The shift from traditional on-premise infrastructure to cloud-based solutions has made it increasingly difficult for businesses to predict and manage their IT costs. With cloud services charging based on usage, organizations need to be aware of the different cloud cost models available to them for effective and intelligent, data-backed investment decisions.
In this article, we’ll explore four types of cloud cost models and management strategies available for businesses using Amazon Web Services and Google Cloud Platform. By understanding the above, organizations can better optimize their cloud spending, eliminate waste, and improve their overall return on investment.
Importance of Cloud Cost Models
Cloud cost management is a critical aspect of any organization that leverages cloud computing. It is essential to understand the different cloud cost models available to choose the one that best fits your organization’s needs.
- Different cloud providers offer different pricing models, and choosing the right one can be challenging.
- The right cloud cost model depends on the organization’s workloads, objectives, and budget.
- Understanding the different pricing models can help you make informed decisions and manage cloud costs more effectively.
Ignoring the importance of cloud cost management can lead to unexpected expenses and resource wastage, affecting the organization’s overall profitability. Therefore, it is crucial to have a deep understanding of cloud cost models and how they impact your organization’s cost management.
Creating a FinOps Strategy
Moreover, understanding the pricing models of cloud services allows organizations to create a FinOps strategy that aligns with their business objectives. A FinOps strategy helps organizations manage their cloud costs by tracking expenses, setting budgets, and creating policies for cloud usage. A well-implemented FinOps strategy can lead to cost savings, increased operational efficiency, and a better understanding of cloud usage across the organization.
To create an effective FinOps strategy, organizations should start by gathering pricing and workflow information for their cloud infrastructure. This information can then be used to identify areas where cost savings can be made and to develop policies and procedures to optimize cloud usage. By incorporating FinOps principles into their cloud management practices, organizations can ensure a culture of accountability, transparency, and cost efficiency.
What are the different Cloud Cost Models?
In this section, we’ll dive into the four types of cloud cost models and their best practices for effective cost management. We’ll discuss the advantages and disadvantages, appropriate use cases, and best practices for each model.
Pay-As-You-Go Model
Amazon Web Services (AWS) and Google Cloud Platform (GCP) both offer the Pay-as-You-Go pricing model. Both providers offer flexible pricing plans that allow customers to scale up or down depending on their computing needs. This model essentially means that resources are charged by the minute.
While AWS refers to its pricing model as pay-as-you-go, GCP uses the term on-demand pricing. Despite the difference in terminology, both refer to the same model in which customers are charged for the computing resources they consume on an as-needed basis.
With the Pay-as-You-Go model, customers only pay for what they use, making it an attractive option for organizations with unpredictable or fluctuating computing needs. This model also allows for easy scalability, as customers can quickly increase or decrease their computing resources as needed.
However, this model can lead to unpredictable costs and unexpected bills if not managed correctly. Customers need to closely monitor their usage and adjust their resources accordingly to avoid overspending.
Advantages & Disadvantages of On Demand Model:
- Flexibility: Organizations can scale their resources up or down based on their changing needs without any financial penalty.
- No upfront costs: Organizations can avoid any upfront costs, making it easier to get started with cloud computing.
- Predictable billing: This model provides transparency and visibility into cloud usage, making it easier for organizations to predict their costs and avoid any surprises in billing.
- No commitment: Organizations can use the service without any long-term commitments or contracts.
- Higher costs: Pay-as-you-go pricing can be more expensive in the long term compared to other models for workloads with predictable resource usage.
- Less predictability: Since costs are dependent on usage, it can be difficult to predict cloud costs accurately for highly variable workloads.
Best practices for managing costs with this model:
- Monitor usage: Keep track of your cloud usage to avoid any surprises in billing.
- Use cost management tools: Use tools like AWS Cost Explorer or Google Cloud Billing to monitor and manage cloud costs.
