Cloudera announced the latest release of Cloudera Data Services, bringing Private AI on premises and giving enterprises secure, GPU-accelerated generative AI capabilities behind their firewall.
Concerns about keeping sensitive data and intellectual property secure is a key factor for what holds back AI adoption from enterprises across industries. According to Accenture, 77% of organisations lack the foundational data and AI security practices needed to safeguard critical models, data pipelines, and cloud infrastructure.
Users can significantly reduce infrastructure costs and streamline data lifecycles, boosting data team productivity. They can also accelerate workload deployment, enhance security by automating complex tasks, and achieve faster time to value for AI deployment. Users now get cloud-native agility behind their firewall, allowing them to scale efficiently without sacrificing security.
Both Cloudera AI Inference Service and AI Studios are now available in the data centre. These tools are designed to tackle the biggest barriers to enterprise AI adoption and have previously been available in cloud-only. This launch empowers organisations to accelerate AI adoption and securely build and run GenAI applications within the security of their own data center to keep sensitive intellectual property behind their firewall. Here’s how each offering delivers value on premises:
- Cloudera AI Inference services, accelerated by NVIDIA, on-premises, is one of the industry’s first AI inference services to provide embedded NVIDIA NIM microservice capabilities. Cloudera now brings its ability to streamline the deployment and management of large-scale AI models to the data center. This secure and scalable engine helps deploy and manage the AI production lifecycle, right in the data center, where data already securely resides.
- Cloudera AI Studios on premises democratises the entire AI application lifecycle, offering low-code templates that empower teams to build and deploy GenAI applications and agents.
According to an independent “Total Economic Impact” (or TEI) study conducted by Forrester Consulting and commissioned by Cloudera, a composite organisation representative of interviewed customers who adopted Cloudera Data Services on premises saw an 80% faster time-to-value for workload deployment, a 20% increase in productivity for data practitioners and platform teams, and overall savings of 35% from the modern cloud-native architecture. The study also highlighted significant operational efficiency gains, with some organisations improving hardware utilisation from 30% to 70% and reporting they needed between 25% to more than 50% less capacity after modernising.
“Historically, enterprises have been forced to cobble together complex, fragile DIY solutions to run their AI on-premises,” said Sanjeev Mohan, industry analyst. “Today the urgency to adopt AI is undeniable, but so are the concerns around data security. What enterprises need are solutions that streamline AI adoption, boost productivity, and do so without compromising on security.”
“Cloudera Data Services On-Premises delivers a true cloud-native experience on-premises, providing agility and efficiency without sacrificing security or control,” said Leo Brunnick, Cloudera’s Chief Product Officer. “This release is a significant step forward in data modernisation, moving from monolithic clusters to a suite of agile, containerised applications.”:
“BNI is proud to be an early adopter of Cloudera’s AI Inference service,” stated Toto Prasetio, Chief Information Officer of BNI. “This technology provides the essential infrastructure to securely and efficiently expand our generative AI initiatives, all while adhering to Indonesia’s dynamic regulatory environment. It marks a significant advancement in our mission to offer smarter, quicker, and more dependable digital banking solutions to the people of Indonesia.”
Cloudera directly addresses the biggest security and intellectual property risks of enterprise AI, allowing customers to accelerate their journey from prototype to production from months to weeks.
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