According to OpenAI, the company has successfully scaled PostgreSQL to support 800 million ChatGPT users while handling millions of queries per second. The organization shared its technical approach to managing this scale in a recent publication.
OpenAI’s scaling strategy relies on several key techniques. The company implemented database replicas to distribute read traffic across multiple instances, reducing load on the primary database. Caching layers were added to minimize repeated queries to the database for frequently accessed data.
To maintain system stability under heavy load, OpenAI deployed rate limiting mechanisms to control the number of requests processed. Additionally, the company used workload isolation to separate different types of database operations, preventing resource contention between critical and non-critical queries.
These architectural decisions enabled OpenAI to maintain PostgreSQL as its database solution despite ChatGPT’s rapid growth to hundreds of millions of users. The company’s approach demonstrates how traditional relational databases can be adapted to handle modern internet-scale applications through careful engineering and optimization strategies.
The technical breakdown provides insight into the infrastructure challenges of operating one of the world’s most popular AI applications.