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AWS Free Tier is a program that allows users to explore and experience a range of AWS services at no cost, up to specified usage limits. This article will provide a comprehensive guide on how to effectively use it. We will cover its purpose, terms of use, supported services, and highlight the benefits it offers to users.

How it works

The AWS Free Tier provides customers with the opportunity to try out some of the AWS services free of charge, enabling them to gain hands-on experience with cloud computing. It consists of three offerings:

• The 12-month Free Tier allows free usage of services within set limits for one year from the date of account activation. 
• The Always Free offer enables ongoing usage within specific limits as long as the user maintains a valid AWS account. 
• Short term Trials provide free usage for a limited period or up to a one-time limit, depending on the selected service.

Terms of Use

To qualify for the AWS Free Tier, users need to have an AWS account. It is available to various customer types, from students and small businesses to large enterprises. However, if an account is linked to an AWS Organization, only one account within the organization can benefit from the Free Tier. The usage of AWS services across all accounts within the organization is aggregated to determine eligibility for the Free Tier. If the free tier limits are exceeded or the user is deemed ineligible, standard pay-as-you-go rates will apply. As part of the AWS Free Tier, you can get started with EC2 for free. 

List of services included in AWS Free Tier

AWS Free tier Limitations

Pay attention – not all AWS services are free. There are no guard rails and you need to remember that your access to services is not restricted by the AWS Free Tier. Moreover, services at the Free Tier have usage limits. You will be charged at standard rates, if exceeding those limits. Also, make sure that services you intend to use are covered and has usage limits applicable to your goals, in order to avoid unexpected charges.

Services not available in the AWS Free Usage Tier

• Reserved Instances
• AWS Support subscription
• Route 53 (still need to pay for hosted availability zones and domains)
• EC2 is limited to a micro instances
• Amazon EKS
• Many of the amounts are high enough that you can get locked in — like 50GB Glacier storage or 1M calls/month to API Gateway
• Free tier is not allowed to be used for for cryptomining
• Limited availability of AMIs
• Amazon S3 RRS storage.

Benefits of AWS Free Tier

1. Exploration and Learning: Users can experiment some of AWS services, gaining hands-on experience and exploring different use cases without incurring costs.
2. Cost-effective Development and Testing: The Free Tier allows users to develop and test applications, proof of concepts, and prototypes without the need for upfront investments.
3. Cost Management and Control: The Free Tier helps users understand the costs associated with different services, enabling them to optimize usage and control expenses.

Conclusion

As you see, despite its limitations AWS Free Tier provides a valuable opportunity for users to explore and leverage the benefits of cloud computing services without incurring initial costs. By understanding the terms of use, exploring the supported services, and taking advantage of the benefits it offers, users can make the most of their cloud journey, empowering themselves for innovation and growth.

Estimating AWS costs is crucial for effective budget planning, cost optimization, and decision-making. By getting an estimate of the costs associated with running your AWS resources, you can allocate your budget wisely, identify areas for cost optimization, compare different resource configurations, forecast future expenses, and make informed decisions. These estimates provide valuable insights into the financial impact of your AWS usage, enabling you to optimize costs, ensure budgetary alignment, and drive accountability within your organization. In this article, we will explore the various tools available for estimating AWS costs.

Where to look for AWS cost estimate?

There are several ways to get AWS cost estimate for your resources. Here are the options:

AWS Cost Explorer

AWS Cost Explorer is a web-based tool provided by AWS that allows you to visualize, analyze, and forecast your AWS costs and usage. It provides pre-built reports, interactive charts, and cost breakdowns, helping you understand your costs at a high level.

It is useful for gaining high-level insights into your AWS costs and usage trends. It provides visualizations and reports that can help you understand how costs are distributed across different services, regions, or tags. It can be used for budget planning, identifying cost anomalies, and analyzing cost drivers.

Pros:

• User-friendly interface with pre-built reports and interactive charts.
• Ability to drill down into cost details and filter data based on various dimensions.
• Cost forecasting feature to estimate future costs.

Cons:

• Limited granularity in some reports, making it difficult to get detailed insights for specific resources.
• It may take a few hours for new cost data to be available in Cost Explorer.
• Advanced cost optimization features are not available within the tool.

AWS Pricing Calculator

The AWS Pricing Calculator is an online tool that allows you to estimate the cost of using AWS services based on your expected usage patterns. You can select specific services, configure resource details, and input usage quantities to generate an estimated monthly cost. It also helps you compare costs across different services, regions, and pricing models to make informed decisions during the planning stage.

Pros:

• Ability to select specific services and configure resource details for accurate cost estimation.
• Options to customize usage patterns, such as choosing reserved or on-demand instances, data transfer volumes, and storage sizes.
• Provides an estimated monthly cost based on your inputs.

