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.