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Warehouse Details

The Warehouse Details view provides a deep, warehouse-specific breakdown of compute consumption, workload activity, and configuration for a single Snowflake warehouse.
It allows FinOps and data platform teams to analyze how a warehouse behaves over time, understand its cost drivers, and identify opportunities for optimization or right-sizing.

This view is used when a warehouse has been identified as cost-heavy, underutilized, or operationally important, and a deeper investigation is needed.

Warehouse details

Warehouse Summary​

A high-level summary card presents the key usage and configuration information for the selected warehouse:

  • Warehouse Name & Size – Identifies the warehouse and its compute capacity tier (e.g., X-Small, Medium, Large).
  • Total Credits Used – Total compute credits consumed within the selected date range.
  • Executed Queries – Number of queries processed by the warehouse.
  • Running Time – Total active time of the warehouse, helping assess utilization efficiency.
  • Last Active – The most recent query execution or resume event.

These metrics provide immediate insight into workload intensity, credit burn, and overall operational footprint.

Credit Consumption Trend​

A line or area chart shows how credits were consumed over time.

Insights Provided​

  • Identify cost spikes or anomalies
  • Detect peak workload periods
  • Compare daily or hourly patterns
  • Correlate credit usage with warehouse size changes or resume events

This view helps teams understand when and why the warehouse consumes the most credits.

Warehouse Configuration​

This section displays the full configuration of the warehouse:

  • Warehouse Size
  • Warehouse Type (Standard, Snowpark-optimized, etc.)
  • Auto-Suspend Timeout
  • Auto-Resume Setting
  • Multi-Cluster Settings (min/max clusters, scaling policy)
  • Current Status (Running or Suspended)

Understanding configuration is critical for diagnosing cost scaling, concurrency behavior, and idle-time credit usage.

Recommendations & Observations​

Based on the warehouse’s actual usage patterns and configuration, this section may highlight:

Right-Sizing Opportunities
If the warehouse size is larger than workload demands.

Auto-Suspend Improvements
When idle time is high or warehouses remain running unnecessarily.

These insights help teams take action to optimize cost and performance directly from the warehouse analysis.

Navigation & Interaction​

Users can:

  • Adjust the date range to analyze short-term or long-term trends
  • Move back to All Warehouses for overview comparison
  • Export usage metrics for governance or reporting

This creates a seamless workflow from account-level FinOps visibility to detailed warehouse-level diagnostics.