AWS Partner Network (APN) Blog
Using ControlMonkey’s Terraform Platform to Govern Large-scale AWS Environments
By Ioannis Moustakis, Sr Solutions Architect, AWS
By Aharon Twizer, CEO, ControlMonkey
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An examination of the evolution of cloud operations in recent years reveals a growing trend among organizations to oversee large-scale cloud environments. There is a clear trend toward an increasing number of businesses overseeing extensive and complex cloud infrastructures. This evolution reflects a broader adoption of cloud computing technologies, driven by factors such as the need for scalability, flexibility, and cost efficiency.
As organizations embrace cloud solutions, they are no longer limited to standalone implementations; instead, they are managing infrastructure across multiple AWS accounts which are distributed across various geographic regions. This multi-account architecture allows organizations to separate workloads, enhance security, and better manage costs, but it also introduces complexities in governance, compliance, and resource allocation.
The Challenges of Managing Large-Scale Cloud Environments
There are a few challenges when managing large-scale environments. First, it becomes increasingly difficult for cloud teams to maintain a clear overview of their asset inventory without the proper tools. Infrastructure as Code (IaC) has consistently been the golden standard for deploying cloud components. Equally important is a clear understanding of cloud environments’ desired versus actual state and a mechanism to consolidate differences between the two.
Second, we notice cloud teams becoming bottlenecks within large organizations. Every request to provision or update infrastructure must go through the cloud operations team slowing operations and increasing the time to market for new features or updates. Furthermore, it is evident that teams are engaging with various interfaces, including the AWS command line interface (CLI), the AWS Management Console, and Application Programming Interfaces (APIs). Maintaining more than one interface to provision and operate infrastructure adds to the complexity. Without robust automation, scaling operations becomes a formidable challenge.
Using a Proactive Cloud Strategy for Large-Scale Cloud Environments
Complex multi-region, multi-account infrastructure architectures require a proactive approach to infrastructure management, focusing on scalability and efficiency. The first step into an effective cloud strategy is adopting IaC. IaC has emerged as a pivotal solution for managing sprawling cloud environments efficiently and effectively. The core advantage of IaC lies in its ability to define and manage the desired state of cloud infrastructure. One of the IaC tools commonly used by AWS customers is Terraform.
IaC allows you to follow the same methodologies you use for your software delivery, such as having your code in a Git repo, which is version-controlled and audited. It also enables a centralized continuous integration and continuous deployment (CI/CD) pipeline for any infrastructure change with the correct policies and guardrails. When organizations begin to implement IaC, they find themselves in a state where their current environments have been established through manual processes, while new environments are generated automatically through automation. Achieving near-complete IaC coverage ensures that almost every aspect of your cloud environment is codified and managed predictably and repeatedly.
In order to make sure you have near-complete IaC coverage for your cloud components running on your AWS environments, a comprehensive and holistic view of your cloud assets inventory is crucial. Additionally, identifying deviations between the desired configuration and the actual state is necessary for achieving operational excellence. By adopting these strategies, cloud teams can ensure that their infrastructure meets current operational standards and is poised for future growth and challenges. This proactive approach to cloud strategy emphasizes not only the importance of maintaining oversight but also the facilitation of innovation and the promotion of strategic autonomy.
ControlMonkey – Your Control Plane for Managing Large-Scale AWS Environments with Terraform
ControlMonkey provides an AI-powered Terraform automation platform that enables organizations to operate infrastructure at scale. As shown in Figure 1, the solution offers a comprehensive control plane for visualizing, deploying, automating, and governing cloud infrastructure managed through Terraform.
Figure 1 – ControlMonkey High-Level Status Report.
Here’s how ControlMonkey addresses these cloud infrastructure challenges:
Cloud inventory view: ControlMonkey offers a unified dashboard that allows users to view all AWS resources, providing clarity on which elements are managed through IaC and which are not.
