Databento
Provides real-time and historical market data APIs for equities, futures, options, and indices with one integration.
Website: https://databento.com/
Cover Block
From the public record
| Field | Value |
|---|---|
| Name | Databento |
| Tagline | Provides real-time and historical market data APIs for equities, futures, options, and indices with one integration [databento.com] |
| Headquarters | Salt Lake City, Utah [Crunchbase] |
| Founded | 2019 [Crunchbase] |
| Stage | Series C [Crunchbase] |
| Business model | API / Developer Platform |
| Industry | Fintech |
| Technology | Software (Non-AI) |
| Geography | Global / Remote-first |
| Growth profile | Venture Scale |
| Founding team | Co-Founders (2), Christina Qi and Andrew Spellman [Crunchbase] |
| Funding label | $100M+ |
| Total disclosed funding | ~$227,000,000 [Crunchbase, retrieved 2026] |
Links
From the public record
- Website: https://databento.com/
- LinkedIn: https://www.linkedin.com/company/databento
The Short Version
PUBLIC Databento provides real-time and historical market data APIs across equities, futures, options, and indices, and it merits investor attention because it has paired a developer-first distribution model with a large late-stage financing cycle in a market that has long been dominated by slower, higher-friction incumbents [Databento, retrieved 2026] [Crunchbase, retrieved 2026] [Hoodline, November 2026]. Founded in 2019 in Salt Lake City, the company is positioning itself around a simple premise: one integration for exchange-sourced market data delivered through APIs, flat files, and raw packet capture, rather than requiring customers to stitch together multiple vendors and infrastructure layers [Databento, retrieved 2026] [FIA MarketVoice, November 2026].
The differentiation, based on public evidence, appears to rest less on a novel asset class and more on delivery architecture and customer experience, including self-serve access, normalized data formats, and exchange coverage that Databento and third-party coverage place at more than 60 venues, with some sources citing more than 70 exchanges [Databento, retrieved 2026] [Crunchbase, retrieved 2026] [Wilmott, October 2026]. That matters because market data has historically been expensive, operationally fragmented, and hard for smaller firms to consume; Databento's usage-based historical pricing and plan-based real-time offering suggest an effort to widen the buyer pool beyond traditional large trading shops [Databento, retrieved 2026].
The founding team brings unusually direct domain exposure for this category. Public sources identify Christina Qi, co-founder and CEO, as a former partner at Domeyard and an MIT alumna, while Andrew Spellman is named as co-founder; together, that gives the company credibility with technical trading customers, even if the public record is still thinner on the commercial bench below the founder level [MIT Sloan, September 2026] [Forbes, 2019] [Databento, retrieved 2026].
On capital formation, the company has disclosed or been reported as having raised roughly $227 million across a $27 million Series A in 2021, a $10 million Series A extension in October 2024, a $97 million Series B led by NEA in July 2026, and a $100 million Series C led by Fifth Down Capital at a reported $1.1 billion valuation in November 2026 [Databento, Nov 2021] [Databento, Oct 2024] [SiliconANGLE, July 2026] [Hoodline, November 2026]. The business model is straightforward but potentially attractive if execution holds: sell market data infrastructure through APIs on a self-serve and enterprise basis, with public reporting indicating a customer footprint of more than 3,000 companies and a team of 39 at the time of the Series C, though those operating metrics are not yet broadly corroborated across multiple independent sources [Wilmott, October 2026] [Hoodline, November 2026].
Over the next 12 to 18 months, the main items to watch are whether Databento can turn exchange coverage and developer goodwill into deeper enterprise standardization, whether customer concentration emerges as it moves upmarket, and whether a small team can sustain service quality while expanding globally and supporting more demanding latency-sensitive workloads [Wilmott, October 2026] [Databento, retrieved 2026]. The recent financing gives the company room to press that case, but the public evidence still leans heavily on company and announcement-based sourcing for product claims, so the operating proof is promising rather than fully established.
Single-source, plausible -- This section combines independent coverage from Crunchbase, MIT Sloan, Hoodline, SiliconANGLE, and Wilmott with company-published materials for product and pricing claims.
Taxonomy Snapshot
| Axis | Value |
|---|---|
| Stage | Series C |
| Business Model | API / Developer Platform |
| Industry / Vertical | Fintech |
| Technology Type | Software (Non-AI) |
| Geography | Global / Remote-First |
| Growth Profile | Venture Scale |
| Founding Team | Co-Founders (2) |
| Funding | $100M+ total disclosed, approximately $227,000,000 |
The Company in Brief
PUBLIC
Databento is a Salt Lake City, Utah-based market-data infrastructure company founded in 2019 by Christina Qi and Andrew Spellman [Crunchbase] [databento.com]. The company describes its core product in plain terms: real-time and historical market data APIs, with access across equities, futures, options, and indices through one integration [databento.com]. That positioning matters because the market-data stack is usually fragmented by asset class, venue, and delivery format, and Databento has framed itself around simplifying that complexity from the outset [databento.com].
