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cloud native AI

Kubernetes Did Not Miss the AI Wave. It Absorbed It

Kubernetes Did Not Miss the AI Wave. It Absorbed It

Enterprise AI is settling onto the cloud native stack, and the strongest evidence is not a vendor roadmap. It is what the ecosystem has already standardized ...
Alan Shimel | September 10, 2026 | AI gateways, AI Inference, AI infrastructure, AI observability, AI operations, cloud native, cloud native AI, cncf, Dynamic Resource Allocation, enterprise AI, gateway API, generative AI, GPU scheduling, kubernetes, Kubernetes AI, Kueue, llm-d, model routing, model serving, platform engineering
Autoscaling AI Workloads on Kubernetes With KEDA and What it Means for Agentic Systems

Autoscaling AI Workloads on Kubernetes With KEDA and What it Means for Agentic Systems

KEDA can scale Kubernetes AI workloads on real demand signals such as queue depth, helping model-serving and agent workloads respond faster while reducing idle compute costs ...
Kishor Patil | August 26, 2026 | agentic AI, AI agents, AI infrastructure, AI model serving, cloud native AI, devops, Event-Driven Autoscaling, horizontal pod autoscaling, HPA, inference scaling, KEDA, Kubernetes AI workloads, Kubernetes autoscaling, Kubernetes Event-Driven Autoscaling, platform engineering, Pub/Sub, queue depth, RabbitMQ, Redis, scale-to-zero, SQS
Kubeflow’s Graduation Is a Vote for Kubernetes as the AI Control Plane

Kubeflow’s Graduation Is a Vote for Kubernetes as the AI Control Plane

Kubeflow’s CNCF graduation signals growing confidence in Kubernetes as a common control plane for production AI workloads, from training and pipelines to governance and inference ...
Alan Shimel | August 19, 2026 | agentic AI, AI infrastructure, AI lifecycle, AI platform, AI Workloads, cloud native AI, cncf, Distributed Training, enterprise AI, GPU scheduling, KServe, Kubeflow, Kubeflow graduation, Kubeflow Pipelines, Kubeflow Trainer, kubernetes, Kubernetes AI, MLOps, OpenTelemetry, platform engineering
Kubernetes Wasn't Built for GPUs. Make It Behave

Kubernetes Wasn’t Built for GPUs. Make It Behave

Kubernetes counts whole GPUs and treats pods as disposable. An LLM pod is neither. Share the silicon with MIG/MPS/time-slicing and stop paying for idle ...
Sneha Gullapalli | August 12, 2026 | A100, AI infrastructure, AI Workloads, cloud native AI, Dynamic Resource Allocation, GPU autoscaling, GPU cost reduction, GPU optimization, GPU partitioning, GPU sharing, GPU time-slicing, GPU utilization, H100, Karpenter, KServe, Kubernetes DRA, Kubernetes GPU scheduling, LLM Inference, model caching, multi-instance GPU, NVIDIA GPU Operator, NVIDIA MIG, NVIDIA MPS, scale-to-zero, VRAM
Stop Treating GPUs Like Web Pods

Stop Treating GPUs Like Web Pods

Kubernetes schedules accelerators as opaque integers, and your bill pays for it. Share the silicon, scale on the right signal and keep weights out of the image ...
Veera Ravindra Divi | August 12, 2026 | AI infrastructure, AI serving, autoscaling, cloud costs, cloud native AI, DCGM exporter, DRA, Dynamic Resource Allocation, GPU costs, GPU scheduling, GPU sharing, GPU utilization, GPUs, inference workloads, KEDA, kubernetes, Kubernetes GPU scheduling, LLM Inference, MIG, model weights, MPS, NVIDIA GPUs, NVIDIA MIG, Prometheus, scale-to-zero, time-slicing
NVIDIA Is Putting Real Skin in the Open AI Game

NVIDIA Is Putting Real Skin in the Open AI Game

Open AI requires community-governed infrastructure and companies willing to contribute code, engineering and costly GPU cycles. NVIDIA is doing exactly that ...
Alan Shimel | July 28, 2026 | accelerated computing, AI compute, AI infrastructure, cloud native AI, cncf, GPU cycles, GPU Dynamic Resource Allocation, GPU scheduling, KAI Scheduler, kubernetes, Kubernetes AI Conformance, Nvidia, open AI infrastructure, open source AI, vendor-neutral APIs
CNCF, cloud native, NVIDIA, AI, Peritus microservices

