Stan is best viewed in my mind as a successor to BUGS (Bayesian Inference Using Gibbs Sampling) which more people may have heard of. In fact, there are lots of players in the probabilistic programming space now, personally I like the model of "Infer.NET" [1] from Microsoft Research, as I find variational and approximate variational inference a good solution to my problems and I like coding models in nearly C# more than in Stan format.
If you want to get started as fast as possible with computational Bayesian inference and don't need the performance and advanced features Stan, I'd recommend emcee [2] which is a lightweight python MCMC sampler.
Final recommendation to the HN crowd: Bayesian Data Analysis 3rd Edition [3] should be on your desk if you are thinking about using any of this software.
[1] http://research.microsoft.com/en-us/um/cambridge/projects/in...]
[2] http://dan.iel.fm/emcee/current/
[3] https://www.crcpress.com/Bayesian-Data-Analysis-Third-Editio...