From $7B/day trader to 24-person startup: Databento's $97M raise

Christina Qi, the former high-frequency trading maven who once traded $7.1 billion a day through her hedge fund Domeyard LP, has just secured a $97 million Series B for Databento, a market data infrastructure startup she runs from a farm in Utah. The round, led by NEA with participation from DRW Venture Capital, Redpoint Ventures, and Tribe Capital, attracted over $300 million in demand—a testament to the acute industry pain point Databento aims to solve. For decades, institutional market data has been dominated by a handful of legacy providers, most notably Bloomberg, whose terminals cost upwards of $20,000 to $27,000 per seat per year and require exhaustive procurement processes. Qi herself recalls sending over 100 emails over 11 months just to get sample data from the world's largest provider, which arrived on a thumb drive via snail mail. Databento flips this model entirely: customers can browse and purchase market data—like Microsoft stock information—in a shopping cart, pay only for what they use via API, and start consuming it in minutes. Already profitable with a lean team of 24, the company operates servers co-located directly inside stock exchanges and has secured over 100 petabytes of additional storage, plans to expand from its current footprint to more than 20 data centers globally. The $97 million brings Databento's total disclosed funding to around $127 million, yet Qi admits the company has barely touched the new capital, as investors urge her to spend faster. By partnering with Nvidia and OpenAI, Databento is positioning its data pipeline to serve AI-driven quantitative analytics, potentially making it an indispensable utility for the next generation of algorithmic trading, risk modeling, and liquidity analysis. This funding event signals a broader trend: the unbundling of Bloomberg’s monolithic terminal bundle, the rise of API-first infrastructure in finance, and the growing acceptance that mission-critical market data can be delivered as a cloud-native, pay-as-you-go service. The implications are far-reaching; if Databento achieves its scale ambitions, it could democratize access to high-quality financial data, accelerate fintech innovation, and pressure legacy vendors to modernize their pricing and distribution models. The road ahead is not without challenge: incumbents enjoy deep institutional relationships, regulatory licensing moats, and brand trust that will not erode overnight. However, Qi’s track record of executing at the highest levels of trading, combined with the company's efficient unit economics and overwhelming investor demand, positions Databento as perhaps the most credible threat to Bloomberg’s data hegemony in a generation.