Cons:

• Requires manual input and configuration, which may be time-consuming for complex architectures.
• Limited to estimating costs for specific services and does not provide a comprehensive view of overall AWS costs.
• May not reflect real-time pricing changes or discounts.

AWS Simple Monthly Calculator

The AWS Simple Monthly Calculator is a basic online tool that helps you estimate the cost of running a specific set of AWS resources. The AWS Simple Monthly Calculator is helpful when you have a specific set of AWS resources in mind and want to estimate the monthly costs of running them. It allows you to configure various resource types and quantities to get a detailed estimate. You can select various input details such as instance types, storage sizes, and data transfer to get an estimated monthly cost.

Pros:

• Provides a granular cost estimate by allowing you to specify resource details.
• Supports a wide range of AWS services and provides options for storage, compute, database, and networking resources.
• Can help in comparing costs between different resource configurations.

Cons:

• Requires manual input for each resource, which can be time-consuming for complex architectures.
• Limited to estimating costs for specific resources and does not provide a comprehensive view of overall AWS costs.
• May not reflect real-time pricing changes or discounts.

AWS Cost and Usage Reports

AWS Cost and Usage Reports provide detailed billing data in a machine-readable format. You can enable this feature to get a comprehensive breakdown of your AWS costs, including resource-level details. You can export this data and perform custom analysis or use third-party tools to generate cost estimates.

Pros:

• Provides detailed billing data in a machine-readable format for custom analysis.
• Offers granular visibility into costs at the resource level, allowing for deeper insights.
• Enables integration with third-party cost management tools for advanced analysis and optimization.

Cons:

• Requires additional effort to set up and configure the reports.
• The data may not be readily available and may take a few hours to generate.
• Requires technical expertise to process and analyze the data effectively.

Third-party tools

There are various third-party tools available that provide more advanced cost management and optimization capabilities for AWS. Some popular options include CloudCheckr, CloudHealth by VMware, and AWS Cost Management partners. These tools often offer additional features beyond getting aws cost estimate, such as cost optimization recommendations and budgeting. Third-party cost management tools are beneficial when you need more advanced cost optimization features, customized reporting, or integration with other systems. They provide comprehensive cost management capabilities in comparison to standard services.

Pros:

• Advanced cost optimization recommendations to identify potential cost-saving opportunities.
• Customizable dashboards and reports for detailed cost analysis.
• Integration with other cloud platforms or business systems for holistic cost management.

Cons:

• Additional cost for using third-party tools.
• Learning curve to understand and leverage the tool’s features effectively.
• Integration and setup may require additional effort.

Summary

Consider your specific requirements, level of granularity needed, and the complexity of your AWS infrastructure when choosing the most suitable tool for estimating your AWS costs.

By having accurate AWS cost estimate, you can effectively manage your cloud costs, optimize resource allocation, and make informed decisions that align with your budgetary goals and business objectives. You should consider your specific requirements, level of granularity needed, and the complexity of your AWS infrastructure when choosing the most suitable tool for estimating your AWS costs. Remember that cost estimation is just the first step in managing your AWS costs effectively. It’s important to monitor your actual usage and costs regularly, implement cost optimization best practices, and set up cost alerts and budgets to ensure cost efficiency and prevent unexpected expenses.

Understanding your business’ unit economics, including the cost of specific features, products, and customers, is crucial to making informed decisions that can drive profitability. For SaaS companies, there are several key unit economic metrics to monitor in order to identify opportunities and plan for the future.

Metrics required for cloud cost analysis

The first important metric is cost per customer. Your customers use specific features within your software and the cloud cost of those features will go to customer cost. Obviously, another required metric is cost per feature. It shows how much each feature costs to your business.

Another metric is lifetime value (LTV), which shows how much value each customer creates for your company over time. LTV increases along with other positive metrics. Customer acquisition costs (CAC) helps you to find out your spending on gaining an additional client. High CAC at SaaS companies means they may have issues performing on the margin.

Churn rate illustrates how many users cancel or fail to renew their subscriptions within a defined period of time. Total revenue shows how much positive cash flow your business has been able to generate, and the number of customers and transactions can help you develop an effective scaling strategy.

Average customer lifetime (ACL) describes how long lasts the usage or subscription for your software. Another useful, universal metric is Gross profit. It illustrates the difference between your cost of sales and revenue. And if you need to examine LTV on the margin, Gross margin per customer lifespan (GML) will help you with it.

Calculating and measuring unit economics involves using formulas, making it easier to track your business’ performance over time and compare it to similar organizations. Some of the most useful formulas include calculating lifetime value, customer acquisition cost, churn rate, and average customer lifetime value.

