Cloudera, known for being the only company that delivers artificial intelligence wherever the data is, announced the release of the new version of Cloudera Data Services, which for the first time allows private generative AI to run within the company’s own data center.
The offering provides GPU-accelerated capabilities, embedded governance, and hybrid portability, making it easier for organizations to build and scale their sovereign data cloud without sensitive information leaving their security perimeter.
In a time when enterprise AI adoption is held back by security risks and intellectual property protection, the company claims that its solution addresses these concerns and reduces prototype-to-production deployment times from months to weeks.
Security, Data Sovereignty, and Speed
According to data from Accenture cited by Cloudera, 77% of organizations lack basic data and AI security practices to protect models, pipelines, and cloud infrastructure.
Cloudera’s solution aims to address this gap by allowing AI workloads to run where the data resides, behind corporate firewalls, thus eliminating the risk of critical information leaks.
The new release is designed not only to enhance security but also to improve data team productivity, optimize infrastructure costs, and shorten deployment cycles.
Controlling Generative AI: New On-Premises Components
Cloudera has incorporated two of its key tools, previously available only in the public cloud, into its on-premises offerings:
Cloudera AI Inference Service (accelerated by NVIDIA):
- One of the industry’s first inference services with integrated microservices capabilities from NVIDIA NIM.
- Enables large-scale deployment and lifecycle management of AI models directly within the data center, where data is already protected.
- Facilitates secure and scalable transition from AI model development to production, especially for generative AI.
Cloudera AI Studios:
- Democratizes the AI application lifecycle by providing low-code templates that empower teams to build and deploy AI applications and agents without extensive technical expertise.
- Accelerates prototyping and fosters collaboration between technical and business teams.
Measured Benefits: Productivity and Cost Savings
An independent Total Economic Impact™ (TEI) study by Forrester Consulting, commissioned by Cloudera, quantified the impact of adopting Cloudera Data Services on-premises:
- 80% faster in delivering value from workload deployment.
- 20% increase in data and platform team productivity.
- 35% cost savings due to a modern, cloud-native architecture.
- Hardware utilization improved from 30% to 70%.
- Capacity requirements reduced by 25% to over 50% after modernization.
From DIY to an Integrated Solution
Industry analyst Sanjeev Mohan highlighted that many companies previously had to “assemble fragile, homemade solutions” for on-premises AI. Cloudera’s offering eliminates that complexity, boosts productivity, and maintains security without compromises.
Moreover, Leo Brunnick, Cloudera’s product director, stated:
“This launch is a significant step toward data modernization. We’ve moved from monolithic clusters to a set of agile, containerized applications that deliver a native cloud-like experience within the data center itself.”
Use Case: Digital Banking in Indonesia
Bank Negara Indonesia (BNI) is among the first to adopt Cloudera’s AI inference service.
Its CIO, Toto Prasetio, explained:
“This technology provides us with the essential infrastructure to securely and efficiently expand our generative AI initiatives, compliant with Indonesia’s evolving regulations. It’s an important step toward delivering smarter, faster, and more reliable digital banking solutions.”
Global Launch with Live Demos
Cloudera is unveiling these advances during its EVOLVE25 data and AI conference in Singapore this week.
The company invites interested parties to register and attend the ClouderaNow event scheduled for October 15, 2025, where detailed demonstrations will show how the platform enables AI to run anywhere the enterprise data resides.
Market Positioning and Competition
Cloudera’s strategy to offer a cloud-native experience in both public and on-premises environments sets it apart.
While companies like Snowflake or Databricks focus on public cloud deployments and multi-environment interconnectivity, Cloudera emphasizes a hybrid, sovereign architecture, vital for heavily regulated sectors like banking, healthcare, government, and defense.
Compared to solutions like VMware Private AI Foundation or Red Hat OpenShift AI, Cloudera provides:
- True hybrid portability with the same suite of services across cloud and on-premises.
- Full data and AI lifecycle integration, not just training and inference.
- Strategic partnerships with NVIDIA to accelerate deployment of advanced models.
A More Sovereign and Secure AI Future
With this update, Cloudera strengthens its role in the sovereign AI discussion—an increasingly relevant issue for governments and large corporations concerned with privacy, data control, and technological dependency.
Deploying private AI within data centers addresses security concerns and offers advantages in latency, costs, and regulatory compliance, especially when moving data across borders isn’t an option.
FAQs
1. What exactly is Cloudera’s private AI?
It is the ability to run generative AI models and other AI applications within the company’s own data center, keeping all data within a controlled environment.
2. Does it require specific hardware?
Yes, Cloudera AI Inference Service benefits from GPU acceleration and NVIDIA NIM capabilities, though the architecture remains flexible for various configurations.
3. How does it differ from public cloud AI?
Public cloud offers elasticity and managed services but involves moving data outside the corporate environment. Cloudera combines cloud-like benefits with total local control.
4. Which sectors stand to benefit most?
Banking, healthcare, government, defense, telecommunications, and any industry with high sovereignty and compliance requirements.
via: cloudera