In this guide, we will talk about Azure cloud cost optimization best practices with real-life implementation examples. We will cover everything from using reserved instances and Azure Cost Management features to Azure DevTest Labs, Database optimization, and much more.

Azure cost optimization, Cloud cost optimization, azure cloud cost optimzation best practices

In today's rapidly evolving digital landscape, businesses are increasingly turning to cloud solutions like Microsoft Azure to power their operations, achieve scalability, and enhance their technological capabilities. 

While the benefits of cloud computing are undeniable, managing costs in the cloud environment can sometimes be a challenge. As organizations embrace the flexibility and power of the cloud, optimizing costs becomes a crucial aspect of maintaining a competitive edge.

In this blog, you will delve into Azure cloud cost optimization and explore key best practices that can help you strike a balance between innovation and financial responsibility. 

Whether you're new to Azure or a seasoned user, these strategies will empower you to maximize your cloud investment without compromising performance or scalability.

Let's get started with the best practices and understand them well, with some practical examples too!

Rightsize Virtual Machines

One of the most effective ways to optimize costs in Azure is by rightsizing your virtual machines (VMs). This involves choosing VM sizes that align with your workload's actual resource requirements and avoiding over-provisioning which can lead to unnecessary costs. 

Azure provides tools like Azure Advisor and Azure Cost Management to help you identify underutilized VMs.

Let's understand it with an example.

Suppose you have a development environment that initially requires a powerful VM (like an Azure VM with 8 vCPUs and 16GB RAM). 

After monitoring usage, you notice that the VM rarely uses more than 50% of its resources. 

You could resize it to a more suitable instance, like one with 4 vCPUs and 8GB RAM, while still meeting your application's needs.

This is how you can use the Azure CLI to accomplish this task:

# Resize a virtual machine

az vm resize --resource-group <resource-group> --name <vm-name> --size <new-size>

Also Read: AWS Cloud Cost Optimization Best Practices

2. Leverage Serverless Services

Azure offers serverless services like Azure Functions and Azure Logic Appsthat allow you to pay only for the computing resources used during the execution of your code or workflows. 

This approach eliminates the need to manage and provision infrastructure continuously, leading to significant cost savings.

Let's say you have a simple data processing task that runs periodically. 

By implementing this task using Azure Functions, you can trigger the function based on events (e.g., new data arrival) and pay only when the function runs, rather than maintaining a dedicated server.

This is a sample code snippet which you can use:

// Azure Function to process data

public static void Run([TimerTrigger("0 */5 * * * *")] TimerInfo myTimer, ILogger log)


log.LogInformation($"C# Timer trigger function executed at: {DateTime.Now}");

// Your data processing logic here


3. Use Azure Reserved Instances

Azure Reserved Instances allow you to pre-pay for virtual machines at a lower rate, which offers substantial cost savings compared to pay-as-you-go pricing. 

This is particularly beneficial for long-running workloads with predictable usage patterns.

Let's look at an example.

If you have a production database that runs 24/7, you can purchase reserved instances for the VMs hosting the database, which can result in significant cost reduction over time.

To achieve this, you can:

  1. Navigate to the Azure Portal.

  2. Go to the "Virtual Machines" section.

  3. Select a VM, and under "Operations," and choose "Purchase reserved instances."

4. Implement Auto Scaling

Auto Scaling allows your application to dynamically adjust the number of resources allocated based on demand. This prevents over-provisioning during periods of low usage and ensures optimal performance during peak times.

Here's an example.

Imagine an eCommerce application that experiences spikes in traffic during holiday seasons. 

By setting up auto-scaling for your web server instances, you can automatically add more instances when traffic increases and scale down when it decreases.

For this, you can:

  1. Navigate to the Azure Portal.

  2. Go to the "Virtual Machine Scale Sets" section.

  3. Configure "Autoscale" settings based on metrics like CPU usage.

Also Read: K8s Cluster Autoscaler vs Karpenter

5. Monitor and Analyze Costs

You should regularly monitor your Azure usage and costs to identify trends, anomalies, and potential areas for optimization. 

Azure Cost Management provides insights into your spending patterns, helping you make informed decisions.

Here's an example.

Use Azure Cost Management to generate reports and dashboards that display your spending breakdown, helping you pinpoint which resources contribute the most to your costs. 

You can set up alerts to notify you when costs exceed a certain threshold.

Go to the "Cost Management + Billing" section to explore cost reports, and usage details, and create budget alerts.

