We begin Chapter 25: Cost Optimization, a topic that is critical in the cloud and that many discover the hard way: an unexpected bill. In the cloud, you pay for what you use (remember the pay-as-you-go model from subchapter 1.3), which is great... but it also means costs can skyrocket if you don't keep them in check. The good news is that AWS offers tools to see, understand, and control what you spend. We'll start with the two fundamental ones: Cost Explorer (to see) and Budgets (to alert).
The problem: the surprise bill
In the cloud, you don't buy servers in advance; you pay for usage, hour by hour. This is flexible, but dangerous if you don't keep an eye on things:
- You leave a large server running unused → it keeps costing you.
- A service scales more than expected → the bill goes up.
- You forget to delete resources from a test → they keep charging you silently.
- An error or attack spikes consumption → huge bill.
The beginner's terror: real stories of people who left something misconfigured and received a bill for thousands of euros at the end of the month. Not out of bad luck, but for not monitoring costs. This is easily avoided with the tools we'll see.
The key is not to operate blindly with money, just as you wouldn't operate blindly with performance (Chapter 24). You need visibility and alerts.
Tool 1: Cost Explorer (see and understand spending)
AWS Cost Explorer is the tool to visualize and analyze what you spend on AWS. It shows your costs in graphs and lets you break them down in many ways to understand where the money is going:
Monthly cost by service: EC2 (servers) ████████████ 450 € RDS (databases) ██████ 220 € S3 (storage) ██ 80 € Others █ 50 € ────────────────────────────────────── TOTAL 800 €
With Cost Explorer you can break down spending by service (what costs me the most?), by tag (how much does each project or team spend? — remember the tags from subchapter 6.x), by region, and by time (how is my spending evolving? Is it going up?).
Analogy: Cost Explorer is like the bank app that categorizes your expenses: it shows you how much you spent on food, transport, entertainment... over the months. Without that app, you'd only see the total at the end and wouldn't know where your money went. With it, you understand your expenses and spot where to cut back.
Cost Explorer is your starting point: before optimizing, you need to know what you're spending on.
Tool 2: Budgets (budgets with alerts)
Seeing your spending is good, but it's reactive (you look at what you've already spent). To be proactive and avoid surprises, you use AWS Budgets: you set a budget (a spending limit) and AWS automatically notifies you when you approach or exceed it.
Budget: "I don't want to spend more than €1,000 a month" → when reaching 80% (€800): ⚠️ ALERT "you're at 80%" → when reaching 100% (€1,000): 🚨 ALERT "you've reached the limit" → if you're projected to exceed it: 📈 ALERT "at this rate you'll go over"
Alerts arrive by email, Slack, etc. (just like CloudWatch alarms from subchapter 24.1, but for money instead of performance). This makes the surprise bill impossible: you find out while it's happening, not at the end of the month.
Analogy: Budgets is like setting an alert on your bank account that notifies you when you're about to spend more than you planned this month. Instead of discovering you're overdrawn when it's too late, you get a timely alert to stop. It gives you control before the problem grows.
How they work together
COST EXPLORER → what am I spending on? (understand, analyze) [reactive] BUDGETS → alert me if I go over the limit (control) [proactive]
Cost Explorer gives you knowledge (where the money goes) and Budgets gives you control (alerts so you don't overspend). Together, they are the foundation of cost management.
Real-world example: a startup sets up, as soon as they start on AWS, a Budget of €500 per month with alerts at 50%, 80%, and 100%. One month, a developer accidentally leaves a very large instance running over a weekend. On Monday, the team receives the Budgets alert: "you're at 80% of the budget and it's only the 10th." They investigate with Cost Explorer, see that EC2 spending has spiked, find the forgotten instance, and shut it down. They avoided a massive bill thanks to a timely alert. Without Budgets, they would have discovered it at the end of the month, with the damage already done.
Basic cost best practices
- Set up a Budget from day one, even if it's small. It's the first thing anyone starting on AWS should do.
- Tag your resources (by project, team, environment) to break down costs in Cost Explorer.
- Review Cost Explorer regularly to spot trends and unusual spending.
- Delete what you don't use (remember
terraform destroyfor temporary environments, subchapter 11.4).
What you should remember
- In the cloud you pay for usage, so costs can skyrocket if you don't monitor them; the dreaded surprise bill is easily avoided with the right tools.
- Cost Explorer visualizes and analyzes your spending: it breaks it down by service, tag, region, and time to understand where the money goes. Like the bank app that categorizes your expenses. It's reactive (see what you've spent).
- Budgets lets you set budgets (limits) and automatically alerts you when you approach or exceed them (even if you're projected to go over). Like a bank alert. It's proactive (control before you overspend).
- Together: Cost Explorer provides knowledge, Budgets provides control. They are the foundation of cost management.
