The CNCF Data Storage in Cloud Native AI White Paper

The CNCF TAG Infrastructure has released a white paper on data storage challenges for AI/ML workloads running on Kubernetes. It covers storage bottlenecks unique to AI workloads — including the small-file trap, compute-storage disaggregation overhead, and differing storage profiles for training vs. inference vs. agentic AI. Key technical areas include data lake houses, vector databases (e.g., Milvus), caching strategies using the CNCF Fluid project, standardized interfaces (CSI, COSI), and modern data pipelines using CDC and Apache Kafka. The paper provides architectural guidance for organizations scaling AI infrastructure on cloud native platforms.