I have personally gotten a lot of mileage from just writing the compute heavy parts of my code in C++ and exposing it to Python with a tool like PyBind11 [1] or NumpyEigen [2]. I find tools like numba and cython to be more trouble than they're worth.
I prototype in python or whatever, then, if the project survives into market and has legs I either buy more hardware or rewrite the expensive parts in C++.
Reduces calendar time, risk, cost. And I'm likely to make better decisions once the code and market is better understood after the prototype is tested under real world conditions and the requirements have changed (like they always seem to do).
+1 for pybind11. I wrote python bindings using pybind11 for two C++ based simulators: MOOSE and Smoldyn. It was surprisingly easy to use given how badly Python C-API and c++ tooling suck. Though you have to create binary wheels for every version of python and platform separately.
pypy is great if you are not already using numpy heavily. Pure python libraries like networkx and myhdl showed 20x speedup when I used it a couple of years ago. For pure python code, pypy provides free lunch.
As a slight contrast to the other responses, I found setting up maturin (Rust + Python) very straightforward since the documentation is recent, and I find it's easy to write parsers in Rust because the ADT syntax is very terse.
