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Custom AI models trained for your use case
Inference.net is a venture-backed team of researchers and engineers who train and host specialized language models for some of the fastest growing companies in the world.
The models we train are:
- Accurate. Our specialized models match or outperform top models from frontier labs.
- Fast. 2–3x faster than the top models from closed-source providers.
- Affordable. Up to 90% cheaper than what you're paying today.
- Scalable. Host on our infrastructure or yours. 99.9% uptime. No rate limits.
- Private. Fully owned and controlled by you. Forever.
If you're spending more than $50,000/month on OpenAI, Anthropic, or Gemini, we can help you save money, improve quality, and ultimately make your business more efficient.
6 weeks from zero to production. Fully managed by our team.
Curious how it works? Talk to an engineer →
Workhorse models
In addition to custom model training, Inference.net trains and hosts a series of open-source models, which we call workhorse models. Workhorse models are purpose-built to solve problems that engineers and product builders often encounter when building AI-native products, like extracting data from HTML, or captioning images.
Workhorse models are very cheap, as good as or better than frontier models and designed to run at scale on our infrastructure. You can use them today via our API without rate limits.
The models available today are:
- ClipTagger-12b - Video frame captioning. As good as Gemini 2.5 Pro. 17x cheaper.
- Schematron-8B - Extract JSON from HTML. Outperforms Gemini Flash 2.0. 5x cheaper.
- Schematron-3B - Smaller variant of Schematron. Built for massive scale.
Why?
We believe LLM inference will be widely integrated into all digital products, systems, and processes. This transition is well underway today. While it's true that some companies are happy to rely on closed-source providers to power their business, others are starting to realize the critical importance of building their own models in order to stay competitive in the long term.
We envision a future where models trained on proprietary data, and tuned to specific workflows, provide fundamentally important value throughout the economy.
We aim to accelerate this process.