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Packer vs Terraform: What are the differences?

Packer and Terraform are both popular tools used in the field of infrastructure automation and orchestration. While they serve different purposes, they have some key differences that set them apart.

  1. Provisioning vs Orchestration: Packer is primarily focused on creating machine images or artifacts, while Terraform is more focused on managing and orchestrating the infrastructure those images will run on. Packer helps to create consistent and reproducible machine images, whereas Terraform helps to provision and manage the infrastructure resources.

  2. Builders vs Providers: Packer uses a concept of builders to create machine images. These builders define where the images will be built, what type of machine they will be built on, and how the image will be created. Terraform, on the other hand, uses providers to manage resources within cloud platforms or infrastructure providers. These providers are responsible for creating, managing, and destroying infrastructure resources.

  3. Image-centric vs Resource-centric: Packer is image-centric, meaning its focus is on the creation and management of machine images. It allows you to build a custom image with desired configurations and software. In contrast, Terraform is resource-centric, focusing on managing infrastructure resources like virtual machines, networks, storage, etc. It provides a declarative way to define the desired state of the infrastructure.

  4. Immutable vs Mutable: Packer promotes the concept of immutable infrastructure, where machine images are built once and deployed multiple times without any modifications. This ensures consistency and eliminates configuration drift. Terraform, on the other hand, supports both immutable and mutable infrastructure. It allows provisioning and continuous modification of infrastructure resources, which can be beneficial in certain use cases.

  5. Single-use vs Multi-use: Packer is typically used in a single-use scenario, where it is run to create a machine image, which is then used to deploy instances. Once the image is created, there is no further interaction with Packer. Terraform, on the other hand, is designed for multi-use scenarios. It allows you to define and manage infrastructure resources as code, facilitating continuous deployment and management of the infrastructure.

  6. Concerns vs Abstractions: Packer primarily focuses on packaging applications and their dependencies into machine images, taking into account concerns like configuration management and provisioning. Terraform, on the other hand, abstracts infrastructure resources and allows you to define the desired state of the infrastructure, taking care of resource provisioning, lifecycle management, and dependency mapping.

In summary, Packer is focused on building machine images and is more image-centric, while Terraform is focused on managing and orchestrating the infrastructure and is more resource-centric. Packer promotes immutable infrastructure, is typically used in a single-use scenario, and is concerned with application packaging. Terraform, on the other hand, supports both immutable and mutable infrastructure, is designed for multi-use scenarios, and abstracts infrastructure resources for efficient management.

Decisions about Packer and Terraform
Julien Fouilhé

It was important for us to use IaC from the very beginning, since we'll be deploying multiple components to multiple environments, and we want those environments to be easily replicated.

While the pragmatic choice would have been the widely used Terraform, we decided to go with Pulumi, which offers a more familiar syntax to describe your infrastructure (the language of your choice, in our case, Typescript). It also has an interesting built-in way of hiding your secrets for you, which makes managing secrets securely a breeze compared to Terraform.

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Hendrik Halkow

Terraform provides a cloud-provider agnostic way of provisioning cloud infrastructure while AWS CloudFormation is limited to AWS.

Pulumi is a great tool that provides similar features as Terraform, including advanced features like policy and cost management.

We see that Terraform has great support in the cloud community. For most cloud services we use, there is an official Terraform provider. We also believe in the declarative model of HCL, which is why we chose Terraform over Pulumi. However, we still keep an eye on Pulumi's progress.

Ansible is great for provisioning software and configuration within virtual machines, but we don't think that Ansible is the right tool for provisioning cloud infrastructure since it's built around the assumption that there is an inventory of remote machines. Terraform also supports more services that we use than Ansible.

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Kirill Shirinkin
Cloud and DevOps Consultant at mkdev · | 3 upvotes · 147.1K views

Ok, so first - AWS Copilot is CloudFormation under the hood, but the way it works results in you not thinking about CFN anymore. AWS found the right balance with Copilot - it's insanely simple to setup production-ready multi-account environment with many services inside, with CI/CD out of the box etc etc. It's pretty new, but even now it was enough to launch Transcripto, which uses may be a dozen of different AWS services, all bound together by Copilot.

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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.

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Sergey Ivanov

We use Terraform to manage AWS cloud environment for the project. It is pretty complex, largely static, security-focused, and constantly evolving.

Terraform provides descriptive (declarative) way of defining the target configuration, where it can work out the dependencies between configuration elements and apply differences without re-provisioning the entire cloud stack.


Terraform is vendor-neutral in a way that it is using a common configuration language (HCL) with plugins (providers) for multiple cloud and service providers.

Terraform keeps track of the previous state of the deployment and applies incremental changes, resulting in faster deployment times.

Terraform allows us to share reusable modules between projects. We have built an impressive library of modules internally, which makes it very easy to assemble a new project from pre-fabricated building blocks.


Software is imperfect, and Terraform is no exception. Occasionally we hit annoying bugs that we have to work around. The interaction with any underlying APIs is encapsulated inside 3rd party Terraform providers, and any bug fixes or new features require a provider release. Some providers have very poor coverage of the underlying APIs.

Terraform is not great for managing highly dynamic parts of cloud environments. That part is better delegated to other tools or scripts.

Terraform state may go out of sync with the target environment or with the source configuration, which often results in painful reconciliation.

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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.
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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

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Pros of Packer
Pros of Terraform
  • 27
    Cross platform builds
  • 9
    Vm creation automation
  • 4
    Bake in security
  • 1
    Good documentation
  • 1
    Easy to use
  • 122
    Infrastructure as code
  • 73
    Declarative syntax
  • 45
  • 28
  • 24
  • 8
  • 8
    Cloud agnostic
  • 6
    It's like coding your infrastructure in simple English
  • 6
    Immutable infrastructure
  • 5
    Platform agnostic
  • 4
  • 4
  • 4
    Automates infrastructure deployments
  • 4
  • 2
  • 2
    Scales to hundreds of hosts

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Cons of Packer
Cons of Terraform
    Be the first to leave a con
    • 1
      Doesn't have full support to GKE

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    - No public GitHub repository available -

    What is 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.

    What is Terraform?

    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.

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