Kubernetes Setup for Web Application Orchestration

When a web application grows to dozens of microservices, manual server management turns into hell. Containers crash, load spikes, deployments fail. Kubernetes is the standard for production environments, automating container management: restart, scaling, rolling updates. Our team has set up Kubernetes for 30+ projects — from startups to enterprise. We guarantee stable operation under any load.

How to Set Up Kubernetes for Web Application Orchestration?

Managed Kubernetes (Yandex Managed Service for Kubernetes, Selectel VK Cloud) eliminates the headache of master nodes, etcd, and updates. You pay only for worker nodes. We recommend using it for production to focus on the application rather than infrastructure. Comparison: a managed cluster pays off with 5+ nodes compared to self-deployment, and administration costs drop by up to 40%.

Minimal Set of Manifests

To get started, five resources are enough: Namespace, Deployment, Service, Ingress, HPA. Below is a working template with explanations.

# namespace.yaml
apiVersion: v1
kind: Namespace
metadata:
  name: myapp
---
# deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: myapp-web
  namespace: myapp
spec:
  replicas: 3
  selector:
    matchLabels: { app: myapp-web }
  template:
    metadata:
      labels: { app: myapp-web }
    spec:
      containers:
        - name: web
          image: registry.example.com/myapp:v1.0.0
          ports:
            - containerPort: 8080
          envFrom:
            - configMapRef: { name: myapp-config }
            - secretRef: { name: myapp-secrets }
          resources:
            requests:
              cpu: "100m"
              memory: "256Mi"
            limits:
              cpu: "500m"
              memory: "512Mi"
          readinessProbe:
            httpGet: { path: /health/ready, port: 8080 }
            initialDelaySeconds: 10
            periodSeconds: 5
          livenessProbe:
            httpGet: { path: /health/live, port: 8080 }
            initialDelaySeconds: 30
            periodSeconds: 30
---
# service.yaml
apiVersion: v1
kind: Service
metadata:
  name: myapp-web
  namespace: myapp
spec:
  selector: { app: myapp-web }
  ports:
    - port: 80
      targetPort: 8080
---
# ingress.yaml
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: myapp-ingress
  namespace: myapp
  annotations:
    cert-manager.io/cluster-issuer: letsencrypt-prod
    nginx.ingress.kubernetes.io/rate-limit: "100"
    nginx.ingress.kubernetes.io/proxy-body-size: "50m"
spec:
  ingressClassName: nginx
  tls:
    - hosts: [example.com]
      secretName: myapp-tls
  rules:
    - host: example.com
      http:
        paths:
          - path: /
            pathType: Prefix
            backend:
              service:
                name: myapp-web
                port: { number: 80 }

We combined the manifests into one block for brevity. ConfigMap and Secret are described in the text — their structure is trivial. Important: secrets are base64-encoded; do not store them in a repository, use SealedSecrets or an external secret store (Hashicorp Vault, AWS Secrets Manager).

Horizontal Scaling and Autoscaling

HPA (HorizontalPodAutoscaler)

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: myapp-hpa
  namespace: myapp
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: myapp-web
  minReplicas: 2
  maxReplicas: 20
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 70
    - type: Resource
      resource:
        name: memory
        target:
          type: Utilization
          averageUtilization: 80

HPA automatically adds replicas when CPU or memory thresholds are exceeded. This is cheaper than keeping 20 pods all the time: at low load it runs 2, at peak up to 20. We saw this save projects during ad campaigns, reducing infrastructure costs by 30-50%.

Periodic Tasks: CronJob

For background tasks (file cleanup, report sending), we use CronJob.

apiVersion: batch/v1
kind: CronJob
metadata:
  name: cleanup-old-files
  namespace: myapp
spec:
  schedule: "0 2 * * *"
  concurrencyPolicy: Forbid
  jobTemplate:
    spec:
      template:
        spec:
          restartPolicy: OnFailure
          containers:
            - name: cleanup
              image: registry.example.com/myapp:latest
              command: ["php", "artisan", "files:cleanup"]
              envFrom:
                - secretRef: { name: myapp-secrets }

Work Process: From Analysis to Deployment

• Analysis — We study the application architecture, SLA requirements, and current issues.
• Design — We design the cluster: number of nodes, storage class, network, ingress controller.
• Implementation — We write manifests, set up CI/CD (GitLab CI, GitHub Actions), integrate monitoring (Prometheus + Grafana).
• Testing — Load testing, fault tolerance verification (chaos engineering).
• Deployment — Deploy to staging and production, train the team.

