Data & AI

PostgreSQL

An open-source relational database with ACID compliance, JSON support, and extensions for full-text search and vector similarity.

01What it is

In production terms.

PostgreSQL stores structured data in tables with foreign keys, transactions, and a mature query planner. Extensions like pgvector add vector search, PostGIS adds geospatial queries, and native JSONB columns handle semi-structured payloads without a separate document store.

Codeaxes defaults to PostgreSQL for most SaaS and PaaS products because it handles relational integrity, complex joins, and reporting queries reliably. pgvector reduces the need for a separate vector database in early-stage AI features, keeping operational surface area smaller.

02Fit

Best-fit use cases.

  1. 01Multi-tenant SaaS with row-level security per organization
  2. 02Transactional order and billing systems requiring ACID guarantees
  3. 03Hybrid workloads combining relational data with JSONB document fields
  4. 04RAG prototypes using pgvector for embedding storage and similarity search

03Trade-offs

Benefits and limits.

Benefits

  • ACID transactions protect financial and inventory data integrity
  • pgvector and JSONB reduce the need for multiple database systems early on
  • Mature replication, backup, and point-in-time recovery tooling
  • Supported as managed service on AWS RDS, Cloud SQL, and Azure Database

Limitations

  • Horizontal write scaling requires sharding or read-replica architectures
  • Very high write throughput workloads may outgrow single-instance limits
  • Schema migrations on large tables require planned downtime or online migration tools

04Ecosystem

Compatible technologies.

05In the field

Example applications.

  • SaaS platform with tenant-scoped data and row-level security policies
  • E-commerce backend with inventory transactions and order history reporting
  • AI product storing user documents, embeddings, and conversation metadata in one database

Use PostgreSQL on a Codeaxes engagement.