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6. Utilize Azure Hybrid Benefit

Azure Hybrid Benefit allows you to use your existing on-premises Windows Server licenses with Azure Virtual Machines, enabling you to save on licensing costs. 

This is especially valuable for workloads that need both on-premises and cloud resources.

When creating or configuring a VM, you should select "Use existing Windows Server licenses" under the "Windows Server" section.

For instance, if you have Windows Server licenses for your on-premises environment, you can apply those licenses to Azure Virtual Machines, effectively reducing the cost of running those VMs in the cloud.

Also Read: Multi-Cloud Management - Challenges & Best Practices

7. Data Storage Optimization

Effectively managing data storage can lead to significant cost savings. Utilize features like Azure Blob Storage's tiering to automatically move data between different storage classes based on access patterns. 

This ensures that frequently accessed data remains in a higher-performance, more expensive tier, while less frequently accessed data is moved to a more cost-effective tier.

For this, Go to the storage account, and under "Lifecycle management," configure rules to transition objects to different tiers.


Imagine you're storing large amounts of archival data. 

By setting up lifecycle management policies on your Azure Blob Storage, you can automatically move data older than a certain threshold to the "Cool" or "Archive" tier, which offers lower storage costs.

Also Read: AWS S3 vs EFS vs EBS

8. Choose the Right Database Service

Selecting the appropriate database service can have a substantial impact on costs. For instance, Azure offers various database options such as Azure SQL Database, Cosmos DB, and more. 

Each service has different performance characteristics and associated costs, so carefully evaluate which one aligns with your application's requirements.

Here's an example for your reference.

If your application needs a highly scalable and globally distributed NoSQL database, Cosmos DB might be the right choice.

However, if you're running a relational database with moderate usage, Azure SQL Database could provide the needed functionality at a lower cost.

Also Read: Hybrid Cloud Security Best Practices

9. Implement Resource Tagging

Resource tagging is a simple yet powerful way to track and categorize your Azure resources.

By assigning tags to resources based on factors like department, project, or environment, you can gain better visibility into your spending patterns and allocate costs more accurately.

Suppose you have multiple development teams using Azure resources. By tagging resources with the respective team names, you can identify which team's resources are driving the most costs and optimize accordingly.

Also Read: Top Cloud Cost Optimization Best Practices to Follow

10. Use Azure DevTest Labs

Azure DevTest Labs allows you to create environments for development and testing purposes while controlling costs. 

You can set up policies to automatically shut down or de-provision virtual machines during off-hours, ensuring you're only paying for resources when they're needed.


Here's an examples. If your development team works during specific hours, you can configure Azure DevTest Labs to shut down their VMs during evenings and weekends to avoid unnecessary costs.

Also Read: Differences between Ansible and Terraform

11. Use Azure Policy and Blueprints

Azure Policy and Blueprints to enable you to enforce governance and compliance standards across your organization's Azure resources.

By defining policies that enforce specific configurations and practices, you can prevent costly resource misconfigurations and ensure adherence to cost-saving guidelines.

Let's look at a quick example.

You can create an Azure Policy that enforces the use of specific VM sizes in a resource group, preventing users from deploying larger, more expensive instances unnecessarily.


"mode": "Indexed",

"policyRule": {

"if": {

"field": "type",

"equals": "Microsoft.Compute/virtualMachines"


"then": {

"effect": "deny",

"details": {

"type": "Microsoft.Compute/virtualMachines/sizes",

"values": ["Standard_D16s_v3", "Standard_E16s_v3"]




Also Read: DevOps vs SRE vs Platform Engineering

12. Optimize Data Transfer Costs

Azure services often involve data transfer between different regions and resources. 

Minimize data transfer costs by strategically placing resources in the same region or utilizing services like Azure Content Delivery Network (CDN) for delivering content closer to end-users.

For this, Go to the storage account, and under "Azure CDN," enable and configure the CDN endpoint.

Let's look at a quick example. If you're hosting a website with a global user base, using Azure CDN can help reduce latency and data transfer costs by caching and delivering content from edge locations.

Also Read: Top 13 Heroku Alternatives

13. Implement Serverless Databases

Serverless database offerings, such as Azure SQL Database serverless and Azure Cosmos DB serverless, allow databases to automatically scale based on demand. 

This eliminates the need to provision and pay for resources that are underutilized, leading to cost savings.

Example: If you have an application with sporadic database usage, Azure SQL Database serverless can automatically scale resources up and down as needed, reducing costs during periods of low activity.

