Get a clear technical comparison of AWS CloudFormation and Terraform as we explore the architecture, state handling, scalability, governance, and real world usage patterns to help engineering teams choose the right Infrastructure as Code approach.
AWS CloudFormation and Terraform are two of the major players dominating the Infrastructure as Code space. And there is good reason for it too. Both of these solve the very same problem, but with a different approach and different design assumptions. Infrastructure as Code shapes how teams build, scale, and govern cloud systems and so the right choice between these two will determine a lot. So to help you out, we brought out this handy guide to help you figure out which one will work best for your real engineering environment.
We skipped over the marketing hype and brought you the trade offs with each that teams can only find out when they decide to scale. Save yourself from the hassle later on and choose the right option now. Here’s everything you need to know.
Terraform
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Let’s start with Terraform and explore the basics of it.
What Terraform does differently
Terraform takes on a novel approach to infrastructure. It approaches infrastructure as a graph problem. Engineers describe desired resources, define relationships, and let the engine calculate execution order. HashiCorp Configuration Language drives this IaC model. This code reads closer to structured code than markup, which changes how teams think about reuse and abstraction.
Terraform supports multiple providers. Each one acts as a translation layer between HashiCorp Configuration Language and an external platform. Azure, Google Cloud, GitHub, Kubernetes, AWS, and a bunch of other SaaS tools expose providers. The Terraform model allows you to orchestrate infrastructure and external services using one workflow.
But remember that Terraform will not magically enable cross-cloud compatibility so team engineers will need to still write the provider specific resources. The benefit comes from the consistent syntax and shared workflow across platforms.
Terraform also exposes logic natively. Loops, conditionals, dynamic blocks, and functions allow teams to operate infrastructure programmatically without ever having to leave the language. You will need this capability when deciding to scale.
How providers shape Terraform usage
Terraform needs explicit provider configuration. Engineers must define authentication, regions, and versions upfront, and it is this explicitness that prevents ambiguity. But at the same time, it also increases your initial setup work.
Each provider defines its own resource types. Naming conventions embed the provider name directly into the resource identifier. That clarity matters in mixed provider codebases because engineers always know which platform owns a resource without scanning context.
Providers also introduce variability here. Community maintained providers differ in quality, update cycles, and documentation depth. Teams adopting Terraform at scale often standardise provider versions aggressively to avoid unexpected behaviour.
Terraform features that impact real teams
The execution plan is what makes Terraform different. It separates intent from action. The engineers review changes before applying them so any accidental deletions are prevented plus, it also makes code reviews meaningful.
Terraform’s state management tracks deployed resources. It does this by storing state locally by default. This can cause collaboration to break down so teams solve this by moving state into remote backends like object storage or managed services. State handling does add a little bit of operational overhead but it is critical to enable drift detection and dependency tracking.
The engine itself determines execution order based on references. Community modules help accelerate adoption but require scrutiny. Mature teams audit modules internally before using them in production.
AWS CloudFormation
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Now, here is a quick brief on Amazon’s AWS CloudFormation IaC approach. Here’s what you need to know.
What AWS optimises for
AWS CloudFormation exists to provision AWS resources using AWS native workflows. It treats infrastructure as declarative stacks defined in YAML or JSON. These formats are quite familiar to engineers but can quickly grow super verbose. CloudFormation integrates deeply with AWS services so if you are deep into the ecosystem, this might make sense for you. You get new AWS features almost first every time before third party tools catch up.
CloudFormation manages state internally. Engineers never handle state files. AWS tracks the deployed resources as stacks and records every operation event. This IaC model reduces operational complexity and simplifies, but within AWS boundaries.
AWS also supports CloudFormation directly so enterprises love contracts that come with CloudFormation expertise. That safety net influences decision making in regulated or risk averse organisations.
Reusability in CloudFormation today
CloudFormation originally had a huge reliance on nested templates for reuse. This approach worked but it did increase complexity. AWS later introduced CloudFormation modules which improved composability without abandoning native syntax.
Even with these modules, CloudFormation favours explicit definitions. Engineers often repeat structures instead of generating them dynamically. This explicitness improves the readability for small stacks but becomes painful at scale.
