You cannot enable AWS S3 Vector on an existing standard S3 bucket because vector functionality requires a completely different bucket type called a “vector bucket.” Vector buckets are purpose-built storage containers that use different APIs, data structures, and optimization strategies compared to standard S3 buckets. To use S3 Vector capabilities, you must create a new vector bucket specifically designed for storing and querying vector data. This architectural decision ensures optimal performance for vector operations while maintaining the reliability and familiarity of standard S3 for traditional file storage needs.
Creating a vector bucket involves using either the AWS Management Console, AWS CLI, or AWS SDKs with the new s3vectors service namespace. In the console, you navigate to the “Vector buckets” section (separate from standard S3 buckets), click “Create vector bucket,” and specify a unique bucket name following the naming requirements (3-63 characters, lowercase letters, numbers, and hyphens only). You must also choose encryption settings during creation - either server-side encryption with S3-managed keys (SSE-S3) or customer-managed KMS keys (SSE-KMS). These encryption settings cannot be changed after bucket creation, making proper planning essential.
After creating your vector bucket, you need to create vector indexes within it to organize your vector data. Each vector index requires configuration of dimension size (matching your embedding model’s output), distance metric for similarity calculations, and optional non-filterable metadata keys. You can create multiple indexes within a single vector bucket (up to 10,000 per bucket) to organize different types of vector data or support different applications. If you have existing vector data in standard S3 buckets or other storage systems, you’ll need to extract that data, potentially reprocess it into the proper vector format, and ingest it into your new vector indexes using the PutVectors API operation.