How to Reduce AWS and Azure Costs Using Right-Sizing, Auto-Scaling, and Resource Scheduling

Cloud computing has emerged as a game-changer for modern companies. It has changed the way they deploy apps, store data, and scale operations. Using cloud services such as AWS or Azure, companies can go the extra mile. They don’t have to wait endless months setting up hardware. Virtual machines, databases, and storage can help them push the envelope and increase their reach. With that being said, the convenience comes at a cost.

Most companies exceed their cloud spending due to poorly managed and used resources. Other reasons include poorly configured infrastructure and inefficient scaling. Not scheduling non-production workloads is another reason for ballooning cloud costs. As per research, organizations waste approximately 32% of their cloud spending. This translates into $200 billion to $230 billion annually.

Additionally, 75% of companies have reported an increase in cloud wastage as their spending increased. This wastage is mainly due to inefficiencies. This blog will explore the three basic foundations of cost optimization strategies. They are as follows:

• Right-Sizing
• Auto-Scaling
• Resource Scheduling

By combining these with governance and continuous monitoring, companies can significantly reduce AWS and Azure costs while ensuring performance and reliability. Enough with the introduction. Let’s get into the details right away.

Understanding Cloud Costs and Why They Increase

Growth of Cloud Spend Across Organizations

Cloud adoption continues to grow year after year. Organizations estimate that over 30% of their IT budget is towards cloud infrastructure. While investment enables agility, it also introduces challenges. The challenge lies in controlling variable, usage-based costs.

Many organizations set up cloud resources once and don’t pay attention to them. They don't bother optimizing it from time to time. After deploying resources, optimization happens once in a blue moon. This leads to inefficiencies such as oversized virtual machines (VMs), idle development environments, and services running 24/7 even when not needed.

6 Common Causes of AWS and Azure Cost Overruns

The most common reasons for cloud cost overruns are as follows:

• Over-provisioned compute resources
• Lack of automation
• No tagging or governance policies
• Round-the-clock non-production systems
• Unused storage volumes and IP addresses
• Lack of centralized cost visibility

Key Concepts - Pay-As-You-Go vs. Reserved Capacity

AWS and Azure use usage-based billing. This means that companies pay for only what they use. They also offer pricing models that provide savings:

On-Demand Pricing

• You pay per hour or per second of actual usage.
• No long-term commitment required.
• Highly flexible and ideal for unpredictable workloads.
• Typically, the most expensive option is used long-term.

Reserved Instances (RI) / Savings Plans

• You commit to using specific resources for 1 to 3 years.
• Significant discounts compared to On-Demand pricing.
• Can offer savings of up to 72%, depending on term length and payment option.
• Best suited for steady, predictable workloads.

Spot Instances / Spot VMs

• Use unused cloud provider capacity at heavily discounted rates.
• Can provide savings of up to 90% compared to On-Demand pricing.
• Instances can be interrupted by the provider on short notice.
• Ideal for fault-tolerant, batch processing, and non-critical workloads.

While the reserved/spot reduces costs greatly, this blog focuses on real-time usage strategies. These strategies boost efficiency through right-sizing, auto-scaling, and resource scheduling.

Right-Sizing - Matching Resources to Demand

Right-sizing is a process where companies align cloud infrastructure to actual workload requirements. This means they invest in only the resources they require, neither less nor more.

What Does Right-Sizing Mean?

Right-sizing involves evaluating compute, storage, and networking resources to determine whether they are appropriately sized for current usage patterns. Too large ⇒ wasted money. Too small ⇒ performance issues.

Think of right-sizing like resizing clothing—the right fit ensures comfort and efficiency.

4 Benefits of Right-Sizing

The four main benefits of right-sizing are as follows:

• Reduce wastage
• Improve application performance
• Allocate budgets more effectively.
• Increase visibility into usage patterns.

