In my experience starting with Julia in 2025, the main thing missing from the ecosystem tends to be boring glue type packages, like a production grade gRPC client/server. I heard HTTP.jl is also slow, but I havn't sufficiently dug into this myself. At least we have an excellent ProtoBuf implementation so you can roll your own performant RPC protocol.

As for the actual numerical stuff I tend to roll my own implementations of most algorithms to better control relevant tradeoffs. There are sometimes issues where a particular algorithm is implemented by a Julia package, but has performance issues / bugs in edge cases. For example, in my testing I wasn't able to get ImageContrastAdjustment CLAHE to run very fast and it had an issue where it throws an exception with an image of all zeros. You also can't easily call the OpenCV version as CLAHE is implemented in OpenCV using an object which doesn't have a binding available in Julia. After not getting anywhere within the ecosystem I just wrote my own optimized CLAHE implementation in Julia which I'm very happy with, this is truly where Julia shines. It's worth noting however that there are many excellent packages to build on such as InterprocessCommunication, ResumableFunctions, StaticArrays, ThreadPinning, Makie, and more. If you don't mind filling in some gaps here and there its completely serviceable.

As for the core language and runtime we are deploying a Julia service to production next release and haven't had any stability/GC/runtime issues after a fairly extensive testing period. All of the Python code we replaced led to a ~40% speedup while improvements to numerical precision led to measurably improved predictions. Development with Revise takes some getting used to but once you get familiar with it you will miss it in other languages. All in all it feels like the language is in a good place currently and is only getting better. I'd like to eventually contribute back to help with some of the ecosystem gaps that impacted me.