- Leverage cost-saving opportunities: Take advantage of savings plans, spot instances, or committed use discounts where possible to reduce cloud costs.
- Optimize resource usage: Use auto-scaling and other optimization techniques to ensure resources are only used when necessary.
- Set budget alerts: Set up budget alerts to receive notifications when cloud costs exceed a certain threshold.
Committed Use Discounts – Reserved Instances Pricing Model
Committed Use Discounts (CUD) and Reserved Instances (RI) pricing are both models that require customers to commit to a certain amount of resource usage for a one or three-year term, and in exchange, they receive a discounted rate on their usage. The discounts vary based on the level of commitment, the length of the commitment, and the type of resource used.
CUD is well-suited for organizations with predictable workloads, as they can commit to a certain level of usage over a longer period of time. This model is beneficial for customers who can accurately forecast their resource needs and who are looking for a cost-effective pricing option.
Similarly, AWS’s Reserved Instances pricing offers different tiers based on the level and length of commitment, as well as the type of instance used. The table lists different Reserved Instance types and their upfront fees, monthly fees, effective hourly rates, and savings over On-Demand rates.
| Instance name | RI upfront fee | RI monthly fees* | RI effective hourly rate** | Savings over On-Demand | On-Demand rate |
|---|---|---|---|---|---|
| a1.medium | $0 | $11.75 | $0.016 | 37% | $0.0255 |
| a1.large | $0 | $23.43 | $0.032 | 37% | $0.0510 |
| a1.xlarge | $0 | $46.94 | $0.064 | 37% | $0.1020 |
| a1.2xlarge | $0 | $93.81 | $0.129 | 37% | $0.2040 |
| a1.4xlarge | $0 | $187.61 | $0.257 | 37% | $0.4080 |
| a1.metal | $0 | $187.61 | $0.257 | 37% | $0.4080 |
| t4g.nano | $0 | $1.90 | $0.003 | 38% | $0.0042 |
| t4g.micro | $0 | $3.87 | $0.005 | 37% | $0.0084 |
| t4g.small | $0 | $7.67 | $0.011 | 38% | $0.0168 |
Compared to On-Demand pricing, the RI pricing model allows customers to save up to 38% on their usage costs, making it an attractive option for organizations with steady-state workloads that can commit to a certain level of usage over a longer period of time.
Advantages & Disadvantages of RIs:
- Predictability: Both pricing models provide predictability in terms of cost, which makes it easier for organizations to budget their cloud expenses.
- Capacity planning: Committing to a certain level of usage over a longer period of time allows organizations to better plan their capacity and ensure they have the resources they need.
- Less flexibility: Unlike the pay-as-you-go model, committed use discounts and reserved instances pricing require customers to commit to a certain level of usage over a longer period of time, which may not be suitable for all organizations.
- Unused resources: Committing to a certain level of usage also means that there may be unused resources, which can result in wasted costs if not managed properly.
- Upfront commitment: Both models require upfront commitment, which can be a barrier for some organizations.
Best practices for managing costs with this model
- Understand your workload: Organizations should have a clear understanding of their workload before committing to CUD or RI. This includes understanding their resource usage, capacity requirements, and usage patterns.
- Use cost management tools: Both GCP and AWS offer cost management tools that allow organizations to monitor their usage and optimize their costs.
- Right-size resources: To avoid unused resources, organizations should ensure they are using the appropriate resource types and sizes for their workload.
- Renew commitments: Both CUD and RI offer the option to renew commitments, so organizations should regularly evaluate their usage and renew commitments if appropriate.
- Consider hybrid usage: Both pricing models can be used in conjunction with other pricing models, such as pay-as-you-go, to optimize cost savings.
Spot Instances – Preemptible VMs Model
Spot instances are a cost-saving option in the AWS and GCP pricing models, where customers can bid on unused compute capacity and potentially receive it at a lower price. In AWS, these instances are called Spot Instances, while in GCP, they are known as Preemptible VMs.
