Not all files are used the same way: some you access daily, others almost never, but you still need to keep them. It would be absurd to pay the same for both. That's why S3 offers several storage classes: different "levels" with varying costs and access speeds. Choosing the right class can drastically reduce your storage bill.
The central idea: cost vs. access frequency
The rule is simple:
The less you access a file, the cheaper it can be to store it… but retrieving it costs more time or money.
Frequent access ◄──────────────────────────► Almost never accessed S3 Standard S3 Standard-IA Glacier (archive) (expensive to (medium) (super cheap to store, store, free slow/expensive to recover) to read)
Analogy: Think about your belongings.
- What you use daily is on your desk (instant access, but desk space is "expensive"). → Standard.
- What you use occasionally you keep in a drawer (a bit more effort). → IA.
- What you almost never use but must keep (old documents) you put in boxes in the storage room (super cheap, but takes time to retrieve). → Glacier.
The main classes
S3 Standard — everyday use
- For: frequently accessed data.
- Storage cost: the highest.
- Access: instant, no extra recovery cost.
- Typical use: files for an active website, app images, data that is read often.
S3 Standard-IA (Infrequent Access) — infrequent access
- For: data you need to have instantly available, but access only a few times a month.
- Storage cost: cheaper than Standard.
- But: you pay a small fee each time you retrieve an object.
- Typical use: recent backups, files you might suddenly need but not daily.
S3 One Zone-IA — cheaper, less resilient
- Same as Standard-IA but stores data in a single availability zone (instead of several).
- Cheaper, but if that AZ has a serious issue, you could lose the data.
- Typical use: data you can regenerate if lost (secondary backups, thumbnails you can recreate).
S3 Glacier — long-term archive
The Glacier family is for archiving data you almost never access but must keep (for example, for legal reasons). There are variants depending on how quickly you need to recover them:
| Variant | Recovery time | Cost |
|---|---|---|
| Glacier Instant Retrieval | Milliseconds (instant) | Cheap to store |
| Glacier Flexible Retrieval | Minutes to hours | Cheaper |
| Glacier Deep Archive | Hours (up to ~12 h) | The cheapest of all |
- Typical use: historical files, backups from years ago, records that must be kept 7-10 years by law.
Real example: A hospital must keep medical records for 15 years by law, but rarely accesses them. They store them in Glacier Deep Archive: they pay a minimal amount to store them and, if they ever need one, accept waiting a few hours to recover it. The savings compared to Standard are huge.
S3 Intelligent-Tiering — let AWS decide for you
If you don't know how often your data will be accessed, this class automatically moves each object to the cheapest tier based on usage. You do nothing; AWS optimizes the cost for you (in exchange for a small monitoring fee).
- Typical use: data with unpredictable or changing access patterns. It's a "safe and convenient" option when in doubt.
Summary table
| Class | Access | Storage cost | Recovery cost | Ideal for |
|---|---|---|---|---|
| Standard | Frequent, instant | High | Free | Active day-to-day data |
| Standard-IA | Infrequent, instant | Medium | Read fee | Recent backups |
| One Zone-IA | Infrequent, 1 AZ only | Lower | Read fee | Regenerable data |
| Glacier Instant | Archive, instant | Low | Fee | Archive you sometimes access |
| Glacier Flexible | Archive, minutes/hours | Very low | Fee | Historical archive |
| Glacier Deep Archive | Archive, hours | Lowest | Fee | Long-term legal retention |
| Intelligent-Tiering | Automatic | Variable | Automatic | Unpredictable access |
How classes are assigned
You can assign the class of an object when uploading it, or let them move automatically over time using lifecycle rules (we'll see this in subchapter 5.3). For example: "files move to IA after 30 days and to Glacier after 90". This way you optimize costs automatically without touching anything.
What you should remember
- S3 offers several storage classes that balance cost and speed/cost of access.
- Standard for day-to-day data; Standard-IA for infrequent access; Glacier for long-term archive (the deeper, the cheaper but slower to recover).
- One Zone-IA saves money at the cost of less resilience (regenerable data).
- Intelligent-Tiering lets AWS optimize cost automatically when you don't know the access pattern.
- Lifecycle rules move objects between classes automatically.
In the next subchapter we'll look at versioning (keeping multiple versions of an object) and lifecycle (automating class transitions and deletion), two key S3 features.
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
