If you're building AI streaming into your app and you asked an agentic coding tool how to do it, you'd probably get a breakdown of SSE versus WebSockets. According to a deep-dive from Ably engineer zknill, that's the wrong conversation entirely. The transport layer is just plumbing—the real complexity lives in what happens after you pick a protocol.

Why Transport Protocol Is the Easy Part

Both SSE and WebSocket designs require the same core architecture: separation of prompt requests and response streams, plus a token cache or data store for handling resume and reconnection scenarios. With SSE, clients POST their prompt and get back a stream ID to connect to. Any server can handle both the request and response because tokens flow through shared storage. WebSockets work almost identically—just with longer-lived connections and more framing overhead to maintain yourself.

The Features Production Actually Demands

Reconnection handling requires sequence IDs on every token so clients can tell servers 'I got up to position 2, start there.' SSE has a Last-Event-ID header built into the spec, but you still need server-side plumbing. Dropped connection detection needs heartbeat messages every 10 seconds or so, plus client timeouts. Cancellation support means checking your cache before writing new tokens—awkward if you're using a queue instead of key-value storage. Token rollup adds another layer: when clients reconnect after missing hundreds of tokens, you don't force them to consume those one-by-one. And once a response completes, compact it into the full answer for history requests rather than replaying the stream. Multi-user and multi-device scenarios? That's where things get genuinely gnarly—user B in a shared chat has no way to know user A just submitted a prompt unless you architect the conversation itself as a streaming entity.

Key Takeaways

  • Token caching is non-negotiable for production—you can't skip it and expect reliable reconnection handling
  • SSE wins on simplicity over WebSockets with zero performance advantage in this architecture
  • Multi-user chat requires rethinking your entire streaming model, not just adding protocol support
  • The gap between GitHub demos and shipped features like cancellation, compaction, and history is massive

The Bottom Line

The AI tools are giving you the easy answer because they don't know about the hard parts. Token streaming in production is a distributed systems problem wearing an HTTP disguise—and if you're not thinking about caching strategy, sequence IDs, and multi-user state from day one, you're building tech debt that'll bite you the moment users actually depend on your product.