It may look like SaaS unit economic metrics are simple to track, but that is not a common case for all of them. Metrics like cost per customer or cost per feature can be challenging at identifying and measuring. For example, AWS monthly bill shows you what you spent on the different services you use in your workload but has no context of how those cloud costs are distributed across business or units. 

It may look like SaaS unit economic metrics are simple to track, but that is not a common case for all of them. Metrics like cost per customer or cost per feature can be challenging at identifying and measuring. For example, AWS monthly bill shows you what you spent on the different services you use in your workload but has no context of how those cloud costs are distributed across business or units. 

Common challenges

Measuring unit cost can be a challenging task for businesses, especially those operating in complex industries. One difficulty lies in accurately allocating costs to specific products or services, as there may be shared expenses that are difficult to attribute to a particular unit. Additionally, there may be hidden cloud costs that are not immediately apparent but still impact the overall unit cost.

Another challenge is determining which costs to include in the calculation of unit cost. For example, some costs may be fixed and not vary with the volume of production, while others may be variable and change based on the quantity produced. It is important to identify and include all relevant costs to ensure an accurate calculation of unit cost.

Moreover, in industries that rely heavily on technology or software, there may be additional complexities in measuring unit cost. For example, determining the cost per feature or per customer may require access to detailed data on software usage and cloud services, which can be difficult to track and quantify accurately.

Summary

Despite the complexity of the approach and its challenges, it is important for businesses to measure and monitor unit cost to make informed decisions about pricing, production, and resource allocation. By utilizing tools and software that can assist with cloud cost analysis and tracking, businesses can more accurately measure unit cost and make data-driven decisions to improve their operations and profitability. 

Our product called Cloud Avocado provides resource management and cost allocation tooling; it also includes customizable dashboards with units’ cost breakdowns, resource utilization data, and valuable insights about your cloud cost. Watch a product overview video below and let us know if you might need a demo for you or your customer.

https://www.youtube.com/watch?v=i06SLSnGd7k

As businesses increasingly turn to cloud computing, there is a growing need for solutions that can automatically scale resources up or down based on demand. Amazon Web Services (AWS) offers two popular services for this purpose: Amazon EC2 Auto Scaling and AWS AutoScaling. While both services offer similar functionality, there are key differences between them that businesses should be aware of when deciding which one to use.

Key differences between Amazon EC2 Auto Scaling and AWS Auto Scaling

Amazon EC2 Auto Scaling is a service that allows you to automatically scale EC2 instances based on demand. It is focused on scaling instances within an Auto Scaling group, and is commonly used to handle varying levels of traffic for web applications or batch processing jobs. It can be configured using either the EC2 console or the AWS CLI, and supports both launch templates and launch configurations.

AWS Auto Scaling, on the other hand, is a more comprehensive service that can scale not only EC2 instances, but also other AWS resources such as DynamoDB tables and Aurora DB clusters. It is designed to work with a wide range of workloads and is highly customizable. This service can be configured using the AWS Management Console, AWS CLI, or AWS SDKs.

Use cases of Amazon EC2 Auto Scaling

One common use case for this service is to ensure that a web application can handle varying levels of traffic. For example, during periods of high traffic, it can automatically spin up additional EC2 instances to handle the increased load, and then scale back down when traffic subsides. This helps ensure that the web application is always responsive and available, even during peak periods.

Another use case is batch processing jobs. For example, if you have a large number of files to process, you can use EC2 Auto Scaling to spin up a fleet of instances to process the files in parallel. Once the job is complete, the instances can be terminated to save costs. This can help you complete batch processing jobs faster and more efficiently.

AWS AutoScaling use case

One use case for AWS Auto Scaling is to scale resources based on demand for a broader range of workloads. For example, you can use it to automatically scale Amazon RDS read replicas in response to changes in read traffic, or to scale DynamoDB tables in response to changes in the number of requests. This can help ensure that your applications and services are always running at optimal capacity, while also minimizing costs.

Another use case for AWS Auto Scaling is to scale resources across multiple availability zones for high availability. For example, you can use it to automatically launch instances in different availability zones to ensure that your applications and services are always available, even if one availability zone goes down.

Conclusion 

oth services are powerful tools for scaling resources in the cloud, but they have key differences in their capabilities and configurations. EC2 Auto Scaling is more focused on scaling EC2 instances within an Auto Scaling group, while AWS AutoScaling can scale a wider range of resources and workloads. By understanding these differences, businesses can make informed decisions about which service to use based on their specific needs and requirements.

AWS cost allocation tags can be a powerful tool for organizations looking to better manage and understand their AWS costs. Users can assign metadata to resources, making it easier to track costs. Whether you are just getting started with AWS or are looking to optimize your AWS cost management practices, understanding cost allocation tags can be an important step toward achieving your goals.

