AWS S3 Vector follows a pay-as-you-use pricing model similar to other AWS services, charging separately for storage, operations, and data transfer without requiring upfront commitments or infrastructure provisioning. The pricing structure includes charges for vector storage based on the amount of data stored in vector indexes, measured in GB-months like standard S3 storage. However, the specific storage rates for S3 Vector may differ from standard S3 due to the specialized indexing and optimization required for vector similarity searches. AWS has indicated potential cost savings of up to 90% compared to traditional vector databases, though exact pricing details aren’t fully published during the preview phase.
Operational charges cover vector-specific API operations including PutVectors for data ingestion, QueryVectors for similarity searches, GetVectors for data retrieval, and index management operations like CreateIndex and ListIndexes. These operations are likely priced per request or per batch, similar to other AWS API pricing models. Query operations may have additional charges based on the number of vectors processed during similarity searches, especially for large indexes where queries must compare against millions of vectors. Data transfer charges apply when moving vector data between AWS regions or out to the internet, following standard AWS data transfer pricing.
The cost advantage of S3 Vector becomes more apparent when compared to traditional vector databases that require dedicated infrastructure with high-performance computing resources and substantial RAM for in-memory operations. With S3 Vector, you avoid infrastructure costs, minimum commitments, and the need to provision compute resources for peak loads. The serverless architecture means you don’t pay for idle capacity, and integration with other AWS services eliminates data transfer costs between services. For organizations implementing RAG applications, AI agent memory systems, or large-scale similarity search capabilities, this pricing model enables use cases that were previously economically unfeasible. However, for applications requiring frequent queries or ultra-low latency, the operational costs might be higher than dedicated vector database solutions optimized for high-throughput scenarios.
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