This is going to be like the biggest, biggest market in tech ever. A lot of companies are making routers because it's fashionable. The model labs have several incentives to go after you eventually. Today we have Alex Atala, co-founder and CEO of Open Router, the unified interface, the gateway to the world of LLMs. They reportedly have had offers from Stripe for $10 billion. They've raised at a valuation of over a billion and a half. They are the market leader and this interview could not come at a more prescient time. In July, we launched 70 models. About one model every 10 hours. America is very, very behind. Still, GLM 5.2 was a really big, big step for open-weight models. There are reports that you are selling to Stripe for $10 billion. Is that going to happen? Ready to go.
Alex, I am so excited for this, dude. I have wanted to make this one happen for a while. I've heard so many things from Matt at Menlo. I've stalked the [__] out of you speaking to Anonyie, even your roommate before this show. Um, so thank you for joining me, dude.
Thank you. It's great to be here.
Now I want to start with a little bit pre-OpenRouter and start on OpenSea. It was a pretty incredible journey. What did you take with you to OpenRouter having seen all that you saw with OpenSea?
Yeah. So, OpenSea, we started as the first NFT marketplace and similar to OpenRouter, it was very small for a long time. Like we kept the team very small until the Series A roughly, or you know, a little bit afterwards. And this was before AI. So, right after NFTs started blowing up in 2020, October of 2020, we were like, 'Oh my goodness, like we are understaffed. The servers are melting. All kinds of like our search index was exploding. We had a couple big outages. It was tough to like keep the site up and it was like, oh my god, we're going to become like the Twitter fail whale but like applied to crypto.' My biggest goal was to have us not be the Twitter fail whale for crypto. And it took a little bit to like create the team, get platform and infrastructure under control, like make sure we could scale, we could predictably scale. In other words, like do load testing to like help the site sustain 10x load even when we weren't seeing that load. Because with crypto, you just don't know. There were like these moments where we would get these incredible traffic spikes and it would be very dependent on the content and the community. And so I built like a lot of infrastructure and scaling, I think, responsibilities then that I took to OpenRouter and spent a lot of time like thinking about, okay, how do we, you know, make something that is going to basically be always up and that people can really count on from an infrastructure point of view, even when there are huge surges in really like tumultuous markets. Which has been very helpful for AI, of course, because like, you know, all companies, like especially Anthropic, have seen like unpredictable growth and we have as well. And you know, we've had like a couple bumps but overall it's been like significantly better. And I like OpenSea just kind of like drilled that into me in a way where I could like take it productively to OpenRouter.
Can I ask you, when you go back to the founding thesis of the company, what has happened in the ecosystem in the model landscape that you did not expect to happen?
Um, okay, well, one thing that we did not expect was that an ecosystem of companies would emerge to host and serve the open-weight models. Like early on, it wasn't clear that that market wasn't going to be a monopoly where like just, you know, the three hyperscalers serve all the open-weight models and startups don't, you know, they're really far behind. In reality, like, you know, how often do you hear people running, you know, GLM on a hyperscaler? Never. Like they're using the inference providers like Fireworks and Together. And there's a, you know, big list that we see doing the best job of hosting all of the open-weight models. And in the early days, we had, I think we called it provider one and provider fallback. We didn't like show which providers were actually doing the hosting because I mean, we weren't really a marketplace. So we were kind of like, we were an exploration tool for like finding and discovering new LLMs and we wanted to build like a marketplace of model labs, but like the inference provider layer, we weren't sure would actually be a marketplace. And it turned out that those companies were doing a way better job than the hyperscalers were, way faster to host the models and figure out these edge cases to hosting them. And uptime was just going to be a constant problem. It wasn't going to like magically get solved by the supply side of the market.
A lot of people suggest that that inference provider layer is a commoditizable element or layer that will be removed or see margin reduction competed out over time. What would you say to that theory?
