A major use for GPU in the cloud is not being limited to the 8-12GB(ish) limits of enthusiast cards. There's similar processing performance, and the ability to store all you need in memory is a much closer reality to why these cards go for $5k
I'm not sure most gtx users doing hpc can be called "enthusiasts". After all, most research is done in these devices, and most models should therefore fit memory requirements (unless you're doing some hardcore - or lazy engineered research model) . Correct me if I'm wrong, but AFAIK the tesla cards are designed to fit regulated markets and are not mass produced like the geforces, therefore the elevated prices.
The only problem is GeForce 1080s (for example) burn out much quicker under heavy load.
They're not designed to sit inside an enterprise chassis
Do you mean that they fail? I have built a mining setup with multiple gpus in the past and haven't lost a card. I am 100% sure that Google is smarter than me at such setups.
Also interested in seeing what sort of failure rates you've had with GPUs. Our's have worked fine - but n=4.