Azure Storage Lifecycle Management for Cost Optimization

Quick Answer

Lifecycle management rules work best when the tier transitions track how access patterns actually change over time, rather than jumping straight to the cheapest possible tier the moment data goes cold. In this scenario, the data is still occasionally accessed after 30 days, just infrequently, which is exactly the access pattern the cool tier is designed for: lower storage cost than hot, in exchange for a modest per-access charge, appropriate when reads still happen sometimes. Waiting until the one-year mark to move data to archive reflects that by then the data has settled into a rarely touched, long-term retention state, where archive's much lower storage cost is worth the delay and cost of rehydration if ever needed again. Skipping straight to archive at 30 days would apply that same rehydration delay and retrieval expense to data still accessed with some regularity, working against the goal rather than for it. Deleting the data at 30 days is disqualified outright, since the requirement is to retain it for seven years, not thirty days. And a premium tier moves in the opposite direction entirely, since premium is optimized for fast performance on frequently accessed data, not for reducing cost on data nobody is actively using. The general pattern: match each tier transition to a genuine shift in access frequency, and never let a tier choice conflict with an explicit retention requirement.