Cloud Cost Optimization: Best Practices with Examples

In the wake of the COVID-19 pandemic, businesses worldwide faced unprecedented challenges that accelerated the adoption of cloud technologies. The shift to remote work, digital transformation, and the need for scalable solutions prompted organizations to migrate their operations to the cloud. While this transition offered numerous benefits, it also introduced concerns about controlling cloud costs. As cost efficiency is a key goal for organizations that use cloud resources, mastering cloud cost optimization has become paramount. This article outlines key best practices and provides some examples to help businesses and their technology management teams balance the costs and benefits of cloud computing.

  1. Right-sizing Instances

One of the most effective ways to lower cloud cost is by adjusting instances sizes to the optimal level. It involves matching the resources of an instance with the workload it supports. By selecting the appropriate instance type and size, businesses can avoid overprovisioning, which can result in unnecessary costs. Regularly monitor and analyse resource utilization to ensure instances are optimized and adjusted as needed.

Example: Imagine a retail website that experiences significant traffic spikes during the holiday season. Instead of keeping high-capacity instances running throughout the year, the business can analyse historical data and use auto scaling to automatically add resources during peak times and reduce them during quieter periods.

2. Choose the Right Pricing Model:

 Cloud providers offer various pricing models, such as On-Demand, Reserved Instances, and Spot Instances. The best one depends on the workload patterns and usage needs. For example, predictable workloads can benefit from Reserved Instances, while non-critical, time-flexible tasks can use Spot Instances for lower costs. By reserving instances for extended periods, businesses can secure lower rates compared to on-demand pricing. Match the model that aligns with your workload characteristics.

Example: A data processing company runs data-intensive workloads. By using Spot Instances during off-peak hours, they can significantly cut costs while still completing the tasks efficiently. However, for critical real-time processes, they might opt for On-Demand Instances to ensure consistent performance.

3. Implement Automated Scaling:

Autoscaling is a feature that cloud providers offer to automatically scale the number of instances according to demand. This dynamic approach prevents overprovisioning during peak times and eliminates the risk of under provisioning during traffic spikes. Implementing autoscaling policies based on predefined metrics or user-defined thresholds can help optimize resource allocation and save money.

Example: A video streaming service experiences variable demand throughout the day. By implementing autoscaling, the service can add more instances when demand increases (e.g., during prime-time viewing) and reduce instances during periods of lower demand, ensuring seamless user experiences without overspending.

4. Monitoring and Analytics:

Continuous monitoring of cloud resource utilization is crucial for identifying cost optimization opportunities. Leverage cloud-native monitoring services or third-party tools to gain insights into usage patterns, performance metrics, and costs. Regularly monitor your application’s usage patterns to identify trends and seasonal to identify idle or underutilized resources that can be right-sized or terminated.

Example: An e-commerce platform observes that traffic surges occur every weekend due to sales and promotions. By monitoring these patterns, the platform can predict the required resources for each weekend and auto scale accordingly. This prevents overprovisioning and minimizes costs during quieter periods.

5. Utilizing Serverless Architectures:

Serverless architectures, such as Function-as-a-Service (FaaS), enable businesses to run applications without provisioning or managing servers. By using serverless computing, businesses can save costs by paying only for the actual compute time used. Serverless architectures automatically scale to match the workload demands, further optimizing resource allocation and reducing costs.

Example: A retail bank can create Azure Functions that execute server-side logic for various banking operations, such as account opening, transaction processing, fraud detection, etc. By using Azure Functions, the bank can pay only for the compute time consumed by each function invocation and scale automatically to handle peak workloads. The bank can also chain functions together to create complex workflows, or use long-running workflows with durable functions for scenarios that require stateful orchestration.

6. Implementing Cost Allocation and Tagging

Proper cost allocation and tagging practices help organizations understand cloud costs at a granular level. By assigning appropriate tags to resources, businesses can track and allocate costs to specific teams, projects, or applications. This visibility enables better cost management, facilitates chargeback mechanisms, and encourages accountability for resource usage. This practice helps in allocating costs transparently and aids in identifying cost-saving opportunities.

Example: A multinational corporation manages multiple projects across various departments in the cloud. By tagging resources with relevant metadata (project names, teams, departments), the company can track spending for each project separately and allocate costs accurately.

  • Optimize Data Storage

Data storage can be a significant source of cloud spending, especially if the data is not managed efficiently. To optimize data storage, you should assess your data storage needs and employ strategies that can reduce the amount of data stored or the cost per unit of storage. Some of these strategies are:

  • Tiered storage: This involves storing data in different tiers based on its frequency of access, performance requirements, and retention policies. For example, you can store frequently accessed data in high-performance SSDs, infrequently accessed data in low-cost HDDs, and archival data in cold storage commonly referred as object storage.
  • Data lifecycle management: This involves defining and implementing policies for data creation, retention, deletion, and archiving. For example, you can set expiration dates for temporary data, delete obsolete data, and move old data to cheaper storage tiers or offline media.
  • Data compression: This involves reducing the size of data by applying compression algorithms or techniques. For example, you can compress text files, images, videos, or databases to save storage space and bandwidth.

By optimizing data storage, you can remove redundant or outdated data that may incur unnecessary expenses. You can also lower the cost of storing and transferring data by choosing the appropriate storage tier and format for your data. This way, you can optimize your cloud costs and improve your data management.

7. Continuous Optimization

Cloud cost optimization is an ongoing process. Regularly review your cloud environment, monitor spending, and adjust strategies as your business evolves. Keeping up with optimization practices ensures long-term cost efficiency.

Example: A software development startup undergoes rapid growth, leading to changing resource requirements. Regularly reviewing their cloud infrastructure, the startup identifies that they are overprovisioning compute resources. They adjust their autoscaling policies and rightsizing strategies to align with the current demands, ensuring optimal cost efficiency.


Each business’s cloud optimization strategy will depend on its unique requirements and goals. By implementing the best practices mentioned in this document, organizations can ensure their cloud resources are utilized optimally, resulting in significant cost savings. Regularly review and adapt these practices to align with changing business requirements and optimize cloud resource usage.


  1. “The Impact of COVID-19 on Cloud Computing,” Gartner, April 2020. Link
  2. “Cloud Cost Management and Optimization,” AWS. Link
  3. “Azure Cost Management and Billing,” Microsoft Azure. Link
  4. “Google Cloud Cost Management Tools,” Google Cloud. Link
  5. “Optimizing Your Costs on AWS,” AWS. Link
  6. “Strategies for Cost Management in the Cloud,” Forbes, October 2020. Link
  7. “Serverless architecture & the next wave of enterprise offerings” McKinsey Digital. Link
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