Sources: DataRobot, which helps companies train and deploy ML models, is in talks to raise ~$300M at a valuation of $2.5B+, bringing its total raised to $700M+
Context & Ripple Effects
DataRobot's fundraising has been compounding fast: a $100M Series D in October 2018 took total raised to $225M, and a ~$200M Series E at a $1B-plus valuation followed in July 2019. The talks Bloomberg reports would nearly double that valuation again in under 16 months.
The round did close, and quickly: the next day DataRobot confirmed a $270M raise led by Altimeter Capital at a $2.7B valuation, making this one of the largest late-stage rounds in enterprise machine-learning tooling at the time.
First-order effects
- The round closes at $270M led by Altimeter at a $2.7B valuation, pushing DataRobot's total raised past $700M and giving the Boston-based AutoML vendor a large war chest against rivals selling model-training and deployment tools to enterprises.
Second-order effects
- Altimeter doubles down and Tiger Global joins: DataRobot raises another ~$250M at a ~$6B pre-money valuation in June 2021, then a $300M Series G at a $6.3B post-money valuation weeks later — the valuation tripling in under a year.
Third-order effects
- That peak mark becomes a liability: senior executives quietly sell about $32M of stock at the reported $6.3B valuation, triggering employee uproar, the resignations of CEO Dan Wright in July 2022 and then the CFO and other executives — a governance failure mode specific to fast-repriced private AI companies.
- If the pattern holds, late-stage AI valuations set in frothy 2020–2021 rounds become internal political fault lines whenever insiders monetize ahead of employees, pressuring boards to formalize secondary-sale rules.
The trend: Enterprise ML platforms rode the 2018–2021 late-stage capital wave to ever-larger rounds at rapidly compounding valuations, with insider liquidity at peak marks emerging as the cycle's recurring flashpoint.