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The two language problem is more specific to scientific computing where literally every popular library is written in more then one language - fast C/C++/Fortran and convenient high-level R/Matlab/Python. NumPy, SciPy, Caffe, Theano, Tensorflow to name a few. I once had to rewrite Matlab + C code (for face tracking) to pure Julia - not only the resulting code was almost twice smaller, it also ran ~20% faster.

I wrote a library in c to replace a Julia one I wrote (and make it more portable) and found that the c version was slower! (Couldn't inline functions as easily in c and was also being more parsimonious about memory usage in c)

They have a pretty good reputation for FFI to most complements, IIRC. eg;

https://docs.julialang.org/en/stable/manual/calling-c-and-fo...

https://github.com/JuliaPy/PyCall.jl

> unless your entire business runs on an AS400

Even there you got support for C, C++, Java amung others as well as the whole IBM family (RPG/ILE, CL, REXX, ...) With somewhat decent bindings in RPG.