Julia is my tool of choice for writing numerical code where performance is critical. I work in computational physics, and have found Julia and its ecosystem to be far nicer than Rust in this space.
It's true that accidental dynamic behaviors are a real concern and can be a performance killer. Fortunately, the language has nice tooling. In VSCode, I often use the visual profiling tool `@profview` to get a flame graph. Anything dynamic gets highlighted in red, and is quick to diagnose. There also exist nice static analysis packages like JET.jl. During development, one can use `report_opt` to statically rule out accidental dynamical behaviors. Such checks can also be incorporated into a project's unit tests. In practice, it's not much of an issue for me anymore. But to be fair, there is a big learning curve for new Julia users. See, e.g., https://docs.julialang.org/en/v1/manual/performance-tips/
It's true that accidental dynamic behaviors are a real concern and can be a performance killer. Fortunately, the language has nice tooling. In VSCode, I often use the visual profiling tool `@profview` to get a flame graph. Anything dynamic gets highlighted in red, and is quick to diagnose. There also exist nice static analysis packages like JET.jl. During development, one can use `report_opt` to statically rule out accidental dynamical behaviors. Such checks can also be incorporated into a project's unit tests. In practice, it's not much of an issue for me anymore. But to be fair, there is a big learning curve for new Julia users. See, e.g., https://docs.julialang.org/en/v1/manual/performance-tips/