- Best practices: set up a Budget from day one, tag resources, review Cost Explorer often, and delete what you don't use.
In the next subchapter, we'll look at tools that go beyond just showing spending: they actively recommend how to save and improve, with Trusted Advisor and Compute Optimizer.
Cloud, AWS & Terraform — From Zero to Expert
Chapter 1 · What is cloud computing
- 1.1 The traditional client-server model
- 1.2 Problems the cloud came to solve
- 1.3 On-premise vs cloud vs hybrid
- 1.4 The three service models: IaaS, PaaS, SaaS
- 1.5 The five pillars of cloud (according to NIST)
- 1.6 Real advantages: elasticity, pay-as-you-go, global availability
Chapter 2 · The cloud market and major providers
- 2.1 AWS, Azure and GCP: differences and market share
- 2.2 Why learn AWS first
- 2.3 Concepts that are universal among providers
Chapter 3 · Regions, availability zones and edge
- 3.1 What is an AWS region and how to choose it
- 3.2 Availability Zones: high availability by design
- 3.3 Edge locations and CloudFront
- 3.4 Latency, resilience and data sovereignty
Chapter 4 · Compute: EC2
- 4.1 Instances: types, families and when to choose each
- 4.2 AMIs, key pairs and Security Groups
- 4.3 Instance lifecycle
- 4.4 Elastic IPs and Placement Groups
- 4.5 Savings Plans vs Reserved vs On-Demand vs Spot
Chapter 5 · Storage: S3
- 5.1 Buckets, objects and keys
- 5.2 Storage classes (Standard, IA, Glacier…)
- 5.3 Versioning and object lifecycle
- 5.4 Bucket policies and ACLs
- 5.5 Static website hosting
Chapter 6 · Networking: VPC
- 6.1 What is a VPC and why you need it
- 6.2 Public and private subnets
- 6.3 Internet Gateway and NAT Gateway
- 6.4 Route Tables and Network ACLs
- 6.5 VPC Peering and endpoints
Chapter 7 · Identity and access: IAM
- 7.1 Users, groups, roles and policies
- 7.2 The principle of least privilege
- 7.3 Identity-based vs resource-based policies
- 7.4 MFA and temporary credentials (STS)
- 7.5 IAM security best practices
Chapter 8 · Managed databases
- 8.1 RDS: engines, Multi-AZ and read replicas
- 8.2 Aurora and its advantages over vanilla RDS
- 8.3 DynamoDB: key-value / document model
- 8.4 ElastiCache for in-memory cache
- 8.5 When to use each type of database
Chapter 9 · Why Infrastructure as Code
- 9.1 Problems with manual provisioning
- 9.2 Declarative vs imperative IaC
- 9.3 Terraform vs CloudFormation vs Pulumi vs CDK
- 9.4 The plan → apply → destroy cycle
Chapter 10 · HCL: the Terraform language
- 10.1 Resource, variable, output, locals blocks
- 10.2 Data types: string, number, bool, list, map, object
- 10.3 Expressions, references and built-in functions
- 10.4 Conditionals and loops (count, for_each, for)
Chapter 11 · Providers and state
- 11.1 How the AWS provider works
- 11.2 The terraform.tfstate file and its importance
- 11.3 Local state vs remote state (S3 + DynamoDB)
- 11.4 Essential commands: init, plan, apply, destroy, fmt, validate
Chapter 12 · Your first real infrastructure in Terraform
- 12.1 Create a VPC with subnets from scratch
- 12.2 Launch a public EC2 instance
- 12.3 Associate a Security Group and an Elastic IP
- 12.4 Outputs and references between resources
- 12.5 Team workflow: PR review of plans
Chapter 13 · Load balancing and auto scaling
- 13.1 Application Load Balancer vs Network Load Balancer
- 13.2 Target Groups, listeners and rules
- 13.3 Auto Scaling Groups: policies and metrics
- 13.4 Warm pools and lifecycle hooks
Chapter 14 · Serverless with Lambda
- 14.1 The Lambda execution model
- 14.2 Triggers: API Gateway, S3, DynamoDB Streams, SQS
- 14.3 Dependency management and layers
- 14.4 Cold starts and strategies to reduce them
- 14.5 Limits and anti-patterns
Chapter 15 · Messaging and events
- 15.1 SQS: standard vs FIFO queues, DLQ
- 15.2 SNS: topics, subscriptions, fan-out
- 15.3 EventBridge: event buses and rules
- 15.4 Patterns: pub/sub, decoupling, saga
Chapter 16 · Content delivery and DNS
- 16.1 Route 53: record types and routing policies
- 16.2 CloudFront: distributions, caches and origins
- 16.3 ACM: free SSL/TLS certificates
- 16.4 WAF integrated with CloudFront
Chapter 17 · Containers on AWS
- 17.1 Docker: quick review of key concepts
- 17.2 ECR: private image registry
