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

Read pinned source

Source commit 1761e96813ebcbd43ed81d19e837219033f0afe5

What was checked

Matches the observed repository HEAD
Yes
Scanner rules are current
Yes
Repository scan complete
No
Skill scan complete
No
Observed behavior
Network or broad shell
Last checked
Nov 3, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
3,424
Repository last updated
Nov 6, 2026
License
Apache-2.0

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt

# 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"`.