The main issue I've had is that the compiler behaviour differs between versions (and it's very difficult to find older releases), so where previously you could run a big model and delegate things to the CPU, now it sometimes won't compile at all. There were also problems where we trained a model in AutoML - using free credits but the real cost would have been over $100 - and the edgetpu compiled model lost a lot of performance for no apparent reason. The developers have been very helpful when I've contacted them, and generally you can get through to real devs (not generic support) who can look at your model for you. Mostly I think you need to take care when training models for these devices, but quantisation-aware training is not trivial to use in Tensorflow and there are only a few off-the-shelf models which are supported in the various toolkits. Model maker looks promising, but it's also finnicky in my experience [1]. I guess that's my main caution: it's often very difficult to figure out why your model performance goes to crap when you compile it, especially if the input TFLite model is fine.
I'm not super worried about hardware availability. They're suffering from the chip shortage like everyone else, so it's not surprising that lead times are long. I was able to buy my device in late 2020 without any trouble.
They also make a nice little environmental sensor + OLED board that's compatible with Pi headers and is the same form factor as the Pi Zero. A bit odd, because on the Coral dev board the heatsink is squarely in the way and you have to buy an extension header for it.
[0] https://github.com/jveitchmichaelis/edgetpu-yolo/blob/main/h...
[1] https://www.tensorflow.org/lite/guide/model_maker
^Remember that time when Intel released an OpenVINO installer that clobbered any existing OpenCV build on your system?