Configuring Pulumi for Infrastructure as Code

Typical situation: you have a TypeScript project, DevOps asks you to learn HCL for Terraform, and you've been writing legacy for six months. We've been through this: in a startup, during a multi-region migration, the Terraform code grew to 500+ lines, while Pulumi assembled the same infrastructure in 150 lines of TypeScript — three times less code with full functionality. That case is one of 10+ projects where we implemented Pulumi. On average, teams spend 2 weeks writing Terraform modules for a typical web application; with Pulumi, it takes 5 days. Pulumi doesn't require learning HCL, lowering the barrier for developers and saving budget.

What Problems Does Pulumi Solve?

• Complex logic and dynamic configs. Terraform with HCL struggles with generating resources based on external data. For example, creating 20 S3 buckets with different policies from a CSV file. In HCL — a tedious loop with count; in Pulumi on TypeScript — a single map with forEach, reducing code by 5x.
• Code reuse. In HCL — modules; in Pulumi — npm packages. You extract common patterns (VPC, cluster, service) into a library and import it as a regular dependency. This reduces duplication and speeds up development by 30%.
• Debugging and testing. Pulumi uses the same tools as development: TypeScript compiler, linter, unit tests. Type errors are caught at compile time, not in production. This reduces debugging time by 30% and lowers maintenance costs.

Why Pulumi is Better Than Terraform?

Pulumi is 3x faster to develop thanks to familiar languages. You use the TypeScript compiler and Jest for testing, not just terraform plan. Type errors are caught at compile time, not in production. Pulumi is also cheaper to operate: no additional tools needed for state management. As stated in the Pulumi documentation: "Pulumi uses familiar programming languages to define and manage cloud resources."

How We Do It: A Real Case

Project: a web application on AWS with RDS PostgreSQL 16, ECS Fargate, and CI/CD via GitHub Actions. Previously, 4 developers maintained Terraform code; each change took 2 days. We migrated to Pulumi in a week, reducing infrastructure management costs by $15,000 per year. The stack consisted of 3 ECS services, 2 application load balancers, 4 security groups, and 2 parameter store secrets.

Stack: TypeScript, Pulumi v3, AWS provider v6, PostgreSQL 16, ECS with Fargate. We used 2 Availability Zones, 100 GB SSD gp3, 256 CPU units, 512 MB memory, 3 tasks per service.

import * as aws from "@pulumi/aws";
import * as awsx from "@pulumi/awsx";
import * as pulumi from "@pulumi/pulumi";

const config = new pulumi.Config();
const dbPassword = config.requireSecret("dbPassword");

const vpc = new awsx.ec2.Vpc("myapp-vpc", {
    numberOfAvailabilityZones: 2,
    enableDnsHostnames: true,
});

const cluster = new aws.ecs.Cluster("myapp-cluster", {
    settings: [{ name: "containerInsights", value: "enabled" }],
});

const db = new aws.rds.Instance("myapp-db", {
    engine: "postgres",
    engineVersion: "16.1",
    instanceClass: aws.rds.InstanceType.T3_Medium,
    allocatedStorage: 100,
    dbName: "myapp",
    username: "myapp",
    password: dbPassword,
    skipFinalSnapshot: !pulumi.getStack().startsWith("prod"),
    vpcSecurityGroupIds: [dbSg.id],
    dbSubnetGroupName: dbSubnetGroup.name,
    storageEncrypted: true,
});

const service = new awsx.ecs.FargateService("myapp-web", {
    cluster: cluster.arn,
    taskDefinitionArgs: {
        container: {
            name: "web",
            image: "registry.example.com/myapp:latest",
            cpu: 256,
            memory: 512,
            essential: true,
            portMappings: [{ containerPort: 8080 }],
            environment: [
                { name: "APP_ENV", value: "production" },
                { name: "DB_HOST", value: db.endpoint },
            ],
            secrets: [
                { name: "DB_PASSWORD", valueFrom: dbPasswordSecret.arn },
            ],
        },
    },
    desiredCount: 3,
    loadBalancers: [{
        targetGroupArn: targetGroup.arn,
        containerName: "web",
        containerPort: 8080,
    }],
});

export const dbEndpoint = db.endpoint;
export const serviceUrl = pulumi.interpolate`https://${loadBalancer.dnsName}`;

After migration, deployment speed increased by 40%, errors halved, and a new developer got up to speed in a day.

Work Process

1. Analysis — audit of current infrastructure, choosing a provider (AWS, GCP, Azure).
2. Design — stack architecture (dev/staging/prod), CI/CD integration.
3. Implementation — writing code in TypeScript/Python, stack configuration.
4. Testing — pulumi preview + unit tests in TypeScript.
5. Deployment — connect to CI/CD (GitHub Actions, GitLab CI) and canary release.

Tip: use Pulumi Cloud for state—it provides locking and change history. This prevents conflicts during parallel deployments.

What's Included

Timeline Estimates

• Typical stack (AWS + PostgreSQL + ECS) — 5-7 days.
• Complex multi-region architecture — up to 14 days.
• Price is calculated individually after an audit. Typical savings: $10,000–$20,000 annually in developer time. Our service costs $3,500 for a basic three-stack setup.

How to Automate Deployment with Pulumi?

We connect Pulumi to your CI/CD in 1 day. Example for GitHub Actions:

- name: Pulumi Deploy
  uses: pulumi/actions@v4
  with:
    command: up
    stack-name: production
    cloud-url: https://api.pulumi.com
  env:
    PULUMI_ACCESS_TOKEN: ${{ secrets.PULUMI_ACCESS_TOKEN }}
    AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
    AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}

At the same time, we configure Pulumi Cloud for state storage and secret rotation.

Comparison: Pulumi vs Terraform

Common Mistakes When Working with Pulumi

• Lack of state lock — if two developers run pulumi up simultaneously, state corruption is possible. Solution: use a backend with locking (S3 + DynamoDB).
• Hardcoded stack dependency — do not use absolute resource names. Always apply pulumi.getStack() and config.require.
• Ignoring preview — pulumi preview saves hours of debugging. Always check the diff before deployment.

Our engineers have over 5 years of IaC experience; we've implemented Pulumi for 10+ projects. We guarantee fault tolerance and compliance with best practices. For more details, see the official Pulumi documentation.

Get a Pulumi setup: we'll assess your project in 1 day, just write to us. Receive a consultation on Pulumi implementation. Contact us for an audit.

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.