SQL, NoSQL & Vector Databases for AI
AI applications need to store structured data, unstructured data, and semantic embeddings — often all at once. Choosing the right database architecture is one of the highest-leverage decisions you'll make.
What you'll learn
- When SQL, NoSQL, and vector databases each shine
- How vector search actually works under the hood
- Why PostgreSQL + pgvector might be all you need
- Designing schemas that serve both traditional queries and AI retrieval
SQL Databases: Still the Foundation
PostgreSQL and MySQL aren't going anywhere. User accounts, subscriptions, permissions, transaction history — this is structured, relational data that SQL handles perfectly. Every AI app still needs a relational database for its core application data.
The good news: you don't have to choose between SQL and vector search. PostgreSQL with the pgvector extension gives you both in one database. That means one connection, one backup strategy, one set of credentials, and full SQL power alongside semantic search.
NoSQL: When Structure Gets in the Way
NoSQL databases like MongoDB, DynamoDB, or Firestore excel at storing data whose shape changes frequently — like conversation histories, user-generated content with varying fields, or AI-generated outputs that don't fit clean schemas.
For AI apps, NoSQL is useful for storing raw conversation logs, flexible metadata, and cached AI responses. But for most teams, PostgreSQL's JSONB columns give you the same flexibility without adding another database to your stack.
The rule of thumb: if you're already running PostgreSQL (and you should be), use JSONB columns before reaching for a separate NoSQL database.
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