Should you standardize on smaller embedding dimensions for RAG retrieval costs in 2026?
Smaller embedding dimensions can materially reduce vector storage and index cost — for large corpora the difference between 3072-dim float32 and compressed 1024-dim representations can exceed 100GB — but the savings only matter if your recall loss stays inside the business tolerance for the workload.