Regarding S2 generally (not just s2-lite), the intent behind it is to turn the core data structure from streaming platforms (like Kafka) into a serverless primitive -- kinda similar to what object storage did for file storage.

So if you are already in the world of working with streaming platforms, S2 gives you a simpler API, bottomless storage (S2 itself uses object storage for durability), and no limits on the quantity of streams you can create and work with. Streams also all have URIs and are directly accessible over REST with granular access controls.

This enables new types of patterns for working with streams, other than just the traditional ones where people typically reach for streaming platforms (like CDC, ETL pipelines, etc). An agent can have its own stream to serialize state onto, for instance; you can use a stream as a durable transport layer -- e.g., you want to reliably provide a flow of data (tokens from a model, financial ticker data, etc) to a user and allow them to resume from exactly where they left off if they are disconnected, for instance; you could use streams as a durable ingest buffer, for collecting data that will eventually reside in an OLAP like Clickhouse.

The durability is for being able to replay the stream, a hash will not let you reconstruct the original message(s).

If you just need ephemeral comms, making it persistent is indeed overkill. But reliability challenges often come up with seemingly ephemeral comms too – think streaming responses from an LLM. The last mile can be pretty flaky e.g. iOS will cancel connections when users background an app. Using a durable stream for persisting the tokens means a client can ask to resume from where it left off / from the beginning of the stream, and the data would be available without having to re-inference.