We built dbt State to stop rebuilding what hadn't changed

dbt State is now generally available across dbt environments, including self-managed orchestrators, Snowflake dbt Projects, and warehouses such as Snowflake, BigQuery, Databricks, and Redshift. It evaluates model code and warehouse metadata on each run to determine whether models need rebuilding, allowing unchanged nodes to be skipped, cloned, reused, or deferred without custom selectors, manifest management, or orchestration logic. The company reports that early users have achieved average compute savings of 15–30%, while some organizations cite reduced job costs, faster development cycles, and simplified deployments. dbt State shifts freshness management from job schedules to model-level configuration through controls such as lag tolerance, enabling teams to consolidate jobs and apply consistent rules across large DAGs. In development, it can automatically reuse fresh production or upstream assets, reducing the need for manual cloning, deferral setup, and costly upstream rebuilds. The product also provides explanations for build decisions, concurrency management, cost insights on the dbt platform, and consumption-based pricing tied to distinct target tables that are reused, skipped, cloned, or deferred each day.

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