Best AWS Right-Sizing Strategies

In AWS, right-sizing is typically applied to EC2 instances, RDS databases, and Elasticache clusters. The tools and steps for AWS right-sizing are as follows:

Tools for AWS Right-Sizing

• AWS Cost Explorer Rightsizing Recommendations
• AWS Compute Optimizer
• CloudWatch Metrics & Alarms

5 Steps to AWS Right-Sizing

• Monitor CPU, memory, disk, and network usage for 7–14 days.
• Identify consistently underutilized instances (e.g., < 20% CPU).
• Evaluate overutilized instances (>70–80% CPU).
• Resize to a more appropriate instance family/size.
• Validate performance after resizing.

Suppose an m5.large instance averages 15% CPU and 30% memory. In this case, resizing to t3.medium or t3.large could cut costs by 50%. The performance impact would be negligible.

Best Azure Right-Sizing Approaches

In Azure, right-sizing is used for Virtual Machines, App Services, SQL databases, and other resources. The tools and steps for the same are as follows:

Tools for Azure 

• Azure Advisor
• Azure Monitor
• Azure Cost Management Plus Billing

Steps for Azure Right-Sizing

• Review Azure Advisor recommendations for underutilized VMs.
• Look at utilization trends in Azure Monitor (CPU, RAM, IOPS).
• Right-size VM tier (e.g., move from D4s_v3 → D2s_v3).
• Validate workload performance post-resize.

When to Right-Size Storage

Right-sizing isn’t just about computation. Storage can be:

• Over-provisioned (expensive disks)
• Underutilized (old snapshots and unattached volumes)

AWS S3

• Using S3 Intelligent-Tiering
• Move inactive objects to Glacier.

Azure Blob Storage

• Use Cool/Archive tiers.
• Delete unneeded blobs and snapshots.

Auto-Scaling - Matching Capacity to Demand Automatically

With auto-scaling, companies can adjust capacity dynamically. This means that they can increase/decrease cloud resources according to demand. They only pay for what they use.

What is Auto-Scaling?

Auto-Scaling automatically increases or decreases the number of compute instances or resources available based on performance triggers. These performance triggers can include CPU utilization, request rate, or custom metrics.

4 Benefits of Auto-Scaling

The benefits of auto-scaling are as follows:

• Reduces over-provisioning
• Provides resiliency
• Improves application responsiveness
• Reduces costs by scaling down during dull periods.

What is AWS Auto-Scaling?

AWS auto-scaling applies to the following:

• EC2 auto scaling groups
• Application auto-scaling (ECS, DynamoDB, Aurora)
• AWS Lambda (scales automatically)

How Does AWS Auto-Scaling Work?

AWS auto-scaling works as follows:

• Define scaling policies (Target Tracking, Step Scaling, and Scheduled Scaling)
• Set metrics (e.g., average CPU = 60%)
• AWS scales instances up or down.

Let’s say an EC2 Auto Scaling Group is set to maintain 60% CPU utilization. In that case, AWS will launch more instances when demand rises and terminate them when demand falls.

What is Azure Auto-Scaling?

Azure auto-scaling is configured via the following:

• Azure VM Scale Sets
• App Service Plans
• Azure Kubernetes Service (AKS)

Key Features of Azure Auto-Scaling

• Scale based on CPU, Memory, HTTP queue length
• Scheduled scaling (e.g., higher capacity on weekdays)

Smart Scaling Policies

For further cost optimization, the following strategies are recommended.

• Target Tracking - Maintains desired average metric (like 50% CPU)
• Predictive Scaling - Uses machine learning (AWS Auto Scaling) to forecast demand.
• Scheduled Scaling - Increases capacity during known peak hours.
• Smart auto-scaling ensures that companies only pay for what they need.

Resource Scheduling - Turning Off Idle Resources

Resource scheduling ensures that cloud services are stopped when the company does not use them. This is valuable for dev/test environments.

What Does Resource Scheduling Mean?

