You get what you pay for... The service is cheap but the on-demand instance randomly disconnected me after a few days. Luckily I could reconnect before anyone else jumped on, otherwise it would be a total loss - all data gone. As expected customer service is nonexistent - "we'll be with you in just a minute" = infinite hold

Good intentions and pricing, poor execution Good intentions and pricing, but so far so-so execution. My custom Docker image did not let me use GPU in TF or PyTorch, and yet the site info said it should be compatible (not with newer CUDA drivers, no - it cannot be, as these drivers are not backward compatible). My custom Jupyter token did not work, and they did not know why. They auto-generated Jupyter token even though I specified my own with a properly set env. variable. The SSH tunnel option did not work... the machines refused SSH connections. I got disconnected after a mere hour of working because the PC (which it was, with a GPU that had an actual fan - meaning: retail hardware, not a proper datacenter GPU, double-check please if you are not violating NVIDIA's hardware licensing agreements by hosting retail hardware like that, in a mock-datacenter fashion). Anyway, I pressed "report this machine" button and it went up again soon, so I did not suffer any data loss (maybe the PC's Jupyter notebook server or other system server simply went down, which the website reported in a very hysterical manner that the machine was "no longer part of their network"). A notch better SLA than an anonymous proxy, because you have actual tech support. Extra star given for the CUDA driver compatibility tests that I managed to conduct (the info provided on each server is very detailed, e.g. on max CUDA version). I hope these are just budding problems, and they will get better soon! Update: I've tested also the Interruptible instances. Waited until I got outbid (for cheapest instances it happens after an 1 hour or so). Then tried to transfer the data out from the stopped instance (you pay for storage of non-running instances precisely for that reason), using the Copy functionality (having spinned up another instance as target). It failed with "Invalid input format" error - I had a text file (JSON) and a binary file (a JPEG). So don't choose these instances if you value your data until such bugs are fixed - if you get outbid, you won't be able to access your data anymore (unless you wait for it to be returned by the person who outbid you, which I did). Update: I was unable to make my containers work with their service, because of non-standard execution mechanisms (these containers work perfectly under both docker and k8s). They still kept charging me even though I could not log in. I subtracted 1 star for being unable to withdraw my money (you have to pre-pay at least 5 USD - I paid 10 - and there is no self-service way to get the unused balance back). I subracted one more start for the age of this service. I've found out that it is now ... 5 years old and it's still full of bugs ("Piotr Inny 15 października, 2018 | 8:26 am Ta usługa to faktycznie beta, niestety pełna bugów."

Incredible support people there Incredible support people there. Super fast support responses. I had a hiccup with my first GPU instance there but they guided me in the right direction and stayed with me until I had a proper instance running. I love the service already.

Vast is amazing Vast is amazing. I have used many GPU rental platforms and vast is my favorite.

If you use verified machines then it's an amazing and cheap service Non verified machines may be bad, offering bad connections and may be unavailable once rebooted, hence the many bad reviews. For this reason, I suggest you to only stick with verified datacenters. I rented an A4000 GPU for many hours and I've never had any problems with internet speed or availability. The price is very convenient at about 0.16$/h. Nothing to complain about, for me it was essential to train some DL models at a small price. Very recommended.