Python may not be known for raw execution speed, but it has earned a reputation for being one of the most developer-friendly languages out there. Its readable syntax, massive ecosystem of libraries, and rapid development cycle make it a favorite among developers in nearly every field. One of Python’s more underrated strengths is its ability to interoperate with other programming languages, especially those that compile down to C or provide a C-compatible interface. That’s where Zig comes in—a rising star in systems programming that offers both performance and safety, and happens to integrate nicely with Python.
Zig can be leveraged in two main ways to enhance Python performance: as a library with a C-compatible interface or as a full Python extension module. The first approach—writing Zig libraries that Python can import and use—is relatively straightforward. Zig has solid C interoperability, making it easy to expose functions to Python through ctypes or cffi. However, because this method relies strictly on C-compatible types, it’s best suited for scenarios where you’re passing simple data structures like integers, floats, or pointers. More complex interactions, such as those involving native Python objects, require a different approach.
That brings us to the second method: building actual Python extensions in Zig by interfacing directly with the CPython API. This is a more complex process that involves working with CPython’s internal structures and memory management model. While it gives you the ability to handle Python objects within Zig, it also ties you more closely to the Python runtime. This can introduce overhead and make debugging more difficult. Additionally, using rich Python objects means you lose some of Zig’s performance benefits, since you’re constrained by the same limitations that slow Python down in the first place.
To get the best of both worlds—Python’s flexibility and Zig’s performance—it’s usually best to keep the interface between the two as lean as possible. Focus on using Zig for performance-critical code that can operate on primitive types or simple C-compatible data structures. Let Python handle the orchestration and high-level logic. This approach is similar to what tools like Cython attempt, but with Zig, you get more control over memory and performance characteristics. By keeping Python and Zig cleanly separated at the interface level, you can write faster programs without sacrificing the ease and power of Python.