πŸ’‘ Explore costs

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Data Explorer is currently a preview feature and not yet available to the public.

Data Explorer’s interactive interface lets you slice Snowflake costs across multiple dimensions, filter to specific resources, and drill down to identify exactly what’s driving spend. The chart and table stay synchronized β€” interactions in one update the other β€” so you always have a visual and detailed view of the same data.

Group By

Group By controls how costs are organized in the chart and table.

  • Select a dimension from the Group By dropdown to see costs sliced by that category.

Account

Shows all Snowflake accounts and the costs they have incurred over the selected time period.

  • Click an account to drill down β€” costs will be grouped by Service Type, filtered to the selected account.

Slingshot Tags

Shows all Slingshot Tags and the costs they have incurred over the selected time period. Tags let you organize costs by your own business categories β€” team, project, cost center, or any custom grouping.

  • Click a tag value to drill down β€” costs will be grouped by Service Type, filtered to the selected tag value.

Snowflake Tags

Shows costs grouped by Snowflake’s native Object Tags β€” key/value pairs applied to your Snowflake resources (warehouses, databases, users, tables). If your organization tags Snowflake objects with business metadata like cost_center, department, or environment, those tags appear here automatically with no additional setup.

Click a tag value to drill down β€” costs will be grouped by Service Type, filtered to the selected tag value.

Service Types

Shows all Snowflake Service Types (Warehouse Metering, Database Storage, AI Services) and the costs they have incurred over the selected time period.

If a Service Type has a related object grouping (e.g., Warehouse Metering maps to Warehouses, Database Storage maps to Databases), you can click it to drill into that object type.

Warehouses

Shows all SQL Warehouses and the costs they have incurred over the selected time period. Each row includes a link to the Warehouse detail page.

Databases

Shows all Databases and the costs they have incurred over the selected time period. Each row includes a link to the Database detail page.

Filter By

Filters narrow the data shown in the chart and table without changing the Group By dimension. Apply one or multiple filters to focus on specific resources.

To apply filters:

  1. Click + Add filter above the chart.
  2. Select one or more dimensions to filter by.
  3. Choose the values you want to include for each dimension.
  4. Click Apply to update the chart and table.

Available filter dimensions:

  • Accounts β€” filter to specific Snowflake accounts
  • Slingshot Tags β€” filter by tag group and tag values
  • Snowflake Tags β€” filter by tag key and tag values
  • Service Types β€” filter to specific service types (Warehouse Metering, Database Storage, AI Services)
  • Warehouses β€” filter to specific SQL Warehouses
  • Databases β€” filter to specific Databases
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Filters persist as you navigate and drill down. You can modify them at any point without losing your current Group By selection.

Drill-down navigation

Data Explorer supports interactive drill-down to help you trace a cost spike to its source. A typical investigation path:

  1. Account β€” identify which account is driving costs
  2. Service Type β€” see which cost category (compute, storage, AI) is responsible
  3. Warehouse β€” pinpoint the specific warehouse

To drill down:

  1. Click any row in the table with a hyperlinked Name.
  2. Data Explorer automatically sets the next Group By dimension and applies the clicked item as a filter.
  3. Continue clicking to drill deeper.
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You can always change the Group By or remove filters manually to explore a different path through your data.

Chart and table interaction

The chart and table are synchronized:

  • Click a chart bar to filter the table to that item and drill down.
  • Select a table row to highlight the corresponding element in the chart.
  • Both views always reflect the same data, filters, and time range.

Understanding the table columns

The table displays detailed information for each item:

ColumnDescription
Cost($)Total cost for the item in the selected date range
% of costThis item’s share of the total cost across all items
Cost ChangeDifference in cost compared to the same-length period immediately before your selected range
% changePercentage difference compared to the same-length period immediately before your selected range
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For example, if your selected date range is March 1 – June 1 (3 months), the Cost Change column compares against December 1 – March 1 (the prior 3 months). If insufficient historical data exists for the comparison period, the column shows “β€””.

Export to CSV

Download the underlying table data for use in spreadsheets or external BI tools.

  1. Click the Export CSV button above the table.
  2. A CSV file downloads with the current table data, reflecting all active filters and groupings.
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The exported data matches exactly what is displayed in the table, including any active filters.

FAQs

How far back can I see cost data?
Data Explorer provides 2 years of history unless otherwise noted for a particular object or resource.
Why can’t I see data for certain warehouses or accounts?
Data Explorer enforces role-based access control (RBAC). You only see cost data for Snowflake objects your role is authorized to manage. Contact your Tenant admin if you need access to additional resources.
How often is cost data updated?
Cost data updates approximately every 24 hours.
What do the Cost Change and % change columns show?

These columns compare total cost in your selected date range against the same-length period immediately before it. For example, if you select a 30-day range, the columns compare against the 30 days prior to your start date. The total costs and proportions you see are determined by the access permissions you have.

If there isn’t enough historical data available for the comparison period, the columns display “β€”” instead of a number.