[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-openai-tunes-postgresql-for-800-million-chatgpt-users":10},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":22,"tags":34,"sources":38,"feedback":42,"feedback_at":22,"cost_usd":42,"total_tokens":42},1080,"openai-tunes-postgresql-for-800-million-chatgpt-users","OpenAI tunes PostgreSQL for 800 million ChatGPT users","OpenAI detailed how it layered replicas, caching and rate limits to push PostgreSQL into the multi‑million‑QPS range.","OpenAI published a technical walk‑through of the PostgreSQL stack that now supports roughly 800 million active ChatGPT users.\n\nThe company added read‑only replicas to spread query load, introduced an aggressive in‑memory cache for hot prompts, and applied per‑user rate limiting to stop any single client from flooding the database. It also separated write‑heavy workloads onto dedicated nodes, ensuring that analytics and logging do not interfere with latency‑critical request handling. Together these tactics lift throughput to several million queries per second while keeping tail latency in the low‑hundreds of milliseconds.\n\nWhy care? ChatGPT’s backend is no longer a bespoke data store; it leans on a mature open‑source system that many enterprises already run. The engineering choices show that, with enough plumbing, PostgreSQL can rival purpose‑built NoSQL services on raw query volume. That may lower the barrier for other AI firms that prefer familiar tooling over custom stacks.\n\nThe move also signals a shift from “scale at any cost” to “scale with proven components.” OpenAI’s approach mirrors what Amazon Aurora did for MySQL‑compatible workloads a few years ago, but applied to a real‑time conversational AI workload. If the pattern sticks, we could see more large language model providers adopting similar hybrid strategies rather than building everything from scratch.\n\nBottom line: OpenAI proved that PostgreSQL, when paired with disciplined replication, caching and workload isolation, can handle the kind of load once thought exclusive to specialized databases. For developers, the takeaway is clear—existing relational tools may be more future‑proof than their hype‑filled alternatives.","[\"openai\",\"postgresql\",\"ai-infrastructure\"]","2026-01-22T12:00:00.000Z","2026-06-16T08:41:27.220Z","2026-06-16T08:41:30.149Z","published",null,[24,30],{"id":25,"reviewer":26,"round":27,"reason":28,"status":29},"editor-r1","editor",1,"Add a clear concluding paragraph summarizing the key takeaway and its relevance for readers.","resolved",{"id":31,"reviewer":26,"round":32,"reason":33,"status":29},"editor-r2",2,"Add a clear concluding paragraph that succinctly summarizes the key takeaway and its relevance for readers.",[35,36,37],"openai","postgresql","ai-infrastructure",[39],{"name":40,"url":41},"OpenAI","https:\u002F\u002Fopenai.com\u002Findex\u002Fscaling-postgresql",0]