# How do companies store and search embeddings without a dedicated tool?

Category: Vector Databases. Intent: informational. Top brand: PostgreSQL.

Page: https://slateindex.ai/prompt/how-do-companies-store-and-search-embeddings-without-a-hmi5tjvufp · data of 2026-11-02

## Brands named

| Brand | Share of engines |
|---|---|
| PostgreSQL | 100% |
| Elasticsearch | 100% |
| OpenSearch | 100% |
| Faiss | 100% |
| Pinecone | 67% |
| MongoDB Atlas | 67% |
| Redis | 67% |
| AWS | 33% |
| Qdrant | 33% |
| Weaviate | 33% |

## Sources

- github.com (3)
- dataaihub.co (2)
- mongodb.com (2)
- agentsunrise.pro (1)
- amazon.com (1)
- carlogino.com (1)
- elastic.co (1)
- gpuwerk.com (1)
- kodekloud.com (1)
- nvidia.com (1)
- oracle.com (1)
- redis.io (1)

## Definitions

- Visibility (0–100): how often and how high AI answers name the brand, weighted by position.
- Mention rate: share of collected AI answers that name the brand.
- Share of voice: the brand's mentions as a share of all brand mentions in the category.
- Citation authority: share of answers naming the brand that cite the brand's own site.

Cite as: SlateIndex, “How do companies store and search embeddings without a dedicated tool?”, https://slateindex.ai/prompt/how-do-companies-store-and-search-embeddings-without-a-hmi5tjvufp, data of 2026-11-02. Licence: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).