Azure Compute Pricing Models: Pay-As-You-Go vs Reserved Instances
Introduction to Azure Compute Pricing Models
Cloud computing has transformed the way businesses manage infrastructure, but with flexibility comes the challenge of managing costs effectively. Microsoft Azure offers multiple pricing models designed to suit different workload types and business needs. In this video, we provide an Overview of Azure Compute Pricing Models and focus on the two most widely adopted options: the Azure Pay-As-You-Go (PAYG) Model and the Azure Reserved Instances (RI) Model.
Whether you are a startup experimenting with new workloads or an enterprise running critical applications, understanding how these models work is essential. PAYG provides maximum flexibility by charging only for resources consumed, while Reserved Instances deliver significant savings for long-term commitments. Knowing when to use each model can help your organization unlock the right balance between agility and cost efficiency. Along the way, we will also explore Azure Use Cases for PAYG vs RI and reveal practical Azure Cost Optimization Strategies that can help you maximize savings without compromising performance.
What You Will Learn in This Video
In this session, you will learn:
- How the Azure Pay-As-You-Go (PAYG) Model works and where it provides the most value
- How the Azure Reserved Instances (RI) Model offers deep discounts for predictable workloads
- Real-world Azure Use Cases for PAYG vs RI and how organizations combine both approaches
- Proven Azure Cost Optimization Strategies that blend flexibility with financial efficiency
- A clear framework to decide which pricing model best fits your cloud workloads
Looking Ahead
This video is part of a broader series on Azure compute management and cost efficiency. Up next, we will take the concepts even further by exploring Using Bicep or ARM Templates to Automate Azure Compute Deployments. This next session will show you how infrastructure-as-code can streamline provisioning, ensure consistency, and help you optimize your Azure environment even more.