AWS S3 Vector is a new storage capability that adds native vector database functionality to Amazon S3, allowing you to store and query vector embeddings directly within S3. Unlike traditional S3 storage that handles files and objects through standard REST operations, S3 Vector introduces specialized “vector buckets” designed specifically for storing numerical vector data and performing similarity searches. This represents the first cloud object storage service with built-in vector search capabilities, eliminating the need for separate vector database infrastructure in many AI applications.
The fundamental difference lies in data structure and access patterns. Traditional S3 stores unstructured data like documents, images, videos, and backups using familiar operations like PUT, GET, and DELETE on object keys. S3 Vector, however, stores structured numerical arrays (vectors) within organized “vector indexes” inside vector buckets. Instead of file-based operations, you use specialized APIs like PutVectors, QueryVectors, and GetVectors through the new s3vectors service namespace. Each vector can include metadata for filtering, and the service automatically optimizes storage for mathematical similarity comparisons rather than simple data retrieval.
Operationally, traditional S3 focuses on high-throughput data storage, backup, and content delivery with global distribution capabilities. S3 Vector prioritizes semantic search performance and cost-effective storage of AI-ready embeddings, supporting up to billions of vectors per index with dimensions up to 4,096. While traditional S3 charges primarily for storage and data transfer, S3 Vector uses a pay-per-use model for vector operations. The service is optimized for workloads with infrequent queries that require sub-second response times, making it ideal for RAG applications, AI agent memory, and large-scale similarity search use cases where traditional vector databases would be cost-prohibitive.