What you'll learn: How infrastructure as code works (and why it became essential), the biggest benefits and tradeoffs, how to implement IaC step by step, and how to test and secure IaC so changes are reviewable and repeatable.

Manual infrastructure management, AKA ClickOps, rarely collapses in one dramatic moment. It decays through manual processes that feel harmless at the time – a console tweak to get a deploy out the door, a one-off IAM exception for a teammate, a "temporary" DNS change made with interactive configuration tools – and after enough of those moves, your computing infrastructure becomes a set of opinions that only exist in people's heads.

That is how snowflakes are born.

Your development environment does not match your staging environment, your deployment environment has invisible exceptions, your production environment carries manual configuration nobody remembers approving, and configuration drift becomes the default operating mode.

Infrastructure as code, often shortened to IaC, is the countermove. You define infrastructure resources in machine-readable definition files, you keep those infrastructure definitions in version control systems, and you let infrastructure automation create and update cloud resources, so the "how infrastructure" part is explicit rather than implicit.

This guide stays practical. You'll get clear definitions, a thin-slice rollout plan, a testing checklist that keeps infrastructure changes boring, and guidance on choosing between IaC tools.

What is infrastructure as code?

Infrastructure as code means you manage infrastructure through configuration files rather than through manual infrastructure management, and the files are treated like real software development artifacts.

You write infrastructure code that describes the infrastructure's desired state, commit it to version control, and use provisioning tools to deploy infrastructure, which makes infrastructure deployments repeatable and reviewable instead of being a sequence of undocumented console clicks.

The reason this became essential is that modern infrastructure is both bigger and faster. Cloud platforms let you create virtual machines, networks, load balancers, IAM roles, storage, and managed cloud services in minutes, and that speed is great until you realize that manual configuration scales linearly with human attention.

IaC gives you a desired state you can diff, an audit trail you can trust, and a way to manage infrastructure changes with the discipline you already apply to application code.

What counts as infrastructure?

Infrastructure is anything that shapes your application infrastructure and your ability to deploy applications safely.

It includes virtual machines and operating systems baselines, networking, subnets, routing, firewalls, load balancers, storage, DNS, IAM, secrets access, logging, and the cloud services that sit between a web app and the users who depend on it. It also includes Kubernetes resources and managed services like databases, queues, and caches, plus the glue, policies, modules, and pipelines that coordinate infrastructure components.

If it can cause downtime or data exposure, put it in infrastructure definitions, because gaps are where configuration drift hides.

IaC vs. scripts vs. config management

IaC describes the desired state. Scripts usually describe steps, which means they can be flexible but also fragile, because they assume order, retries, and current reality in ways that break as systems evolve. Configuration management tools overlap, but they typically focus on converging an existing machine toward a configuration, which is why they're popular for operating systems hardening and service setup, while IaC is the usual tool for infrastructure provisioning of the underlying cloud infrastructure.

Infrastructure as code: how IaC works

IaC tools turn infrastructure code into a plan for infrastructure changes, then they execute that plan against a cloud provider API. The difference between declarative IaC and an imperative approach is how much intent the tool can infer from your inputs.

Declarative IaC

Declarative IaC describes an end state and lets the tool figure out how to reach it.

You write IaC configuration files that declare the desired state, the tool performs dependency mapping, and then it produces a plan that reviewers can read before anything changes. This is why declarative systems dominate day-to-day infrastructure management, because they are easier to reason about and they surface destructive changes early.

Terraform and OpenTofu are common examples, and they use HashiCorp Configuration Language (HCL), a domain-specific language format designed for predictable planning. Here's a small code example:

resource "aws_security_group" "web" {
  name   = "web-${var.env}"
  vpc_id = var.vpc_id
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  ingress {
    from_port   = 443
    to_port     = 443
    protocol    = "tcp"
    cidr_blocks = ["0.0.0.0/0"]
  }
}

This code creates an AWS security group named web-<env> inside a specific VPC, allowing incoming HTTPS traffic from anywhere on the internet.

Imperative IaC

Imperative IaC describes steps. You tell the system what to do, in what order, often using familiar programming languages, and that power can be valuable when you need complex multi-IaC workflows or tight coupling to application behavior.

The tradeoff is that reviews can become about control flow rather than intent, and the "what will this really do" question becomes harder to answer without running it. Imperative tooling can be a great fit, but it needs stricter guardrails because the surface area for human error grows quickly.

What is infrastructure as code in DevOps?

In DevOps, IaC is the bridge that makes infrastructure changes behave like application changes.

A pull request proposes an update, continuous integration runs validation and produces a plan, peers review the diff, and continuous delivery applies changes through automation tools rather than through a human session in a console.

That pipeline makes infrastructure deployments less dependent on individual system administrators and more dependent on a repeatable development process.

IaC as a collaboration layer

IaC works as a collaboration layer because it gives everyone the same artifact to discuss in a pull request. Reviewers see the infrastructure code diff and the plan output in one place, which makes conversations concrete, while operations teams can encode standards that software developers can extend without requesting console access for every change.

