OpenAI's PostgreSQL Secret: 800 Million Users on a Single Instance?

In the fast-paced world of AI, we often hear about cutting-edge technologies and massive datasets. But what about the humble, yet mighty, database holding it all together? OpenAI, the company behind ChatGPT, has recently pulled back the curtain on how it's scaling its popular AI models and API platform to an astounding 800 million users, and the answer might surprise you: PostgreSQL.

Forget the hype around distributed databases and complex sharding for a moment. OpenAI's approach is a masterclass in optimizing a single, robust PostgreSQL instance. They're managing all writes on one Azure PostgreSQL Flexible Server and leveraging nearly 50 read replicas across multiple regions to handle the colossal read load. The result? Millions of queries per second with impressive low double-digit millisecond p99 latency and five-nines availability. This is not just impressive; it challenges conventional scaling wisdom and offers invaluable insights for enterprise architects.

Challenging the Scaling Dogma

For years, the go-to advice for massive scale has been to shard databases or migrate to distributed SQL solutions. While these methods have their merits, they also introduce significant complexity. Application code needs to be aware of shard routing, distributed transactions become a headache, and operational overhead skyrockets. OpenAI, however, took a different path. Instead of re-architecting prematurely or succumbing to "scale panic," they focused on deeply understanding their workload patterns and operational constraints.

As OpenAI engineer Bohan Zhang noted, PostgreSQL has been a critical, under-the-hood data system powering their core products. With their load growing more than 10x in the past year, the pressure was on. Their solution wasn't to discard PostgreSQL but to optimize it ruthlessly. Key to their success were targeted optimizations like connection pooling, which slashed connection times from 50ms to a mere 5ms, and sophisticated cache locking to prevent 'thundering herd' problems.

Why PostgreSQL Still Reigns Supreme (for some workloads)

PostgreSQL is well-suited for the heavily read-oriented operational data of ChatGPT and OpenAI's API. However, its multiversion concurrency control (MVCC) can present challenges under heavy write loads, as it involves creating new row versions and can lead to write amplification. OpenAI didn't fight this limitation; they built their strategy around it.

Their hybrid approach is particularly insightful: no new tables in PostgreSQL. New workloads are directed to sharded systems like Azure Cosmos DB, and existing write-heavy, horizontally partitionable workloads are migrated out. Everything else that remains in PostgreSQL receives aggressive optimization. This strategy allows enterprises to avoid a complete overhaul of their existing infrastructure, focusing instead on identifying and addressing specific bottlenecks.

Key Takeaways for Enterprises Scaling Up

OpenAI's journey offers several actionable strategies for businesses of all sizes:

• Build Layered Operational Defenses: Implement a multi-pronged approach including cache locking, connection pooling, and rate limiting at various levels (application, proxy, query). Workload isolation is also crucial to prevent less critical traffic from impacting core services.
• Mind Your ORM-Generated SQL: While Object-Relational Mapping (ORM) frameworks offer development convenience, they can hide performance pitfalls. OpenAI discovered a single ORM-generated query joining 12 tables that caused significant incidents under load. Regularly reviewing and monitoring these queries in production is essential.
• Enforce Strict Operational Discipline: Establish clear guidelines for schema changes, enforcing strict timeouts and prohibiting full table rewrites. Implement automatic termination for long-running queries and enforce rate limits during data backfilling to avoid performance degradation and maintenance issues.

The core lesson from OpenAI's experience is that read-heavy workloads with burst writes can thrive on a single-primary PostgreSQL instance for longer than commonly assumed. The decision to shard or migrate should be driven by actual workload patterns, not just user counts or the latest tech trends.

For AI applications, which often feature read-intensive operations and unpredictable traffic spikes, this approach is particularly relevant. OpenAI's strategy is a powerful reminder that optimizing proven infrastructure and migrating selectively can be far more effective than a wholesale re-architecture. It’s about working smarter, not just bigger.