How are they different
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Syntax and expressiveness
Terraform’s HashiCorp Configuration Language supports expressions, functions, and iteration directly. So with Terraform, engineers have to write fewer lines to express complex intent. The logic stays close to resource definitions.
CloudFormation uses declarative templates with limited intrinsic functions so engineers will need to frequently compensate by introducing custom resources backed by Lambda functions. That workaround increases surface area and operational risk.
Terraform encourages abstraction. CloudFormation encourages explicit declaration. Neither approach here is going to be universally better. The difference becomes obvious once infrastructure spans hundreds of environments.
The learning curve
Terraform introduces novel concepts unfamiliar to traditional infrastructure teams. State management, providers, and modules require discipline. Once teams internalise these concepts, the productivity increases sharply.
CloudFormation feels approachable for AWS focused engineers. YAML and JSON reduce syntactic friction, but debugging template errors often frustrates newcomers due to verbose error messages and strict formatting rules.
So in a nutshell, AWS familiarity will shorten CloudFormation onboarding and Terraform needs a broader platform understanding.
Dynamic infrastructure creation
Terraform is built to handle repetition natively. Engineers simply generate dozens or hundreds of similar resources using loops and conditionals. Changes here propagate cleanly across all your environments.
CloudFormation needs more explicit duplication or modularisation. This approach increases the template size and the maintenance effort. Teams often reach for AWS CDK or SAM to escape these limits, but these tools introduce another layer of abstraction.
Functions and computation
With Terraform, you get a rich function library. Engineers manipulate strings, dates, numbers, files, and encodings inline. This capability eliminates external scripting for common tasks.
Now compare that with CloudFormation. It offers a small set of intrinsic functions. CloudFormation engineers need to create Lambda-backed custom resources to perform basic computations. This pattern works well but introduces latency, permission requirements, and failure modes unrelated to the infrastructure itself.
State and drift
Terraform’s state file represents reality as Terraform understands it. Drift detection highlights discrepancies between code and deployed resources. Teams must secure and manage state carefully to avoid corruption.
CloudFormation, on the other hand, stores state implicitly within AWS. Drift detection exists but it operates very differently from Terraform. AWS tracks changes but limits visibility into unmanaged modifications.
Provider scope and ecosystem
Terraform helps manage your infrastructure beyond cloud providers. GitHub repos, monitoring platforms, identity systems, and SaaS tools integrate into the same workflow. This breadth ensures true infrastructure orchestration.
CloudFormation remains AWS centric. Custom resources extend reach but does suffer from lack of first class support.
Terraform’s ecosystem evolves much faster. CloudFormation’s ecosystem aligns tightly with AWS release cycles.
Cost considerations
Both tools cost very little to run directly. Most of the expenses will be coming from managed services, enterprise support, and the infrastructure they provision.
Terraforms’ open source core remains completely free. Managed offering adds costs but reduces the operational burden.
CloudFormation charges per operation at negligible rates. AWS resource costs are going to dominate the overall spend in this case.
Enterprise support and governance
AWS Enterprise Support covers CloudFormation deeply. Large organisations value that alignment. Now compare it with Terraform. Terraform offers commercial support through HashiCorp and community support through open source channels. However, it is important to remember that support quality is going to vary based on the provider.
Common usage patterns
Teams having to manage multiple clouds or external services often will gravitate towards Terraform thanks to its open source nature. One workflow covers infrastructure and tooling.
AWS heavy teams building regulated architectures often standardise on CloudFormation. The tight integration is great if you want to simplify audits and governance.
Large organisations will frequently mix both tools to get the best of both worlds.
Using Terraform and CloudFormation together
There are massive benefits to this. Terraform can provision CloudFormation stacks directly. This pattern will allow teams to standardise orchestration while preserving AWS native templates where needed. Hybrid usage does increase complexity but solves organisational constraints.
Choosing between them without buzzwords
Here’s everything in a nutshell for you.
Terraform is great at abstraction, scale, and platform breadth. CloudFormation excels at AWS native depth and operational simplicity. Neither tool is going to replace architectural thinking. Both are there to amplify good practices and expose bad ones faster.
Infrastructure as Code is only going to succeed when teams understand their systems more directly. These tools are not here to remove complexity, they just help to surface it earlier. Teams that understand these differences make better long term decisions. So choose wisely.
