Overview

Terraform

Stacks18.9K

Followers14.7K

Votes344

GitHub Stars47.0K

Forks10.1K

Pulumi

Stacks271

Followers293

Votes25

GitHub Stars24.1K

Forks1.3K

Pulumi vs Terraform: What are the differences?

# Introduction
This Markdown code provides a comparison between Pulumi and Terraform highlighting key differences between the two infrastructure as code tools.

1. **Programming Language Support**: Pulumi allows users to define infrastructure using familiar programming languages like TypeScript, Python, Go, and .NET, while Terraform uses its own declarative language called HashiCorp Configuration Language (HCL). This allows developers to leverage their existing skills and tools with Pulumi.
2. **State Management**: Pulumi uses its own state management system that can be stored in a variety of backends like local files, S3, or Azure Blob Storage, while Terraform relies on a single state file that can be stored locally or remotely in a backend like S3 or Azure Storage. Pulumi's approach can provide more flexibility and scalability in managing infrastructure state.
3. **Resource Dependencies**: Pulumi automatically manages dependencies between resources, ensuring that they are created and destroyed in the correct order, while Terraform requires users to explicitly define resource dependencies using the "depends_on" attribute. This can make defining complex infrastructure easier and more intuitive in Pulumi.
4. **Real-Time Updates**: Pulumi provides real-time updates and previews of changes, allowing users to see the impact of their code changes before applying them, while Terraform requires users to run the "terraform plan" command to preview changes. This can help in reducing errors and providing more visibility into the infrastructure changes with Pulumi.
5. **Cross-Resource Inputs**: Pulumi allows resources to reference output values of other resources directly within the same program, enabling easier sharing of data between resources, while Terraform requires the use of remote state and data sources to achieve similar functionality. This can lead to more streamlined and readable code in Pulumi.
6. **Support for Cloud Providers**: Pulumi offers support for a wide range of cloud providers and services, including AWS, Azure, Google Cloud, and Kubernetes, while Terraform also supports multiple cloud providers but may have limited support for specific services. This can make Pulumi a more versatile choice for multi-cloud environments or specific cloud services.

In Summary, the key differences between Pulumi and Terraform lie in programming language support, state management, resource dependencies, real-time updates, cross-resource inputs, and support for cloud providers, providing users with different options based on their specific needs and preferences.

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Advice on Terraform, Pulumi

Sung Won

Nov 4, 2019

Decidedon

Google Cloud IoT Core

Terraform

Python

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

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.

384k views384k

Comments

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

Terraform	Pulumi
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.	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.
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	Containers - Deploy a Docker container to production in 5 minutes using your favorite orchestrator.; Serverless - Stand up a serverless API or event handler in 5 minutes using a real lambda in code.; Infrastructure - Manage cloud infrastructure or hosted services using infrastructure as code.; CoLaDa - Embrace containers, lambdas, and data, using a modern, multi-cloud framework.
Statistics
GitHub Stars 47.0K	GitHub Stars 24.1K
GitHub Forks 10.1K	GitHub Forks 1.3K
Stacks 18.9K	Stacks 271
Followers 14.7K	Followers 293
Votes 344	Votes 25
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	Pros 8 Infrastructure as code with less pain 4 Best-in-class kubernetes support 3 Simple 3 Can use many languages 2 Great CLI
Integrations
Heroku Amazon EC2 CloudFlare DNSimple Microsoft Azure Consul Equinix Metal DigitalOcean OpenStack Google Compute Engine	No integrations available

What are some alternatives to Terraform, Pulumi?

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 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 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 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 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

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 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

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 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.