Ex-hedge fund trader raises $97M to make Wall Street data cheaper

Databento occupies a narrow but valuable gap in financial infrastructure. The company's founder built and then exited from Domeyard LP, a high-frequency trading hedge fund, and applied that operational expertise to a frustration she observed firsthand: the data supply chain for financial markets is both expensive and inefficient. The problem is real—Qi recalls spending more than 100 emails over 11 months just to procure data from Bloomberg, with sample data arriving on a thumb drive via snail mail. Her technical edge is twofold: specialized chips that capture and clean raw order-level data from 80-plus venues in real-time, and the ability to deliver that data over standard internet infrastructure rather than proprietary networks. That combination has attracted a diverse customer base spanning institutional buyers (who value full market coverage) and emerging segments like AI labs and students (who value affordability and low minimum orders).

Databento's capital raise and continued profitability at a small team size ($97 million（約160億円） raised, only $127 million（約200億円） total disclosed funding, 24 employees, and still profitable each month) suggest the unit economics are favorable—demand is strong enough that the company is reinvesting rather than spending aggressively to grow headcount. The planned expansion to 20+ data centers and a doubling of storage capacity indicates the company is moving from a niche player serving exchanges and a few large customers to a platform-scale business. The stated goal is to reach "way past a billion" in value, a claim that will be tested against the historical pattern Qi herself acknowledges: most scrappy data startups have been acquired before scaling to generational size (MayStreet was acquired by LSEG in 2022; BML was acquired at Series B for around $250 million（約400億円）).