Together AI positions open-weight AI models as the enterprise moat for cost, control and IP
Enterprises racing to deploy AI at scale are discovering that the biggest constraint isn’t model capability anymore — it’s control. As agentic AI moves from experimentation into core business processes, companies are rethinking whether handing proprietary data to closed frontier models is a risk worth taking, opening the door for open-weight AI models.
That shift is fueling explosive growth for the companies building the infrastructure layer beneath open-source AI. Token usage on open-weight models has surged as enterprises weigh cost, compliance and intellectual property against the convenience of closed systems, according to Vipul Ved Prakash (pictured), co-founder and chief executive officer of Together AI Inc., which recently raised $800 million in Series C funding at an $8.3 billion valuation.
“One of the things that we have seen over the last year is there’s been almost a stampede towards open-weights models, which we serve and we allow our customers to post-train and adapt to their data,” Prakash said. “We’ve seen a 10,000-times increase in the number of tokens being processed through open-source models. I think they have really become now a workhorse of agentic AI in a way that was just not there a year ago.”
