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# The great migration: Why every AI platform is converging on Kubernetes

**[CNCF](https://daily.dev/sources/cncf)** · 6 min read · 3 upvotes · 0 comments

## Summary

Kubernetes has evolved from a microservices platform into the de facto infrastructure for AI workloads. By 2026, 82% of container users run Kubernetes in production and 66% of organizations use it for generative AI inference. The post traces three eras of Kubernetes adoption and covers the full AI stack: data processing with Apache Spark and Kubeflow, ML pipeline orchestration with Argo Workflows, distributed training with gang scheduling tools like Kueue and Volcano, LLM inference serving with vLLM/KServe, and agentic workloads using LangGraph and KEDA. GPU optimization strategies including MIG, time-slicing, DRA, and Karpenter are discussed, along with multi-cluster scheduling for large-scale AI. Emerging areas include control plane scalability beyond etcd, unified agent operators, and a new CNCF AI conformance program.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.cncf.io/blog/2026/03/05/the-great-migration-why-every-ai-platform-is-converging-on-kubernetes/>

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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#kubernetes](https://daily.dev/tags/kubernetes), [#gpu](https://daily.dev/tags/gpu), [#mlops](https://daily.dev/tags/mlops), [#ai-inference](https://daily.dev/tags/ai-inference)

[View this post on daily.dev](https://daily.dev/posts/the-great-migration-why-every-ai-platform-is-converging-on-kubernetes-8cmgl222i)

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