Rapidly scaling online storage to serve over 1 billion ChatGPT users

Every OpenAI product depends on fast, reliable access to data, whether someone is logging in, checking their Codex settings, or starting a new conversation in ChatGPT. Each of those actions may require many separate data lookups before the product can respond. If those requests are slow, the product feels slow. If those requests fail, the product stops working entirely.

Habitat is the online storage platform we built so OpenAI products can quickly and reliably access needed information. Habitat now handles more than 70 million requests every second, supporting products used by over 1 billion people each week, across almost 40 geographic regions. Habitat first launched to support GPTs at DevDay 2023, starting as a simple Python client-side library connected to a single database. Today, it’s a complex distributed system that serves more than 500 petabytes of data.

Habitat is the online storage platform we built so OpenAI products can quickly and reliably access needed information.

Building and operating infrastructure at this scale is no easy feat, but also not particularly challenging. What made our situation unique is the unprecedented rate at which we’ve had to scale to support staggering user growth and product demand while simultaneously building out a mature platform. Often, system engineers build for 10x scale, and hope for it to hold for a few years while preparing for the next 10x. In our case, we've grown more than 10x year-over-year for the last three years. As a result, building and operating Habitat has been a series of tactical decisions and sequencing: understanding each component at the lowest level to squeeze as much juice out of our existing stack, while fending off storage and compute capacity crunches to buy time for foundational investments.

Habitat started from a simple idea: product engineers shouldn’t need to think about database management. Habitat first launched to support GPTs at DevDay 2023 as a small Python library that interacted with ChatGPT’s main server. It supported a small set of operations that mapped under the hood to the database application, Azure Cosmos DB.

As product needs evolved, it was even easy for product developers to add to the shared library support for features like client-side caching, compression, or encryption.

As OpenAI grew, Habitat had to grow with it: first by becoming reliable enough for mission-critical product traffic, then fast enough for global users, and finally, to deftly operate at massive scale. This post is the first in a two-part series on how we scaled online storage. In this post, we’ll share how Habitat evolved, why we turned it from a library into a service, and how we stretched a service written in an uncommon serving stack language—Python—into a reliable storage platform layer.

In a future post, we’ll go into detail about how we made multi-tenancy reliability at scale, our layered strategy for optimizing read performance, and how we scaled our partnership with Azure Cosmos DB to reliably handle unprecedented demand.

The library’s job was to give product teams a simple way to store and retrieve data without needing to master the underlying details. Habitat took care of the necessary work: figuring out what kind of data was involved, where it should come from (or go), whether the request was allowed, and so on.