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.

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.

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 Without lifecycle policies or regular purging, tables accumulate outdated data.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.

S3 Standard is the default storage class and is often used by default even for data that is rarely accessed. Keeping large volumes of infrequently accessed data in S3 Standard leads to unnecessary costs. Data such as backups, logs, archives, or historical snapshots are often strong candidates for migration to colder tiers like S3 Glacier or Deep Archive. If access patterns are unknown or variable, S3 Intelligent-Tiering can reduce costs without requiring manual transitions.

Managed Disks frequently remain detached after Azure virtual machines are deleted, reimaged, or reconfigured. Some may be intentionally retained for reattachment, backup, or migration purposes, but many persist unintentionally due to the lack of automated cleanup processes. When these detached disks are also inactive—showing no read or write activity—they represent unnecessary ongoing costs. Identifying and removing these orphaned disks can produce meaningful savings without affecting any active workloads.