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.

MemoryDB now supports Valkey, a drop-in replacement for Redis OSS offering significant cost and performance advantages. However, many deployments still default to Redis OSS, incurring higher hourly costs and unnecessary data write charges. For compatible workloads, continuing to use Redis OSS instead of Valkey represents a missed opportunity for savings and modernization.

Provisioned capacity mode is appropriate for workloads with consistent or predictable throughput. However, when write capacity is significantly over-provisioned relative to actual usage, it results in wasted spend. This inefficiency is especially common in dev/test environments, legacy systems, or workloads that have tapered off over time but were never adjusted.

Provisioned capacity mode is appropriate for workloads with consistent or predictable throughput. However, when read capacity is significantly over-provisioned relative to actual usage, it results in wasted spend. This inefficiency is especially common in dev/test environments, legacy systems, or workloads that have tapered off over time but were never adjusted.

Azure SQL databases often use the default backup configuration, which stores backups in RA-GRS storage to ensure geo-redundancy. While suitable for high-availability production systems, this level of resilience may be unnecessary for development, testing, or lower-impact workloads.

Using RA-GRS without a business requirement results in avoidable costs. Downgrading to LRS or ZRS — where appropriate — can significantly reduce monthly backup storage spend. This change has no impact on backup frequency or retention behavior, only the underlying storage replication method.

Azure SQL Database resources are frequently overprovisioned due to default configurations, conservative sizing, or legacy requirements that no longer apply. This inefficiency appears across all deployment models:

* Single Databases may be assigned more DTUs or vCores than the workload requires * Elastic Pools may be oversized for the actual demand of pooled databases * Managed Instances are often deployed with excess compute capacity that remains underutilized

Because billing is based on provisioned capacity, not actual consumption, organizations incur unnecessary costs when sizing is not aligned with workload behavior. Without regular reviews, these resources become persistent sources of waste — especially across dev/test environments or variable workloads.

Azure Database for PostgreSQL – Flexible Server often defaults to general-purpose D-series VMs, which may be oversized for many production or development workloads. PostgreSQL typically does not require a high sustained high CPU, making it well-suited to memory-optimized (E-series) or burstable (B-series) instances.

When actual usage consistently falls below the provisioned capacity — particularly CPU — the deployment may be overprovisioned, resulting in unnecessary compute charges. Choosing the wrong VM family or size leads to silent overspend, especially in long-lived database environments.

Azure SQL deployments often reserve more storage than needed, either due to default provisioning settings or anticipated future growth. Over time, if actual usage remains low, these oversized allocations generate unnecessary storage costs.

In Elastic Pools, resizing can be done through standard configuration updates. In Managed Instances, reducing storage may require a shrink operation to reclaim unused space before reallocation is permitted. Without regular review, these overprovisioned environments persist as silent cost contributors.

An Azure SQL Elastic Pool continues to incur costs even if it contains no databases. This can occur when databases are deleted, migrated to single-instance configurations, or consolidated elsewhere — but the pool itself remains provisioned. In such cases, the pool becomes an idle resource consuming budget without delivering value.

This inefficiency often goes undetected in large or decentralized environments where cleanup workflows are manual or inconsistent. Since the pool reserves compute and storage regardless of usage, it silently contributes to unnecessary spend over time.