HashiCorp [Private Offer Only]

I started my journey with HashiCorp Terraform  in 2023 at Toolbox Tech , coming from a background with no prior experience in cloud or infrastructure. After going through a DevOps learning path focused on GCP, I was able to quickly get up to speed. My first major project involved using Terraform  to build and integrate a platform for AutoAvaliar, a Brazilian company that connects vehicle sellers to buyers.

Over the last 12 months, my work has spanned both Google Cloud  Platform and Microsoft Azure . Terraform has been the cornerstone of my workflow in both environments. The difference it makes is night and day. Before, deploying infrastructure meant navigating the cloud console, a tedious process of 'click, wait, click, wait.' Now, we define and deploy complex infrastructure entirely through code, which is repeatable, version-controlled, and incredibly fast.

I work at Toolbox Tech , where we provide services to clients like Embracon and AutoAvaliar. Terraform is essential in all this work, allowing us to rapidly provision infrastructure for our internal projects and for our clients.

For example, I recently worked on a project integrating Azure  Kubernetes  Service with Azure Key Vault . I noticed the team was creating all their key vaults manually through the console. I built a GitHub Actions  pipeline that automates the entire process. Now, creating a new Key Vault is as simple as adding a configuration file to a GitHub  repository, which has been a huge time-saver for them.

Similarly, in GCP, I have developed a comprehensive suite of standardized Terraform modules for a wide array of services, including Cloud Run, App Engine, Firestore, and Cloud SQL. This empowers developers: instead of manually setting up their services, they can now use a CI/CD pipeline. Once their application code is ready, the pipeline uses our Terraform modules to provision the necessary services and automatically deploy the app to production.

Ultimately, HashiCorp Terraform is an essential tool for us. It empowers us to accelerate our development cycles, reduce manual errors, and create robust, standardized infrastructure across multiple cloud providers.

Terraform has fundamentally improved our organization by:

1. Accelerating Delivery: We now deploy infrastructure in minutes, not hours.
2. Reducing Errors: Automating with code eliminates manual configuration mistakes.
3. Improving Security: We centrally manage infrastructure and avoid sharing sensitive cloud credentials.
4. Controlling Costs: The state file gives us a clear view of all provisioned resources, helping us eliminate waste.
5. Standardizing Deployments: Reusable modules ensure consistency and best practices.

The three most valuable features for me are the state file, reusable modules, and the simplicity of HCL.

1. The State File: This is Terraform's core strength. It acts as a single source of truth for our infrastructure, allowing us to track every resource, plan changes safely, and detect any manual modifications. Before Terraform, we lacked visibility into who was creating what, leading to higher costs. Now, with the state file, we have full control and can easily remove unused resources.

2. Reusable Modules: The ability to create and share modules is a game-changer. At Toolbox Tech , we build our own modules to enforce standards and encapsulate complexity. This allows developers to provision complex infrastructure, like a full CI/CD pipeline that deploys to Cloud Run and sets up New Relic  alerts, without needing to be Terraform experts themselves. The public Terraform Registry is also a fantastic resource for getting started quickly.

3. HCL (HashiCorp Configuration Language): The syntax is declarative and very easy to read, making it simple for new team members to learn and contribute. It's much more approachable than using a general-purpose programming language for infrastructure.

A built-in feature to generate a visual diagram or graph of the infrastructure directly from the state file would be incredibly valuable. This would allow teams to easily visualize all deployed resources and their dependencies without relying on third-party tools.

I have been using the solution for three years.

For anyone starting with Terraform, my advice is to invest time in learning the core concepts, as the official documentation is excellent and primarily in English. Also, while AI tools can help generate boilerplate code, they don't replace a solid understanding of how Terraform works, especially regarding state management. Always review AI-generated code carefully before applying it to your infrastructure.

We primarily use HashiCorp Terraform  for our infrastructure deployments and cloud deployments. As a cloud architect and infra DevOps architect, we use HashiCorp Terraform  to deploy our code, builds, and set up CI/CD pipelines. We create Terraform code and deploy it through Terraform Enterprise.

Regarding state management and orchestration capabilities of HashiCorp Terraform's impact on our workflows, we had two different environments. Initially, we used Jenkins  for our CI/CD pipelines. Because we primarily work on Azure , we use remote state files for Azure  deployments. Jenkins  runs the HashiCorp Terraform code, and then the state file gets saved in our Azure storage as a remote location. Whenever we require that state file, we can retrieve it from there.

At a later stage, we enhanced the environment by setting up Terraform Enterprise in our environment. Currently, we have it within our environment. We set up Terraform Enterprise with HashiCorp's license. We create modules from HashiCorp Terraform using the Azure ARM provider, and then we set up those modules to apply Azure best practices. We provide this to the development team and other teams so they can reuse these modules and deploy secure codes. We also manage upgrades and the complete lifecycle of TFE, and we provide these services to our other teams.

One of the major benefits of HashiCorp Terraform is that it is platform-agnostic because it supports multiple cloud platforms. This is the biggest advantage.

The state file is one of the key features of HashiCorp Terraform that helps us because whenever there is a drift, it actually helps us identify those and reset the environment to the actual desired state.

From our environment, we have enabled the health monitoring and drift detection features in HashiCorp Terraform. These have been really useful for our environment. It helps us where we do not have HashiCorp Terraform code for the environments and get that created, and whenever there is a manual change, we get to know that.