Workloads with flexible start and end times, or that can be interrupted and resumed without affecting the end-user experience, are ideal for spot instances. These can include batch processing, data analysis, and simulations. Spot instances are not recommended for workloads that require consistent performance or must run continuously.
It is important to note that spot instances do not provide any service level agreements (SLAs), and there is a possibility that the instances may be terminated at any time. Therefore, customers need to architect their applications to handle interruptions gracefully and to have a backup plan in case of unexpected termination.
| EC2 Spot Instances | EC2 On-Demand Instances |
|---|---|
| Cost-effective alternative to EC2 On-Demand instances | Typically more expensive than EC2 Spot Instances |
| Users bid on unused EC2 computing capacity | Users pay the full price for the EC2 computing capacity |
| Applications can be terminated at any time | Applications are guaranteed to run until the user stops it or terminates the instance |
| Ideal for flexible, stateless applications | Ideal for applications that require guaranteed computing capacity |
Best practices for Spot Instances – Preemptible VMs
- Use fault-tolerant architectures: Since Preemptible VMs can be interrupted at any time, it’s essential to design applications that can handle failures and restart automatically.
- Use auto-scaling: By using auto-scaling, organizations can quickly scale up or down depending on demand and optimize the use of Preemptible VMs.
- Monitor instances: Organizations should closely monitor the instances and set up alerts to detect any potential interruption.
- Conduct Cloud Cost Audits: Cloud Cost Audits help you determine where your expenditure is going and categorize them by priority. This helps in cost optimization.
- Use in non-critical workloads: Preemptible VMs are ideal for non-critical workloads such as batch processing, data analysis, or testing. Organizations should avoid using them for critical workloads that require high availability and stability.
Savings Plan – Sustained Use Discounts Model
Savings Plans is a pricing model offered by AWS , (Sustained Use Discounts in GCP) that provides customers with significant cost savings on their cloud computing usage. With Savings Plans, customers commit to a certain level of usage over a one or three-year term and receive discounted rates on their usage. The discounts vary based on the level of commitment and the type of resource used.
Compared to other pricing models like On-Demand, SUDs can help customers save up to 72% on their cloud computing costs. This pricing model is ideal for organizations with consistent usage patterns and predictable workloads. Customers can choose to purchase Savings Plans either with an upfront payment or on a monthly basis.
Best practices for using Savings Plans & Sustained Use Discounts
- Analyze your usage patterns: Before committing to a Savings Plan, it’s important to analyze your usage patterns and determine if a one or three-year commitment makes sense. It’s also important to understand which resources are being used the most and how they can be optimized to save costs.
- Optimize resource allocation: Savings Plans offer a high level of flexibility, allowing you to use them across different instance families, sizes, and regions. To maximize your cost savings, it’s important to optimize resource allocation and use the most cost-effective options for your workload.
- Consider other pricing models for fluctuating workloads: If your workloads are fluctuating and unpredictable, it may be more cost-effective to use other pricing models such as On-Demand or Spot Instances. Savings Plans are best suited for organizations with consistent usage patterns and predictable workloads.
- Take advantage of the pricing model benefits: Savings Plans offer significant cost savings compared to other pricing models. To take full advantage of these benefits, it’s important to commit to a usage level that aligns with your needs and closely monitor your usage to avoid overage charges.
Conclusion
Understanding the different cloud cost models available, including On-Demand, Spot Instances, Reserved Instances, and Savings Plans, is essential for organizations looking to optimize their cloud spending. By selecting the right cost model for their workload, organizations can save significant costs on their cloud computing usage.
However, optimizing cloud costs is an ongoing process, and it requires a continuous effort to ensure that resources are being utilized effectively.
Ultimately, a well-implemented cloud cost management tool can help organizations achieve cost optimization and resource utilization, enabling them to scale their cloud infrastructure in a sustainable and efficient manner.
