How teams scaled on object storage: 3 real case studies
3 of the 94 engineering write-ups in our database mention Object storage in their stack. They come from 3 companies (Axiom, Datadog and GitLab) and were published between 2022 and 2026. Techniques that show up most often alongside Object storage: storage/compute separation (2). 2 of the 3 state concrete scale numbers, for example "single agent invocation can need 5-10 GB of transfer and 30+ s of setup" (GitLab). Each card below links to our short summary and the original post.
Scaling Git for the agentic software factory
GitLab is building next-gen source control for thousands of concurrent AI agents working on large repositories. With conventional Git, a single agent invocation can mean 5 to 10 GB of data transfer and more than 30 seconds of setup just to answer one question. Next-gen SCM keeps the Git protocol but runs stateless compute that scales horizontally against shared object storage, including S3-compatible storage customers control, and lets agents request only the files and history they need instead of cloning whole repos. In early internal tests, workloads ran up to 50x faster with up to 1,000x less network traffic, which GitLab calls peak results it is still validating. It also notes CI is the next bottleneck: GitLab.com customers created 40% more pipelines and pushed 50% more code over the past year.
Haydex: From Zero to 178,600,000,000 rows a second in 30 days
Axiom turned a failed filter-index prototype into a production system for its event store, letting queries skip data and scan 178.6B rows per second.
Introducing Husky, Datadog's third-generation event store
Datadog's Husky is a distributed, schemaless column store that keeps event data on object storage.