AWS CloudFormation vs Packer: What are the differences?
AWS CloudFormation: 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; Packer: Create identical machine images for multiple platforms from a single source configuration. Packer automates the creation of any type of machine image. It embraces modern configuration management by encouraging you to use automated scripts to install and configure the software within your Packer-made images.
AWS CloudFormation and Packer 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, Packer provides the following key features:
- Super fast infrastructure deployment. Packer images allow you to launch completely provisioned and configured machines in seconds, rather than several minutes or hours.
- Multi-provider portability. Because Packer creates identical images for multiple platforms, you can run production in AWS, staging/QA in a private cloud like OpenStack, and development in desktop virtualization solutions such as VMware or VirtualBox.
- Improved stability. Packer installs and configures all the software for a machine at the time the image is built. If there are bugs in these scripts, they'll be caught early, rather than several minutes after a machine is launched.
"Automates infrastructure deployments" is the top reason why over 36 developers like AWS CloudFormation, while over 24 developers mention "Cross platform builds" as the leading cause for choosing Packer.
Packer is an open source tool with 9.09K GitHub stars and 2.48K GitHub forks. Here's a link to Packer's open source repository on GitHub.
Expedia.com, Redox Engine, and TimeHop are some of the popular companies that use AWS CloudFormation, whereas Packer is used by Instacart, Oscar Health, and MAK IT. AWS CloudFormation has a broader approval, being mentioned in 197 company stacks & 77 developers stacks; compared to Packer, which is listed in 115 company stacks and 21 developer stacks.
What is AWS CloudFormation?
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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 .
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.
Manage infrastructure as codes. Native AWS solution so it has better support to AWS resources than Terraform, also can leverage AWS Business Support.