Cost Insights Private betaEnterpriseEnterprise +

Private beta feature

Cost Insights is a private beta feature. To request access, contact your account manager.

Cost Insights shows estimated costs and compute time for your dbt projects and models directly in the dbt platform, so you can measure and share the impact of optimizations like state-aware orchestration.

State-aware orchestration makes your dbt workflows more efficient by reusing models and tests instead of running full rebuilds. When this is enabled, Cost Insights helps you demonstrate the resulting cost reductions and efficiency gains.

With Cost Insights, you can see:

• How much your dbt models cost to run: See the compute cost and times for each model and job in your warehouse's native units.
• The cost reductions from using state-aware orchestration: Understand the cost reduction when state-aware orchestration reuses unchanged models.
• Cost trends over time: Track your warehouse spend and optimization impact across your dbt projects.

The Cost Insights section is available in different dbt platform areas and lets you view your cost data and the impact of state-aware optimizations across various dimensions:

• Project dashboard
• Catalog on Model page
• Job details page

Prerequisities

To view cost data, ensure you have:

• A dbt account with dbt Fusion Engine enabled. Contact your account manager to enable Fusion for your account.
• One of the roles listed in Assign required permissions.
• A supported data warehouse: Snowflake, BigQuery, or Databricks.

For setup instructions, see Set up Cost Insights.

Understanding cost and reduction estimates

note

Cost estimates are intended for visibility and optimization, not billing reconciliation.

dbt calculates the cost of running your dbt models using your data warehouse’s usage metadata and billing context. dbt computes costs daily using up to the last seven days of available data.

Warehouse-specific logic

The following sections explain how costs are calculated for each supported warehouse. Expand each section to view the details.

 Snowflake

dbt computes Snowflake query costs using Snowflake's query attribution data and your credit price (price_per_credit). Query attribution data is always available for Snowflake. dbt pulls the per_credit price directly from Snowflake when available; otherwise, dbt uses the configured or default value in the dbt platform. For more information about configuring or viewing these values, see Configure Cost Insights settings.

Formula:

credits_per_query * price_per_credit

Where:

• credits_per_query - Cloud services, compute, and query acceleration credits attributed to the query. dbt sources this value from QUERY_ATTRIBUTION_HISTORY. For more information, see the Snowflake documentation.
• price_per_credit - Your Snowflake credit price (from Snowflake system tables when available, otherwise from your configured input or the default rate).

 BigQuery

BigQuery does not expose per-query cost directly in system tables. Instead, dbt estimates cost by combining query usage with a pricing input (either from your configuration or the default rate).

• On-demand pricing

  The cost is determined by how much data each query processes. The usage shows the amount of that data billed for the query.

  Formula:

  data_processed_per_query * price_per_tib

  Where:

  • data_processed_per_query - Total data billed for the query (normalized to TiB). dbt sources this value from information_schema.jobs.total_bytes_billed. For more information, see the BigQuery documentation.
  • price_per_tib - BigQuery on-demand price per TiB (from your configuration or the default rate).

• Capacity pricing (reservations)

  The cost is determined by how long each query runs on reserved compute. The usage shows the amount of that reserved compute time consumed by a query.

  Formula:

  compute_time_per_query * price_per_slot_hour

  Where:

  • compute_time_per_query - Total slot time used by the query (in hours). dbt sources this value from information_schema.jobs.total_slot_ms. For more information, see the BigQuery documentation.
  • price_per_slot_hour - BigQuery capacity price per slot-hour (from your configuration or the default rate)

• Cached queries Queries served from cache do not consume compute and are counted as $0.

 Databricks

Databricks does not directly attribute usage to individual queries. Instead, dbt estimates per-query cost by proportionally allocating Databricks Units (DBUs) based on how long each query ran during a billing period.

• Queries that run longer receive a larger share of usage.
• Usage is converted to dollars using your list price.

Formula:

usage_per_query * cost_per_dbu

Where:

• usage_per_query - DBUs attributed to the query.
• cost_per_dbu - Dollar cost per DBU for the relevant stock-keeping unit. For information about the pricing system table, see the Databricks documentation.

Databricks reports usage in billing windows. These windows are periods of time where a compute resource consumed a known number of DBUs. Queries have their own start and end times. For information about the billing usage system table, see the Databricks documentation.

