# Julia's biggest success\[es\] so far?
**URL:** https://discourse.julialang.org/t/julias-biggest-success-es-so-far/80044
**Category:** Community
**Created:** [April 26, 2022, 10:44am UTC](https://discourse.julialang.org/t/julias-biggest-success-es-so-far/80044 "2022-04-26T10:44:14Z")
**Posts on this page:** 7
**Page:** 1
### Author:  [@Palli](https://discourse.julialang.org/u/Palli)
#### Post date: [April 26, 2022, 10:44am UTC](https://discourse.julialang.org/t/julias-biggest-success-es-so-far/80044/1 "2022-04-26T10:44:14Z")
I’m thinking of a) [enabling] commercial success and/or b) research success and/or c) good behind-the scenes package, likely at 1.0+. [And maybe Julia website in production?]
For a) you might not know of a specific company, and nothing might be obvious from looking at the public package repository.
However, I believe Pumas.AI (I believe part of COVID vaccine success) might be the largest success so far, a company built on SciML. Any other ideas?
DifferentialEquations.jl enables SciML, and is now under that umbrella. Note, I do not think general machine learning (e.g. Flux.jl) is yet a big success for Julia in the commercial sense, and even for use in research, could be wrong, at least not the mainstream SOTA research I follow. SciML is however not typical ML, and is a huge success.
SciML is a huge ecosystem, some of it rather new, e.g. ReservoirComputing.jl not yet at 1.0.
The first state-of-the-art Julia package was I believe JuMP.jl, but only recently after 10 years hit 1.0. I count it under b) research success and c), hopefully helped some companies too, likely wouldn’t/couldn’t know easily.
Genie.jl for building websites is now at version 4.18.0 whatever you want to read into that. [EDIT: It’s claimed ready for production, see from the horses-mouth below, so striking out repeatign incorrect claim to the contrary.]. It is used in production for intranet sites (for “billion dollar companies”), and also publicly open website.
[Julialang.org](http://Julialang.org) however uses Franklin.jl for static website. It’s the most high-profile website run (in some sense) by Julia.
DataFrames.jl is now at version 1.3.3 a good candidate for c).
Is there a good way to filter the General registry for packages already at 1.0+? Anyone has code or can come up with, or maybe possible at [juliahub.com](http://juliahub.com)?
Other Julia success companies I can think of:
Invenia is also a company built on mainly I think Julia with an impressive github: [Invenia Technical Computing · GitHub](https://github.com/invenia)
RelationalAI (likely not yet a commercial success).
Blackrock (not built on Julia originally, but adopted Julia, still using?), largest such company. Julia is also used in other finance companies, likely too new to for companies to build on it only.
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### Author:  [@essenciary](https://discourse.julialang.org/u/essenciary)
#### Post date: [April 26, 2022, 11:09am UTC](https://discourse.julialang.org/t/julias-biggest-success-es-so-far/80044/2 "2022-04-26T11:09:55Z")
Can you please share the Reddit thread where I’d claim that Genie is not production ready? That’s certainly not true. I have personally developed quite a few apps for various consultancy clients and institutional customers, some of them being in production for years now (mostly backend/intranet apps though). In addition, I have collaborated with users and contributors that deploy Genie apps at work, some working at billion dollar companies. Not really at liberty to share details though, as it’s usually the case.
We’re now starting to put more effort to feature user apps on [GenieFramework.com](http://GenieFramework.com) - but for an example of a great recent app that’s been running in production for over 3 months, with public access and public repo, people can check [https://pkgs.genieframework.com](https://pkgs.genieframework.com) for a good example of:
- reactive data dashboard
- with REST API
- with SwaggerUI API
- currently crunching 1,700,545 rows of package downloads data to compute stats in real time
- on a puny 1GB RAM Heroku server (with some 1/2 of that RAM available)
- with avg server response time of 500 ms
- featuring lots of best practices including container/Docker deployments with prebuilt Julia sys img, caching, CDN support, MVC architecture, DRY, etc.
Version 4.18 means 4 breaking versions so far 🙂 But more importantly it’s about over 3-4 years of continuous improvements, over 1500 commits and some 60 awesome contributors. In addition, Genie 5 will be released in a couple of weeks, as our first performance focused release, which should address the concerns of some of the people in the Julia community around Genie’s speed.
