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  1. Stackups
  2. DevOps
  3. Build Automation
  4. Infrastructure Build Tools
  5. Google Cloud Deployment Manager vs Packer vs Terraform

Google Cloud Deployment Manager vs Packer vs Terraform

OverviewComparisonAlternatives

Overview

Packer
Packer
Stacks573
Followers566
Votes41
Terraform
Terraform
Stacks22.9K
Followers14.7K
Votes344
GitHub Stars47.0K
Forks10.1K
Google Cloud Deployment Manager
Google Cloud Deployment Manager
Stacks24
Followers113
Votes5

Google Cloud Deployment Manager vs Packer vs Terraform: What are the differences?

# Introduction
Google Cloud Deployment Manager, Packer, and Terraform are all infrastructure as code tools used to automate and manage cloud resources.

1. **Hosting Environment**: Google Cloud Deployment Manager is specific to Google Cloud Platform, while Packer and Terraform can be used with multiple cloud providers, making them more versatile options for multi-cloud environments.
   
2. **Configuration Language**: Google Cloud Deployment Manager uses YAML or Jinja templates, Packer uses JSON, and Terraform uses HashiCorp Configuration Language (HCL). This difference in syntax may influence developer preference and ease of use for each tool.
   
3. **Resource Provisioning**: Google Cloud Deployment Manager mainly focuses on setting up resources in Google Cloud Platform, whereas Packer is primarily used for machine image creation, and Terraform is known for provisioning and managing infrastructure across various cloud providers.
   
4. **State Management**: Google Cloud Deployment Manager directly manages resources within Google Cloud, Packer does not maintain any state, and Terraform has a state file that keeps track of the current state of the infrastructure, facilitating updates and modifications.
   
5. **Community Support**: Terraform has a large and active community with numerous pre-built modules and plugins, while Google Cloud Deployment Manager and Packer have smaller communities and may have fewer readily available resources and support.
   
6. **Deployment Workflow**: Google Cloud Deployment Manager provides a workflow framework for building, running, and viewing deployments, Packer focuses on creating machine images and automating image builds, and Terraform offers a comprehensive workflow for managing infrastructure configuration, applying changes, and tracking state.

In Summary, Google Cloud Deployment Manager is Google Cloud-specific, while Packer and Terraform offer more flexibility across multiple cloud providers, each with unique features and advantages in infrastructure automation and management.

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Detailed Comparison

Packer
Packer
Terraform
Terraform
Google Cloud Deployment Manager
Google Cloud Deployment Manager

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.

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.

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

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.;Greater testability. After a machine image is built, that machine image can be quickly launched and smoke tested to verify that things appear to be working. If they are, you can be confident that any other machines launched from that image will function properly.
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.;Change Automation: Complex changesets can be applied to your infrastructure with minimal human interaction. With the previously mentioned execution plan and resource graph, you know exactly what Terraform will change and in what order, avoiding many possible human errors
-
Statistics
GitHub Stars
-
GitHub Stars
47.0K
GitHub Stars
-
GitHub Forks
-
GitHub Forks
10.1K
GitHub Forks
-
Stacks
573
Stacks
22.9K
Stacks
24
Followers
566
Followers
14.7K
Followers
113
Votes
41
Votes
344
Votes
5
Pros & Cons
Pros
  • 27
    Cross platform builds
  • 8
    Vm creation automation
  • 4
    Bake in security
  • 1
    Easy to use
  • 1
    Good documentation
Pros
  • 121
    Infrastructure as code
  • 73
    Declarative syntax
  • 45
    Planning
  • 28
    Simple
  • 24
    Parallelism
Cons
  • 1
    Doesn't have full support to GKE
Pros
  • 2
    Automates infrastructure deployments
  • 1
    Infrastracture as a code
  • 1
    Easy to deploy for GCP
  • 1
    Fast deploy and update
Cons
  • 1
    Only using in GCP
Integrations
Amazon EC2
Amazon EC2
DigitalOcean
DigitalOcean
Docker
Docker
Google Compute Engine
Google Compute Engine
OpenStack
OpenStack
VirtualBox
VirtualBox
Heroku
Heroku
Amazon EC2
Amazon EC2
CloudFlare
CloudFlare
DNSimple
DNSimple
Microsoft Azure
Microsoft Azure
Consul
Consul
Equinix Metal
Equinix Metal
DigitalOcean
DigitalOcean
OpenStack
OpenStack
Google Compute Engine
Google Compute Engine
Jinja
Jinja
Python
Python
Google Cloud Storage
Google Cloud Storage
Google Compute Engine
Google Compute Engine
Google Cloud SQL
Google Cloud SQL

What are some alternatives to Packer, Terraform, Google Cloud Deployment Manager?

Ansible

Ansible

Ansible is an IT automation tool. It can configure systems, deploy software, and orchestrate more advanced IT tasks such as continuous deployments or zero downtime rolling updates. Ansible’s goals are foremost those of simplicity and maximum ease of use.

Chef

Chef

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.

Capistrano

Capistrano

Capistrano is a remote server automation tool. It supports the scripting and execution of arbitrary tasks, and includes a set of sane-default deployment workflows.

Puppet Labs

Puppet Labs

Puppet is an automated administrative engine for your Linux, Unix, and Windows systems and performs administrative tasks (such as adding users, installing packages, and updating server configurations) based on a centralized specification.

Salt

Salt

Salt is a new approach to infrastructure management. Easy enough to get running in minutes, scalable enough to manage tens of thousands of servers, and fast enough to communicate with them in seconds. Salt delivers a dynamic communication bus for infrastructures that can be used for orchestration, remote execution, configuration management and much more.

AWS CloudFormation

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.

Fabric

Fabric

Fabric is a Python (2.5-2.7) library and command-line tool for streamlining the use of SSH for application deployment or systems administration tasks. It provides a basic suite of operations for executing local or remote shell commands (normally or via sudo) and uploading/downloading files, as well as auxiliary functionality such as prompting the running user for input, or aborting execution.

AWS OpsWorks

AWS OpsWorks

Start from templates for common technologies like Ruby, Node.JS, PHP, and Java, or build your own using Chef recipes to install software packages and perform any task that you can script. AWS OpsWorks can scale your application using automatic load-based or time-based scaling and maintain the health of your application by detecting failed instances and replacing them. You have full control of deployments and automation of each component

Scalr

Scalr

Scalr is a remote state & operations backend for Terraform with access controls, policy as code, and many quality of life features.

Pulumi

Pulumi

Pulumi is a cloud development platform that makes creating cloud programs easy and productive. Skip the YAML and just write code. Pulumi is multi-language, multi-cloud and fully extensible in both its engine and ecosystem of packages.

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