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dbt AI

AI-enhanced SQL-based data transformation platform for building reliable analytics data models.

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  • Open source
  • Free forever
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What is dbt AI?

dbt AI is an open-source platform that helps data teams build and maintain reliable analytics data models using SQL. It combines traditional dbt functionality with AI assistance to speed up writing and refactoring data transformations. The tool is designed for analytics engineers, data engineers, and organisations that need to version control their data pipelines, document dependencies, and ensure data quality at scale. The platform focuses on transforming raw data into clean, modelled datasets that analysts and business intelligence tools can trust. By integrating AI capabilities, dbt AI aims to reduce boilerplate code writing and help teams spot potential issues in their SQL transformations. It's particularly useful for teams already invested in SQL-based workflows who want to improve productivity without switching tools.

Key features

AI-assisted SQL writing

Get suggestions and code completions when writing data transformations

Version control integration

Track changes to your data models alongside your code repositories

Data lineage and dependencies

Visualise how data flows through your transformations from source to output

Testing and documentation

Define tests to catch data quality issues and auto-generate documentation for your models

Modularity

Build reusable transformation logic that can be shared across projects

dbt Cloud integration

Connect to the managed dbt Cloud service for scheduling and monitoring

Pros & cons

Advantages

  • Open-source with no licensing costs, making it accessible for teams of any size
  • Strong community support and extensive documentation from years of dbt adoption
  • Works with most major data warehouses, including Snowflake, BigQuery, Redshift, and others
  • AI assistance helps accelerate development without requiring you to leave the SQL environment

Limitations

  • Requires familiarity with SQL and basic command-line tools, so there's a learning curve for non-technical users
  • Setup and configuration can be complex for first-time users, particularly when integrating with existing data infrastructure
  • AI features depend on having sufficient context about your data models, which takes time to build up in new projects

Use cases

Building a central data warehouse: Define transformations that clean and model raw data for company-wide analytics

Maintaining data quality: Set up automated tests to catch issues in your transformations before they reach analysts

Scaling analytics infrastructure: Use dbt's modularity to manage hundreds of data models across growing organisations

Documenting data pipelines: Auto-generate lineage documentation so teams understand where data comes from and how it's transformed

Speeding up analytics development: Use AI suggestions to write SQL transformations faster, especially for repetitive patterns

Ready to try dbt AI?

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Pricing

Open Source (Free)

Free

Full access to dbt Core for SQL transformations, local development, version control integration, testing and documentation features. Requires self-hosting and management.

dbt Cloud (Paid)

Varies

Hosted development environment, scheduling and orchestration, monitoring and alerting, metadata API access. Pricing typically based on job runs and team size, but specific tiers not detailed in public documentation.

Get started with dbt AI

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Get dbt AI
  • Open source
  • Free forever