Observed Signal · Jan 22, 2026 · Product & Technology Development · Source: OpenAI Blog · Impact: 3/5 · Sentiment: Positive
Powering 800 Million ChatGPT Users with PostgreSQL
OpenAI published an engineering post describing how it scaled PostgreSQL to support millions of queries per second and serve 800 million ChatGPT users. The team retained a single-primary Azure PostgreSQL flexible server for writes and operated nearly 50 geo-distributed read replicas, while migrating shardable, write-heavy workloads to sharded systems such as Azure Cosmos DB. Key techniques included aggressive query optimization, workload isolation, PgBouncer connection pooling (reducing average connection time from 50ms to 5ms), cache locking to prevent cache-miss storms, rate limiting, strict schema-change controls, and testing cascading replication with Azure to scale replicas. OpenAI reports low p99 read latency, five-nines availability, and only one SEV-0 Postgres incident in the past 12 months.
OpenAI demonstrates that PostgreSQL can be scaled to very large read-heavy workloads using cloud-managed Postgres, read-replica strategies, connection pooling, and migration of writes to sharded systems. The techniques and operational lessons are relevant to large-scale platform and data infrastructure teams.
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Key Takeaways & Evidence Grounding
- OpenAI’s PostgreSQL load grew more than 10x over the past year.
- OpenAI uses a single-primary Azure PostgreSQL flexible server instance with nearly 50 read replicas across regions.
- OpenAI migrated shardable, write-heavy workloads to sharded systems such as Azure Cosmos DB and disallows adding new tables to the PostgreSQL deployment.
- OpenAI deployed PgBouncer for connection pooling, reducing average connection time from ~50 ms to ~5 ms in benchmarks.
- OpenAI is testing cascading replication with Azure PostgreSQL to scale beyond ~50 replicas while managing WAL shipping and failover complexity.
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