To attribute usage to queries:

1. dbt identifies which billing windows each query overlaps.
2. dbt calculates how long the query ran during each window.
3. dbt allocates DBUs proportionally based on the query’s share of total execution time on that compute resource during the same window.

Conceptually:

DBUs_in_window * (query_runtime / total_query_runtime_in_window)

dbt sums this across all overlapping windows to get usage_per_query.

Cost reduction calculation

dbt calculates cost reductions by comparing actual costs to what costs would have been without model reuse. To do this, dbt uses data from the last seven days (where available) and performs the following steps:

1. Calculates the average cost per model build.
2. Counts how many times a model was reused instead of rebuilt.
3. Multiplies the reused model count by the average cost per build to determine total cost reduction.

Formula:

average_cost_per_build * reuse_count

dbt calculates reductions per model and per deployment environment (production and staging), based on recent historical runs.

Additional notes:

• dbt calculates estimated costs and savings daily.
• Pricing inputs come from warehouse system tables (where available), connection-level configuration, or default list prices.

Example

The following example shows how dbt calculates cost reductions. Looking back seven days, assuming a model runs on two distinct days:

Day	Total cost	Total executions
Day 1	$5	5
Day 2	$10	10
Total	$15	15

Loading table...

The average cost per execution: $15 ÷ 15 runs = $1 per run

If the model was reused eight times instead of rebuilt during this same period, the estimated cost reduction is: $1 average cost per run * 8 reuses = $8

Considerations

Keep the following in mind when using Cost Insights:

Data collection and refresh

• Cost Insights uses your platform metadata credentials to access warehouse system tables. No separate credentials are needed beyond the platform metadata setup.
• Cost data refreshes daily and reflects the previous day's usage, which means there is a lag of up to one day between when a job runs and when its cost data appears.
• You need sufficient permissions to query warehouse metadata tables.

Cost accuracy

• dbt calculates costs using warehouse-reported usage data and applies default credit or compute costs based on standard warehouse pricing.
• If you have custom pricing agreements with your warehouse provider, override the default values in your account settings to ensure accurate cost reporting. For more information, see Set up Cost Insights.
• Update your cost variables whenever your warehouse pricing contracts change to maintain accurate tracking.
• Changes to cost variables only apply to future calculations — historical cost data remains unchanged.

Optimization data

• Optimization and usage reduction data is available once state-aware orchestration is enabled and begins reusing models across runs.
• For accounts already using state-aware orchestration, run at least one full model build within the last 10 days before enabling Cost Insights to establish a baseline for cost reduction calculations. If you don't see cost reduction data, run a full build to establish the baseline.
• Cost Insights currently calculates estimated reductions in warehouse compute usage at the model level and will expand to include tests and seeds in the future.

Exporting data

• You can export cost data as a CSV file for further analysis and reporting. For more information, see Explore cost data.

Related FAQs

Why might my actual warehouse costs differ from displayed costs?

Cost Insights shows estimates based on warehouse-reported usage and your configured pricing variables. Adjustments and differences may occur if:

• Your warehouse has custom pricing that differs from the default compute credit unit.
• There are discounts or credits applied at the billing level that aren't reflected in usage tables.
• Costs include other charges beyond compute.

Costs Insights in the dbt platform is designed to be directionally accurate, showing you dbt-specific components rather than matching your billing exactly.

How often is cost data refreshed?

Cost data refreshes daily and reflects the previous day's usage. This means there is a lag of up to one day between when a job runs and when its cost data appears in Cost Insights.

How do I troubleshoot if cost data isn't appearing?

If cost data isn't appearing in Cost Insights, check the following:

• Verify that platform metadata credentials are configured in your account settings and that the credential test is passing. For more information, see Set up Cost Insights.
• Ensure you have one of the required permissions to view cost data. For more information, see Assign required permissions.
• Confirm that at least one job is running in a production environment. Cost data only appears after jobs have executed.
• Cost data refreshes daily and reflects the previous day's usage, which means there is a lag of up to one day between when a job runs and when its cost data appears. If you just ran a job, wait until the next day to see the data.
• After enabling Cost Insights, dbt looks back 10 days to build baselines for cost reduction calculations. If you don't see cost reduction data, ensure you have sufficient job history within the last 10 days.

Does the Cost Insights feature incur warehouse costs?

dbt issues lightweight, read-only queries against your warehouse to retrieve metadata and to power features such as Cost Insights. dbt scopes and filters these queries to minimize impact, and most customers see negligible costs (typically on the order of cents).

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