> What is then your initial latency if i start an AI job 'fresh'? You still need to hit the backend right? How long do you then keep this data in your cache?
That's correct, and it's something that we can tune if there's a specific need. For AI use cases specifically, we're working on adding functionality to "pre-load" the cache with your data. For example, you would be able to call an API that says "I'm about to start a job and I need this directory on the cache". We would then be able to fan out our infrastructure to download that data very quickly (think hundreds of GiB/s) -- much faster than any individual instance could download the data. Then your job would be able to access the data set at low-latency. Does that sound like it would make sense for you?
> Btw. while your experience works well for Netflix, in my company (also very big), we have LoBs and while different teams utilize their storage in a different way, none of us are aligned on a level that we would benefit directly from your solution.
I'm not totally sure what you mean here. I don't anticipate that a large organization would have to 100% buy-in to Regatta in order to get benefits. In fact, this is the reason why we are so intent on having a serverless product that "scales to 0". That would allow each of your teams to independently try Regatta without needing to spend hundreds of thousands of dollars on something Day 1 for the entire company.
> From a pure curiosity point of view: Do you have already enough customers which have savings? What are their use cases? The size of their setups?
These are pretty intimate details about the business, and I don't think I can share very specific data. However, yes -- we do have customers who are realizing massive savings (50%+) over their existing set ups.