What's Included in the Result

• Kubernetes manifests: Deployment, Service, Ingress, HPA, CronJob, ConfigMap, Secret.
• CI/CD pipeline: automatic image builds, deployment via Helm or Kustomize.
• Monitoring and alerts: Grafana dashboards, alerts to Telegram/Slack.
• Documentation: architecture diagram, developer instructions.
• Team training: 2-3 sessions on cluster operations.
• 2 weeks of post-launch support — bug fixes, Q&A.

Timeline

Cost is calculated individually based on project complexity. Request a free consultation — we'll evaluate your project.

Comparison with Docker Compose

Docker Compose is good for local development, but in production it cannot handle high load. Kubernetes offers built-in scaling, self-healing, service discovery, and rolling updates. For example, at 1000 RPS, Kubernetes survives a node failure without losing requests, while Docker Compose does not.

Why Is Correct Container Resource Configuration Important?

Incorrect requests and limits lead to OOM kills or cluster underutilization. We use Vertical Pod Autoscaler based on historical data to pick optimal values. This reduces infrastructure costs by up to 40% and increases stability. Learn more about resources in the Kubernetes documentation.

Typical Mistakes in Kubernetes Setup

• Missing readiness and liveness probes: the pod is considered healthy but does not respond to requests.
• Too large limits overload the cluster and degrade neighboring pod performance.
• Storing secrets in plain text creates a leak risk.
• Ignoring resource quotas allows one pod to consume all resources.

We help avoid these pitfalls. Contact us for a detailed consultation.

Case Study: E-Commerce Platform

A client with a high-load e-commerce platform (800 RPS average, 5000 RPS peak on Black Friday) was using Docker Compose in production. Servers crashed monthly, scaling took 30 minutes manually. We migrated to a Kubernetes cluster on Yandex Managed Kubernetes with HPA and rolling updates. Result: deployment time dropped from 30 minutes to 2 minutes, infrastructure costs reduced by 35% due to right-sizing and autoscaling. During peak load, the system scaled from 5 to 25 pods automatically without any downtime.

Conclusion

Kubernetes is a powerful but complex tool. Proper setup requires experience. Our team has 10+ years in DevOps and over 50 implementations. We guarantee stability and speed. Get a free consultation — let's discuss your project and propose the best solution.

We regularly encounter a situation: "The site is not opening" at 3 a.m. — and it turns out that the VPS disk is full because nginx logs haven't been rotated for six months. Or the server went down under load on the day of an advertising campaign launch because the shared hosting had a limit of 50 concurrent connections. Setting up hosting and deployment is not about "where it's cheaper" but about what happens when something goes wrong. Our team helps avoid such incidents by designing infrastructure that accounts for real load patterns.

When to choose Vercel and Netlify?

Vercel is built for Next.js — deploy in one push, preview deployments for every PR, automatic CDN, Edge Functions, ISR without configuration. For frontend projects and JAMstack, it's the optimal choice: no operational overhead, time-to-deploy measured in minutes.

Real limitations: Vercel Serverless Functions run in us-east-1 by default (latency for Europe +80–100ms), Function timeout 300 seconds on Pro, Bandwidth 1TB/month on Pro. For heavy backend, you need workers or a separate server.

Netlify is closer to static sites and Edge Functions based on Deno Deploy. Build minutes are the main limitation on the free tier.

Why is Docker the foundation of predictable deployment?

"It works on my machine" — classic. Docker solves this through environment containerization. But a bad Dockerfile creates new problems.

A typical mistake: copying everything into the image without .dockerignore, resulting in an 800MB image instead of 80MB. node_modules inside the image weighs as much. Correct approach: multi-stage build.

FROM node:20-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
COPY . .
RUN npm run build

FROM node:20-alpine AS runner
WORKDIR /app
COPY --from=builder /app/.next ./.next
COPY --from=builder /app/node_modules ./node_modules
COPY --from=builder /app/package.json ./package.json
EXPOSE 3000
CMD ["npm", "start"]

Final image: 180MB instead of 1.2GB. CI build time is reduced due to layer caching — if package.json hasn't changed, the layer with npm ci is taken from cache.

