Fivetran and dbt Labs have officially completed their merger. The merger, originally announced in October 2025 with combined revenue of $600 million, builds an open data infrastructure layer for AI agents. George Fraser remains CEO, and Tristan Handy becomes President. Together, they serve more than 100,000 data teams worldwide.
Fivetran + dbt Labs position themselves as the infrastructure layer that enables reliable AI agents. Fivetran ensures continuous data synchronization and completeness. dbt enriches that data with tested business logic, shared semantic context, and governance. Together, the platform supports any cloud, engine, and tool built on open standards, even if the organization’s architecture is still under development.
According to Fivetran’s own Agentic AI Readiness Index 2026, 60 percent of enterprises are investing millions in agentic AI, while only 15 percent have a data foundation capable of supporting those workloads safely and effectively. A separate Fivetran survey indicates that 85 percent of enterprises are running agentic AI on a data foundation that isn’t yet ready for it.
“The companies that deploy AI successfully over the next decade will be the ones whose agents can be trusted to act,” said Tristan Handy, President and co-founder. “Trust is built at the infrastructure layer, on high-quality tooling and on open standards.”
First joint product innovations
Upon completion of the merger, Fivetran + dbt Labs will immediately launch a series of joint products. dbt Core v2.0 (alpha) is available as open source under an Apache 2.0 license, built on the new Fusion engine runtime. dbt State (preview) acts as a caching layer for data pipelines, building only what has changed. The company claims this can reduce infrastructure costs by 30 percent or more. dbt Wizard (beta) offers autonomous support for writing, refactoring, and debugging data models.
In addition, the companies are introducing Agents Schema, an open-source standard that designates a single schema in the data warehouse as a shared context layer for AI agents. Metric definitions, semantic models, and dbt lineage are stored in regular SQL tables. The standard is compatible with any warehouse, ingestion tool, or SQL-capable agent and operates within existing security and governance policies.