The public record shows a business that has scaled in discrete funding steps rather than through a long list of acquisitions or corporate restructurings. Crunchbase and the company website indicate an initial Series A in November 2021, followed by an additional $10 million Series A financing announced in October 2024, which the company said brought total Series A funding to $30 million [databento.com] [Crunchbase]. Public company materials and Crunchbase also list later institutional backing from firms including New Enterprise Associates, DRW Venture Capital, Redpoint Ventures, Tribe Capital, and Fifth Down Capital, placing Databento in the venture-scale category even if the legal entity details are not surfaced in the cited sources here [Crunchbase] [databento.com].
Single-source, plausible -- Confirmed with company website and Crunchbase; legal entity detail was not established in the cited sources used for this section.
What They Have Built
MIXED
Databento is selling market data as infrastructure rather than as a traditional terminal or managed feed relationship. Public materials describe real-time and historical market data APIs for equities, futures, options, and indices, with delivery options that include APIs, flat-file downloads, and raw packet capture [databento.com, retrieved 2026]. The product pitch is a single integration across asset classes and venues, which the company says now spans more than 60 trading venues as a licensed distributor and direct provider of market data [databento.com, retrieved 2026] [Crunchbase, retrieved 2026].
The technical claim that matters most is normalization at the market-structure layer. In founder interviews and industry coverage, Databento describes rebuilding market-data systems from the hardware up, with its own servers colocated inside exchanges and feed handlers for more than 70 exchanges, so customers can consume venue data in a unified format instead of managing separate exchange-specific schemas [Fortt Knox, 2026] [sCommerce, retrieved 2026]. A 2026 interview summarized that approach as putting market data into one common language across books and venues, which is directionally consistent with the company website's one-integration message [Sloanies Talking with Sloanies, 2026] [databento.com, retrieved 2026].
Commercially, the product appears designed for self-serve adoption first, then enterprise expansion where needed. Databento has said historical data is priced by data consumed, while real-time access sits behind tiered plans, and public interviews describe a self-serve buying motion rather than a field-sales-led model [databento.com, retrieved 2026] [Fortt Knox, 2026]. Some of the stronger performance claims, including a 6.1 microsecond median gateway latency and the assertion that it is now a provider for AI and agentic workflows, rest primarily on company statements, so they are better read as positioning than as independently verified product benchmarks [databento.com, retrieved 2026].
Unconfirmed -- Core product scope is corroborated by the company website and secondary databases, but several material technology and performance claims in this section rely on company statements or interview coverage.
Market Size and Demand
PUBLIC
The market matters now because Databento sits at the junction of three durable spend pools, exchange market data, developer infrastructure, and institutional trading software, at a moment when buyers want lower-friction access to cleaner data rather than another terminal seat [Fortune, July 2026] [FIA MarketVoice, November 2026].
Public source material does not provide a direct third-party TAM, SAM, or SOM for Databento’s exact category, so the cleaner way to size the opportunity is through analogous markets rather than invented precision. Fortune framed Databento’s ambition against Bloomberg and LSEG, which places the company in the broader financial data and workflow stack rather than in a narrow API niche alone [Fortune, July 2026]. FIA MarketVoice and Wilmott describe a customer set that spans quantitative trading firms, brokerages, asset managers, digital-asset companies, and other institutional users, which suggests the reachable market is defined by firms that need exchange-sourced data programmatically, not only by traditional market-data desks [FIA MarketVoice, November 2026] [Wilmott, October 2026].
The demand signal in the public record is less about category size than about workload shift. Databento’s product pitch, real-time and historical market data through one API, flat-file downloads, and raw packet capture, maps to a broader move inside capital markets toward self-serve infrastructure and software-defined research and execution workflows [Databento] [FIA MarketVoice, November 2026]. That matters because a buyer who previously assembled exchange entitlements, feed handlers, storage, and normalization internally can now compare that stack against an external API platform on speed of onboarding and engineering cost, not just on raw data access [Fortt Knox, 2026] [Sloanies Talking with Sloanies, 2026].
