theCUBE + NYSE Wired: The FinTech Exchange | Christina Qi, Databento

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play_circle_outline Machine-First Normalized Global Exchange Data API for Quants, Traders, Institutions, Fintechs, and AI Labs

play_circle_outline Infrastructure: bare-metal servers, colocation, ~50–60 global physical venues

play_circle_outline High customer retention, limited competition due to technical infrastructure expertise

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Christina Qi, Databento

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Gemma Allen

>> Palo Alto Studio Connection, Silicon Valley and Wall Street.

Christina Qi

>> I'm John Furrier, co-hosting with David Vellante, my co-host.

Gemma Allen

>> Welcome to theCUBE Studio here at the New York Stock Exchange. I'm Gemma Allen, co-host of NYSE Wired Fintech Exchange, and we talk constantly about AI transforming finance. But there is a much less sexy question underneath all of it. What data are you actually feeding the machine? Because the stock closing at $102 tells you almost nothing about what happened on the way there. Who was buying? Who pulled the order? How deep was the market? What happened in the milliseconds before that price moved? Joining me now to have a conversation about how that infrastructure is being built is Christina Qi, co-founder and CEO at Databento. Welcome, Christina.

Christina Qi

>> Thanks for having me.

Gemma Allen

>> So help me understand Databento. First of all, I love the name. It certainly makes sense to me. I was just planning bento boxes for my children this week. But help me understand what it is that you guys are actually building.

Christina Qi

>> So we strive to be the single source of truth for financial information. So we're a financial market data API provider is what we do in a nutshell. We aggregate financial stock price information from various exchanges or venues across the globe, including the NYSE. And then we normalize that data and distribute that to currently about 100,000 or so users around the world.

Gemma Allen

>> Wow. Okay. So help me understand who is using that data, for what purpose? Are you creating data for quant traders, for, I'm guessing, probably not retail customers, right? I assume that it's probably quite a technical interaction. Bring it to life a little bit. Who is the key customer? Who did you build this with in mind?

Christina Qi

>> So we actually originally built this product for quantitative traders. It could actually be for anybody, honestly. So we do have a lot of retail traders who use the product. We have students who use the product, people for the first time who have never actually touched a quantitative, maybe not, maybe don't even know how to code actually, who can use the product as well. But then also people who are very sophisticated, including some of the largest financial institutions in the world who use the product as well. So it does range very greatly between the different types of users. And I think recently what surprised us is that we do have a new type of user that uses the product. We have a lot of AI labs that have also picked up on the product. So yeah, I think that's probably been a bigger surprise for us is that we didn't design the product originally for that type of category, but that has been a big, like, I guess, like tailwind for us in recent times. Yeah.

Gemma Allen

>> So give me a scenario here. You are a quant trader and you want to understand all— I'm looking at the board here— NVIDIA stock buys between 9:30 and 10 a.m., right? And information isn't necessarily easily ingestible. You guys can actually kind of solve for scenarios like that, bring it to life. How does it compare to the world of the Bloomberg Terminal, for example, that we're all so familiar with here on Wall Street?

Christina Qi

>> For sure. So the difference and the reason why we started Databento to begin with is actually because I ran a hedge fund before Databento, and when we were dealing with the previous generation of data providers, we noticed that they were designed for human consumption, essentially. So they were designed around a human staring at a terminal-like product. And we noticed that even today, most data is being consumed by machines, not by humans. And so we wanted to design a data provider that was meant for machine consumption, not for human consumption. And that way we could basically help be the data backbone of any kind of product and help other products launch faster, go to market faster. You can build products on top of us. So think of us as like we're designed with the builder in mind. Rather than for an end user. So, if you pull up a stock price on an app today, like any app on your phone, that data might come from us already. So that's also what we do as well. We do serve a lot of fintech startups that use us to build apps and products on top of us. Yeah.

Gemma Allen

>> And if you're any sort of enthusiast and you pull up data on your phone of the NVIDIA stock price, right, you don't necessarily get the underlying data or the story or the structure, right? What brought it there? What that kind of broader story looks like. Is a lot of this around kind of piecing together that trajectory, like that broader story? And where is the value in that data? Where is that realized?

