Find out where your dbt spend is going and what you can cut without touching your pipeline logic.
Describe your dbt project setup and get a cost analysis, models to defer or cache, estimated savings and a migration checklist if you are considering alternatives.
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fct_orders — optimised
{{ config(
materialized='view'
) }}{{ config(
materialized=
'incremental'
) }}Illustrative — optimisations are specific to your model SQL and warehouse.
Every card is something members actually do inside Datamata — not a vague promise.
Model run frequency versus usage — the culprits are almost always a handful of high-frequency rebuilds nobody monitors.
Specific models to defer, cache or drop with estimated savings for each change.
SQLMesh, Coalesce or Dagster migration steps if the numbers make switching worth it.
Cost breakdown by model category — high-frequency rebuilds are usually the culprit
Feature deep-dives
See it work
Model run frequency versus usage — the culprits are almost always a handful of high-frequency rebuilds nobody monitors.
Try dbt Cost Optfct_orders — optimised
{{ config(
materialized='view'
) }}{{ config(
materialized=
'incremental'
) }}Illustrative — optimisations are specific to your model SQL and warehouse.
Go deeper
Specific models to defer, cache or drop with estimated savings for each change.
Browse all premium toolsIn practice
SQLMesh, Coalesce or Dagster migration steps if the numbers make switching worth it.
Try dbt Cost OptBuilt for Premium outcomes
Real decisions, not generic templates. Every workflow runs on live market data so your moves are backed by what employers are asking for now.
Cost breakdown by model category — high-frequency rebuilds are usually the culprit
Specific models to defer, cache or drop based on your run schedule
Estimated monthly savings with concrete actions to get there
Quick start guide
Enter your project size, warehouse and run schedule — the optimizer needs enough context to estimate real savings.
Enter model count, warehouse type, plan tier and rough run schedule.
Tip: If you have dbt Cloud run history, note which models run most frequently — that is usually where the savings are.
The analysis flags the highest-cost patterns with estimated monthly spend.
Implement deferrals and caching first — migration is worth evaluating only if the savings are substantial.
Tip: Migration requires testing — budget at least a sprint before switching orchestrators on a production project.
Run the dbt Auditor on the models flagged as expensive — poorly written CTEs compound the cost problem.
What's included
Cost breakdown by model category — high-frequency rebuilds are usually the culprit
Specific models to defer, cache or drop based on your run schedule
Estimated monthly savings with concrete actions to get there
Optional migration checklist for SQLMesh, Coalesce or Dagster if you are ready to move
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