State, retrieval, caching, and queues

AI Data Infrastructure

An AI application needs durable product data, temporary coordination state, retrieval indexes, permissions, and evidence about model behavior. This guide separates those jobs so you can choose databases and services for their actual role rather than forcing every concern into a vector store.

Decisions this guide helps you make

Which data is authoritative, derived, temporary, or safe to rebuild.
Where conversation, agent, job, and evaluation state should live.
When PostgreSQL is enough and when a specialized system earns its complexity.
How retrieval freshness and access control stay consistent with source data.

01

Choose the system of record

Vectors are only one part of an AI product. Users, permissions, jobs, billing, conversations, evaluations, and audit events still need durable state.

Platform decision

Neon vs Supabase

Compare serverless PostgreSQL with a broader backend platform for AI SaaS.

Supabase vs Firebase

Choose database, authentication, storage, and functions for an AI application.

MongoDB vs PostgreSQL

Evaluate document flexibility against relational integrity and PostgreSQL extensions.

Read guide → Foundation

Database architecture for AI applications

Place relational state, documents, vectors, caches, queues, and audit logs.

PostgreSQL vs SQLite vs MySQL

Choose a database across local prototypes and multi-tenant AI production.

02

Add fast, temporary state

Caches, queues, rate-limit counters, and agent checkpoints have different durability and latency needs.

Redis vs Memcached

Choose between a simple cache and a richer state, queue, and coordination layer.

Redis maxmemory troubleshooting

Keep cache and queue memory policies from destabilizing an AI backend.

PostgreSQL vs SQLite

Know when a local prototype needs a production database.

03

Build retrieval as a system

Useful RAG requires ingestion, metadata, permissions, freshness, evaluation, and observability—not only embeddings.

Deploy a RAG pipeline

Connect application infrastructure around retrieval and generation.

Build a RAG system that scales

Choose retrieval, chunking, reranking, and evaluation deliberately.

AI application architecture

Connect retrieval state to jobs, APIs, authentication, and failure handling.