Snowflake automatic clustering consumes credits to reorganize table data around a clustering key. The cost is separate from the warehouse credits used by queries against the table.
Automatic clustering maintains table organization as rows are inserted, updated, or deleted. The amount of work depends on table size, the clustering key, and the frequency and distribution of changes. A table that changes often can require more maintenance than a table with the same size and few updates.
The service can reduce query scans when filters align with the clustering key. That benefit has value only when the reduction in query cost or latency offsets the credits used for maintenance.
Snowflake exposes automatic clustering history and credit usage through account usage views. Review maintenance credits alongside query history, bytes scanned, and query execution time. A table with a low query volume may not recover its maintenance cost.
Clustering on a column that queries rarely filter does not provide the expected pruning benefit. A key that matches common predicates and has useful data distribution is more likely to justify the service.
Measure the table before enabling automatic clustering, then compare maintenance credits with the change in query performance and warehouse consumption. Snowflake documents automatic clustering and the clustering history view.