This inefficiency occurs when an EBS volume has provisioned IOPS levels that consistently exceed the actual I/O requirements of the workload it supports. This can happen when performance buffers are estimated too high, usage patterns change over time, or default settings are left unadjusted. Provisioned IOPS above the included baseline generate ongoing charges that may not reflect actual utilization, resulting in avoidable cost.

Multipart upload allows large files to be uploaded in segments. Each part is stored individually until the upload is finalized by a “CompleteMultipartUpload” request. If this final request is never issued—due to a timeout, crash, failed job, or misconfiguration—the parts remain stored but are effectively useless: they do not form a valid object and cannot be retrieved. Without a lifecycle policy in place to clean up these incomplete uploads, the orphaned parts persist and continue to incur storage charges indefinitely.

This inefficiency occurs when an EC2 instance remains in a running state but is not actively utilized. These instances may be remnants of past projects, forgotten development environments, or temporarily created for testing and never decommissioned. If an instance shows consistently low or no CPU, network, or disk activity—and no active connections—it likely serves no operational purpose but continues to generate ongoing compute and storage charges.

This inefficiency occurs when a VM is deallocated but its attached managed disks are still active and incurring storage charges. While compute billing stops for deallocated VMs, the disks remain provisioned and billable. These disks often persist unintentionally after a VM is paused, retired, or left unused in dev/test environments, resulting in waste if not explicitly cleaned up.

ListBucket requests are commonly used to enumerate objects in a bucket, such as by backup systems, scheduled sync jobs, data catalogs, or monitoring tools. When these operations are frequent or target buckets with large object counts, they can generate disproportionately high request charges. In many cases, real-time enumeration is not necessary and can be replaced with more efficient alternatives like S3 Inventory, which provides object metadata on a scheduled basis at lower cost.

This inefficiency occurs when a blob container intended for long-term or infrequently accessed data continues to store objects in higher-cost tiers like Hot or Cool, instead of using the Archive tier. This often happens when containers are created without lifecycle policies or default tier settings. Over time, storing archival data in non-archival tiers results in avoidable cost without any performance benefit, especially for compliance data, backups, or historical logs that rarely need to be accessed.

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.

When an EKS cluster remains on a Kubernetes version that has reached the end of standard support, AWS begins charging an additional Extended Support fee. These charges often arise from delays in upgrade cycles, uncertainty about workload compatibility, or overlooked legacy clusters. If the workload does not require the older version, continuing to run the cluster in this state results in unnecessary cost and technical risk.

This inefficiency occurs when an EC2 instance is stopped but still has one or more attached EBS volumes. Although the compute resource is not generating charges while stopped, the attached volumes continue to incur full storage and performance-related costs. These volumes are often overlooked in cost reviews, especially if the instance is temporarily paused or has been left in a stopped state long-term. Without regular validation, these volumes may represent unused capacity that delivers no value.

This inefficiency occurs when an RDS cluster remains provisioned but is no longer serving any workloads and has no active database connections. Unlike underutilized resources, these clusters are completely idle—showing no query activity, background processing, or usage over time. They often persist in dev, staging, or legacy environments where the workload has been retired or moved, yet the cluster remains active and continues to generate ongoing compute and storage costs.