One issue we have faced with HashiCorp Terraform is that sometimes the latest features that Microsoft suggests are added in their services take some time for HashiCorp Terraform to get enhanced. Sometimes there is that lag where we want some service or configuration which is not yet enabled in the HashiCorp Terraform module.

The initial setup process for HashiCorp Terraform was fine and possible without major challenges. However, currently, our organization helps set up governance and policies, including those related to the OS. There is a challenge where our organization is recommending a specific OS, which is currently not supported by HashiCorp Terraform.

I have been working with HashiCorp Terraform for the last 10 years.

Since it is based on Azure, HashiCorp Terraform is highly scalable.

From the maintenance point of view, it is not difficult to maintain HashiCorp Terraform because primarily all those resources or the infrastructure we set up in Azure, and we have the high availability setup. From that perspective, there haven't been major challenges.

In terms of the stability and reliability of HashiCorp Terraform, I have not had any performance issues with it. There was only one challenge when we installed one of the patches in HashiCorp Terraform, and we lost some database. That was one challenge, which was not a known issue. Later, they identified that there was one defect in that code. Apart from that, there have been no challenges.

Since it is based on Azure, HashiCorp Terraform is highly scalable.

Whenever we face any issues, we escalate it to HashiCorp technical support, and we get that support. We have regular interaction with them. I would rate the HashiCorp technical support an eight on a scale of one to ten, where ten is the best.

From a configuration management perspective, we have used Jenkins and SCCM. My experience is limited to that, but some of my team members have used Ansible  and others as well.

The major benefit of HashiCorp Terraform in comparison to Jenkins and SCCM is that these are only configuration management tools, but HashiCorp Terraform is a language where you can deploy those, and you provide those providers for different cloud environments. This gives it a tactical advantage. It deals with having a seat, or it always helps having a lesser technical landscape. We don't have to have additional tools or any other feature to develop that IaC  code as well.

When we did the POC and wanted to set up our production environment, it took us around three to four months. Now that we have set up that pipeline for the upgrades, it doesn't take us that long. For the production deployment, currently, it takes us around two weeks, excluding the testing, the stage deployment, development, and deployment.

We have not calculated the measurable benefits recently from using HashiCorp Terraform. On a high level, I would say it has improved our overall environment, speed, and excellence by approximately 20%.

I can provide feedback regarding my experience working with HashiCorp Terraform. We haven't found any major challenges with the integration of HashiCorp Terraform. Frankly speaking, we haven't explored it completely, but for our requirements, it has been working fine.

I have not had any AI-driven projects or AI-driven tasks, nor have I utilized any AI within HashiCorp Terraform yet. I might not be the right person regarding the HashiCorp Terraform licensing part because I haven't been involved from the costing perspective.

Based on my experience, I would recommend HashiCorp Terraform to others, majorly because of the advantages of being platform-agnostic and all those other features. My overall rating for HashiCorp Terraform is eight out of ten.

I use it to deploy our Kubernetes  clusters and Boomi  as well. I can run Boomi  by itself and not worry about Terraform  and Kubernetes , and that's okay.

However, my use case requires me to run it in a cluster environment to scale up and scale down accordingly and manage costs. This is why I chose Kubernetes. Although it's possible to manually roll it out, I have security requirements to consider for a secure environment. To make it repeatable, I use Bicep or Terraform , as I'm mostly running in Azure .

I decided on Terraform to manage deployments across multiple environments. This includes a dev environment, a QA environment, and now a production environment. I test and then run Terraform to deploy these environments. In AI, it's more about setting up your environment.

I develop AI-based products and use Terraform to create a consistent and secure environment in Azure . Once deployed, I get a security score from Azure and receive recommendations for improvements. If a resource is noncompliant under SOC 2, type one or two, ISO, or HIPAA, I make the changes in Terraform, redeploy, and observe score improvements. Terraform aids in ensuring my deployments are correctly configured across environments.

The most valuable feature is predictability. When I need to put something, it is repeatable. I can deploy it in one environment, change the parameter, and it will work in another. If changes are needed, I track who did what because my Terraform scripts are version-controlled. This reduces guesswork and trial and error from the UI, replacing it with code, which enhances maintainability. In cloud environments, manual configurations can be forgotten or misunderstood if the original person leaves, but Terraform ensures documentation and consistent configurations.

The area for improvement is the speed at which resources become available. When a new resource is introduced in AWS  or Azure, especially since I work mostly in Azure, how quickly can it be integrated into my Terraform scripts ensuring rapid availability of resources is crucial.

I have used the solution for two to three years now.

The solution is stable because I can define which version I want to use. I am not at the mercy of the latest version. I can choose a stable version and decide whether to upgrade based on its performance.

The solution scales well. Terraform runs only when our pipelines run and parallelizes resource creation/updates whenever possible.

The setup is straightforward, but mastering it may take a day or two. Maintenance requires significant expertise. The challenge lies in understanding the environments being created, not Terraform itself.

I am using the free version. The paid version is necessary when you run it and manage it in the environment, however, I only use Terraform runtimes. The documentation is excellent, and understanding how to read it is crucial.

Once I understand the different concepts of Terraform, it becomes straightforward, allowing for further capabilities. A major challenge was applying Terraform to existing resources, yet now the new import method is much improved.

My product rating is eight out of ten.