How to Understand a Large Terraform Based Project

Published on 19 Jan 2026 by Adam Lloyd-Jones

To understand a massive Terraform project effectively, you must treat the infrastructure as “executable documentation” where the code itself serves as the primary source of truth for the system’s architecture. Navigating a project with over 100 folders requires a systematic approach that balances high-level structural analysis with deep dives into resource dependencies and automated workflows.

1. Decipher the Repository and File Structure

The first step is to recognize how the project separates concerns, as large-scale Terraform typically moves away from monolithic stacks to avoid “automatically breaking many machines at once”.

Distinguish “Live” vs. “Modules”: Large projects are often split into a live repository, which defines specific instances of infrastructure (e.g., a production database), and a modules repository, which contains the “blueprints” or reusable libraries used to build those instances.
Identify Environments and Components: Standard layouts typically feature top-level folders for environments like stage, prod, mgmt, and global. Within these, you will find components such as vpc for networking, services for applications, and data-storage for databases.
Standard File Naming: Look for the core files in each directory: main.tf usually contains the primary resources, variables.tf defines the inputs (the “API”), and outputs.tf specifies what data the module returns for use by others.

2. Visualize the Resource Graph

Because Terraform is a declarative language, the order in which code appears in files is irrelevant; the resource graph determines the true execution order.

Generate Dependency Maps: You can use the terraform graph command to output the project’s Directed Acyclic Graph (DAG) of dependencies in DOT format.
Use Interactive Tooling: For a project this large, static graphs are difficult to read, so use a tool like Rover to generate an interactive, web-based UI that allows you to click through resources, variables, and outputs to see their connections.
Trace Remote State: Identify how data flows between the 100+ folders by searching for terraform_remote_state data sources; these indicate where one component is reading the “return values” of another previously deployed component.

3. Analyze the “Module API” (Inputs and Outputs)

Input and output variables act as the contract between different parts of the infrastructure.

Examine Variables: The variables.tf files tell you what can be customized in a module, such as instance types or network CIDR blocks. Descriptions within these blocks are critical for understanding the intent behind each parameter.
Examine Outputs: The outputs.tf files reveal what a module exposes to the rest of the system, such as a database endpoint or a load balancer DNS name. This helps you understand which modules are “providers” of data and which are “consumers”.

4. Reconcile Code with the “Real World”

To understand the current state of the infrastructure, you must look at the Terraform State file, which acts as a mapping from your code to real-world resource IDs.

Speculative Plans: Running terraform plan in a directory will show you a “diff” of what would change if the code were applied today, helping you see how much configuration drift has occurred between the source code and the live environment.
Inspect State Metadata: Reviewing the state via terraform state list or terraform state show lets you see every resource Terraform is currently managing in a specific folder without needing to look at the cloud provider’s console.

5. Review Operational Workflows

Understanding how the code is deployed is just as important as the code itself.

Check for Wrappers: Look for a terragrunt.hcl file; Terragrunt is often used in large projects to keep backend configurations DRY (Don’t Repeat Yourself) across many folders and to manage dependencies between those folders.
Analyze CI/CD Pipelines: Browse the .github/workflows or azure-pipelines.yml files to see the “pre-flight checklists” like terraform validate, tflint, and security scanners such as Checkov or tfsec.
Executable Documentation: Check the examples/ directory within modules; these folders often contain the simplest, working implementations of complex modules and are the best place to start if you want to understand how a component is intended to function.

6. Utilize Automated Documentation Tools

Instead of reading every line of code, leverage tools that summarize the project.

Terraform-Docs: If the project uses terraform-docs, it will likely have README.md files in each folder with autogenerated tables describing every input, output, and resource.
Provider Documentation: If you encounter unfamiliar resource types (e.g., aws_eks_cluster), refer to the Terraform Registry documentation for that specific provider to understand the required arguments and behavior.

Adam Lloyd-Jones

Adam is a privacy-first SaaS builder, technical educator, and automation strategist. He leads modular infrastructure projects across AWS, Azure, and GCP, blending deep cloud expertise with ethical marketing and content strategy.