Christina Qi

>> Yeah, yeah. So what's interesting is we decided to hyperfocus on just what's called market data, which is just the financial stock price data instead of alternative data, which would be like, let's say satellite data on the parking lots or shipment data or earnings calls or things like that that are more broad that might be surrounding that stock price, where it might go or where it could head, stuff like that. And the reason why we decided to focus on just market data is just because, one, everybody needs market data. Every company needs that within our industry in order to basically survive. We've even had, you know, we have a lot of customers who have invested in our company, for example, or want to invest in our company because they're like, if you die, we die. So market data is like a critical need for a lot of these companies. Whereas if you branch into alternative data, it's just less critical of a need for these guys. And so it's harder to sell alternative data and convince companies that they need that type of data. And so it just came purely down to a preference on what do we want to focus on? And plus, my specialty is more in the market data realm. Market data, not saying that alternative data is not important, it's just that that's just what we chose to focus on.

Gemma Allen

>> Yeah. So maybe this is, and I'm sure it is, a false assumption, but in this world of AI where we're hearing more and more about data accessibility, about the democratization of data and information, I would have assumed that a lot of large quant houses are building their own pipes, building their own platforms. What is actually happening though? Because clearly there's a real market need for this.

Gemma Allen

>> Yeah.

Gemma Allen

>> what sorts of conversations are you having with the buyers across those industries?

Christina Qi

>> Yeah, so we were surprised by the AI Labs using us to begin with. I think, the reason why they're using our product, one is that we're a product that AI can't replicate very easily because to give you guys a sense of maybe the differences, we were not just simply reselling data. We actually rebuilt the entire stack from the ground up, all the infrastructure behind the data as well. So that comes down to even things like the bare metal servers, the colocation, all that infrastructure we actually built— rebuilt, I guess, from the ground up. And so as a result of that, because it is a lot of hardware behind the scenes, you can't just vibe code that kind of company from the ground up. And so as a result of that, a lot of these AI labs end up using us as customers actually, rather than just doing it themselves, just because it's so much easier to use us from the perspective of a customer.

Gemma Allen

>> So, yeah, that is fascinating, and that is not something I would have guessed when I looked at this company. So you're not necessarily another cloud license spend on your end, or even a Frontier Labs spend, it's something much deeper, right? So talk me through the company, what's the footprint like then? How many of you guys, where do you exist? Right. What does Databento's actual hardware/software footprint look like?

Christina Qi

>> Yeah, so we are co-located currently at various venues kind of globally. We've signed across, I think, about 15 different venues currently globally and over 50 different physical locations. But in terms of actual venues or so, it's probably going to be about 60 different locations or 60 different venues, sorry, globally. So hopefully that will give us coverage across the US, Europe, and Asia in the, let's say, near-ish future. So that's something that we're really excited about just in terms of our roadmap. Currently, we only have US coverage as well as a little bit of European coverage. So what's fascinating is that we do have about over 100,000 users on Databento right now, but our product is incomplete, meaning that we only have a limited number of datasets. And the more global coverage we have, the more likely we are to actually gain more customers, because a lot of customers need global coverage in order to switch over to us as their primary data source.

Gemma Allen

>> So, and what is the kind of chicken and egg problem for the datasets? Like, where is that information aggregated from? How do you collect and build on that?

Christina Qi

>> Yeah, so it's aggregated from these stock exchanges. Yeah, like the NYSE, where we have to sign licenses to redistribute this data. We do have to pay for that license as well as aggregate historical data as well. So there is an initial fee, or we have to pay that cost to be able to get all that historical data dump to come in. We have to also have the servers to be able to capture that data, to be able to store that data as well. So it is quite costly to be able to do that initially. So we think of it almost like an investment. Thankfully, one thing that's really nice is that we do have a lot of customers who are willing to pay us in advance now to be like, hey, if you go out to this venue, we'll pay you in advance as a day one customer so that once you have that data, you'll have a day one customer to be willing to buy the data from you. So that eliminates that risk. For us.

