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dbt

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Transform data in your warehouse with SQL

From $100/mo (free plan available)
Freemium Developer Tools Data & Databases Testing & QA Documentation
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Reviews on G2
209 reviews tracked

The Bottom Line

Price
From $100/mo (free plan available). Compare plans

Key facts

  • Data transformation tool for analytics engineers
  • SQL-based modeling with version control
  • Industry standard for modern data stacks

Pros

  • Industry standard
  • Great community
  • Excellent documentation
  • Free tier available

Cons

  • Learning curve
  • Requires SQL knowledge
  • Cloud pricing can add up

What is dbt?

Editorial review
dbt transforms data in your warehouse using SQL. Write SELECT statements, and dbt handles dependencies, testing, and documentation-software engineering practices applied to analytics code. Version control for transformations. Tests ensure data quality. Documentation generates automatically from code. Data teams treat dbt as essential because it brought engineering discipline to analytics workflows that used to be undocumented SQL scripts.

Available on: Web

how we evaluate Source getdbt.com ↗

Ratings Across the Web

4.7 (209 reviews)

dbt holds an aggregate rating of 4.7 out of 5 from 209 reviews across G2, last checked September 17, 2026.

G2 209 reviews
4.7/5

Ratings aggregated from independent review platforms. Learn more

Key Features

  • SQL transformations
  • Version control
  • Testing
  • Documentation
  • Lineage
  • Scheduling
  • IDE

Pricing Plans

Free Trial

Pricing checked Oct 10, 2026

dbt plans and prices, checked October 2026
PlanPriceDetails
Developer

$0

Free tier

  • 1 developer seat
  • 3K models/month
  • 1 project
+2 more
  • Browser IDE
  • Job scheduling
Starter

$100/per user/month

Small teams

  • 5 developer seats
  • 15K models/month
  • dbt Catalog
+2 more
  • dbt Semantic Layer
  • API access
Enterprise

Custom

Large orgs

  • 100K models/month
  • 30 projects
  • dbt Mesh
+2 more
  • dbt Canvas
  • Cost optimization
Calculate your cost View full pricing

Is dbt worth the price?

Fair value

dbt Cloud pricing shifted to a consumption-based model in 2024, adding per-model-run charges on top of per-seat fees.

The Developer plan is free for 1 user with limited runs, enough to learn dbt. Starter at $100/user/mo gets teams up to 5 seats with 15,000 model runs included.

The hidden cost: model run overages at $0.01 per run add up fast for teams running complex DAGs hourly. dbt Core (open source) is free and handles the same transformations, dbt Cloud's value is in the IDE, scheduler, and governance features.

Hidden Costs & Gotchas

Model run overages

Starter includes 15,000 runs/month. Every run beyond costs $0.01.

Consumption-based pricing makes costs unpredictable

unlike flat per-seat pricing, your bill varies monthly based on how many models run.

Queried metrics (Semantic Layer) are separately metered

Starter includes 5,000 queries/month. Dashboards hitting the Semantic Layer can burn through this in days if not monitored

dbt Cloud IDE is the only supported development environment on paid plans, you cannot use VS Code or another editor without losing CI/CD integration. This creates IDE lock-in

Multiple projects require Enterprise

Starter limits you to 1 project. Teams managing separate data products or domains need Enterprise at 4x the cost

dbt Core is free but requires significant DevOps investment

setting up Airflow/Dagster for scheduling, managing environments, and building CI/CD pipelines costs engineering time that dbt Cloud replaces with managed infrastructure

Annual billing is required on Enterprise, no monthly option. Starter offers monthly billing but at higher effective rates than annual

Full dbt pricing: every plan, hidden costs and alternatives

Reviews

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4.7/5

Across 209 verified user reviews on G2

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Best dbt Alternatives

Top alternatives based on features, pricing, and user needs.

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Still deciding?

Most buyers shortlist 2 or 3 tools before committing. Pull a side-by-side comparison or browse the full alternatives shortlist below.

dbt vs Looker Head-to-head: features, pricing, who wins dbt vs Sigma Computing Head-to-head: features, pricing, who wins dbt vs Metabase Head-to-head: features, pricing, who wins

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dbt FAQ

How does dbt help data teams manage their analytics code?

dbt allows data teams to apply software engineering practices to their analytics code by enabling version control for transformations. It helps manage dependencies between data models and automates testing to ensure data quality within the warehouse.

What kind of user benefits most from dbt?

dbt is ideal for data analysts and engineers who are comfortable with SQL and want to bring more discipline and reliability to their data transformation workflows. It helps teams maintain high data quality and provides automatic documentation for their analytics projects.

How is dbt priced?

dbt offers a free tier for users to get started with its core functionalities. For organizations requiring more extensive usage or advanced features, paid plans are available.

Can dbt be used for data quality assurance?

Yes, dbt includes testing capabilities that allow users to define and run tests on their data transformations. These tests help ensure the quality and integrity of the data within the warehouse.

How does dbt compare to tools like Looker for data transformation?

dbt focuses specifically on transforming data within the warehouse using SQL, applying software engineering principles like version control and testing to the process. Looker, while also working with data, is primarily a business intelligence and data visualization platform that sits on top of transformed data.

What are the main trade-offs when adopting dbt?

Adopting dbt requires users to have SQL knowledge and presents a learning curve for those new to its methodology. Additionally, for cloud-based implementations, the associated cloud infrastructure costs can accumulate.

How does dbt generate documentation for data models?

dbt automatically generates documentation directly from the analytics code written by users. This feature ensures that data models are well-documented and understandable, improving collaboration and maintainability for data teams.

Source: getdbt.com

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