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.
Auto-Clustering consumption is billed as Snowflake compute credits. Costs increase based on the volume of data reclustered and the frequency of clustering operations, regardless of query activity.
Optimize data loading practices by using incremental loads and pre-sorting data where possible to minimize disruption to partition structures Redesign cluster key selections to prioritize columns commonly used in query filters and joins, limit the number of keys, and order by cardinality Disable or adjust clustering maintenance for low-value or rarely queried tables to reduce unnecessary overhead Periodically monitor clustering costs and modify configurations based on evolving query access patterns
