Production AI on the data you can't move.
Private AI on Cloudera means running generative AI models entirely inside your own security perimeter. Models are served by Cloudera AI Inference on your infrastructure, on governed data in the Cloudera Data Platform, so prompts, documents, and outputs never reach a public model endpoint. Duczer East delivers it for regulated enterprises, built to clear model-risk and audit review.
Proven with Industry Leaders
The demo works. The review kills it.
Your team builds something impressive — then it hits model-risk, the CISO, or compliance, and the answer is no, because the data went somewhere it can't go.
The fix isn't a better demo. It's an architecture where regulated data never leaves your control, every model action is traceable, and the evidence to approve it is produced as part of delivery. That's what private AI on Cloudera is.
One governed stack, entirely inside your boundary.
Cloudera's principle is to bring AI compute to the data instead of moving data to the AI. Every layer below runs in your environment — on-prem or private cloud.
Private AI inference on Cloudera
Private AI inference means the model runs on infrastructure you control, so prompts, retrieved documents, and outputs never go to a public model API. On Cloudera, that is Cloudera AI Inference: a model-serving service with embedded NVIDIA NIM microservices, available on premises as well as in the cloud.
It runs on NVIDIA GPU servers. How many, and with how much memory, depends on the models you serve, their size, and the concurrency and response times you need. We size the infrastructure with your hardware vendor and validate it against your workloads before you commit. If model traffic also needs its own policy and audit point, see our guide to the AI gateway.
Cloudera AI Inference, with embedded NVIDIA NIM microservices for accelerated model serving.
On premises in your data center, or in your private or public cloud. On-premises availability since August 2025.
NVIDIA GPU servers, sized to your models, concurrency, and response-time needs with your hardware vendor.
Grounded answers your reviewers can follow
Keeping data in-perimeter settles where the AI runs. It does not settle whether the answers are right. A model retrieving documents still has to know that two systems mean different things by the same word, and a reviewer still has to see why an answer came out the way it did.
Semantic intelligence is the layer we build for that: an ontology and knowledge graph of your business, mapped to the governed data in Cloudera, with a trace from every answer back to its sources. It is the part of private AI that Duczer East brings to the platform, and it comes from our data engineering consulting practice. Keeping that governed data current, rather than a nightly copy, is the job of Cloudera streaming.
Shared definitions
One ontology of your business terms, so "customer", "counterparty", and "account holder" resolve the same way in every answer.
Knowledge graph
Entities and relationships mapped to governed data in Cloudera, so retrieval follows how your business actually connects.
Decision trace
A record of the sources and steps behind every answer, so model-risk and audit reviewers can follow how it was produced.
We deliver the use case — and the evidence to approve it
The platform is Cloudera's and NVIDIA's. What gets you to production is the domain depth and delivery judgment to satisfy the people whose job is to say no. That's the part we own. Our Cloudera consulting practice runs the build, and Cloudera support keeps the platform sound once the models are live.
Readiness & scope
A fixed-scope assessment of your data, governance, and the target use case — so the build starts on solid ground and the risks are known up front.
Build in-perimeter
Stand up Cloudera AI Inference with NVIDIA NIM, wire in your governed data, and build the use case with grounded, citation-backed outputs.
Clear the review
We produce the lineage, controls, and traceability your model-risk, CISO, and examiner reviews require — and move it into production.
Two production-ready starting points
AML investigator copilot
An in-perimeter copilot that triages financial-crime alerts — pulling transaction history, KYC records, prior filings, and adverse media into one place and drafting the investigation narrative, with every claim cited to source.
PHI-safe document intelligence
In-perimeter summarization and Q&A over PHI-laden records — clinical-note summarization, prior-authorization drafting, or adverse-event triage — with grounded, cited answers reviewers can trust.
