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.

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.

Replication instances are commonly left running after migration tasks are completed, especially when DMS is used for one-time or project-based migrations. Without active replication tasks, these instances no longer serve any purpose but continue to incur hourly compute costs. In large organizations or decentralized migration projects, these idle instances may go unnoticed, contributing to persistent background spend.