3 Years and 25 Exabytes of Data
Cloudera is used by the largest global companies across industries—including healthcare, life sciences, financial services, manufacturing, and high tech—to collectively manage over 25 exabytes of data and drive real-time insights via AI and analytics. We’ve helped and learned from hundreds of enterprises using large language models (LLMs) to build applications, assistants, and agents that power productivity and transform processes. Over the last three years, open and closed models have thrived and application architectures have evolved from RAG to agentic. However, one theme remains consistent: combining proprietary enterprise data and context with generative AI models.
What is Private AI?
Private AI refers to the ability to use all your proprietary data to build and run AI models, applications and agents—whether you use public cloud or on-premises infrastructure—without data or insight being shared outside of your organization in any way.
When AI is built privately, all your training data, configurations, and resulting fine-tuned models are kept within your security perimeter, ensuring every step of model creation remains entirely under your control.
When AI is run privately, all your model endpoints are within your security perimeter, so all of your prompts and context sent to models and the responses received stay within your organization’s environment.
Simply put, Private AI is about taking the tremendous innovation in AI while guaranteeing zero external exposure of sensitive data.
What are the key tenets of Private AI?
A Private AI platform must be built on an open foundation, have the ability to leverage public and on- premises infrastructure alike, and have seamless integration across the data and AI life cycle. Let’s look at what this means:
1. Open Source is not just a Philosophy—It’s the Foundation of Private AI
The momentum behind open-source AI is undeniable. Early models like BLOOM and Falcon proved that open-source AI had the potential to compete with proprietary alternatives in both scale and capability. This paved the way for models like Llama, bringing state-of-the-art language AI within reach of businesses eager to tailor solutions to their unique needs. Today, advancements like DeepSeek continue to push boundaries in code generation, reasoning, and operational efficiency. Yet this is just the beginning. The open-source community thrives on iteration, and tomorrow’s models will raise the bar again, becoming smaller, faster, and more specialized.
Open source is foundational at Cloudera: Cloudera AI is designed to embrace this continuous wave of innovation. We empower customers to adopt any open-source model, whether it’s an early model like Bloom and Falcon, a versatile workhorse like Llama, or cutting-edge reasoning model like DeepSeek. Our platform enables seamless transitions between AI model generations, eliminating the need for costly infrastructure overhauls. Today, our customers are evolving their workflows on Cloudera, from leveraging text-generation models for summarization to harnessing advanced reasoning capabilities for mission-critical challenges like code optimization and decision automation. With the same platform, they’re laying the groundwork for tomorrow’s multimodal AI, where models unify text, data, and visual inputs to solve complex problems that once required siloed tools and teams.
This agility isn’t incidental, it’s intentional. By supporting every phase of the open-source journey, we enable users to turn disruption into opportunity, giving customers the freedom to experiment, scale, and future-proof their AI investments without compromise.
2. Bringing AI Compute to Data
AI’s transformative potential depends on a simple principle: models are only as powerful as the data that fuels them. When data and AI systems operate in isolation, challenges arise. Data stored in disconnected systems becomes difficult to access, leading to delays in insights, fragile pipelines, and models that lack the real-time context needed for accurate decisions. Moving data between fragmented tools also increases risk, compromising security and compliance.
At Cloudera, we unify data and AI into a single, cohesive lifecycle. The Cloudera platform and Cloudera AI services are built to work as one integrated system, where data flows seamlessly into AI workflows—governed, secure, and optimized for performance. Shared metadata, security policies, and compute resources eliminate costly data duplication and movement. Every prediction is traceable back to its origin, ensuring transparency and trust.
This integration is core to Cloudera’s design. By unifying the data and AI lifecycle, models stay updated with the latest information while respecting strict access controls and audit requirements. Organizations shift from experimenting with AI to deploying it at scale, turning raw data into actionable results. The outcome? AI that delivers real-world impact—accelerating innovation without sacrificing security, speed, or governance.
3. Private AI, Even in the Public Cloud
Early AI adoption was defined by limitations. Organizations restricted their AI usage to non-sensitive datasets—drafting generic content, analyzing public trends, or automating routine tasks—because moving proprietary data outside their environments posed unacceptable risks. They still do. As a result, mission-critical workflows remained untouched: financial institutions couldn’t safely analyze transaction logs, healthcare providers avoided patient record insights, and manufacturers hesitated to optimize operations with proprietary sensor data.
As the only true hybrid platform for data and AI, Cloudera redefines what’s possible—our customers can run the same AI workload on every cloud and data center, all within their virtual company firewall. With Private AI, enterprises deploy models like Llama3 and DeepSeek directly within their existing data environments—whether in a data center or in a secured AWS or Microsoft Azure cloud, or a hybrid architecture. When all the enterprise data can be used with AI, our customers evolve from basic tasks like generating reports to solving mission-critical challenges—analyzing proprietary sensor data to optimize operations, detecting anomalies in real-time transaction logs, or personalizing customer interactions—all governed by their encryption, access policies, and compliance guardrails.
This is AI without asterisks: your data stays yours, models adapt to your infrastructure, and innovation aligns with your risk tolerance. Cloudera ensures privacy isn’t a constraint but the foundation for AI that transforms every corner of your business—securely, seamlessly, and on your terms.
Cloudera AI: Build and Run Private AI
We built Cloudera AI to ensure you don’t need to choose between innovation and control–instead you can wield both. Our services enable users to build and run AI privately:
- Cloudera AI Workbench accelerates AI development with a flexible, low-to-high-code platform for building and fine-tuning models, creating applications, and agents—by leveraging private data to transform ideas into production-ready solutions faster.
- Cloudera AI Inference is our production-ready service for deploying AI models, applications, and agents at enterprise scale. Its natively integrated, optimized model microservices accelerate inference speeds by 36x on the Cloudera platform, enabling responsive, high-performance AI operations with predictable total cost of ownership (TCO)—so you never have to choose between speed, scale, or cost.
- Cloudera AI Registry acts as the central hub for your end-to-end AI lifecycle—bridging model development and operations. With access to hundreds of optimized models from open-source communities and our AI partners, it ensures cutting-edge advancements are always readily available, easily integrated, and adaptable to your ongoing AI initiatives.
By anchoring our strategy in open-source agility, enterprise-grade security, and a relentless focus on private AI, we empower organizations to break free from yesterday’s compromises. The AI revolution isn’t coming—it’s here. And with Cloudera, it’s yours to shape on your terms.
Take the Next Step
Ready to build AI on your own terms? Learn more about Cloudera’s Private AI capabilities and see how enterprises are putting them into action:
Attending the NVIDIA GTC AI Conference March 17 - 21 in San Jose, CA? Visit our booth #2303 or catch our session: Accelerate AI Innovation with Cloudera’s AI Studios, hosted by Robert Hryniewicz, Director of Enterprise AI, on March 20th at 1:40 PT in the ESJCC Hall 2 Theater.
Alternatively, you can Experience Cloudera AI Inference services by taking advantage of our FREE 5-day trial.
