Every real application needs multiple environments: one for development to test, another for staging (pre-production) to validate, and production where the real users are. In this chapter, we’ll see how to manage those environments with Terraform. We’ll start with a tool that Terraform itself provides: workspaces. But beware, we’ll also see why they have important limitations.
Why You Need Multiple Environments
You should never test changes directly in production: it would be like experimenting with real customers watching. That’s why environments are separated:
Development (dev) → test freely, can break without issue Staging (stg) → production replica to validate before launching Production (prod) → the real environment, with users; no experimenting here
Each environment usually has the same infrastructure structure (a VPC, some servers, a database...), but with differences: production is bigger and more robust, development is smaller and cheaper. The challenge is: how do I manage the same code for multiple environments without duplicating everything?
What Are Workspaces
Remember Terraform’s state (Chapter 11): the file that records which resources exist. A workspace is, essentially, a separate state within the same code. By switching workspaces, Terraform uses a different state, allowing you to have multiple copies of the same infrastructure without mixing them.
Same Terraform code ├── workspace "dev" → its own state → development infrastructure ├── workspace "stg" → its own state → staging infrastructure └── workspace "prod" → its own state → production infrastructure
The basic commands:
terraform workspace new dev # create a workspace terraform workspace select prod # switch to another workspace terraform workspace list # list workspaces
Within the code, you can know which workspace you’re in with terraform.workspace, and adapt values accordingly:
locals {
# in production we use large instances; elsewhere, small ones
instance_type = terraform.workspace == "prod" ? "t3.large" : "t3.micro"
}Analogy: workspaces are like having multiple saved games of the same video game. The game (the code) is the same, but each save (workspace) has its own independent progress (state). You switch saves and work on a different reality without affecting the others.
The Limitations (Important)
Workspaces seem like the perfect solution, but they have serious problems you should know about before adopting them to manage environments. In fact, the community does not recommend using workspaces to separate dev/stg/prod. Let’s see why.
- Same Code, Same Backend: Little Real Separation
All workspaces share the same code and the same backend (the same state bucket). This means the separation between production and development is fragile: a mistake can affect multiple environments, and there’s no strong barrier between them.
- Risk of Using the Wrong Environment
Since you switch environments with a simple workspace select, it’s easy to forget which one you’re in and apply a change in the wrong place. Imagine running an apply thinking you’re in dev when you’re actually in prod. ⚠️ This mistake has caused real incidents.
- Hard to Have Very Different Configurations
If your environments are very different (not just in size, but in structure: production has components that development doesn’t), forcing everything into the same code with conditionals (terraform.workspace == ...) becomes convoluted and hard to read.
- Poor Visibility
It’s not obvious, looking at the code, what’s in each environment. Everything is mixed and depends on which workspace you’re in, making it hard to understand and audit the infrastructure.
When SHOULD You Use Workspaces?
Workspaces have their place in simple cases:
- When environments are almost identical and only minor details change (a size, a name).
- To create temporary test copies (for example, one environment per development branch, which you create and destroy quickly).
- In small projects where strong separation is not critical.
When NOT To? (And What To Do Instead)
To manage serious environments (dev/stg/prod in a company), the general recommendation is not to use workspaces, but rather a strategy of directories separated by environment, which we’ll see in the next subchapter (19.2). That strategy gives a much clearer, safer, and more visible separation.
Key Conclusion: workspaces exist and are useful for simple cases, but they are not the best tool to separate production from development in real projects. Know them, but be aware of their limits.
What You Should Remember
- Every real application needs multiple environments (dev, staging, production) to never experiment directly on real users.
- A Terraform workspace is a separate state within the same code, allowing multiple copies of the infrastructure (like “saved games” of the same game). Managed with
terraform workspace new/select/list. - Important limitations: they share code and backend (weak separation), it’s easy to use the wrong environment (dangerous), things get complicated if environments are very different, and they offer poor visibility.
- Use them for simple cases (almost identical environments, temporary copies), but not as the main way to separate dev/stg/prod in serious projects.
- The recommended alternative for serious environments is directory separation, which we’ll see next.
In the next subchapter, we’ll see that recommended strategy: organizing your infrastructure in directories per environment.
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
