Kubernetes
Engineering.
We design, deploy, and rescue production Kubernetes clusters. From managing massive EKS footprints to building custom operators, we ensure your orchestrator works for you, not against you.
Clusters Sized to Reality, Not Defaults
Most clusters we get called in to rescue were never really designed. They accreted, one `kubectl apply` at a time, until requests and limits were guesses, autoscaling reacted instead of predicting, and nobody could say with confidence what the next traffic spike would do. We rebuild the scheduling and scaling story from measured usage instead of defaults copied out of a tutorial.
Resource requests and limits derived from actual VPA recommendations over a real traffic window, not copy-pasted defaults that leave nodes either starved or wastefully idle.
Cluster Autoscaler or Karpenter tuned against your actual pod bin-packing pattern, so scale-up latency during a traffic spike is measured in seconds, not the several minutes a naive config produces.
Network policies default-deny at the namespace boundary, with explicit allow rules. No flat cluster network where any pod can reach any other pod.
PodDisruptionBudgets and topology spread constraints set deliberately, so a node drain or AZ failure doesn't take out every replica of a critical service at once.
What we build
Cluster Rescue & Stabilization
Stabilizing out-of-control clusters suffering from OOM kills, DNS timeouts, and configuration drift, usually starting with a full audit of requests/limits against real usage.
Cost Optimization
Implementing predictive autoscaling, Spot/Karpenter instance orchestration, and resource request/limit tuning based on measured VPA data, not guesswork.
Security & Compliance
Enforcing strict RBAC, default-deny network policies, and OPA Gatekeeper or Kyverno admission policies for SOC2/HIPAA environments, verified with policy tests in CI.
Custom Operators & Controllers
Extending the Kubernetes API with Go-based operators for internal workflows that off-the-shelf Helm charts and CRDs don't cover.
What we fix
Unpredictable cluster scaling during traffic spikes, with scale-up latency measured in minutes instead of seconds
Runaway cloud costs from over-provisioned worker nodes and requests/limits set by guesswork
Complex and brittle Helm chart deployments that break on every minor version bump
Service mesh (Istio/Linkerd) implementations that add latency and complexity without a clear owner
Flat cluster networking with no default-deny policy, so a compromised pod can reach anything
PodDisruptionBudgets missing or misconfigured, so a routine node drain causes a customer-facing outage
Stop guessing.
Start shipping.
Thirty minutes with a senior engineer. No sales deck, no junior SDRs. Just a technical conversation about what's actually breaking or slowing you down.