Kubernetes You Can Sleep Through
Cluster design, auto scaling, service mesh, and GitOps that make Kubernetes boring. And boring is beautiful in production.
What This Actually Means
Kubernetes is the most powerful container orchestration platform ever built. It is also one of the most complex. The difference between a Kubernetes cluster you trust and one that wakes you up at 3 AM comes down to design decisions made before the first pod is scheduled.
We build Kubernetes clusters that are reliable, observable, and cost effective. Cluster design that follows production best practices. Auto-scaling that responds to demand without over-provisioning. Service mesh for traffic management and security. GitOps workflows that make deployments predictable and auditable.
If Kubernetes has been harder to manage than you expected, or if you are planning a migration and want to get it right the first time, we will help you build a cluster that you can trust through the night.
What's Actually Going Wrong
Cluster complexity that requires a PhD to manage
CNI plugins, ingress controllers, storage classes, RBAC, pod security policies, resource quotas, node affinity-the surface area of Kubernetes configuration is enormous. Your team spends more time managing Kubernetes than deploying applications.
Auto-scaling that doesn't work when you need it
Horizontal Pod Autoscaler configured with wrong metrics. Cluster Autoscaler that doesn't provision nodes fast enough. Vertical Pod Autoscaler that causes pod churn. Scaling doesn't work when traffic spikes.
Costs that spiral as the cluster grows
Node costs, persistent volumes, load balancers, data transfer, and control plane charges. Without proper cost allocation and optimization, your Kubernetes cluster becomes a significant line item on your cloud bill.
Why The Usual Approach Doesn't Work
Traditional VM-based deployment wastes resources and creates environment drift. Each service gets its own VM or instance, leading to utilization rates below 20%. Scaling requires provisioning new VMs, which takes minutes. Auto-scaling is impractical.
Docker Compose and Swarm are simpler but lack Kubernetes advanced features: auto-healing, rolling updates, service discovery, secrets management, and the vast ecosystem of tools and extensions. You trade capability for simplicity.
Managed Kubernetes services (EKS, AKS, GKE) handle the control plane but do not solve cluster design. A badly designed cluster on any managed service will still have networking issues, scaling problems, and operational overhead. The managed service handles the plane; you handle everything else.
How We Solve It Differently
We design clusters with clear resource boundaries: namespaces for tenancy, resource quotas for fairness, node pools for workload isolation, and taints/tolerations for specialized hardware. Your cluster is organized for multi-team use without interference.
Auto-scaling is configured with realistic metrics and proper limits. Horizontal Pod Autoscaler, Cluster Autoscaler, and Vertical Pod Autoscaler are tuned for your workload patterns. Scaling events are tested and monitored. Your cluster responds to demand automatically.
Cost management is built into the cluster design. Node sizing, right-sizing of resource requests/limits, spot instances for fault-tolerant workloads, and namespace-level cost allocation ensure that you know exactly what your cluster costs and why.
What You Get
Production cluster design
Node pool architecture, namespace organization, network policy, RBAC, and resource management. Your cluster is designed for reliability, security, and multi-team use.
Intelligent auto scaling
HPA, VPA, and Cluster Autoscaler configured for your workload patterns. Scaling that responds to demand without over-provisioning. Tested scaling events that work when traffic spikes.
Service mesh (Istio or Linkerd)
Traffic management, observability, and security for microservices. Mutual TLS, traffic splitting, circuit breaking, and distributed tracing without application changes.
GitOps deployment workflow
ArgoCD or Flux for declarative, version-controlled deployments. Git is the source of truth for cluster state. Deployments are auditable, reviewable, and automatically reconciled.
How We Work
Cluster architecture design
We design the cluster topology: node pools, networking model, storage strategy, and namespace hierarchy. Design decisions are documented with rationale.
Cluster provisioning and configuration
Cluster is provisioned as infrastructure as code. CNI, ingress, monitoring, logging, and security are configured. GitOps tooling is installed and connected to your repositories.
Application deployment and GitOps setup
Applications are containerized and deployed through GitOps workflows. Helm charts or Kustomize configurations are created. Rollout strategies are configured and tested.
Ongoing operations and optimization
Cluster monitoring, cost optimization, upgrade planning, and team training. Your team can manage the cluster independently with runbooks and operational documentation.
Tools We Use
Who Benefits Most
Why DiVentra Labs
Production experience when it matters
We have designed, deployed, and operated Kubernetes clusters for production workloads serving millions of users. We know what breaks and how to prevent it.
GitOps-first approach
Git is the single source of truth for cluster state. Deployments are declarative, auditable, and automatically reconciled. Changes are made through pull requests, not kubectl commands.
Cost-conscious design
We design clusters that are cost effective without sacrificing reliability. Right-sized nodes, spot instances, resource limits, and cost allocation provide visibility and control over spending.
Questions? We Have Answers.
Should I use managed Kubernetes (EKS/AKS/GKE) or self-hosted?
Managed Kubernetes for almost everyone. The control plane management overhead of self-hosted is rarely justified unless you have specific compliance or customization requirements that managed services can't meet. EKS, AKS, and GKE all provide reliable control planes with reasonable pricing.
How do you handle persistent storage in Kubernetes?
We use CSI drivers for cloud provider storage (EBS, Azure Disk, Persistent Disk) for ReadWriteOnce workloads, and network file systems (EFS, Azure Files, Filestore) for ReadWriteMany. StatefulSets with stable storage identities for databases. Operator-based solutions (e.g., Zalando Postgres Operator) for production databases.
What is the right approach for Kubernetes networking?
CNI choice depends on your requirements: Calico for network policy and performance, Cilium for eBPF-based networking and security, AWS VPC CNI for native VPC integration on EKS. Service mesh (Istio or Linkerd) for microservices communication, observability, and security.
How do you manage Kubernetes upgrades?
Managed Kubernetes services handle control plane upgrades; we focus on node pool upgrades and application compatibility. Upgrade process: review breaking changes, update workloads for compatibility, upgrade node pools with rolling strategy, validate applications post-upgrade.
How do you secure a Kubernetes cluster?
RBAC with least-privilege access, network policies for pod-level segmentation, pod security standards (baseline or restricted), secrets management through external providers (HashiCorp Vault, AWS Secrets Manager), image scanning in CI/CD, and regular cluster audits with tools like kube-bench and kube-hunter.
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