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Guides, deep dives, and opinionated takes on Kubernetes, platform engineering, and developer experience - from the engineers building Qovery.
Published weekly. Each piece is written by an engineer, reviewed by the team, and ships only when it says something the docs don't. No fluff, no listicle padding.
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Deep technical writing on Kubernetes operations, platform engineering patterns, compliance automation, and the shift toward agentic infrastructure management.
Shadow IT Is Back - And Vibe Coding Made It 10x Worse
AI coding tools are the new Shadow IT - but instead of rogue Trello boards, they have OAuth access to your code repos, cloud accounts, and production databases. Here's what's already gone wrong, and how platform engineering fixes it.
[Alan] From nginx to Envoy: What Actually Happens When You Swap Your Proxy in Production
Migrating from nginx Ingress to Envoy Gateway? Discover how Alan migrated 100+ services in one month, the technical hurdles they faced (like Content-Length normalization), and why staging isn't always enough.
Kubernetes management in 2026: mastering Day-2 ops with agentic control
The cluster coming up is the easy part. What catches teams off guard is what happens six months later: certificates expire without a single alert, node pools run at 40% over-provisioned because nobody revisited the initial resource requests, and a manual kubectl patch applied during a 2am incident is now permanent state. Agentic control planes enforce declared state continuously. Monitoring tools just report the problem.
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.
How to automate environment sleeping and stop paying for idle Kubernetes resources
Scaling your deployments to zero is only half the battle. If your cluster autoscaler does not aggressively bin-pack and terminate the underlying worker nodes, you are still paying for idle metal. True environment sleeping requires tight integration between your ingress layer and your node provisioner to actually realize FinOps savings.
10 best Kubernetes management tools for enterprise fleets in 2026
The structure, table, tool list, and code blocks are all worth keeping. The main work is fixing AI-isms in the prose, updating the case study to real metrics, correcting the FAQ format, and replacing the CTAs with the proper HTML blocks. The tool descriptions need the "Core strengths / Potential weaknesses" headers made less template-y, and the intro needs a sharper human voice.
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.
Qovery Skill for AI Agents: Deploy Apps in One Prompt
Use Qovery from Claude Code, OpenCode, Codex, and 20+ AI Coding agents
Stopping Kubernetes cloud waste: agentic automation for enterprise fleets
Agentic Kubernetes resource reclamation is the practice of using an autonomous control plane to continuously identify, suspend, and delete idle infrastructure across a multi-cloud Kubernetes fleet. It replaces manual cleanup and reactive autoscaling with intent-based policies that act on business state, eliminating the configuration drift and cloud waste typical of unmanaged fleets.
Top 10 Rancher alternatives in 2026: beyond cluster management
Rancher solved the Day-1 problem of launching clusters across disparate bare-metal environments. But in 2026, launching clusters is no longer the bottleneck. The real failure point is Day-2: managing the operational chaos, security patching, and configuration drift on top of them. Rancher is a heavy, ops-focused fleet manager that completely ignores the application developer. If your goal is developer velocity and automated FinOps, you must graduate from basic fleet management to an intent-based Kubernetes Management Platform like Qovery.
What is Kubernetes? The reality of Day-2 enterprise fleet orchestration
Kubernetes focuses on container orchestration, but the reality on the ground is far less forgiving. Provisioning a single cluster is a trivial Day-1 exercise. The true operational nightmare begins on Day 2. Teams that treat multi-cloud fleets like isolated pets inevitably face crushing YAML configuration drift, runaway AWS bills, and severe scaling bottlenecks.
Agentic AI infrastructure: moving beyond Copilots to autonomous operations
The shift from AI copilots to autonomous agents is redefining infrastructure requirements. Discover how to build secure, stateful, and compliant Agentic AI systems using Kubernetes, sandboxing, and observability while meeting EU AI Act standards
Building a single pane of glass for enterprise Kubernetes fleets
A Kubernetes single pane of glass is a centralized management layer that unifies visibility, access control, cost allocation, and policy enforcement across § cluster in an enterprise fleet for all cloud providers. It replaces the fragmented practice of switching between AWS, GCP, and Azure consoles to govern infrastructure, giving platform teams a single source of truth for multi-cloud Kubernetes operations.
10 best practices for optimizing Kubernetes on AWS
Optimizing Kubernetes on AWS is less about raw compute and more about surviving Day-2 operations. A standard failure mode occurs when teams scale the control plane while ignoring Amazon VPC IP exhaustion. When the cluster autoscaler triggers, nodes provision but pods fail to schedule due to IP depletion. Effective scaling requires network foresight before compute allocation.
How to deploy a Docker container on Kubernetes (and why manual YAML fails at scale)
Deploying a Docker container on Kubernetes requires building an image, authenticating with a registry, writing YAML deployment manifests, configuring services, and executing kubectl commands. While necessary to understand, executing this manual workflow across thousands of clusters causes severe configuration drift. Enterprise platform teams use agentic platforms to automate the entire deployment lifecycle.
Managing Kubernetes deployment YAML across multi-cloud enterprise fleets
At enterprise scale, managing provider-specific Kubernetes YAML across multiple clouds creates crippling configuration drift and operational toil. By adopting an agentic Kubernetes management platform, infrastructure teams abstract cloud-specific configurations (like ingress controllers and storage classes) into a single, declarative intent that automatically reconciles across 1,000+ clusters.
How Kubernetes works at enterprise scale: mastering Day-2 operations
Kubernetes is a distributed orchestration engine that automates container deployment and scaling. At an enterprise level, its core mechanisms-control planes, schedulers, and worker nodes-provide foundational infrastructure resiliency. However, operating these components natively across thousands of clusters creates massive configuration drift, requiring intent-based control planes to manage Day-2 FinOps, RBAC, and multi-cloud abstraction globally.
GPU orchestration guide: How to auto-scale Kubernetes clusters and slash AI infrastructure costs
To stop GPU costs from destroying SaaS margins, teams must transition from static to consumption-based infrastructure by utilizing Karpenter for dynamic provisioning, maximizing hardware density with NVIDIA MIG, and leveraging Qovery to tie scaling directly to business metrics.
Everything you need to know about Kubernetes autoscaling at fleet scale
When engineers configure pod autoscaling, they instinctively tie the Horizontal Pod Autoscaler (HPA) to CPU utilization. If the application is actually bound by memory or downstream database connections, the cluster sits idle while incoming requests time out. Diagnosing hundreds of outages reveals a clear pattern: effective elasticity requires scaling on custom application queues, not just default hardware thresholds.
Managed Kubernetes comparison: EKS vs. GKE for multi-cloud fleets
When comparing Amazon EKS and Google Kubernetes Engine (GKE), GKE often provides a more automated, hands-off experience with its Autopilot mode and rapid release channels. EKS excels in hybrid cloud integrations and government cloud support. However, at fleet scale, organizations frequently use both, requiring an agentic control plane to enforce global cost governance and standardize Day-2 operations across multi-cloud environments.
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