Inefficiency arises when MVs are either underused or misused.
Proper evaluation of workload patterns and strategic use of MVs is critical to achieve a net cost benefit.
Organizations may experience unnecessary Snowflake spend due to inefficient query-to-warehouse routing, lack of dynamic warehouse scaling, or failure to consolidate workloads during low-usage periods. Third-party platforms offer solutions to address these inefficiencies:
Choosing between these solutions depends heavily on the organization's internal capabilities and desired balance between control and automation.
Excessive Auto-Clustering costs occur when tables experience frequent and large-scale modifications ("high churn"), causing Snowflake to constantly recluster data. This leads to significant and often hidden compute consumption for maintenance tasks, especially when table structures or loading patterns are not optimized. Poor clustering key choices, unordered data loads, or frequent full-table replacements are common drivers of unnecessary Auto-Clustering activity.
Retention of stale data occurs when old, no longer needed records are preserved within active Snowflake tables. Without lifecycle policies or regular purging, tables accumulate outdated data.
Because Snowflake’s compute charges are tied to how much data is scanned, retaining large volumes of inactive or irrelevant data can drive up both storage and query execution costs unnecessarily.
Ingesting a large number of small files (e.g., files smaller than 10 MB) using Snowpipe can lead to disproportionately high costs due to the per-file overhead charges. Each file, regardless of its size, incurs the same overhead fee, making the ingestion of numerous small files less cost-effective. Additionally, small files can increase the load on Snowflake's metadata and ingestion infrastructure, potentially impacting performance.
Snowflake automatically maintains previous versions of data when tables are modified or deleted. For tables with high churn—meaning frequent INSERT, UPDATE, DELETE, or MERGE operations—this can cause a significant buildup of historical snapshot data, even if the active data size remains small.
This hidden accumulation leads to elevated storage costs, particularly when Time Travel retention periods are long and data change rates are high. Often, teams are unaware of how much snapshot data is being stored behind the scenes.
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.
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.
