GraphLab, Now Dato, Raises $18.5M for Machine-Learning Applications
The Series B round comes from Vulcan Capital …
Context & Ripple Effects
In early 2015, GraphLab rebranded as Dato and pulled down an $18.5M Series B from Vulcan Capital — wait, no: Vulcan backed this round directly, while TigerGraph's own big round came years later. The raise positioned the company, born out of Carnegie Mellon's GraphLab project, as an early bet that machine-learning tooling could be a standalone venture category rather than academic software.
That thesis aged into a durable funding lane: SafeGraph's later $45M Series B claimed more than 7,000 data scientists using its ML data-management product, and Kumo's $18.5M Series A led by Sequoia at a $100M valuation applied graph neural networks to enterprise prediction — both walking ground adjacent to what Dato was building in 2015.
First-order effects
- Dato gains a war chest from Vulcan Capital specifically earmarked for machine-learning applications, letting it hire and ship against rivals that are still pre-revenue research projects.
- Vulcan Capital gets an early-mover position in commercial ML tooling, a sector that barely existed as a venture line when the round closed.
Second-order effects
- The round helped legitimize graph-plus-ML infrastructure as investable, clearing the path for later large checks such as TigerGraph's $105M Series C and SafeGraph's data-management raises aimed at the same data-science buyer.
- Enterprises evaluating ML platforms gained a funded, dedicated vendor option, forcing general-purpose analytics suppliers to decide whether to bundle ML capabilities or cede that budget line.
Third-order effects
- If the pattern holds — successive rounds for graph databases, ML data management, and graph neural networks — the ML tooling layer consolidates into a distinct industry stratum between cloud infrastructure and end applications, with specialized vendors rather than incumbents owning it.
- A decade of sustained funding for graph-centric startups suggests the underlying bet of the Dato round wins on structure even where individual companies do not: capital keeps arriving because enterprises keep needing to operationalize models over connected data.
The trend: Machine-learning tooling evolved from a niche 2015 bet like Dato's into a decade-long venture category spanning graph databases, ML data management, and graph neural networks.