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Imagine you’re a DevOps engineer managing microservices on AWS. How would you configure auto-scaling to handle high traffic without wasting any resources?

Your teammate is setting up an auto-scaling policy using Terraform. The goal is to automatically add more EC2 instances when CPU usage gets too high so the application can handle more API requests during peak traffic.

However, there is a mistake in the Terraform configuration; the CPU utilization threshold is set to 10% instead of a higher, more reasonable value, like 60-70%. Because of this, even small activities like health checks and background tasks trigger auto-scaling. As a result, the system keeps launching new EC2 instances unnecessarily, even when they are not needed. 

By the end of the month, the organization is hit with an unexpected bill of $18,000. The mistake was small, but its impact was significant.

This kind of problem is common in organizations where multiple teams manage their own resources independently. The data team, led by a data platform engineer, is responsible for managing large-scale data processing using Amazon EMR and analytics workloads on Amazon Redshift. These engineers ensure that data infrastructure scales efficiently without incurring excessive costs.

The infrastructure team manages networking resources such as VPCs, subnets, and security groups, ensuring connectivity and security across all environments. The application team deploys microservices using Amazon ECS or EKS, running workloads on EC2 instances or Fargate. Meanwhile, the ML team might use Amazon SageMaker with GPU-powered instances for training models. 

Without a centralized system to track and manage these resources, misconfigurations, such as over-provisioning, idle instances, or conflicting configurations, can easily go unnoticed. When different teams work separately without a shared system, unexpected cost spikes are more likely. 

When cloud costs increase unexpectedly, it indicates inefficiencies in how resources are configured and managed within your organization. Resolving these issues requires more than just cost tracking. It needs an approach where resource provisioning, scaling, and cost management are closely aligned and built into the infrastructure setup.

Platform engineering philosophy is the practice of building and maintaining an internal developer platform that acts as a bridge between developers and the underlying cloud infrastructure. It provides a structured system where cost management, resource provisioning, and scaling are built into daily workflows. This makes sure that resources are used efficiently, scaling is handled correctly, and cloud expenses stay within the organization’s budget.

Platform Engineering vs DevOps​

Before diving into the differences, let's first address the question: What is platform engineering? Platform engineering is the practice of building and maintaining an internal developer platform that acts as a bridge between developers and the underlying cloud infrastructure.

Now, scaling your cloud resources efficiently and keeping the cloud costs under control at the same time are some of the major challenges for many organizations. Mismanaged resources, misconfigurations, and the absence of centralized monitoring often lead to some unnecessary expenses. To address such issues, it is important to understand how platform engineering philosophy and DevOps differ in their approach.

Now, here is a comparison table of platform engineering philosophy and DevOps, focusing on their roles in resource management and cost optimization:

Platform engineering is all about building a shared system that simplifies how DevOps or Infrastructure teams manage their infrastructure and control cloud costs. Standardizing processes and enforcing clear rules helps reduce errors and makes sure that resources within your infrastructure are used effectively.

On the other hand, DevOps focuses more on teamwork and flexibility. It improves team collaboration and speeds up the deployments as well, but this flexibility can sometimes lead to inconsistent practices and higher cloud costs.

For organizations struggling with increasing cloud expenses and resource inefficiencies, combining platform engineering with DevOps can strike the right balance. It brings structure to resource management while still allowing teams to work efficiently.

How Does Infrastructure Platform Engineering Philosophy Help in Cost Management?

Now as we know the differences between platform engineering and DevOps, it’s clear that platform engineering takes a more structured approach for managing your cloud infrastructure. This philosophy is particularly effective in addressing cloud cost challenges by integrating cost management directly into resource provisioning, scaling, and governance processes.

Standardizing Resource Provisioning

One of the main reasons for higher cloud costs is allocating resources without proper planning or provisioning instances that are larger than necessary. For example, using an EC2 instance like m5.4xlarge instead of m5.large increases your cloud expenses without matching the actual needs of the application. Similarly, not setting up lifecycle policies for S3 buckets can lead to unused data, such as old logs or temporary files, being stored for long periods, which can also add up to storage costs.

These modules define how resources should be configured, including instance sizes, network configurations, and tags. Since they are created by a central or platform team, they ensure that all considerations, such as performance, cost efficiency, and security are accounted for before provisioning. For example, an EC2 module can guide teams to select an instance size that fits both the application’s needs and the organization’s budget at the same time. It can also include tagging rules to track spending and identify resources that are no longer needed within your infrastructure. By using these modules, teams can avoid over-provisioning and configure their resources correctly.

