AWS CloudFormation vs Terraform: What are the differences?
Developers describe AWS CloudFormation as "Create and manage a collection of related AWS resources". 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. On the other hand, Terraform is detailed as "Describe your complete infrastructure as code and build resources across providers". 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 CloudFormation and Terraform belong to "Infrastructure Build Tools" category of the tech stack.
Some of the features offered by AWS CloudFormation are:
- AWS CloudFormation comes with the following ready-to-run sample templates: WordPress (blog),Tracks (project tracking), Gollum (wiki used by GitHub), Drupal (content management), Joomla (content management), Insoshi (social apps), Redmine (project mgmt)
- No Need to Reinvent the Wheel – A template can be used repeatedly to create identical copies of the same stack (or to use as a foundation to start a new stack)
- Transparent and Open – Templates are simple JSON formatted text files that can be placed under your normal source control mechanisms, stored in private or public locations such as Amazon S3 and exchanged via email.
On the other hand, Terraform provides the following key features:
- Infrastructure as Code: Infrastructure is described using a high-level configuration syntax. This allows a blueprint of your datacenter to be versioned and treated as you would any other code. Additionally, infrastructure can be shared and re-used.
- Execution Plans: Terraform has a "planning" step where it generates an execution plan. The execution plan shows what Terraform will do when you call apply. This lets you avoid any surprises when Terraform manipulates infrastructure.
- Resource Graph: Terraform builds a graph of all your resources, and parallelizes the creation and modification of any non-dependent resources. Because of this, Terraform builds infrastructure as efficiently as possible, and operators get insight into dependencies in their infrastructure.
"Automates infrastructure deployments" is the primary reason why developers consider AWS CloudFormation over the competitors, whereas "Infrastructure as code" was stated as the key factor in picking Terraform.
Terraform is an open source tool with 17.4K GitHub stars and 4.77K GitHub forks. Here's a link to Terraform's open source repository on GitHub.
Instacart, Slack, and Twitch are some of the popular companies that use Terraform, whereas AWS CloudFormation is used by TimeHop, Custora, and NASA Jet Propulsion Laboratory. Terraform has a broader approval, being mentioned in 490 company stacks & 298 developers stacks; compared to AWS CloudFormation, which is listed in 195 company stacks and 75 developer stacks.
What is AWS CloudFormation?
What is Terraform?
Need advice about which tool to choose?Ask the StackShare community!
Sign up to add, upvote and see more prosMake informed product decisions
What are the cons of using Terraform?
Sign up to get full access to all the companiesMake informed product decisions
Sign up to get full access to all the tool integrationsMake informed product decisions
LaunchDarkly is almost a five year old company, and our methodology for deploying was state of the art... for 2014. We recently undertook a project to modernize the way we #deploy our software, moving from Ansible-based deploy scripts that executed on our local machines, to using Spinnaker (along with Terraform and Packer) as the basis of our deployment system. We've been using Armory's enterprise Spinnaker offering to make this project a reality.
We use Terraform because we needed a way to automate the process of building and deploying feature branches. We wanted to hide the complexity such that when a dev creates a PR, it triggers a build and deployment without the dev having to worry about any of the 'plumbing' going on behind the scenes. Terraform allows us to automate the process of provisioning DNS records, Amazon S3 buckets, Amazon EC2 instances and AWS Elastic Load Balancing (ELB)'s. It also makes it easy to tear it all down when finished. We also like that it supports multiple clouds, which is why we chose to use it over AWS CloudFormation.
I use Terraform because it hits the level of abstraction pocket of being high-level and flexible, and is agnostic to cloud platforms. Creating complex infrastructure components for a solution with a UI console is tedious to repeat. Using low-level APIs are usually specific to cloud platforms, and you still have to build your own tooling for deploying, state management, and destroying infrastructure.
However, Terraform is usually slower to implement new services compared to cloud-specific APIs. It's worth the trade-off though, especially if you're multi-cloud. I heard someone say, "We want to preference a cloud, not lock in to one." Terraform builds on that claim.
Terraform Google Cloud Deployment Manager AWS CloudFormation
Our base infrastructure is composed of Debian based servers running in Amazon EC2 , asset storage with Amazon S3 , and Amazon RDS for Aurora and Redis under Amazon ElastiCache for data storage.
We are starting to work in automated provisioning and management with Terraform , Packer , and Ansible .
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
Manually clicking around the AWS UI or scripting AWS CLI calls can be both a slow and brittle process.
We needed to be able to reconstruct CloudRepo's infrastructure in case of disaster or moving to another AWS Region.
Setting up our infrastructure with CloudFormation allows us to update it easily as well as duplicate or recreate things when the need arises.
Opstax uses CloudFormation for anything infrastructure related! CloudFormation allows us to use infrastructure-as-code as a constant blueprint/map of our environment. It means we can accurately and efficiently deploy replicated or new infrastructure with no time wasted clicking around and no human error.
Terraform makes it so easy to deploy AWS and Google Cloud services, with the declarative approach avoiding so many headaches of manual work and possible mistakes.
Manage infrastructure as codes. Native AWS solution so it has better support to AWS resources than Terraform, also can leverage AWS Business Support.
- Infrastructure as Code.
- Central tool to deploy all infratructure: AWS, CloudFlare, StatusCake
The entire AWS environments is described and setup using Terraform.