For many data aficionados, Postgres is the way to store and manage relational data. It’s efficient, it’s straightforward, and it plays nice with other tools. But even Postgres has its limits.

When you’re working with a relentless stream of live data, you may catch Postgres chugging along on the struggle bus: slower queries, more maintenance requirements, and enough database upkeep to make your autovacuum beg for a pit stop.

Developers are increasingly turning to Postgres as a foundation for AI applications, thanks in part to its flexibility and extensive ecosystem. But you might hit its limits even faster as you add AI agents to your pipeline. Agentic workflows often increase database activity. You’ll likely see more frequent database requests and a need to retain more data over time.

As your tables and indexes grow, keeping queries fast may require more behind-the-scenes attention. Vanilla Postgres may have handled your time-series data just fine at first; that is, until keeping Postgres running becomes a job of its own.

Join the conversation: On October 21, we’ll sit down with Matty Stratton, Head of Developer Advocacy and Docs at Tiger Data, to discuss how scale stretches vanilla Postgres to its limits and how you can extend it with TimescaleDB. He’ll walk through a live demo of TimescaleDB and point out which Postgres pain points emerge as data volume and velocity increase.

What's the biggest thing standing between you and scaling Postgres for time-series data today? *

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