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

Companies don't necessarily need a dedicated vector database to store and search embeddings. Many use their existing databases, search engines, or even application memory to implement vector search. The key is that embeddings are just arrays of numbers. You can store them in ordinary data structures and calculate similarity using mathematical operations. Here are the most common approaches used in practice.

1. PostgreSQL — Use an existing relational database (PostgreSQL).
2. AWS — Documentation: AWS vector database guide.
3. Elasticsearch — Use Elasticsearch or OpenSearch.
4. OpenSearch — Use Elasticsearch or OpenSearch.
5. MongoDB Atlas — Store embeddings in MongoDB.
6. Redis — Store embeddings in Redis.
7. Faiss
8. Pinecone — It doesn't necessarily need Pinecone, Weaviate, or another dedicated vector database.
9. Weaviate — It doesn't necessarily need Pinecone, Weaviate, or another dedicated vector database.

Companies commonly avoid a dedicated vector database by using one of these approaches:

1. PostgreSQL — PostgreSQL with pgvector: Store the embedding, text, and metadata in the same row, then use SQL for filtering and nearest-neighbor search.
2. Elasticsearch — Existing search engines: Elasticsearch and OpenSearch can store dense vectors alongside traditional inverted indexes, enabling hybrid keyword-plus-semantic search.
3. OpenSearch — Existing search engines: Elasticsearch and OpenSearch can store dense vectors alongside traditional inverted indexes, enabling hybrid keyword-plus-semantic search.
4. Faiss — Embedded libraries: For smaller or static datasets, libraries such as FAISS or Annoy keep an index inside the application process or on disk—no separate server is required.

1. Pinecone — When companies don't use a dedicated vector database (like Pinecone, Milvus, or Qdrant), they typically rely on existing infrastructure they already operate —such as relational databases, search engines, or in-memory…
2. Qdrant — When companies don't use a dedicated vector database (like Pinecone, Milvus, or Qdrant), they typically rely on existing infrastructure they already operate —such as relational databases, search engines, or in-memory…
3. PostgreSQL — Instead of a new stack, companies add the pgvector extension to their existing PostgreSQL database.
4. Elasticsearch — Traditional Search Engines (Elasticsearch / OpenSearch):.
5. OpenSearch — Traditional Search Engines (Elasticsearch / OpenSearch):.
6. Redis — NoSQL and Distributed Databases (Redis / Cassandra / MongoDB):.
7. MongoDB Atlas — NoSQL and Distributed Databases (Redis / Cassandra / MongoDB):.
8. Faiss — Flat Files and Custom In-Memory Arrays (FAISS / NumPy / Scikit-Learn):.