Development and test environments on Compute Engine are commonly provisioned and left running around the clock, even if only used during business hours. This results in wasteful spend on compute time that could be eliminated by scheduling shutdowns during idle periods. GCP enables scheduling via native tools such as Cloud Scheduler, Cloud Functions, or Terraform automation. Stopping VMs during off-hours preserves boot disks and instance metadata while halting compute billing.
Non-production EC2 instances are often provisioned for daytime-only usage but remain running 24/7 out of convenience or oversight. This results in unnecessary compute charges, even if the workload is inactive for 16+ hours per day. AWS supports automated schedules to stop and start instances at predefined times, allowing organizations to retain data and instance configuration without paying for unused runtime. Implementing a shutdown schedule for inactive periods (e.g., nights, weekends) can reduce compute costs by up to 60% in typical non-prod environments.
Non-production Azure VMs are frequently left running during off-hours despite being used only during business hours. When these instances remain active overnight or on weekends, they generate unnecessary compute spend. Azure offers built-in auto-shutdown features that allow teams to define daily stop times, retaining disk data and configurations without paying for VM runtime. Implementing scheduled shutdowns in dev/test environments is a simple, low-risk optimization that can reduce compute costs by 30–60%.
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 In aggregate, outdated Elasticsearch clusters contribute to significant silent spend unless proactively addressed.
Domains running outdated OpenSearch versions — particularly OpenSearch 1.x — begin to incur AWS Extended Support charges once they fall outside of the standard support period.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 In large organizations, outdated versions can silently drive meaningful spend over time, especially across many small or idle domains.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.
In many environments, users launch Databricks clusters for development or analysis and forget to shut them down after use. When no auto-termination policy is configured, these clusters remain active indefinitely, incurring unnecessary charges for both Databricks and cloud infrastructure usage. This inefficiency is especially common in interactive clusters that are user-managed, ephemeral, or exploratory in nature. While Databricks provides built-in support for cluster auto-termination, teams often overlook it unless it's enforced through workspace policies. Without this safeguard in place, idle clusters can persist unnoticed for hours or days, leading to avoidable cost.
Workloads in private subnets often access AWS services like S3 or DynamoDB. If this traffic is routed through a NAT Gateway, it incurs both hourly and data processing charges. However, AWS offers VPC Gateway Endpoints (for S3/DynamoDB) and Interface Endpoints (for other services), which provide private access paths that bypass the NAT Gateway entirely. When teams fail to use VPC endpoints — often due to default routing configurations or lack of awareness — they unnecessarily route internal service calls through a costlier, public-facing path. This leads to persistent and avoidable spend.
Elastic IPs are often provisioned but forgotten — left unassociated, or still attached to EC2 instances that have been stopped. In either case, AWS treats the EIP as idle and applies an hourly charge. Although the cost per hour is relatively small, these charges accumulate quietly, especially across environments with frequent provisioning, decommissioning, or ephemeral workloads. Many organizations overlook the fact that even a single EIP attached to a stopped instance is billable. Without periodic review, this creates persistent, low-visibility waste across AWS accounts.
