AWS S3 Vector has several important limitations and quotas that developers need to consider when designing their applications. Each vector bucket can contain up to 10,000 vector indexes, and each vector index can store tens of millions of vectors, though AWS hasn’t published exact limits for individual vector counts per index. Vector dimensions are constrained between 1 and 4,096, which accommodates most common embedding models but may limit use cases requiring higher-dimensional representations. The service currently supports only floating-point (float32) vector data, excluding binary embeddings that some applications use for storage efficiency.
Metadata limitations include a maximum of 10 non-filterable metadata keys per vector index, with these keys being unchangeable after index creation. Each vector can have associated metadata, but there are size limits on both the total metadata per vector and the filterable metadata size, though specific byte limits aren’t detailed in the documentation. Regional availability is currently limited during the preview phase, with S3 Vector available only in select regions including Northern Virginia (us-east-1), Ohio (us-east-2), and Frankfurt (eu-central-1) in Europe. This geographic limitation may affect global applications requiring low-latency access from multiple regions.
Operational limitations include the immutability of key vector index parameters after creation. Once you set the dimension size, distance metric, and non-filterable metadata keys for an index, these cannot be modified, requiring careful planning or index recreation for changes. Vector bucket encryption settings are also permanent after creation. Performance characteristics are optimized for infrequent queries rather than high-throughput scenarios, making S3 Vector less suitable for applications requiring thousands of queries per second or ultra-low latency responses. The service is designed for sub-second query performance, which may not meet requirements for real-time applications needing millisecond response times.
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