If backup retention settings are too high or old automated backups are unnecessarily retained, costs can accumulate rapidly. RDS backup storage is significantly more expensive than equivalent storage in S3. For long-term retention or compliance use cases, exporting backups to S3 (e.g., via snapshot export to Amazon S3 in Parquet) is often more cost-effective than retaining them in RDS-native format.

RDS Multi-AZ deployments are designed for production-grade fault tolerance. In non-production environments, this configuration doubles the cost of database instances and storage with little added value. Unless explicitly required for high-availability testing, Multi-AZ in dev, staging, or test environments typically results in avoidable expense.

RDS reader nodes are intended to handle read-only workloads, allowing for traffic offloading from the primary (writer) node. However, in many environments, services are misconfigured or hardcoded to send all traffic—including reads—to the writer node. This results in underutilized reader nodes that still incur full hourly charges, while the writer node becomes a performance bottleneck and may require upsizing to handle unnecessary load. This inefficiency reduces cost-effectiveness and resilience, especially in high-throughput or scalable architectures.

RDS workloads often evolve — changing engine types, rightsizing instances, or shifting to Aurora or serverless models. When these changes occur after Reserved Instances have been purchased, the existing commitments may no longer match active usage. This results in silent overspend, as underutilized RIs continue billing without offsetting usage.

Unlike Convertible EC2 RIs, RDS RIs cannot be exchanged. Selling unused RDS RIs is not supported. In rare cases, AWS Support may approve a goodwill adjustment, but this is not guaranteed. The most effective way to recover value is to steer eligible workloads back toward the reserved configuration.

Many RDS workloads continue to run on older x86 instance types (e.g., db.m5, db.r5) even though compatible Graviton-based options (e.g., db.m6g, db.r6g) are widely available. These newer families deliver improved performance per vCPU and lower hourly costs, yet are often not adopted due to legacy defaults, inertia, or lack of awareness.When workloads are not tightly bound to architecture-specific extensions (e.g., x86-specific binaries or drivers), switching to Graviton typically requires no application changes and results in immediate savings. Persisting with x86 in eligible use cases results in avoidable overpayment for compute.

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.

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 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.