PostgreSQL vs MySQL at scale: how real companies scaled each
Published 2026-10-19 · Built only from first-party engineering write-ups in our database. Every number below comes from the linked source.
Both scale to very large workloads in our database. On PostgreSQL, OpenAI runs ChatGPT on a single primary with nearly 50 read replicas, and Figma and Notion sharded Postgres themselves (a custom query proxy at Figma, workspace-keyed logical shards at Notion). On MySQL, GitHub runs 5.5 million QPS across 50+ clusters, Slack (2.3 million QPS) and Shopify's Shop app shard with Vitess, and Meta's TAO caches a graph stored in sharded MySQL.
PostgreSQL vs MySQL: side by side
| PostgreSQL | MySQL | |
|---|---|---|
| Stories in our database | 14 | 10 |
| Companies | Atlassian, Datadog, Figma, GitLab, Notion, OpenAI, PlanetScale, Supabase and Tiger Data (Timescale) | Canva, GitHub, Meta, PlanetScale, Shopify and Slack |
| Published | 2021–2026 | 2013–2026 |
| Techniques alongside it | connection pooling (3), horizontal sharding (3), read replicas (3), benchmarking (2) | database migration (3), horizontal sharding (3), caching (2), online migration (2) |
| Scale numbers stated in sources | 118.5M QPS sustained for 16 minutes (PlanetScale); +53% avg write latency with remote_apply (pgbench TPC-B) (Datadog); 800 million users (OpenAI) | hundreds of millions of users (Canva); $5.1 million sales/minute (Black Friday 2025 peak) (Shopify); 5.5 million QPS (GitHub) |
Verdict
The difference in these stories is tooling, not raw limits. The MySQL teams often reach for Vitess to shard. The Postgres teams here leaned on PgBouncer, read replicas and sharding in their own application layer.
Counts are stories in How They Scaled tagged with each side, see the PostgreSQL and MySQL pages. They describe what these teams wrote about, not market share or benchmark results.