Spot Instances are designed to be short-lived, with frequent interruptions and replacements. When AWS Config continuously records every lifecycle change for these instances, it produces a large number of CIRs. This drives costs significantly higher without delivering meaningful compliance insight, since Spot Instances are typically stateless and non-critical. In environments with heavy Spot usage, Config costs can balloon and exceed the value of tracking these transient resources.
By default, AWS Config is enabled in continuous recording mode. While this may be justified for production workloads where detailed auditability is critical, it is rarely necessary in non-production environments. Frequent changes in development or testing environments — such as redeploying Lambda functions, ECS tasks, or EC2 instances — generate large volumes of CIRs. This results in disproportionately high costs with minimal benefit to governance or compliance. Switching non-production environments to daily recording reduces CIR volume significantly while maintaining sufficient visibility for tracking changes.
Many organizations keep Datadog’s default log retention settings without evaluating business requirements. Defaults may extend retention far beyond what is useful for troubleshooting, performance monitoring, or compliance. This leads to unnecessary storage and indexing costs, particularly in non-production environments or for logs with limited value after a short period. By adjusting retention per project, environment, or service, organizations can reduce spend while still meeting compliance and operational needs.
AWS Graviton processors are designed to deliver better price-performance than comparable Intel-based instances, often reducing cost by 20–30% at equivalent workload performance. OpenSearch domains running on older Intel-based families consume more spend without providing additional capability. Since Graviton-powered instance types are functionally identical in features and performance for OpenSearch, continuing to run on Intel-based clusters represents unnecessary inefficiency.
When multiple tasks within a workflow are executed on separate job clusters — despite having similar compute requirements — organizations incur unnecessary overhead. Each cluster must initialize independently, adding latency and cost. This results in inefficient resource usage, especially for workflows that could reuse the same cluster across tasks. Consolidating tasks onto a single job cluster where feasible reduces start-up time and avoids duplicative compute charges.
Changing a Google Cloud billing account can unintentionally break existing Marketplace subscriptions. If entitlements are tied to the original billing account, the subscription may fail or become invalid, prompting teams to make urgent, direct purchases of the same services, often at higher list or on-demand rates. These emergency purchases bypass previously negotiated Marketplace pricing and can result in significantly higher short-term costs. The issue is common during reorganizations, mergers, or changes to billing hierarchy and is often not discovered until after costs have spiked.
When Marketplace contracts or subscriptions expire or change without visibility, Azure may automatically continue billing at higher on-demand or list prices. These lapses often go unnoticed due to lack of proactive tracking, ownership, or renewal alerts, resulting in substantial cost increases. The issue is amplified when contract records are siloed across procurement, finance, and engineering teams, with no centralized mechanism to monitor entitlement status or reconcile expected versus actual billing.
In many organizations, AWS Marketplace purchases are lumped into a single consolidated billing line without visibility into individual vendors. This lack of transparency makes it difficult to identify which Marketplace spend is eligible to count toward the EDP cap. As a result, teams may either overspend on direct AWS services to fulfill their commitment unnecessarily or miss the opportunity to right-size new commitments based on existing Marketplace purchases. In both cases, the absence of vendor-level detail hinders optimization.
Azure Marketplace offers two types of listings: transactable and non-transactable. Only transactable purchases contribute toward a customer’s MACC commitment. However, many teams mistakenly assume that all Marketplace spend counts, leading to missed opportunities to burn down commitments and risking budget inefficiencies. Selecting a non-transactable listing, when a transactable equivalent exists, can result in identical services being acquired at higher effective cost due to lost discounts. This confusion is exacerbated when procurement and engineering teams do not coordinate or consult Microsoft's guidance.
Many organizations mistakenly believe that all AWS Marketplace spend automatically contributes to their EDP commitment. In reality, only certain Marketplace transactions, those involving EDP-eligible vendors and transactable SKUs, will count towards a portion of their EDP commitment. This misunderstanding can lead to double counting: forecasting based on the assumption that both native AWS usage and Marketplace purchases will fully draw down the commitment. If the assumptions are incorrect, the organization risks failing to meet its EDP threshold, incurring penalties or losing expected discounts.