Right now we're in a massively supply constrained market where, and it's likely going to be supply constrained for a while, where all the inference providers are short, pretty much constantly short. And you're like, okay, so GPUs are really, really beneficial and like why doesn't Google or Amazon or Azure run around and like buy up all the GPUs and take all these inference providers out of business? Well, the people making the GPUs don't want that. Like, one of Nvidia's top priorities is not having customer concentration. They want lots of customers to all have like separate allocations of GPUs. They want the heterogeneity of the market. They want like competition on the compute layer. And this is good for the ecosystem, like users also want this. It's good for Nvidia and it's good for end users as well. It allows these inference providers to kind of like come up with new innovations on like how to serve the models better. Even a single model like Kimi K3, like Moonshot just posted a benchmark showing all the inference providers and how well they're serving Kimi K3. And they're pretty different numbers for like benchmarks that are really static, that are well-known. We post this continuously all the time. We always are like benchmarking all of the models on all of the inference providers, all the open-weight providers and finding really different results constantly. The results change over time. These models are like very, they're very emotional. They're very like, they're very like non-deterministic. So, um...
I had Lynn on the show from Fireworks and she said that, you know, I said about Gavin Baker and a token is a token is what he said, and she kind of corrected me that a token is not a token actually because one provider can make a token go so much further than another token. It's like how do you get to the store? Well, you can drive around the whole block or you can drive straight to the store. Tokens can be made more efficient and go further and that's the job of the provider.
Yeah, I agree with that. I think that, you know, in some ways we are providing a service to help people discover providers and ultimately when one provider is making a token go further, we spend an enormous amount of time on our router, central router tech, so that that provider immediately gets more traffic as soon as we detect that like there's a quality improvement or a speed up or a price reduction happening, immediately starts getting more traffic. And this stuff happens like 24/7, every single like, every five minutes there are big changes for the big models. And so it like actually does make the experience better.
You can only invest in one inference provider. Which one do you invest in?
I probably have to stay, you know, stay neutral on this. I really like the inference providers that are doing custom hardware and very, very like low-level optimizations. I like providers that are also trying to figure out how to make customization easier. So like today you fine-tune models and you create this like new like fully independent model from the base model. Many inference providers are kind of like, you know, creating these LoRAs or some call them like cartridges that are much more portable potentially between models. And we might see a future where like when you do a fine-tune and you want to like change the base model layer, it only costs like maybe a few hundred, maybe a few dozen dollars to change it.
It's okay. I understood that Fireworks is your favorite. It's okay. I get it. Mine too. My question is when Lynn was on the show, she was like, 'Oh, you don't want to rent your own intelligence. You want to own it.' And we're going to see companies have specialized models which is trained on their own data and proprietary to them. In a world of every company having specialized models that's really tuned to them and their preferences, is that good for an OpenRouter business or not?
Oh, definitely. I mean, our...
Because you'd stick on one model which is yours, proprietary, trained on yours, and not be open to the diaspora of models that is available.
Uh, no, I disagree. I think our mission from the very beginning has been to increase neurodiversity in AI for the whole ecosystem. And we really believe that like a multimodel future is inevitable. And when you start, let's say like, let's say there's one model that like, you know, hypothetically, let's say you're right. Let's say there's one model that fulfills all of your desires, either within your company or like as a consumer. And every, you know, more and more people start using that model and then someone decides, you know what, I'm going to like create a neurodivergent model. I'm going to create a model that's like a little bit different, that like talks a little differently, that has ideas that the first model like could never have come up with because it's like completely different data that's being used to train it. Then it kind of creates inevitable demand to use both models. But creativity is not a, it's not verifiable. There's, you can't really put an easy number on creative ideas. And when you use two models together, you're more likely to get creative ideas than if you just use one. It's just a fact if that other model was trained in a different way on that different data set or has like made a big update. So consolidation on one model just seems like, it just doesn't make any sense to me.
Totally get you. So you'll have companies which have like a core workflow or their core which is their own specialized model and then they'll use a plethora of other models and they'll use OpenRouter for those other model selection.
