Ollama raises $65M to connect 9 million developers to open-weight models, with 80% of Fortune 500 already using the platform

Speaker 2: Great

Speaker 1: Thank to see

Speaker 3: you so much.

Speaker 1: Let me tell you about CrowdStrike. Your business is AI. Their business is securing it. CrowdStrike secures AI and stops breaches. Up next, we have Jeff Morgan from Ollama. He's the cofounder and CEO. This is his first appearance for the massive series b. Jeff, how are you doing? Welcome to the show.

Speaker 8: Thanks for having me. Doing great. So excited to be here. Big fan of the show as well.

Speaker 1: Thank you. Please introduce yourself and your music company. I wanna get to the bottom of how the

Speaker 8: hell you got 80% of Fortune

Speaker 1: five hundred using your product. But first, introduce yourself and the company.

Speaker 2: Yeah. I'm Jeff. I'm the

Speaker 8: CEO and cofounder of Alama. Alama is the largest network for developers to access open models. K. You download Alama. Get connected right away to open models like GLM five two, or you can download them locally and run them right on your laptop for more kind of edge low latency use cases.

Speaker 1: Okay. Tell us about the round. Jordy's warming up the gong. How much did you raise? Who from?

Speaker 8: 65,000,000. The lead was Tamash Tungus, and we had existing investors participate too, like that.

Speaker 1: Awesome. Fantastic. Tons of GitHub stars. Why how does this fit into like the the value add to businesses versus just downloading the model themselves from from an open platform, getting the the actual the weights themselves, deploying things, or just going with an API? Like, how are you talking to Fortune 500 customers about how you will actually improve their experience with something like GLM 5.2?

Speaker 8: You know, the power of open source is how fast it can build trust with developers around the world.

Speaker 1: And it

Speaker 8: ends up a large number of developers work at work at companies that work at, you know, a lot of those in the Fortune 500 or Global 10,000. And open source has a special, you know, capability where you can deploy it in your own environment and not have to think about a ton of security and compliance. And what that means is, look. Like, you can take an open model, which already has open weights, and that's kind of why open models are perfect for these use cases, and run them without any approval really as a developer and and get real successful results all without, you know, having to expose your data or, you know, even incur big costs. So, you know, that's really been the driving force of being able to to get into these large businesses. Mhmm. And to your point, look, like, you know, just the way it's being open isn't enough. You need a way to deploy them, to run them, to make sure it works on your hardware. For the cloud models, you know, you really need to make sure that you're running them in a secure environment where your company can access them. You know, a lot of a lot of businesses we talk to, especially Fortune 500, they need these open models hosted in The US and in Europe. And that's just a requirement, and it's it's a need they have. And so, you know, put put that all together. It's it's so surprising and incredible how fast it can get adopted.

Speaker 1: So what does I mean, you say you're you're you're talking to these Fortune 500 companies, but I imagine that the fact that you have so many GitHub stars means that there's a lot of self serve activity. When does a customer crossover to an enterprise relationship with you?

Speaker 8: Yeah. Generally, it starts with the individual dev. Right? They bring it they bring it to work. A lot of them use it, you know, for personal productivity Sure. And they bring it to their team. And once you're using a team, it's not a one one person store anymore. Right? There there's a team there. Right? And everything from security to technical architects, IT teams. Mhmm. You know? And and these folks, they they need not just a product that's really, you know, easy to use and self serve, but they they need a solution that really kinda end to end covers things like safety and monitoring and logging and data storage and protection. Like, these are all components of a successful, you know, agent deployment. And so, you know, that's when it becomes a multiparty environment. And, you know, as we know, like, that's when you need a solution. And and on our side, you know, we need a team to be there to help help those customers.

Speaker 2: Mhmm. What, what set of models are you most excited about for the back half of this year in the open weights world?

Speaker 8: Oh. I mean, I think with GLN five two, we just had another massive moment in open models. And, you know, a lot of by far, at least from what we know publicly, is the highest token volume of accessing VLAN five two. And so I'm excited for that because I think there's gonna be a series of new models that are long horizon. They're focused on these really hard agentic use cases. There's gonna unlock so many use cases in enterprise that, you know, the prior generation of open models couldn't. Mhmm. You know, and the gap between open models and the frontier models is shrinking. And so, you know, I I think at that point, you we're able to get to these incredible use cases that just weren't there, you know, three or four months ago.

Speaker 1: Take me through some of the game theory in the open source community around those rumors that we heard that there might be export controls on open weights models coming out of China soon. If we stop getting frontier or near frontier open source models from China for free, is the next step that you would see an American company step up, NVIDIA or maybe Meta changes their strategy? How are you thinking the the open source ecosystem would evolve if China changes their strategy?

