Storing And Querying Vectors

This walks you through Amazon S3 Vectors, AWS's cost-effective vector storage service for embeddings you don't need to query constantly. It covers the full workflow from creating buckets and indexes to storing and querying vectors, with clear decision points on when to use OpenSearch instead (high QPS, hybrid search). The skill is thorough about immutable configuration gotchas like dimension and distance metric choices, and it handles the Bedrock integration for generating embeddings. Good for RAG systems where you need cheap long-term storage with occasional queries. The troubleshooting table actually covers the common IAM permission trip-ups with the s3vectors namespace.

skills.sh installs: 7K
GitHub stars: 3.4K
last updated: Aug 21, 2026

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Install

$ npx -y skills add aws/agent-toolkit-for-aws --skill storing-and-querying-vectors --agent claude-code

Installs into .claude/skills of the current project.

Files

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".