dbt as open business logic layer
Julia Schottenstein, Product Manager at dbt Labs, on the business model of open source
Keeping business logic in an open framework turns dbt from a tool into shared infrastructure for the data stack. A company can write the SQL models, tests, docs, and metrics that define how revenue, retention, or CAC are calculated, and keep owning that logic even if it changes warehouses, BI tools, or deployment models. That lowers switching costs for users, and lets dbt sell the workflow around the standard instead of locking up the standard itself.
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This is why dbt Core stays free while dbt Cloud is paid. Core is the compiler and framework for writing transformations. Cloud sells the surrounding work, browser development, CI checks, scheduling, hosted docs, governance, and semantic layer features that make large teams faster and safer.
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Vendor neutrality matters because many companies run more than one warehouse or more than one BI tool. When business definitions live above Snowflake, Databricks, or Looker, teams do not have to rewrite the same metric logic in each system, and can keep one source of truth for how tables and metrics are built.
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The closest software analogy is Git and GitLab. Git is the open standard developers trust to hold source code. GitLab monetizes the management layer around it. dbt follows the same pattern, with adoption landing through open source and monetization happening through collaboration, control, and enterprise workflow features.
The next step is for dbt to become the control plane where business logic sits above an increasingly multi-cloud data world. As Snowflake and Databricks bundle more native transformation features, dbt is expanding from transformation into orchestration, cataloging, observability, and semantic governance so the open standard remains the anchor and the commercial product captures more of the daily workflow.