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Interviews / / What is the difference between a vector database and a regular SQL database?

Questions

What is the difference between a vector database and a regular SQL database?

conceptual GenAI & RAG
How Different Experience Levels Approach This
Junior Engineer
Surface Level

Vector databases store embeddings.

Senior Engineer
Production Ready

Yes, but the key difference is how they search.

SQL databases search by exact matches or range queries:

SELECT * FROM products
WHERE price > 100
  AND category = 'electronics';

Vector databases search by semantic similarity — they find items that are close in meaning, not exact matches.

Example query: How do I deploy a Lambda function?

A vector DB will return documents about:

  • serverless deployment
  • AWS Lambda setup
  • function deployment guides

— even if those exact words aren’t in the query.

When to Use a Vector DB for RAG

  • Use a vector DB when queries are natural language (chatbots, Q&A systems)
  • Use SQL when queries are structured (“Show me all orders from last week”)

In Real RAG Systems, Both Are Used Together

  • Vector DB (Pinecone, Weaviate, Bedrock Knowledge Bases) → semantic search
  • SQL (PostgreSQL + pgvector) → metadata filtering

Example Workflow

  1. Filter by metadata in SQL: “Only search docs published after 2024”
  2. Then run semantic search in the vector DB on that subset

This hybrid approach is faster and more accurate than pure vector search.

What Makes the Difference?
  • Context over facts: Explains when and why, not just what
  • Real examples: Provides specific use cases from production experience
  • Trade-offs: Acknowledges pros, cons, and decision factors