SHORT ANSWER
A vector database stores embeddings, lists of numbers that capture the meaning of text, images or other data, and returns the items most similar to a query in milliseconds. It is the retrieval engine behind most semantic search and RAG systems. You often don't need a dedicated product: PostgreSQL with pgvector, Azure AI Search or Databricks Vector Search cover many enterprise cases.
A vector database stores and searches embeddings. An embedding is a list of numbers, often hundreds or thousands long, that an AI model produces to represent the meaning of a piece of content. Items with similar meaning sit close together, so the database can quickly answer the question “what is most similar to this?”
How does a vector database work?
An embedding model turns each document chunk, image or record into a vector.
The database stores each vector with metadata such as source, date and access permissions.
An index, usually HNSW (hierarchical navigable small world) or IVF, arranges the vectors for approximate nearest neighbour search.
At query time, the same model embeds the question, and the database returns the closest vectors, filtered by metadata.
Many systems merge the results with keyword search and re-rank them before they reach the LLM.
Why does it matter for enterprise AI?
Vector search is the retrieval step in retrieval-augmented generation, so it caps the quality of your AI answers. It also powers semantic document search, product recommendations, duplicate detection and similar-case lookup in support. The enterprise questions are practical. Can you enforce document permissions in the filter, keep the index in sync with source systems and run the platform securely with the team you have?
Vector databases vs. other retrieval options
| Keyword search | Vector database | Knowledge graph |
|---|---|---|---|
Matches on | Exact words | Meaning | Entities and relationships |
Good for | Codes, names, exact terms | Natural-language questions | Multi-step, connected questions |
Example tools | Elasticsearch, BM25 | pgvector, Pinecone, Qdrant | Neo4j |
Setup effort | Low | Medium | Higher |
What matters more than the database?
The store you pick matters less than the pipeline around it: how you split documents, which embedding model you use, how you store metadata and permissions, and how you refresh the index when sources change. Switching embedding models later means re-embedding everything, so test two or three candidates on your own content before you commit.
Vectors also work well as glue. On an enterprise GraphRAG chatbot, similarity search wasn’t our only retrieval method. We used cosine similarity to attach each meeting transcript and status report chunk to a verified person, project or department in a knowledge graph. Because every chunk had to land on an existing node, the graph ended up with zero duplicate entities.
How RUBICON helps with vector search
We build retrieval pipelines for enterprise assistants that combine vector search, keyword search and graph data where the questions call for it. See our AI agents services, or our architects can look at your retrieval setup with you.
Related terms
Frequently asked questions
What is the difference between a vector database and a regular database?
A regular database finds rows by exact matches, ranges or keywords. A vector database finds items by similarity of meaning, comparing embeddings with measures such as cosine similarity. So a search for 'terminate the contract' can find a passage about 'ending the agreement early', even though the words differ.
Do I need a dedicated vector database?
Not always. Many platforms you may already run support vector search, including PostgreSQL with pgvector, Azure AI Search, Elasticsearch, MongoDB Atlas and Databricks Vector Search. A dedicated product such as Pinecone, Weaviate, Milvus or Qdrant makes sense at very large scale or for specialised features. Reusing an existing platform usually keeps security and operations simpler.
What is hybrid search?
Hybrid search runs vector similarity and traditional keyword search side by side, then merges the results, often with a re-ranking model. It beats vectors alone on product codes, names, legal references and acronyms that embeddings may miss. Most production RAG systems use hybrid search rather than pure vector search.
How much data can a vector database handle?
Modern vector databases handle millions to billions of vectors. As a rough guide, one million chunks with 1,536-dimension embeddings need about 6 GB of raw vector storage before index overhead. Cost depends mainly on the number of vectors, their dimensions, the index type and the number of queries per second.
Related case study
Enterprise GraphRAG Chatbot with Neo4j | Case Study
How RUBICON's Two Layer Fixed Entity Architecture eliminated data bottlenecks for a multi team enterprise, delivering a conversational AI system that gives leadership instant project clarity, without hallucinations.
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