Yes. And I think that when companies make, like to get back to your question, when they make their own, you know, their own model trained on their own data, like you are, the ecosystem around you is all doing the same thing. You have to like play out the game theory for these things a little bit. Like if everybody is doing this as well and all the model labs are creating new models constantly using new data that they've acquired, that they've bought from other companies, that's all like potentially data that's valuable to you. What is in your best interests? It's to go and try out those other models and like see if you can be more productive with them. If you can like, you know, merge them together to get better state-of-the-art performance. If you can reduce your costs using these other models, they're all, whether your goal is to like reduce your or improve your margins or grow your company, like you are incentivized to go use what the ecosystem creates. So the model that you made, you're going to have to continuously improve it to keep up and it's never going to win the whole market. This is going to be a massive market. This is going to be like the biggest, biggest market in tech ever and biggest market probably in human history. No one's going to win all of it. You're not going to build a model that wins all of it. So you might as well build a model that like is known to specialize in something very useful and that's very important to your company and your business and be known for that specialty. And I think a lot of enterprises are going to move that direction, make their own models, make their own branded intelligence. So your brand is a big part of your moat and that model will like be a way your brand carries around.
You mentioned the immense time that you spend on the routing technology that you have. A lot of people are thinking that we're seeing the commoditization of the routing technology. You're seeing Ramp release products like this. I mentioned earlier of Merge, a company we invested in, release that product. Several are releasing kind of routing technology similar or claiming to be similar. Are we seeing the commoditization of this layer?
I think a lot, yeah, a lot of companies are making routers because it's fashionable. I think they're, you know, they're seeing growth happen here and or they're making gateways at least. First, I think there's two issues with that. First, it immediately puts you in the mindset of copying instead of like, you know, winning something. You're sort of, you're playing to play. You're playing to exist rather than playing to win. And maybe you're just trying to like play to serve your existing customer base. And you want to see some AI growth happen. I think, you know, immediately kind of like puts that gateway like many, many months behind the companies that are fully focused on it. Like I am 100% focused on building the best router and gateway and LLM marketplace and it shows in our product and, you know, the benchmarks that we create internally and how we see ourselves compared to the competition. This is not a side quest for us, like it may be for some other companies. The other problem is that it reduces the leverage of all of your users. So like I really deeply believe in giving users and developers more leverage. Like fundamentally giving them access to more models is about giving them more leverage over all the innovations that happen in AI. You want to be able to like access them all. You want to reduce your dependency on any individual one. If, you know, you build on top of a router or a gateway that, you know, doesn't give you access to the full market or full flexibility or full customizability, it doesn't give you like the full leverage of the whole ecosystem, then you're kind of like being cut out. You're cutting out all your employees at your company of things that they need. And so like OpenRouter is fundamentally about giving people more choice because that gives them more leverage.
You do that at a price at 5.5% take.
That was sort of our pay-go plan. We then added an enterprise plan with like a totally different pricing model and it's been very successful so far. It's kind of based on like committed spend and then, you know, no fees on that committed spend.
Because that was going to be my question. Ultimately, companies will like love it small and then as you scale like [__], this is really freaking expensive. I'll just build my own routing tech now because it's become such a significant part of my cost base actually.
I mean, I kind of think it like some of those companies don't, you know, just haven't realized we have like an enterprise plan and some of them, and some of it is like our fault for not having like a better, I think more detailed pricing model. We're soon going to introduce like a kind of a business self-serve plan that also just, you know, makes it make a lot more sense. And if you have your own inference, like if you bring your own inference to OpenRouter, if you bring your own keys, that fee goes away. So it's a fairly like, you know, for inference that we are providing you, like when you go into OpenRouter's capacity and you're not on our enterprise plan, that's when that fee comes in. Otherwise like, you know, we need to be able to like predict demand a little bit. So that's why we do these committed spend.
What will be the main revenue line of OpenRouter in three years' time?
