Kubernetes Management

The Kubernetes Reality Check

Kubernetes solves real problems — service discovery, rolling deployments, resource isolation, horizontal scaling. But it also introduces a surface area of operational complexity that most growth-stage companies underestimate. By the time you've configured ingress controllers, set up monitoring, tuned pod disruption budgets, and built a CI/CD pipeline that actually works, you've spent six engineering-months on infrastructure instead of product.

We help teams get the benefits of Kubernetes without the full-time platform team it typically requires.

What We Handle

Cluster Architecture

Every cluster we build starts with the same question: what does your workload actually need? A team running 12 microservices has fundamentally different requirements than a team running GPU-heavy ML training jobs. We design for your reality, not for a conference talk.

Standard decisions we help teams make:

• Managed vs. self-hosted — EKS, GKE, and AKS handle the control plane. For most teams, that's the right call. We'll tell you when it isn't.
• Node pool strategy — right-sizing instance types, separating workloads by resource profile, configuring spot/preemptible nodes for cost savings on fault-tolerant workloads
• Networking — CNI selection, network policies, service mesh decisions (usually: you don't need one yet)

Deployment Pipelines

We build deployment pipelines that give your team confidence. That means:

• GitOps workflows — infrastructure state lives in Git, changes deploy through pull requests, rollbacks are a revert commit away
• Progressive delivery — canary deployments that automatically roll back if error rates spike, blue-green deployments for zero-downtime database migrations
• Environment parity — staging environments that actually mirror production, not a single-node cluster running everything on one machine

Observability

A cluster without observability is a liability. We set up:

• Metrics — Prometheus or cloud-native equivalents, with dashboards that show what matters (pod resource usage, request latency, error rates) instead of everything
• Logging — structured logs aggregated to a central store with retention policies that balance cost and debuggability
• Alerting — alerts that page on-call for actual incidents, not for expected behavior. We've seen teams with 200 alert rules where 180 were noise. That gets fixed.

Security & Compliance

Production clusters need guardrails:

• RBAC — role-based access that follows principle of least privilege, not a single admin kubeconfig shared over Slack
• Pod security — non-root containers, read-only filesystems where possible, network policies that restrict east-west traffic
• Image scanning — automated vulnerability scanning in CI, admission controllers that block unscanned images from deploying
• Secrets management — external secret stores (Vault, cloud KMS) instead of Kubernetes Secrets in plain base64

Ongoing Operations

For teams that need it, we provide ongoing cluster operations:

• Kubernetes version upgrades — tested in staging, rolled out with zero downtime, documented for your team
• Cost optimization — monthly reviews of resource requests vs. actual usage, right-sizing recommendations, reserved instance planning
• Incident support — when something breaks at 2 AM, having someone who's debugged Kubernetes networking issues before is worth a lot

When Kubernetes Is Wrong

We'll tell you if Kubernetes isn't the right answer. If you're running three services and a database, a container orchestrator adds complexity you don't need. A well-configured PaaS or even a couple of well-automated VMs might be the right call until you hit the scale where Kubernetes starts paying for itself. That's usually around 10-15 services with meaningful scaling requirements.