S3 Vectors Concepts
What is S3 Vectors?
Section titled “What is S3 Vectors?” Amazon S3 Vectors
Amazon S3 Vectors — a purpose-built vector storage capability within S3 that enables similarity search over embeddings without a separate vector database. is a purpose-built vector storage capability
within S3. It enables similarity search over embeddings without managing a separate vector database
(no Pinecone, no pgvector, no OpenSearch).
Core concepts
Section titled “Core concepts”
Vector Bucket
Section titled “Vector Bucket”A Vector Bucket
A specialized S3 bucket type (aws_s3vectors_vector_bucket) that hosts vector indexes for similarity search. is a specialized S3 bucket type that hosts vector indexes. Think of it as the “database” that contains your “tables” (indexes).
- Created via
aws_s3vectors_vector_bucketTerraform resource - Named with a globally unique name (this demo uses a random prefix)
- Contains one or more vector indexes
Vector Index
Section titled “Vector Index”A Vector Index
A named index (aws_s3vectors_index) within a vector bucket defining dimension, distance metric, and data type. is a named index within a vector bucket. It defines how vectors are stored and searched:
| Property | Value in this demo | Meaning |
|---|---|---|
| Dimension | 1024 | Number of floats per vector. Must match embedding model output exactly. |
| Distance metric | cosine | Measures angle between vectors. Best for text embeddings (direction matters more than magnitude). |
| Data type | float32 | 32-bit floating point precision for each value. |
Vectors
Section titled “Vectors”Each stored vector consists of:
- Key — A unique identifier (e.g.,
article-a1b2c3d4-chunk-000) - Data — The float array (1024 values for this demo)
- Metadata — Arbitrary key-value pairs (title, URL, date, feed, etc.)
Metadata is returned with search results, eliminating the need for a separate database lookup.
How it compares
Section titled “How it compares”