Cloud infrastructure often runs 24/7, even when employees have completed their working hours. With resource scheduling, non-production systems are shut down automatically during off-hours.

Workloads Ideal for Resource Scheduling

Schedule non-production environments such as the following:

• Dev, Test, QA
• Staging
• UAT
• Sandbox

These environments don’t work overnight or during weekends.

AWS Resource Scheduling

In AWS, scheduling can be enabled using:

• AWS Instance Scheduler (Lambda + CloudWatch Rules + DynamoDB)
• AWS Systems Manager Automation
• EventBridge scheduled rules

Examples include the following:

• Automatically stop dev EC2 instances at 7 PM.
• Restart at 7 AM on weekdays.

8 Steps to Implement Resource Scheduling

1. Identify Resources to Schedule

• Determine which resources should be scheduled (e.g., Dev, Test, QA EC2 instances).
• Ensure production workloads are excluded.
• Group resources logically (by environment, project, or team).

2. Apply Consistent Resource Tagging

Add tags such as:

• Environment = Dev/Test
• Schedule = OfficeHours
• Owner = TeamName

Tagging allows automation tools to identify which instances to start/stop.

3. Choose a Scheduling Method

Select one of the following approaches:

• AWS Instance Scheduler (official solution)
• Amazon EventBridge + AWS Lambda
• AWS Systems Manager Automation
• Custom scripts via CLI or SDK

4. Define the Schedule

• Set start and stop times (e.g., 7 AM – 7 PM, Monday–Friday).
• Account for time zones.
• Define exceptions for holidays or special events.

5. Deploy the Automation Setup

Depending on the method:

• Deploy the AWS Instance Scheduler CloudFormation template
• Create a Lambda function to stop/start an instance.
• Configure EventBridge rule (cron expression)
• Assign the necessary IAM roles and permissions.ns

6. Test in a Non-Production Environment

• Validate that instances stop and start at the correct times.
• Confirm that no production services are impacted.
• Monitor logs for errors.

7. Monitor and Optimize

• Use CloudWatch logs to verify executions.
• Review billing reports to measure cost savings.
• Adjust schedules based on actual usage patterns.

8. Implement Governance Controls

• Enforce tagging policies.
• Set up cost alerts via AWS Budgets.
• Review scheduled resources periodically.
• Azure Resource Scheduling
• Azure supports scheduling using the following:
• Azure Automation Runbooks
• Azure Logic Apps
• Azure DevTest Labs Auto-Shutdown
• Companies must use tags to identify resources and apply schedules.

8 Steps to Implement Azure Resource Scheduling

Below are the core steps to implement Azure resource scheduling.

1. Identify Resources to Schedule

Determine which resources can be safely stopped (e.g., Dev/Test VMs, non-production databases, App Services).

Exclude production or mission-critical systems.

Group resources logically (by environment, department, or workload).

2. Implement Proper Resource Tagging

Apply consistent tags such as:

• Environment: Dev/Test
• Schedule: BusinessHours
• Owner: TeamName
• Tags allow automation to target specific resources.

3. Choose a Scheduling Method

Select one of the following Azure-native options:

• Azure Automation (Runbooks) – For advanced, script-based scheduling.
• Azure DevTest Labs Auto-Shutdown – For built-in VM auto-stop.
• Azure Logic Apps – For low-code scheduling workflows.
• Azure Functions and Timer Trigger – For serverless scheduling automation.

4. Create Stop and Start Automation

• Create a scheduled job to stop resources after business hours.
• Create a scheduled job to start resources before business hours.
• Ensure scripts handle multiple resource types if required.

5. Configure Schedules

Define time-based triggers (e.g., 7 PM stop, 7 AM start).

Adjust for:

• Weekdays vs weekends
• Public holidays
• Different time zones (if applicable)

6. Assign Permissions

Grant automation accounts appropriate IAM roles (e.g., Contributor or custom role).

