AWS Infrastructure for Growing Startups: Choosing the Right Tools

You've outgrown Heroku. Your AWS bill is climbing. Someone mentions "Infrastructure as Code" and suddenly you're drowning in acronyms: Terraform, CloudFormation, CDK, Pulumi.

This guide cuts through the noise and helps you choose the right infrastructure tools for your stage—without over-engineering or under-investing.

The Real Question

The question isn't "which IaC tool is best?" It's "what level of infrastructure maturity do we need right now?"

Different stages require different approaches:

Stage	Typical AWS Spend	Infrastructure Approach
Pre-seed	£0-500/month	Managed platforms (Heroku, Railway)
Seed	£500-3k/month	Basic AWS with some automation
Series A	£3k-15k/month	Full IaC, multiple environments
Series B+	£15k+/month	Advanced automation, cost optimisation

Key insight: The infrastructure that works for a 3-person pre-seed team will fail a 15-person Series A team. The infrastructure that works for Series A would be over-engineering for pre-seed.

The Tool Landscape

Terraform

What it is: A tool that lets you define infrastructure in configuration files and apply them consistently.

Best for: Most startups post-seed who need flexibility and industry-standard tooling.

# Example: Creating an RDS database
resource "aws_db_instance" "main" {
  identifier        = "myapp-production"
  engine            = "postgres"
  engine_version    = "14"
  instance_class    = "db.t3.medium"
  allocated_storage = 100

  db_name  = "myapp"
  username = "admin"
  password = var.db_password

  backup_retention_period = 7
  multi_az               = true
}

Pros:

• Industry standard (investors recognise it)
• Huge community and ecosystem
• Works with any cloud provider
• Plenty of examples and documentation

Cons:

• Separate language to learn (HCL)
• State file management requires attention
• Initial learning curve

Verdict: The default choice for most startups. Well-documented, widely understood, and signals engineering maturity.

AWS CloudFormation

What it is: AWS's native infrastructure-as-code service.

Best for: Teams deeply committed to AWS who prefer native tooling.

# Example: Creating an RDS database
Resources:
  Database:
    Type: AWS::RDS::DBInstance
    Properties:
      DBInstanceIdentifier: myapp-production
      Engine: postgres
      EngineVersion: "14"
      DBInstanceClass: db.t3.medium
      AllocatedStorage: 100
      MasterUsername: admin
      MasterUserPassword: !Ref DBPassword
      MultiAZ: true

Pros:

• Native AWS integration
• No external state file to manage
• Built-in drift detection
• No additional tools to install

Cons:

• AWS-only (no flexibility for multi-cloud)
• More verbose syntax
• Smaller community than Terraform

Verdict: Good choice if you're 100% AWS and prefer native tooling. Less common in the startup ecosystem.

AWS CDK

What it is: Define infrastructure using familiar programming languages (TypeScript, Python, etc.).

Best for: Teams with strong developers who prefer code over configuration files.

// Example: Creating an RDS database
import * as rds from 'aws-cdk-lib/aws-rds';
import * as ec2 from 'aws-cdk-lib/aws-ec2';

const database = new rds.DatabaseInstance(this, 'Database', {
  engine: rds.DatabaseInstanceEngine.postgres({
    version: rds.PostgresEngineVersion.VER_14,
  }),
  instanceType: ec2.InstanceType.of(
    ec2.InstanceClass.T3,
    ec2.InstanceSize.MEDIUM,
  ),
  vpc: vpc,
  multiAz: true,
});

Pros:

• Use languages your team knows
• Full programming capabilities (loops, conditionals)
• Strong type checking and IDE support
• High-level abstractions reduce boilerplate

Cons:

• AWS-only
• Can be over-engineered ("just because you can doesn't mean you should")
• Steeper learning curve for infrastructure concepts

Verdict: Good for TypeScript/Python-heavy teams. Can lead to over-engineering if not disciplined.

Pulumi

What it is: Like CDK but works with any cloud provider.

Best for: Multi-cloud teams or those who want CDK-style experience with flexibility.

Pros:

• Use familiar programming languages
• Works with AWS, GCP, Azure, etc.
• Growing community

Cons:

• Smaller ecosystem than Terraform
• Less documentation and examples
• Less familiar to most engineers

Verdict: Consider if you need multi-cloud and prefer code over configuration. Otherwise, Terraform is usually the safer choice.

Our Recommendation by Stage

Pre-Seed: Don't Use IaC Yet

At this stage, you need to ship product, not manage infrastructure.

Approach:

• Use managed platforms (Heroku, Railway, Render)
• Or use AWS console for simple setups
• Document what you create (a simple spreadsheet works)

Why: Every hour spent on infrastructure is an hour not spent on product. Your infrastructure needs will change dramatically as you find product-market fit.