Gemma Allen

>> And that type of market conversation also shows the appetite and the need, right, that there is in ensuring that you do have data access in the right places and at the right endpoints 5 years from now. So talk me through the tech here. It sounds like you guys are building something pretty unique. How proprietary is everything you're building? You mentioned the frontier labs, they're customers of yours. Are you a customer of theirs? How is this build happening, especially at the speed you are alluding to there?

Christina Qi

>> Yeah, the tech behind the scenes is quite complex actually. That's why, I guess that's why we have so many customers across those different industries. And it's also why there's not a lot of, it's interesting because I guess the best way to put it is there's a reason why we were, by a lot of people in the VC world, described as a meme stock in the VC world in the sense that we raised a round of funding recently, but we weren't trying to fundraise. The VCs came over to us and they're begging basically to invest in us. And we were trying to figure out why, and it was actually because we were one of the few non-AI companies that was doing very well. And we were like, wait, there's so few non-AI companies really? What's going on? Um, it turns out that there's just not a lot of good non-AI companies. And then even comparing us to an AI company, in AI there's just so much competition in the startup space right now. There's just so many AI companies and the competition's really fierce. But in the market data space, there's not as much competition. And a lot of it's just because there's not a lot of people who have the knowledge to be able to build that infrastructure from the ground up. And then also, if you think about Databento, we have really good retention of customers as a result of that, just 'cause there's not a lot of other alternatives to really go to as a result. A lot of the startups in this space are more focused on retail, which is also a really great market, by the way. There's a lot of really great providers. Usually when a retail company or retail trader, when they're ready to take it to the next level, they'll usually switch over to Databento when they're ready for a more powerful solution. And so, that's who we usually cater to, is when someone's ready for something just slightly more powerful.

Gemma Allen

>> that is a fantastic problem to have, right? VCs coming to you trying to throw money at you. How much did you raise? What stage are you guys

Christina Qi

>> at?Yeah, so we raised a $97 million Series B, and that was led by NEA. Okay. Yeah, and that was— that closed, just earlier this year. Yeah. And so to give you a sense of why, we actually— we didn't need to raise the funding, we actually broke even earlier this year. So thankfully we're in a strong financial position, which is kind of rare for startups these days. I feel like a lot of startups are losing a lot, burning cash, losing money. Thankfully we were pretty good about how we spend. And so thankfully we're in a good position. But we figured our product is incomplete actually. So we were like, let's take the money because we do need to build out that data center presence globally. And we do want to complete that product. And the other thing we thought about was also a lot of data companies at the Series B stage in our industry, they tend to exit, by the way. They tend to get acquired around the Series B stage. We decided not to. We were like, let's continue building because we feel like we're still at the start line of our product. We haven't run the race yet just because our product is incomplete. So we're like, let's build out the product. And I feel like our customers would be so disappointed in us if we sold the company at this stage.

Gemma Allen

>> So I love that. And I love to see a female founder as well fighting the fight. And I have to tell you, it's fantastic. So I want to go back to something you said though, which is, we're not an AI company, right? That's an interesting line to lead with in 2026 because like you said, everyone is now an AI company, right? But when we boil that down, what exactly do you mean by that? Because I'm sure there is a lot of AI built into your technology, right? And especially as it relates to discoverability and data accessibility and all of those things. So how do you differentiate between what you just described there and an AI-native SaaS startup, let's say, or any type of startup.

Christina Qi

>> Yeah, that's actually true, right? For example, most of our doc site is being read by AI today, and that's something that we have to be aware of. Whereas for a lot of the incumbents, their entire doc site is offline. It's like a PDF or it's an attachment. They have to email you the docs. For us, we're like, we recognize that AI is the primary consumer of our documentation. And so we're like, let's put it online. Not only is it online, but let's make it AI-friendly so that an AI agent can read that instead. So that makes it easier for our end user. So we're trying to do things to make it easier for our customers behind the scenes so that when our customers do choose to use AI, their life will be easier, right? That being said, our product is not an AI product, meaning we're not building agents ourselves or we're just not ourselves in the AI industry. We're not actually consuming tokens ourselves, which is really hilarious.

