How much is QuantLib used in industry and how much street cred does it have?
It's a very good and useful question. And it is bloody hard to answer just like many other questions relating to the proprietary nature of how banks and funds implement their core technology. A better route may be to ask on the QL lists, and/or to inquire as to who actually attented the first Quantlib forum in London last month. Another route would be to see who StatPro lists as clients.
I have been near QL for a long time based on my RQuantLib bindings to GNU R. That obviously covers only a subset of users (those who like R) as well as functionality but I can assure you that I have been in contact with a number of places about it.
Which makes perfect sense: this is open source code, so you can always bring it in to at least provide a benchmark or reference implementation.
Based on anecdata (conversations with other quants), not much. Banks develop their own models and if they do outsource the effort, they pay someone for the code + support (there are companies like Numerix or Pricing Partners which do that). QuantLib is criticized for being poorly documented and convoluted.
My own observation is that the parts of QuantLib I was interested in (LMM) weren't particularly advanced, so if I were to make a call, I would see no point to make an effort to integrate this code with the rest of my bank's systems.
I've never heard of it, but I've only been in the industry 2.5 years. Our C++ guys haven't even mentioned it either. They prefer using PACK/LAPACK which is mostly rooted in academia & heavily debugged. We also make heavy use of the IMSL FORTRAN libraries for hardcore statistical computation and Extreme Optimization (for .NET).
One of our other researches has reported some interest in the Intel Math Kernel library, but that faded once they saw the price tag.
To echo what @quant_dev said, we much prefer to build our own models.