Purpose of tagging in AWS

Applying tags is crucial for almost every AWS user because it helps to organize and manage resources in a more efficient and effective way. Without tags, it can be difficult to keep track of resources, especially in larger and more complex environments. Tagging resources provides a way to add metadata to resources, making it easier to identify, categorize, and manage them.

Imagine a scenario where an organization has hundreds or thousands of EC2 instances running across multiple regions and accounts. Without tags, it would be challenging to keep track of which instances are used for development, testing, or production environments. With tags, it becomes easier to filter, search, and organize instances based on their purpose, making it simpler to manage resources and ensure that they are used appropriately.

In addition to making it easier to manage resources, tags can also be used for aws cost allocation and chargeback. By tagging resources with specific values, such as department or project, organizations can allocate costs to the appropriate teams or individuals. This can help to improve accountability and reduce waste, as teams are more likely to be mindful of their resource usage when they are aware of the associated costs.

Also, tags can be used to improve security and compliance. By tagging resources with specific values, such as classification level or compliance requirements, organizations can ensure that sensitive or regulated resources are properly secured and monitored. This can help to reduce the risk of security breaches and ensure that organizations are meeting their compliance obligations.

Avoiding cost allocation tags issues

There are many guides on applying tags to your resources, yet there are several common mistakes that users can make when doing it in AWS. Here are a few examples and some tips on how to avoid them:

• Inconsistent tagging

One of the most common mistakes is using inconsistent or incomplete tags across different resources. This can make it difficult to search and organize resources and can lead to confusion when trying to determine the purpose of a resource. 

To avoid this mistake, establish a clear set of tagging conventions and ensure that all resources are tagged consistently. Consider using automation tools like AWS Config or AWS Resource Groups to enforce tagging standards across your environment.

• Overcomplicating tags

Another mistake is creating overly complex tags that are difficult to understand or manage. For example, using tags with multiple values or long descriptions can make it challenging to search for resources and allocate costs accurately. 

To avoid this mistake, keep tags simple and easy to understand. Use descriptive values that are easy to search for, and avoid creating tags with multiple values unless necessary.

• Using non-standard or ambiguous tags 

Non-standard or ambiguous tags can make it difficult to manage and monitor resources. For example, using abbreviations or acronyms that are not commonly understood can lead to confusion, while using vague tag values like “miscellaneous” or “other” can make it hard to understand the purpose of a resource. 

To avoid this mistake, use standard tag values and conventions that are commonly understood across your organization.

• Failing to update tags

Another common mistake is failing to update tags as resources change or evolve over time. For example, if a resource is repurposed or decommissioned, it is important to update the associated tags to reflect the new state of the resource. To avoid this mistake, establish a process for reviewing and updating tags on a regular basis. Consider using automation tools to alert you when resources are no longer being used or are no longer compliant with tagging standards.

By avoiding these common mistakes and following best practices for tagging, users can ensure that their resources are properly categorized, making it easier to manage and monitor them over time.

Summary

Applying tags is crucial for almost every AWS user because it helps to organize and manage resources more efficiently, allocate costs more accurately, and improve security and compliance. By following best practices for tagging, organizations can ensure that their resources are properly categorized, making it easier to manage and monitor them.

Our cost optimization expert can help you to set up or improve AWS cost allocation tags. Just use this link to book a meeting if you have any questions on this matter.

Containerization has revolutionized software development and deployment, allowing organizations to build, test, and deploy applications faster and more efficiently. However, as the number of containers grows, managing and scaling them becomes increasingly challenging. Container orchestration is the process of managing and automating the deployment, scaling, and operation of containerized applications. In this article, we will explore twelve tools for container orchestration.

What is Container Orchestration and How You Benefit from It

Container orchestration is the process of managing and automating the deployment, scaling, and operation of containerized applications. Containers are a lightweight and portable way to package software code and dependencies, making it easier to move applications between different environments.

Container orchestration provides a way to manage containers at scale, making it easier to deploy and run applications in a distributed environment. With container orchestration, you can:

• Simplify deployment: Container orchestration makes it easier to deploy applications by automating the process of creating, deploying, and updating containers.
• Improve scalability: Container orchestration allows you to scale your applications up or down based on demand, making it easier to handle spikes in traffic.
• Enhance reliability: Container orchestration provides features such as load balancing and self-healing, which help ensure that your applications are always available.
• Increase efficiency: Container orchestration allows you to optimize resource usage by running multiple containers on a single host, reducing infrastructure costs.

To take advantage of container orchestration, you need to use a container orchestration platform. There are several container orchestration platforms available, each with its own features and benefits. In the next section, we’ll take a closer look at some of the popular container orchestration tools.