Automating Scaling

Next, let’s talk about scaling, one of the trickiest parts of managing cloud infrastructure. We’ve all been there; when setting up a new EC2 instance or Kubernetes cluster, there’s always some guesswork involved. To be safe, we often over-provision and end up paying for idle resources, or we under-provision and run into performance issues. When scaling isn’t properly configured, extra instances may be added during high traffic but don’t scale down when demand drops, leading to unnecessary costs.

Platform engineering helps by automating scaling based on real-time usage. Instead of relying on fixed thresholds or manual adjustments, scaling policies automatically adjust resources based on actual workload demands. For example, EC2 instances can be set to scale up when CPU usage goes above 70% and scale down when it drops below 30%. Kubernetes clusters can scale pods based on memory usage or request load, ensuring resources are always in line with demand.

By integrating these automated scaling policies, resources are provisioned only when needed and removed as soon as traffic decreases. This enables self-service capabilities, allowing development teams to deploy applications without worrying about over-provisioning or unexpected costs. Instead of manually adjusting scaling settings, teams can rely on predefined rules set by the platform engineering team, ensuring resources are used efficiently.

Enforcing Policies

Now, let’s look at policy enforcement, which plays an important role in controlling cloud costs. When teams create resources without following standard rules, tracking expenses and optimizing usage becomes much harder. For example, if EC2 instances and S3 buckets are not tagged properly, it becomes difficult to determine which project, cost center, or team owns them. As a result, unused instances may keep running unnoticed, adding to cloud costs without anyone being accountable for them.

Platform engineering philosophy addresses this by enforcing governance policies that define how resources should be created, monitored, and destroyed. These policies require teams to follow some specific rules, such as applying cost allocation tags, setting spending limits, and restricting certain instance types. For example, an organization might enforce a rule where every EC2 instance must have tags like Project: BillingSystem, Owner: DevOpsTeam, and Environment: Production to improve cost monitoring. Another policy could prevent the provisioning of high-cost instances like r5.8xlarge unless explicitly approved.

By integrating these policies into the platform, organizations establish pre-defined rules that everyone in the team must follow. This makes sure that cost control is part of daily operations, prevents unnecessary spending, simplifies audits, and keeps cloud costs within the organization's budget.

Organizations use various platform engineering tools to enforce policies and manage cloud resources efficiently. Tools like Backstage, Crossplane, and Firefly help standardize provisioning, enforce cost controls, and provide self-service capabilities for developers.

Setting Up Monitoring for Cloud Costs (Hands-On)

Now that we have seen how platform engineering philosophy helps control your cloud costs through provisioning, scaling, and policy enforcement, the next step in the list is monitoring your cloud costs. Without proper cost monitoring, unexpected expenses can build up and go unnoticed until they appear in your billing report. 

To prevent this, we will set up an automated cost monitoring system using Terraform, AWS Budgets, and AWS Cost Explorer. This setup will help you track your cloud spending, send alerts when costs exceed a defined threshold, and provide insights into which resources contribute the most to the overall bill.

To track costs, we first need to create some AWS resources. We will provision an EC2 instance and an S3 bucket using Terraform. The EC2 instance will be a t2.micro running in us-east-1, and the S3 bucket will be private. These resources will be tagged with relevant metadata such as Name, Environment, and Owner to help track expenses in AWS Cost Explorer. The following Terraform configuration provisions these resources.

provider "aws" {

  region = "us-east-1"

}

resource "aws_instance" "monitoring_ec2" {

  ami       = "ami-0e86e20dae9224db8"

  instance_type = "t2.micro"

  subnet_id = "subnet-02efa144df0a77c13"

  tags = {

Name    = "monitoring-ec2"

Environment = "dev"

Owner   = "team-a"

  }

}

resource "aws_s3_bucket" "monitoring_bucket" {

  bucket = "cost-monitoring-bucket-cycloid-93829"

  acl = "private"

  tags = {

Name    = "monitoring-s3-bucket"

Environment = "dev"

Owner   = "team-a"

  }

}

Once the Terraform configuration is ready, we need to initialize Terraform to download the necessary provider plugins. Running the terraform init command will set up the working directory for Terraform.

At this point, Terraform will initialize the AWS provider, ensuring that all required dependencies are installed.

After initializing Terraform, we should verify what changes will be applied by running the terraform plan command. This helps confirm that Terraform will create the correct resources before actually deploying them.

Once the plan is reviewed, we can proceed with applying the configuration. Running the terraform apply command will create the EC2 instance and S3 bucket as defined in the Terraform configuration.

