Cloudera launched Cloudera Blueprints, a set of prebuilt AI development packages aimed at helping customers in certain industries more successfully build and deploy agents and other AI tools.
Introduced during Evolve, Cloudera's annual user conference in New York City, Cloudera Blueprints is built on a native integration with Nvidia Agent Toolkit that makes the platform's features available in Cloudera's environment. Through the Nvidia integration, Cloudera users can combine Nvidia's AI reference workflows with Cloudera's data management, security and governance capabilities to build AI applications they can trust in production and deploy in any environment, including private clouds and on premises.
The Blueprints are rolling out with accelerators aimed at highly regulated industries with a lot of end-user data. They include Build Your Own Transaction Foundation Model to help financial institutions create foundation models that target fraud detection, risk management and customer experience, and Ambient Healthcare Agents will automate clinical documentation.
In addition, Blueprints include AI Agent for Telecom Network Configuration Planning to help telecommunications companies optimize network performance and Synthetic Video Detection so enterprises can identify AI-generated video content that strengthens digital trust, identity verification and fraud prevention.
Perhaps the most significant aspect of Cloudera's Blueprints are that they enable customers to run their AI in any environment, which is especially valuable for enterprises in highly regulated industries, according to David Menninger, an analyst at ISG. He noted that his firm's research shows that two-third of all organizations are evaluating or piloting AI projects in local environments.
"The most common reasons are for data security and privacy, operating costs and managing AI usage at scale," he said. "The Cloudera Blueprints will help those enterprises whose sensitive data spans environments and cannot easily move to the cloud accomplish those objectives."
Blueprints isn't unique, Menninger continued. For example, Cloudera rivals Databricks and Snowflake each provide tools aimed at accelerating AI development. However, enabling development and deployment across on-premises, cloud and edge environments is a potential differentiator for Cloudera.
"The accelerator concept is well established," Menninger said. "Cloudera's strongest differentiation is applying these workflows across [environments], especially where data residency and operational control matter."
Tackling trouble building AI
Despite all the focus on AI over the past few years, including ever-increasing investments in building agents and other AI tools, most enterprises still struggle to develop AI that can be trusted to deliver accurate responses in production environments.
According to McKinsey's State of AI in 2026 report, although more enterprises are scaling agents than in 2025, only 40% of organizations with annual revenues of $1 billion or more have been able to launch agents at scale. That represents an increase from 27% one year earlier but is still well below even 50%. The percentage is even lower for those reporting annual revenues below $1 billion, remaining flat from 2025 with just 22% successfully scaling agentic AI projects.
This announcement is significant on two levels. First, Cloudera is helping AI adopters accelerate and standardize their agentic projects, which is a big step forward. … Second, Nvidia is emerging as a serious contender on many layers of the AI stack.
Kevin Petrie
Analyst, BARC U.S.
By simplifying AI development via Blueprints that customers can follow to build task-specific agents using Nvidia's Agent Toolkit in conjunction with their prepared and governed proprietary data, Cloudera is providing capabilities designed to improve the likelihood of building agents that perform as intended.
New Cloudera tools that help customers keep data consistent are important additions, according to Sanjeev Mohan, founder and principal of analyst firm SanjMo.
"We used to say in machine learning that 90% of the time is spent preparing data -- the same is now true for AI," Sanjeev Mohan, founder and principal of analyst firm SanjMo, told TechTarget, noting that teams often struggle to access, prepare and govern their data.
"Blueprints bundle Nvidia's reference workflows with the pipelines, security and lineage needed to run them on real enterprise data," Mohan continued. "This is crucial for regulated companies whose data sits on-premises or in sovereign environments and can’t be shipped off to wherever the AI runs."
Meanwhile, by enabling customers to run their AI in on-premises environments as well as the cloud, Cloudera is addressing the data sovereignty and compliance problems many enterprises in highly regulated industries face when trying to put agents into production.
"Getting the same experience on-premises as in the cloud has been one of the big hurdles to AI adoption," Mohan said. "That's where Cloudera stands out. A lot of enterprise data is going to stay in data centers, private clouds and at the edge, and Blueprints run right there."
Kevin Petrie, an analyst at BARC U.S., noted that his firm's research shows that fewer than half of all organizations standardize context across teams and platforms. In addition, integrating with Nvidia is valuable for Cloudera customers given Nvidia's innovative capabilities for AI development."This announcement is significant on two levels," Petrie told TechTarget. "First, Cloudera is helping AI adopters accelerate and standardize their agentic projects, which is a big step forward … Second, Nvidia is emerging as a serious contender on many layers of the AI stack, from hardware to models, context engineering and agent development."
However, he added that competing vendors including AWS, Databricks and Snowflake also partner with Nvidia, so Cloudera's integration with Nvidia Agent Toolkit does not provide a significant competitive advantage.
While broadly designed to simplify AI development and provide customers with a direct path toward building agents they can trust in production, specific benefits of Cloudera Blueprints include the following, according to the vendor:
- A faster and easier development process.
- Deployment in any environment, including public clouds and on-premises, so that sensitive and sovereign data does not have be moved.
- Integrations with Nvidia NIM microservices and Nvidia's family of Nemotron models to simplify data preparation and data inferencing.
- An open source foundation so users have freedom to choose AI models, infrastructure and deployment environments without being tied to a single vendor.
Actual accelerators, or AI hype?
While the concept of a blueprint for building agents directly targets some of the trouble enterprises face when trying to make AI projects production-ready, none of the capabilities that data and AI vendors have been adding over the past few years have been the panacea that suddenly leads to successful development.
Many vendors, including the hyperscale cloud providers and Cloudera's data platform peers Databricks and Snowflake, have made connecting agents with context a focus of their product development this year. Meanwhile, blueprints are a common means to try simplifying complex processes. They've each resulted in some improvement, as evidenced by McKinsey's data, but so far not been an elixir for the many ills that plague AI development projects.
Cloudera Blueprints will help users, but not solve every problem, according to Mohan, who noted that Blueprints reduce burdens such as figuring out which models to use, what data to feed them, how to evaluate results and what guardrails to put in place.
"Blueprints shorten the path to a credible proof of concept," he said. "[But] to make them more effective, I'd want to see clear guidance on adapting a [Cloudera] Blueprint to a customer's own data, built-in evaluation and observability so teams can show [and agent is] production ready, and published cost and performance numbers across deployment environments."
Menninger noted that while Cloudera's Blueprints, in theory, will help simplify and speed AI development, each enterprise still has unique needs. Therefore, their effectiveness may be limited, depending on the customer.
"The challenge with any blueprint is adapting or extending those blueprints to meet an enterprise’s unique business requirements," he said. "But a blueprint allows the enterprise to concentrate their efforts on those activities that are unique to their organization."
Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.