Overview of Available Tools

1. Kubernetes – Kubernetes is currently the most popular container orchestration tool in the market. It’s an open-source platform that provides a wide range of features such as automatic scaling, load balancing, and self-healing.
2. Docker Swarm – Docker Swarm is a native clustering tool for Docker containers. It’s easy to use and provides features such as rolling updates and service discovery.
3. Mesos – Apache Mesos is a distributed systems kernel that provides features such as resource isolation and dynamic allocation. It supports multiple container runtimes, including Docker.
4. Nomad – Nomad is a simple and flexible container orchestration tool that supports multiple scheduling algorithms and can run on multiple platforms.
5. Rancher – Rancher is an open-source container management platform that provides features such as multi-cluster management, automated deployment, and centralized logging.
6. Amazon ECS – Amazon Elastic Container Service (ECS) is a fully-managed container orchestration service that runs on Amazon Web Services (AWS). It provides features such as automatic scaling and integration with other AWS services.
7. Google Kubernetes Engine – Google Kubernetes Engine (GKE) is a fully-managed Kubernetes service that runs on Google Cloud Platform (GCP). It provides features such as automatic scaling and integration with other GCP services.
8. Azure Kubernetes Service – Azure Kubernetes Service (AKS) is a fully-managed Kubernetes service that runs on Microsoft Azure. It provides features such as automatic scaling and integration with other Azure services.
9. OpenShift – OpenShift is a container application platform that provides features such as integrated container registry, automated builds, and continuous delivery.
10. Docker Enterprise – Docker Enterprise is an enterprise-grade container platform that provides features such as container security, automated operations, and integrated registry.
11. Portainer – Portainer is a simple and easy-to-use container management platform that provides features such as user management, container management, and container logs.
12. Kubernetes Operations (kOps) – kOps is a tool that automates the deployment, scaling, and management of Kubernetes clusters on AWS. It provides features such as automated updates and backup and restore.

Each of these container orchestration tools has its own strengths and weaknesses, so it’s important to choose the one that best fits your specific needs. 

Сonclusion

Сontainer orchestration has become an essential part of modern software development and deployment, and it’s important to choose the right tool for your organization’s specific needs. The twelve container orchestration tools discussed in this article are likely to be popular in 2023, and each one offers unique features and benefits. Kubernetes remains the most popular tool in the market, but there are other options such as Docker Swarm, Mesos, Nomad, Rancher, Amazon ECS, GKE, AKS, OpenShift, Docker Enterprise, Portainer, and kOps. By enabling container cost optimization, you can take full advantage of the benefits of container orchestration, such as simplifying deployment, improving scalability, enhancing reliability, and increasing efficiency, while also reducing infrastructure costs. With the right container orchestration tool, organizations can streamline their software development and deployment processes, improve application performance, and ultimately deliver better experiences to their users.

If you experience issues with choosing the most appropriate tool that will fit your needs, you can schedule a call with our Cloud Expert. 

COGS is a reflection of how much money you spend on the goods or services your customers buy. It describes the direct expenses required in creating and maintaining subscription-based software services at a software-as-a-service (SaaS) business. Anyone who has ever used any cloud services can confirm that the cost of cloud computing can become one of the largest expenditure items on that list. Cloud costs are very erratic; they shift as you add more clients, commission more features, and drive more traffic with viral marketing campaigns. You need to be accurate with your cloud management, finance, and even engineering in order to maintain your metric at the needed level. For instance, the costs of testing software features in production or increasing your customer’s resources can suddenly boost COGS. This gets us to the idea that using cloud cost optimization to track and cut back on wasteful spending is an excellent method to raise COGS.

In the previous article about COGS overview, we gave you a straightforward example of how to calculate COGS for a typical SaaS company and discussed the benefits of tracking this statistic. This article will provide you with recommendations on how to handle your AWS cloud cost optimization so that it reduces your COGS.

Implement cost allocation

You might never know how much you spend on each feature, client, or project in your app if you can’t track costs back to them. It could mean being unable to report higher gross margins that would increase your valuation, attract investors, and make it easier to service operating costs.

Unfortunately, most cloud service providers combine all fees into a single monthly invoice. As a result, it is highly challenging to eliminate items like non-production resources from the COGS calculation. However, each SaaS company’s COGS will vary depending on its particular business strategy, industry, compliance rules, and other elements. To suit your unique business requirements and development process, you must apply cost allocation.

Identifying, aggregating, and assigning costs to cost objects are the steps in the cost allocation process. Products, research projects, customers, sales regions, and departments are a few examples of cost objects. Businesses can determine the costs associated with running various applications and services within their organization by creating explicit cost allocation.

