# Underutilized Snowflake warehouse | Cloud Efficiency Hub Canonical: https://www.pointfive.co/efficiency-hub/inefficiencies/underutilized-snowflake-warehouse Underutilized Snowflake warehouses occur when a workload is assigned a larger warehouse size than necessary. By: Simar Arora Updated: 2026-09-28 [Cloud Efficiency Hub](https://www.pointfive.co/efficiency-hub) # Underutilized Snowflake Warehouse The short version Underutilized Snowflake warehouses occur when a workload is assigned a larger warehouse size than necessary. [Simar Arora](https://www.linkedin.com/in/simarpreet-arora/) Value Engineer - FinOps @ Snowflake Snowflake service [Snowflake Virtual Warehouse](https://www.pointfive.co/efficiency-hub/cloud-services/snowflake-virtual-warehouse) Category [Compute](https://www.pointfive.co/efficiency-hub/service-category/compute) Reference CER-0168 Type Overprovisioned Resource ## Explanation Why the waste happens and who it affects. For example, a workload that could efficiently execute on a Medium (M) warehouse may be running on a Large (L) or Extra Large (XL) warehouse.This leads to unnecessary credit consumption without a proportional benefit to performance. Underutilization is often driven by early provisioning decisions that were not later reassessed, or by a desire for marginal speed improvements that do not justify the increased operational cost. A specific case is warehouses used to run Snowflake Cortex AI Functions such as AI\_COMPLETE or AI\_CLASSIFY. These functions are billed separately by tokens, but the calling warehouse keeps billing credits while the query runs. Snowflake recommends a warehouse no larger than MEDIUM for these calls and states that a larger warehouse does not increase performance, so reusing a Large or bigger ETL warehouse for AI batch jobs adds warehouse credits with no benefit. ## Billing model The pricing dimensions that drive this cost. Snowflake charges based on the size and active runtime of a warehouse. Larger warehouses consume more credits per second of operation compared to smaller sizes, and relevant size should be used depending on workload requirements. Cortex AI Function queries Token or page charges for the function, plus the calling warehouse's per-second credits by size while the query runs ## How to detect 5 checks to find it in your estate. - Analyze historical query activity for each warehouse to assess typical concurrency levels and peak usage patterns - Evaluate whether workloads experience query queuing or whether the warehouse is frequently underloaded - Review warehouse credit consumption relative to query volume and complexity over a representative time period - Confirm with data engineering or application teams whether the warehouse can be downsized without affecting SLAs or performance requirements - Join SNOWFLAKE.ACCOUNT\_USAGE.CORTEX\_AI\_FUNCTIONS\_USAGE\_HISTORY (QUERY\_ID, WAREHOUSE\_ID, FUNCTION\_NAME, MODEL\_NAME) to QUERY\_HISTORY to find Cortex AI Function queries running on warehouses larger than MEDIUM ## How to fix 4 ways to remove the waste. - Right-size the Snowflake warehouse by selecting a smaller size (e.g., from L to M, or M to S) that adequately supports workload performance and concurrency needs. - Implement a periodic review process to reassess warehouse sizing based on observed usage patterns and changes in workload requirements - Coordinate with business and engineering teams to validate any SLA requirements before resizing - Route Cortex AI Function workloads to a dedicated warehouse no larger than MEDIUM, as Snowflake recommends, with a short auto-suspend ## Documentation Vendor references for pricing and configuration. - [Snowflake Credit Consumption Table snowflake.com](https://www.snowflake.com/legal-files/CreditConsumptionTable.pdf) - [Warehouse Load History docs.snowflake.com](https://docs.snowflake.com/en/sql-reference/functions/warehouse_load_history) - [Cost considerations for Cortex AI Functions docs.snowflake.com](https://docs.snowflake.com/en/user-guide/snowflake-cortex/aisql-cost) - [CORTEX\_AI\_FUNCTIONS\_USAGE\_HISTORY view docs.snowflake.com](https://docs.snowflake.com/en/sql-reference/account-usage/cortex_ai_functions_usage_history) ## Related inefficiencies [Browse the library](https://www.pointfive.co/efficiency-hub) - Snowflake Virtual Warehouse CER-0141 ### [Inefficient Workload Distribution Across Warehouses](https://www.pointfive.co/efficiency-hub/inefficiencies/inefficient-workload-distribution-across-warehouses) Many organizations assign separate Snowflake warehouses to individual business units or teams to simplify chargebacks and operational ownership. This often results in redundant and underutilized warehouses, as workloads frequently do not... Compute - Snowflake Virtual Warehouse CER-0132 ### [Suboptimal Warehouse Auto-Suspend Configuration](https://www.pointfive.co/efficiency-hub/inefficiencies/suboptimal-warehouse-auto-suspend-configuration) 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... Compute - Snowflake Virtual Warehouse CER-0129 ### [Suboptimal Query Timeout Configuration](https://www.pointfive.co/efficiency-hub/inefficiencies/suboptimal-query-timeout-configuration) 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.... Compute --- Source: the public page above. Product screenshots and illustrative interfaces are examples, not live customer data.