Beyond the Model: Why AI Agent Orchestration Requires Cloud-Native Engineering

AI agent orchestration is a distributed systems challenge. Cloud-native engineering provides the resilience, observability, security and scalability needed for production AI ...
Nithiya Dharshini | July 22, 2026 | agent communication, agentic AI infrastructure, AI agent orchestration, AI governance, AI infrastructure, AI observability, AI scalability, AI security, AI workflow monitoring, AI workload management, automated scaling, cloud native AI, cloud-native engineering, containerized AI services, distributed AI systems, distributed tracing, enterprise AI agents, event-driven architecture, GitOps, Kubernetes for AI, multi-agent systems, platform engineering, production AI systems, resilient AI systems
Docker, AI, GenAI, Kubernetes, docker, ai, cloud-native AI Carbon Relay

AI-driven Kubernetes in Action: Exploring AI-Assisted Kubernetes Operations 

Discover how AI is transforming Kubernetes from reactive troubleshooting to proactive, intelligent automation. Learn about essential AIOps tools, resource optimization strategies, and the challenges of managing AI-enabled container orchestration at scale ...
Joydip Kanjilal | April 20, 2026 | AI-powered Kubernetes, anomaly detection, autonomous Kubernetes, cloud native AI, container orchestration, K8sGPT, kubectl-ai, Kubeflow, Kubernetes AIOps, resource optimization
metrics, agents, ai, cloud-native, cloud native, project, istio, ambient, architecture, modules, cloud native, CCoE Kubernetes Virtana CrowdStrike cloud native

Measuring AI-Driven Automation: The Metrics That Prove Whether Your Platform is Actually Getting Smarter 

AI is reshaping cloud-native operations, but old automation metrics mislead. Learn the modern KPIs—MTTR, action quality, autonomy and cognitive load reduction ...
Ankush Dhar | December 3, 2025 | agentic AI systems, AI action quality, AI automation metrics, AI cloud operations, AI governance metrics, AI incident response, AI-driven reliability engineering, AIOps performance metrics, autonomous remediation, cloud cost optimization AI, cloud native AI, cloud platform automation, cloud-native automation KPIs, cognitive load reduction SRE, explainable AI operations, false action rate, MTTR reduction, operational intelligence, predictive incident prevention, SRE automation
kubernetes, network, logistics, security, Traefik Tigera container security Calico Red Hat Dynatrace

Google Extends Kubernetes Service to Safely Run Agentic AI Workloads

At KubeCon + CloudNativeCon North America 2025, Google unveiled major GKE upgrades — including an AI sandbox, inference gateway, pod snapshots, and 130,000-node clusters — to optimize and secure agentic AI workloads ...
Mike Vizard | November 13, 2025 | agentic AI, AI compute provisioning, AI infrastructure, AI security, AI Workloads, cloud native AI, GKE updates, Google AI platform, Google Cloud, Google Kubernetes Engine, KubeCon 2025, Kubernetes AI, Kubernetes AI workloads, Kubernetes conference, pod snapshots
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The Economics of Infrastructure Modernization
4 November 2026
The Economics of Infrastructure Modernization
Beyond SLSA and Sigstore: Securing the Developer Origin in the Age of Agentic Coding
20 October 2026
AI Agents Can Now Take Software Apart. DevOps Teams Need a Plan
20 October 2026
Microsoft Foundry Puts AI Agents on a Schedule and Under Watch
20 October 2026
AI Security: Machine-Speed Threats, Multi-Layered Defense
1 December 2026
Agentic Runtime Security on AWS: Identity, Least Privilege, and Audit for AI Agents with IBM Verify Identity Access and HashiCorp Vault
23 November 2026
Agentic Runtime Security on AWS: Identity, Least Privilege, and Audit for AI Agents with IBM Verify Identity Access and HashiCorp Vault
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17 November 2026

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Karmada Federated Control Plane for Kubernetes Achieves CNCF Graduation

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Microsoft Bakes Linux Container Support Directly into Windows

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Scaling GitOps With Release Promotion
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Scaling GitOps With Release Promotion

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Kubernetes and Infrastructure as Code in the Age of Agentic Development
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