Scaling PostgreSQL to power 800 million ChatGPT users

For years, PostgreSQL has been one of the most critical, under-the-hood data systems powering core products like ChatGPT and OpenAI’s API. As our user base grows rapidly, the demands on our databases have increased exponentially, too. Over the past year, our PostgreSQL load has grown by more than 10x, and it continues to rise quickly.

Our efforts to advance our production infrastructure to sustain this growth revealed a new insight: PostgreSQL can be scaled to reliably support much larger read-heavy workloads than many previously thought possible. The system (initially created by a team of scientists at University of California, Berkeley) has enabled us to support massive global traffic with a single primary Azure PostgreSQL flexible server instance⁠(opens in a new window) and nearly 50 read replicas spread over multiple regions globally. This is the story of how we’ve scaled PostgreSQL at OpenAI to support millions of queries per second for 800 million users through rigorous optimizations and solid engineering; we’ll also cover key takeaways we learned along the way.

Cracks in our initial design

After the launch of ChatGPT, traffic grew at an unprecedented rate. To support it, we rapidly implemented extensive optimizations at both the application and PostgreSQL database layers, scaled up by increasing the instance size, and scaled out by adding more read replicas. This architecture has served us well for a long time. With ongoing improvements, it continues to provide ample runway for future growth.

Scaling PostgreSQL to millions of QPS

To mitigate these limitations and reduce write pressure, we’ve migrated, and continue to migrate, shardable (i.e. workloads that can be horizontally partitioned), write-heavy workloads to sharded systems such as Azure Cosmos DB, optimizing application logic to minimize unnecessary writes. We also no longer allow adding new tables to the current PostgreSQL deployment. New workloads default to the sharded systems.