In non-production environments, enabling Multi-AZ Redis clusters introduces redundant replicas that may not deliver meaningful business value. These replicas are often kept in sync across Availability Zones, incurring both compute and inter-AZ data transfer costs. For development or test clusters that can tolerate occasional downtime or data loss, a single-AZ deployment is typically sufficient and significantly less expensive.

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.

Multi-AZ deployment is often essential for production workloads, but its use in non-production environments (e.g., development, test, QA) offers minimal value. These environments typically do not require high availability, yet still incur the full cost of redundant compute, storage, and data transfer. This results in unnecessary spend without operational benefit.

When a Lambda function processes messages from an SQS queue but fails to handle certain messages properly, the same messages may be returned to the queue and retried repeatedly. In some cases, especially if the Lambda is also writing messages back to the same or a chained queue, this can create a recursive invocation loop. This loop results in high invocation counts, prolonged execution, and unnecessary costs, particularly if retries continue without a termination strategy.

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.

Provisioned load balancers continue to generate costs even when they are no longer serving meaningful traffic. This often occurs when applications are decommissioned, testing infrastructure is left behind, or backend services are removed without deleting the associated frontend configurations. Without ingress or egress traffic, these load balancers offer no functional value but still consume billable resources, including forwarding rules and reserved external IPs.

Load balancers incur charges based on provisioned bandwidth shape, even if backend traffic is minimal. If traffic is low, or if only one backend server is configured, the load balancer may be oversized or unnecessary, especially in test or staging environments.

Without lifecycle policies, data in OCI Object Storage remains in the default storage tier indefinitely—even if it is rarely accessed. This can lead to growing costs from unneeded or rarely accessed data that could be expired or transitioned to lower-cost tiers like Archive Storage.

Block volumes that are not attached to any instance continue to incur charges. These often accumulate after instance deletion or reconfiguration. Unlike boot volumes, unattached data volumes may be harder to track if not labeled or tagged clearly.

OCI Object Storage buckets accrue charges based on data volume stored, even if no activity has occurred. Buckets that haven't been read from or written to in months may contain outdated data or artifacts from discontinued projects.