# Amazon OpenSearch

> Overkill for most apps. Postgres full-text plus pgvector covers search.

- Verdict: Skip
- Vendor: AWS
- Layer: [Data](https://cloudlesslabs.com/stack/layers/data.md)
- Last reviewed: 18 October 2026, by Nicolas Dao (Cloudless Labs, Sydney)
- Web page: https://cloudlesslabs.com/stack/aws-opensearch/. The whole stack in one file: https://cloudlesslabs.com/stack/brief.md

A cluster to run for a search box that Postgres already handles. Only for very large text corpora.

## Use it when

- Very large text corpora with heavy faceting and aggregations.

## Don't use it when

- Typical app search. Postgres full-text search plus pgvector does it, with no cluster to run.

## Provisioning

Official Pulumi package · Official Terraform provider

## Use instead

Postgres full-text search plus pgvector, next to the data you already have.

## Alternatives

- [pgvector](https://cloudlesslabs.com/stack/pgvector/index.md): Semantic and hybrid search in Postgres.

## Worked examples

- [pgvector or S3 Vectors?](https://cloudlesslabs.com/stack/guides/vectors/index.md): Where should my embeddings live?

## Where it fits

- [Semantic search and RAG](https://cloudlesslabs.com/stack/use-cases/search.md): don't. A cluster to size, patch and pay for around the clock, to do what Postgres full-text search plus pgvector already does next to your data. Only worth it for very large text corpora. Instead: [pgvector](https://cloudlesslabs.com/stack/pgvector/index.md).

## Related

- [Data](https://cloudlesslabs.com/stack/layers/data.md): every tool in this layer, with its verdict
- [The whole stack in one file](https://cloudlesslabs.com/stack/brief.md): every use case and tool, for questions that span many
- [Map of every Markdown file on cloudlesslabs.com, with what each one answers](https://ask.cloudlesslabs.com)