The way I see it, TF is about to pull _way_ ahead thanks to XLA JIT/AOT. All of a sudden you get the ability to fuse things at a much more granular level, which could reduce memory bandwidth requirements by a lot. Frameworks like Torch can't do any fusing at all, since their computation is fully imperative. Tactical win for imperative frameworks, I suppose, but strategically functional graph is the way to go. DB people realized this in the 70s, ML people are realizing this now.
TF is way behind on UI, which is why it's making Keras its front-end. It's fairly slow on multi-GPUs compared to Torch and neon. It might pull ahead in performance on GCE, but that's just for lockin.
TF is in a fortunate position of having several UIs at this point. It's a lower level framework with a lot of power. If you don't need all that power, Keras or TFLearn or Slim are pretty great. If you do, it's there for you. I see no evidence that Google's goal with TF is to lock you into anything, and especially GCE. I'm a former Google employee, and I can tell you unequivocally — that's not how Google actually works.