AWS CloudFormation vs Google Cloud Deployment Manager

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AWS CloudFormation

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AWS CloudFormation vs Google Cloud Deployment Manager: What are the differences?

AWS CloudFormation and Google Cloud Deployment Manager are two popular infrastructure-as-code (IaC) tools used for managing cloud resources. Below are the key differences between these two services.

  1. Cloud Provider Integration: One major difference between AWS CloudFormation and Google Cloud Deployment Manager is the cloud provider integration they offer. AWS CloudFormation is specific to AWS and integrates tightly with the AWS ecosystem, providing seamless resource provisioning and management within the AWS platform. On the other hand, Google Cloud Deployment Manager is designed specifically for Google Cloud Platform (GCP) and provides similar capabilities within the GCP environment.

  2. Syntax and Configuration Language: AWS CloudFormation uses JSON or YAML templates as its syntax and configuration language. These templates define the desired state of the infrastructure, including resources, dependencies, and configurations. In contrast, Google Cloud Deployment Manager uses YAML or Python configurations to define and provision resources. This difference in syntax and configuration language allows users to choose their preferred format based on their familiarity and comfort.

  3. Resource Coverage: Another significant difference lies in the extent of resource coverage provided by AWS CloudFormation and Google Cloud Deployment Manager. AWS CloudFormation offers broad coverage for AWS resources, including various EC2 instances, RDS databases, S3 buckets, and more. In contrast, Google Cloud Deployment Manager has a narrower coverage, focusing primarily on GCP resources like VM instances, Cloud Storage buckets, and Cloud SQL databases.

  4. Template Reusability: AWS CloudFormation provides the concept of nested stacks, allowing users to reuse templates by referencing them within other templates. This enables modularization and reduces duplication of infrastructure code. Google Cloud Deployment Manager lacks a native feature for template reuse, although users can achieve similar functionality by separating resource configurations into reusable YAML or Python files.

  5. Implementation Approach: AWS CloudFormation takes an imperative approach to infrastructure provisioning, where it largely relies on manual resource creation and configuration statements. In contrast, Google Cloud Deployment Manager follows a declarative approach, where users specify the desired state of the infrastructure, and the tool automatically handles resource creation and configuration. This difference in implementation can influence the overall user experience and preference.

  6. Maturity and Ecosystem: AWS CloudFormation has been in the market for a longer time and has a mature ecosystem, with extensive community support, a rich library of pre-built templates, and a comprehensive documentation base. Google Cloud Deployment Manager, being a relatively newer service, has a growing ecosystem that is not as extensive as AWS CloudFormation. However, with the popularity of GCP increasing, the ecosystem is continuously expanding.

In summary, CloudFormation is an AWS service that uses declarative YAML or JSON templates for defining and provisioning AWS infrastructure, while Google Cloud Deployment Manager achieves similar goals on Google Cloud Platform using YAML or Python templates.

Decisions about AWS CloudFormation and Google Cloud Deployment Manager
Kirill Shirinkin
Cloud and DevOps Consultant at mkdev · | 3 upvotes · 146.9K views

Ok, so first - AWS Copilot is CloudFormation under the hood, but the way it works results in you not thinking about CFN anymore. AWS found the right balance with Copilot - it's insanely simple to setup production-ready multi-account environment with many services inside, with CI/CD out of the box etc etc. It's pretty new, but even now it was enough to launch Transcripto, which uses may be a dozen of different AWS services, all bound together by Copilot.

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Because Pulumi uses real programming languages, you can actually write abstractions for your infrastructure code, which is incredibly empowering. You still 'describe' your desired state, but by having a programming language at your fingers, you can factor out patterns, and package it up for easier consumption.

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Sergey Ivanov

We use Terraform to manage AWS cloud environment for the project. It is pretty complex, largely static, security-focused, and constantly evolving.

Terraform provides descriptive (declarative) way of defining the target configuration, where it can work out the dependencies between configuration elements and apply differences without re-provisioning the entire cloud stack.


Terraform is vendor-neutral in a way that it is using a common configuration language (HCL) with plugins (providers) for multiple cloud and service providers.

Terraform keeps track of the previous state of the deployment and applies incremental changes, resulting in faster deployment times.

Terraform allows us to share reusable modules between projects. We have built an impressive library of modules internally, which makes it very easy to assemble a new project from pre-fabricated building blocks.


Software is imperfect, and Terraform is no exception. Occasionally we hit annoying bugs that we have to work around. The interaction with any underlying APIs is encapsulated inside 3rd party Terraform providers, and any bug fixes or new features require a provider release. Some providers have very poor coverage of the underlying APIs.

