New engine enables faster analytics delivery, lower cloud costs, and trusted data pipelines built for AI at scale
PHILADELPHIA, May 28, 2025 /PRNewswire/ — dbt Labs, the leader in standards for AI-ready structured data, today unveiled the new dbt Fusion engine, a monumental evolution of the technology that powers dbt. Fusion, built on Rust and equipped with native SQL comprehension, introduces a lightning-fast developer experience that delivers productivity, data velocity, and platform intelligence to drive substantial cost savings. dbt Labs also launched its VS Code extension, unlocking broad access to the power of Fusion for local developers, and is introducing a free, source-available version of the Fusion engine with a subset of features. These foundational enhancements, along with several others announced today and tailored to bring data analysts into the dbt workflow, will empower organizations to scale analytics in the age of AI.
“AI is completely changing the way we interact with data, and dbt is in a prime position to drive the next phase of innovation in the market,” said Tristan Handy, founder and CEO, dbt Labs. “Fusion is the most significant evolution of dbt in its history. It gives enterprise data teams the control, speed, and intelligence they need to scale analytics and AI responsibly while keeping costs down.”
Fusion Engine Brings SQL Comprehension to dbt
The dbt Fusion engine now powers the entire dbt platform, from the CLI that is in use by over 60,000 teams today, to dbt Orchestrator, Catalog, Studio, and the other commercial products powering dbt Labs’ rapid growth. Fusion introduces powerful SQL comprehension and a host of other capabilities that collectively deliver a best-in-class developer experience, all while empowering organizations to operate with the highest quality, context-rich data while optimizing costs.
New capabilities include lightning-fast parse times, up to 30x faster than dbt Core, allowing large dbt projects to execute in milliseconds instead of minutes. Instant feedback loops and live error detection now uncover and surface parse, compilation and logic errors as code is being written – and before running code against the warehouse – optimizing both developer efficiency and data platform costs.
Fusion also brings state-awareness to dbt and with it, a new level of intelligence to how dbt orchestrates pipelines. With state-aware orchestration, available in beta for commercial customers running Fusion, dbt will automatically run jobs as soon as sources are fresh and limit builds to only the models that changed. This helps organizations save on data platform compute and maintain pipeline velocity. Early customer feedback indicates an average 10% cost savings as a result, with additional savings expected as Fusion matures. Organizations can validate these savings in the new cost management dashboard (in preview for Snowflake users), which offers visibility into costs at the warehouse, project, model, and environment level, helping to identify inefficiencies earlier.
Other standout functionality includes:
- Powerful IntelliSense, which autocompletes SQL functions, model names, columns, macros and more;
- Instant refactoring to rename models or columns and see references update project-wide;
- Go-to-definition, allowing users to jump to definitions in a single click, a useful feature for large projects with many models and macros;
- Hover insights, which enable users to see context on tables, columns and functions without leaving code;
- Live CTE previews directly inside dbt models, for faster validating and debugging;
- Rich lineage, in context, allowing developers to see lineage at the column or table level as they develop, without breaking flow; and
- View compiled code, which gives a live view of the SQL code built by models, alongside dbt code.
“The data team at Bilt is very excited to roll out the new dbt Fusion engine,” said James Dorodo, VP of Data Analytics at Bilt Rewards. “The improvements it brings will address many of the pain points we currently face in our development cycle, and we believe it will provide a step function increase in our velocity.”
Multiple Paths to Fusion Engine Access
Fusion is now available for eligible dbt projects on Snowflake, with support for Databricks, BigQuery, and Redshift coming soon. dbt Labs is also introducing its VS Code extension, the sole way to access the full power of the Fusion engine while developing locally. Now, wherever developers are doing their work, they can do so backed by Fusion. The VS Code extension is downloadable now from the VS Code Marketplace.
In addition, dbt Labs is making a subset of Fusion’s capabilities broadly available via a new source-available license. This will provide users in the dbt community free access to Fusion’s robust developer experience features.
AI Adoption Drives The Need For More Quality Data and Unified Standards
A major disruption is underway in analytics, driven by and in service to AI. According to the latest State of Analytics Engineering report, organizations rank AI at the top of their budget priority lists. As these investments grow, the pressure on data teams to deliver trustworthy, contextual data – and a scalable AI strategy – has never been greater. Yet inconsistent standards and fragmented workflows often result in a lack of a single source of truth, making it difficult to scale AI initiatives responsibly.
To address this challenge, dbt Labs launched the dbt MCP server, leveraging Model Context Protocol to enable seamless, universal connectivity between AI systems and the governed, structured data in dbt.
As the standard for creating governed, trustworthy datasets on top of structured data, dbt unifies models, metrics, documentation, and testing into one collaborative environment. The dbt MCP server gives business users the confidence that all AI endpoints are fueled by context-rich, reliable data, no matter how the AI stack evolves.
“dbt Labs’ continuous investment in revolutionizing the developer experience aligns well with our commitment to giving customers the very best platform for all of their data engineering needs, with low costs and accelerated performance in the AI Data Cloud,” said Chris Child, VP of Product, Data Engineering, Snowflake. “As more of our joint customers adopt AI across their businesses, we know these critical data initiatives require context to be successful. The dbt MCP server complements our investments in AI, and now, with the Fusion engine powering dbt, we’re eager to see how much more productive and successful our joint customers will be.”
dbt Empowers Data Analysts with New Features
In conjunction with the rollout of the Fusion engine, VS Code extension and dbt MCP server, dbt Labs launched a suite of new governed, accessible features designed to bring data analysts into the dbt workflow, including:
- dbt Canvas, a new AI-powered drag-and-drop visual editing experience that enables analysts less familiar with dbt or SQL to create new and edit existing dbt models within a governed environment.
- dbt Insights, a new AI-powered query interface that lets analysts perform ad-hoc analysis by asking questions about their data models in SQL or natural language and get answers faster, without waiting on engineering.
- Extending the functionality of dbt Catalog, formerly known as dbt Explorer, to include search and lineage for overall Snowflake assets alongside your dbt models, making it easier to explore your full data environment. Support for other data platforms is coming soon.
These features expand the impact of dbt across the analytics workflow, empowering more collaborators to build, analyze, and explore the data they need, within a governed environment. By supporting governed, scalable self-service, teams reduce engineering bottlenecks, minimize security risks, cut compute costs, and ensure high quality data across their analytics workflow.
“Fivetran and dbt have a long history of innovation and delivering on the promise of the modern data stack. With the launch of the dbt Fusion engine and dbt Labs’ acquisition of SDF Labs, dbt Labs is accelerating what’s possible with data and AI,” said Taylor Brown, COO and co-founder, Fivetran. “We’re excited to have SDF Labs co-founder Elias DeFaria join us at the Fivetran booth at Snowflake Summit next week to showcase the next wave of tooling.”
These new features will be the focus of dbt Labs’ upcoming presences at Snowflake Summit (June 2-5, booth #1808) and Databricks Summit (June 9-12, booth #326). The dbt Labs team will be onsite at both industry events to discuss and demo the power of Fusion and these new capabilities. To learn more and book a meeting, visit https://www.getdbt.com/events/summit/snowflake-summit-2025.
About dbt Labs
Since 2016, dbt Labs has been on a mission to help data practitioners create and disseminate organizational knowledge. dbt is the standard for AI-ready structured data. Powered by the dbt Fusion engine, it unlocks the performance, context, and trust that organizations need to scale analytics in the era of AI. Globally, more than 60,000 data teams use dbt, including those at Siemens, Roche and Condé Nast.
Learn more at getdbt.com, and follow dbt Labs on LinkedIn, X, Instagram, and YouTube.
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