I mean, I think it's going to depend on the economy in so many ways. If the overall AI market keeps growing the way it's been growing over the next four years, you know, with like 10 to 15x every year or potentially more. It's a lot of growth. You know, I think under that world, I would expect people to continue to underestimate how much inference they're going to need. And thus our revenue is going to be dominated by, you know, the same things that dominate it today, which is like us helping people with unplanned inference capacity, both enterprises and startups. And like that's what OpenRouter is best at. Like when you need to try models that you weren't expecting, you need to try when you're like using more inference than you thought you were going to use on particular models, like we make sure that that is not going to be an issue for your company by providing the best failover and best uptime. And this is really, really a good thing to do when the market is like continuously underestimating its inference needs and growing at this rate. If this growth rate continues over the next four years, I mean, it's going to be a wild amount of growth. Like the economy has some limits to it. I can see like major SMB, you know, SaaS like growing for us and needing to grow for us whenever, you know, if growth like does not keep going 10x, 12x, 15x per year.
We've seen token prices fall 90% give or take in like 18 months. Is the reduction of token prices helpful or hurtful to your business? Because obviously you have a take on spend. If they come down and spend is more efficient, seemingly it's bad for your business. You have a shrinking pie to take from.
Well, a lot of people talk about the Jevons paradox that, you know, when prices go down by 10x, the usage increases by more than 10x. But like no one has really done a great job modeling it there. We do have a lot of spot stories that confirm it. For example, GPT-5.6 Luna on OpenRouter. OpenAI cut prices by 5x and then in coordination with us by another 2x. So in total, the price of Luna has dropped 10x on OpenRouter over the last two weeks. And guess how much usage has grown? 13x. So it's a close to perfect Jevons paradox story where you drop prices 10x and usage grows by more than 10x, just a bit more. And also the usage is pretty stable. Like it grew, you know, it flattened out but at 13x and then, you know, it's been kind of like growing at the same rate that it was growing before it hit the 13x multiple. So that's pretty interesting and it's a pretty like low variable, like there are few other confounding variables in the story. And it was also done in the middle of DeepSeek launching and having a really, really good price and GLM having a really good price. Like now Luna is being used more than GLM on OpenRouter. GLM used to be like one of the top like three, four models by token volume and now Luna is past it. This is the first time OpenAI has had a model on our platform in the top, you know, three to five models by token volume in an extremely long time. So it was a really big and interesting move.
How reflective of the market are your token volumes? Because it's about, I may get this wrong, about maybe one and a half, two percent of say token volumes. And so how reflective are they? Because a lot of people when I say, oh, the top five models when I look at OpenRouter are all Chinese, what does that mean? They'll go, oh well Harry, no offense to OpenRouter, but like it's not reflective of the market and most people who use frontier, it doesn't go through that, like they use frontier APIs and so it's not counted. To what extent are your rankings reflective of true token usage?
We try to estimate how they're off by, you know, just surveying people. Sometimes we're looking at like the surveys other people have done. I think we definitely have a bias to people who believe our thesis, which is that the future is multimodel and companies who want multiple models. And there are still companies out there, I basically very rarely run into them now, but there's still companies out there that are just like, oh yeah, we're an OpenAI shop, like we've won, you know, we only do OpenAI models and so we're not going to see any of those companies. And I think those companies are primarily focused on like the hyperscalers, OpenAI, Anthropic and Gemini. So we do probably like undercount the frontier models. But I think like over time our thesis is becoming more and more common to see in other companies and the moment that like they're like, oh yeah, like we need to use other models, then our data becomes more representative and as we scale up the data becomes more representative in general. So my hope is that like that we, that the, it just becomes like better and better data over time.
Can I ask you? Alex Carp said on CNBC in his rather wonderfully energetic way that companies are terrified of working with frontier model providers. Do you think they are?
So I haven't seen what he talked about there when I talk to our customers. But there was definitely like a little, there was some skittishness that the, particularly when Claude Design came out around Figma and that part I did see. And I do think that there are like real concerns for a company that is kind of building like a, you know, thin, you know, for like Figma is very different, but if a startup is only building a like go-to-market wrapper around intelligence, like hey we are, you know, we're a company that kind of like brings AI to this market and does so by like doing the right integrations and like customizing the system prompt, you're going to be fine if the model labs don't care about that market, which there will be many markets like that. But the model labs have several incentives to go after you eventually. One is getting multiple teams within companies they do care about to be dependent on them. So this is my theory behind why like Claude Design was strategic. While it's not like a massive amount of revenue for Anthropic, like probably not a significant amount of revenue, it does get the design team to really care about Anthropic models. And so the companies that like they want, like they now have another team that really wants to stick to Anthropic. So that team strategy makes this, you know, like can make you compete with the model labs. And so I think like companies like that that find themselves like, oh we're like building a product for a team that has now become strategic for the model labs for like companies they actually care about, that's where I see probably the most near-term threat.
