I believe the toolchain of Rust is nicer in that you get a relatively small sized and self-contained rust library that can be easily distributed with a python p

I believe the toolchain of Rust is nicer in that you get a relatively small sized and self-contained rust library that can be easily distributed with a python package. Julia can't be easily bundled this way because you need to ship the entire runtime, with all the gubbins this entails. On the other hand: having used pyo3 to integrate rust with python in the past, the biggest pain is simply to reconcile the dynamism of python with AOT compiled rust code. There's a lot of noise at the interface from the large amount of type checking to unpack the specific types of numpy arrays coming across. Do you want to have your rust code work with all sorts of integer bit lengths? That'll be a code path for each. If you have a number of input types, have fun coercing them all. This can be alleviated with macros to a degree, but that just hides the problem really. Julia is JIT compiled, which means that there are no problems with types being determined only at run time. In fact, this one fact makes integration at the code level much nicer with Julia. So all in all it's a tradeoff really. I also found the tooling for Rust to be much more robust and stable (1 year ago admittedly).