AWS S3 Vector does not natively support hybrid search capabilities that combine keyword-based and vector-based search within a single query operation. The service is designed specifically for vector similarity search using mathematical distance calculations between embeddings, without built-in text indexing or keyword search functionality. S3 Vector focuses on semantic search through vector embeddings, where you must generate vector representations of both your stored content and search queries using embedding models before performing similarity comparisons.
However, you can implement hybrid search architectures by combining S3 Vector with other AWS services that provide complementary search capabilities. The most common approach involves integrating S3 Vector with Amazon OpenSearch Service, which excels at traditional keyword search, full-text search, and advanced filtering. You can store your content in both systems: text and metadata in OpenSearch for keyword search, and vector embeddings in S3 Vector for semantic search. Your application can then query both services simultaneously and merge results based on relevance scores, user preferences, or application logic to provide comprehensive search results.
Another hybrid approach leverages Amazon Bedrock Knowledge Bases, which can orchestrate searches across different modalities while using S3 Vector as the underlying vector storage. When building RAG applications, you might implement a two-stage search process where keyword filtering narrows the search space using metadata filters in S3 Vector, followed by semantic similarity search within the filtered results. You can also use preprocessing techniques to extract keywords from documents and store them as filterable metadata in S3 Vector, enabling basic keyword filtering within vector searches. While these approaches require additional architecture complexity compared to purpose-built vector databases with native hybrid search, they provide flexibility to optimize each search type independently and leverage AWS’s managed services for different aspects of the search experience.
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