(I work on GCP)
Then consider the possible applications of that at Google scale -- there are "an awful lot" of images on the web, over 13PB of photos in Google photos last year [1], a gajiggle of photos in street view and google maps, an elephant worth in google plus, and probably a few trillion I'm not even thinking of. :)
Same applies, of course, to Translate, and to RankBrain, also mentioned as NNs running on the TPU. 100B words per day translated [2], and .. many, many, many Google Searches per day, even if RankBrain primarily targets the 15% of never-before-seen queries [3].
Add that to the fact that GPUs are poorly-suited to realtime inference because of the large batch size requirements, and it's a solid first target.
[1] https://en.wikipedia.org/wiki/Google_Photos [2] http://www.k-international.com/blog/google-translate-facts/ [3] https://www.bloomberg.com/news/articles/2015-10-26/google-tu...
(work at Google Brain on Mondays, but speakin' for myself here.)
Most of us are probably better off building a few workstations at home with high-end cards. The hardware will be more efficient for the money. But if you're considering hiring someone to manage all your machines, power-efficiency and stability become more important than the performance/upfront $ ratio.
There's also FPGAs, but they tend to be much lower quality than the chips Intel or Nvidia put out so unless you know why you'd want them you don't need them.