# Blog post: Rust vs Julia in scientific computing

**URL:** https://discourse.julialang.org/t/blog-post-rust-vs-julia-in-scientific-computing/101711
**Category:** Offtopic
**Tags:** rust
**Created:** [July 17, 2023, 10:17pm UTC](https://discourse.julialang.org/t/blog-post-rust-vs-julia-in-scientific-computing/101711 "2023-07-17T22:17:20Z")
**Posts on this page:** 1
**Showing post:** 10

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### Author: ![giordano](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/giordano/32/2166_2.png) [@giordano](https://discourse.julialang.org/u/giordano)
#### Post date: [July 18, 2023, 12:08am UTC](https://discourse.julialang.org/t/blog-post-rust-vs-julia-in-scientific-computing/101711/10 "2023-07-18T00:08:52Z")

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The talk

[![](https://global.discourse-cdn.com/julialang/original/3X/e/d/ed1db7847fcc37bce565c087b50fe0b4385193af.jpeg "Paul Goulart - Conic optimization using the Clarabel solver") ](https://www.youtube.com/watch?v=4mDCUHb7XLY)

from the same workshop was also quite interesting: they’re using Julia for prototyping because it’s easier to implement and debug the algorithm, and Rust for deployment, because it’s easier to use from Python/C/C++, because in the end all their users are in Python. Quoting from their slides at 3:46

> - Julia easier for debugging “math” problems.
> - Rust easier for debugging “code” problems.

From what they say, the first point is mainly due to the fact in Julia you can more easily and dynamically inspect objects to see what’s really going on in the math.

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