Gemma Allen

>> That is

Christina Qi

>> fascinating.Yeah, we're not a company that consumes tokens behind the scenes or anything. It was funny because we actually did a startup accelerator, and during the accelerator, pretty much almost all the other startups in the accelerator were AI companies, and they're talking about tokens. And I remember I was the dumb one in the, like, the dumb founder. I was like, what's a token? And they were like, they couldn't believe that I was the only one that didn't know what a token was at the time, because that was how out of the loop I was in terms of the AI space, because we've been building this far along without ever consuming a token.

Gemma Allen

>> That is absolutely— I have to say, from where I'm sitting honestly, you're truly unique, right? you're probably the only person who has said that, maybe to me, anything ever. And I kind of love it, to be honest. Maybe the traditionalist in me is like, go, Christina. But how are you monetizing this? What's the business model here? we're very familiar with the world of Bloomberg terminals and how extremely lucrative that life is for many. How do you make money off the back of this?

Christina Qi

>> Yeah. So we have two different types of models. When most users come in, they want to try it before they buy it, right? And in our industry, there's a common saying, data is bought, not sold. I'm not going to cold call a leader in this industry. I'm not going to call Ray Dalio and be like, did you know you need data? And then he's not going to be like, yes, I need data. that's so unrealistic of a scenario. And so instead, what happens is when someone on the team needs data, they're going to look for a data provider that services their use case. they're going to come in and they're going to buy it. And so we recognize that that's the most common use case. For example, if SpaceX as a company has an IPO one day, right? Suddenly everybody needs data from that company. And so they're going to come in and buy that data. And so data's bought, not sold. And we recognize that people want to try it, they want to buy it, and then that's the whole model. And so we have a usage-based model like that where, at first they want to pay on a usage basis for— it might just be a single stock, a single security, one product. And then after that, when they want to scale upwards, they don't want to pay for, they want pricing that's more scalable and they want certainty in the pricing. And so instead, when they scale up, they do want to pay a monthly fee. And so then after that, we can have a monthly contract or an annual recurring contract that is more standard with the standard B2B SaaS subscription kind of model. And so we have it both ways. So users can pay on a pay-as-you-go kind of plan, usage-based, and then they can scale upwards into a more traditional plan once they're ready to go for that upgrade.

Gemma Allen

>> Okay, so prove the value, lock them in. So Christina, you mentioned $97 million. you also described a very CapEx-heavy footprint there. And certainly in terms of what you're building out, what does the next kind of year or so look like for you and the team? What are you spending that $97 million on? Close us out with the journey ahead.

Christina Qi

>> Yeah, so for us, we're really excited to finally expand out to Asia for the first time. So getting Asia coverage expanding more out to Europe as well, and then also expanding out to different asset classes too. So we've had a lot of demand for FX, for fixed income, for crypto, so for different asset classes, also for event contracts as well. So there's just a lot of demand for different things. We're hoping to be able to fulfill those demands as soon as we can for all of our customers, and then also just continue to build and make our customers happy. So I think that's the number one thing is just continue to fulfill that mission of being that single source of truth for data. And also for me, I think the big thing is also just making sure that my team is happy. I think a big reason why we didn't sell the company is also I feel like my team would be really disappointed in me if we sold the business early and called it quits. I feel like no one's quit the company in so long, such a long period of time that if we were the first to quit the company and just leave, then the team would be so sad. And I'm like, okay, let's continue staying so that we can continue building and hopefully expanding the size of the team, hopefully without ruining the culture. So I'm a little nervous about that, but hopefully we'll get there and be able to still keep this culture.

Gemma Allen

>> Well, I certainly hope you go the whole mile here, Christina, and raise as much money as you can. Maybe one day ring the bell here at the New York Stock Exchange. God knows we need another female trailblazer like yourself to do that. So thank you so much for joining us.

Christina Qi

>> Thank you.

