Kubernetes for AI
Kubernetes for AI is the practice of operating Kubernetes clusters at fleet scale - with cost control, automated upgrades, and agent-safe governance - so both human teams and AI agents can run and manage workloads reliably.
Kubernetes became the default substrate for modern workloads - and now for AI. But one cluster is exciting; ten clusters across three clouds is two full-time engineers doing nothing but maintenance, and AI agents bombarding the control plane only raises the stakes.
Managing Kubernetes for AI means solving fleet management, cost optimization, automated upgrades, and - critically - giving AI agents a governed path to operate clusters without breaking them.
These guides cover the full lifecycle: from cluster operations and cost control to agentic cluster management.
The Best Tools for Integrating AI Agents with Kubernetes in 2026
A practical guide to the best tools for both using AI agents to manage Kubernetes (AIOps) and running AI agent workloads on Kubernetes infrastructure in 2026.
How Kubernetes AI Agents Improve Cluster Management
AI agents compress Kubernetes incident diagnosis from 45 minutes to seconds, eliminate YAML authoring toil, and shift resource tuning from static to continuous. Here is what changes concretely when they enter your workflow.
Kubernetes management in 2026: mastering Day-2 ops with agentic control
What Kubernetes management involves after the cluster is running: certificate expiry, node pool waste, upgrades and multi-cluster drift.
Kubernetes observability at scale: how to cut APM costs without losing visibility
The instinct when setting up Kubernetes observability is to instrument everything and send it all to your APM vendor. That works fine at ten nodes. At a hundred, the bill becomes a board-level conversation. The less obvious problem is the fix most teams reach for: aggressive sampling. That is how intermittent failures affecting 1% of requests disappear from your monitoring entirely.
Kubernetes cost optimization: agentic FinOps for enterprise fleets
The three pillars of Kubernetes spend (Compute, Network, and Storage) compound rapidly at enterprise scale. While manual cost-cutting works for a single cluster, managing 1,000+ clusters requires an agentic FinOps approach. By automating resource right-sizing, Spot instance orchestration, and idle environment shutdowns, organizations can eliminate cloud waste without sacrificing production stability.
10 best Red Hat OpenShift alternatives to reduce licensing costs
For years, Red Hat OpenShift has been the safe choice for heavily regulated, on-premise environments. It operates as a secure fortress. But in the public cloud, that fortress acts as an expensive prison. Paying proprietary per-core licensing fees on top of your standard AWS or GCP compute bill is a redundant "middleware tax." Escaping OpenShift requires decoupling your infrastructure from your developer experience by running standard, vanilla Kubernetes paired with an agentic control plane.
What a Preview Environment per Agent PR Really Costs, and How to Cap It
A transparent cost model for one full-stack preview environment per coding-agent pull request on an existing EKS cluster, built from AWS us-east-1 list prices read on 7 November 2026: cost per hour, per PR and per month, and how delete-on-merge, on-demand previews, schedules and Spot change the bill.
Argo CD or Qovery Free for Agent PR Environments
An honest comparison for teams already running Kubernetes: what the Argo CD ApplicationSet pull request generator gives you per coding-agent PR, what you still build around it (images, databases, DNS, secrets, cleanup, PR comments, agent access), where Argo CD is the better choice, and where Qovery Free fits. They can also run side by side.
Claude Code + GitHub Actions: Deploy Every Claude PR to Its Own Environment
Set up anthropics/claude-code-action so @claude turns issues into branches and pull requests, then let Qovery preview environments deploy each Claude PR to its own full-stack environment on your Kubernetes cluster, post the URL on the PR, and delete it on merge.
Ephemeral Environments on EKS for Agent Pull Requests: Step-by-Step Setup
Connect an existing Amazon EKS cluster to Qovery with qovery cluster install and Helm, turn one environment into a blueprint, enable preview environments, and give every pull request opened from a coding agent its own ephemeral environment that is deleted on merge. Runs on Qovery Free.
Preview Environments for AI Coding Agents: The Full-Stack Guide
Agent-written pull requests need a running environment before anyone can review them. What a full-stack preview environment needs (services, databases, seed data, scoped secrets, cleanup, cost caps), how Vercel, Netlify, Argo CD ApplicationSets, homegrown GitHub Actions + Terraform and Qovery compare, and how to run it on your own cluster for free.
Qovery Free: A Live Environment for Every Agent PR, on Your Own Cluster
Qovery Free connects to a Kubernetes cluster you already run and gives each pull request from Claude Code, Cursor or Codex its own full-stack preview environment. 3 users, unlimited AI agents, 1,000 deployment minutes a month, no credit card, no time limit.