A second tailwind is the widening buyer base for structured market data. Wilmott reported that Databento supports more than 3,000 companies worldwide and covers more than 20 petabytes of raw market data across more than 70 exchanges, while Hoodline and the company tied recent financing to demand from frontier AI labs as well as traditional trading customers [Wilmott, October 2026] [Hoodline, November 2026] [Databento]. The AI angle should be treated cautiously because the named customers are not disclosed, but the underlying point is credible: machine-driven research, backtesting, and agentic finance tools all increase the value of normalized historical and real-time datasets even when the end use is not high-frequency trading [Hoodline, November 2026] [Databento].
The closest adjacent and substitute markets are established terminals and enterprise data platforms on one side, and internal buildouts or direct exchange relationships on the other. Fortune explicitly positioned Databento as competing with incumbent financial-information providers, while FIA MarketVoice emphasized the firm’s direct-from-colocation architecture and packet-level delivery, which are more relevant to technical users than to classic desktop terminal buyers [Fortune, July 2026] [FIA MarketVoice, November 2026]. That split is important because it implies Databento does not need to replace a full terminal workflow to win budget, it can instead displace pieces of in-house market-data infrastructure or become the programmatic layer sitting beside incumbent desktops [Fortt Knox, 2026].
Regulation and market structure are part of the setup, even if the available sources stop short of forecasting their impact. Market data remains shaped by exchange licensing, redistribution rules, and entitlement complexity, which creates both a moat and a constraint for vendors that want to aggregate feeds at scale [Databento] [Crunchbase]. The same factors that make this a sticky market also limit how quickly newer providers can expand venue coverage, compress pricing, or simplify contracting, so execution in this category tends to depend on operational discipline as much as on product design [FIA MarketVoice, November 2026] [Wilmott, October 2026].
| Analogous market framing | What the public sources support | Relevance to Databento |
|---|---|---|
| Financial data and workflow incumbents | Databento has been described as building against providers such as Bloomberg and LSEG [Fortune, July 2026] | Suggests budget can come from large enterprise data and workflow spend, not only niche API budgets |
| Institutional trading infrastructure | Customer categories include quantitative trading firms, brokerages, asset managers, and commodities firms [FIA MarketVoice, November 2026] [Wilmott, October 2026] | Supports a SAM centered on professional firms that need exchange-sourced data programmatically |
| AI and data infrastructure buyers | The company said it signed enterprise contracts with five of the six largest frontier AI labs [Databento] [Hoodline, November 2026] | Points to adjacent demand from model training, evaluation, and agentic workflow builders |
The practical read is that this is a broad but uneven market. The addressable spend is likely large by analogy, but the near-term capture pool depends less on headline market size than on whether Databento can keep converting technically sophisticated users who care about latency, coverage, normalized schemas, and self-serve access.
Single-source, plausible -- This section relies on named public sources including Fortune, FIA MarketVoice, Wilmott, Hoodline, Crunchbase, and Databento, but it uses analogous market framing rather than a directly cited third-party TAM report.
Who Else Is Fighting for This
Competitive Analysis
MIXED Databento is positioned as an API-first market-data provider that sits between legacy terminal and feed vendors on one side, and do-it-yourself exchange connectivity on the other [databento.com, retrieved 2026] [Crunchbase, retrieved 2026].
The competitive map is easiest to read in layers. At the incumbent end, Bloomberg and LSEG remain the broadest reference points in public coverage because they combine extensive data rights, brand trust, and deep institutional distribution, even if the available source set here does not establish direct product parity field by field [Fortune, July 2026]. At the other end are internal build paths used by sophisticated trading firms, where teams buy exchange feeds directly and run their own handlers, storage, and normalization stack. Databento's pitch is that one integration can replace part of that assembly burden across equities, futures, options, and indices, with delivery through APIs, flat files, and packet capture [databento.com, retrieved 2026] [FIA MarketVoice, November 2026].
That leaves Databento competing less as a full terminal substitute than as infrastructure. Public evidence points to two practical wedges. First, the company says it is a licensed distributor and direct provider for more than 60 trading venues, while separate coverage describes feed handlers for more than 70 exchanges and infrastructure spanning more than 20 petabytes of raw market data [databento.com, retrieved 2026] [Wilmott, October 2026] [Crunchbase, retrieved 2026] [sCommerce, retrieved 2026]. Second, its go-to-market appears unusually self-serve for a category that has historically depended on sales-heavy enterprise relationships, with Christina Qi describing a model built around programmatic access rather than traditional vendor packaging [Fortt Knox, 2026] [databento.com, retrieved 2026]. That edge is real if developer adoption keeps compounding, but it is also perishable because data rights and exchange relationships are expensive to maintain, and the larger vendors already own long-standing procurement channels.