Also Read: Why Application Performance Monitoring is Important?

14. Monitor and Optimize Network Resources

Network resources, such as Virtual Networks and Load Balancers, contribute to costs. 

Monitor network traffic patterns and optimize resources by eliminating unnecessary components and ensuring efficient routing.

Example: If you have a load balancer distributing traffic to multiple VMs, analyze traffic patterns to determine if all VMs are needed. You can adjust the number of instances behind the load balancer based on traffic volume.

Here is a Code Snippet (Azure CLI - Scale VM Instances in a Load Balancer Set):

# Scale the number of instances in a VM scale set

az vmss scale --resource-group <resource-group> --name <vmss-name> --new-capacity <new-instance-count>

15. Explore Azure Spot VMs

Azure Spot VMs allow you to use spare compute capacity at a significantly reduced cost compared to regular VMs. 

These VMs are suitable for workloads that can handle interruptions and are cost-effective for batch processing and temporary tasks.

Consider a scenario where you need to process a large dataset periodically. 

By utilizing Azure Spot VMs for this task, you can achieve cost savings while still completing the job within a reasonable timeframe.

Also Read: Internal Developer Platform - IDP

16. Use Azure Monitor and Application Insights

Azure Monitor and Application Insights provide deep insights into the performance and availability of your applications and services.

By proactively identifying performance bottlenecks and resource usage patterns, you can optimize your resources to reduce costs while maintaining optimal user experiences.

Suppose you're running a web application. By analyzing telemetry data from Application Insights, you can identify pages or API endpoints with high latency. 

Optimizing the code and database queries for these specific areas can lead to reduced resource consumption and improved performance.

Also Read: Kubernetes Cost Optimization Tools

17. Implement Azure Logic Apps for Automation

Azure Logic Apps allow you to automate workflows and integrate different services. By automating manual tasks, you can reduce human intervention and the potential for errors, ultimately saving time and resources.

Example: Imagine a scenario where you need to process data from an incoming email and store it in a database. By creating a Logic App that triggers new emails, extracts the relevant

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18. Utilize Azure Policy Initiative Definitions

Azure Policy Initiative Definitions allow you to group related Azure Policy definitions together, simplifying the process of assigning multiple policies to resources. 

This ensures consistent governance and compliance while optimizing resource configurations.

Suppose you have a compliance requirement to enforce encryption for data at rest. You can create an Azure Policy Initiative Definition that includes policies for different resource types (VMs, storage accounts, databases) to ensure that encryption is enforced across the board.

Code Snippet (Azure Policy Initiative Definition - JSON):

  "displayName": "Data Encryption Initiative",
  "description": "Enforces data encryption requirements",
  "policyDefinitions": [
      "policyDefinitionId": "/providers/Microsoft.Authorization/policyDefinitions/<policy-def-id-1>",
      "parameters": {},
      "type": "BuiltIn"
    // Add more policy definitions here

Also Read: Self-hosted Kubernetes vs AWS ECS

19. Optimize Container Resources with Azure Kubernetes Service (AKS)

If you're using containers, Azure Kubernetes Service (AKS) can help optimize resource usage by dynamically scaling and managing containers based on demand. 

It ensures you're running the right number of containers to meet workload requirements without over-provisioning.

Example: Suppose you have a microservices application with varying traffic patterns. AKS can automatically adjust the number of pods based on CPU and memory usage, ensuring optimal resource utilization.

Also Read: Horizontal Scaling vs Vertical Scaling

20. Explore Azure Cost Management APIs for Custom Insights

Azure Cost Management APIs allow you to programmatically access cost and usage data, enabling you to build custom dashboards, reports, and insights tailored to your organization's needs. 

This gives you more flexibility in analyzing costs and optimizing spending.

Source: Microsoft Cost Optimization

Suppose you want to create a custom dashboard that displays real-time cost data for specific resource groups. By using the Cost Management APIs, you can fetch cost data and present it in a format that's most meaningful to your stakeholders.

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Summary of Azure Cost Optimization Best Practices

In the world of Azure Cloud, saving money while using its features is really important. We talked about many ways to do this. 

Like making sure virtual computers are the right size, using services that only cost when you use them, and planning how much you need. 

We also learned about tools to watch your spending and fix things if they're not right. Using special rules and automation can help too. Remember, this is something you keep doing to always make sure you're using Azure in the best way for your money. 

So, you're now ready to use Azure smartly and save money at the same time!