This inefficiency occurs when an App Service Plan is sized larger than required for the applications it hosts. Plans are often provisioned conservatively to handle anticipated peak demand and are not revisited after workloads stabilize. Because pricing is tied to the plan’s SKU rather than real-time usage, oversized plans continue to incur higher costs even when CPU and memory utilization remain consistently low.

This inefficiency occurs when a function has steady, high-volume traffic (or predictable load) but continues running on default Lambda pricing, where costs scale with execution duration. Lambda Managed Instances runs Lambda on EC2 capacity managed by Lambda and supports multi-concurrent invocations within the same execution environment, which can materially improve utilization for suitable workloads (often IO-heavy services). For these steady-state patterns, shifting from duration-based billing to instance-based billing (and potentially leveraging EC2 pricing options like Savings Plans or Reserved Instances) can reduce total cost—while keeping the Lambda programming model. Savings are workload-dependent and not guaranteed.

This inefficiency occurs when Savings Plans are purchased within the final days of a calendar month, reducing or eliminating the ability to reverse the purchase if errors are discovered. Because the refund window is constrained to both a 7-day period and the same month, late-month purchases materially limit correction options. This increases the risk of locking in misaligned commitments (e.g., incorrect scope, amount, or term), which can lead to sustained underutilization and unnecessary long-term spend.

This inefficiency occurs when workloads are constrained to run only on Spot-based capacity with no viable path to standard nodes when Spot capacity is reclaimed or unavailable. While Spot reduces unit cost, rigid dependence can create hidden costs by requiring standby standard capacity elsewhere, delaying deployments, or increasing operational intervention to keep environments usable. GKE explicitly recommends mixing Spot and standard node pools for continuity when Spot is unavailable.

This inefficiency occurs when Kubernetes Jobs or CronJobs running on EKS Fargate leave completed or failed pod objects in the cluster indefinitely. Although the workload execution has finished, AWS keeps the underlying Fargate microVM running to allow log inspection and final status checks. As a result, vCPU, memory, and networking resources remain allocated and billable until the pod object is explicitly deleted.

Over time, large numbers of stale Job pods can generate direct compute charges as well as consume ENIs and IP addresses, leading to both unnecessary spend and capacity pressure. This pattern is common in batch-processing and scheduled workloads that lack automated cleanup.

This inefficiency occurs when workloads with predictable, long-running compute usage continue to run entirely on on-demand pricing instead of leveraging Committed Use Discounts. For stable environments, such as production services or continuously running batch workloads, failing to apply CUDs results in materially higher compute spend without any operational benefit. The inefficiency is driven by pricing choice, not resource overuse.

This inefficiency occurs when production and non-production applications are hosted within the same App Service Plan. Production workloads often require higher availability, performance, or scaling characteristics, driving the plan toward larger or higher-cost SKUs. When non-production workloads share that plan, they inherit the higher cost structure even though their availability and performance requirements are typically much lower, resulting in unnecessary spend.

This inefficiency occurs when pod resource requests—often inflated by sidecar containers—push total memory or CPU just over a Fargate sizing boundary. Because Fargate adds mandatory system overhead and only supports fixed resource combinations, small incremental increases can force a pod into a much larger billing tier. This results in materially higher cost for marginal additional resource needs, especially in workloads that run continuously or at scale.

This inefficiency occurs when Provisioned Concurrency is enabled for Lambda functions that do not require consistently low latency or steady traffic. In such cases, reserved capacity remains allocated and billed during idle periods, creating ongoing cost without proportional performance or business benefit. This is distinct from standard Lambda execution charges, which are purely usage-based.

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.