The company is most exposed where breadth and bundled workflow matter more than clean API delivery. Bloomberg and LSEG can absorb pricing pressure, cross-sell into desktop and analytics workflows, and defend strategic accounts through contracts that span far beyond market data alone, according to the way Fortune framed Databento's ambition to "rival Bloomberg" [Fortune, July 2026]. Databento also does not appear, from the public record here, to own the primary user interface layer that many discretionary market participants still prefer. If the buyer wants a full research, execution, compliance, and desktop environment, the evidence in this source set does not suggest Databento is trying to win that category [databento.com, retrieved 2026] [Fortune, July 2026].
The most plausible 18-month scenario is a continued split between infrastructure winners and workflow incumbents. Bloomberg looks like the winner if bundled desktop distribution remains the center of gravity for large institutional budgets, because Databento's API-led motion does not obviously dislodge that installed base from the evidence available here [Fortune, July 2026]. The more exposed player, if self-serve developer procurement keeps gaining share in quantitative, brokerage, and AI-oriented use cases, is the in-house build path itself: fewer firms will want to recreate exchange connectivity and normalization if Databento can keep offering broad venue coverage with a small team and one integration surface [Wilmott, October 2026] [databento.com, retrieved 2026] [FIA MarketVoice, November 2026].
Single-source, plausible -- This section relies on public company materials plus independent coverage from Fortune, Wilmott, FIA MarketVoice, Crunchbase, and sCommerce, but named direct competitors beyond Bloomberg and LSEG are only partially established in the source set.
Opportunity
PUBLIC
The prize here is unusually large because market data sits upstream of trading, brokerage, asset management, and a growing set of AI workflows, so the company that makes that data programmable and easy to buy can become infrastructure rather than a point solution [Databento, retrieved 2026] [Wilmott, October 2026].
The headline opportunity is straightforward: Databento could become the default developer-facing market data layer for firms that do not want to stitch together exchange licenses, multiple vendors, and their own normalization stack [Databento, retrieved 2026] [FIA MarketVoice, November 2026]. That outcome is reachable, not merely aspirational, because the public record already shows three ingredients that usually have to be built separately: broad venue coverage, with 60-plus trading venues cited by the company and 70-plus exchanges referenced in outside coverage; a product surface that spans real-time APIs, historical APIs, flat files, and raw packet capture; and evidence that sophisticated buyers are willing to underwrite the model, including NEA leading the July 2026 Series B and Fifth Down Capital leading the November 2026 Series C [Databento, retrieved 2026] [Crunchbase, retrieved 2026] [Wilmott, October 2026] [Hoodline, November 2026]. The small-team profile matters too. Public reporting tied the November 2026 Series C to 39 employees, which suggests the company may be scaling distribution and infrastructure with relatively high operating use if customer adoption continues [Hoodline, November 2026].
The upside paths are easier to see when broken into a few concrete routes.
| Scenario | What happens | Catalyst | Why it's plausible |
|---|---|---|---|
| API standard for quant and fintech builders | Databento becomes the default first integration for startups, brokerages, and internal developer teams that need normalized market data across asset classes | Continued self-serve adoption around a single API, usage-based historical pricing, and broader venue coverage [Databento, retrieved 2026] | The company already positions around one integration for equities, futures, options, and indices, and outside coverage describes the wedge as operational simplicity versus fragmented incumbent workflows [Databento, retrieved 2026] [FIA MarketVoice, November 2026] |
| Enterprise backbone for institutions modernizing data stacks | Larger trading firms and asset managers use Databento as a primary feed for selected workflows, then expand across desks and geographies | More enterprise references after the NEA-led Series B and continued board support from Rick Yang and Danielle Lay [SiliconANGLE, July 2026] [Crunchbase, retrieved 2026] | Public reporting says an unnamed major quantitative hedge fund selected Databento as its primary options-market-data provider, which is a stronger signal than a generic pilot because it points to production use in a demanding segment [FIA MarketVoice, November 2026] |
| Data supplier for AI-native finance products | Databento becomes the market-data layer used by AI labs and AI-native financial applications that need structured historical and real-time inputs | More enterprise contracts with frontier AI labs and product packaging tailored to machine-driven research and execution [Databento, retrieved 2026] | The company said it had signed enterprise contracts with five of the six largest frontier AI labs, and public reporting also frames AI workflows as a new buyer class for the platform, even if the named customers are not disclosed [Databento, retrieved 2026] [Hoodline, November 2026] |
The compounding mechanism is not a classic consumer network effect. It is a supply-and-distribution flywheel built on integration effort, normalization, and trust. Each additional venue and feed handler makes the API more useful to developers who want one schema across fragmented markets; each additional customer segment, from quant firms to brokerages to AI buyers, improves the return on maintaining that ingestion layer; and each new enterprise deployment can widen the historical corpus and product packaging sold through the same platform [Databento, retrieved 2026] [sCommerce, retrieved 2026] [Wilmott, October 2026]. There are early signs this flywheel is already turning. Outside coverage describes more than 20 petabytes of raw market data, support for more than 3,000 companies, and a self-serve distribution model that reduces dependence on a large sales force [Wilmott, October 2026] [Fortt Knox, 2026]. If that mix holds, one product improvement can propagate across many cohorts without a linear increase in headcount.
