If auto-suspend settings are too high, warehouses can sit idle and continue accruing unnecessary charges. Tightening the auto-suspend window ensures that the warehouse shuts down quickly once queries complete, minimizing credit waste while maintaining acceptable user experience (e.g., caching needs, interactive performance).
If no appropriate query timeout is configured, inefficient or runaway queries can execute for extended periods (up to the default 2-day system limit). For as long as the query is running, the warehouse will remain active and accrue costs. Proper timeout settings help terminate inefficient queries, free up compute capacity, and allow the warehouse to become idle sooner, making it eligible for auto-suspend once the inactivity timer is reached.
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.
Standard Load Balancers are frequently provisioned for internal services, internet-facing applications, or testing environments. When a workload is decommissioned or moved, the load balancer may be left behind without any active backend pool or traffic — but continues to incur hourly charges for each frontend IP configuration.Because Azure does not automatically remove or alert on inactive load balancers, and because they may not show significant outbound traffic, these resources often persist unnoticed. In dynamic or multi-team environments, this can result in a growing number of unused Standard Load Balancers generating silent, recurring costs.
Snapshots are often created for short-term protection before changes to a VM or disk, but many remain in the environment far beyond their intended lifespan. Over time, this leads to an accumulation of snapshots that are no longer associated with any active resource or retained for operational need.Since Azure does not enforce automatic expiration or lifecycle policies for snapshots, they can persist indefinitely and continue to incur monthly storage charges. This inefficiency is especially common in development environments, migration efforts, or manual backup workflows that lack centralized cleanup.Snapshots older than 30–90 days, especially those not tied to a documented backup strategy or workload, are strong candidates for review and removal.
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.
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.
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.
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.
