AWS S3 Vector supports vector search and retrieval through a dedicated set of APIs that perform mathematical similarity comparisons between stored vectors and query vectors. When you submit a search query using the QueryVectors API, you provide a query vector (typically generated using the same embedding model used for stored data), specify the number of results you want (topK parameter), and optionally include metadata filters. The service calculates distances between your query vector and every vector in the specified index using the configured distance metric, then returns the most similar vectors ranked by similarity score.
The search process begins with vector indexes that organize your data within vector buckets. Each index is configured with specific parameters including dimension size (1-4,096), distance metrics (such as cosine similarity for semantic search or Euclidean distance for spatial data), and metadata configuration. When you perform a search, S3 Vector automatically handles the mathematical computations required for similarity comparison across potentially millions of vectors. The service supports filtered searches using metadata attributes, allowing you to restrict results to specific categories, time periods, or other criteria stored as key-value pairs with each vector.
Performance and retrieval capabilities are optimized for sub-second query response times, making S3 Vector suitable for interactive applications despite being built on object storage infrastructure. The service automatically optimizes vector storage and indexing as you add, update, and delete vectors over time, maintaining search performance without manual intervention. You can retrieve not only the most similar vectors but also their distance scores and associated metadata, enabling rich search experiences. The system supports batch operations for efficiency and provides consistent read-after-write behavior, meaning newly added vectors are immediately available for search operations. Integration with other AWS services like Bedrock Knowledge Bases further simplifies retrieval workflows by automatically handling query vector generation and result processing.
Will Amazon S3 vectors kill vector databases or save them?
S3 vectors looks great particularly in terms of price and integration into the AWS ecosystem. So naturally, there are a lot of hot takes. I’ve seen folks on social media and in engineering circles say this could be the end of purpose-built vector databases—Milvus, Pinecone, Qdrant, and others included. Bold claim, right?
As a group of people who’s spent way too many late nights thinking about vector search, we have to admit that: S3 Vectors does bring something interesting to the table, especially around cost and integration within the AWS ecosystem. But instead of “killing” vector databases, I see it fitting into the ecosystem as a complementary piece. In fact, its real future probably lies in working with professional vector databases, not replacing them.
Check out James’ post to learn why we think that—looking at it from three angles: the tech itself, what it can and can’t do, and what it means for the market. We’ll also share S3 vectors’ strenghs and weakness and in what situations you should choose an alternative such as Milvus and Zilliz Cloud.
Will Amazon S3 Vectors Kill Vector Databases—or Save Them?
Or if you’d like to compare Amazon S3 vectors with other specialized vector databases, visit our comparison page for more details: Vector Database Comparison