Kubernetes Deployment and Management Services

Pod in CrashLoopBackOff while nodes are 15% loaded? Start with the real problem

Typical picture: kubectl describe pod shows OOMKilled, limits set randomly, monitoring not configured, autoscaling absent. The cluster was "just launched" — and now it eats budget but doesn't handle load. We’ve deployed over 50 production clusters across multiple clouds, built a team of 10 certified engineers, and know how to turn chaos into predictable orchestration. We select a managed service (EKS, GKE, AKS) for your infrastructure, configure HPA/VPA/KEDA, implement GitOps via Helm and ArgoCD, and guarantee cluster operation.

Why managed Kubernetes: EKS, GKE or AKS?

Three platforms solve the same task differently. The summary table helps you choose for your scenario.

Kubernetes base architecture is the same, but the choice of managed service determines operational costs. EKS offers maximum AWS compatibility: IRSA, ALB via AWS Load Balancer Controller, EBS CSI Driver. On EKS it's optimal to use Karpenter instead of Cluster Autoscaler — nodes start in 30–60 seconds versus 3–5 minutes, and it can mix spot/on-demand via NodePool. Karpenter launches nodes up to 6x faster than the standard autoscaler, directly cutting scaling delays.

GKE is the most "native" Kubernetes (Google created it, Kubernetes project). GKE Autopilot eliminates the need to manage node pools — you pay only for pods. AKS is convenient in the Microsoft ecosystem: Azure AD for RBAC, Key Vault via CSI, pipelines via GitHub Actions.

How do HPA, VPA, and KEDA work? Example with real savings

Improper autoscaling either can't handle load or burns money. Let's examine three mechanisms. HPA (Horizontal Pod Autoscaler) — scaling by CPU. Basic manifest:

apiVersion: autoscaling/v2 kind: HorizontalPodAutoscaler metadata: name: api-hpa spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: api minReplicas: 2 maxReplicas: 20 metrics: - type: Resource resource: name: cpu target: type: Utilization averageUtilization: 70 behavior: scaleDown: stabilizationWindowSeconds: 300 policies: - type: Percent value: 10 periodSeconds: 60 

stabilizationWindowSeconds: 300 — a must. Without it, HPA oscillates replicas under pulsating load. The Percent: 10 policy limits scale-down speed. Real production uses custom metrics: RPS via Prometheus Adapter, RabbitMQ queue depth.

VPA (Vertical Pod Autoscaler) adjusts correct requests/limits. Off mode gives recommendations, Auto restarts pods. In production, we often use Off with weekly Rolling Update. VPA in Auto mode is incompatible with HPA on CPU — they conflict.

KEDA (Kubernetes Event-Driven Autoscaling) scales based on external sources: SQS queue length, Kafka lag, HTTP RPS. It can scale to zero — convenient for batch workers.

apiVersion: keda.sh/v1alpha1 kind: ScaledObject metadata: name: worker-scaledobject spec: scaleTargetRef: name: queue-worker minReplicaCount: 0 maxReplicaCount: 50 triggers: - type: aws-sqs-queue metadata: queueURL: https://sqs.eu-west-1.amazonaws.com/123456789/jobs queueLength: "5" awsRegion: eu-west-1 authenticationRef: name: keda-aws-credentials 

queueLength: "5" — one worker per 5 messages. 100 messages arrive — 20 workers spin up. Queue empty for 5 minutes — workers go to zero. Savings on instances can reach 40–60% of the cost of a fixed number of pods. Monetarily, this amounts to $2,000–$5,000 monthly savings for an average 50‑microservice project.

Case: a startup of 50 microservices saved significantly per month. The client had fixed nodes costing $8,000/month, load was pulsating (nightly batch tasks). We implemented HPA by RPS and KEDA for queues — without changing code, the bill dropped to $3,200/month with the same throughput. Project payback — 3 weeks. Reach out for an autoscaling audit and see how much you can save.

