The CNCF Data Storage in Cloud Native AI White Paper

2026-07-08 · Source: Cloud Native Computing Foundation · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Data Science & Analytics · Depth: Advanced, quick

Summary

The CNCF Technical Advisory Group for Infrastructure (TAG Infrastructure) released its "Data On Kubernetes – Data Analytics and AI/ML Workloads" white paper on July 8, 2026. This comprehensive guide addresses critical data bottlenecks encountered when deploying large-scale AI and ML workloads within cloud-native environments. It details challenges like the "small-file trap," decoupled bottlenecks leading to low GPU utilization, and the distinct storage demands of varying workload profiles. The paper outlines key technical pillars, including the use of Data Lake Houses and Vector Databases like Milvus for RAG, distributed caching with CNCF Fluid, and standardized interfaces such as CSI and COSI. Furthermore, it provides a granular breakdown of storage requirements across the AI lifecycle, covering throughput-oriented model training, latency-sensitive model inference with KV and Prefix Caching, and the complex memory needs of emerging Agentic AI systems.

Key takeaway

For MLOps Engineers or AI Architects deploying AI/ML workloads on Kubernetes, this white paper is essential for optimizing data storage. You should evaluate your current infrastructure against the outlined challenges like the small-file trap and decoupled bottlenecks. Implement strategies leveraging Data Lake Houses, Vector Databases, and CNCF Fluid for caching to enhance GPU utilization and manage diverse storage profiles across training, inference, and agentic AI phases. This will help you build more resilient and performant cloud-native AI systems.

Key insights

Cloud-native AI/ML workloads demand specialized data storage strategies to overcome bottlenecks and optimize performance across their lifecycle.

Principles

• Traditional storage architectures are insufficient for high-performance AI/ML workloads.
• Data locality and caching are crucial for eliminating transfer lag in AI/ML.
• Storage requirements vary significantly across AI lifecycle phases: training, inference, and agentic AI.

Method

Implement a structured approach to cloud-native AI data management by integrating Data Lake Houses, Vector Databases, distributed caching, and standardized storage interfaces.

In practice

• Utilize CNCF Fluid for orchestrating distributed caching within Kubernetes.
• Employ CSI and COSI for bridging block/file and object storage layers.
• Adopt Apache Kafka and CDC for real-time streaming data pipelines.

Topics

• Cloud Native AI
• Kubernetes Data Storage
• Vector Databases
• Data Locality
• Container Storage Interface
• Agentic AI

Code references

• cncf/toc

Best for: CTO, VP of Engineering/Data, Director of AI/ML, MLOps Engineer, AI Architect, Data Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Cloud Native Computing Foundation.