When you use the cloud, you’re not always renting “the same thing.” You can rent anything from a bare computer to a fully ready-to-use application. These levels are called service models, and there are three: IaaS, PaaS, and SaaS. Understanding them is key because they define how much work you do and how much the provider does.
The Pizza Analogy 🍕
The most famous (and useful) way to understand this is to think about how you can eat a pizza:
- Make it at home from scratch: you buy flour, tomato, cheese… and cook. Total control, lots of work. → On-premise.
- Buy a frozen pizza: it’s already made, you just use the oven. → IaaS.
- Order pizza delivery: it arrives ready, you set the table and drinks. → PaaS.
- Go to a restaurant: you sit and eat; you don’t do anything. → SaaS.
In all cases you eat pizza, but each time you delegate more work. Let’s look at each model in IT.
IaaS — Infrastructure as a Service
What you rent: the basic components. Virtual servers, storage, networks. The provider gives you the “virtual hardware” and you install and manage everything else: operating system, patches, your application, etc.
Analogy: you rent an empty apartment. The walls and floor are there; you bring the furniture.
You manage: operating system, software, configuration, your application. The provider manages: physical servers, virtualization, physical network, datacenter.
Examples in AWS:
- EC2 (virtual servers) — we’ll see this in Chapter 4.
- S3 (storage) — Chapter 5.
- VPC (networks) — Chapter 6.
When to use it: when you need control and flexibility, for example to migrate an existing application as-is or install very specific software.
PaaS — Platform as a Service
What you rent: a ready-to-use platform where you just upload your code. The provider takes care of the operating system, patches, scaling, and server. You only worry about your application and your data.
Analogy: you rent a furnished apartment. You arrive with your suitcase and move in.
You manage: your code and your data. The provider manages: everything below (OS, runtime, servers, scaling).
Examples in AWS:
- AWS Elastic Beanstalk (you upload your app and AWS deploys it).
- AWS Lambda (you upload a function, AWS runs it) — Chapter 14.
- RDS (managed database) — Chapter 8.
When to use it: when you want to focus on coding and not on managing servers. Ideal for small teams that want to move fast.
SaaS — Software as a Service
What you rent: the final, finished application. You don’t install or program anything: you just use it, usually from the browser.
Analogy: you go to a hotel. You don’t bring furniture, cook, or clean. You just enjoy.
You manage: nothing technical, just your data within the app. The provider manages: absolutely everything.
Examples you already use:
- Gmail, Office 365, Dropbox, Slack, Netflix, Salesforce.
When to use it: when you need a solution and don’t want to build anything. Most of the tools you use daily are SaaS.
The “Who Does What” Table
This table summarizes who is responsible for each layer. ✅ = you manage it; ☁️ = the provider manages it.
| Layer | On-premise | IaaS | PaaS | SaaS |
|---|---|---|---|---|
| Application | ✅ | ✅ | ✅ | ☁️ |
| Data | ✅ | ✅ | ✅ | ☁️ |
| Runtime / Middleware | ✅ | ✅ | ☁️ | ☁️ |
| Operating system | ✅ | ✅ | ☁️ | ☁️ |
| Virtualization | ✅ | ☁️ | ☁️ | ☁️ |
| Physical servers | ✅ | ☁️ | ☁️ | ☁️ |
| Network and datacenter | ✅ | ☁️ | ☁️ | ☁️ |
Notice the pattern: the further down the table you go towards SaaS, the fewer things you manage. That means less work, but also less control.
Which is better?
None is “better”: it depends on how much control you need versus how much work you want to save.
Real example: A company sets up its product like this:
- Uses Gmail (SaaS) for email, because they don’t want to maintain a mail server.
- Deploys their application with Lambda (PaaS) to avoid managing servers.
- But their video processing system, which needs special configuration, runs on EC2 (IaaS) for total control.
Mixing models according to each need is normal and recommended.
What you should remember
- IaaS = you rent the basic infrastructure; you manage the OS and above. (More control, more work.)
- PaaS = you rent a platform; you just upload your code. (Balance.)
- SaaS = you use a finished application. (Less work, less control.)
- The pizza analogy and the “who does what” table will help you place any service.
- It’s common to combine all three models as needed.
In the next subchapter we’ll look at the five pillars that formally define what the cloud is according to NIST, the reference organization.
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
