Introduction

Behind the scenes of OpenAI’s most popular products, including ChatGPT, sits a surprisingly traditional but highly optimized database: PostgreSQL.

In a recent technical deep dive, OpenAI engineers explained how they scaled PostgreSQL to support over 800 million users and millions of queries per second, proving that with the right engineering discipline, PostgreSQL can operate far beyond commonly assumed limits.

This article simplifies what OpenAI achieved, why it matters, and how they made it work. Full technical details are credited to OpenAI’s engineering team, and readers are encouraged to follow their original article for an in-depth explanation.

Background and Context

As ChatGPT usage exploded globally, database traffic grew more than 10x in a single year. Every user action generates reads and writes, putting extreme pressure on backend systems.

Rather than immediately adopting complex distributed databases, OpenAI chose to push PostgreSQL to its maximum potential, especially for read-heavy workloads. The result is a highly resilient architecture built on a single primary PostgreSQL instance supported by nearly 50 global read replicas running on Azure.

What This Architecture Does, Simply Explained

At a high level, OpenAI’s PostgreSQL setup works like this:

• One primary database handles all writes.
• Dozens of read replicas spread across regions handle user queries.
• Most user requests are reads, so they never touch the primary.
• Heavy write workloads are gradually moved to sharded systems like Azure Cosmos DB.
• Intelligent caching and rate limiting prevent sudden traffic spikes from overwhelming the system.

This design allows ChatGPT to stay fast and reliable even during massive traffic surges.

Key Engineering Strategies (Non-Technical View)

1. Reducing Pressure on the Primary Database

Since only one server can accept writes, OpenAI minimizes anything that touches it. Reads are pushed to replicas, and unnecessary writes are eliminated at the application level.

2. Aggressive Query Optimization

A small number of inefficient database queries can slow everything down. OpenAI continuously audits and rewrites expensive queries, especially those generated automatically by ORMs.

3. High Availability and Failover

The primary database runs in high-availability mode with a hot standby. If it fails, a backup is promoted quickly, minimizing downtime.

4. Connection Pooling at Scale

To avoid exhausting database connections, OpenAI uses PgBouncer to reuse connections efficiently, cutting connection times from 50 ms to about 5 ms.

5. Smart Caching to Avoid Traffic Storms

When cache systems fail, databases often collapse under sudden load. OpenAI uses cache-locking so only one request repopulates missing data, preventing mass database hits.

6. Workload Isolation

Low-priority features are isolated from critical traffic. If one feature misbehaves, it does not take down ChatGPT as a whole.

Why This Matters

Many organizations assume PostgreSQL cannot scale to extreme global workloads. OpenAI’s experience challenges that assumption.

Their system delivers:

• Millions of queries per second
• Low double-digit millisecond latency
• Five-nines availability
• Support for hundreds of millions of users with minimal outages

This proves that PostgreSQL, when engineered carefully, remains a viable backbone even at internet-scale.

Credit and Further Reading

This summary is based on the original engineering article by Bohan Zhang, Member of Technical Staff at OpenAI.
For deep technical insights, diagrams, and implementation-level details, readers should follow and read the original OpenAI article directly.