Gemma Allen

>> I'm Gemma Allen here at theCUBE Studio at the New York Stock Exchange. This is FinTech Exchange, one of our shows with NYSE Wired. Thanks for watching.

Christina Qi, Databento

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Gemma Allen

>> Palo Alto Studio Connection, Silicon Valley and Wall Street.

Christina Qi

>> I'm John Furrier, co-hosting with David Vellante, my co-host.

Gemma Allen

>> Welcome to theCUBE Studio here at the New York Stock Exchange. I'm Gemma Allen, co-host of NYSE Wired Fintech Exchange, and we talk constantly about AI transforming finance. But there is a much less sexy question underneath all of it. What data are you actually feeding the machine? Because the stock closing at $102 tells you almost nothing about what happened on the way there. Who was buying? Who pulled the order? How deep was the market? What happened in the milliseconds before that price moved? Joining me now to have a conversation about how that infrastructure is being built is Christina Qi, co-founder and CEO at Databento. Welcome, Christina.

Christina Qi

>> Thanks for having me.

Gemma Allen

>> So help me understand Databento. First of all, I love the name. It certainly makes sense to me. I was just planning bento boxes for my children this week. But help me understand what it is that you guys are actually building.

Christina Qi

>> So we strive to be the single source of truth for financial information. So we're a financial market data API provider is what we do in a nutshell. We aggregate financial stock price information from various exchanges or venues across the globe, including the NYSE. And then we normalize that data and distribute that to currently about 100,000 or so users around the world.

Gemma Allen

>> Wow. Okay. So help me understand who is using that data, for what purpose? Are you creating data for quant traders, for, I'm guessing, probably not retail customers, right? I assume that it's probably quite a technical interaction. Bring it to life a little bit. Who is the key customer? Who did you build this with in mind?

Christina Qi

>> So we actually originally built this product for quantitative traders. It could actually be for anybody, honestly. So we do have a lot of retail traders who use the product. We have students who use the product, people for the first time who have never actually touched a quantitative, maybe not, maybe don't even know how to code actually, who can use the product as well. But then also people who are very sophisticated, including some of the largest financial institutions in the world who use the product as well. So it does range very greatly between the different types of users. And I think recently what surprised us is that we do have a new type of user that uses the product. We have a lot of AI labs that have also picked up on the product. So yeah, I think that's probably been a bigger surprise for us is that we didn't design the product originally for that type of category, but that has been a big, like, I guess, like tailwind for us in recent times. Yeah.

Gemma Allen

>> So give me a scenario here. You are a quant trader and you want to understand all— I'm looking at the board here— NVIDIA stock buys between 9:30 and 10 a.m., right? And information isn't necessarily easily ingestible. You guys can actually kind of solve for scenarios like that, bring it to life. How does it compare to the world of the Bloomberg Terminal, for example, that we're all so familiar with here on Wall Street?

Christina Qi

>> For sure. So the difference and the reason why we started Databento to begin with is actually because I ran a hedge fund before Databento, and when we were dealing with the previous generation of data providers, we noticed that they were designed for human consumption, essentially. So they were designed around a human staring at a terminal-like product. And we noticed that even today, most data is being consumed by machines, not by humans. And so we wanted to design a data provider that was meant for machine consumption, not for human consumption. And that way we could basically help be the data backbone of any kind of product and help other products launch faster, go to market faster. You can build products on top of us. So think of us as like we're designed with the builder in mind. Rather than for an end user. So, if you pull up a stock price on an app today, like any app on your phone, that data might come from us already. So that's also what we do as well. We do serve a lot of fintech startups that use us to build apps and products on top of us. Yeah.

Gemma Allen

>> And if you're any sort of enthusiast and you pull up data on your phone of the NVIDIA stock price, right, you don't necessarily get the underlying data or the story or the structure, right? What brought it there? What that kind of broader story looks like. Is a lot of this around kind of piecing together that trajectory, like that broader story? And where is the value in that data? Where is that realized?

