Kubernetes unlocks real container orchestration โ autoscaling, self-healing, zero-downtime deployments, and multi-region workloads. We design, provision, and operate clusters that your team can hand workloads to confidently, without becoming Kubernetes experts first.
The moment you have more than a handful of containers, the limits of manual orchestration become painful and expensive.
Running dozens of Docker containers with docker-compose โ scaling is manual and fragile. A single server failure takes everything down with no automatic recovery.
A container crash takes down the service โ no automatic restart or rescheduling. On-call engineers are alerted for incidents that should self-heal.
Rolling updates require downtime because there's no orchestration layer to manage traffic shifting between old and new versions.
No horizontal autoscaling โ either over-provision (expensive) or under-provision (crashes under load). Both options cost money or users.
Secrets and config hardcoded in container images or passed unsafely through environment variables โ no rotation, no audit trail.
Multi-region deployments are a manual, error-prone process per environment โ no consistent tooling, no unified deployment model.
We design clusters that are appropriately sized for your workloads โ not over-engineered, not under-built. Every architectural decision is documented with the reasoning behind it.
Production Kubernetes is more than running pods โ it's autoscaling, observability, security, and GitOps working as a unified system.
EKS, AKS, GKE, or self-hosted with Kubeadm, K3s, or Talos Linux โ configured for high availability from day one.
Deployments, StatefulSets, DaemonSets, Jobs, and CronJobs โ each workload type configured with appropriate health probes and resource limits.
HPA, VPA, KEDA event-driven scaling, and Cluster Autoscaler โ pods and nodes scale with demand, not a calendar reminder.
Istio or Linkerd for mutual TLS between services, distributed tracing, traffic management, and fine-grained observability.
Declarative app definitions, automated sync policies, app-of-apps pattern, and multi-cluster management from a single pane.
NGINX Ingress or Traefik, Gateway API, cert-manager for automatic TLS, and NetworkPolicy for traffic isolation.
Prometheus metrics, Grafana dashboards per namespace, Loki log aggregation, and kube-state-metrics for cluster-level visibility.
RBAC least-privilege, NetworkPolicies, OPA Gatekeeper admission control, Pod Security Admission, and image scanning in CI.
We migrate workloads incrementally โ your existing services stay online throughout the transition to Kubernetes.
Review current deployment architecture, identify Kubernetes readiness per service, and document dependencies and stateful components.
Cluster architecture, node pool strategy, namespace model, RBAC structure, networking approach, and storage class selection.
Cluster creation, VPC networking, storage classes, base monitoring stack, ingress controller, and cert-manager configuration.
Containerise remaining services, write Helm charts for each workload, and migrate services incrementally with parallel running periods.
ArgoCD installation and configuration, app-of-apps pattern, environment promotion strategy, and automated sync policies.
Cluster upgrade management, node pool right-sizing, monthly capacity reviews, runbook documentation, and optional managed operations.
Proven tools chosen for reliability and your team's ability to operate them long-term.
Kubernetes is industry-agnostic โ compliance requirements differ, the engineering principles don't.
We don't over-engineer. A 3-service application doesn't need a multi-cluster federation. We match cluster complexity to actual requirements.
Engineers leave every engagement knowing how to debug pod failures, scale deployments, and plan Kubernetes upgrades โ not just consume infrastructure.
Every cluster change is a git commit with a reviewer. No undocumented kubectl applies in production โ every state change is auditable.
We've run production Kubernetes clusters across EKS, AKS, GKE, and self-hosted for 8+ years โ across cloud providers, industries, and compliance regimes.
A payment processing platform was running 140 EC2 instances with static provisioning โ over-provisioned by 4x during off-peak hours and under-provisioned during end-of-month batch runs. We migrated them to EKS with Cluster Autoscaler and KEDA-driven scaling on SQS queue depth, collapsing 140 VMs to a cluster of 12โ35 nodes depending on real load.
140 static EC2 instances provisioned for peak load, sitting at 12% CPU utilisation at night. Deployments required a manual Ansible runbook and a 20-minute maintenance window.
EKS cluster with Karpenter node autoprovisioning, KEDA scaling on SQS transaction queue depth, and ArgoCD for GitOps deployments. Helm charts for all 22 microservices.
Significant reduction in infrastructure cost through autoscaling vs static provisioning. Deployments changed from manual Ansible runbooks to zero-downtime rolling updates triggered by git push. Scale-out time reduced dramatically during batch peaks.
โ Success Stories
Verified Client Reviews on Every Engagement