- Specialization
- Data Engineering
- Business
- Software Applications
- Market
- Cross Industry
- Ecosystem
- Not classified
- Customers
Developersand 1 more customers valuesLess
- Enterprises
More details
- Organization type
- Commercial Company
- Ecosystem
- Not classified
- Business
- Software Applications
- Specialization
- Data Engineering
- Market
- Cross Industry
- Customers
- Developers, Enterprises
- Delivery models
- Hosted Software
- Revenue models
- Subscription, Usage Based
- Listing status
- Not classified
- Latest equity round
- Series D
- Reported funding amount
- Not classified
About
dbt Labs builds dbt, a data transformation and governance platform used by data teams to create reliable analytics and AI-ready data. It sells to organizations ranging from startups to large enterprises, differentiating through open standards, software-engineering practices, governed business logic, and portability across clouds, data engines, and tools.
Market
dbt competes in cloud data transformation, analytics engineering, and the broader data-infrastructure market for trusted analytics and AI-ready structured data. It positions dbt as an open, modern standard that applies software-engineering practices such as testing, versioning, documentation, metadata, and lineage to data work. Its differentiation is executing transformations where the data already lives while adding governance, stateful orchestration, cost optimization, and AI-oriented context rather than focusing only on data movement or visualization.
Data-intensive organizations that use cloud data platforms and need trusted data products for analytics, operations, or AI. The primary users and buyers are data practitioners, analytics engineers, and data/analytics leaders; the evidence does not specify a narrow company-size segment.
At a Glance
Problem
Modern data teams need to turn raw, changing data into reliable, governed, reusable data for analytics and AI, but transformation work can become slow, fragile, and expensive. Rebuilding every pipeline on every run wastes warehouse compute and engineering time, while manual scheduling and orchestration delay answers and increase the risk of breakage. The core use case is creating trusted, tested data models that analysts and engineers can reuse across reporting, decision-making, and AI or agent initiatives.
Product / Service
Dbt provides an open-source transformation foundation through dbt Core alongside a proprietary, platform-connected commercial offering. Teams develop data models primarily in SQL and apply software-engineering practices such as testing, validation, documentation, versioned development, and investigation; the dbt platform brings these workflows into a browser-based environment for development, scheduling, and governance. Its Fusion engine adds faster parsing and richer capabilities, while dbt State identifies what has changed and skips unnecessary builds, reducing production warehouse compute by an average of 30% according to the company.
Market
Dbt competes in data transformation and the modern data-stack market, particularly the analytics-engineering layer between data ingestion and business or AI consumption. Its positioning is strengthened by an open-source ecosystem and broad warehouse support, while alternatives and adjacent competitors include SQLMesh, Coalesce, Matillion, Informatica, and Google Cloud Dataform. Fivetran is now a strategic combination rather than a direct competitor: the companies describe Fivetran as the ingestion standard and dbt as the transformation standard.
The available evidence indicates meaningful adoption rather than a pre-revenue product: more than 4,500 projects were running on Fusion, and the company describes the broader Fivetran-dbt combination as the backbone of the modern data stack for thousands of organizations. The evidence does not disclose revenue or a valuation, so commercial scale beyond these adoption indicators cannot be quantified here.
Founders & Leadership
Funding History
Not disclosed in the retrieved funding records
Andreessen Horowitz
Sequoia Capital
Altimeter Capital, Andreessen Horowitz, Sequoia Capital
Altimeter Capital
Recent News
At dbt Summit 2026, dbt Labs announced a set of product capabilities: dbt v2 and dbt State were generally available, while dbt Wizard and Explore Mode were in public preview, and Wizard CLI and dbt Charts were in public beta. The company’s Summit recap also described dbt v2 as a modern Rust-based engine for dbt.
dbt announced that dbt Core v1.12 is generally available on August 17, 2026. The release includes UDF enhancements, simpler Iceberg catalog and Semantic Layer specifications, an opt-in Rust-based parser, and other workflow improvements.
Fivetran and dbt Labs completed their all-stock merger, initially operating as Fivetran + dbt Labs. The combined company serves more than 100,000 data teams and announced joint innovations focused on trusted data infrastructure for AI agents.
Google Cloud recognized dbt Labs with a 2026 Partner of the Year Award in Data and Analytics: Data Pipelines and Governance, citing its contribution to customer success.
Fivetran and dbt Labs announced an all-stock merger agreement. The companies said the combined business would approach $600 million in annual recurring revenue.
Active Roles
26Business Model
dbt monetizes its hosted platform through tiered subscriptions: a free Developer plan, a $100-per-seat-per-month Starter plan, and custom-priced Enterprise and Enterprise+ plans. It also offers usage-based products such as dbt State, priced according to daily active target tables.