Follow the principle of least privilege.

7. Test in a Non-Production Environment

• Run scheduling scripts manually first.
• Validate start/stop behavior.
• Confirm no impact on dependent services.

8. Enable Monitoring and Alerts

Use Azure Monitor to track job execution.

Set alerts for:

• Failed automation runs
• Resources that did not stop/start as expected

9. Document and Govern

• Document scheduling policies.
• Enforce tagging using Azure Policy.
• Periodically review and adjust schedules as business needs change.

Tools and Tagging for Scheduling

Tagging resources (e.g., Environment: Dev, Owner: TeamA) helps automation scripts stop and start resources only where needed.

7 Steps to Reduce Costs with Right-Sizing, Auto-Scaling, and Scheduling

Below are seven steps to reduce costs with right-sizing, auto-scaling, and scheduling.

Tag Everything

Continuous tagline ensures visibility and governance. The most common tags include the following:

• Environment - Identifies whether the resource belongs to development, test, or production.
• Owner - Specifies the team/individual to manage the resource.
• CostCenter - Highlights the team/department that must pay for the resource.
• Project - Shows the project that the resource belongs to. This allows the company to track the usage and costs easily.

With tagging, tools like AWS Cost Explorer and Azure Cost Management can organize and manage cloud costs better.

Set up Monitoring and Cost Dashboards

AWS CloudWatch and Cloud Explorer - CloudWatch monitors real-time resource metrics. On the other hand, Cost Explorer analyzes spending trends and factors that drive costs. This is crucial for decisions related to optimization.

Azure Monitor and Azure Cost Management - Azure Monitor can track performance and utilization metrics. Azure Cost Management reviews spending patterns and spots opportunities to cut costs.

Review Right-Sizing Recommendations

In AWS

• Run Cost Explorer Rightsizing
• Review Compute Optimizer findings

In Azure

• Review Azure Advisor recommendations
• Validate performance requirements before resizing.

Define Auto-Scaling Policies

Determine the metrics for scaling. Examples include the following:

• CPU Usage
• Requests Per Second
• Queue Length
• Custom Business Metrics
• Deploy auto-scaling with alerting.

Apply Resource Schedules

Companies can use automation to perform the following actions:

• Shut down during non-business hours (mostly nighttime).
• Resume during business hours
• Exclude production systems.

Care should be taken to ensure that the schedules consider global teams and public holidays.

Enforce Governance and Alerts

Create policies for the following:

• Generating alerts when costs spike.
• Prevent resource creation without tags.
• Review reserve instance purchases periodically.
• Use AWS Organizations and Azure Policy.

Educate Teams

Engineering teams must be educated about how their technical decisions impact costs. They must know how to read the following:

• Hourly Dashboards
• Billing Alerts
• Regular Reviews
• Teams must appoint a champion for cost optimization.

Cost Management Tools for Azure and AWS

AWS Cost Optimization Tools

• AWS Cost Explorer - Visualize cost trends.
• AWS Compute Optimizer - Right-sizing recommendations.
• Trusted Advisor - Cost optimization checks.
• CloudWatch - Metrics and alarms
• AWS Alerts - Budget alerts

Azure Cost Optimization Tools

• Azure Cost Management and Billing - Cost Visualization
• Azure Advisor - Right Sizing and Recommendations
• Azure Monitor - Resource Metrics
• Azure Policy - Governance

For deeper insights and automation, companies can use the following tools:

• CloudHealth
• Spot.io
• Turbonomic
• Apptio

3 Common Mistakes and How to Avoid Them

Below are three common mistakes that companies make with AWS and Azure. We have also mentioned tips on how to avoid such mistakes.

Focusing Solely on Compute

Most companies don’t consider storage, bandwidth, and database costs. The solution lies in including all the major services. They are as follows:

• S3 / Blob
• NAT Gateways
• DBS Disks

Scaling Too Conservatively

Performance suffers when thresholds are very conservative. The solution lies in combining target tracking with slow cooldown periods.

