AWS for DevOps

AWS for DevOps #

AWS is a strong fit for teams that need broad service coverage, mature enterprise governance patterns, and flexible compute choices from serverless to Kubernetes.

Overview #

Typical AWS DevOps stacks combine:

  • Identity and governance with IAM, Organizations, and SCPs.
  • Compute platforms including Lambda, ECS, EKS, and EC2.
  • Delivery automation with CodePipeline/CodeBuild or GitHub Actions/GitLab CI.
  • Operations with CloudWatch, X-Ray, CloudTrail, and Config.

When to use AWS / decision criteria #

Choose AWS when you need:

  • Deep service breadth for heterogeneous workloads.
  • Mature multi-account governance patterns.
  • Native managed options for event-driven and containerized architectures.

Tradeoffs to plan for:

  • Service sprawl can increase operational complexity.
  • IAM policy design requires discipline for least privilege at scale.
  • Multi-account networking and shared services require clear standards.

Architecture patterns #

1) Multi-account landing zone #

  • Separate production, non-production, and shared services accounts.
  • Use Organizations + SCPs for baseline guardrails.
  • Centralize logs and security findings in dedicated accounts.

2) Kubernetes platform (EKS) #

  • Use environment-specific clusters for risk isolation.
  • Use IAM Roles for Service Accounts (IRSA) for workload auth.
  • Standardize add-ons: ingress, autoscaling, observability, policy enforcement.

3) Serverless services (Lambda) #

  • Trigger from API Gateway, SQS/SNS, or EventBridge.
  • Keep functions small and event-focused.
  • Use reserved concurrency and alarms to control blast radius.

Security and cost guardrails #

Security baseline #

  • Enforce MFA and short-lived federated access.
  • Block root account usage except break-glass.
  • Enable CloudTrail organization-wide and protect log buckets.
  • Store secrets in AWS Secrets Manager or SSM Parameter Store.

Cost baseline #

  • Tag resources by owner, service, and environment.
  • Set budgets and anomaly alerts per account.
  • Right-size compute and use autoscaling defaults.
  • Use Savings Plans/Reserved Instances for steady workloads.

Implementation examples #

Example Terraform bootstrap snippet # 

provider "aws" {
  region = var.region
}

resource "aws_s3_bucket" "tf_state" {
  bucket = "${var.org_name}-${var.env}-tf-state"

  tags = {
    Owner       = var.owner
    Environment = var.env
    Service     = "platform"
  }
}

resource "aws_s3_bucket_versioning" "tf_state" {
  bucket = aws_s3_bucket.tf_state.id

  versioning_configuration {
    status = "Enabled"
  }
}

Example CI/CD flow #

  1. Pull request runs tests and security scans.
  2. Build and push artifact (container/image/package).
  3. Deploy to staging via automated pipeline.
  4. Run smoke checks and then promote to production.
  5. Emit deployment events and monitor SLO indicators.

Example Terraform baseline #

  • Account vending and baseline IAM roles.
  • CloudTrail, Config, guardrails, and centralized logging.
  • Reusable VPC and network modules.

Migration/adoption path #

  1. Establish a multi-account landing zone before first production workloads.
  2. Move identity federation and short-lived credentials into CI/CD pipelines.
  3. Migrate one service at a time, starting with stateless services on ECS/EKS/Lambda.
  4. Add mandatory guardrails (SCPs, Config, budgets) before scaling account count.
  5. Define an SLO + incident runbook baseline before onboarding critical services.

Pitfalls / anti-patterns #

  • Single AWS account for every environment.
  • Long-lived access keys in CI/CD systems.
  • Unbounded IAM wildcard permissions.
  • Missing tagging strategy and cost ownership.

References #