Enterprises racing to deploy AI at scale are discovering that the biggest constraint isn\u2019t model capability anymore \u2014 it\u2019s 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,\u00a0opening the door for open-weight AI models.<\/p>\n
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<\/a> (pictured), co-founder and chief executive officer of Together AI Inc., which recently raised $800 million in Series C funding<\/a> at an $8.3 billion valuation.<\/p>\n \u201cOne of the things that we have seen over the last year is there\u2019s been almost a stampede towards open-weights models, which we serve and we allow our customers to post-train and adapt to their data,\u201d Prakash said. \u201cWe\u2019ve 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.\u201d<\/p>\n Prakash spoke with theCUBE\u2019s John Furrier<\/a> at the RAISE Summit<\/a> in Paris, during an exclusive broadcast on theCUBE, SiliconANGLE Media\u2019s livestreaming studio. They discussed the rise of open-weight AI models, enterprise agent harnesses and how sovereignty concerns are reshaping AI infrastructure decisions. (* Disclosure below.)<\/p>\n Open-weight AI models reshape enterprise cost and control equations<\/p>\n Cost is a major driver of the shift, but it\u2019s not the only one. Together AI\u2019s 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.<\/p>\n \u201c[Open-weight models] are important for a couple of reasons,\u201d Prakash said. \u201cOne is cost. \u2026 The other is control. 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.\u201d<\/p>\n Enterprises increasingly worry that sending proprietary business processes into closed frontier models effectively hands competitors a blueprint, Prakash noted, pointing to public comments<\/a> from Palantir Technologies Inc. CEO Alex Karp on the same tension. That anxiety is growing as Together AI\u2019s own volume signals how fast agentic workloads are scaling.<\/p>\n \u201cWe were serving 30 billion tokens a month 9 months ago,\u201d he said. \u201cWe are serving over 400 trillion tokens a month now. So, there is an incredible appetite.\u00a0It\u2019s become a compute-bound business.\u201d<\/p>\n Enterprises are responding by building their own \u201charnesses\u201d \u2014 orchestration loops that let them swap models underneath an application with near-zero switching cost, Prakash explained. That flexibility, paired with data control, is turning open infrastructure into a durable competitive advantage rather than just a budget line item.<\/p>\n \u201cYou are not sharing your data with a company that trains models,\u201d Prakash said. \u201cYou have complete control on data residency, what happens with that data, and you can still mix and match multiple models within your harnesses to get the best results. I think this starts becoming a moat in that you\u2019re deploying AI effectively in the enterprise \u2026 all the while you\u2019re also creating these AI assets that you now own, and it becomes part of your intellectual property.\u201d<\/p>\n Here\u2019s the complete video interview, part of SiliconANGLE\u2019s and theCUBE\u2019s coverage of\u00a0RAISE Summit<\/a>:<\/p>\n (* Disclosure: TheCUBE is a paid media partner for the RAISE Summit event. Neither Solidigm, the headline sponsor of theCUBE\u2019s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)<\/p>\n Photo: SiliconANGLE<\/p>\n Support our mission to keep content open and free by engaging with theCUBE community. Join theCUBE\u2019s Alumni Trust Network, where technology leaders connect, share intelligence and create opportunities.<\/p>\n 15M+ viewers of theCUBE videos, powering conversations across AI, cloud, cybersecurity and more About SiliconANGLE Media<\/p>\n SiliconANGLE Media is a recognized leader in digital media innovation, uniting breakthrough technology, strategic insights and real-time audience engagement. As the parent company of SiliconANGLE<\/a>, theCUBE Network<\/a>, theCUBE Research<\/a>, CUBE365<\/a>, theCUBE AI<\/a> and theCUBE SuperStudios \u2014 with flagship locations in Silicon Valley and the New York Stock Exchange \u2014 SiliconANGLE Media operates at the intersection of media, technology and AI.<\/p>\n Founded by tech visionaries John Furrier and Dave Vellante, SiliconANGLE Media has built a dynamic ecosystem of industry-leading digital media brands that reach 15+ million elite tech professionals. Our new proprietary theCUBE AI Video Cloud is breaking ground in audience interaction, leveraging theCUBEai.com neural network to help technology companies make data-driven decisions and stay at the forefront of industry conversations.<\/p>\n","protected":false},"excerpt":{"rendered":"Enterprises racing to deploy AI at scale are discovering that the biggest constraint isn\u2019t model capability anymore \u2014…\n","protected":false},"author":2,"featured_media":105405,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[179,7493,54644,11153,396,54643],"class_list":["post-105404","post","type-post","status-publish","format-standard","has-post-thumbnail","category-agentic-ai","tag-agentic-ai","tag-agentic-artificial-intelligence","tag-control-and-ip","tag-jonathan-anthony","tag-siliconangle","tag-together-ai-positions-open-weight-ai-models-as-the-enterprise-moat-for-cost"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/105404","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=105404"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/105404\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/105405"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=105404"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=105404"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=105404"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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