You should migrate from text-embedding-ada-002 when you have clear evidence that a newer embedding model materially improves your application’s retrieval or grouping quality, or when you need capabilities that are hard to achieve with your current setup. In other words, migrate when relevance is a bottleneck (users can’t find what they need, RAG answers cite the wrong chunks, clustering feels noisy) and you’ve already addressed the “cheap wins” like better chunking, metadata filtering, deduplication, and query rewriting. If text-embedding-ada-002 is meeting your accuracy, latency, and cost targets, there’s no urgent need to change just because something newer exists.
A practical migration decision is usually driven by measurement. Create an evaluation set of real queries (and expected relevant documents), then compare models using top-k recall and ranking metrics like MRR. Also check operational impacts: embedding throughput, storage growth (vector size and count), and any downstream ranking logic. A common strategy is a dual-write period: for new or updated documents, generate both the old and new embeddings and store them side-by-side. Then you can run A/B tests in production: route a small percentage of traffic to the new index, compare click-through, dwell time, “search refinement” rates, or human-judged relevance, and roll forward only if the improvement is consistent.
Vector databases make migration much less painful. With Milvus or Zilliz Cloud, you can maintain two collections (one for text-embedding-ada-002, one for the new model), keep identical metadata fields, and switch retrieval by configuration. That also helps with rollback: if you see unexpected regressions, you can flip back instantly without re-ingesting everything. In many teams, the right moment to migrate is when you’re already doing a re-embedding pass (for example, you changed chunking rules or added a new corpus), because you’re paying the ingestion cost anyway. For more information, click here：https://zilliz.com/ai-models/text-embedding-ada-002