Website Scaling: When Load Grows

A website starts to slow down at 500 RPS, and the server hits CPU limits — it's time to scale. Many clients come with the problem: "We bought a powerful server, but the load didn't drop." We've encountered this many times. Scaling infrastructure is not about replacing a small server with a big one. It's an architectural process: first optimization, then horizontal scaling, then vertical (if needed). The wrong order leads to wasted budget without solving the problem. Our engineers with ten years of experience help navigate this path without downtime. We guarantee stability at every stage — all changes go through a stage environment, ensuring 99.9% uptime. One client — an online store with 2000 RPS traffic — after architecture reorganization reduced infrastructure costs by 40% while doubling the load. Budget savings amounted to up to 40% of previous expenses.

How to Diagnose Bottlenecks

Before scaling, understand where the bottleneck is. We use standard Linux utilities:

# CPU, I/O, memory, database, network diagnostics
top -b -n 1 | head -20
iostat -x 1 5
free -m && vmstat 1 5
mysql -e "SHOW PROCESSLIST;"
psql -c "SELECT pid, now()-pg_stat_activity.query_start AS duration, query FROM pg_stat_activity WHERE state != 'idle' ORDER BY duration DESC LIMIT 10;"
ss -s
# Load testing with k6, ab, wrk
k6 run --vus 100 --duration 30s script.js
ab -n 10000 -c 100 https://mysite.com/
wrk -t12 -c400 -d30s https://mysite.com/

According to Wikipedia, horizontal scaling is often more cost-effective for high-load systems.

Why Caching is the First Step

Caching provides the fastest ROI. One project — an online store on Laravel — after setting up Redis and Nginx fastcgi_cache reduced database load by 80% at the same RPS. Here's the configuration:

# Nginx: static caching and FastCGI cache for PHP
location ~*\.(css|js|jpg|png|gif|ico|woff2)$ {
    expires 1y;
    add_header Cache-Control "public, immutable";
}
fastcgi_cache_path /tmp/nginx-cache levels=1:2 keys_zone=MYAPP:100m inactive=60m;
fastcgi_cache_key "$scheme$request_method$host$request_uri";

Redis additionally caches application data: php artisan config:cache, route:cache, view:cache.

CDN and Load Balancing

CDN (Cloudflare, CloudFront) offloads the server from static content. We configure a rule: static content is cached at the Edge, API is passed through. This requires setting Cache-Control headers in code: const cacheControl = isStatic ? 'public, max-age=31536000' : 'no-cache';. A load balancer (Nginx, HAProxy) distributes traffic among application replicas — essential for horizontal scaling.

Vertical vs Horizontal Scaling: Which is Better?

Horizontal scaling (adding replicas) is 3–5 times more effective than vertical scaling (upgrading hardware) under high load because it distributes traffic and provides fault tolerance. Vertical scaling is simpler initially but runs into physical limits.

Database Optimization

Even with caching, the database is often a bottleneck. We find slow queries via EXPLAIN ANALYZE and add indexes:

EXPLAIN ANALYZE SELECT * FROM products WHERE category_id = 5 ORDER BY created_at DESC LIMIT 20;
CREATE INDEX CONCURRENTLY idx_products_category_created ON products (category_id, created_at DESC);

Connection pooling using PgBouncer reduces database load by 2–3 times. We also configure connection pools for applications: pdo_mysql.default_socket and max_connections in the MySQL config.

How Task Queues Work

Heavy operations — email sending, PDF generation, image processing — should not be executed synchronously. We use queues: Laravel Queue + Redis, RQ, or RabbitMQ. This offloads the web server and improves responsiveness. Example with Laravel:

dispatch(new ProcessImageJob($file));
// The frontend does not wait for completion — the user gets an instant response

[program:queue-worker]
process_name=%(program_name)s_%(process_num)02d
command=php /var/www/artisan queue:work redis --sleep=3 --tries=3
numprocs=2
autostart=true
autorestart=true
user=www-data

Horizontal Scaling with Kubernetes

When one server is not enough, add replicas. Kubernetes with HorizontalPodAutoscaler automatically scales the number of pods based on CPU:

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

Service Separation and Queues

Gradually split the monolith into microservices:

• API Gateway (Nginx/Kong)
• Auth Service (stateless JWT)
• Content Service
• Media Service (separate for file uploads)
• Search Service (Elasticsearch)

Heavy operations are moved to queues: instead of synchronous processing, we use Laravel Queue + Redis, executing dispatch(new ProcessImageJob(...)).

Architecture by Load Level

Rule: scale what has been measured as a bottleneck. Don't scale assumptions.

What's Included in the Work

1. Audit of current architecture and bottlenecks (1-2 days)
2. Setup of caching and CDN
3. Database optimization: indexes, configs, pooling
4. Application containerization and orchestration setup
5. Load testing before and after changes
6. Documentation and instructions for the team

Timeline and Budget

Audit and optimization under load (without changing architecture) — 1-2 weeks. Migration to horizontal scaling — 2-6 weeks. The cost is calculated individually after assessing your system, but optimization savings usually amount to 30-50% of current costs. Get a consultation — our certified engineers will analyze your infrastructure and propose a plan. Order an infrastructure audit — we will identify bottlenecks and develop a scaling plan.

Experience with over 50 scaling projects, stability guarantee — all changes go through a stage environment.

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.