When EC2 instances, Lambda functions, or containerized workloads access AWS-managed services without VPC Endpoints, that traffic exits the VPC through a NAT Gateway or Internet Gateway. This introduces unnecessary egress charges and NAT processing costs, especially for data-intensive or high-frequency workloads.
AWS CloudTrail enables event logging across AWS services, but when multiple trails are configured to log overlapping events — especially data events — it can result in redundant charges and unnecessary storage or ingestion costs. This commonly occurs in decentralized environments where teams create trails independently, unaware of existing coverage or shared logging destinations.Each trail that records data events contributes to billing on a per-event basis, even if the same activity is logged by multiple trails. Additional costs may also arise from delivering duplicate logs to separate S3 buckets or CloudWatch Log groups. While separate trails may be justified for audit, compliance, or operational segmentation, unintentional duplication increases both cost and operational complexity without added value.
Engineers often enable verbose logging (e.g., debug or trace-level) during development or troubleshooting, then forget to disable it after deployment. This results in elevated log ingestion rates — and therefore costs — even when the detailed logs are no longer needed. Because CloudWatch Logs charges per GB ingested, persistent debug logging in production environments can create silent but material cost increases, particularly for high-throughput services.In environments with multiple teams or loosely governed log group policies, this issue can go undetected for long periods. Identifying and deactivating unnecessary debug-level logging is a low-risk, high-leverage optimization.
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.
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 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%.
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.
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.
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.
