We use Julia in production at RelationalAI. Our cloud hosted next generation knowledge graph management system is a highly performant database and query optimization engine, and it's written 100% in Julia.

We love Julia for the same reasons others have stated here: It's easy and intuitive to write, making it productive to work with for both our engineers and our mathematicians. It's reflection abilities make performance tuning and optimization easier than any experience I've had before. The ability to fine tune the assembly emitted for your hotspots _matters_, and it's straightforward to do in Julia. And for the rest of the code, being easy to write, easy to read, and close to the math are all a big win.

I've built production software being shipped to pharmaceutical companies and used in FDA submissions using Julia (pumas.ai) because it is quite mature in the field of modeling and simulation in comparison to other languages. The stability of the language along with the ability to easily get fast code is the main reason, and it's the reason why if you go to something like the American Conference on Pharmacology (ACoP) you'll hear lots of mentions of Julia.

I've also built several production systems and it has nothing but nightmare. Stack traces are meaningless and there were so many issues with things like ISO Time Zones, HTTP requests (why isn't there a solid http request library? its a mess), Docker problems with compilation, start times were atrocious and still are I think, package management is insane, setting up an internal julia repository is a total nightmare - just go on SO and search for it.

I've lived through this.