Now that the EC2 instance and S3 bucket are deployed, the next step is to create a budget to track cloud costs. Here, we will define a monthly budget of $1 and enable cost tracking. This will allow us to detect any unexpected costs early and take action before they increase further. The following Terraform configuration sets up the AWS Budgets resource.

resource "aws_budgets_budget" "monthly_cost_budget" {

  name          = "monthly-cost-budget"

  budget_type   = "COST"

  limit_amount  = "1"

  limit_unit    = "USD"

  time_unit     = "MONTHLY"

  cost_types {

include_credit        = false

include_discount      = true

include_other_subscription = true

include_recurring     = true

include_tax           = true

  }

}

Before applying this configuration, we verify it by running the terraform plan  command.

Once verified, apply the changes with the terraform apply command to create the budget in AWS. Terraform will now deploy the budget, making it visible in the AWS Budgets console.

Now, having a budget in place is useful, but we also need a way to get notified when spending reaches a certain threshold. Instead of checking the budget through the AWS console, we can set up an email alert that will notify us when expenses exceed 80% of the budget. This makes sure that any unexpected charges are detected early. The following Terraform configuration creates an AWS Budget Action that sends an email alert when spending goes above 80% of the budget.

resource "aws_budgets_budget_action" "email_alert" {

  budget_name = aws_budgets_budget.monthly_cost_budget.name

  action_type = "NOTIFICATION"

  notification {

notification_type = "ACTUAL"

comparison_operator = "GREATER_THAN"

threshold = 80.0

threshold_type = "PERCENTAGE"

subscriber {

   subscription_type = "EMAIL"

   address = "saksham@infrasity.com"

}

  }

}

Run the terraform apply command to apply the changes and activate the budget alert.

Once the budget is created, AWS will start tracking costs, and if the spending exceeds 80% of the defined limit, an email notification will be sent to the configured address. Below is an example of an AWS Budgets alert showing that the actual cost has exceeded the set threshold.

With AWS Budgets and alerts in place, cost tracking becomes automated, making sure that responsible teams are notified when spending exceeds the set limit. Without this setup, unexpected charges could easily go unnoticed until the billing cycle ends.

Best Practices for Sustainable Cloud Cost Management 

Now that we have a system in place to monitor our cloud costs, the next step is making sure that cloud costs remain optimized over time as well. Just having budgets and alerts is not enough; organizations need to follow some best practices to prevent unnecessary spending and improve long-term cost efficiency.

Conduct Regular Cost Audits

Cloud environments scale over time as teams provision new resources. Without regular audits, unused or oversized instances, idle storage, and misconfigured services can lead to unnecessary cloud costs. Reviewing cloud spending every month or week helps identify such resources. For example, an audit might reveal EC2 instances running 24/7 when they are only needed during business hours. Switching such instances to a scheduled start/stop policy or using spot instances can significantly reduce expenses.

Enforce Strict Resource Tagging Policies

When multiple teams work in the same cloud environment, tracking which resources belong to which project becomes difficult. Without proper resource tagging, identifying and decommissioning unused resources becomes difficult. Enforcing a tagging policy that includes attributes like Project, Environment, and Owner ensures clear cost allocation. Using Terraform modules that require tags at the time of provisioning helps enforce this consistently across the organization.

Implement Cost Alerts and Budgets

Setting up cost alerts prevents unexpected charges by notifying teams when spending exceeds predefined limits. AWS Budgets, as configured earlier, help track total cloud costs, but organizations can extend this to monitor specific services like RDS, Lambda, and EBS snapshots. Alerts can also detect sudden spikes in usage, helping teams investigate and address cost irregularities before they become a significant issue.

By following these best practices, organizations can maintain better control over their cloud expenses. Conducting regular audits, enforcing tagging policies, and setting up cost alerts ensure cloud costs remain predictable and aligned with business needs. When combined with platform engineering principles, these measures create a sustainable approach to managing cloud costs at scale.

Now, in the earlier hands-on setup, we created an AWS budget to track cloud costs, but there were some limitations to it as well. The budget only starts tracking after the resources are deployed, and alerts are triggered only after the threshold is exceeded. This means there is no way to see the cost impact before provisioning a resource. If a large instance type like m5.4xlarge is selected instead of a smaller one, there is no immediate breakdown to show how much the cost will increase. Without visibility into expected costs upfront, most of the teams risk deploying oversized resources and only realizing the impact when the budget alert is triggered.

For more ways to reduce cloud costs, check out this blog on Cloud Cost Optimization.

