This inefficiency occurs when an RDS database instance is deleted but its manual snapshots or retained backups remain. Unlike automated backups tied to a live instance, these backups persist independently and continue generating storage costs despite no longer supporting any active database. This is distinct from excessive retention on active databases and typically arises from incomplete cleanup during decommissioning.

This inefficiency occurs when analysts use SELECT * (reading more columns than needed) and/or rely on LIMIT as a cost-control mechanism. In BigQuery, projecting excess columns increases the amount of data read and can materially raise query cost, particularly on wide tables and frequently-run queries. Separately, applying LIMIT to a query does not inherently reduce bytes processed for non-clustered tables; it mainly caps the result set returned. The “LIMIT saves cost” assumption is only sometimes true on clustered tables, where BigQuery may be able to stop scanning earlier once enough clustered blocks have been read.

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 an Azure Virtual WAN hub is provisioned with more capacity than required to support real network traffic. Because hub costs scale with the number of configured scale units, overprovisioned hubs continue to incur higher charges even when traffic levels remain consistently low. This commonly happens when hubs are sized for peak or anticipated demand that never materializes, or when traffic patterns change over time without corresponding capacity adjustments.

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 Azure SQL Managed Instances continue running on legacy General Purpose or Business Critical tiers despite the availability of the next-gen General Purpose tier. The newer tier enables more granular scaling of vCPU, memory, and storage, allowing workloads to better match actual resource needs. In many cases, workloads running on Business Critical—or overprovisioned legacy General Purpose—do not require the premium performance or architecture of those tiers and could achieve equivalent outcomes at lower cost by moving to next-gen General Purpose.

This inefficiency occurs when backup data remains in a Recovery Services Vault after the original protected resource has been deleted. These orphaned backups continue to consume storage and generate cost despite no longer supporting an active workload. In addition, long-retained backups that are rarely accessed are often kept in higher-cost tiers, increasing storage spend without providing additional value.

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 licensed Azure DevOps users remain assigned after individuals leave the organization or stop using the platform. These inactive users continue to generate recurring per-user charges despite providing no ongoing value, leading to unnecessary spend over time.

This inefficiency occurs when teams assume AWS Marketplace SaaS purchases will contribute toward EDP or PPA commitments, but the SaaS product is not eligible under AWS’s “Deployed on AWS” standard. As of May 1, 2025, AWS Marketplace allows SaaS products regardless of where they are hosted, while separately identifying products that qualify for commitment drawdown via a visible “Deployed on AWS” badge.

Eligibility is determined based on the invoice date, not the contract signing date. As a result, Marketplace SaaS contracts signed prior to the policy change may still generate invoices after May 1, 2025 that no longer qualify for commitment retirement. This can lead to Marketplace spend appearing on AWS invoices without reducing commitments, creating false confidence in commitment progress and increasing the risk of end-of-term shortfalls.