How does Helm simplify release management?

Helm is the de facto standard for deployment. But helm upgrade --install without flags is a path to disaster. Structure via overlay:

charts/api/ Chart.yaml values.yaml # base values values.prod.yaml # production overrides values.staging.yaml # staging overrides templates/ 

helm upgrade api ./charts/api -f values.yaml -f values.prod.yaml --atomic --timeout 5m — --atomic flag rolls back the release if pods don't start within 5 minutes. Without it, the cluster may remain in a semi-updated state.

Helm standardizes deployment and version management. Helm Secrets via SOPS + Age or AWS KMS stores encrypted secrets in repository. Alternative — External Secrets Operator, which fetches secrets from AWS Secrets Manager or HashiCorp Vault.

When are Kubernetes Operators needed?

Operators are controllers that understand the business logic of stateful applications. Ready-made solutions for production:

• CloudNativePG — PostgreSQL: automatic failover, PiTR backups to S3, replica management via CRD.
• Strimzi — Kafka: manages brokers, topics, users; TLS out of the box.
• VictoriaMetrics Operator — monitoring: compatible with Prometheus CRD, consumes much less memory.

If no ready operator exists, we write our own using Kubebuilder (Go). Typical case — operator for hot-reload configuration: watches changes in ConfigMap, calls /reload endpoint of the application.

How to configure RBAC without cluster-admin?

RBAC is not "set and forget". Typical mistake: give the team cluster-admin "for speed". Then accidental kubectl delete namespace production.

Principle of least privilege via Role and RoleBinding:

apiVersion: rbac.authorization.k8s.io/v1 kind: Role metadata: name: developer namespace: production rules: - apiGroups: ["apps"] resources: ["deployments"] verbs: ["get", "list", "watch"] - apiGroups: [""] resources: ["pods", "pods/log"] verbs: ["get", "list", "watch"] 

For kubectl exec, need to explicitly add pods/exec, for port-forward — pods/portforward. CI/CD systems get a ServiceAccount with rights only on Deployment patch in specific namespaces.

Audit log is enabled by default in managed clusters. Audit policy for production logs all write operations and pods/exec.

Why are Network Policies critical for security?

By default in Kubernetes, all pods can communicate with each other — unacceptable for production.

apiVersion: networking.k8s.io/v1 kind: NetworkPolicy metadata: name: api-ingress namespace: production spec: podSelector: matchLabels: app: api policyTypes: - Ingress ingress: - from: - podSelector: matchLabels: app: nginx-ingress ports: - protocol: TCP port: 8080 

api receives traffic only from the ingress controller. Database — only from api. Monitoring — only from namespace monitoring. Implementation requires a CNI with Network Policy support: Calico or Cilium (eBPF is faster).

Typical timelines and scope of work

Cost is calculated individually after audit. Average infrastructure savings after implementing autoscaling — $2,000–$5,000 per month, projects pay off in 3–6 months.

Process

1. Audit — load profiling, security analysis, current cost assessment.
2. Design — managed service selection, autoscaling scheme, RBAC, network policies.
3. Deployment — cluster setup, Helm charts, monitoring (Prometheus + Grafana) and alerting.
4. Testing — load tests, failure scenarios (pod failure, node failure), canary deployment.
5. Launch — traffic migration, blue-green or canary, SLO commitment.
6. Support — runbook, post-release support for 1 month with SLA 99.9%.

What's included

• Audit of current cluster and architectural recommendations.
• Deployment of a managed cluster with configured autoscaling, RBAC, Network Policies, monitoring (Prometheus + Grafana).
• Documentation of architecture and runbook.
• Team training (2–4 hour workshop).
• Post-release support for 1 month with SLA 99.9%.

Get a consultation on choosing a managed solution and setting up autoscaling for your workloads. Contact us for an audit of your cluster — we'll assess the current state and propose a Kubernetes orchestration plan with guaranteed results.