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

Chef vs Terraform

OverviewDecisionsComparisonAlternatives

Overview

Chef
Chef
Stacks1.3K
Followers1.1K
Votes345
Terraform
Terraform
Stacks22.9K
Followers14.7K
Votes344
GitHub Stars47.0K
Forks10.1K

Chef vs Terraform: What are the differences?

Introduction: Chef and Terraform are both popular configuration management tools used in the DevOps ecosystem. However, they have key differences that set them apart in terms of their approach and functionality.

  1. Language and Configuration: Chef primarily uses Ruby as its configuration language, allowing users to write recipes and cookbooks in Ruby syntax. On the other hand, Terraform uses HashiCorp Configuration Language (HCL) or JSON for its configuration files. This difference in languages can impact the comfort level and ease of use for developers.

  2. Granularity: Chef operates at a more granular level, allowing users to define configurations at the resource level, specifying the desired state for each resource. This enables fine-grained control over configurations and is well-suited for managing complex infrastructure. In contrast, Terraform operates at a higher level of abstraction, treating the infrastructure as a whole and focusing on the provision and orchestration of resources.

  3. Scalability and Resource Management: Chef is primarily designed for managing server configurations and software installations on individual nodes. It excels at configuring and maintaining a large number of nodes in a scalable manner. In contrast, Terraform specializes in managing infrastructure as code and provisioning resources from cloud providers or infrastructure providers. It is especially suitable for managing cloud resources and supports multiple providers.

  4. Human Readability: Chef recipes and cookbooks are generally more human-readable due to the use of Ruby as the configuration language. This makes it easier for developers and operators to understand, modify, and collaborate on configurations. On the other hand, Terraform's HCL configuration files may be less familiar to some developers, especially those who are not familiar with HashiCorp tools, but its syntax is relatively easy to learn for most users.

  5. Procedural vs. Declarative: Chef follows a procedural approach where a series of ordered steps are executed to converge the current state of a system with the desired state. It relies on idempotent resource declarations and imperative logic. In contrast, Terraform follows a declarative approach, allowing users to describe the desired state of the infrastructure, and Terraform itself handles the provisioning and orchestration to achieve that state. This declarative nature simplifies infrastructure management and reduces the risk of configuration drift.

  6. Community and Ecosystem: Both Chef and Terraform have active communities and ecosystems. However, Chef has been around for a longer time and has a more mature community and a vast number of pre-built cookbooks and resources available. Terraform, being a part of the larger HashiCorp ecosystem, benefits from its integration with other tools like Vault and Consul.

In Summary, Chef and Terraform differ in their configuration languages, granularity of control, scalability, human readability, approach (procedural vs. declarative), and community/ecosystem support. These differences make them suited for different use cases and preferences within the DevOps community.

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Advice on Chef, Terraform

Sung Won
Sung Won

Nov 4, 2019

DecidedonGoogle Cloud IoT CoreGoogle Cloud IoT CoreTerraformTerraformPythonPython

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

2.25M views2.25M
Comments
Timothy
Timothy

SRE

Mar 20, 2020

Decided

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.
385k views385k
Comments
Daniel
Daniel

May 4, 2020

Decided

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.

426k views426k
Comments

Detailed Comparison

Chef
Chef
Terraform
Terraform

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.

Access to 800+ Reusable Cookbooks;Integration with Leading Cloud Providers;Enterprise Platform Support including Windows and Solaris;Create, Bootstrap and Manage OpenStack Clouds;Easy Installation with 'one-click' Omnibus Installer;Automatic System Discovery with Ohai;Text-Based Search Capabilities;Multiple Environment Support;"Knife" Command Line Interface;"Dry Run" Mode for Testing Potential Changes;Manage 10,000+ Nodes on a Single Chef Server;Available as a Hosted Service;Centralized Activity and Resource Reporting;"Push" Command and Control Client Runs;Multi-Tenancy;Role-Based Access Control [RBAC];High Availability Installation Support and Verification;Centralized Authentication Using LDAP or Active Directory
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 Forks
-
GitHub Forks
10.1K
Stacks
1.3K
Stacks
22.9K
Followers
1.1K
Followers
14.7K
Votes
345
Votes
344
Pros & Cons
Pros
  • 110
    Dynamic and idempotent server configuration
  • 76
    Reusable components
  • 47
    Integration testing with Vagrant
  • 43
    Repeatable
  • 30
    Mock testing with Chefspec
Pros
  • 121
    Infrastructure as code
  • 73
    Declarative syntax
  • 45
    Planning
  • 28
    Simple
  • 24
    Parallelism
Cons
  • 1
    Doesn't have full support to GKE
Integrations
Amazon EC2
Amazon EC2
Rackspace Cloud Servers
Rackspace Cloud Servers
Microsoft Azure
Microsoft Azure
HP Cloud Compute
HP Cloud Compute
Joyent Cloud
Joyent Cloud
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

What are some alternatives to Chef, Terraform?

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.

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.

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

cPanel

cPanel

It is an industry leading hosting platform with world-class support. It is globally empowering hosting providers through fully-automated point-and-click hosting platform by hosting-centric professionals

Webmin

Webmin

It is a web-based interface for system administration for Unix. Using any modern web browser, you can setup user accounts, Apache, DNS, file sharing and much more. It removes the need to manually edit Unix configuration files.

Mina

Mina

Mina works really fast because it's a deploy Bash script generator. It generates an entire procedure as a Bash script and runs it remotely in the server. Compare this to the likes of Vlad or Capistrano, where each command is run separately on their own SSH sessions. Mina only creates one SSH session per deploy, minimizing the SSH connection overhead.

Puppet Bolt

Puppet Bolt

It is an open source orchestration tool that automates the manual work it takes to maintain your infrastructure. Use it to automate tasks that you perform on an as-needed basis or as part of a greater orchestration workflow.

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