dbt State: build only what's changed, skip the rest.

Every job used to rebuild every model in the lineage, every single time, whether anything upstream actually changed or not. dbt State fixes that. On every run, it checks your metadata and model SQL to see what's changed. If nothing changed, it skips the build by reusing existing state at zero compute, cloning existing state at minimal cost, or auto-deferring to production state in development. If something did change, it builds. This works for full models, incremental models, snapshots, seeds, and tests. The result: fresher data, lower warehouse costs, and no custom workflows or manual orchestration to maintain. Just turn dbt State on and run your jobs as often as you need. Declare freshness and dependency rules directly in your project code, set a max staleness window so models rebuild only when they're due, and move orchestration logic from imperative scheduling into declarative, version-controlled configuration. Data teams are already seeing the impact: CarGurus cut compute 9%, built 35% fewer models, and reduced Snowflake costs by 15%. Fanatics saw 25 to 30% model reuse and roughly 15% in Snowflake savings on their pilot. Obie Insurance is saving at least 30% on compute costs from model reuse alone. dbt State works whether you're orchestrating in the dbt platform, running dbt Core locally, or using an external orchestrator. Get started: https://us1.dbt.com/register?_dbtsrc=dbt-state Read the docs: https://docs.getdbt.com/docs/deploy/dbt-state-setup Learn more: https://www.getdbt.com/product/dbt-state #dbt #dbtState #AnalyticsEngineering #DataEngineering #DataOps