OpenAI’s latest Habitat engineering account describes the storage work behind products serving more than a billion users. The October 23 post explains how the platform evolved its use of Azure Cosmos DB, including the tradeoffs between isolated workloads and shared capacity.
The problems are familiar distributed-systems problems at unusual scale: noisy neighbors, hot keys, capacity planning, caching, and data placement. Model improvements alone cannot make a service responsive when application storage is slow or an overloaded tenant disrupts unrelated users.
For infrastructure teams, the case study is useful because it exposes operational tradeoffs instead of only reporting growth. The lesson to test locally is how isolation, caching, and workload movement affect reliability under uneven demand.
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