Cloudera expands AI inference and unified data access to on-premises environments, enabling secure, governed enterprise AI across clouds and data centers.
As enterprises shift from AI pilots to production systems, a new question has taken center stage: not whether to deploy AI, but where it should run — and how securely it can access critical data.
Cloudera said Monday it is expanding its AI inference and analytics capabilities into on-premises environments, a move aimed at giving organizations tighter control over cost, compliance and latency as AI workloads scale.
The company is bringing Cloudera AI Inference and Cloudera Data Warehouse with Trino into customer data centers, while also enhancing Cloudera Data Visualization to support AI-driven workflows across cloud, edge and on-premises deployments.
AI Where the Data Lives
For years, the enterprise debate centered on cloud migration. Now, as AI systems increasingly require access to sensitive data, companies are reconsidering whether moving that data is necessary — or wise.
Cloudera cited its recent enterprise AI and data architecture report, which found that nearly half of companies store their data in warehouses. Rather than exporting that data to external environments for model inference, Cloudera’s strategy is to bring AI to the data itself.
With AI Inference now available on premises and powered by NVIDIA infrastructure — including Blackwell GPUs, Dynamo-Triton Inference Server and NIM microservices — organizations can deploy large language models, fraud detection systems, computer vision and other AI workloads directly within their own data centers.
The pitch is straightforward: avoid unpredictable cloud costs, reduce compliance exposure and maintain control over data privacy and latency.
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Unified Access Across the Estate
Cloudera is also extending its Data Warehouse with Trino into data center environments, offering centralized security, governance and observability across distributed data estates.
The updated Data Visualization platform adds AI-generated chart summaries, resilient AI features for handling transient issues, detailed AI query logging for traceability and simplified administrative controls designed to streamline single sign-on management.
Leo Brunnick, Cloudera’s chief product officer, said the goal is to give enterprises “a superior level of control and flexibility” by allowing them to deploy AI and analytics “exactly where their most critical data resides.”
Pat Lee, vice president of strategic enterprise partnerships at NVIDIA, said the collaboration enables customers to scale AI inference while maintaining predictable economics and data-center efficiency.
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The Production Moment
The announcement reflects a broader enterprise shift. As AI moves from experimentation to steady-state production, cost structures, auditability and governance are becoming as important as model performance.
Cloudera’s bet is that the future of enterprise AI will not be cloud-only or on-prem-only — but unified, governed and portable across both.
In the race to operationalize AI at scale, control may prove just as valuable as capability.
