Backpressure in AI streaming: change how tokens move, keep each response one message

AI streaming backpressure can arise during inference, in worker queues, at the delivery layer, or in client rendering, with delivery posing particular challenges because common techniques can alter how a response is stored and recovered. Buffering can create unbounded queues and latency, batching reduces publish frequency but splits responses into multiple stored records, and repeatedly rewriting a single record preserves one response but incurs growing database-write costs. The material recommends evaluating delivery systems with slow-consumer and late-joiner tests, measuring whether lagging viewers can catch up independently and whether reconnecting or newly joining viewers can immediately access the complete response generated so far. It presents Ably message appends as an alternative that keeps a streamed response as one growing message while sending only new text, using ordering, rollups, recovery, and full-state catch-up to limit update frequency and support reconnections. It also notes that applications must still manage model capacity, queue limits, cancellation, client rendering policies, idempotency for external side effects, and long-term storage of completed responses.

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