Boston-based DataRobot, which makes tools for enterprises to automate building machine learning models, raises $100M Series D, bringing total raised to $225M
Context & Ripple Effects
This October 2018 round is the opening move in what became one of the steepest capital escalations in enterprise machine learning. DataRobot's automated model-building tools had already attracted $125M before this $100M Series D, and within a year the company followed with a ~$200M Series E led by Sapphire Ventures at a $1B+ valuation.
From there the ladder only climbed: a $270M Altimeter-led round at $2.7B in late 2020, then a $300M Series G at a $6.3B post-money valuation by mid-2021 — roughly a thirtyfold valuation jump from the point this article captures. The Series D matters because it funded the platform expansion, including the data-prep capability acquired via Paxata, that made those later rounds possible.
First-order effects
- With $225M total raised, DataRobot gains the balance sheet to scale its automated-ML platform sales and engineering ahead of rivals still raising smaller enterprise-AI rounds.
Second-order effects
- The war chest converts directly into M&A capacity — the following year DataRobot deployed it on Paxata, a data-prep vendor that had itself raised $90M, pulling a key upstream step of the ML workflow in-house.
- Competing AutoML vendors face a rival that can bundle data preparation, model building, and deployment under one heavily capitalized roof, pressuring them toward their own large raises or consolidation.
Third-order effects
- The trajectory from a $225M-total startup to a $6.3B valuation in under three years shows how quickly enterprise-AI platforms attracted mega-rounds — and, per the later executive stock-sale uproar and CEO resignation, how fast-rising private valuations can outpace governance inside the companies they fund.
The trend: Enterprise automated-machine-learning is consolidating around a few heavily capitalized platforms whose funding cadence, not product features alone, sets the competitive pace.