Cloudera Links AI With Enterprise Data Via New ‘Blueprints’ Offering

At its Evolve2026 event this week data platform developer Cloudera also expanded the roster of its Enterprise AI Ecosystem of technology partners in a move to make it easier for partners and customers to develop production-ready AI applications that tap into enterprise data.

Cloudera has launched a portfolio of ready-to-deploy AI software that the data platform developer says will help partners and customers accelerate the development and deployment of AI applications that use their own enterprise data.

The new Cloudera Blueprints, introduced at this week’s Cloudera Evolve26 event in New York, combine Nvidia’s reference AI workflows with Cloudera’s enterprise data management, security, and governance capabilities that the company says will help organizations move AI use cases from proof of concept to production.

“Blueprints get you to being able to solve business problems very quickly,” said Leo Brunnick, Cloudera chief product officer, in an interview with CRN. “These are the pieces and the parts that you need, and these are the steps that you take in order to attack those business problems.”

The Blueprints announcement follows the August debut of Cloudera Anywhere Cloud, the company’s new hybrid data and AI platform that’s scheduled for general availability on Nov. 30.

Brunnick touted the combination of Anywhere Cloud and the new Blueprints as “a game changer” for Cloudera’s go-to-market channel partners because of what he called the “convergence of convenience and control”—the former from the new platform’s reduced implementation/integration time and costs, and the latter from the system’s ability to maintain data security and data sovereignty.

The development of Cloudera Blueprints deepens the company’s already extensive alliance with AI infrastructure giant Nvidia. In August Cloudera teamed with Nvidia to develop native GPU acceleration for Apache Spark 4.1 in Cloudera Data Engineering.

Expanding Cloudera’s Enterprise AI Ecosystem of AI technology and ISV partners, a subset of Cloudera’s partner program, was a major theme at the Evolve26 event. The company announced that it is now working with AI model developer Mistral, graph database developer Neo4j, voice AI and speech model developer Cartesia, and high-performance data infrastructure developer Vast Data.

“The AI ecosystem is all about making it easier for our customers, as well as our partners, to be able to plug in and make AI production- and execution-ready,” said Michelle Hoover, Cloudera senior vice president of global alliances and channels, in an interview with CRN.

“These are third-party engines that can plug into Cloudera’s Anywhere Cloud and provide the building blocks for our partners to more easily build AI applications for their customers,” Hoover said, speaking of the latest additions to the company’s Enterprise AI Ecosystem.

A major hurdle many businesses face as they attempt to scale up AI applications is accessing, preparing and governing the data those applications need in production—especially in regulated industries such as financial services and the public sector.

“One of the things you need most for AI is to get your data ready and to know where it resides,” Hoover said. “Customers’ proprietary data is the greatest asset they have. So what Cloudera is doing is enabling our customers to leverage that data for enterprise AI.”

“This is going to be an exciting opportunity for our partners because Cloudera Anywhere Cloud makes it easier for our partners to build AI solutions on top,” the channel chief said.

Cloudera Blueprints Details

Cloudera Blueprints, based on Nvidia AI Blueprints, are pre-configured AI solutions that run directly on Cloudera AI, the company’s cloud-native toolkit for building and managing generative and agentic AI software.

Blueprints connects Nvidia’s AI reference workflows with Cloudera’s enterprise data and infrastructure, according to the company, making it possible to build and deploy applications on the same platform across public clouds, on-premises data centers and edge computing systems while maintaining consistent security, governance and control.

The result is a faster path from AI prototype to production, according to the company, and the ability to deploy AI wherever enterprise data is stored.

“Nvidia and Cloudera share a commitment to helping enterprises accelerate AI adoption and realize the full value of their investments,” said Pat Lee, Nvidia vice president of strategic enterprise partnerships, in a statement. “By combining Nvidia’s accelerated computing and AI software stack with Cloudera’s hybrid data platform, we’re making it easier for organizations to deploy AI wherever their data resides, whether in the cloud, on premises, or at the edge.”

Some of the first Blueprints span functions in the financial services, healthcare and telecommunications industries. One agent, for example, helps financial institutions develop transaction-based models to improve fraud detection, risk assessment, and personalized customer experiences. A new agent for the healthcare space automates clinical documentation chores. And a “synthetic video detection” agent helps organizations identify AI-generated video content.

AI Survey Findings

Also at Evolve2026, Cloudera released the results of its 2026 State of Enterprise AI Survey of 1,500 IT leaders and decision-makers at the director level or above across nine global markets. The survey, commissioned by Cloudera, was conducted in September by Wakefield Research.

Among the findings: Ninety-eight percent said they had pulled back an AI deployment because of failure, hallucinations or unexpected behavior, with 68 percent experiencing these issues “sometimes” or “frequently.”

“Everybody [hasn’t] gotten across the trust boundary, trusting the AI to really support key decision making,” said Brian Martin, Cloudera CTO of applied AI and vice president of industry solutions, in an interview with CRN, commenting on the survey results.

Martin said that in many cases it’s not that the AI outputs are wrong, but rather it’s a question of “change management” for people to accept AI results. But he also noted that “companies have to be looking for ways” to develop a “level of control, observability and isolation” for AI technology.

The cost of AI is becoming real for many executives: 89 percent said they have limited or redesigned AI use cases due to rising token consumption. Governance and compliance are a constraint on scaling AI, 75 percent of respondents said, while 55 percent reported fast-than-expected growth in AI governance and compliance costs.

“I think everyone is sort of recognizing that cost is the issue now and we’re moving from speed to efficiency,” Martin said of the survey’s findings around AI expenses. “I think the fact is, the costs [of AI] are coming due and companies are seeing the unbudgeted and unplanned expenses start to hit their books in really powerful and meaningful ways. ‘Token maxxing’ is given way to ‘token maximizing’ in a play to be more efficient and derive real value out of [AI initiatives].”

Fifty-eight percent of those surveyed said that moving AI workloads between environments required moderate or major system redesign—a sign of the difficulty of adapting AI deployments as requirements change.