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You do not need a separate vector database. PostgreSQL handles AI workloads. Use extensions like pgvector to store embeddings and application data together.
AI products need three main things:
- Vector search for RAG.
- Metadata for prompts and costs.
- Time tracking for audits.
Follow these patterns to keep your database fast.
Separate your AI traffic. Use a dedicated schema for AI tables. This protects your core transactions. Use read replicas for heavy search traffic.
Split your storage. Put vectors in one table. Put text and JSON in another. This keeps the vector table slim and fast.
Pick the right index. Avoid brute force searches. Use IVF or HNSW structures for similarity search.
Batch your writes. Do not insert one row at a time. Use bulk inserts. This reduces overhead on your system.
Find slow queries. Use pg_stat_statements. Look for high total time and low cache hits. Fix facts, not hunches.
Tune autovacuum. AI logs grow fast. Set table-specific vacuum settings. Partition tables by time to stop bloat.
Use PL/Rust for speed. Move heavy logic into the database. This stops you from moving millions of rows to your app.
Set AI-specific goals. Track search latency and ingest speed.