built-in object types—lists, dicts, sets—you’re once again shackled to the traditional runtime and its performance ceiling.
So what’s the path forward? That’s the billion-dollar question. The Python community is exploring answers—from Just-In-Time (JIT) compilation to alternate interpreters like PyPy and projects like Faster CPython. But whatever the solution, it’ll have to strike a delicate balance: preserve the elegance and flexibility that make Python beloved, while finding new ways to make it run faster—without sending developers out of the language to do so.