PostgreSQL
Strip the buzzwords off most production AI systems and you find Postgres doing the real work: application state, agent checkpoints, workflow queues, audit trails, and — with pgvector — the embeddings too. It's the most consequential unglamorous choice in your stack, and it rewards being taken seriously.
We've run Postgres under genuine load for years: schema design that anticipates the query patterns, indexes chosen from evidence rather than superstition, connection pooling that doesn't melt at peak, and high-availability setups that turn a node failure into a non-event. Whether it's RDS, Cloud SQL, or self-hosted on Kubernetes, the goal is the same — a database nobody has to think about at 2 a.m.
Schema and query design
Data models built for your access patterns, with the indexes to back them.
Performance rescue
Slow-query forensics, locking analysis, and the config tuning defaults never gave you.
pgvector for AI workloads
Embeddings, hybrid search, and agent state in the database you already trust.
High availability and recovery
Replication, failover, and tested backups — recovery you've rehearsed, not hoped for.
Kubernetes and cloud operations
Postgres run properly on K8s operators, RDS, or Cloud SQL — pooling, monitoring, upgrades.
Postgres or a specialized database for our AI workload?
Start with Postgres unless the numbers say otherwise. pgvector handles most embedding workloads, JSONB handles most document needs, and LISTEN/NOTIFY plus SKIP LOCKED handles most queues. One database your team knows beats four your team doesn't — complexity is a cost, not a badge.
Our Postgres is slow. New hardware or new schema?
Evidence first. pg_stat_statements and a day of profiling usually find the culprits: missing indexes, N+1 queries, bloat, or a connection storm. Most "we need a bigger instance" situations are a handful of fixable queries. Scaling hardware to outrun a bad query plan just delays the invoice.
Can Postgres be our vector database too?
For most enterprise corpora, yes. pgvector with HNSW indexes handles tens of millions of embeddings with solid latency, and keeping vectors beside their source rows simplifies permissions, joins, and freshness. We'll tell you honestly when your scale demands a dedicated engine.
Put PostgreSQL to work — in production.
One forward-deployed engineer, embedded in your stack, owning the outcome from discovery to production. Weeks, not quarters.
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