[pushkarg]

Correct, we are super early so there is no self-serve yet.

The primary usecase for this is serving features for ML inference (since eventual consistency is ok and sacrificing write latency for reads is a fair tradeoff). Right now, this is done by using a traditional client-server DB at the moment (Redis/DynamoDB/etc) - or if you're a big tech company that cares about latency you can implement this on your own (https://doordash.engineering/2022/05/03/how-we-applied-clien...).

As far as self-hosting goes - yes writes will be definitely faster. IKV is fully open source so we're not opposed to it, just haven't figured out the details yet (since self hosting will mostly be useful to very large usecase)

At the core, we use in-memory hashmaps that reference memory-mapped files. So, when a dataset doesn't fit in RAM - it spills to disk automatically.

Cold start - the database is seeded with a "base image", that is built periodically by the backend. That's how a user can add new nodes to their cluster, and still avoid any RPCs.

That being said, if you don't have 10TB of disk, you have to partition IKV (and by extension your application). We support partitioning by allowing documents (the data) to declare partitioning keys. If one shard/partition cannot fit on disk - the store won't startup.