Gzip and Brotli Compression: Full Guide for Nginx and Apache

Imagine: your site loads in 5 seconds, but 80% of the time is spent transferring JavaScript bundles. You've already optimized images, enabled caching, but speed barely improved. Most likely, you missed transport-level compression. We configure Gzip and Brotli on the server, reducing content size by 3–10x without code changes. Traffic savings can reach 70% with Brotli — lowering hosting and CDN costs.

Brotli gives 15–25% better compression than Gzip at comparable CPU load. One of our projects — an online store with a 1.2 MB bundle. After enabling Brotli with pre-compressed static assets, the size dropped to 280 KB, and LCP fell from 4.2 s to 1.8 s. For static files we use pre-compressed assets — .gz and .br files are generated at build time, the server serves them without re-compression. This saves up to 30% CPU time during peak loads.

Why Brotli outperforms Gzip?

Brotli uses a dictionary for HTML/JS/CSS, enabling tighter packing. For example, app.js 500 KB compresses to 130 KB (Brotli) vs 150 KB (Gzip). The difference is especially noticeable on mobile networks with limited bandwidth. Additionally, Brotli supports compression levels up to 11, allowing maximum compression for static assets during build. However, for on-the-fly compression, high levels (9–11) heavily load the CPU, so level 6 is recommended for dynamic content.

How to minimize CPU load during compression?

The primary method is to use pre-compressed static assets. .gz and .br files are generated once during the project build (e.g., using vite-plugin-compression), and the server serves them directly, saving CPU time on every request. For dynamic content (HTML, JSON), we apply on-the-fly compression at level 6 — a balance between compression ratio and load. During peak hours, you can disable compression for large files via the gzip_min_length 256 directive. CPU monitoring allows timely adjustment of compression levels — if load exceeds 80%, consider lowering the level or increasing gzip_min_length. Reducing CPU load lowers server resource costs.

Nginx: Gzip + Brotli

# /etc/nginx/nginx.conf or /etc/nginx/conf.d/compression.conf

# Gzip — supported everywhere
gzip on;
gzip_vary on;
gzip_proxied any;
gzip_comp_level 6;        # 1-9, CPU/compression balance; 6 is a good spot
gzip_min_length 256;      # don't compress very small files
gzip_types
    application/javascript
    application/json
    application/xml
    application/rss+xml
    image/svg+xml
    text/css
    text/html
    text/javascript
    text/plain
    text/xml
    font/woff
    font/woff2;

# Brotli — requires ngx_brotli module
# Install: apt install libnginx-mod-brotli
brotli on;
brotli_comp_level 6;
brotli_types
    application/javascript
    application/json
    text/css
    text/html
    text/plain
    image/svg+xml
    font/woff2;

Verification:

curl -H "Accept-Encoding: br" -I https://example.ru/
# Response should contain: Content-Encoding: br

curl -H "Accept-Encoding: gzip" -I https://example.ru/
# Response: Content-Encoding: gzip

Pre-compressed static assets

For static files (JS, CSS, bundles) — generate .gz and .br files at build time, serve directly. No CPU overhead per request:

// vite.config.ts
import { defineConfig } from 'vite';
import viteCompression from 'vite-plugin-compression';

export default defineConfig({
    plugins: [
        viteCompression({ algorithm: 'gzip',   ext: '.gz' }),
        viteCompression({ algorithm: 'brotliCompress', ext: '.br' }),
    ]
});

# Nginx: serve pre-compressed files
location ~* \.(js|css|woff2)$ {
    gzip_static  on;    # looks for .gz version
    brotli_static on;   # looks for .br version (ngx_brotli module)
    expires 1y;
    add_header Cache-Control "public, immutable";
}

How to verify compression is working?

Use curl or the Network tab in DevTools. The response should include a Content-Encoding: br or gzip header. Also check for Vary: Accept-Encoding — it tells caching servers that content depends on encoding. For step-by-step diagnostics, you can use online services, like header checking tools.

If the client doesn't support Brotli, Nginx automatically falls back to Gzip or uncompressed content. Ensure the gzip on directive is enabled — this guarantees a fallback. Using a CDN with Brotli support further accelerates content delivery and reduces load on the origin server.

Typical compression results

Compression levels: recommendations

Levels 1–3 — low CPU load, weak compression. 4–6 — balanced. 7–9 — high compression, but CPU grows non-linearly: level 9 takes 3x more time than 6. For pre-compressed files, use 11 (Brotli only) — compiled once at build time.

Apache

# .htaccess
<IfModule mod_deflate.c>
    AddOutputFilterByType DEFLATE text/html text/css application/javascript
    AddOutputFilterByType DEFLATE application/json image/svg+xml font/woff2
</IfModule>

Compression setup process

1. Analyze current server configuration (Nginx/Apache).
2. Choose compression levels (Gzip 6, Brotli 6 — balanced).
3. Configure exclusions (non-compressible formats: PNG, JPEG, MP4).
4. Generate pre-compressed files in the build (Vite, Webpack).
5. Verify response headers (Content-Encoding, Vary).
6. Monitor CPU load.

What's included in the work

• Configuration of Gzip and Brotli on the server (Nginx/Apache).
• Setup of pre-compressed static assets (if using a bundler).
• Header verification and compression testing.
• Documentation of changes made.
• Recommendations for compression levels based on your traffic.

Our engineers have been configuring compression for over 5 years — we've optimized load speed for 100+ projects, reducing LCP and TTFB by 40–60%. Want the same? Contact us — we'll perform an audit and implement Gzip and Brotli compression in one day. Order compression setup and get a consultation on accelerating your site today.

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.