Deploying a Cost-Optimized Compute and Storage Solution
Pluralsight Hands-On Lab — Azure Fundamentals
At a Glance
| Platform | Pluralsight |
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| Category | Azure Cost Optimization |
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| Lab Type | Guided + Challenge Mode |
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| Environment | Azure Portal |
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| Completed | 2026 |
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Overview
Cost optimization in Azure involves selecting right-sized compute, low-redundancy storage tiers, and automated lifecycle policies to reduce long-term infrastructure spend without sacrificing availability. In this lab, I deployed a Virtual Machine Scale Set with autoscaling using cost-efficient B2s instances and an Azure Load Balancer, provisioned a storage account with LRS redundancy, uploaded application data to a Cool-tier blob container, and configured a lifecycle management rule to automatically transition blobs to Cool storage after 30 days of inactivity.
What I Did
- Created a Virtual Machine Scale Set (vmss-costopt) with Flexible orchestration and autoscaling enabled, reviewing the default scaling rules: scale out at CPU > 80%, scale in at CPU < 20%, with a minimum of 2 and maximum of 20 instances
- Selected the B2s VM size from the B-series as a cost-optimized compute option balancing performance and cost
- Configured an Azure Load Balancer (vmss-lb) for the scale set to distribute traffic across instances
- Created a storage account (stcostopt + unique suffix) with Locally Redundant Storage (LRS) to minimize redundancy costs
- Created a private blob container named "appdata" and uploaded a file with the access tier set to Cool at upload time, reducing storage costs for infrequently accessed data
- Configured a lifecycle management rule (tier-by-age) to automatically move blobs last modified more than 30 days ago to Cool storage, automating long-term cost reduction
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