Many organizations assign separate Snowflake warehouses to individual business units or teams to simplify chargebacks and operational ownership. This often results in redundant and underutilized warehouses, as workloads frequently do not require the full capacity of even the smallest warehouse size.
By consolidating compatible workloads onto shared warehouses, organizations can maximize utilization, reduce idle runtime across the fleet, and significantly lower total credit consumption. Cost allocation can still be achieved using Query Billing Attribution.
In Azure, it’s common for public IP addresses to be created as part of virtual machine or load balancer configurations. When those resources are deleted or reconfigured, the IP address may remain in the environment unassigned. While Basic SKUs are free when idle, Standard SKUs incur ongoing hourly charges, even if the address is not in use.Unassigned Standard public IPs provide no functional value but continue to generate cost, especially in environments with high churn or inconsistent cleanup practices.
Many Redis and Memcached clusters still use legacy x86-based node types (e.g., cache.r5, cache.m5) even though Graviton-based alternatives are available. In-memory workloads tend to be highly compatible with Graviton due to their simplicity and reliance on standard CPU and memory usage patterns.Unless constrained by architecture-specific extensions or strict compliance requirements, most ElastiCache clusters can be transitioned with no application-level changes. Failing to migrate to Graviton results in unnecessary compute spend and missed opportunities to improve cache efficiency.
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.
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.
When ECS clusters are configured with an Auto Scaling Group that maintains a minimum number of EC2 instances (e.g., min = 1 or higher), the instances remain active even when there are no tasks scheduled. This leads to idle compute capacity and unnecessary EC2 charges.Instead, ECS Capacity Providers support target tracking scaling policies that can scale the ASG to zero when idle and automatically increase capacity when new tasks or services are scheduled. Failing to adopt this pattern results in persistent idle infrastructure and unnecessary costs in ECS environments that do not require always-on 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.
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.
