Build what’s changed, skip what hasn’t.
dbt State builds only what has changed. Cut warehouse compute 30%, ship fresher data, and iterate faster. Locally or in the cloud. Currently in preview.
Stop rebuilding. Start saving.
Whether you're orchestrating in the dbt platform, running dbt Core locally, or using an external orchestrator, dbt State optimizes every model run for speed, performance, and efficiency.
Optimize costs
Reduce warehouse compute by 30% on average* by only building models when upstream data or code has actually changed. Stop paying to rebuild what hasn't.
Run freely
No more rationing runs. Set a schedule that supports your SLAs for fresher data, and iterate faster without fear of expensive mistakes.
Reduce complexity
No custom workflows, no manual orchestration. Turn on dbt State and your jobs optimize themselves.
Intelligence built into every run.
On every run, dbt State checks your metadata and model SQL to see what’s changed. When upstream data or code has changed, it builds the model. Otherwise, it skips the build by:
- Reusing existing state — zero compute, zero risk
- Cloning existing state with minimal compute cost
- Auto-deferring (in development) to production state
This works for full models, incremental models, snapshots, seeds, and tests.
In production: fresher data, lower costs.
Eliminate hours spent maintaining custom workflows and manual orchestration. Just turn dbt State on, and run your jobs as often as you need. That means fresher data, less complexity, and meaningful warehouse savings without changing how you work.
Tuned configurations
Declare freshness and dependency rules directly in your project code. Set a max staleness window so models rebuild only when they’re due — not every run. Move orchestration logic from imperative scheduling into declarative, version-controlled configurations.
Iterate faster, without the risk
dbt State skips and clones models in development and auto-defers to production state — so you can start building immediately with no manual overhead and no fear of costly mistakes.
Go deeper on dbt State.
Join us July 15th for demos of the platform and local experience, a real customer story, and Q&A.
Trusted by data teams
Before dbt State, every job rebuilt every model in the lineage. Every. Single. Time. Now, with dbt State, dbt checks if source data changed. If it didnt, the model is skipped. For us, that resulted in a 9% compute reduction, 35% fewer models built, and a 15% reduction in Snowflake costs.
Parag Shah, VP of Data, CarGurus
Declaring SLAs at the model level and letting it decide what actually needs to run has already delivered 25-30% model reuse and roughly 15% in Snowflake savings on our pilot project.
Alvin Chai, Senior Analytics Engineer, Fanatics
Read Obie Insurance's storyWere saving at least 30% on compute costs, just from reusing models with dbt State
Tyson Doberneck, Senior Data Engineer
Reuse more, save more.
dbt State pricing is usage-based. You pay based on the benefit from model reuse. dbt State cost is measured using Daily Active Target Tables. Daily active target tables (DATT) are measured as the number of distinct target tables for which dbt State performs a unique skip or clone, and unique test reuse operations on a given calendar day.
Visit the docs billing page to learn more.
Frequently asked questions
Stop rebuilding unchanged models. Start saving today.
Get started in minutes — whether you run dbt Core or the dbt platform.