People are always talking about spot pricing when GPU compute comes up which is valid but not for the ML use-case (which this article is discussing). Spot is 100% incompatible with ML training. You need to be guaranteed that your many hour/multiple day job is going to complete which is antithetical to spot. At Paperspace, our goal is to provide a steady state low price that you can depend on because our primary audience is ML. Spot would be more ideal from an efficiency perspective but it’s just not viable in this context.
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Now that is patently false. Maybe you should start with your disclosure of working for a competitor before spreading such misinformation. Hourly checkpointing is a thing and built into frameworks such as Tensorflow. Making it very easy to save your work just before interruption, that is IF you are interrupted given your spot price is too low, and then resuming whenever you'd like, i.e. when prices fall back down below your max threshold. I've compared cost with Paperspace and it's not even close. The cost saving on AWS are huge, especially on the most expensive gpu instances which is what we machine learners use.
Look, as a consumer of gpu compute, I want there to be as much competition in this space as possible, so I root for you guys, but as it stands you're nowhere even close.