Using Cycloid to Estimate and Manage Cloud Costs

This is where Cycloid simplifies cloud cost management by integrating cost estimation directly into the resource provisioning workflow. Instead of manually setting budgets, navigating Cost Explorer, and provisioning resources through Terraform, Cycloid provides everything in one place.

Before Cycloid, teams had to set up cost controls manually, define budgets, configure alerts, and provision resources from scratch with a lot of guesswork. It was easy to over-provision, miss cost spikes, or struggle with fragmented tracking across multiple tools. What if there was a centralized platform that handled all of this automatically? Cycloid simplifies cloud cost management by integrating cost estimation directly into the resource provisioning workflow. Instead of manually setting budgets, navigating Cost Explorer, and provisioning resources through Terraform, Cycloid provides everything in one place, ensuring that cost visibility and control are built into the process from the start.

Cycloid centralizes infrastructure management, allowing teams to organize deployments under structured projects and environments.

Once inside a project, Cycloid allows users to define cloud configurations without writing Terraform configurations. Teams can specify the AWS account, region, VPC ID, SSH key, and instance type through a UI-driven approach, ensuring consistency across deployments.

What sets Cycloid apart is its side-by-side cost estimation during resource creation. Unlike AWS Budgets, which tracks expenses after deployment, Cycloid provides real-time cost visibility before provisioning. This helps teams adjust configurations early to prevent unnecessary expenses.

The platform team defines infrastructure stacks in Cycloid, ensuring consistency across environments. These configurations are stored in Cycloid’s GitHub private repository and can be deployed using predefined modules via the UI.

Cycloid’s stack pipeline runs Terraform with built-in governance, making sure that infrastructure deployments follow predefined policies. This means that teams don’t have to manually enforce security rules, cost limits, or instance configurations; everything is checked automatically before provisioning. The pipeline applies standard practices for instance sizing, network configurations, and tagging, reducing misconfigurations and ensuring compliance across all the environments.

Developers can use Cycloid’s pipeline-driven workflow to deploy infrastructure directly without writing Terraform configurations. The UI provides cost estimates before deployment, and teams can track spending across projects in Cycloid’s dashboard.

Cycloid also offers additional features like cloud carbon footprint tracking and deeper cost visibility to help organizations maintain sustainable and optimized cloud spending.

Once the configurations are set, Cycloid automatically runs a pipeline to deploy the infrastructure. It pulls the Terraform stack, executes a terraform plan step to generate an execution plan, and then applies the changes to provision resources.

The pipeline ensures deployments follow a structured process without requiring manual intervention. It also manages Terraform state to keep track of existing resources and prevent conflicts. This automated workflow makes infrastructure provisioning more predictable while keeping costs in check as well.

With real-time cost estimation, teams can compare instance types or adjust configurations before deployment to balance cost and performance. Once the resource configuration is finalized, it can be deployed directly from Cycloid, making sure that infrastructure changes are cost-optimized before affecting the cloud environment.

By integrating cost estimation with provisioning and ongoing cost tracking, Cycloid makes sure that cloud cost management is proactive rather than reactive. Instead of relying on post-deployment budgets and alerts, teams can optimize cloud spending at the time of provisioning, ensuring predictable and well-managed cloud costs.

Do you want to take control of your cloud costs before deploying to the infrastructure? Try Cycloid now and make every resource count.

Conclusion 

Till now, you should have a clear understanding of how misconfigurations and unmanaged resources lead to high cloud costs. We covered how platform engineering, cost monitoring, and Cycloid help control expenses. By following these practices, teams can optimize cloud spending and avoid unexpected charges.

Frequently Asked Questions 

• How do you control cloud costs?

  Cloud costs can be controlled by right-sizing resources, setting budgets and alerts, automating scaling, and regularly reviewing usage to eliminate waste. Using Infrastructure as Code (IaC) helps standardize provisioning and prevent over-allocation.

• How can you optimize costs with cloud computing?
  Optimizing cloud costs involves using auto-scaling, spot instances, serverless computing, and reserved instances where applicable. Regular audits, enforcing tagging policies, and tracking expenses with cost-monitoring tools also help.

• How to save cost on cloud?

  To save cloud costs, avoid idle resources, use cost-effective storage options, and leverage discounts like reserved and spot instances. Implement automation to scale resources only when needed.

• How to control cost in Azure?

  Azure costs can be controlled by setting up Azure Cost Management, enabling budgets and alerts, using reserved instances, and optimizing virtual machines and storage based on usage patterns.

• What is Azure Cost Management tool?

  Azure Cost Management is a built-in tool that helps track, analyze, and optimize cloud spending in Azure. It provides reports, cost alerts, and recommendations to manage expenses effectively.