A lot of very useful statistical models can't benefit from the GPU, and for them I think Stan is the better tool. There's a reason it's basically the default choice for people who want to use probabilistic programming for Bayesian statistics.
It's probably not the best tool for AI/ML type models. But for statisticians who want to use Bayesian methods it's close to perfect.
Haha I knew "legacy" would ruffle some feathers, by which I mean Stan was pretty much the first application-ready PPS on the block -- robust toolbox of methods, actively developed/supported.
The last slide of this presentation on SMC inference in PPLs is a nice view of the PPS landscape (not to mention the whole deck is a great intro by Lawrence Murray): http://www.it.uu.se/research/systems_and_control/education/2...
I've never actually used BUGS or JAGS for anything but I would consider at least BUGS to truly be legacy software. To me the word "legacy" means you shouldn't pick it for a new project.
It's generally used more for modeling and prediction than for creating "products", if that makes sense. More popular among people with statistics or social science backgrounds than among programmers and computer scientists.
If you dig around Stan-related websites you can see various companies and institutions that use it. One I found quite quickly was Metrum Research Group, which does consulting work for the pharmaceutical industry.
https://metrumrg.com/