Do you think Claude Design will have a meaningful impact on the Figma business? I speak to many founders today who are bluntly switching from Figma to Claude Design and it's cannibalizing their Figma usage. Do you see that and do you think that will happen?
So I saw a lot of designers try out Claude Design, including our own. But so far, I haven't heard of the repeat story. I don't know, honestly. Like, I have not talked to very many designers about this. I certainly haven't heard a lot of chatter about Claude Design. And like if you just look at the numbers for Figma, they're quite good. Like very, very incredible earnings. So, um...
This is why you don't want to be public, dude. You see like you great numbers, Figma down. I'm like, poor Dylan. Like, give what?
Do you know what I mean? Like, really? Come on. We were talking about like the different models that we have on offer and where the company's willing to work with frontier models. The rate of model development feels immense. Do you think we will see the same rate of model development continue over the next year, two years, three years?
Frontier model development or general model?
A general model, both frontier and open.
Just because I mean every single day there's two, three, four new models.
In July we launched 70 models. It's about one model every 10 hours. There's some agent labs starting too that are all going to kind of like probably make models eventually. Like Jeff Dean is starting an agent lab right now from Google. The companies that are known for making agents have an incentive to create their own model, a very clear incentive to create their own models and distribute it through the agent. And we haven't even seen the start of that. Like sorry, we've seen the start of it but we haven't seen it really pick up. Like Cognition has a model, Cursor has a model. Does Lovable have a model yet? I don't think so.
Yeah, so the agent labs are going to, I think, develop models. This pressure from both the GPU makers like Nvidia to like create more competition in the space and create more diversity in the space, plus us, plus investors who just want to try new things that like all could like improve intelligence in some neurodivergent way. I think those are strong incentives. I think that there's still, they're enough to like incentivize more founders to make new labs. And if like American open-weight models pick up in steam then it gives these new labs a base to train on that's not Chinese, which will then probably create more American new labs.
Do you think we should be concerned by the rate and quality of Chinese open models?
We should. We're behind. America is very, very behind still. I think things are picking up and I think, you know, we have Poolside, we have Thinking Machines, we have RC.
Do you feel a sense of responsibility for that? And what I mean by that is like, you know, you are a routing business and you could route a company to a Chinese model that who knows, people are worried about backdoors, CCP involvement. You could be the deliverer of that to those models. Do you feel a sense of responsibility for that?
So we do feel a responsibility to have safe access for all of these models. Like customer trust is like our paramount goal. If one of these models is unsafe to use, you know, generally considered unsafe, we pull it from the platform. If there's like a way to use it in an unsafe way, I mean, there's a way to use like all the models in an unsafe way, then we believe in using technology to make it safe and to like work with the model labs themselves to figure out how they're doing it on their side so that we can be state-of-the-art or better. We spend an enormous amount of time making sure that like our practices match what the best things that we're seeing coming out of the labs or are better. And because we're like a very good, because we're a way of like exploring all the models and finding them for the first time, we're a good focal point for like deploying safety measures across your whole company. For example, we have prompt injection protection. You can just turn it on and immediately flag prompts that look like prompt injection that's trying to happen. We have PII redaction. We have like a couple different things that you can automatically just turn on with a click and get an added safety layer on top of all of your inference. And we build that so that enterprises feel like they can safely like deploy new models and that their employees can try them out. I think of the models a little bit like the internet. You know, you can't just like ban the internet at your company because there's some like bad things on the internet. You can create guardrails and you should. You need to use AI to build the best possible guardrails that you can.