Vercel Preview Environments vs Full-Stack Previews for Agent-Written Backends
Vercel preview deployments are excellent for frontends. Coding-agent PRs often change APIs, workers, migrations and databases too. Here is what a full-stack preview needs, how to pair a Vercel frontend preview with a backend preview on your own Kubernetes cluster, with a working GitHub Actions workflow, and when to use which.
ECS vs EKS in 2026: The ECS Limits AWS Won't Raise, and When They Matter
If ECS works for your team, no single limit forces you off it. Growing teams still hit a few hard ECS and Fargate quotas that AWS won't raise, starting with 100 target groups per load balancer for preview environments. Here are the limits, the numbers, and how to add EKS for previews without moving production.
Northflank Security Incident (October 30, 2026): What Happened and How to Protect Your Secrets
On October 30, 2026, Northflank told all users to assume their environment variables were decrypted and to rotate every secret. Here is the verified timeline, what to rotate first, why shared platforms keep getting hit after Railway, and how Qovery limits the blast radius, including where we still hold secrets.
Plug Qovery Into the Kubernetes Cluster You Already Run
BYOK on EKS, GKE and Kapsule now runs through the Qovery Operator: install it once with Helm on a cluster you already run, and deploy Qovery's component updates from the Console. Now in Preview, for clusters without cert-manager, ExternalDNS or Envoy Gateway.
How to Stop Secrets Leaking From Your Deployment Platform, and How Qovery Stores Yours
Railway's disclosure of leaked customer environment variables is a reminder that deployment platforms hold the keys to everything. Here is how secrets leak from platforms, the practices that prevent it or limit the damage, and exactly how Qovery stores secrets across its control plane and your data plane, with AWS Secrets Manager, Parameter Store, GCP Secret Manager, Doppler and Infisical.
Sandbox Environments for AI Agents: A Full, Isolated Stack for Every Task
A code sandbox like Daytona or E2B isolates a process. A sandbox environment isolates a whole stack: services, databases, queues and configuration, cloned for one agent task and destroyed when it is done. Here is how sandbox environments work, what they are good for, and how to run them on Qovery.
GCP Out of Capacity? How to Offload Workloads to AWS Without Running a Second Platform
Google says it is compute constrained, and GKE teams now hit ZONE_RESOURCE_POOL_EXHAUSTED on CPU machine families, not only GPUs. Here is what the error means, what Google recommends, what AWS can and cannot offer, and how to offload workloads from GCP to AWS with one platform instead of two.
Where to Find GPU Capacity in 2026: Hyperscalers, Neoclouds and Regional Clouds
Google, CoreWeave and Nebius all say they are short of GPUs. Here is where capacity still exists in 2026, which GPU clouds offer managed Kubernetes, how to connect a second cloud to your first one, and how to run GPU services on all of them from one platform.
Inside a DevOps Team Extension engagement: an infrastructure review and a Typesense architecture session
How Qovery assessed a growing B2B SaaS company's infrastructure across about 150 controls, what the review with their team changed, and why a Typesense high-availability project turned into a sharding plan.
Replacing ingress-nginx with Envoy Gateway on Hundreds of Clusters: What the Plan Missed
In February we planned to move every Qovery-managed cluster from the community ingress-nginx controller to Envoy Gateway by the end of March. It took until October. Here is the rollout model, every class of problem we hit, and what I would do differently.
The 12 Tools I Actually Use to Automate Kubernetes Deployments (2026)
A practitioner's category-by-category breakdown of the best tools for automating Kubernetes deployments in 2026 - Argo CD, Flux CD, Helm, Kustomize, KEDA, Kyverno, OPA Gatekeeper, Karpenter, Cluster Autoscaler, Rancher, Portainer, Terraform and Crossplane - plus which 3 to 5 you actually need, and how a platform like Qovery automates the whole chain from cluster creation to upgrades in under 15 minutes.
6 Best Cloud Cost Optimization & FinOps Tools in 2026
Compare the top cloud cost optimization and FinOps tools - Vantage, nOps, CloudZero, CloudAware, PointFive, and Qovery - across spend visibility, Kubernetes rightsizing, and migration cost control.
Longer, question-shaped articles that work through a single buying decision end to end. They are not listed on the blog index.
Frequently asked
How do AI agents manage Kubernetes clusters?
How do you reduce Kubernetes cloud costs with AI?
Delegate Kubernetes operations. Keep control.
Let Qovery manage your fleet - upgrades, scaling, cost, and agent governance - across any cloud, on your own clusters.