I have written quite a bit of cython code as well and at least the last time I looked cython was much better documented than numba (it has been a couple of years though so things might have improved on the numba side), and I would agree with the previous poster is generally quite well documented.

Also I am specifically referring to the documentation on creating ahead of time compiled packages using Numba vs Cython, but perhaps that was unclear.

FWIW Numba's JIT caches the compiled function as long as you don't call it again with different type signatures (eg. int32[] vs int64[])

I've succesfully deployed numba code in an AWS lambda for instance -- llvmlite takes a lot of your 250mb package budget, but once the lambda is "warm" the jit lag isn't an issue.

That said, if you absolutely want AOT you'll have to use Cython or some horrible hack dumping the compiled function binary.

Exactly!

You realize that Scikit-learn is written mostly in Cython (where high performance is needed)? It is a part of the most influential ML library in existence.

Also pandas

You're aware pandas and most of scipy is cython, right?

I like numba, but cython is clearly used more in the popular packages