Rightsizing (subchapter 25.3) saves by adjusting the size of resources. Now we’ll look at another very powerful savings lever, but through a different path: getting significant discounts (up to 70%) in exchange for committing to use AWS for a period of time. The two ways to do this are Savings Plans and Reserved Instances. Understanding the difference can save you a lot of money.
The idea: discount in exchange for commitment
By default, in AWS you pay on-demand: you use a resource for an hour, you pay for that hour, no strings attached. It’s flexible, but it’s the most expensive option. AWS offers you a deal: if you commit to using a certain amount of resources for 1 or 3 years, they give you a big discount (up to 70%).
On-demand payment: €100 (flexible, no commitment, more expensive)
With commitment: €30-50 for the same thing (you commit for 1-3 years)
└─ savings of up to 70% ─┘Analogy: it’s like a public transport pass vs. single tickets. The single ticket (on-demand) you buy whenever you want, no commitment, but it’s expensive if you travel a lot. The monthly or annual pass (the commitment) requires you to pay upfront for a period, but the price per trip is much cheaper. If you know you’ll use transport consistently, the pass saves you a lot. AWS works the same way: if you know you’ll use resources continuously, committing is very worthwhile.
This makes sense for AWS (you guarantee them stable usage, they can plan) and for you (you pay much less for what you were going to use anyway).
When it makes sense to commit
The key is predictability. Commitment is worthwhile for the part of your usage that is stable and constant, that “base” you know you’ll need no matter what for months or years:
Usage over time: ┌──────────────────────────────┐ │ variable peaks │ ← this, on-demand (flexible) │ ╱╲ ╱╲ ╱╲ ╱╲ │ │──────────────────────────── │ ← this stable "base", │ constant base usage │ with commitment (discount) └──────────────────────────────┘
The smart strategy: cover your base usage (what you always need) with commitments (discount), and leave the variable peaks on-demand (flexibility). This way you combine savings and flexibility.
⚠️ The risk of commitment: if you commit to using a certain amount and then don’t use it (because your project shrank or changed), you pay anyway (you already committed). That’s why you should only commit the part you’re sure you’ll use. For the uncertain part, better to use on-demand.
Reserved Instances (the classic, more specific option)
Reserved Instances (RI) are the classic way to commit, and they are specific: you reserve a specific type of resource (for example, “a type X server in region Y”) for 1 or 3 years, in exchange for the discount. Since they are specific, they are somewhat rigid: the discount applies to that specific type.
Reserved Instance: "I commit to a 'medium' type server
in Europe for 3 years" → big discount on THAT resourceSavings Plans (more modern and flexible)
Savings Plans are the more modern and flexible option. Instead of committing to a specific type of resource, you commit to spending a certain amount of money per hour (for example, “I commit to spending at least €10 per hour on compute”) for 1 or 3 years, and you get the discount. The big advantage: that commitment is applied flexibly to whatever you use (different server types, different regions, even Lambda or Fargate, depending on the plan).
Savings Plan: "I commit to spending €10/hour on compute for 1 year"
→ the discount is applied AUTOMATICALLY to whatever you use,
even if you change server type or regionAnalogy: a Reserved Instance is like a pass for a specific subway line (it only works for that line). A Savings Plan is like a transport pass that works for subway, bus, and train interchangeably: you commit to a spend, but you use it for whatever you need. Much more flexible if your needs change.
Comparison
| Reserved Instances | Savings Plans | |
|---|---|---|
| You commit to | A specific type of resource | A spend amount per hour |
| Flexibility | More rigid (specific resource) | More flexible (applies to whatever you use) |
| Seniority | Classic form | More modern form (recommended) |
| Discount | Up to ~70% | Up to ~70% |
| Commitment | 1 or 3 years | 1 or 3 years |
General recommendation: for most cases, Savings Plans are the preferred option today due to their flexibility (you get a similar discount but adapt better to changes). Reserved Instances still make sense in very specific and stable cases.
Real world example: a company has been on AWS for a while and sees, with Cost Explorer, that it has a very stable base usage of compute (its production applications run 24/7 all year). Until now they paid everything on-demand. They purchase a 1-year Savings Plan that covers that base usage, committing to an hourly spend they know they’ll have anyway. Result: a 55% discount on that part of their bill, without changing anything in their infrastructure. And since they chose a Savings Plan (not RI), when they later migrated some servers to other types, the discount kept applying automatically. They left the occasional peaks on-demand to maintain flexibility.
How this fits with the rest of the cost strategy
Rightsizing (25.3) → adjusts the SIZE (don’t pay for excess capacity)
Savings Plans / RI (this) → DISCOUNT for committing your stable base usage
│
▼
Combine them: first adjust the size (rightsizing),
THEN buy commitments on the correct size💡 Important order: do rightsizing first and then buy Savings Plans. If you commit to oversized resources, you’ll be “locking in” waste for years. Adjust the size first, and only commit to what you really need.
What you should remember
- By default you pay on-demand (flexible but more expensive). AWS offers big discounts (up to ~70%) in exchange for committing to use resources for 1 or 3 years. Like a transport pass vs. single tickets.
- It’s worthwhile for your stable and predictable base usage; leave the variable peaks on-demand. ⚠️ If you commit and don’t use it, you pay anyway: only commit what’s certain.
- Reserved Instances (RI): classic form, you commit to a specific type of resource. More rigid.
- Savings Plans: modern and flexible form, you commit to a spend amount per hour that is automatically applied to whatever you use (different types, regions, Fargate/Lambda). Recommended in general for their flexibility.
- 💡 Do rightsizing first and then buy commitments, so you don’t “lock in” oversized resources.
In the last subchapter of the chapter we’ll look at the discipline that encompasses all this cost management as a team practice: FinOps.
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
