The process of indexing vector data in AWS S3 Vector begins with creating a vector index within your vector bucket, which serves as the organizational structure for your vectors. You must first configure the index parameters that cannot be changed later: dimension size (1-4,096 to match your embedding model), distance metric (such as cosine similarity for text embeddings or Euclidean distance for spatial data), and optional non-filterable metadata keys. This configuration step is critical because all vectors added to the index must conform to these specifications. You can create up to 10,000 vector indexes per bucket, allowing you to organize different types of vector data or support multiple applications within a single bucket.
Once your vector index is configured, you ingest vector data using the PutVectors API operation, which accepts batches of vectors for efficient processing. Each vector submission includes a unique key identifier, the vector data as an array of floating-point numbers, and optional metadata as key-value pairs. For example, when indexing document embeddings, you might generate vectors using Amazon Bedrock’s Titan Text Embeddings model and include metadata like document title, creation date, and category. The service supports batch operations, allowing you to insert multiple vectors in a single API call to improve throughput. During ingestion, S3 Vector validates that each vector matches the index dimension requirements and automatically begins optimizing the internal data structures for similarity search performance.
S3 Vector handles the complex indexing algorithms automatically, optimizing vector storage and search structures as you add, update, and delete vectors over time. The service maintains strong consistency, meaning newly indexed vectors are immediately available for similarity searches without waiting for background processing. Unlike traditional vector databases that require manual index rebuilding or optimization, S3 Vector continuously maintains optimal search performance without user intervention. You can monitor indexing progress and vector counts through CloudWatch metrics and use the ListVectors API to enumerate stored vectors within an index. The indexing process scales automatically to handle billions of vectors per index while maintaining sub-second query performance for typical workloads.
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