Most conversations about vector databases start and end with embeddings. Discussions typically center around how they're generated, which model produced them, how many dimensions they carry. Embeddings get all the attention, but they aren't what determines whether your AI search, RAG pipeline, or recommendation engine feels instant or painfully slow in production.That comes down to indexing.