Terraform is not great for managing highly dynamic parts of cloud environments. That part is better delegated to other tools or scripts.

Terraform state may go out of sync with the target environment or with the source configuration, which often results in painful reconciliation.

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I personally am not a huge fan of vendor lock in for multiple reasons:

  • I've seen cost saving moves to the cloud end up costing a fortune and trapping companies due to over utilization of cloud specific features.
  • I've seen S3 failures nearly take down half the internet.
  • I've seen companies get stuck in the cloud because they aren't built cloud agnostic.

I choose to use terraform for my cloud provisioning for these reasons:

  • It's cloud agnostic so I can use it no matter where I am.
  • It isn't difficult to use and uses a relatively easy to read language.
  • It tests infrastructure before running it, and enables me to see and keep changes up to date.
  • It runs from the same CLI I do most of my CM work from.
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Context: I wanted to create an end to end IoT data pipeline simulation in Google Cloud IoT Core and other GCP services. I never touched Terraform meaningfully until working on this project, and it's one of the best explorations in my development career. The documentation and syntax is incredibly human-readable and friendly. I'm used to building infrastructure through the google apis via Python , but I'm so glad past Sung did not make that decision. I was tempted to use Google Cloud Deployment Manager, but the templates were a bit convoluted by first impression. I'm glad past Sung did not make this decision either.

Solution: Leveraging Google Cloud Build Google Cloud Run Google Cloud Bigtable Google BigQuery Google Cloud Storage Google Compute Engine along with some other fun tools, I can deploy over 40 GCP resources using Terraform!

Check Out My Architecture: CLICK ME

Check out the GitHub repo attached

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Pros of AWS CloudFormation
Pros of Google Cloud Deployment Manager
  • 43
    Automates infrastructure deployments
  • 21
    Declarative infrastructure and deployment
  • 13
    No more clicking around
  • 3
    Any Operative System you want
  • 3
  • 3
    Infrastructure as code
  • 1
    CDK makes it truly infrastructure-as-code
  • 1
    Automates Infrastructure Deployment
  • 0
  • 2
    Automates infrastructure deployments
  • 1
    Fast deploy and update
  • 1
    Infrastracture as a code
  • 1
    Easy to deploy for GCP

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Cons of AWS CloudFormation
Cons of Google Cloud Deployment Manager
  • 4
  • 2
    No RBAC and policies in templates
  • 1
    Only using in GCP

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What is AWS CloudFormation?

You can use AWS CloudFormation’s sample templates or create your own templates to describe the AWS resources, and any associated dependencies or runtime parameters, required to run your application. You don’t need to figure out the order in which AWS services need to be provisioned or the subtleties of how to make those dependencies work.

What is Google Cloud Deployment Manager?

Google Cloud Deployment Manager allows you to specify all the resources needed for your application in a declarative format using yaml.

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What companies use AWS CloudFormation?
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See which teams inside your own company are using AWS CloudFormation or Google Cloud Deployment Manager.
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What are some alternatives to AWS CloudFormation and Google Cloud Deployment Manager?
AWS CodeDeploy
AWS CodeDeploy is a service that automates code deployments to Amazon EC2 instances. AWS CodeDeploy makes it easier for you to rapidly release new features, helps you avoid downtime during deployment, and handles the complexity of updating your applications.
Chef enables you to manage and scale cloud infrastructure with no downtime or interruptions. Freely move applications and configurations from one cloud to another. Chef is integrated with all major cloud providers including Amazon EC2, VMWare, IBM Smartcloud, Rackspace, OpenStack, Windows Azure, HP Cloud, Google Compute Engine, Joyent Cloud and others.
With Terraform, you describe your complete infrastructure as code, even as it spans multiple service providers. Your servers may come from AWS, your DNS may come from CloudFlare, and your database may come from Heroku. Terraform will build all these resources across all these providers in parallel.
AWS Elastic Beanstalk
Once you upload your application, Elastic Beanstalk automatically handles the deployment details of capacity provisioning, load balancing, auto-scaling, and application health monitoring.
AWS Config
AWS Config is a fully managed service that provides you with an AWS resource inventory, configuration history, and configuration change notifications to enable security and governance. With AWS Config you can discover existing AWS resources, export a complete inventory of your AWS resources with all configuration details, and determine how a resource was configured at any point in time. These capabilities enable compliance auditing, security analysis, resource change tracking, and troubleshooting.
See all alternatives