Julia can do both static and dynamic dispatch. The latter works with function barriers works to maintain codebases that are impossible to achieve with just one or the other.

There's no resolving trade-off because when Julia knows types and call time (or a union of them ) it can inline function calls.

Anyway, neither numba nor cython has the combin of features that allows for one to define a fast custom abstract array and have another package seamlessly define a subtype that works fast and with other data structures, overloading only the necessary functions. Then a third package can come and build on top using traits from the first and some weird solver.