Christina Qi

>> Yeah, yeah. So what's interesting is we decided to hyperfocus on just what's called market data, which is just the financial stock price data instead of alternative data, which would be like, let's say satellite data on the parking lots or shipment data or earnings calls or things like that that are more broad that might be surrounding that stock price, where it might go or where it could head, stuff like that. And the reason why we decided to focus on just market data is just because, one, everybody needs market data. Every company needs that within our industry in order to basically survive. We've even had, you know, we have a lot of customers who have invested in our company, for example, or want to invest in our company because they're like, if you die, we die. So market data is like a critical need for a lot of these companies. Whereas if you branch into alternative data, it's just less critical of a need for these guys. And so it's harder to sell alternative data and convince companies that they need that type of data. And so it just came purely down to a preference on what do we want to focus on? And plus, my specialty is more in the market data realm. Market data, not saying that alternative data is not important, it's just that that's just what we chose to focus on.

Gemma Allen

>> Yeah. So maybe this is, and I'm sure it is, a false assumption, but in this world of AI where we're hearing more and more about data accessibility, about the democratization of data and information, I would have assumed that a lot of large quant houses are building their own pipes, building their own platforms. What is actually happening though? Because clearly there's a real market need for this.

Gemma Allen

>> Yeah.

Gemma Allen

>> what sorts of conversations are you having with the buyers across those industries?

Christina Qi

>> Yeah, so we were surprised by the AI Labs using us to begin with. I think, the reason why they're using our product, one is that we're a product that AI can't replicate very easily because to give you guys a sense of maybe the differences, we were not just simply reselling data. We actually rebuilt the entire stack from the ground up, all the infrastructure behind the data as well. So that comes down to even things like the bare metal servers, the colocation, all that infrastructure we actually built— rebuilt, I guess, from the ground up. And so as a result of that, because it is a lot of hardware behind the scenes, you can't just vibe code that kind of company from the ground up. And so as a result of that, a lot of these AI labs end up using us as customers actually, rather than just doing it themselves, just because it's so much easier to use us from the perspective of a customer.

Gemma Allen

>> So, yeah, that is fascinating, and that is not something I would have guessed when I looked at this company. So you're not necessarily another cloud license spend on your end, or even a Frontier Labs spend, it's something much deeper, right? So talk me through the company, what's the footprint like then? How many of you guys, where do you exist? Right. What does Databento's actual hardware/software footprint look like?

Christina Qi

>> Yeah, so we are co-located currently at various venues kind of globally. We've signed across, I think, about 15 different venues currently globally and over 50 different physical locations. But in terms of actual venues or so, it's probably going to be about 60 different locations or 60 different venues, sorry, globally. So hopefully that will give us coverage across the US, Europe, and Asia in the, let's say, near-ish future. So that's something that we're really excited about just in terms of our roadmap. Currently, we only have US coverage as well as a little bit of European coverage. So what's fascinating is that we do have about over 100,000 users on Databento right now, but our product is incomplete, meaning that we only have a limited number of datasets. And the more global coverage we have, the more likely we are to actually gain more customers, because a lot of customers need global coverage in order to switch over to us as their primary data source.

Gemma Allen

>> So, and what is the kind of chicken and egg problem for the datasets? Like, where is that information aggregated from? How do you collect and build on that?

Christina Qi

>> Yeah, so it's aggregated from these stock exchanges. Yeah, like the NYSE, where we have to sign licenses to redistribute this data. We do have to pay for that license as well as aggregate historical data as well. So there is an initial fee, or we have to pay that cost to be able to get all that historical data dump to come in. We have to also have the servers to be able to capture that data, to be able to store that data as well. So it is quite costly to be able to do that initially. So we think of it almost like an investment. Thankfully, one thing that's really nice is that we do have a lot of customers who are willing to pay us in advance now to be like, hey, if you go out to this venue, we'll pay you in advance as a day one customer so that once you have that data, you'll have a day one customer to be willing to buy the data from you. So that eliminates that risk. For us.

