Updating vector data in AWS S3 Vector involves using the same PutVectors API operation that you use for initial ingestion, with the service automatically handling overwrites based on vector keys. When you submit a vector with an existing key within an index, S3 Vector replaces the previous vector data and metadata with the new values. This update process is atomic and immediately consistent, meaning the updated vector becomes available for similarity searches right away. You can update the vector data itself, associated metadata, or both simultaneously by providing the complete vector object with the same key identifier.
Deleting vectors requires using the DeleteVectors API operation, which removes specified vectors from an index based on their unique keys. You can delete individual vectors by providing a single key or perform batch deletions by specifying multiple keys in one operation. The deletion process is also immediately consistent, removing the specified vectors from search results without delay. When planning updates or deletions, consider that these operations may affect similarity search results for existing queries, especially if you’re modifying vectors that frequently appear in search results.
Managing vector lifecycle requires careful consideration of key management and data versioning strategies. Since vector keys must be unique within an index, you should establish consistent key naming conventions that support your update patterns. For applications requiring version history, you might include version identifiers in keys or maintain separate indexes for different data versions. Large-scale updates or deletions should be performed in batches to optimize performance, and you should monitor the operations through CloudWatch metrics to ensure successful completion. The service automatically optimizes internal indexing structures after updates and deletions, maintaining search performance without requiring manual reindexing. However, frequent updates to large portions of your vector data might impact query performance temporarily as the service rebalances internal structures.
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