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 In aggregate, outdated Elasticsearch clusters contribute to significant silent spend unless proactively addressed.
Domains running outdated OpenSearch versions — particularly OpenSearch 1.x — begin to incur AWS Extended Support charges once they fall outside of the standard support period.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 In large organizations, outdated versions can silently drive meaningful spend over time, especially across many small or idle domains.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.
BigQuery incentivizes efficient data retention by cutting storage costs in half for tables or partitions that go 90 days without modification. However, many teams unintentionally forfeit this discount by performing broad or unnecessary updates to long-lived datasets — for example, touching an entire table when only a few rows need to change. Even small-scale or programmatic updates can trigger a full reset of the 90-day timer on affected data. This behavior is subtle but expensive: it silently doubles the storage cost of large datasets for another 90-day cycle, even when the data itself is mostly static. Without intentional safeguards, organizations may repeatedly reset their discounted storage window without realizing it.
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.
Non-production environments such as development, testing, or staging often do not require the high availability, failover capabilities, and premium storage performance offered by the Business Critical tier. Running these workloads on Business Critical unnecessarily inflates costs. Choosing a lower-cost tier like General Purpose typically provides sufficient performance and availability for non-production use cases, significantly reducing ongoing database expenses.
This inefficiency occurs when a table remains in the default Standard storage class despite having minimal or infrequent access. In these cases, switching to Standard-IA can significantly reduce monthly storage costs, especially for archival tables, compliance data, or legacy systems that are still retained but rarely queried.
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.
