随着隐私规则的收紧和成本的攀升企业将AI带入数据
每个企业AI策略最终都会出现同样的问题：专有数据是应该前往模型，还是应该前往模型？
随着隐私规则的收紧和地缘政治风险的增加，更多的组织正在选择第二种选择。 这正在改变数据平台提供者的自我表现，Cloudera Inc.围绕部署灵活性制定了AI战略。 公司并未要求客户将数据交给外部AI服务。 相反，据Cloudera产品总监Wim Stop(图片)称，它的目的是在数据已经存在的环境中运行AI。
" 与其说`只要给我们你的所有数据，我们就会给你一个基于这些数据的模型 '，我们转过身去。 “我们说，`我们会把AI带到数据所在的地方和数据所在的地方'。
在CUBE, SiliconANGLE Media的直播室独家播出时，他们讨论了企业如何在保持控制和管理成本的同时将AI带入数据的问题。 (* 下文披露。 )
主权转移到企业中心
Cloudera最近朝这个方向发展，是和Mistral AI SAS合作，带来 Mistral的模型进入Cloudera的数据平台。 尤其对于欧洲的客户来说，模型制造者的本地根部与其技术一样重要，Stoop指出。
“当然，对于我们欧洲来说，有一个法国组织，一个当地公司，提供这些模型和完整的堆栈对我们的许多客户来说是非常有吸引力的，”Stoop 称。 Mistral是一个非常容易的选择
AI位于成熟曲线的顶端，它从主权数据，主权云和当地操作开始，根据Stoop. 欧盟的《数字操作复原力法》和美国的《CLOUD法》等规则正在促使各组织证明它们拥有控制权，而不是简单地声称拥有控制权。 但仅靠控制并不能让AI脱离试验阶段。 对于许多公司来说，企业AI策略中更大的障碍是大规模运行模型的成本，因为生成符物需要大量的硬件。
“如果你为此使用公共提供者，你越是使用你的模型，你就越受到成本打击。” “我如何在组织内高水平地利用一个好的模型，而不承受可怕的代价？我认为，这是各组织目前面临的最大挑战。”
以及CUBE的Cloudera EVOLVE26报导：
(*披露：CUBE是EVOLVE26 2026赛事的付费媒体合作伙伴。 CUBE活动报道的发起人Cloudera和其他赞助者对CUBE或SiliconANGLE上的内容都没有编辑控制。 ).
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Enterprises bring AI to their data as privacy rules tighten and costs climb
Every enterprise AI strategy eventually comes down to the same question: Should proprietary data travel to the model, or should the model come to the data?
As privacy rules tighten and geopolitical risk rises, more organizations are choosing the second option. That is changing how data platform providers present themselves, and Cloudera Inc. has built its AI strategy around deployment flexibility. The company doesn’t ask customers to hand their data to an external AI service. Instead, it aims to run AI inside the environments where that data already lives, according to Wim Stoop (pictured), product director at Cloudera.
“Rather than saying, ‘Just give us all your data and we’ll in return provide you access to a model that’s then contextualized based on that data,’ we turn things around,” Stoop said. “We say, ‘We’ll bring AI to where the data is and where the data lives.'”
Stoop spoke with theCUBE’s Rebecca Knight and Paul Gillin at Cloudera EVOLVE26, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how enterprises can bring AI to their data while maintaining control and managing costs. (* Disclosure below.)
Sovereignty moves to the center of enterprise AI strategy
Cloudera’s latest move in that direction is a partnership with Mistral AI SAS, which brings Mistral’s models into Cloudera’s data platform. For European customers in particular, the model maker’s local roots matter as much as its technology, Stoop noted.
“Certainly for us in Europe, having a French organization, a local company, that provides these models and provides the complete stack is very attractive for a lot of our customers,” Stoop said. “Mistral was a very easy choice for us to say, ‘Those are the ones that we’re going with.'”
AI is at the top of a maturity curve that starts with sovereign data, sovereign cloud and local operations, according to Stoop. Rules such as the European Union’s Digital Operational Resilience Act and the U.S. CLOUD Act are pushing organizations to prove they have control rather than simply claim it. But control alone won’t move AI out of the pilot phase. For many companies, the bigger obstacle in an enterprise AI strategy is the cost of running models at scale, because generating tokens takes a lot of hardware.
“If you use a public provider for that, the more you use your model, the more you get hit on cost,” Stoop said. “How do I get that high utilization of a good model inside my organization without being exposed to horrific costs? That, I think, is the biggest challenge that organizations are struggling with right now.”
Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of Cloudera EVOLVE26:
(* Disclosure: TheCUBE is a paid media partner for the EVOLVE26 2026 event. Neither Cloudera, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
Photo: SiliconANGLE
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