Data privacy and security in AWS S3 Vector are handled through multiple layers of protection, starting with mandatory encryption for all stored vector data. The service supports server-side encryption using either Amazon S3 managed keys (SSE-S3) or customer-managed AWS KMS keys (SSE-KMS), with encryption settings configured during vector bucket creation and unchangeable afterward. All vector buckets have Amazon S3 Block Public Access settings permanently enabled and cannot be disabled, ensuring that vector data cannot be accidentally exposed to public access. This provides an additional security layer beyond what’s available in standard S3 buckets.
Access control operates through AWS Identity and Access Management (IAM) using the dedicated s3vectors service namespace, allowing for granular permissions specific to vector operations. You can create IAM policies that grant access to individual vector indexes, all indexes within a vector bucket, or all vector buckets in an account. This fine-grained control enables you to implement principle of least privilege access, where users and applications only receive permissions for the specific vector resources they need. Service Control Policies in AWS Organizations can also restrict S3 Vector operations across multiple accounts, providing centralized governance for enterprise deployments.
Data isolation and compliance features ensure that vector data remains protected throughout its lifecycle. Vector buckets exist within your AWS account boundary with the same network isolation and VPC endpoint support as other AWS services, enabling private connectivity without internet exposure. The service maintains audit trails through AWS CloudTrail, logging all vector operations for compliance and security monitoring. Since vectors often represent sensitive information (like proprietary documents or personal data), the metadata system allows you to store classification tags and implement data governance policies. For organizations with specific compliance requirements, S3 Vector inherits AWS’s extensive compliance certifications and can be used in regulated industries with appropriate additional controls and data handling procedures.
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