Docker Compose for local development and simple production scenarios: application + PostgreSQL + Redis in one configuration. For production on a single server, it's a perfectly viable option if there's no requirement for horizontal scaling.

More about containerization — Wikipedia: Docker.

How to set up Nginx as a reverse proxy?

Nginx in front of the application is standard for VPS and dedicated servers. Main functions: SSL termination, gzip, static files, rate limiting, upstream load balancing.

A configuration often done incorrectly: worker_processes auto — number of processes equals CPU count. worker_connections 1024 — that's 1024 per worker process. With 4 CPUs and 1024 connections = 4096 concurrent connections. For a high-traffic site, you need worker_connections 4096 and set keepalive_timeout 65.

For static assets with hash in the filename:

location ~* \.(js|css|woff2|png|webp)$ {
    expires 1y;
    add_header Cache-Control "public, immutable";
}

immutable tells the browser: don't revalidate this file even on hard refresh. This only works correctly with content-hashed filenames (which Vite/webpack do by default). Documentation — Wikipedia: Nginx.

AWS: flexibility and complexity

EC2 + Auto Scaling Group — classic for horizontal scaling. AMI with pre-installed application, Launch Template, ASG with min/desired/max instances, Application Load Balancer. When CPU > 70% for 3 minutes — scale out, when CPU < 30% for 15 minutes — scale in. Health check via ALB removes unhealthy instances from rotation.

ECS Fargate — containers without managing EC2. Deploy a Docker image, specify CPU/memory (512 CPU units = 0.5 vCPU, from 512MB memory), Fargate launches it. More expensive than Lambda, but no cold start and no timeout limitations. Suitable for long-running processes, WebSocket servers, heavy workers.

RDS for PostgreSQL with Multi-AZ: automatic failover in 1–2 minutes when primary fails. Read Replicas for scaling reads. RDS Proxy for connection pooling — Lambda functions cannot hold long-term connections, the proxy buffers this.

Kubernetes: when it is justified

K8s adds significant operational complexity. Justified when: multiple teams deploy independent services, fine-grained resource allocation per service is needed, canary deployments and blue/green without downtime are required.

AWS EKS, GKE, or managed k8s from Hetzner (cheaper). Helm charts for standard services. Horizontal Pod Autoscaler based on CPU and custom metrics (RPS via Prometheus).

For most startups and medium-sized projects, Kubernetes is overkill. ECS or Fly.io provide 80% of the capabilities with 20% of the operational complexity.

Monitoring and alerting

A server without monitoring is waiting for an incident. Minimal stack: Prometheus + Grafana (or Grafana Cloud for managed), alerting on disk > 80%, memory > 85%, CPU > 90% over 5 minutes, error rate > 1%. Uptime via Better Uptime or Upptime (self-hosted).

Logs: Loki + Grafana or CloudWatch Logs Insights. Structured JSON logs (winston, pino) are mandatory — otherwise, log searching becomes a pain.

What is included in hosting setup

• Audit of current infrastructure and load profiling
• Selection of target architecture (VPS, AWS, serverless, Kubernetes)
• Setting up CI/CD pipeline (GitHub Actions, GitLab CI) with automatic deployment
• IaC via Terraform or Pulumi (infrastructure as code)
• Configuration of Nginx, SSL certificates, HTTP/2, brotli
• Monitoring and alerting (Prometheus + Grafana, PagerDuty)
• Documentation of runbooks and team training

Additionally, contact us if you need migration from current hosting or integration with external services.

Work process

1. Audit of current infrastructure (2–5 days)
2. Selection of target architecture with load and budget justification (1–3 days)
3. Setting up CI/CD pipeline (GitHub Actions, GitLab CI) (2–5 days)
4. IaC via Terraform or Pulumi (3–10 days)
5. Setting up monitoring and alerting (2–5 days)
6. Documentation of runbooks and team training (1–3 days)

Our experience — 7 years on the market, over 50 projects, guarantee of operability after deployment.

Timeline

• Basic deployment on VPS with Docker + Nginx + CI/CD: 1–2 weeks.
• Setting up AWS infrastructure with Auto Scaling, RDS, CDN: 3–6 weeks.
• Migration to EKS from scratch: 6–12 weeks.
• Setting up Vercel/Netlify for JAMstack: 3–5 days.

The cost is calculated individually depending on complexity and scope of work. Get a consultation — we'll evaluate your architecture in one day.