Introduction
We’re excited to announce that we have raised $11.8MM in Series Seed funding, led by Multicoin Capital and a16z CSX, with participation from Topology Ventures, Founders, Inc., and an exceptional group of angel investors. This capital will accelerate our mission to help companies own their AI destiny by training and hosting custom models that are faster, more affordable, and more accurate than what the Big Labs offer today.
A fork in the road
Every company building with AI faces an impossible choice: pay unsustainable prices to OpenAI, Anthropic, and Google for general-purpose models, or compromise on quality with cheaper alternatives. This dependency on frontier labs creates three critical risks: spiraling costs that limit scale, a lack of control over core business infrastructure, and the inability to differentiate when everyone uses the same models.
We believe companies shouldn't have to choose between quality and cost. They shouldn't have to send sensitive data to third-party servers. And they certainly shouldn't build their competitive advantage on a foundation they do not control.
Where we stand
Over the past year, our team has trained and deployed custom language models for some of the fastest-growing AI-native companies in the world. These are companies that started out using OpenAI, scaled quickly, and realized the precariousness of their situation. We've proven that custom, specialized models can match or exceed frontier model performance while being an order of magnitude cheaper and 2-3x faster.
Our approach is simple: we take the specific, repeatable tasks that businesses run millions of times—extracting data from documents, captioning images, classifying content—and train purpose-built models that excel at exactly those tasks. These models are up to 100 times smaller than GPT-5 class models and deliver identical or superior results for their specialized domains.
Specialized models have a direct impact on business outcomes. Companies spending millions of dollars annually on API calls can reduce costs by up to 90%. Applications constrained by latency can suddenly serve real-time use cases. Businesses concerned about data privacy can run models privately on their own infrastructure.
Most importantly, companies have full domain over the AI models that power their core products.
Moving forward
The next decade will witness two parallel tracks in AI development. The Big Labs will continue pushing the limits of scaling and fundamental research with massive, general-purpose models for open-ended tasks like coding, creative writing, complex reasoning, and exploratory analysis. These models will remain expensive but essential for a broad set of use cases.
Simultaneously, a new ecosystem of specialized models will emerge to power the repetitive, high-volume tasks that constitute the majority of AI usage from real businesses. We envision a world where businesses rely on frontier labs for cutting-edge capabilities while owning and operating their own custom models that power core operations.
In addition to better economics, custom models provide long-term defensibility that is impossible to find with closed-source providers. When every company has access to the same GPT models, differentiation disappears. Custom models trained on proprietary data and optimized for specific workflows become a moat that competitors (or model providers themselves) cannot easily replicate.
We've set an ambitious goal: train 1,000 custom models over the next three years. That's one company per day gaining control over their AI infrastructure, billions of dollars redirected from API costs to product innovation, and thousands of new AI-native features that wouldn't have been economically viable before.
To support this mission, we're also releasing our Workhorse models—open-source, purpose-built models for common tasks that any developer can use today. Our ClipTagger-12b matches Gemini 2.5 Pro's video captioning quality at 17x lower cost. Our Schematron model outperforms Gemini Flash 2.5 at structured extraction while being 5x cheaper. These models prove that when you optimize for specific tasks rather than general capability, you can beat the frontier in costs, latency, and quality.
Why now
The economics of AI are reaching a tipping point. As companies scale from prototypes to production — from thousands of requests to billions of requests — the cost of relying on frontier labs becomes untenable. Meanwhile, the open-source model ecosystem has matured dramatically over the last few years, and new post-training techniques make it possible to match frontier model capabilities with fewer parameters.
We built Inference.net because we fundamentally believe that companies need to own their destiny when it comes to AI. The companies that will win in the AI era aren't those with the biggest budgets for API calls—they're those with models tailored to their exact needs, running on infrastructure they control, improving with every iteration.
Join us
This funding enables us to expand our research into new frontiers of model and infrastructure performance while scaling our ability to serve more companies. But capital is just the beginning. We're building the infrastructure layer that will power the next generation of AI applications.
If you're spending more than $50,000 per month on closed-source AI providers, we can help you cut costs by up to 90% while improving performance. If you're building the future of AI-native applications, we can give you the control and economics to make that future possible.
The transition from renting intelligence to owning it has begun. We're here to accelerate it.
Own your model. Scale with confidence.
Schedule a call with our research team to learn more about custom training. We'll propose a plan that beats your current SLA and unit cost.