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.
Azure Cosmos DB is optimized for low-latency, globally distributed workloads—not long-term storage of infrequently accessed data. Yet in many environments, cold data such as logs, telemetry, or historical records is retained in Cosmos DB due to a lack of lifecycle management.
Many legacy workloads still run on older Elasticsearch versions — particularly 5.x, 6.x, or 7.x — due to inertia, compatibility constraints, or lack of ownership. Once these versions exceed their standard support window, AWS begins charging an hourly Extended Support fee for each domain. These fees are often missed in cost reviews, especially in environments that are inactive but still provisioned. In aggregate, outdated Elasticsearch clusters contribute to significant silent spend unless proactively addressed.
Domains running outdated OpenSearch versions — particularly OpenSearch 1.x — begin to incur AWS Extended Support charges once they fall outside of the standard support period. These charges are persistent and apply even if the domain is inactive or lightly used. Many teams overlook this cost when delaying upgrades or maintaining non-critical environments like dev, test, or staging. In large organizations, outdated versions can silently drive meaningful spend over time, especially across many small or idle domains.
Many workloads default to using Redis or Memcached without evaluating whether a lighter or more efficient engine would provide equivalent functionality at lower cost. Valkey is a Redis-compatible, open-source engine supported by ElastiCache that may offer improved price-performance and licensing benefits. For read-heavy or stateless workloads that don’t require Redis-specific features (e.g., persistence, advanced replication), Valkey can often be used as a drop-in replacement. Memcached, while simple, lacks key features like replication and persistence, and may be less cost-effective for certain access patterns. Choosing the wrong engine can result in overpaying for capabilities that aren’t needed — or missing opportunities to optimize.
ElastiCache clusters are often sized for peak performance or reliability assumptions that no longer reflect current workload needs. When memory and CPU usage remain consistently low, the node is likely overprovisioned. For Redis, memory is typically the primary sizing constraint, while Memcached workloads may be more CPU-sensitive. In dev, staging, or lightly used production environments, some nodes may be entirely idle.It's important to evaluate usage patterns in context — for example, replica nodes in Redis Multi-AZ configurations may show low utilization by design, but still serve a high-availability purpose. However, in non-critical environments or where HA is not required, those nodes can often be downsized or removed. Additionally, older ElastiCache instance types (e.g., r4, m3) are frequently less cost-efficient than newer generations like r6g or r7g, offering further savings through modernization.
