What is the business model? How are they making money?
- JuliaPro is the go-to product for anyone who wants to use Julia – professionals, engineers, software developers and data scientists. Beginners and experts can build better software quicker and dramatically improve performance.
- JuliaRun is for scalable deployment of Julia in production for real time analytics and large-scale parallel simulations in the public cloud or a private cluster.
- JuliaBox lets data scientists, quants and strats run Julia in Jupyter notebooks right in the browser. JuliaBox is the number one choice for universities teaching Julia since students can get started with Julia in seconds with no installation required.
- JuliaFin is a suite of Julia packages that simplify the workflow for quantitative finance, reducing the gap between the quant and the trader. JuliaFin provides the fastest path from market data to market transactions by leveraging the full power of Julia for implementing new trading strategies where time is of the essence.
EDIT: Oh, and consulting/training:
JuliaPro is basically VisualStudio Express vs. Pro (e.g. there's a free version and then an expensive version with better integrations, better enterprise level tools, etc).
JuliaRun is basically a cloud service (e.g. charge for hosting) designed for julia code.
JuliaBox seems to be a cloud IDE / google docs / notebook style product. Probably with a similar model to github eventually (e.g. pay for teams / commercial use, etc).
JuliaFin seems to be a way to charge banks a lot more money for the same stuff.
- JuliaRun is for easily scalable deployment of code, either for cloud or local servers.
- JuliaBox is as you say, available here https://juliabox.com/
- JuliaFin is completely separate, and currently consists of Bloomberg integration and the Miletus contract specification and pricing package.
(I work for Julia Computing)
It's a little sad when we are trying to figure out how to make money for free software and the answer ends up being "sell (some) software non-freely". Okay, so it's impossible to make money without selling non-free software? There can only be one Red Hat in the world?
I don't think it's any consolation that you are also producing free software; pretty much every major software vendor nowadays does that while keeping all the interesting secret sauce secret.
Mostly: JuliaPro is essentially a nice convenient installer of a huge collection of open source work (though I can't say for sure, as I'm not really involved in that part). A few small components (e.g. the Excel plugin) are not open, and we are moving to MKL for some stuff, so that will change slightly though.
Open source business models are hard (as you're no doubt aware). You may disagree, but I think Julia Computing has so far done a good job of balancing the open source vs business aspect, which is part of the reason I originally joined.
So, not more money for the same stuff, but more money for a lot more other stuff. Some of these components are proprietary, but many are open source.
Or, as we used to call it, just "software". They're selling software.
If Julia hasn't figured out how to make money without restricting customers via legal or technical means, trying to lock them in, or putting software in their machines that controls their machines without the full knowledge and consent of the users (i.e. source code and permission), then I'm not really interested in what they're doing economically.
I don't understand. Julia is not stable at all yet. Who would want to teach it now? What would be the point?
I'm sure the main draw will be companies that don't want to be stuck with a GPL license in their product.
From the website:
The core of the Julia implementation is licensed under the MIT license. Various libraries used by the Julia environment include their own licenses such as the GPL, LGPL, and BSD (therefore the environment, which consists of the language, user interfaces, and libraries, is under the GPL).
$1500 per year
You can't support 100 people strong developer collective on proceeds from licensing of such product at all, given how small that commercial "data science" market is.