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. ] The post Together AI positions open-weight AI models as the enterprise moat for cost, control and IP appeared first on SiliconANGLE.
Key Takeaways
- Open-weight AI models give enterprises savings, control and data sovereignty for agentic AI as they reconsider handing proprietary data to closed models.
UPDATED 09:14 EDT / JULY 14 2026 AI Together AI positions open-weight AI models as the enterprise moat for cost, control and IP by Jonathan Anthony Enterprises racing to deploy AI at scale are discovering that the biggest constraint isn't model capability anymore - it's control.
- "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.
- These models can be run in the compute environment that the customer wants, following the compliance and data loss and the security requirements for the customer.
" Enterprises increasingly worry that sending proprietary business processes into closed frontier models effectively hands competitors a blueprint, Prakash noted, pointing to public comments from Palantir Technologies Inc.
- So, there is an incredible appetite.
- all the while you're also creating these AI assets that you now own, and it becomes part of your intellectual property.
Stats & Key Facts
- #UPDATED 09:14 EDT / JULY 14 2026 AI Together AI positions open-weight AI models as the enterprise moat for cost, control and IP by Jonathan Anthony Enterprises racing to deploy AI at scale are discovering that the biggest constraint isn't model capability anymore - it's control.
- #, which recently raised $800 million in Series C funding at an $8.
- #"We've seen a 10,000-times increase in the number of tokens being processed through open-source models.
- #Together AI's customers see cost differences between open and closed models ranging from six to 60 times, Prakash said, a gap that becomes decisive once AI runs at production scale rather than in a demo.
Open-weight AI models give enterprises savings, control and data sovereignty for agentic AI as they reconsider handing proprietary data to closed models. UPDATED 09:14 EDT / JULY 14 2026 AI Together AI positions open-weight AI models as the enterprise moat for cost, control and IP by Jonathan Anthony 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.
"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.
For more details please read the original article at SiliconANGLE AI.
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