Gemma Allen

>> And that type of market conversation also shows the appetite and the need, right, that there is in ensuring that you do have data access in the right places and at the right endpoints 5 years from now. So talk me through the tech here. It sounds like you guys are building something pretty unique. How proprietary is everything you're building? You mentioned the frontier labs, they're customers of yours. Are you a customer of theirs? How is this build happening, especially at the speed you are alluding to there?

Christina Qi

>> Yeah, the tech behind the scenes is quite complex actually. That's why, I guess that's why we have so many customers across those different industries. And it's also why there's not a lot of, it's interesting because I guess the best way to put it is there's a reason why we were, by a lot of people in the VC world, described as a meme stock in the VC world in the sense that we raised a round of funding recently, but we weren't trying to fundraise. The VCs came over to us and they're begging basically to invest in us. And we were trying to figure out why, and it was actually because we were one of the few non-AI companies that was doing very well. And we were like, wait, there's so few non-AI companies really? What's going on? Um, it turns out that there's just not a lot of good non-AI companies. And then even comparing us to an AI company, in AI there's just so much competition in the startup space right now. There's just so many AI companies and the competition's really fierce. But in the market data space, there's not as much competition. And a lot of it's just because there's not a lot of people who have the knowledge to be able to build that infrastructure from the ground up. And then also, if you think about Databento, we have really good retention of customers as a result of that, just 'cause there's not a lot of other alternatives to really go to as a result. A lot of the startups in this space are more focused on retail, which is also a really great market, by the way. There's a lot of really great providers. Usually when a retail company or retail trader, when they're ready to take it to the next level, they'll usually switch over to Databento when they're ready for a more powerful solution. And so, that's who we usually cater to, is when someone's ready for something just slightly more powerful.

Gemma Allen

>> that is a fantastic problem to have, right? VCs coming to you trying to throw money at you. How much did you raise? What stage are you guys

Christina Qi

>> at?Yeah, so we raised a $97 million Series B, and that was led by NEA. Okay. Yeah, and that was— that closed, just earlier this year. Yeah. And so to give you a sense of why, we actually— we didn't need to raise the funding, we actually broke even earlier this year. So thankfully we're in a strong financial position, which is kind of rare for startups these days. I feel like a lot of startups are losing a lot, burning cash, losing money. Thankfully we were pretty good about how we spend. And so thankfully we're in a good position. But we figured our product is incomplete actually. So we were like, let's take the money because we do need to build out that data center presence globally. And we do want to complete that product. And the other thing we thought about was also a lot of data companies at the Series B stage in our industry, they tend to exit, by the way. They tend to get acquired around the Series B stage. We decided not to. We were like, let's continue building because we feel like we're still at the start line of our product. We haven't run the race yet just because our product is incomplete. So we're like, let's build out the product. And I feel like our customers would be so disappointed in us if we sold the company at this stage.

Gemma Allen

>> So I love that. And I love to see a female founder as well fighting the fight. And I have to tell you, it's fantastic. So I want to go back to something you said though, which is, we're not an AI company, right? That's an interesting line to lead with in 2026 because like you said, everyone is now an AI company, right? But when we boil that down, what exactly do you mean by that? Because I'm sure there is a lot of AI built into your technology, right? And especially as it relates to discoverability and data accessibility and all of those things. So how do you differentiate between what you just described there and an AI-native SaaS startup, let's say, or any type of startup.

Christina Qi

>> Yeah, that's actually true, right? For example, most of our doc site is being read by AI today, and that's something that we have to be aware of. Whereas for a lot of the incumbents, their entire doc site is offline. It's like a PDF or it's an attachment. They have to email you the docs. For us, we're like, we recognize that AI is the primary consumer of our documentation. And so we're like, let's put it online. Not only is it online, but let's make it AI-friendly so that an AI agent can read that instead. So that makes it easier for our end user. So we're trying to do things to make it easier for our customers behind the scenes so that when our customers do choose to use AI, their life will be easier, right? That being said, our product is not an AI product, meaning we're not building agents ourselves or we're just not ourselves in the AI industry. We're not actually consuming tokens ourselves, which is really hilarious.

