PRODUCTION SCENARIO
A publisher is building an Amazon Bedrock knowledge base that will hold 400 million text chunks. Vector storage is the largest cost line in the project. The team can accept slightly less precise retrieval in exchange for lower storage cost.
Which embedding choice will meet these requirements?
Answering here is anonymous. Nothing is saved unless you sign in.
Show answer and explanation
Answer: Binary embeddings in a vector store that supports binary vectors
Binary vectors use 1 bit per dimension where float32 vectors use 32 bits, so they cut vector storage sharply at some cost in how precisely they represent the text, which is the trade the publisher has agreed to. Reusing the existing float32 index does not work: AWS requires both an embedding model and a vector store that support binary vectors. Adding dimensions increases storage rather than reducing it. Moving to higher-capacity nodes provisions room for the cost instead of removing it.