Sacra estimates dbt Labs hit $96M in annual recurring revenue (ARR) in 2024, representing 14% year-over-year growth. This marks a significant deceleration from previous years, as the company grew over 400% in 2022 and 71% in 2023, reaching $73M ARR.

The company generates revenue primarily through dbt Cloud subscriptions, charging $100 per user seat and $0.01 per successful data transformation. With over 5,000 paying customers and 50,000 teams using dbt weekly, the platform has achieved significant market penetration among data teams.

Enterprise customers, particularly Fortune 500 companies, represent a growing revenue segment with 85% year-over-year growth in adoption. The company has seen 90% year-over-year growth among customers spending $100,000+ annually on dbt Cloud, indicating strong enterprise expansion.

Geographic expansion is driving additional growth, with customers now spanning 43 countries, representing 27% year-over-year growth in international markets. Recent office openings in Austin and Dublin, along with expansion efforts in APAC, particularly Japan, Australia, and New Zealand, suggest continued focus on global market penetration.

dbt Labs was last valued at $4.2 billion during its Series D financing in February 2022 for a 221x multiple off $19M in estimated ARR. The company has raised a total of $410 million across 4 funding rounds, with the Series D round contributing $220 million.

dbt Labs was founded in 2016 by Drew Banin, Tristan Handy, and Connor McArthur as a consulting company before pivoting to focus on building their open-source data transformation tool.

dbt found product-market fit as a SQL-based transformation framework for analytics engineers who needed to apply software engineering best practices to data analytics workflows. The tool resonated strongly with data analysts working in cloud data warehouses like Snowflake, BigQuery, and Redshift.

The core dbt product enables data teams to transform raw data into analytics-ready datasets using SQL. Users write modular SQL transformations that can be version controlled, tested, and documented. These transformations are organized into directed acyclic graphs (DAGs) that define how data should flow through an organization's analytics pipeline.

dbt Cloud extends this functionality with a web-based IDE, scheduling capabilities, and collaboration features. Teams use it to develop and deploy data transformations, monitor job runs, and maintain data documentation. The platform includes a semantic layer that standardizes metric definitions across an organization's analytics tools.

dbt Labs is a data transformation platform company that monetizes through dbt Cloud, a commercial offering built on top of their open-source dbt Core product. The company provides tools for analytics engineers to transform data within cloud data warehouses like Snowflake, BigQuery, and Databricks.

The company operates a hybrid business model combining open-source and commercial offerings. dbt Cloud is sold through per-seat subscriptions at $100 per user with additional usage-based fees of $0.01 per successful data transformation. Enterprise plans include additional features for security, compliance, and governance needs.

dbt Labs employs a product-led growth strategy where users often start with the free open-source dbt Core before upgrading to dbt Cloud for enhanced features and scalability. This creates a natural expansion path as organizations grow their data teams and transformation needs.

The company differentiates itself through warehouse-agnostic capabilities and deep integration with the modern data stack. Their platform approach extends beyond basic transformation to include orchestration, observability, and cataloging features. By positioning dbt Cloud as a comprehensive "data control plane," they create multiple vectors for expansion within customer organizations while maintaining their core focus on transformation workflows.

dbt Labs operates in the data transformation and analytics engineering market, which spans across multiple segments of the modern data stack.

Snowflake and Databricks are building native transformation capabilities directly into their platforms. Both companies have significantly larger scale - Databricks at $43B valuation and Snowflake with $2.8B in revenue. These platforms offer integrated solutions that combine storage, compute, and increasingly, transformation capabilities.

Emerging competitors like SQLMesh and MetricFlow offer alternative approaches to data transformation, though they lack dbt's community adoption. Dataform, acquired by Google, provides similar functionality but remains primarily focused on BigQuery users. Matillion offers a low-code ETL/ELT solution with visual interfaces, targeting users who prefer GUI-based workflows over SQL.

Companies like Dagster and Meltano are "re-bundling" transformation capabilities with enhanced orchestration and scheduling features. These platforms compete by offering end-to-end workflow management, while traditional orchestrators like Apache Airflow complement rather than compete with dbt.

As dbt expands into cataloging and observability, it increasingly competes with specialized vendors like Collibra (data cataloging) and Metaplane (data observability). These companies offer deeper functionality in their respective niches but lack dbt's broad transformation capabilities and community adoption.

The competitive landscape is shifting as larger platforms pursue vertical integration while specialized tools focus on specific segments of the data stack. dbt's position as a vendor-neutral transformation layer with strong community adoption remains distinctive, though maintaining this advantage requires continuous innovation in adjacent capabilities.

dbt Labs has tailwinds from the rapid growth of cloud data warehousing and the increasing importance of AI/ML data pipelines, with opportunities to expand into adjacent markets like data quality management, metadata management, and AI-powered data operations.

The $3.5B data quality market represents a natural expansion opportunity for dbt Labs. The company's position at the transformation layer gives it unique visibility into data quality issues. By expanding its testing and validation capabilities, dbt could capture a significant portion of this market while providing more value to existing customers.

As organizations struggle with data discovery and governance, dbt Labs could leverage its existing semantic layer to build comprehensive metadata management solutions. The metadata management market is expected to reach $10B by 2025. dbt's existing integration with major data platforms positions it well to become the system of record for data assets.

dbt Labs' recent launch of dbt Copilot signals its entry into AI-assisted data operations. The company could expand this capability to automate complex data transformations, testing, and documentation. This market opportunity extends beyond traditional transformation tools into the broader DataOps space, estimated at $7B by 2026.

With 50% of enterprises using multiple data platforms, dbt's cross-platform mesh technology positions it to become the central orchestration layer for enterprise data stacks. This expansion could help dbt capture a larger share of enterprise IT budgets while increasing switching costs for existing customers.

Platform dependency and commoditization: dbt's core value proposition relies heavily on cloud data warehouses like Snowflake and Databricks. These platforms are increasingly building native transformation capabilities that could reduce the need for separate transformation tools. While dbt's vendor-neutral position has been a strength, it could become a weakness if major platforms successfully bundle competitive features.

Community-commercial tension: dbt Labs faces a delicate balance between maintaining its open-source community roots and driving commercial growth through dbt Cloud. The widening "chasm" between self-deployed dbt Core and the paid Cloud offering risks alienating the developer community that drove initial adoption. This tension could slow enterprise adoption if organizations perceive a disconnect between the open-source tool their teams love and the commercial platform they're being sold.

Feature expansion risks: dbt's expansion into cataloging, orchestration, and observability represents a significant departure from its focused transformation roots. This broader platform approach increases product complexity and puts dbt in direct competition with specialized tools. The move could dilute the company's core value proposition and strain engineering resources as they attempt to match feature parity across multiple product categories.