Explanation
Why the waste happens and who it affects.
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.
Billing model
The pricing dimensions that drive this cost.
- Storage - each materialized view stores its query results, which adds to the account's monthly storage usage
- Maintenance compute - when the base table changes, a Snowflake-managed background service updates every materialized view defined on it, billed as serverless credits in 1-second increments (the rate is listed in the Serverless Feature Credit Table of the Snowflake Service Consumption Table)
- Maintenance cost grows with the number of materialized views per base table, the amount of data that changes, and whether the views are clustered; suspending maintenance usually only defers these costs
How to detect
4 checks to find it in your estate.
- Analyze query history to identify expensive, frequently executed queries that could benefit from materialization.
- Review existing MVs for read activity to detect underutilized or idle materialized views.
- Compare MV refresh costs and storage footprint against active query savings to assess net cost effectiveness.
- Validate whether candidate workloads involve relatively stable datasets appropriate for materialization.
How to fix
5 ways to remove the waste.
- Create materialized views for high-cost, repetitive queries where refresh costs are low relative to compute savings.
- Check eligibility first: Snowflake materialized views require Enterprise Edition and can query only a single table (no joins, window functions, or UDFs), so for expensive repeated queries with joins or complex logic consider alternatives such as dynamic tables
- Decommission materialized views that incur maintenance and storage costs without sufficient query usage.
- Implement periodic reviews of MV usage and refresh behavior as data volumes and access patterns evolve.
- Engage data engineering teams to tune MV designs for optimal cost-benefit balance (e.g., selective columns, filtered subsets).
Documentation
Vendor references for pricing and configuration.