You don’t need to create anything by yourself; most vendor-provided or third-party cloud cost optimization tools provide you with built-in tagging functionality.

To link AWS costs to conventional cost-allocation dimensions, organizations frequently use tags like cost center/business unit, client, or project. However, any tag can be included in a cost allocation report. This makes it easier to link expenses to technical or security factors like particular apps, environments, or compliance initiatives.

Cost Analysis for the Cloud

Analyzing your cloud costs is the next step in lowering COGS with cloud cost optimization.

You must determine how much you are spending on cloud services, which expenses should be included as part of COGS, and what is causing the costs of each of these items to increase.

You can find areas where expenses can be reduced by analyzing your cloud costs: idle resources, overprovisioned resources, and ineffective instance usage. You can start optimizing your cloud costs to lower your COGS only after you have a thorough understanding of them.

Regardless of your requirements or background, it makes sense to think about using a cloud cost optimization tool to obtain basic cost analysis capabilities, such as cost monitoring and alerts on cost changes.

Choose the Appropriate Pricing

You can significantly reduce your COGS costs by choosing one of the many pricing options offered by AWS. When a component of your environment can fulfill the requirements for a certain commitment, you can switch to a more appropriate billing type to cut down the costs.

On-Demand

With this type of pricing, you can avoid the high fixed costs and complicated planning, buying, and maintaining hardware requirements, only paying for the resources you use.

Reserved instances

For companies that use cloud services for extended periods of time, reserved instances (RI) are a cost-effective choice. Businesses can see significant cost savings by reserving cloud capacity for a predetermined time period as opposed to using on-demand instances because reserved instances have lower hourly rates than on-demand instances. Businesses can lower their cloud costs and, as a result, their COGS by using reserved instances.

Savings Plans

Another customizable pricing structure that offers savings of up to 72% on your AWS computing utilization is called Savings Plans. No matter the instance family, size, operating system, tenancy, or AWS Region, this pricing model offers cheaper charges for the use of Amazon EC2 instances. Similar to EC2 Reserved Instances, Savings Plans offer significant savings over On-Demand Instances in return for a commitment to consume a certain amount of compute power (measured in $/hour) over a one or three-year period.

Also, you can keep buying RIs to keep them compatible with your current cost management procedures, and they will operate in conjunction with savings plans to lower your entire expenditure.

Spot Instance

A sort of Amazon EC2 instance called a spot instance enables you to place a bid on available EC2 capacity. Spot instance costs can be up to 90% less than on-demand instance costs. Workloads that can be interrupted, such as batch processing, rendering, or data analysis, can use this option.

Monitor and Improve Your Resource Use

According to Gartner, 35% of cloud spend is wasted. Overprovisioning of resources, which results in unnecessary expenses, is one of the key causes. To find inefficiencies and chances for cost savings, it becomes important to set up ongoing monitoring of cloud environment usage.

You can rightsize your resources to correspond with their actual demand using the analytics you already acquired. On the one hand, you can prevent overprovisioning resources, which can result in waste and extra costs. On the other hand, you can also prevent underprovisioning resources, which can result in poor performance and additional expenses.

Sometimes the choice between these rightsizing alternatives is harder than it appears to be. Thus the best advice is to inform the budget owners with your cost and usage data that is tied to the business units whenever you run onto a crossroads. This will assist you in making decisions that not only reduce costs but also aligns with the business strategy and goals.

As an illustration, imagine you are the Engineering Manager for a SaaS business that provides a messaging platform and stores the messages of your clients for no charge. One day, you realize that 30% of your COGS is spent on storage. Present this information and decide whether you need to start charging more for storage or whether it would be better to employ an engineering solution to decrease the price of storage.

Use Cloud Cost Optimization Tools

There are a lot of ways how a typical cloud cost optimization tool can assist companies in lowering COGS by offering real-time cost analysis. Yet, there are a few more elements that can assist you in lowering the price of your production.

Automatization of Routine 

By configuring auto events, such as assigning tags or ownership for each new resource you can cover more instances and do more accurate allocation. Turning instances on and off automatically in accordance with provided timetables allows you to spend less time on reducing your direct cost manually; moreover, it provides higher results.

View Cost Breakdown

An average tool provides you with a detailed cost breakdown right from the box. Use it to analyze your costs and understand where your money is being spent. This can help you identify areas where you can reduce costs by adjusting usage patterns, selecting a lower-cost service or changing pricing type.

Optimization opportunities

These tools can assist companies in identifying areas where they can cut back on cloud costs, such as unused resources, idle instances, or ineffective instance usage.

Calculate Upcoming Costs

Some tools use your prior usage patterns to forecast your costs in the future. This can assist you in budgeting for upcoming costs and selecting the best use of resources.

