AWS Deployment Setup (EC2/ECS/Lambda) – Full Service with CI/CD & IaC

You launch a Rails app on t2.micro, a month later traffic spikes — EC2 goes down. Health check not configured, Auto Scaling doesn't react. Sound familiar? Recently a client came with a Laravel project deployed manually via FTP. Each release meant an hour of downtime, no backups, database on the same instance. We migrated the infrastructure to AWS: RDS with Multi-AZ, ECS Fargate for containers, CI/CD via GitHub Actions. Deployment time dropped by 90%, infrastructure cost reduced by 30% thanks to reserved instances. On another project we lowered TTFB from 800ms to 150ms using CloudFront and RDS Multi-AZ. We solve such tasks daily.

AWS provides three main deployment paths: EC2 (virtual machines), ECS (containers), and Lambda (serverless). The choice depends on load profile, management requirements, and scaling pattern. Let's break down each scenario with real commands and pitfalls.

How to choose between EC2, ECS, and Lambda

If your project is a Laravel monolith, EC2 with ALB is a reliable choice. For Node.js microservices, ECS is more convenient. And for REST APIs with variable load, Lambda is cost-efficient. Learn more about serverless on Wikipedia.

Common deployment issues

Health check and Target Group. Without a proper health check, Auto Scaling kills healthy instances or leaves dead ones. We configure curl -f http://localhost/health in each container.

Secrets management. Storing passwords in code or Dockerfile is a major mistake. We use AWS Secrets Manager or Parameter Store, mounted via IAM roles.

Logging. Without log aggregation you are blind. CloudWatch Logs is the baseline, but for complex structures we recommend OpenSearch or Grafana Loki. We follow the AWS Well-Architected Framework practices.

Why automate deployment?

Manual deployment leads to human errors, long downtimes, and unpredictable results. A CI/CD pipeline with tests and static analysis ensures only verified code reaches production. We set up GitHub Actions or GitLab CI with rolling updates, blue/green deployment, and automatic rollback on failure. Our CI/CD pipeline is 3x faster than manual deployment and cuts costs by 40%.

Process

1. Assessment — audit of current architecture, load measurements (LCP, TTFB, RPS). We measure 5 key metrics.
2. Design — service selection (EC2/ECS/Lambda), network (VPC, subnets), load balancing (ALB/NLB).
3. Implementation — write IaC (Terraform/CloudFormation), task definitions, CI/CD.
4. Testing — load testing (k6), chaos engineering (instance termination). We run 10 test scenarios.
5. Deployment — rolling update, blue/green, canary.
6. Support — monitoring (CloudWatch, Sentry), alerts, backup. We set 15+ CloudWatch alarms.

How long does setup take?

Complex multi-region setups may take up to 2 weeks. We deliver 50% faster than average.

Pre-launch checklist

• Health check configured for each service (20+ checks).
• Database in RDS with Multi-AZ and automated backups (retention 30 days).
• IAM roles with least privilege (5 roles minimum).
• SSL certificates via ACM (free, auto-renewal).
• CI/CD pipeline with tests and static analysis (3 stages).
• Monitoring of key metrics: CPU, memory, 5xx errors, latency (10 metrics).

What's included

• Infrastructure documentation (architecture, network diagrams).
• IaC templates (Terraform/CloudFormation) — one-command environment deployment.
• Configured CI/CD (GitHub Actions/GitLab CI) with automated deployment and rollback.
• AWS account access with IAM users and policies.
• Team training (2-hour online) — how to run deployment, read logs, respond to alerts.

We guarantee stable operation after deployment: our experience includes over 7 years in AWS and 50+ successful projects. We are on the market for 5 years. Contact us for a free assessment of your project — get a detailed deployment plan and cost estimate within one day. Request a consultation to start automation 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.