Individual skill
storing-and-querying-vectors
Store and query vector embeddings using Amazon S3 Vectors, a cost-effective long-term vector storage service with its own API namespace (s3vectors). Triggers on: create S3 vector bucket, vector index, store embeddings, semantic search, RAG vector storage, similarity search, vector database, migrate from other vector databases. Do NOT use for: querying tabular data (use querying-data-lake), S3 object storage, or hundreds/thousands of sustained QPS (use OpenSearch).
plugins/aws-data-analytics/skills/storing-and-querying-vectors/SKILL.md
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# Store and Query Vectors with Amazon S3 Vectors
## Overview
Amazon S3 Vectors is a cost-effective AWS service for storing and querying vector embeddings at scale. Optimized for long-term storage with subsecond latency for cold queries, as low as 100ms for warm queries.
## Decision Guide
- **Hundreds/thousands of sustained queries per second (QPS)**: Wrong tool. Recommend OpenSearch.
- **Hybrid search, aggregations, faceted search**: Recommend OpenSearch with S3 Vectors as storage engine. For OpenSearch integration, search AWS docs for `"Using S3 Vectors with OpenSearch Service"`.
- **Tiered (bulk + hot)**: S3 Vectors for storage + OpenSearch Serverless for real-time. See `references/limits-and-patterns.md`.
- **Cost-effective storage, infrequent queries, RAG**: S3 Vectors is the right fit. Proceed.
For latest guidance, search AWS docs for `"S3 Vectors best practices"`.