Monitor and Alert on Cost Changes

The tool can monitor your usage patterns and alert you when costs change significantly. This can help you stay on top of your expenses and take action to reduce costs when necessary.

Conclusion

In conclusion, cloud cost optimization is a crucial strategy for businesses that want to reduce their COGS. By analyzing cloud costs, utilizing reserved and spot instances, optimizing resource usage, using comfortable cloud cost optimization tools, and implementing cost allocation and accountability, businesses can reduce their cloud costs and with that ultimately reduce their COGS.

Hope this article was useful and interesting for our readers. In one of our next articles, we are going to publish materials with examples on how to apply the described plan to a real AWS cloud cost optimization use case.

Cloud computing has revolutionized the way businesses operate. It offers flexible, scalable, and cost-effective IT solutions. However, as businesses move their operations to the cloud, they need to pay close attention to their cost of goods sold (COGS). This metric is required to make sure they are achieving maximum profitability. COGS represents the direct costs associated with producing and delivering a product or service. It also includes the cost of cloud infrastructure, development costs, and other expenses as part of the cloud computing cost. Businesses that track this metric can understand their profitability better. Also, it helps to make informed decisions about pricing, resource allocation, cost optimization, and investment.

How to calculate COGS

Let’s say a SaaS company provides a project management tool to its customers. This company had $80,000 in allocated cloud infrastructure costs during a particular period. It includes the cost of servers, storage, and other cloud services used to deliver the app. Also, the company incurred $30,000 in other direct costs: salaries for developers and customer support.

Let’s look at COGS formula for a SaaS product. We can calculate the cost of producing and delivering the product for the period with all cloud costs included, as follows:

Allocated Cloud Infrastructure Cost + Other Direct Costs = Cost of Goods Sold

$80,000 + $30,000 = $110,000

So the cost of producing and delivering the project management tool for the period was $110,000. It includes the cost of cloud infrastructure and other direct expenses.

Cost of cloud computing in COGS

Let’s look at reasons why tracking COGS is important for businesses.

Understanding the Cost of Cloud Infrastructure

The cloud computing cost can become unpredictable. It happens because of the flexibility vendors offer to businesses to scale their IT infrastructure. Each company needs to track the cost of its resources to understand the direct costs associated with operating in the cloud. It helps in making informed decisions about resource allocation.

Optimizing Resource Utilization

Also tracking COGS can help to identify areas where the company can optimize its resource utilization. This includes identifying underutilized resources, monitoring resource usage, improving resource allocation, and results in insights for cost optimization.

Improving Pricing Strategies

COGS awareness can help businesses develop more effective pricing strategies. Companies need to monitor direct cloud costs associated with producing and delivering products or services. It allows them to set prices that cover their expenses and generate profit. Businesses can compete more effectively in the market and generate higher revenue.

Identifying Cost Savings

It is much easier to identify areas for potential cost savings while tracking COGS. It requires identifying inefficiencies, waste, and unnecessary expenses associated with cloud costs. By optimizing operations and reducing the cost of cloud computing, businesses can improve profitability and achieve long-term success.

Meeting Financial Reporting Requirements

Tracking COGS is also important for meeting financial reporting requirements. To calculate accurate financial statements, a company needs to track its direct costs and COGS. Both are necessary for gross profit, which is an important metric for investors, lenders, and other stakeholders.

Summary

In summary, tracking COGS in cloud computing is very important for businesses. It provides valuable insights into the direct costs associated with operating in the cloud. Also, it helps businesses to optimize their resource utilization, improve pricing strategies and achieve cost savings. Last but not least – it meets common financial reporting requirements. Moreover, while tracking COGS, businesses can enable cloud cost optimization to achieve higher profitability and compete more effectively in the market.

In our next articles, we’re going to find out what are the possible areas for reducing COGS and show you some of our real-life examples of achieving this goal.

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In today’s fast-paced and competitive business world, lots of organizations are constantly looking for ways to improve their operations along with mastering AWS cost optimization. One approach has merged these goals and gained significant popularity in recent years, it is FinOps. In this article, we’ll take a closer look at what FinOps is, and 5 key facts that can help you better understand it.

What is FinOps?

FinOps (short for Financial Operations) is a practice that aims to optimize cloud costs by bringing together people, processes, and tools. This approach emphasizes collaboration between finance, operations, and engineering teams to ensure that cloud spending is transparent, accurate, and aligned with business objectives. FinOps provides a framework for managing cloud costs by leveraging automation, analytics, and data-driven decision making.

