A token is not a message: stop storing AI responses like event logs

Storage design determines whether clients joining an AI response mid-stream can retrieve the accumulated text, know its status, and continue receiving updates without gaps or duplicates. The comparison examines ephemeral SSE or WebSocket delivery, which retains no prior content; durable chunk logs such as Redis Streams or NATS JetStream, which preserve fragments but require consumers to reassemble, deduplicate, and detect completion; repeated database-record updates, which provide a readable partial response but can create substantial write load and lag when batching; and message appends, which maintain a growing readable message. The article argues that message appends, presented through Ably’s platform, allow late joiners to receive the full response so far followed by live updates while avoiding per-token database writes, with the completed response later persisted in a database. It notes that building an equivalent system independently requires distributed handling of ordering, retries, readable state, live-history handoff, cancellation, failures, capacity, and monitoring. The discussion also identifies practical constraints around retention periods, message-size limits, publisher ordering, SDK availability, authentication, and the distinction between appends as a data model and SSE or WebSockets as delivery transports.

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