I was able to convince my coworkers to stop working on a Python project (a simulator for a scientific instrument) once we all realised that we would have not been able to keep using plain Python because of performance. When the alternatives where (1) to force everybody to learn either C or Fortran, or (2) to rewrite everything in Julia, they asked me to provide examples of both solutions. After I showed them a few different approaches (pybind, cbind, f2py...), there was full consensus for Julia. We moved what we implemented so far (it was not to much, luckily), and so far nobody has regretted the choice.
The problem in using two languages is that you do not have just to learn a new language besides Python. You have also to figure out how to properly interface them. (f2py was considered the easiest solution, but we feared that the 1- vs 0-based array indexing would have likely caused many bugs.)
Did you evaluate Cython? I'm not anti-Julia, but I like that my Cython code is useable out of the box from Python, with no wrapping, and then users can continue to use their Jupiter + Python scripting workflows with performant bespoke modules complemented by the full Python ecosystem.
Someday I'll do a project in Julia. But for some such projects, Rust seems fully guaranteed to be performant while Julia might or might not be, so I might still lean towards Rust (unless one of the high quality packages of Julia removes a lot of development time, which is a decent possibility).
I used it for another project but was not impressed. When I tested it, the documentation was scarce, and the deployment is harder than Julia because it needs a working C compiler alongside the CPython interpreter.
The problem in using two languages is that you do not have just to learn a new language besides Python. You have also to figure out how to properly interface them. (f2py was considered the easiest solution, but we feared that the 1- vs 0-based array indexing would have likely caused many bugs.)