AWS S3 Vector integrates natively with Amazon Bedrock Knowledge Bases, providing a fully managed end-to-end RAG (Retrieval Augmented Generation) workflow that significantly simplifies implementation. When you create a knowledge base in Amazon Bedrock and select S3 Vector as your vector store, Bedrock automatically handles document ingestion from your S3 data sources, text chunking, embedding generation using your chosen model, and storage in S3 Vector indexes. This integration eliminates the need to manually generate embeddings or manage the data pipeline, making advanced AI capabilities accessible without deep vector database expertise.
The Bedrock integration extends to Amazon SageMaker Unified Studio, where you can develop and test knowledge bases using S3 Vector as the underlying storage layer. This allows data scientists and ML engineers to experiment with different embedding models, chunking strategies, and retrieval configurations while leveraging S3 Vector’s cost advantages for storing large knowledge bases. The integration supports various document formats and automatically manages synchronization when source documents change. You can query the knowledge base through Bedrock’s APIs, which automatically convert natural language questions into vector embeddings, search the S3 Vector index for relevant content, and provide context to foundation models for response generation.
Integration with Amazon Kendra is not directly available as a native feature, since Kendra uses its own search infrastructure optimized for enterprise document search with different indexing and query mechanisms. However, you can build complementary architectures where S3 Vector handles semantic search for embeddings-based retrieval while Kendra manages traditional keyword and entity-based enterprise search. For SageMaker integration beyond Unified Studio, you can use S3 Vector within custom ML pipelines by accessing it through the AWS SDKs in SageMaker notebooks or processing jobs. Additionally, S3 Vector integrates with Amazon OpenSearch Service, allowing you to export vector data for high-performance search scenarios or implement tiered architectures where S3 Vector provides cost-effective storage and OpenSearch handles high-throughput, low-latency queries.
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