An AI application’s database stores the reliable state around an unreliable model: users, permissions, runs, tool calls, source metadata, evaluations, and billing references. PostgreSQL, SQLite, and MySQL can all play a role, but at different stages and operational boundaries.
Short answer
- SQLite for local AI tools, desktop applications, tests, and single-process prototypes.
- PostgreSQL as the strongest general default for multi-tenant AI SaaS, relational state, JSON metadata, and advanced data needs.
- MySQL when existing infrastructure, expertise, compatibility, or managed-service standards make it the pragmatic production choice.
| Requirement | SQLite | PostgreSQL | MySQL |
|---|---|---|---|
| Embedded/local deployment | Excellent | Requires server | Requires server |
| Concurrent multi-tenant writes | Limited by architecture | Strong | Strong |
| Relational integrity | Good | Strong and feature-rich | Strong |
| Flexible JSON metadata | Available | Strong querying/tooling | Available |
| Operational simplicity | Highest locally | Managed service recommended | Managed service recommended |
| AI-specific extensions | Limited ecosystem | Broad extension ecosystem | Evaluate external services |
Prototype with SQLite deliberately
SQLite is ideal when an Ollama desktop utility, coding assistant, or evaluation tool needs one portable file. It is not a toy database, but its single-file concurrency and deployment model differ from a networked service.
Use transactions, enable appropriate journal settings, and keep a migration path. Do not assume moving to PostgreSQL later is a connection-string change; SQL behavior, types, concurrency, and deployment all need testing.
PostgreSQL for production AI state
PostgreSQL fits users, organizations, model runs, tool approvals, audit logs, and source permissions. JSON columns can retain provider-specific metadata without abandoning relational constraints. Extensions may support vector retrieval, but benchmark them against dedicated alternatives rather than declaring every Postgres database a vector platform.
MySQL when the estate already fits
MySQL remains a valid production database for application state. If the company has reliable MySQL operations, schemas, and integrations, switching solely because PostgreSQL is popular in AI tutorials may add risk without value. Evaluate exact query, JSON, search, and retrieval requirements.
Separate database from queue and object storage
Do not store large uploaded documents or generated media as ordinary database rows by default. Use object storage. Do not treat status polling as a complete queue; use durable job execution for long-running agents and inference tasks.
Migration triggers
Move beyond SQLite when concurrent writers, remote access, high availability, tenant isolation, online migrations, or centralized backups become requirements. Reconsider any database when retrieval latency, geographic placement, compliance, or operational cost no longer meets measured needs.
Decision rule
Use SQLite for local-first and bounded tools, PostgreSQL as the default new AI SaaS database, and MySQL when its existing ecosystem is an asset. The model provider does not decide your database; application state does. Continue with AI Data Infrastructure and running AI applications locally.