How teams scaled Python: 4 real case studies
4 of the 73 engineering write-ups in our database mention Python in their stack. They come from 3 companies (OpenAI, Discord and Dropbox) and were published between 2017 and 2026 (1 undated). Techniques that show up most often alongside Python: rewrite (2). It's often paired with Rust (3). 3 of the 4 state concrete scale numbers, for example "1B+ users across 40 regions" (OpenAI). Each card below links to our short summary and the original post.
Rapidly scaling online storage to serve over 1 billion ChatGPT users (Part II)
OpenAI's Habitat storage platform went from one shared Azure Cosmos DB container, to dedicated single-tenant containers that pushed capacity planning onto every product team, and back to a shared container by default once Azure added hot-key throttling, request-priority QoS and a new serverless mode where each physical partition scales to 10K RU/s and splits on RU usage. After failover and failback outages in late 2024, OpenAI and Azure now co-own testing and monitoring. Replicating small latency-critical datasets widely, a Valkey lookaside cache invalidated through the Cosmos DB change feed, and a Rust rewrite of the proxy cut global read p50 from about 120 ms to about 10 ms.
Rapidly scaling online storage to serve over 1 billion ChatGPT users
OpenAI's Habitat storage platform grew from a Python client library over Cosmos DB into a central service handling 70M+ requests per second. The team ran many low-concurrency Python processes to keep asyncio delay down, switched aiohttp connection reuse from LIFO to FIFO to stop a metastable failure, used Envoy for connection fan-in, kept a deliberately simple NoSQL API, then rewrote the service in Rust.
How Discord Indexes Billions of Messages
Discord built message search on many small Elasticsearch clusters, sharding by guild and indexing lazily through a queue.
Rewriting the heart of our sync engine
Dropbox rewrote its desktop sync engine in Rust with a new data model built for correctness and testability.