Google Cloud details zero-downtime embedding model upgrades in AlloyDB AI

[Google Cloud] Google Cloud published a technical blog post describing how to upgrade vector embedding models in AlloyDB AI with zero downtime. According to the post, upgrading embedding models requires re-embedding the entire dataset, rebuilding indexes and coordinating schema migrations without interrupting user queries; common pitfalls include incompatible vector spaces between models, index rebuild overhead, and DDL operations that acquire exclusive locks and can stall read and write transactions. Google Cloud said AlloyDB AI's model endpoint management provides in-database access to first-party, third-party or self-hosted embedding models, while auto embeddings generates new embeddings in the background at up to 3,000 per second and keeps them current as source data changes. ScaNN index builds are 10x faster than open source HNSW, according to the post. The walkthrough migrates the ecomm.product_reviews table from text-embedding-005 (768 dimensions) to gemini-embedding-001 (3,072 dimensions) while serving live traffic.