Seed: Start Simple

Once you have paying customers and basic traction:

Approach:

• Learn Terraform basics
• Codify your core infrastructure (database, cache, storage)
• Set up two environments: staging and production
• Use managed services (RDS, ElastiCache) over self-managed

Example structure:

infrastructure/
├── main.tf           # Provider and backend config
├── database.tf       # RDS configuration
├── networking.tf     # VPC, subnets, security groups
├── staging.tfvars    # Staging-specific values
└── production.tfvars # Production-specific values

Why: This gives you reproducibility and disaster recovery without massive investment.

Series A: Full IaC

With more engineers and complexity:

Approach:

• Everything in Terraform (or your chosen tool)
• Automated deployment via CI/CD
• Multiple environments (dev, staging, production)
• Module structure for reusability
• Remote state with locking

Example structure:

infrastructure/
├── modules/
│   ├── vpc/          # Reusable VPC module
│   ├── database/     # Reusable RDS module
│   └── application/  # Application infrastructure
├── environments/
│   ├── dev/
│   │   ├── main.tf
│   │   └── terraform.tfvars
│   ├── staging/
│   └── production/
└── README.md

Why: At this scale, manual infrastructure management becomes a bottleneck. IaC enables team growth and operational reliability.

Common Mistakes to Avoid

1. Over-Engineering Early

Mistake: Building complex module hierarchies and abstractions for a 2-person team.

Reality: Simple, readable Terraform is better than clever Terraform. You can refactor later.

Bad:

module "super_flexible_database" {
  source = "./modules/database"

  # 47 configurable parameters...
  enable_read_replicas = false
  cross_region_backup_enabled = false
  parameter_group_family = "postgres14"
  # ... etc
}

Good:

resource "aws_db_instance" "main" {
  identifier        = "myapp-production"
  engine            = "postgres"
  engine_version    = "14"
  instance_class    = "db.t3.medium"
  allocated_storage = 100

  # Clear, readable, direct
}

2. Ignoring State Management

Mistake: Storing Terraform state locally or in Git.

Reality: Multiple people running Terraform with local state will corrupt your infrastructure.

Fix: Use remote state from day one:

terraform {
  backend "s3" {
    bucket         = "mycompany-terraform-state"
    key            = "production/terraform.tfstate"
    region         = "eu-west-2"
    encrypt        = true
    dynamodb_table = "terraform-locks"  # Prevents concurrent runs
  }
}

3. Storing Secrets in Code

Mistake: Committing database passwords or API keys to your Terraform files.

Reality: Once a secret is in Git, it's compromised forever.

Fix: Use AWS Secrets Manager or Parameter Store:

data "aws_secretsmanager_secret_version" "db_password" {
  secret_id = "myapp/production/db-password"
}

resource "aws_db_instance" "main" {
  # ...
  password = data.aws_secretsmanager_secret_version.db_password.secret_string
}

4. No CI/CD Integration

Mistake: Running terraform apply manually from laptops.

Reality: Manual applies lead to inconsistent environments and no audit trail.

Fix: Integrate with your CI/CD pipeline:

# .github/workflows/terraform.yaml
name: Terraform

on:
  pull_request:
    paths: ['infrastructure/**']
  push:
    branches: [main]
    paths: ['infrastructure/**']

jobs:
  plan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Terraform Plan
        run: terraform plan -out=plan.tfplan
        working-directory: infrastructure/production

  apply:
    if: github.ref == 'refs/heads/main'
    needs: plan
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Terraform Apply
        run: terraform apply -auto-approve plan.tfplan
        working-directory: infrastructure/production

What Investors Look For

Technical due diligence typically examines:

Area	Good Signal	Red Flag
Infrastructure definition	"Everything is in Terraform"	"Our CTO set it up manually"
Disaster recovery	"We can recreate our environment in 2 hours"	"It would take a while to figure out"
Change management	"Pull request, code review, automated apply"	"We change things in the console"
Access control	"Only two senior engineers have admin access"	"Everyone shares the AWS root credentials"
Cost visibility	"We track spend by service and environment"	"We get surprised by AWS bills"

Getting Started Today

If you don't have IaC yet:

1. Week 1: Install Terraform, complete the official tutorial
2. Week 2: Write Terraform for your database (the most critical resource)
3. Week 3: Add your VPC and security groups
4. Week 4: Set up remote state and basic CI/CD

You don't need to convert everything overnight. Start with critical resources and expand from there.

Summary

The right infrastructure tooling depends on your stage:

Pre-seed: Use managed platforms. Don't invest in IaC yet.

Seed: Learn Terraform, codify critical resources, use managed AWS services.

Series A+: Full IaC with CI/CD, multiple environments, proper access controls.

The goal isn't to have the most sophisticated infrastructure—it's to have infrastructure that matches your needs today while positioning you for growth tomorrow.

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Need help setting up your AWS infrastructure or preparing for technical due diligence? Get in touch for a consultation.