Gemma Allen

>> That is

Christina Qi

>> fascinating.Yeah, we're not a company that consumes tokens behind the scenes or anything. It was funny because we actually did a startup accelerator, and during the accelerator, pretty much almost all the other startups in the accelerator were AI companies, and they're talking about tokens. And I remember I was the dumb one in the, like, the dumb founder. I was like, what's a token? And they were like, they couldn't believe that I was the only one that didn't know what a token was at the time, because that was how out of the loop I was in terms of the AI space, because we've been building this far along without ever consuming a token.

Gemma Allen

>> That is absolutely— I have to say, from where I'm sitting honestly, you're truly unique, right? you're probably the only person who has said that, maybe to me, anything ever. And I kind of love it, to be honest. Maybe the traditionalist in me is like, go, Christina. But how are you monetizing this? What's the business model here? we're very familiar with the world of Bloomberg terminals and how extremely lucrative that life is for many. How do you make money off the back of this?

Christina Qi

>> Yeah. So we have two different types of models. When most users come in, they want to try it before they buy it, right? And in our industry, there's a common saying, data is bought, not sold. I'm not going to cold call a leader in this industry. I'm not going to call Ray Dalio and be like, did you know you need data? And then he's not going to be like, yes, I need data. that's so unrealistic of a scenario. And so instead, what happens is when someone on the team needs data, they're going to look for a data provider that services their use case. they're going to come in and they're going to buy it. And so we recognize that that's the most common use case. For example, if SpaceX as a company has an IPO one day, right? Suddenly everybody needs data from that company. And so they're going to come in and buy that data. And so data's bought, not sold. And we recognize that people want to try it, they want to buy it, and then that's the whole model. And so we have a usage-based model like that where, at first they want to pay on a usage basis for— it might just be a single stock, a single security, one product. And then after that, when they want to scale upwards, they don't want to pay for, they want pricing that's more scalable and they want certainty in the pricing. And so instead, when they scale up, they do want to pay a monthly fee. And so then after that, we can have a monthly contract or an annual recurring contract that is more standard with the standard B2B SaaS subscription kind of model. And so we have it both ways. So users can pay on a pay-as-you-go kind of plan, usage-based, and then they can scale upwards into a more traditional plan once they're ready to go for that upgrade.

Gemma Allen

>> Okay, so prove the value, lock them in. So Christina, you mentioned $97 million. you also described a very CapEx-heavy footprint there. And certainly in terms of what you're building out, what does the next kind of year or so look like for you and the team? What are you spending that $97 million on? Close us out with the journey ahead.

Christina Qi

>> Yeah, so for us, we're really excited to finally expand out to Asia for the first time. So getting Asia coverage expanding more out to Europe as well, and then also expanding out to different asset classes too. So we've had a lot of demand for FX, for fixed income, for crypto, so for different asset classes, also for event contracts as well. So there's just a lot of demand for different things. We're hoping to be able to fulfill those demands as soon as we can for all of our customers, and then also just continue to build and make our customers happy. So I think that's the number one thing is just continue to fulfill that mission of being that single source of truth for data. And also for me, I think the big thing is also just making sure that my team is happy. I think a big reason why we didn't sell the company is also I feel like my team would be really disappointed in me if we sold the business early and called it quits. I feel like no one's quit the company in so long, such a long period of time that if we were the first to quit the company and just leave, then the team would be so sad. And I'm like, okay, let's continue staying so that we can continue building and hopefully expanding the size of the team, hopefully without ruining the culture. So I'm a little nervous about that, but hopefully we'll get there and be able to still keep this culture.

Gemma Allen

>> Well, I certainly hope you go the whole mile here, Christina, and raise as much money as you can. Maybe one day ring the bell here at the New York Stock Exchange. God knows we need another female trailblazer like yourself to do that. So thank you so much for joining us.

Christina Qi

>> Thank you.

Gemma Allen

>> I'm Gemma Allen here at theCUBE Studio at the New York Stock Exchange. This is FinTech Exchange, one of our shows with NYSE Wired. Thanks for watching.