--- {"title": "PyOverdrive vs Cython in 2026: Pricing, Setup, Features", "description": "PyOverdrive and Cython compared on what each does, what each costs, how each is installed and what to check before you pick one for Python speed.", "url": "https://saydeploy.com/pyoverdrive-vs-cython/", "date": "2026-10-24"} --- PyOverdrive and Cython compared on what each does, what each costs, how each is installed and what to check before you pick one for Python speed. Cython is an optimising static compiler for Python [^1]. PyOverdrive patches supported NumPy functions so existing code uses faster paths [^2]. Both are open source [^1] [^2]. | Option | Licence | Price | Install | Python version | |---|---|---|---|---| | Cython | Open source Apache 2.0 License [^1] | Freely available [^1]; no paid price published | not published | 3.0.x supports Python 2.7 and 3.5 and later [^1] | | PyOverdrive | Open source MIT License [^2] | No paid price published | pip straight from the GitHub repository [^2] | Python 3.12 or newer [^2] | ## The short answer Cython is an optimising static compiler for both the Python programming language and the extended Cython programming language [^1]. PyOverdrive patches supported NumPy functions so your existing code uses faster paths, and calls it does not support run on stock NumPy [^2]. For a compiler for Python code, Cython's page covers compiling Python code [^1]. PyOverdrive's sources cover faster paths for certain NumPy calls [^2], a check that compares them with stock NumPy on your machine [^2] and a way to turn them off [^2]. ## What Cython is built for Cython's own page calls it an optimising static compiler for both the Python programming language and the extended Cython programming language [^1]. It is freely available under the open source Apache 2.0 License [^1]. ## What PyOverdrive is built for PyOverdrive is made by the team that publishes this site. Calling pyoverdrive.enable() patches supported NumPy functions so your existing code uses faster paths, and calls it does not support run on stock NumPy [^2]. A fast path runs only when a check confirms the input is in its measured range; anything else, or a fast path that raises an error, goes to stock NumPy [^2]. Threaded paths cover np.sin, cos, tan, exp, log, log10 and tanh on C-contiguous float64 and float32 arrays, above a size floor calibrated per op and dtype (3e5-3e6 elements) [^2]. np.linalg.qr on large stacks of 3x3 matrices uses a vectorized closed-form Householder, and np.linalg.eigvalsh has closed-form 2x2 and 3x3 paths for float batches above a size threshold [^2]. Large np.einsum calls go through NumPy's own optimize=True planner once they pass a measured size threshold [^2]. np.isin on 1-D StringDType or object arrays can use hash-set membership [^2]. pyoverdrive.explain() tells you which path a call would take and why, without running the call [^2]. ## Pricing and fees Cython is freely available under the open source Apache 2.0 License [^1]. Its page also notes that PayPal takes 5-15% fees for small non-EUR payments to the project [^1]. PyOverdrive is open source under the MIT License [^2]. No paid tier price is stated in its sources, so none is published here. ## Setting it up and daily use The Cython sources here do not describe installation or daily use, so check its own documentation. PyOverdrive needs Python 3.12 or newer [^2]. You install it with pip straight from the GitHub repository [^2]. The package is marked OS independent, and the test suite has been run on Windows and on Linux x86-64 [^2]. Its version is 1.0.0 and its status is Beta [^2]. You call pyoverdrive.enable() once [^2]. python -m pyoverdrive --demo times the headline operations with stock NumPy and with PyOverdrive on your machine, in about 20 seconds [^2]. python -m pyoverdrive --calibrate re-times the threaded paths on your CPU and switches off any that do not pay there [^2]. ## Things to check before you choose Ask what you need to speed up: your own Python code, or calls to NumPy functions you already use. Cython is described as a compiler for Python [^1], and PyOverdrive's claims speak of supported NumPy functions [^2]. Check your Python version. PyOverdrive needs Python 3.12 or newer [^2], and the Python version row of the table on this page shows what each tool requires. Check maturity. PyOverdrive is version 1.0.0 in Beta [^2]. Check whether your array sizes and types fall in the ranges the fast paths cover [^2], and run the self check on your machine [^2]. ## How to choose If you want a compiler for Python and the extended Cython language, Cython's page describes that [^1]. If you need to match your Python version, the Python version row in the table shows what each tool requires. If you want existing NumPy code to use faster paths after one call, PyOverdrive's claims describe that [^2]. If you want to see why a call takes a given path, PyOverdrive offers explain() [^2], and you can turn paths off when needed [^2]. If you want a free open source tool, both sources say so [^1] [^2]. ## Frequently asked questions ### Is Cython free to use? Cython is freely available under the open source Apache 2.0 License [^1]. Its page also says PayPal takes 5-15% fees for small non-EUR payments to the project [^1]. No paid plan is stated in the facts here. ### Is PyOverdrive free and open source? PyOverdrive is open source under the MIT License [^2]. The sources here do not state a price or any paid tier, so none is published here. Check the project's own pages if you need more detail on future plans or support terms before relying on it. ### How do you install PyOverdrive? You install it with pip straight from the GitHub repository [^2]. It needs Python 3.12 or newer [^2]. The Cython sources here do not describe installation, so check its own documentation for that tool. ### What Python versions does Cython support? The comparison table on this page has a Python version row for each tool, so check that row against the Python version your project uses. PyOverdrive needs Python 3.12 or newer [^2]. Cython's 3.0.x releases support Python 2.7 and 3.5 and later [^1]. ### How can PyOverdrive be checked against plain NumPy? python -m pyoverdrive --selfcheck compares every fast path with stock NumPy on your own machine [^2]. python -m pyoverdrive --demo times the headline operations with stock NumPy and with PyOverdrive in about 20 seconds [^2]. pyoverdrive.disable() restores the original NumPy functions [^2]. It is open source under the MIT License. [^2] - [Visit PyOverdrive](/go/pyoverdrive-vs-cython) ## Sources [^1]: Cython, cython.org, read 2026-10-07 (https://cython.org) [^2]: PyOverdrive's own description of itself (the product of this site's publisher) (/go/pyoverdrive-vs-cython)