# Semantic search and RAG

> Search by meaning, or feed an LLM your documents. Nicolas Dao's picks for this job (Cloudless Labs, Sydney), in order of preference, what he would avoid, and what to use instead.

- Use case 10 of 21 in The Stack, last reviewed 21 October 2026
- Web page: https://cloudlesslabs.com/stack/use-cases/search/

Picks, in order of preference:

1. **[pgvector](https://cloudlesslabs.com/stack/pgvector/index.md)** (My default): When a user is waiting and results depend on your data and permissions.
2. **[Amazon S3 Vectors](https://cloudlesslabs.com/stack/aws-s3-vectors/index.md)** (Use when it fits): When the collection is huge, cold or per client, and 100 ms is fine.

Don't:

- ~~Amazon OpenSearch~~: 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).

Worked example: [pgvector or S3 Vectors?](https://cloudlesslabs.com/stack/guides/vectors/index.md)

## The picks in brief

- [pgvector](https://cloudlesslabs.com/stack/pgvector/index.md): My default. Embeddings next to the data they describe, for when a user is waiting for the answer. Pricing: Mostly the storage on a Postgres you already pay for: about $5 a month for 1M vectors on an existing Neon database.
- [Amazon S3 Vectors](https://cloudlesslabs.com/stack/aws-s3-vectors/index.md): Use when it fits. Vectors at S3 prices, for huge, cold or per-client collections where 100 ms is fine. Pricing: Sydney storage $0.066/GB-month; about $2.50 a month for 1M vectors, $15–154 for 10M, $76–473 for 100M (vs $2,000–12,000 to hold 100M in Postgres). No servers to size.

## Related

- [pgvector or S3 Vectors?](https://cloudlesslabs.com/stack/guides/vectors/index.md): the worked example for this job, with costs
- [Data](https://cloudlesslabs.com/stack/layers/data/index.md): every tool in this layer
- [AI](https://cloudlesslabs.com/stack/layers/ai/index.md): every tool in this layer
- [Every use case and tool in one file](https://cloudlesslabs.com/stack/brief.md)
- [Map of every Markdown file on cloudlesslabs.com, with what each one answers](https://ask.cloudlesslabs.com)