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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.

Pierre Mavro

CTO & Co-founder

Learn how to use the Model Context Protocol (MCP) to transform static runbooks into intelligent, real-time investigation tools for Kubernetes and cert-manager.

Romain Gérard

Staff Software Engineer

By adopting GitOps and utilizing management platforms like Qovery, fintech teams can automatically generate DORA-compliant audit trails, transforming regulatory compliance from a manual, time-consuming chore into an automated, native byproduct of their infrastructure.

Mélanie Dallé

Senior Marketing Manager

For most enterprises, a Kubernetes migration is pitched as a finite project. In reality, Day-1 deployment is merely the start of an infinite marathon. As clusters scale, a quiet financial crisis emerges. When your highest-paid senior engineers spend 40% of their week on cluster upgrades, certificate rotations, and manual node scaling, you are paying a permanent tax on innovation.

Mélanie Dallé

Senior Marketing Manager

Operations form the backbone of successful infrastructure delivery, but the specific requirements of each phase demand completely different tooling and workflows. For platform architects managing enterprise fleets, understanding how to transition from initial planning to long-term fleet automation is critical. A Kubernetes migration is not the finish line. If you treat Day-2 as an afterthought, your platform engineers will drown in operational toil, configuration drift, and spiraling cloud costs.

Morgan Perry

Co-founder

Scaling Kubernetes on AWS requires balancing node-level elasticity with pod-level demands. A critical architectural oversight occurs when teams misconfigure the Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA) to compete over the same metrics, causing scaling thrash. Effective Day-2 operations demand standardizing these configurations across your fleet to prevent runaway cloud costs.

Morgan Perry

Co-founder

A multi-cluster Kubernetes architecture distributes application workloads across geographically separated clusters rather than a single environment. This strategy strictly isolates failure domains, ensures regional data compliance, and guarantees global high availability, but demands centralized Day-2 control to prevent exponential cloud costs and operational sprawl.

Morgan Perry

Co-founder

Choosing how to run Kubernetes on AWS dictates your operational burden for years. A standard failure mode is defaulting to Fargate to avoid managing EC2 instances, only to discover it lacks DaemonSet support. If your observability stack relies on node-level agents, Fargate forces a massive architectural rewrite to inject sidecars into every pod.

Morgan Perry

Co-founder

A standard trap is trusting the in-tree Kubernetes service load balancer on AWS. When you delete a Service of type LoadBalancer, the in-tree controller frequently fails to delete the underlying AWS resources. You end up with dozens of orphaned Network Load Balancers silently racking up massive cloud bills. Transitioning to the out-of-tree AWS Load Balancer Controller is mandatory to stop the bleeding.

Pierre Mavro

CTO & Co-founder