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When DeepSeek-R1 first emerged, the prevailing fear that shook the industry was that advanced reasoning could be achieved with less infrastructure.
As it turns out, that’s not necessarily the case. At least, according to Together AI, the rise of DeepSeek and open-source reasoning has had the exact opposite effect: Instead of reducing the need for infrastructure, it is increasing it.
That increased demand has helped fuel the growth of Together AI’s platform and business. Today the company announced a $305 million series B round of funding, led by General Catalyst and co-led by Prosperity7. Together AI first emerged in 2023 with an aim to simplify enterprise use of open-source large language models (LLMs). The company expanded in 2024 with the Together enterprise platform, which enables AI deployment in virtual private cloud (VPC) and on-premises environments. In 2025, Together AI is growing its platform once again with reasoning clusters and agentic AI capabilities.
The company claims that its AI deployment platform has more than 450,000 registered developers and that the business has grown 6X overall year-over-year. The company’s customers include enterprises as well as AI startups such as Krea AI, Captions and Pika Labs.
“We are now serving models across all modalities: language and reasoning and images and audio and video,” Vipul Prakash, CEO of Together AI, told VentureBeat.
The huge impact DeepSeek-R1 is having on AI infrastructure demand
DeepSeek-R1 was hugely disruptive when it first debuted, for a number of reasons — one of which was the implication that a leading edge open-source reasoning model could be built and deployed with less infrastructure than a proprietary model.
However, Prakash explained, Together AI has grown its infrastructure in part to help support increased demand of DeepSeek-R1 related workloads.
“It’s a fairly expensive model to run inference on,” he said. “It has 671 billion parameters and you need to distribute it over multiple servers. And because the quality is higher, there’s generally more demand on the top end, which means you need more capacity.”
Additionally, he noted that DeepSeek-R1 generally has longer-lived requests that can last two to three minutes. Tremendous user demand for DeepSeek-R1 is further driving the need for more infrastructure.
To meet that demand, Together AI has rolled out a service it calls “reasoning clusters” that provision dedicated capacity, ranging from 128 to 2,000 chips, to run models at the best possible performance.
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