Your AI app generates three kinds of data at once: events, vector embeddings, and the analytics you run on top of them. The default move is three systems, three sets of tooling, and pipelines to keep them in sync.
TimescaleDB handles all three in the Postgres you already run.
Hypertables ingest events at scale. pgvector and pgvectorscale store and search embeddings. Continuous aggregates keep analytics live without re-querying everything.
Same SQL, same tools, one system to operate. No pipelines to sync, no drift, no second database to feed.
Point it at your heaviest workload and watch it stay fast as data grows. It's still Postgres. Start on Tiger Cloud and get $1000 in credits.
