November 30, 2023

Compute Commitment And Usage-Based Strategies across Azure

3 min read

Cost Optimization on Azure
Cost Optimization on Azure
Cost Optimization on Azure

Optimizing costs in Azure can be a challenge, especially when navigating complex pricing models associated with compute resources. Insights into Compute Commitment and Usage-Based Strategies across Microsoft Azure can help organizations with efficient resource utilization, resource allocation and cost efficiency. 

Understanding Compute Commitment in Azure

Compute Commitment pertains to an organization committing to use certain compute resources for a specific duration in exchange for discounted pricing compared to a pay-as-you-go model. 

In the Azure ecosystem, this commitment could be in the form of Azure Reserved Virtual Machine Instances (RIs) and Azure Savings Plan for Compute. 

Let's take a closer look into each of them:

Azure Reserved Virtual Machine Instances (RIs)

Azure Reserved Virtual Machine Instances (RIs) are compute resources that can help you save up to 72% compared to pay-as-you-go pricing by committing to a one-year or three-year plan. 

Azure RIs are specific to service and region and the discounts are on the infrastructure component of VM costs for Windows and Linux, excluding license expenses. However, users can utilize Azure Hybrid Benefit separately for license cost savings as well. 

When combined with Azure Hybrid Benefit, the discounts can be further enhanced up to 80%. Discounts are applied on an hourly basis and payments can be either done upfront or monthly. Total cost remains the same and you don’t pay any extra fees when you choose to pay monthly. RIs can be purchased through the Azure portal. 

Azure Savings Plan for Compute

With Azure savings plans for compute, organizations can commit to spending a fixed hourly amount on compute resources for one or three years and save up to 65% compared to pay-as-you-go prices. The biggest discounts are automatically applied first. Unlike RIs, here you can launch resources in any region with any instance series or operating system. You can purchase a Savings Plan via the Azure portal or with the Savings plan Order Alias API. 

See how the savings plan for compute works in the graph below.

Understanding Usage-Based Strategies in Azure

In contrast to commitment-based models where businesses are charged regardless of whether they utilize their resources, in Usage-Based pricing models businesses are charged only when they use a service. 

There are no long-term contracts and the billing is calculated according to the resources used and the duration of usage, often billed on an hourly basis. This Pay-As-You-Go pricing offers flexibility in scaling resources based on your changing requirements. 

Azure Virtual Machines, Azure Functions and Azure Spot Virtual Machines are good examples of this model that we can look into:

  • Azure Virtual Machines: Virtual Machines are fundamental compute resources from Azure that operate on a pay-as-you-go model, allowing businesses flexibility without any long-term commitments. 

  • Azure Functions: Azure Functions lets you run code without managing servers. You can write your code in any language and you only pay for the time your code runs, and you can scale up or down as needed. You can also use Azure Functions to build event-driven systems, web APIs, workflows, and more.

  • Azure Spot Virtual Machines: Spot Virtual Machines allow businesses to purchase unused compute capacity at significant cost savings up to 90% off compared to pay-as-you-go prices. You pay up to the maximum price that you set, however, at any point instances could be evicted if Azure no longer has available compute capacity or if someone is paying a higher price than the maximum price set by you. Hence, Spot VM instances are ideal for workloads that can be interrupted. Here also you only pay for the duration you use the VMs.

Hurdles and Challenges

Let's explore some challenges with tools and solutions that can help organizations optimize their cloud computing costs and resource management.

  • Variable Workloads and Complex Pricing Models: Accurately predicting future computing needs could be difficult looking at the dynamic nature of businesses and selecting a wrong pricing model can significantly impact cloud computing costs. Hence tools like Microsoft Cost Management + Billing and Azure Advisor can come in handy to forecast future compute needs and optimize compute resources to make more informed decisions.

  • Resource Monitoring and Management: The lack of visibility into resource utilization can lead to inefficient usage of your resources and increased costs. Azure Monitor is useful here, offering insights into performance, availability, and utilization. These insights enable organizations to proactively identify and address potential issues before they impact performance or cause unnecessary costs.

What to remember before getting started

Amnic delivers a cloud cost observability platform that helps measure and rightsize cloud costs continuously. It is agentless, secure and allows users to get started in five minutes at no cost. 

Amnic provides a suite of features such as cost explorer, K8s visibility, custom dashboards, benchmarking, anomaly detection, alerts, K8s optimization and more. Businesses save 25-30% on their cloud costs, even on pre-optimized environments. 

Amnic provides precise recommendations across network, storage and compute, based on your cloud infrastructure to identify high costs and industry best practices to reduce them. With Amnic, organizations can successfully build a roadmap towards lean cloud infrastructure and build a culture of cost optimization among their teams.  

Visit to learn more about how you can get started on your cloud cost optimization journey. 

Reference Links
  1. Azure Reserved Virtual Machine Instances (RIs) Link to article

  2. Azure Savings Plan for Compute Link to article

  3. Azure Virtual Machines (Pay-As-You-Go) Link to article

  4. Azure Functions Link to article

  5. Azure Spot Virtual Machines Link to article

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