- 17.3 ECS: task definitions, services, Fargate vs EC2
- 17.4 EKS: when Kubernetes and when not
Chapter 18 · Modules: reuse and composition
- 18.1 Anatomy of a Terraform module
- 18.2 Input variables, outputs and dependencies
- 18.3 Local modules vs Terraform Registry modules
- 18.4 Module versioning with Git tags
- 18.5 Design of generic vs domain-specific modules
Chapter 19 · Workspaces and environment management
- 19.1 Terraform workspaces: use cases and limitations
- 19.2 Directory strategy per environment (dev/stg/prod)
- 19.3 Terragrunt: DRY for environment configurations
- 19.4 Environment variables and .tfvars files
Chapter 20 · Remote backends and locking
- 20.1 Configure S3 + DynamoDB as backend
- 20.2 State locking: avoiding team corruption
- 20.3 State migration between backends
- 20.4 terraform import: bring existing resources into state
Chapter 21 · Infrastructure testing
- 21.1 Terraform validate and fmt in CI
- 21.2 Checkov and tfsec: static security analysis
- 21.3 Terratest: integration tests in Go
- 21.4 Contract testing between modules
Chapter 22 · Terraform in CI/CD
- 22.1 Basic pipeline: lint → plan → apply in GitHub Actions
- 22.2 Atlantis: GitOps for Terraform
- 22.3 Terraform Cloud / HCP Terraform
- 22.4 Drift detection and automatic reconciliation
Chapter 23 · Defense in depth
- 23.1 AWS Organizations and Service Control Policies
- 23.2 AWS Config: continuous compliance
- 23.3 GuardDuty: threat detection
- 23.4 Security Hub: centralized view
- 23.5 KMS: key management and rotation
- 23.6 Secrets Manager vs Parameter Store
Chapter 24 · Observability: logs, metrics and traces
- 24.1 CloudWatch Logs, metrics and alarms
- 24.2 CloudWatch Dashboards and Contributor Insights
- 24.3 X-Ray: distributed tracing
- 24.4 OpenTelemetry on AWS
- 24.5 Managed Grafana and Managed Prometheus
Chapter 25 · Cost optimization
- 25.1 AWS Cost Explorer and budgets with alerts
- 25.2 Trusted Advisor and Compute Optimizer
- 25.3 Rightsizing: how to detect overprovisioning
- 25.4 Savings Plans vs Reserved Instances: strategic decision
- 25.5 FinOps: culture and processes to control spending
Chapter 26 · High availability and disaster recovery
- 26.1 RTO and RPO: defining objectives
- 26.2 Strategies: backup/restore, pilot light, warm standby, multi-site
- 26.3 Route 53 health checks and automatic failover
- 26.4 AWS Backup: centralized backup policy
Chapter 27 · AWS Well-Architected Framework
- 27.1 The six pillars: operational excellence, security, reliability, performance efficiency, cost optimization, sustainability
- 27.2 Well-Architected Tool: formal reviews
- 27.3 How to apply the framework in design decisions
Chapter 28 · Serverless architectures at scale
- 28.1 Event-driven architecture with Lambda + EventBridge
- 28.2 Saga pattern for distributed transactions
- 28.3 Step Functions: orchestration of complex workflows
- 28.4 Lambda@Edge and CloudFront Functions
Chapter 29 · Data platforms on AWS
- 29.1 Data Lake with S3, Glue and Athena
- 29.2 Kinesis Data Streams and Firehose for streaming
- 29.3 Redshift: data warehousing at scale
- 29.4 Lake Formation: data governance
Chapter 30 · Multi-account and landing zones
- 30.1 Why separate workloads into different accounts
- 30.2 AWS Control Tower and Account Factory
- 30.3 Centralized log and security management
- 30.4 Terraform at multi-account scale with shared modules
Chapter 31 · Platform Engineering and Internal Developer Platform
- 31.1 Golden paths and abstractions over Terraform
- 31.2 AWS Service Catalog
- 31.3 Backstage as a developer portal
- 31.4 Terraform modules as internal product
Chapter 32 · Relevant AWS certifications
- 32.1 Cloud Practitioner: is it worth it?
- 32.2 Solutions Architect Associate → Professional
- 32.3 DevOps Engineer Professional
- 32.4 Specialty: Security, Database, Networking
- 32.5 HashiCorp Terraform Associate
Chapter 33 · Projects to consolidate what you've learned
- 33.1 Project 1: serverless blog (S3 + CloudFront + Lambda + DynamoDB)
- 33.2 Project 2: REST API with ECS Fargate + RDS + ALB
- 33.3 Project 3: data platform with Glue + Athena + Redshift
- 33.4 Project 4: multi-account landing zone with Terraform and Control Tower
