Every embedding model gets deprecated eventually. A practitioner's guide to migrating a production RAG pipeline without breaking search quality or your budget.

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- Embedding model providers (OpenAI, Cohere, Google, AWS) deprecate older models on a regular cadence. When it happens, every vector in your index needs to be regenerated.

- Embeddings from different models are geometrically incompatible, even when dimensions match. There is no shortcut: you have to re-embed.

- Three production strategies: blue-green index deployment (build a parallel index and cut over), mixed-model indexes with RRF fusion (migrate gradually while keeping both queryable), and embedding space alignment (promising research, but no confirmed production deployments yet).

- Standard A/B testing is misleading for embedding swaps because the retrieval step itself changes. Use LLM-as-judge for offline validation and canary rollouts with automated rollback.

- Build for migration from day one: version your embeddings, store the original text alongside the vectors, and keep a retrieval evaluation harness ready. Teams that treat the embedding model as a permanent decision scramble when the deprecation notice arrives.