Speaker 8: Yeah. You know, we like to work backwards from from our customers. What are they trying to do? And they for by and large, you know, they may have preference on specific, you know, geographies where the models are from, but by and large, they're adopting both. Right? They're and some mix of open models and and frontier models as well. To your point, like, I think The US models are absolutely stepping up. They're incredible. The Nemo Tron three Ultra model is just amazing and is able to accomplish some of these long running agent tasks. Mhmm. And then also, you know, one of the most downloaded models on the llama is a US model. It's the Gemma model. And Oh, yeah. You know, this is, like, a super amazing team at at DeepMind that's putting them out. The new ones are, you know, agent ready. Like, they can run coding agent loops. They can accomplish much harder tasks. And so, look, I think it's really up to the customer. If they want a US entirely US built model designed from scratch, that's there. If they want a Chinese model, which is often the case, they it it's less about where the model's from. It's like, where does it run? And is it running next to your data, which, you know, a lot you can deploy it locally. Mhmm. And then able to deploy with safeguards. And ends up a lot of customers, they're not looking for, like, where the model's from. They just wanna make sure that they're running it properly and safely so that they, you know, they can have a a understanding of what's gonna you know, what could go wrong, but what could go right. And and there's a lot of safety tooling that can be deployed to help with that. They have tons of appetite for that.

Speaker 1: What do you see your role as in terms of benchmarking, reality checking, vibe checking, different models, helping enterprises that work with you to make the right decision, pick the right tool for the job?

Speaker 8: You know, our job fundamentally is to connect the 9,000,000 developers on Ollama to the right model for the right task. And that's that's step one.

Speaker 6: Mhmm.

Speaker 8: And so just by having that sheer volume and the this critical mass of devs, we're able to already understand just from, you know, our community which models are performing right for the right tasks. That that's a starting point. I think from there, it's really collaborating with the model labs, and we're launch partners with every major model lab. And just making sure that, you know, the best parts of the model are shining through through OLAMA

Speaker 1: Mhmm.

Speaker 8: Including what are they capable for, what are their benchmarks, how can customer customers benchmark it for their own use cases. Sure. It it all comes down to a lot of software tooling and and and and and, you know, a community in a in a network. And that's Mhmm. You know, what we built and and it's what makes Alama special for developers.

Speaker 1: Got it. $65,000,000 raised. Is this like, what are you using the money for? Because you don't have the crazy training costs because you're more of a gateway. Is this head count off

Speaker 2: 2,000 BDRs.

Speaker 8: Yeah. You hit the nail on the head. Look. We've we've put out a you know, on our site, hey. We're launching a Teams plan. We were inundated with thousands of teams that wanna use Olema.

Speaker 4: Yeah.

Speaker 8: And, you know, that's gonna that that's the core mission. It's like, look. We've got this critical mass of devs. How do we go solve problems for businesses back to what we were just talking about? And that takes a team, so, obviously, we're expanding. We got here with 14 people to to company of this magnitude. Mhmm. But there's a much bigger team

Speaker 5: to be around

Speaker 7: the market. Very good.

Speaker 8: And then and, course, you know, one thing Alama does very special for the larger open models is we host it on US and European servers.

Speaker 1: Okay.

Speaker 8: A lot of the consumption of open models is going to China or is going to servers where

Speaker 3: Sure.

Speaker 8: It's there's there's no data retention guarantees. And that's so important for companies. And so that's a compute, investment we're making and really enabling, you know, every business in the world to access the most powerful models on compute that's secure and safe in The US or Europe.

Speaker 2: Will we ever settle the debate on whether the gap between open and frontier models is closing or widening? Because depending on what sort of group somebody is a part of, they tend to have one one view or or the other. But it but I think in in reality, it's probably always kind of going like going like this to some degree. But what's your view?

Speaker 8: I think you're right. It's oscillating. I mean, I'm a daily GLM five two user through Ollama right now, and it's replaced 80% of my coding work. Wow. And I think a lot of that's gonna be true of a lot of customers. As for the gap, like, to your point, think it it may widen. It may shrink. I think overall, it's shrinking. But but, ultimately, customers are gonna use a mix. And, you know, for the bulk of their use cases, they're gonna reach for these open models because they can tune them to be much faster. They're obviously much cheaper. And there's always gonna be use cases where you need the frontier. I don't if we'll ever settle the debate. I think, ultimately, the gap will will continue to shift. I think that's what makes it exciting. Right? It's like every every three months, we're able to do something new. We're able to run better agents, and quickly open models will catch up and really enable a whole wave of customers that wanna run open models to do that, you know, in their own environment or to customize it to the point where, like, they can even make it more more powerful. The last thing I'll say too is, you know, customers are readily taking these open models and customizing them, and they're actually getting better results often than

Speaker 6: Yeah.

Speaker 8: You know, just a a stock frontier model. Yeah. And, you know, I think we're just at the beginning of that transformation.

Speaker 1: Very cool. Well, congratulations on the progress

Speaker 2: Awesome to meet you.

Speaker 1: In the round.

Speaker 2: Congrats

Speaker 1: to the for coming on the show. And have a great rest of your week.

Speaker 3: We'll talk

Speaker 2: to soon.

Speaker 1: Goodbye. Awesome. Thanks a

Speaker 2: lot. Cheers.