Inefficiency arises when MVs are either underused or misused. When high-cost, repetitive queries are not backed by MVs, workloads consume unnecessary compute resources. When MVs exist but are rarely queried, their background refresh and storage costs accumulate without offsetting savings. Proper evaluation of workload patterns and strategic use of MVs is critical to achieve a net cost benefit.

Search Optimization can enable significant cost savings when selectively applied to workloads that heavily rely on point-lookup queries. By improving lookup efficiency, it allows smaller warehouses to satisfy performance SLAs, reducing credit consumption. However, inefficiencies arise when: Search Optimization is not enabled on critical lookup-heavy tables, forcing oversized warehouses. It is enabled unnecessarily on infrequently queried data, adding avoidable costs. Warehouse sizing is not adjusted after Search Optimization is implemented, missing the primary cost-saving opportunity. Regular review of query patterns and warehouse sizing is essential to maximize the intended benefit of Search Optimization.

Inefficient execution of repeated queries occurs when common query patterns are frequently executed without optimization. Even if individual executions are successful, repeated inefficiencies compound overall compute consumption and credit costs. By analyzing Snowflake's parameterized query metrics, organizations can identify top repeated queries and optimize them for better performance, resource usage, and cost-efficiency.

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.

GCP VM instances are often provisioned with more CPU or memory than needed, especially when using custom machine types or legacy templates. If an instance consistently consumes only a small portion of its allocated resources, it represents an opportunity to reduce costs through rightsizing. Without proactive reviews, these oversized instances can remain unnoticed and continue to incur unnecessary charges.

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.

Underutilized Snowflake warehouses occur when a workload is assigned a larger warehouse size than necessary. For example, a workload that could efficiently execute on a Medium (M) warehouse may be running on a Large (L) or Extra Large (XL) warehouse.

This leads to unnecessary credit consumption without a proportional benefit to performance. Underutilization is often driven by early provisioning decisions that were not later reassessed, or by a desire for marginal speed improvements that do not justify the increased operational cost.