Achieve 99% IaC and Terraform coverage: ControlMonkey’s AI-powered Terraform Import engine can use your existing cloud infrastructure and generate both the Terraform code and Terraform state file, so you don’t need to re-provision your infrastructure, but simply import it. By achieving near-complete IaC coverage, teams can avoid using graphical user interfaces (GUIs) to create resources manually.
Disaster Recovery: Assists with implementing a Disaster Recovery strategy – Having all your cloud resources under IaC management and the necessary tooling to manage and operate infrastructure effectively is a prerequisite for an effective Disaster Recovery strategy.
Infrastructure Delivery Standardization: ControlMonkey provides best practices and standardization to deliver infrastructure at scale with out-of-the-box policies for cost, compliance, security, and tagging.
Self-Service Catalog: ControlMonkey provides a way to share predefined, compliant infrastructure blueprints for teams that are less familiar with Terraform. The end user is required to select a blueprint, input the necessary variables, and subsequently establish their infrastructure efficiently.
Drift Detection & Remediation: ControlMonkey provides a drift detection mechanism that alerts you on any difference between the desired configuration and the actual state. Once a drift is identified, ControlMonkey provides a remediation path by fixing the Terraform code for you with our AI-powered code generation or reconciling your actual state with the desired state.
The combined capabilities provide an effective cloud strategy that allows you to manage AWS infrastructure at scale.
Real-world Use Case with ControlMonkey
We will explore a practical application involving a customer of ControlMonkey, focusing on a case study of a medium-sized enterprise that operates with a cloud team consisting of six members, operating remotely.
Before using ControlMonkey, this team faced three main challenges in their cloud operations. First, while they managed most of their AWS infrastructure with Terraform, other resources were not managed by Terraform, creating uniformity issues. This inconsistency blocked cloud engineers from having a uniform way to manage their infrastructure. Furthermore, manual changes, led to drifts between the Terraform code and the actual state, detected only during subsequent modifications. Finally, there was no centralized Terraform pipeline for making changes to the infrastructure, so all Terraform ‘Plan’ and ‘Apply’ commands were run on the cloud team’s personal laptops. This lack of centralization hindered the ability to implement and enforce policies and adversely affected both auditability and visibility of deployments.
The team’s focus on manual processes and reactive troubleshooting prevented them from delivering new initiatives and critical business outcomes.
After adopting ControlMonkey, the team gained complete visibility of their AWS environments through a comprehensive cloud resource inventory. As shown in Figure 2, the team achieved 99% Terraform coverage through ControlMonkey’s Terraform Import Engine, which automatically generated infrastructure code for their existing AWS resources.
Figure 2 – Infrastructure as Code coverage Increasing over time after ControlMonkey adoption.
The cloud team was also able to centralize their infrastructure collaboration changes by implementing ControlMonkey’s Terraform GitOps CI/CD pipeline, replacing local Terraform command execution. As shown in Figure 3, the solution enables automated assessment of security, cost, and compliance impacts through pre-configured policy packages when pull requests are created, significantly reducing manual reviews and preventing configuration errors through proactive quality gates.
Figure 3 – Pull Request Decoration with Policy Enforcement Feedback.
The team can swiftly investigate production incidents using ControlMonkey’s drift detection and manual activity scanner. They possess a transparent audit log that details the modifications made to resource configurations, including the identity of the individual responsible and timing of these changes, thereby considerably decreasing the time required for investigations from minutes to seconds.
Conclusion
Cloud teams are increasingly tasked with delivering infrastructure changes quickly and reliably. As we look to the future, it’s clear that such teams will manage more infrastructure than they do today, making manual management unfeasible. Thus, automation becomes essential.
IaC provides a unified approach to managing complex cloud environments. However, IaC is merely the foundational layer. The next generation of infrastructure management at scale involves adding automation capabilities that enable you to manage and operate your environments at scale. ControlMonkey establishes a new benchmark for cloud automation by supporting best practices and bolstering automation that enables scalable infrastructure delivery.
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ControlMonkey – AWS Partner Spotlight
ControlMonkey is an AWS Specialization Partner and Terraform Operations platform that enables networking and DevOps teams to take a proactive DevOps strategy regarding cloud operations.