Fivetran, DBT Labs complete merger to form data layer for AI
The combined capabilities of the newly formed company provide the infrastructure, including data integration and preparation, for agents and other cutting-edge applications.
The merger of the two, first revealed in October 2025, became official on Monday, creating a new company that combines the data integration capabilities of Fivetran with the data transformation and data modeling capabilities of DBT Labs.
Although financial terms of the all-stock transaction were not disclosed, Fivetran was valued at $5.6 billion in September 2021 when it raised $565 million in venture capital funding, while DBT Labs was valued at $4.2 billion in February 2022 when it raised $222 million from venture capitalists.
The combined entity will operate as Fivetran + DBT Labs with former Fivetran CEO George Fraser serving as the new company's CEO and former DBT Labs CEO Tristan Handy serving as president.
Given that Fivetran and DBT Labs bring separate but complementary capabilities to Fivetran + DBT Labs, their combination is logical, according to Devin Pratt, an analyst at IDC.
"Fivetran moves the data and DBT makes it trustworthy," he said. "Together they cover the two things that buyers care about most right now, [which are] data quality and AI readiness."
However, whether existing customers -- particularly those of DBT Labs, which began as an open source project -- stay loyal as the new company evolves remains to be seen, Pratt continued.
"The technology fit is the easy part," he said. "The real test is keeping the DBT open-source community's trust through the transition."
Donald Farmer, founder and principal of TreeHive Strategy, similarly noted that Fivetran and DBT Labs are a strong technological fit. But given Fivetran's history as a closed-source SaaS vendor "with a reputation for complex, aggressive consumption-based pricing models" and DBT Labs' open-source ethos, there could be culture clashes as the two join forces, he cautioned.
"If they integrate well and operate as a single platform they can eliminate some of the complexity of the data stack," Farmer said. "And they do share many customers already. But they may be less compatible in business terms. ... Bringing these two communities together is going to be a real challenge."
Ultimately, the motivation behind the merger might be an initial public stock offering, he added.
"Perhaps the real driver is that neither company was likely to successfully IPO individually [and] this merger consolidates their annual recurring revenue to cross a threshold required for a successful public listing," Farmer said.
Technological fit
While Fivetran and DBT Labs each developed user bases as independent vendors, as agentic AI becomes more prevalent across enterprises and data management evolves to become a foundational layer for multi-agent systems, some vendors are turning their platforms into end-to-end systems for data and AI.
The technology fit is the easy part. The real test is keeping the DBT open-source community's trust through the transition.
Devin PrattAnalyst, IDC
Those that have the capital to compete -- hyperscale cloud vendors such as AWS, Google Cloud and Microsoft along with data platform providers including Databricks and Snowflake -- are expanding. As they do so, it makes it difficult for niche vendors to remain independent, which is leading to consolidation.
Some formerly independent companies have opted to sell to broader platform vendors. For example, Informatica is now part of Salesforce, Confluent was bought by IBM, and Dremio was acquired by SAP. Rather than find buyers to become small pieces of larger wholes, Fivetran and DBT Labs elected to merge to expand beyond their specialties.
Together, they can provide a data infrastructure layer designed to prepare data for AI, including the semantic modeling capabilities and business logic that help feed agents the contextually relevant data they require to deliver accurate, trustworthy outcomes.
In addition, with DBT Labs' origins in the open-source community, the combined Fivetran + DBT Labs platform includes open standards that work across all clouds, engines and tools so that customers can use the data management architecture of their choice and avoid becoming too closely aligned with any single vendor.
Pratt noted that IDC research shows that 97% of organizations want to reduce the number of products they use for data management. However, only 12% want to use a single vendor. Therefore, though the space for specialists is shrinking, there remains room for independent vendors such as Fivetran + DBT Labs that provide more than one niche capability but aren't end-to-end data and AI platforms.
"Specialists can still thrive, as long as they slot cleanly into a core or get big enough to be that core," Pratt said. "Combining, as Fivetran and DBT have, is one way to do that rather than waiting to be bought."
Farmer likewise noted that despite ongoing consolidation, there remains a place for independent vendors. In particular, independent vendors with unique engineering approaches can survive given that integration becomes the focus amid acquisitions rather than innovation.
"Independents do have opportunities, especially if they can support a methodology and community," Farmer said. "When independents -- like Confluent, Dremio or DBT -- get absorbed, their engineering resources are inevitably redirected from product innovation toward integration and from the interests of their community towards alignment with enterprise sales."
New capabilities
Beyond the merger, Fivetran + DBT Labs unveiled its first new features. They include the following:
Agents Schema, an open source standard for providing context to agents that designates one schema in a data warehouse or data lake that is compatible across systems as the shared context layer for agentic AI.
DBT Core 2.0, the latest version of DBT Labs' open source Fusion engine for data transformation using SQL and Python code.
DBT State, a caching layer for data pipelines aimed at enabling users to reduce infrastructure costs.
DBT Wizard, an autonomous assistant that uses context including lineage and defined metrics from DBT projects for model authoring, refactoring and debugging.
DBT Core 2.0, DBT State and DBT Wizard are in various states of testing and not yet generally available.
"I like DBT State," Farmer said. "In an era where CFOs are cracking down on unpredictable bills, if DBT can cut infrastructure costs … with smart caching, that's a solid -- and testable -- claim."
Pratt, meanwhile, noted the potential value of Agents Schema.
"The hardest part of getting agents into production isn't the model, it's giving the agent context it can trust," he said. "Agents Schema goes straight at that, as an open standard the customer owns rather than one more lock-in."
Looking ahead, now that Fivetran and DBT Labs have merged, Pratt recommended that the company continue to stress and honor the openness on which DBT Labs was founded. With some vendors making it difficult to integrate with third parties, and others enabling only some interoperability, Fivetran + DBT Labs could stand apart from at least some competitors by fully embracing openness.
"Staying open is their biggest asset," Pratt said. "The opportunity now is to pair that openness with strong governance and automation, the things buyers value most, which would position them to keep their users and attract new ones."
"They need to fully open up DBT's semantic layer," he said. "A semantic layer must integrate with outside tools to be useful."
Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.
