We have seen two ways to automate Terraform: a generic pipeline (GitHub Actions) and a specialized tool that you host yourself (Atlantis). There is a third way: using a managed service that takes care of everything, offered by the very company that created Terraform. It's called Terraform Cloud (recently renamed HCP Terraform). In this subchapter, you'll see what it offers and when it's appropriate.
What is Terraform Cloud / HCP Terraform
Terraform Cloud (now HCP Terraform, where HCP stands for "HashiCorp Cloud Platform") is HashiCorp's managed platform—the company that created Terraform—for running and managing Terraform. It's like having everything we've set up manually (remote state, locking, pipelines, plan review) but offered as a ready-to-use service, without you having to build or maintain anything.
Before (manually): you set up the S3+DynamoDB backend, the pipeline, the secrets... With HCP Terraform: all of that is solved as a managed service
Analogy: if setting up your own backend and pipeline is like cooking at home (total control, but you buy, cook, and clean), HCP Terraform is like going to a restaurant: you get the complete and ready service, for a price. You don't maintain the kitchen, you just enjoy the result.
What it offers, all-in-one
HCP Terraform brings together in a single platform many of the things that in previous chapters we configured piece by piece:
- Managed remote state
It stores and manages your state (Chapter 11) for you, with locking (subchapter 20.2), versioning, and encryption included. You don't need to set up the S3 + DynamoDB backend from subchapter 20.1: it's already solved.
- Remote execution of Terraform
It runs plan and apply on their servers, not on your laptop or in your pipeline. This provides consistency (always the same environment) and security (credentials live on the platform, not on scattered machines).
- Workflow with plan review
Integrated with Git, it runs the plan automatically on every change and displays it for review and approval (the flow from subchapter 12.5), just like Atlantis but as a managed service.
- Management of variables and secrets
It securely stores the variables and secrets (subchapter 19.4) that your configurations need, without you having to set up your own system.
- Private module registry
It offers a private registry for your organization's modules (remember the Registry from subchapter 18.3), with versioning, to share them among teams.
- Governance controls (in advanced plans)
It allows you to define policies (with Sentinel or OPA) that automatically check that the infrastructure complies with company rules before being applied—similar to the security analysis in subchapter 21.2, but as mandatory policies.
Comparison with the other options
| Own pipeline (GitHub Actions) | Atlantis | HCP Terraform | |
|---|---|---|---|
| Who maintains it | You (you build it) | You (you host it) | HashiCorp (managed) |
| Setup effort | Medium | Medium-high | Low |
| State and locking | You set it up (S3+DynamoDB) | You set it up | Included |
| Cost | Low (you pay for the cloud) | Low (you pay for the server) | Has free and paid plans |
| Control/privacy | Total | Total | You depend on the service |
| Ideal for | Flexibility, technical teams | Self-hosted GitOps | Speed, less maintenance |
Note: HCP Terraform has a free tier for small teams and paid plans for organizations. It's worth checking the current prices according to your size.
When to choose HCP Terraform?
It makes sense when:
- You want to start quickly without setting up backend, pipeline, or maintenance. It's the most "turnkey" option.
- You don't want to maintain your own CI/CD infrastructure for Terraform (neither a complex pipeline nor a self-hosted Atlantis).
- You value integrated governance and collaboration features (policies, module registry, team management).
- Your team prefers to pay for a service in exchange for saving operational time.
When NOT to?
- If you want total control over where everything runs and where your credentials live (some companies, by policy, prefer everything in their own cloud).
- If your case is simple and a basic GitHub Actions pipeline (subchapter 22.1) is enough for you at no additional cost.
- If you prefer not to depend on an external platform.
The decision: the three ways
In summary, to automate Terraform you have three paths, and none is the universal "right one":
1. Own pipeline (GitHub Actions...) → flexible, you build it 2. Atlantis (self-hosted) → specialized GitOps, you maintain it 3. HCP Terraform (managed) → turnkey, maintained by HashiCorp
Choose according to your team size, how much maintenance you want to take on, your control/privacy needs, and your budget. Many teams start with a simple pipeline and evolve as they grow.
What you should remember
- Terraform Cloud / HCP Terraform is HashiCorp's managed platform for running and managing Terraform: it offers as a service everything you would set up manually. Like going to a restaurant instead of cooking at home.
- It includes: managed remote state with locking, remote execution, workflow with plan review, management of variables and secrets, private module registry, and governance controls.
- Compared to the other options: less maintenance (managed by HashiCorp) in exchange for depending on the service and its cost (has a free tier and paid plans).
- Choose it if you want to start quickly and not maintain your own CI/CD infrastructure; avoid it if you need total control/privacy or your case is simple and a basic pipeline is enough.
- The three ways to automate Terraform: own pipeline, Atlantis (self-hosted), or HCP Terraform (managed). Choose according to team, maintenance, control, and budget.
In the last subchapter of the chapter (and of Part V) we will see an important problem that automation helps solve: drift detection and automatic reconciliation.
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