The Role of Automation

Automation is a key component of FinOps. By automating the process of monitoring cloud costs, organizations can gain real-time visibility into their spending and quickly identify areas where cost savings can be achieved. For example, FinOps tools can automate the process of tagging cloud resources, which makes it easier to track costs and attribute them to specific departments or teams. Automation can also be used to enforce policies around cost optimization, such as automatically shutting down unused resources.

The Importance of Collaboration

Collaboration is another essential aspect of FinOps. To effectively manage cloud costs, finance, operations, and engineering teams must work together towards common goals. By breaking down silos between these teams, organizations can achieve a more holistic view of their cloud spending and identify opportunities for optimization. Collaboration can also lead to better decision making, as teams can leverage their diverse perspectives and expertise to make informed choices about cloud resource allocation.

The Role of Analytics

FinOps relies heavily on data analytics to inform decision making. By collecting and analyzing data about cloud spending, organizations can gain insights into where their money is going and identify areas where costs can be reduced. For example, analytics tools can be used to identify unused resources, spot trends in spending, and forecast future costs. By using data to drive decision making, organizations can optimize their cloud spending and achieve better financial outcomes.

The Benefits of FinOps

By adopting a FinOps approach, organizations can achieve greater cost transparency, which allows them to better understand their cloud spending and make informed decisions about resource allocation. FinOps also promotes collaboration and alignment between finance, operations, and engineering teams, which can lead to more efficient and effective use of cloud resources. And by leveraging automation and analytics, organizations can achieve significant cost savings, which can free up resources for other strategic initiatives.

Summary

In summary, FinOps is a practice that brings together people, processes, and tools to optimize cloud costs. It relies on automation, collaboration, and data analytics to achieve greater cost transparency and identify opportunities for cost savings. By adopting a FinOps approach, organizations can achieve better financial outcomes and free up resources for other strategic initiatives. If you’re looking to improve your organization’s cloud cost management, FinOps is definitely worth exploring.

Elastic Compute Cloud is a cloud computing service provided by Amazon Web Services (AWS) that offers users the ability to rent virtual machines in the cloud via the scalable computing service known as EC2. EC2 instances are virtual servers that provide users with various computing resources such as CPU, memory, and storage. These instances can be tailored to a user’s needs and utilized for various tasks including program operation, website hosting, and big data analysis. One of the popular cloud cost optimization questions we get is if one can really save money by choosing appropriate instance type? Yes, its possible, but you need alway track the latest updates on types and generations to make the most appropriate choice that fits your needs and of course be aware of current pricing models. Our team prepared a short cheat sheet that may be useful for beginners. 

Existing EC2 Types

Currently, there are over 200 different EC2 instance types available through Amazon, each with distinctive features and costs. These instances can be divided into several categories including general-purpose, compute-, memory-, storage-, and GPU- and FPGA-optimized instances. 

General-purpose instances	This type can be used for a variety of workloads, including development and test environments, small- to medium-sized databases, and web and application servers.
Compute-optimized instances	These are best for high-performance computing applications including batch processing, video transcoding, and scientific modeling.
Memory-optimized instances	This type of instances is designed for memory-intensive applications like high-performance databases, large data analytics, and in-memory caches
Storage-optimized instances	Instances of this type is an ideal choice for databases, data warehouses, and big data processing.
GPU instances	This is a good item to choose when searching something tailored for demanding graphic workloads such as machine learning, scientific simulations, and video rendering.
FPGA instances	A type of instances that are optimized to accelerate specialized applications such as financial modeling and genomics research.

Instance generation

Remember, that EC2 instance generations are a way to group comparable instance types that are released at the same time. Each instance creation includes several instance types tailored for specific workloads and or use cases. For example, the M5 generation of general-purpose instances includes the M5d, M5a, and M5n instance types.The M5d instance type is optimized for applications that require high-speed, low-latency local storage, while the M5a instance type is designed for applications that require a balance of compute and memory resources. The M5n instance type, on the other hand, is optimized for applications that require high-speed networking. By grouping these instances into a generation, users can easily compare and choose the best instance type for their workload.

Remember about pricing model

AWS provides users with three pricing tiers for EC2 instances: on-demand, reserved, and spot. With on-demand instances, users can pay for compute power by the hour without any upfront payments or long-term obligations. Reserved instances offer a considerable hourly cost savings in exchange for a one- or three-year commitment. Spot instances allow users to bid on available EC2 capacity, and the hourly price is determined by supply and demand. Choose pricing wisely to get the most benefit from the instances you’ve chosen. 

Summary

In conclusion, EC2 instance types offer users a variety of choices to meet their computing needs. Understanding the various instance types and how they can be optimized will enable users to select the best instance type for their workload. AWS also offers pricing options that allow users to tailor their charges based on their needs. EC2 instances enable users to create scalable and cost-effective cloud applications.