For small-scale server-side deployments both frameworks are easy to wrap in e.g. a Flask web server.
For mobile and embedded deployments, TensorFlow works really well. This is more than what can be said of most other deep learning frameworks including PyTorch.
Deploying to Android or iOS does require a non-trivial amount of work in TensorFlow.
You don’t have to rewrite the entire inference portion of your model in Java or C++.
Other than performance, one of the noticeable features of TensorFlow Serving is that models can be hot-swapped easily without bringing the service down.
Read on for the full comparison.