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  1. Stackups
  2. DevOps
  3. Continuous Deployment
  4. Server Configuration And Automation
  5. Atlas vs GeoEngineer vs Terraform

Atlas vs GeoEngineer vs Terraform

OverviewComparisonAlternatives

Overview

Terraform
Terraform
Stacks22.9K
Followers14.7K
Votes344
GitHub Stars47.0K
Forks10.1K
Atlas
Atlas
Stacks33
Followers125
Votes0
GeoEngineer
GeoEngineer
Stacks52
Followers65
Votes0
GitHub Stars401
Forks54

Atlas vs GeoEngineer vs Terraform: What are the differences?

Introduction:
---
Key Differences Between Atlas, GeoEngineer, and Terraform:
---

1. **Backend Configuration**: Atlas provides a managed backend service, while Terraform allows you to configure backends on different platforms. GeoEngineer, on the other hand, requires you to handle the backend configuration manually, offering more flexibility but also requiring more management effort.

2. **Workflow Automation**: Atlas offers a GUI and API for workflow automation, making it easier to manage infrastructure changes. Terraform focuses on CLI-driven automation, offering a robust command-line interface for managing infrastructure as code. GeoEngineer integrates with Terraform, allowing for automation through code but lacks the additional GUI and API features of Atlas.

3. **Collaboration and Version Control**: Atlas provides built-in collaboration and version control features, allowing teams to work together on infrastructure changes efficiently. Terraform supports version control systems like Git but does not offer built-in collaboration features. GeoEngineer, being built on top of Terraform, inherits Terraform's version control capabilities but does not provide additional collaboration features.

4. **Community Support and Ecosystem**: Terraform has a larger community and ecosystem, with extensive documentation, plugins, and modules available. Atlas offers enterprise support and integration with other HashiCorp tools, catering more towards organizations requiring premium support. GeoEngineer benefits from Terraform's ecosystem but may lag behind in terms of community-developed resources compared to Terraform.

5. **Cost Structure**: Atlas follows a subscription-based pricing model, offering different tiers based on usage and features. Terraform is open-source and free to use, while GeoEngineer is also open-source and does not require any additional costs. Organizations need to consider their budget and requirements when choosing between these tools based on their cost structures.

6. **Customization and Extensibility**: Terraform and GeoEngineer offer a high level of customization through resource providers, allowing users to extend functionality as needed. Atlas provides a more opinionated approach, focusing on simplicity and ease of use, which may limit the extent of customization available compared to Terraform and GeoEngineer.

In Summary, understanding the key differences between Atlas, GeoEngineer, and Terraform is crucial for organizations to make an informed decision on which infrastructure as code tool best aligns with their needs and preferences.

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Detailed Comparison

Terraform
Terraform
Atlas
Atlas
GeoEngineer
GeoEngineer

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.

Atlas is one foundation to manage and provide visibility to your servers, containers, VMs, configuration management, service discovery, and additional operations services.

GeoEngineer uses Terraform to plan and execute changes, so the DSL to describe resources is similar to Terraform's. GeoEngineer's DSL also provides programming and object oriented features like inheritance, abstraction, branching and looping.

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
One command to develop any application: vagrant up;One command to deploy any application: vagrant push
-
Statistics
GitHub Stars
47.0K
GitHub Stars
-
GitHub Stars
401
GitHub Forks
10.1K
GitHub Forks
-
GitHub Forks
54
Stacks
22.9K
Stacks
33
Stacks
52
Followers
14.7K
Followers
125
Followers
65
Votes
344
Votes
0
Votes
0
Pros & Cons
Pros
  • 121
    Infrastructure as code
  • 73
    Declarative syntax
  • 45
    Planning
  • 28
    Simple
  • 24
    Parallelism
Cons
  • 1
    Doesn't have full support to GKE
No community feedback yet
No community feedback yet
Integrations
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
No integrations available
Ruby
Ruby

What are some alternatives to Terraform, Atlas, GeoEngineer?

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

Packer

Packer

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.

Scalr

Scalr

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

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