Not Evaluating Reserved Pricing

When cloud usage becomes steady and predictable, companies should switch to better options. Discounted pricing options, such as Savings Plans or Reserved Instances, can reduce long-term costs.

9 Best Practices to Reduce AWS and Azure Costs

Below are the ten best practices to reduce AWS and Azure costs.

• Monitor cloud usage and spending continuously using cost management tools.
• Right-size virtual machines and databases based on real usage.
• Enable auto-scaling to match resources with actual demand.
• Shut down or schedule non-product resources during non-business hours.
• Delete unused storage, snapshots, and unattached disks.
• Use Reserved Instances or Savings Plans for steady workloads.
• Use Spot Instances or Spot VMS for flexible workloads
• Set up budget alerts to detect unexpected cost spikes beforehand.
• Review cloud costs monthly and optimize continuously.

Conclusion

Reducing AWS and Azure costs goes beyond cutting resources. Companies must have a plan and act wisely when reducing costs. Using a mix of techniques such as right-sizing, auto-scaling, and resource-scheduling, companies can ensure that they pay for the cloud capacity they use.

• Right-Sizing - Ensures workloads run smoothly on aligned infrastructure.
• Auto-Scaling - Resources scale dynamically as per business demands.
• Scheduling - Non-production systems run only when required. Not 24/7/365.

The combination of these techniques can significantly reduce cloud costs. Simultaneously, it does not affect performance or reliability. Companies must understand that cloud optimization is not a one-time task. It is a continuous process where they have to monitor usage regularly and review recommendations. Also, teams must be educated about the benefits and how they can contribute to cost savings.

Using the right tools, clear rules, and a proactive approach, managing cloud costs can be easy. Don’t view them as a burdensome activity. When used correctly, cloud spending can become an investment that boosts business growth. A proactive approach ensures addressing issues before they turn into obstacles. Smart cloud optimization saves money that companies can use for other purposes. This can include innovation, new projects, or business expansion.

FAQs

How should a company go about choosing a cloud management tool?

A company must choose a cloud management tool by considering various factors. These factors include budget, how easy it is to use, and features. Ideal features in a cloud management tool are cost-tracking automation and monitoring. Attention should be paid to the tool’s integration, security, and scalability features as well. Lastly, the development team must be able to use the tool without any issues.

Who can identify idle resources in a cloud and how?

From cloud engineers to FinOps teams, anyone can identify idle resources. They can identify idle resources using monitoring tools such as AWS CloudWatch or Azure Monitor. Usage reports and metrics shed light on resources that are running but unutilized.

When to use AWS and Azure?

Both AWS and Azure have their share of pros and cons. Below are situations when companies should use AWS and Azure.

• AWS - Ideal when the company wants diverse services. Both startups and enterprises wanting flexibility can use AWS. It can help them achieve a robust, global presence.
• Azure - This is best for companies that use Microsoft tools a lot. Azure integrates smoothly with multiple Microsoft tools and systems.

When can a company see the ROI of cloud management tools?

Usually, companies can see measurable ROI from cloud management tools within 3 to 6 months. The most notable signs are cost optimization, optimized resource utilization, and reduced operational overheads.

How do Azure and AWS support hybrid cloud?

Azure is generally considered a better option for hybrid. This is due to services such as the following.

• Azure Arc
• Azure Stack

Azure supports hybrid through the following:

• AWS Outposts
• AWS Local Zones

Azure has an edge over AWS in hybrid enterprise setups.

What are the most popular cloud platforms in 2026?

The three most popular cloud platforms in 2026 are as follows:

• Amazon Web Services (AWS) - 30% to 32% of market share.
• Microsoft Azure - 20% to 28% of market share.
• Google Cloud Platform - 11% to 14% of market share.