Files that show no read or write activity over an extended period often indicate redundant or abandoned data. Keeping inactive files in higher-cost storage classes unnecessarily increases monthly spend. Implementing proactive archiving, deletion workflows, and safety features like Blob Soft Delete or Versioning improves cost efficiency while protecting against accidental data loss.
Buckets without Autoclass enabled can accumulate infrequently accessed data in more expensive storage classes, inflating monthly costs. Enabling Autoclass allows GCS to automatically move objects to lower-cost tiers based on observed access behavior, optimizing storage costs without manual lifecycle policy management. Activating Autoclass reduces operational overhead while maintaining seamless access to objects across storage classes.
This inefficiency arises when snapshots are retained long after they’ve served their purpose. Snapshots may have been created for backups, migrations, or disaster recovery plans but were never deleted—even after the related workload or volume was decommissioned. Over time, these unused snapshots accumulate, continuing to incur storage costs without providing operational value.
This inefficiency occurs when small files are stored in S3 storage classes that impose a minimum object size charge, resulting in unnecessary costs. Small files under 128 KB stored in Glacier Instant Retrieval, Standard-IA, or One Zone-IA are billed as if they were 128 KB. If these small files are accessed frequently, S3 Standard may be a better fit. For infrequently accessed small files, transitioning them to archival storage classes like Glacier Flexible Retrieval or Deep Archive can optimize storage spend. Poorly tuned lifecycle policies often allow small files to remain in suboptimal storage classes indefinitely.
This inefficiency occurs when legacy volume types such as gp2 or io1 remain in use, even though AWS has released newer types—like gp3 and io2—that offer better performance at lower cost. Gp3 allows users to configure IOPS and throughput independently of volume size, while io2 provides higher durability and more predictable performance than io1. These newer volumes are generally more cost-effective and can be adopted without re-architecting workloads. Many teams continue using outdated types due to default AMIs, automation templates, or simple oversight.
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 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.