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### Author:  [@Palli](https://discourse.julialang.org/u/Palli)
#### Post date: [April 26, 2022, 11:25am UTC](https://discourse.julialang.org/t/julias-biggest-success-es-so-far/80044/3 "2022-04-26T11:25:43Z")
Well he wrote Genie “author” (I assumed you) claimed it and I responded, and he did yesterday respond repeating it for “Genie fella”, so it might be better if the denial came from you:
[https://www.reddit.com/r/Julia/comments/u8shoe/state\_of\_web\_assembly\_in\_julia/i5n5l5w/?context=3](https://www.reddit.com/r/Julia/comments/u8shoe/state_of_web_assembly_in_julia/i5n5l5w/?context=3)
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### Author:  [@essenciary](https://discourse.julialang.org/u/essenciary)
#### Post date: [April 26, 2022, 11:28am UTC](https://discourse.julialang.org/t/julias-biggest-success-es-so-far/80044/4 "2022-04-26T11:28:16Z")
Cool, thanks. Who knows what he meant/understood. But yes, I’ll follow up, worth clarifying.
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### Author:  [@NiclasMattsson](https://discourse.julialang.org/u/NiclasMattsson)
#### Post date: [April 26, 2022, 12:48pm UTC](https://discourse.julialang.org/t/julias-biggest-success-es-so-far/80044/5 "2022-04-26T12:48:27Z")
Here’s a short testimony by @Satvik about a commercial success that I bookmarked last year. It’s (probably deliberately) vague about the details but I found it impressive and convincing nonetheless.
> [@Julia's applicable context is getting narrower over time?](https://discourse.julialang.org/t/julias-applicable-context-is-getting-narrower-over-time/55042/5):
>
> We recently added Julia to our codebase, where we’d previously used Python only for 3 years. This was motivated by optimizing a major process that was a key part of our product and cost $15k/month to run. As you can imagine, we had already heavily optimized it in Python. Everything was using numpy and calling C libraries beneath the hood, but besides that we employed many performance tricks that resulted in very ugly and unpythonic code. We had several very talented engineers look at this, inclu…
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### Author:  [@mbauman](https://discourse.julialang.org/u/mbauman)
#### Post date: [April 26, 2022, 3:34pm UTC](https://discourse.julialang.org/t/julias-biggest-success-es-so-far/80044/6 "2022-04-26T15:34:26Z")
There’s probably about a billion dollars worth of predominantly Julia-based companies:
- RelationalAI just announced [$122 million in funding](https://twitter.com/molhamaref/status/1518930170924584960). Relational has their own language (Rel), but lots of Julia folk are there under the hood.
- Beacon Biosignals had a [$27 million Series A](https://www.crunchbase.com/organization/beacon-biosignals)
- Julia Computing had a [$24 million Series A](https://www.crunchbase.com/organization/julia-computing)
- Invenia had a [$5 million Series A and an undisclosed Series B](https://www.crunchbase.com/organization/invenia/company_financials)
- Pumas-AI
- Banyan Computing
Putting on my Julia Computing hat, many more success stories using the language appear on our [case studies page](https://juliacomputing.com/case-studies/) — even for folks that aren’t directly working with our company.
The foremost research project in my mind is the [Clima project](https://clima.caltech.edu).
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### Author:  [@Satvik](https://discourse.julialang.org/u/Satvik)
#### Post date: [April 26, 2022, 3:38pm UTC](https://discourse.julialang.org/t/julias-biggest-success-es-so-far/80044/7 "2022-04-26T15:38:40Z")
Thanks for the mention! We’ve continued to move a lot of our Python code to Julia since then (and to write a lot of new Julia code), and have generally found that our average Julia code is 2-5x faster and more memory-efficient than our most optimized Python/numpy code.
One of the biggest gains was that Julia’s macro system allowed us to switch from calculating our features directly to building and evaluating a computational graph, which allowed us to skip a lot of redundant calculations. The fact that Julia’s for loops are fast also made it easy for us to do incremental updates, whereas in Python we’d essentially had to settle on batch refreshes every time we wanted new data.
I’m happy to go into more detail on any of these points, I just don’t know what people are interested in.