The benefits of infrastructure as code: speed, consistency, and fewer errors

IaC improves speed because infrastructure provisioning stops being a queue of manual processes that are error-prone, but the deeper benefit is consistency.

When the same infrastructure definitions produce the same environment shape, you get fewer surprises, fewer deployment failures, and less operational drag, because you're not constantly reconciling differences between environments.

Environment cloning and repeatability

Environment cloning is the practical test. If you can create a development environment, a staging environment, and a production environment from the same repo with different inputs, you can onboard faster, you can create ephemeral stacks for testing, and you can debug issues without guessing whether you are looking at the same environment.

It also changes your relationship with legacy infrastructure, because rebuilds and migrations become engineering work instead of careful ritual.

Reducing configuration drift and snowflakes

Configuration drift is the gap between what your infrastructure definitions say and what exists in reality. It often starts with a fast console change to fix an incident, but it becomes a long-term risk when the exception never returns to code. IaC is the antidote because it makes drift visible:

Plans show unexpected diffs
Enforced workflows make it easier to avoid manual configuration
Drift becomes something you detect and resolve, not something you accept as fate

How does infrastructure as code improve security?

IaC improves security by replacing hidden manual configuration with controlled change visibility. Version control provides traceability. Pull requests provide peer review. Automation provides consistent deployments. Together, they reduce the chance that a risky one-off change slips into production unnoticed, and they improve auditability because you can point to the exact infrastructure changes, approvals, and apply logs.

PR-based review and change visibility

Security teams care about who changed what, when, and why, and PR-based workflows answer that in a way auditors understand. The diff shows the intent, the plan shows the impact on cloud resources, and the discussion provides context that does not disappear when a chat thread scrolls away.

Policy as code guardrails

Policy as code is where teams turn standards into enforceable rules.

OPA and Conftest are common choices, and the rules are usually pragmatic: no public storage buckets, required tags, restricted IAM, and tighter constraints for production environment changes than for a development environment.

These checks run in continuous integration, so the system blocks unsafe changes before apply. Terrateam's documentation is a useful reference for how policy checks fit into PR workflows.

Drift detection and continuous compliance

Drift is both a reliability and a security problem, because unknown state is hard to defend. Environment drift detection compares cloud resources to your IaC configuration files on a schedule, surfaces differences, and helps you maintain continuous compliance without turning it into a quarterly scramble.

IaC tools and platforms: which one's the best?

There's no universal winner. The best choice depends on whether you are single-cloud or multi-cloud, whether your team prefers configuration files or code tools, and how much governance you need around approvals and policy.

Picking a platform is about ecosystem fit, not about finding the "most powerful" option.

Approach	Fits when	Tradeoff
Cloud-native IaC	One cloud provider and deep feature use	Portability costs
Multi-cloud declarative	Multiple clouds or many SaaS providers	State and workflow ownership
Language-driven IaC	Software developers want full languages	Harder change visibility

Cloud-native IaC (AWS and Azure examples)

On AWS, CloudFormation and CDK cover most needs. CloudFormation is declarative. CDK lets you write code that synthesizes templates, which can be attractive when you want abstractions while still using the provider's deployment engine for AWS infrastructure. On Azure, Azure Resource Manager is the deployment system, ARM templates are the original format, and Bicep is the newer authoring layer that improves readability.

Here's is an example Bicep definition for Azure, used to create (or manage) an Azure Resource Group:

resource rg 'Microsoft.Resources/resourceGroups@2022-09-01' = {
  name: 'app-${env}'
  location: location
}

Multi-cloud IaC (Terraform and OpenTofu, plus alternatives)

Terraform and OpenTofu are popular when portability matters, and they cover a broad set of cloud services through providers, which makes them useful for infrastructure provisioning that spans cloud infrastructure and cloud application resources. Read our Terraform vs. OpenTofu comparison to find out which would work best for your organization.

Pulumi is a serious alternative for teams that want to use familiar programming languages end-to-end.

Terrateam fits on top of Terraform or OpenTofu, providing PR-native plans, apply control, policy checks, approvals, and drift detection, and if you want to try that workflow, you can sign up to Terrateam.

The "best" platform depends on…

The real decision factors are

Governance
Workflow
Scale
Integration needs

Governance includes policy and permissions. Workflow includes how well the tool fits pull requests and CI. Scale includes whether your repo model, monorepo, or multi-repo, can grow without state sprawl.

How to implement infrastructure as code: a step-by-step rollout plan

IaC adoption works when it starts with a thin slice and grows through repetition. The goal is to build a workflow that makes infrastructure changes boring and safe, then expand the surface area.

Start with a thin slice

Start with one service or one environment that exercises networking, identity, and at least one managed service. Define boundaries and naming conventions early, because these decisions become the shape of your repo, and use the slice to practice writing IaC configuration files that produce the same environment every time.

