What is Amazon S3 Vectors?
Amazon S3 Vectors is the first cloud object store with native support for storing and querying vectors. Embeddings generated from text, images, or audio can be stored directly in S3 and searched through a native vector API. Until now, working with embeddings at scale required a dedicated vector database with its own infrastructure, scaling, and operations. S3 Vectors moves this task into object storage, making the storage and search of vectors significantly more cost-efficient.
The service addresses the cost problem of billion-scale vector data. Applications such as Retrieval Augmented Generation (RAG) and semantic search produce large volumes of embeddings, and keeping them in specialized databases over time is expensive. S3 Vectors is fully serverless: there is no infrastructure to provision and no capacity planning. AWS states that the total cost of uploading, storing, and querying vectors can be reduced by up to around 90 percent compared with dedicated vector databases.
Core Features
- Vector buckets and vector indexes: Two new resource types organize vector data. A vector index stores up to 2 billion vectors, and a vector bucket holds up to 10,000 indexes, enabling tens of billions of vectors per bucket.
- Native vector search: Similarity search using cosine or euclidean distance metrics with sub-second query latency. Frequently queried indexes return results in around 100 milliseconds or less.
- Metadata filtering: Key-value metadata attached to vectors allows filtering of search results, for example by source, date, or category.
- Automatic optimization: S3 Vectors automatically optimizes vector data as datasets grow, without manual intervention in the index structure.
Common Use Cases
Retrieval Augmented Generation (RAG): S3 Vectors serves as a cost-efficient backend for RAG applications and integrates directly with Amazon Bedrock Knowledge Bases. Documents are stored as embeddings and retrieved at runtime as context for language models.
Semantic search and AI agents: Applications with semantic or similarity search store their embeddings durably in S3. The long-term memory of AI agents can likewise be kept and queried at low cost.
Indexing of media and documents: Large collections of images, videos, or documents are indexed as vectors to enable content-based search, recommendations, or automated content analysis.
Benefits
- Significantly lower cost for vector data compared with dedicated vector databases, up to around 90 percent according to AWS
- Fully serverless with no infrastructure to provision and automatic scaling into the billion range
- Native integration with Amazon Bedrock Knowledge Bases, SageMaker Unified Studio, and OpenSearch Service
- Available in EU regions and therefore usable for data-sovereign architectures
Integration with innFactory
As an AWS Reseller, innFactory supports you with the adoption and operation of this service.
Typical Use Cases
Frequently Asked Questions
What is Amazon S3 Vectors?
Amazon S3 Vectors is the first cloud object store with native support for storing and querying vectors. Instead of running a separate vector database, you store embeddings directly in S3 and query them through a native API. The service is fully serverless and requires no infrastructure to provision.
When should I use Amazon S3 Vectors?
S3 Vectors suits RAG backends, semantic search, long-term memory for AI agents, and indexing large volumes of media or documents. It is especially useful for large, less latency-sensitive vector datasets where low storage cost matters more than minimal query latency.
How much does Amazon S3 Vectors cost?
Billing follows a pay-as-you-go model: you pay for uploading, storing, and querying vectors. Compared with dedicated vector databases, AWS states the total cost can be up to around 90 percent lower. Current rates are listed on the S3 pricing page.
How does S3 Vectors integrate with other AWS services?
S3 Vectors integrates natively with Amazon Bedrock Knowledge Bases as a RAG backend, with Amazon SageMaker Unified Studio, and with Amazon OpenSearch Service. Through OpenSearch you can implement a tiered strategy: low-cost storage of rarely queried vectors in S3 and high-performance search for frequently used data in OpenSearch.