Short answer: the factors that influence lifecycle policies for AI-generated images are access frequency, useful life, regional compliance, and audit importance; keep frequently read images hot, archive or delete them after the useful window, and choose a specialist when immutable or public objects are required.

Large AI-generated images make lifecycle policy a systems decision, not a housekeeping checkbox. A useful policy watches four signals: how often an image is read, how long it remains useful, what retention law applies in its region, and whether the original must remain available for an audit. For a developer tool processing receipts, I would keep recent and frequently reviewed originals in hot storage, archive or delete derived material after its business window, and automate the transition only after legal retention rules have been translated into explicit dates.

The hard part is the boundary between throughput and evidence.…