Put everything in version control

Once you have a slice, commit everything. Make pull requests the only path for infrastructure changes, treat version control as the source of truth, and avoid manual configuration as a habit, not as a suggestion. When an emergency forces a console change, reconcile it back into infrastructure code quickly so configuration drift does not become permanent.

Standardize environments (dev, staging, prod)

Standardize environment shape across dev, staging, and prod, and parameterize differences rather than copying configuration files. Use modules when they capture stable conventions, and avoid over-modularization that hides intent and slows reviews.

Wire IaC into CI/CD and approvals

Run plans on every pull request, and apply only after review. Keep credentials tight and short-lived. Keep apply in automation. This ties infrastructure deployments to the same gates you already trust for application code.

How to test infrastructure as code

Testing IaC is a set of checks that make bad changes fail early.

Static checks

Formatting, validation, and linting are fast, and they catch broken syntax, missing required fields, and common mistakes. Run them on every pull request.

Plan-based review checks

Treat the plan output as a test artifact. Reviewers should notice destructive actions, unexpected diffs, missing tags, and surprising replacements, because those are where outages begin.

Policy tests

Test policy rules the same way you test code, then enforce them in CI, so guardrails do not depend on memory.

Integration tests in ephemeral environments

When a change touches critical paths, networking, IAM, or load balancers, it can be worth provisioning an ephemeral environment, running a smoke test, and tearing it down. IaC makes this practical because provisioning is repeatable.

How to learn infrastructure as code

Learning IaC sticks when you build something real, then iterate.

Choose a "home base" tool

Pick one tool aligned with your cloud provider, CloudFormation or CDK for AWS, Bicep for Azure via Azure Resource Manager, or Terraform and OpenTofu for multi-cloud. The point is to learn a mental model, not to sample every ecosystem.

Learn by shipping

Build a sandbox that provisions networking, compute, least-privilege IAM, and a managed service, deploy applications, then destroy everything, and repeat until you stop needing manual processes to "fix" things.

Level up into workflows

Then learn workflows. Use pull requests, add continuous integration checks, document modules, and practice keeping secrets out of repos and state.

Common IaC pitfalls (and how to avoid them)

The most common IaC pitfalls are predictable. Over-modularization hides intent. State sprawl creates coordination overhead. Secrets leakage turns version control into a liability. Unmanaged drift makes the repo untrustworthy. Skipping reviews reintroduces human error through a different door.

The fixes are equally predictable. Keep modules boring, align state with ownership, use proper secret stores, run drift detection, and make PR review the default path for infrastructure changes.

Making IaC safer with GitOps pull request workflows

GitOps workflows make IaC operationally safe because they force every change through the same visible loop. A pull request triggers an automated plan, policy checks evaluate the change, reviewers approve, and apply is controlled, auditable, and consistent. You can read our guide to using GitOps with Terraform to get started.

Terrateam is designed to make that loop work for Terraform and OpenTofu, and the overview in our docs explains the model.

What the workflow looks like

A PR triggers plan generation and posts the plan output back to the PR, reviewers evaluate the diff and the plan, then approval unlocks apply. The apply is logged, permissions are enforced, and the change is tied to the repo history.

Policy enforcement and approvals at scale

At scale, guardrails matter. Policy as code enforces baseline rules. Approvals enforce higher scrutiny for production environment changes. Permissions limit who can apply where, and that keeps infrastructure automation safe even as your organization grows.

Conclusion

Infrastructure as code works because it turns infrastructure management into repeatable engineering practice.

You replace manual processes with versioned infrastructure code, you manage infrastructure changes through pull requests, you apply continuous integration checks and policy guardrails, you automate infrastructure, and you let infrastructure automation deploy infrastructure consistently across cloud platforms.

If you adopt IaC with a thin-slice rollout, standardize environments, and treat plan review and drift detection as normal work, you end up with fewer surprises and a calmer on-call rotation.

If you want to operationalize Terraform or OpenTofu with PR-native plans, policy checks, approvals, and auditable applies, Terrateam is built for that. You can sign up to try Terrateam.

Infrastructure as code FAQs

What is infrastructure as code in DevOps?

In DevOps, infrastructure as code means infrastructure changes use the same development process as application code. Pull requests propose changes, continuous integration validates and plans them, reviewers approve, and automation applies them, which gives development and operations teams shared change visibility and fewer manual handoffs.

How does infrastructure as code improve security?

IaC improves security by reducing manual infrastructure management, increasing traceability through version control systems, enabling peer review through pull requests, and enforcing guardrails through policy as code and environment drift detection, which makes risky changes harder to slip into a production environment unnoticed.

Which infrastructure-as-code platform is best?

The best platform depends on your cloud provider strategy and workflow needs. Cloud-native options such as CloudFormation, CDK, ARM templates, Bicep, and Azure Resource Manager can be great in a single-cloud world, while Terraform and OpenTofu are strong multi-cloud provisioning tools, and language-driven approaches can fit teams that want familiar programming languages, as long as they invest in reviewability, testing, and governance.

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