This inefficiency occurs when an Azure Savings Plan is scoped too narrowly relative to where eligible compute usage actually runs. When usage is spread across multiple subscriptions or fluctuates significantly (for example, development and test workloads that are frequently stopped and started), a narrowly scoped Savings Plan may not consistently find enough eligible usage to consume the full commitment. As a result, part of the committed hourly spend goes unused while other eligible workloads outside the scope continue to incur on-demand charges.
Azure supports broader scoping options—such as Management Group or Shared scope—that allow the commitment to be applied across a larger pool of eligible compute. Selecting an overly restrictive scope can therefore directly drive underutilization, even when sufficient total usage exists across the tenant.
Many organizations purchase Software Assurance or subscription-based Windows and SQL Server licenses that entitle them to use Azure Hybrid Benefit. However, if the setting is not applied on eligible resources, Azure continues charging pay-as-you-go rates that already include Microsoft licensing costs. This oversight results in paying twice—once for the on-premises license and once for the built-in Azure license. The inefficiency often goes unnoticed because licensing configurations are not centrally validated or enforced. Enabling AHUB can reduce costs by up to 40% for Windows server VMs and up to 30% for SQL Databases.
Azure provides VM families across three major CPU architectures, but default provisioning often leans toward Intel-based SKUs due to inertia or pre-configured templates. AMD and ARM alternatives offer substantial cost savings; ARM in particular can be 30–50% cheaper for general-purpose workloads. These price differences accumulate quickly at scale.
ARM-based VMs in Azure (e.g., Dps_v5, Eps_v5) are suited for many common workloads, such as microservices, web applications, and containerized environments. However, not all applications are architecture-compatible, especially those with dependencies on x86-specific libraries or instruction sets. Organizations that skip architecture evaluation during provisioning miss out on cost-efficient options.
Many organizations choose a VM SKU and version (e.g., `D4s_v3`) during the initial planning phase of a project, often based on availability, compatibility, or early cost estimates. Over time, Microsoft releases newer hardware generations (e.g., `D4s_v4`, `D4s_v5`) that offer equivalent or better performance at the same or reduced cost. However, existing VMs are not automatically migrated, and these newer versions are often overlooked unless intentionally evaluated.
This inefficiency tends to persist because switching to a newer version typically requires VM deallocation and resizing, which introduces temporary downtime. As a result, outdated VM series versions continue to run indefinitely, even in environments where brief downtime is acceptable. The cost delta between series versions—especially across certain families like `D`, `E`, or `F`—can be significant when scaled across environments or multiple VMs. Note that VM series versions (v3, v4, v5) are distinct from VM generations (Gen 1 vs Gen 2), with series versions representing the primary opportunity for cost optimization.
Non-production Azure VMs are frequently left running during off-hours despite being used only during business hours. When these instances remain active overnight or on weekends, they generate unnecessary compute spend. Azure offers built-in auto-shutdown features that allow teams to define daily stop times, retaining disk data and configurations without paying for VM runtime. Implementing scheduled shutdowns in dev/test environments is a simple, low-risk optimization that can reduce compute costs by 30–60%.
This inefficiency arises when a virtual machine is left in a stopped (deallocated) state for an extended period but continues to incur costs through attached storage and associated resources. These idle VMs are often remnants of retired workloads, temporary environments, or paused projects that were never fully cleaned up. Without clear ownership or automated cleanup, they can persist unnoticed and accumulate avoidable charges.
Azure VMs are frequently provisioned with more vCPU and memory than needed, often based on template defaults or peak demand assumptions. When a VM operates well below its capacity for an extended period, it presents an opportunity to reduce costs through rightsizing. Without regular usage reviews, these inefficiencies can persist indefinitely.
