[Melonotromo]

Your pricepoint is very bad. The overprovicioning statement in your Post indicated that you would be a 'cheap' alternative but 100gb for $5?

I'm also not sure that its a good architecture to have your servers inbetween my S3. If i'm on one cloud provider, the traffic between their S3 compatible solution and my infrastructure is most of the time in the same cloud provider. And if not, i will for sure have a local cache rcloning the stuff from left to right.

I also don't get your calculator at all.

[huntaub]

Thanks for the feedback. If price is the single blocker for teams to try the product, I'd love to discuss more. Please send me an email at hleath [at] regattastorage.com.

> If i'm on one cloud provider, the traffic between their S3 compatible solution and my infrastructure is most of the time in the same cloud provider

This is exactly right, and it's why we're working to deploy our infrastructure to every major cloud. We don't want customers paying egress costs or cross-cloud latency to use Regatta.

> I also don't get your calculator at all.

This could probably use a bit more explanation on the website. We're comparing to the usage of local devices. We find that, most often, teams will only use 15% of the EBS volumes that they've purchased (over a monthly time period). This means that instead of paying $0.125/GiB-mo of storage (like io2 offers), they're actually paying $0.833/GiB-mo of actual bytes stored ($0.125/15%). Whereas on Regatta, they're only paying for what they use -- which is a combination of our caching layer ($0.20) and S3 ($0.025). That averages out closer to $0.10/GiB stored, depending on the amount of data that you use.

[Melonotromo]

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?

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.

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?

[huntaub]

> 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.