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Databricks spend template

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Use the AI Cost Monitoring: Databricks template (the Databricks spend template) to monitor your organization’s Databricks AI spend and usage across AI-specific SKUs, including Model Serving, Vector Search, AI Gateway, Agent Bricks, foundation model fine-tuning, agent evaluation, and serverless GPU compute.

You can use this template to review an executive cost overview, cost drivers by SKU and usage type, and workspace-level spend detail.

This template is set up with sample data, so you can explore the analysis before you connect your own data.

This template tracks the AI-specific billing SKUs for Databricks and does not include general Databricks compute costs, such as all-purpose clusters, SQL warehouses, or jobs.

System and user requirements

The ability to use the AI Cost Monitoring: Databricks template requires the following:

  • You must be assigned an account type with the Create, edit, and publish workbooks permission enabled.
  • To use your own data, your organization must be connected to Databricks, with access to the billing usage and list price tables in the system.billing schema and the workspace table in the system.access schema. For details, see Data sources. Otherwise, you can use the template with the provided sample data.

Data sources

The template uses sample data. If you want to use your own data, you can swap the sources to use the following tables from the Databricks organization connected to Sigma.

TableDatabricks tableDetails
DATABRICKS_BILLING_USAGEsystem.billing.usageUsage records for AI products (for example, Model Serving, Vector Search, AI Gateway, and Agent Bricks), including quantity, SKU, workspace, and account.1 Used for the Executive Overview spend KPIs, trends, and cost drivers.
DATABRICKS_LIST_PRICESsystem.billing.list_pricesList price by SKU, cloud, and effective date range. Joined to usage data to compute your costs.2
DATABRICKS_WORKSPACESsystem.access.workspaces_latestWorkspace metadata (workspace ID and name). Used to enrich usage with workspace names via lookup.

1The template filters billing usage to AI-specific SKUs, so general compute usage (for example, all-purpose clusters or SQL warehouses) doesn’t appear even if it’s present in the source table.

2Cost is computed as [Usage Quantity] × [Default Price] using the list price effective for that usage date. If your organization has negotiated pricing that differs from list price, or your list prices are expressed in Databricks Units (DBUs) rather than USD, the template’s calculated totals can differ from your actual invoiced cost.

A SKU or workspace might appear empty if your account has no usage for it in the selected period, or if that usage is not yet available in Databricks.

Create a workbook from the template

To create a workbook from this template:

  1. Go to Home > Templates, then select the AI Cost Monitoring: Databricks template to review its details.

  2. Click Use template. The template opens as an exploration named after the template using sample data.

  3. To use your own data, click Change sources:

    In the Swap data sources overview modal, Sigma attempts to match each source to your own tables. Because the column names match the Databricks billing and usage schema, most sources match automatically.

    1. To replace a matched source, select the source drop-down menu, then select a new source.
    2. Click Swap now, then explore the changes to confirm your data looks correct.

  4. In the Filters menu, set the date range to scope every page to the period you care about.

  5. To create an editable, publishable document, in the document header, click Save as, then name and save the workbook.

For more details, see Create documents from templates.

About this template

The template contains Executive overview and Workspaces pages. The template also includes a README page to document the purpose of the template, source tables, and setup steps.

Executive overview

This page answers “How much is Databricks AI costing us?” and includes:

  • A Monthly Spend KPI with the period-over-period change.
  • A spend trend by month.
  • Top 5 SKUs by Spend, with a Show control for the number of results and a Spend By toggle for SKU or Workspace. Top SKUs typically include Agent Bricks compute, foundation model fine-tuning, serverless GPU compute, agent evaluation, and external model serving.
  • Usage-type mix over time, an area chart that splits usage into DBU, GPU_HOUR, and TOKEN.
  • SKU & workspace detail, a table of total usage quantity and total cost by SKU, usage unit, and workspace.

Workspaces

This page answers “Which workspaces are driving Databricks AI spend?” and includes:

  • Spend by Workspace, a bar chart of total spend per workspace.
  • Workspace Usage Detail, a table of total usage quantity and total cost by workspace and SKU.