BigQuery incentivizes efficient data retention by cutting storage costs in half for tables or partitions that go 90 days without modification. However, many teams unintentionally forfeit this discount by performing broad or unnecessary updates to long-lived datasets — for example, touching an entire table when only a few rows need to change. Even small-scale or programmatic updates can trigger a full reset of the 90-day timer on affected data. This behavior is subtle but expensive: it silently doubles the storage cost of large datasets for another 90-day cycle, even when the data itself is mostly static. Without intentional safeguards, organizations may repeatedly reset their discounted storage window without realizing it.

While stopping an RDS instance reduces runtime cost, AWS enforces a 7-day limit on stopped state. After this period, the instance is automatically restarted and resumes incurring compute charges — even if the database is still not needed. This creates waste in cases where teams intended to pause the environment but failed to manage its lifecycle beyond the 7-day window. Without proper automation or teardown workflows, stopped RDS instances silently become active and billable again. The best practice for long-term inactivity is to snapshot the database and delete the instance entirely. If the instance must remain available for fast recovery, automation should be in place to re-stop it upon restart.

Non-production environments such as development, testing, or staging often do not require the high availability, failover capabilities, and premium storage performance offered by the Business Critical tier. Running these workloads on Business Critical unnecessarily inflates costs. Choosing a lower-cost tier like General Purpose typically provides sufficient performance and availability for non-production use cases, significantly reducing ongoing database expenses.

This inefficiency occurs when a table remains in the default Standard storage class despite having minimal or infrequent access. In these cases, switching to Standard-IA can significantly reduce monthly storage costs, especially for archival tables, compliance data, or legacy systems that are still retained but rarely queried.

This inefficiency occurs when an RDS instance uses a high-cost storage type such as io1 or io2 but does not require the performance benefits it provides. In many cases, provisioned IOPS are set at or below the free baseline included with gp3 (3,000 IOPS and 125 MB/s). In such scenarios, continuing to use provisioned IOPS storage results in elevated cost with no functional advantage. These misconfigurations often persist due to legacy templates, default settings, or a lack of periodic review.

This inefficiency occurs when a DynamoDB table is no longer accessed by any active workload but continues to accumulate storage charges. These tables often remain after a project ends, a feature is retired, or data is migrated elsewhere. Without any read or write activity, the table provides no functional value and becomes a cost liability.

This inefficiency occurs when an RDS instance remains in the running state but is no longer actively serving application traffic. These instances may be remnants of retired applications, paused development environments, or workloads that were migrated elsewhere. If an instance shows no active connections and sustained inactivity across CPU and memory metrics, it is likely idle and generating unnecessary costs.

This inefficiency occurs when an RDS instance is consistently operating below its provisioned capacity—for example, showing low CPU, or memory utilization over an extended period. This often results from conservative initial sizing, decreased workload demand, or failure to review and adjust after deployment. Running oversized RDS instances leads to unnecessary compute and licensing costs without delivering additional value.

This inefficiency occurs when an RDS instance is allocated significantly more storage than it consumes. For example, a 2TB volume might contain only 150GB of actual data. Since RDS does not allow reducing allocated storage on existing instances, these volumes continue to incur charges based on total provisioned size—not actual usage. This often goes unnoticed in long-running databases that no longer require their original allocation.

When an RDS cluster is not upgraded in time, it can fall out of standard support and incur Extended Support charges. This often happens when upgrade cycles are delayed, blocked by compatibility issues, or deprioritized due to competing initiatives. Over time, these fees can add up significantly. Staying on an outdated version also increases operational risk and reduces access to engine improvements, performance enhancements, and security patches.