The size of the win is best framed against the valuation datapoint already on the tape. Databento's November 2026 Series C was reported at a $1.1 billion valuation, after a $97 million Series B only months earlier [Hoodline, November 2026] [Ventureburn, November 2026]. If the company executes on the enterprise-backbone or AI-data-layer scenarios above, the upside case is not simply adding more customers, it is moving from a funded infrastructure startup to a durable control point in financial-data delivery. That could support a business worth several multiples of the last round valuation, meaning a multi-billion-dollar outcome from here (scenario, not a forecast), particularly if market data spend continues to shift toward API-native and usage-based consumption rather than bundled terminal-style procurement [Databento, retrieved 2026] [FIA MarketVoice, November 2026]. The public evidence is still too thin to anchor that to revenue multiples with precision, but the combination of deep infrastructure, broad venue coverage, and signs of institutional adoption makes the upside legible rather than speculative.
Single-source, plausible -- Section relies on a mix of company materials and single-source publisher reporting, with key opportunity claims partially corroborated by Crunchbase, Wilmott, Hoodline, FIA MarketVoice, and the company website.
Sources
From the public record
[databento.com, retrieved 2026] Databento | Market data APIs and solutions for every firm | https://databento.com/
[Crunchbase, retrieved 2026] Databento | Crunchbase Company Profile & Funding | https://www.crunchbase.com/organization/databento
[Hoodline, November 2026] Databento Raises $100M Series C at $1.1B Valuation | https://hoodline.com/2026/11/salt-lake-city-s-databento-hits-1-1b-valuation-with-100m-round-just-months-after-series-b/
[FIA MarketVoice, November 2026] Innovators Pavilion 2021 - Where are they now? | https://www.fia.org/marketvoice/articles/innovators-pavilion-2021-where-are-they-now
[Wilmott, October 2026] Databento Raises $97M Series B to Expand Market Data Platform | https://www.wilmott.com/databento-raises-97m-series-b-to-expand-market-data-platform/
[MIT Sloan, September 2026] Christina Qi, SB ’13 | https://mitsloan.mit.edu/alumni/christina-qi-sb-13
[Forbes, 2019] Christina Qi | Forbes | https://www.forbes.com/profile/christina-qi/
[Databento, Nov 2021] Databento raises $27M Series A to continue democratizing market data | https://databento.com/blog/announcing-27m-series-a
[Databento, Oct 2024] Databento raises $10M in Series A+ to drive global expansion | Databento Blog | https://databento.com/blog/databento-10m-additional-funding-2024
[SiliconANGLE, July 2026] Market data platform startup Databento closes $97M round after drawing $300M in investor demand | https://siliconangle.com/2026/07/09/market-data-platform-startup-databento-closes-97m-round-drawing-300m-investor-demand/
[Fortune, July 2026] Christina Qi left behind a hedge fund trading $7 billion a day for a farm in Utah. Her new startup just raised $97 million to rival Bloomberg | Fortune | https://fortune.com/2026/07/09/christina-qi-left-a-hedge-fund-trading-7-billion-a-day-for-a-farm-in-utah-startup-raised-97-million-to-rival-bloomberg-lseg-databento/
[Databento] Databento | Market data APIs and solutions for every firm | https://databento.com/
[Fortt Knox, 2026] Christina Qi on Databento and rebuilding market data infrastructure | https://fortt.media/knox
[sCommerce, retrieved 2026] Databento company profile | https://www.scommerce.com/company/databento
[Ventureburn, November 2026] Databento Raises $100 Million at $1.1B Valuation | https://ventureburn.com/databento-raises-100-million-series-c-expansion/
Articles about Databento
- Databento's $100 Million Series C Backs a Bet on Self-Serve Market Data — The seven-year-old startup, now valued at $1.1 billion, is betting a developer-first model can upend how quants and AI labs buy financial data.