Why we chose Ceph as part of our storage-related solutions for production-scale AI

In the fast-paced world of DevOps and cloud infrastructure, there is a natural gravitation toward tools that offer instant gratification. We value the "Day 1" experience: the single binary download, the five-minute setup, and the immediate results. When a tool allows you to go from zero to a working prototype in the time it takes to drink a coffee, it gains adoption rapid-fire. However, when you are architecting modern AI-ready cloud infrastructure from the ground up, the laws of physics – and the definition of success – are fundamentally different. We aren't simply hosting static websites or lightweight user databases. We are building the high-throughput pipelines required to feed petabytes of training data into hungry H100/H200 GPU clusters. We are managing Retrieval-Augmented Generation (RAG) workflows where millisecond latency isn't just a metric; it’s the difference between a functional product and a failed user experience. In this high-stakes environment, the pressure to take inf