The same EC2 instance can cost you very different prices depending on how you buy it. Choosing the right purchase model can save you up to 90%. This subsection explains the four main options—On-Demand, Reserved Instances, Savings Plans, and Spot—and when to use each. This is one of the pieces of knowledge that saves the most money in real life.
The central idea: commitment in exchange for a discount
AWS rewards you for committing. The more you commit (to using for a long time, or to accepting interruptions), the bigger the discount you get. The more flexibility you want, the more you pay.
More flexibility ◄──────────────────────────► More discount
On-Demand Savings Plans / Reserved Spot
(expensive, (medium, you commit (super cheap,
no strings to use for 1-3 years) but interruptible)
attached)On-Demand: pay for what you use, no commitment
This is the default model: you pay per second or hour of use, with no commitment. You turn it on when you want, turn it off when you want.
- Advantage: maximum flexibility, zero commitment.
- Drawback: it is the most expensive per hour.
When to use it:
- Unpredictable or short-term workloads.
- Testing, development, and experiments.
- When you are starting out and don’t know your usage pattern yet.
- As a base while you decide whether to commit.
Example: You’re learning AWS and spin up instances for a while to practice. On-Demand is the right choice: you don’t want to commit to anything.
Reserved Instances (RI): commit for 1 or 3 years
You commit to using a specific instance type for 1 or 3 years in exchange for a discount of up to ~72% compared to On-Demand.
- Advantage: great savings for stable workloads.
- Drawback: you’re tied to an instance type and a term. Less flexible.
When to use them: constant and predictable workloads that you know will be there for years (for example, a database that is always on).
Note: Reserved Instances are the “classic” model. Today, for most cases, AWS recommends Savings Plans (see below) because they are more flexible with similar savings.
Savings Plans: flexible discount for committing spend
Savings Plans are the evolution of Reserved Instances. Instead of committing to a type of instance, you commit to spending a fixed amount per hour (for example, “at least €10/hour on compute”) for 1 or 3 years. In return, that usage is billed at a big discount (up to ~72%).
- Advantage: savings similar to RI, but much more flexible: the discount applies even if you change instance type, size, or even region (depending on the plan). Some plans also cover Lambda and Fargate.
- Drawback: you commit to a minimum spend during the term, whether you use it or not.
When to use them: stable workloads where you want to save but keep flexibility to change instances over time. This is the recommended option today for most constant compute bases.
We’ll look at the strategic decision between Savings Plans and Reserved Instances in detail in Chapter 25 (cost optimization).
Spot Instances: super cheap, but interruptible
Spot Instances use AWS’s spare capacity and sell it at discounts of up to 90%. The catch: AWS can take them back at any time (with about 2 minutes’ notice) if it needs that capacity.
- Advantage: lowest price by far.
- Drawback: they can be interrupted without real warning. Not suitable for things that can’t tolerate interruptions.
When to use them:
- Jobs that can be resumed if interrupted (batch processing, rendering, data analysis).
- Fault-tolerant systems that spread work across many instances and can handle losing some.
- Don’t use them for a critical database or a server that can’t go down.
Real example: A company processes thousands of videos overnight. It uses Spot Instances: if AWS takes some away, the work simply continues on others or is retried. They pay a fraction of the normal price for a job that isn’t urgent and doesn’t need to be always available.
Comparative table
| Model | Discount | Commitment | Can be interrupted | Ideal case |
|---|---|---|---|---|
| On-Demand | 0% | None | No | Testing, unpredictable workloads |
| Reserved Instances | Up to ~72% | 1-3 years, fixed type | No | Stable and predictable workload |
| Savings Plans | Up to ~72% | 1-3 years, spend/hour | No | Stable workload with flexibility |
| Spot | Up to ~90% | None | Yes | Interruption-tolerant jobs |
Real strategy: combine them
In practice, companies mix the models to optimize the bill:
Example of a combined strategy:
- Savings Plans for the base capacity that is always on (base web servers, databases).
- On-Demand to absorb occasional traffic spikes.
- Spot for batch jobs that aren’t urgent (overnight processing).
This way they cover their minimum load cheaply and with commitment, scale flexibly, and minimize costs for what can be interrupted.
What you should remember
- The price of EC2 depends on how you buy: more commitment = more discount.
- On-Demand: no commitment, most expensive. For testing and unpredictable workloads.
- Reserved Instances: commit to type + term (1-3 years), high savings. The classic model.
- Savings Plans: commit to spend per hour (1-3 years), high savings and flexibility. Recommended today for stable workloads.
- Spot: up to 90% discount, but interruptible. Only for interruption-tolerant jobs.
- In real life, all four are combined to minimize the bill.
With this, you finish Chapter 4 and master compute in AWS. In Chapter 5 we’ll look at the other major pillar: object storage with S3.
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
