MXNet is actually pretty good. It got to the "mixing eager and graph mode" semantics before either PyTorch or TensorFlow did. On top of that, it's also blazing fast (usually the fastest of the frameworks).
Admittedly, I've never used MXNet so it might have more issues that I'm not aware of. Judging from the benchmarks I've seen, however, MXNet got a lot of things right.
Unluckily, I just don't think it added enough on top of PyTorch or TensorFlow for people to consider switching. People switched from TensorFlow to PyTorch because eager mode was just so much easier to use.
My former employer pulled in a few AWS data scientists to consult with us on a few projects and based on my interactions it seemed like they were under some directive to strongly discourage anything that wasn't a built-in AWS plug-and-play sagemaker algorithm. It was not a positive experience because of course most of them are half baked.
MxNet is fantastic though. It's usually faster than tensorflow, has a pytorch like "eager" API that doesn't suck, and can still use symbolic graphs. Amazing documentation too (for the Gluon API).
Admittedly, I've never used MXNet so it might have more issues that I'm not aware of. Judging from the benchmarks I've seen, however, MXNet got a lot of things right.
Unluckily, I just don't think it added enough on top of PyTorch or TensorFlow for people to consider switching. People switched from TensorFlow to PyTorch because eager mode was just so much easier to use.