Cloudera AI vs Databricks Mosaic AI for regulated models
Both platforms serve and govern generative AI well. The deciding difference for a regulated enterprise is where the models, the data, and the control plane are allowed to live. We implement Databricks and Snowflake as well as Cloudera, so this comparison is about fit, not a sale.
| What to test | Cloudera AI | Databricks Mosaic AI |
|---|---|---|
| Where it runs | On premises, private cloud, or public cloud. | In your public cloud account. No on-premises or disconnected deployment. |
| Control plane | Runs in your environment for on-premises deployments. | Databricks-operated SaaS, with a live connection required. |
| Model serving | Cloudera AI Inference with embedded NVIDIA NIM microservices, inside your boundary. | Mosaic AI Model Serving on Databricks-managed serverless compute, plus Foundation Model APIs and external model access. |
| Governance | Cloudera SDX: Ranger access control, Atlas catalog and lineage. | Unity Catalog. |
| GenAI tooling | Cloudera AI Studios: Agent, RAG, Fine Tuning, and Synthetic Data Studios. | Mosaic AI tools for agents, vector search, and evaluation. |
| Fits best when | Models and data must stay in your data center, or you already run Cloudera. | Your estate is cloud-first and a vendor-hosted control plane is acceptable. |
Product capabilities change quickly. We verify current features against your specific architecture before they go into a design.
Private AI on Cloudera, answered
What is private AI on Cloudera? +
Private AI runs the entire inference stack inside your security perimeter, so prompts, proprietary data, and model outputs never leave your environment. On Cloudera, it's delivered through Cloudera AI Inference powered by NVIDIA NIM, served on top of governed data in the Cloudera Data Platform.
Can Cloudera run LLMs on-premises? +
Yes. Since August 2025, Cloudera AI Inference and Cloudera AI Studios are available on premises as part of Cloudera Data Services, so large language models can be served on GPU infrastructure in your own data center, next to governed data in the Cloudera Data Platform.
What is Cloudera AI Inference? +
Cloudera AI Inference is Cloudera’s model-serving service for production AI. It serves large language models and other models as endpoints inside your environment, uses embedded NVIDIA NIM microservices for accelerated inference, and runs in the cloud or on premises.
What is private AI inference? +
Private AI inference means running a model’s inference, the step where it reads a prompt and produces an answer, on infrastructure you control instead of a public model API. Prompts, retrieved documents, and outputs stay inside your perimeter, which is what most regulators and data-residency rules expect for sensitive data.
Is Cloudera AI or Databricks Mosaic AI better for regulated models? +
It depends on where your models and data must live. Cloudera AI runs on premises, in a private cloud, or in public cloud, with model serving inside your boundary. Databricks runs in your cloud account with a Databricks-operated control plane, and its serverless model serving runs on Databricks-managed compute; there is no on-premises Databricks. If models and data must stay in your data center, Cloudera is the fit. If cloud-hosted serving is acceptable, either can work. We implement Databricks and Snowflake as well as Cloudera, so the recommendation follows your constraints.
What hardware does private AI on Cloudera need? +
NVIDIA GPU servers. The number of GPUs and the memory you need depend on which models you serve, their size, and the concurrency and response times you require. We size the infrastructure with your hardware vendor and validate it against your workloads before you commit.
How does Duczer East help with private AI on Cloudera? +
We deliver it end to end: a fixed-scope readiness assessment, the in-perimeter build on Cloudera AI Inference, the semantic intelligence layer that grounds answers in your business definitions, and the evidence model-risk, CISO, and examiner reviews require. Once it is live, our Cloudera support practice keeps the platform sound.
Does my data leave my environment? +
No. Cloudera brings AI compute to the data rather than sending data out to an external model. Models are served in-perimeter — on-prem or in your private cloud — so regulated data stays inside your control and never reaches a public model endpoint.
Which models can run on it? +
It's model-agnostic. Open and commercial models such as NVIDIA Nemotron, Llama, and Mistral, enterprise models such as Cohere, and your own fine-tuned models can all be served in-perimeter. We help you choose and govern the right model for each regulated use case rather than locking you to one vendor.
How does it clear model-risk and audit review? +
Cloudera SDX provides lineage and access controls, and grounded, citation-backed outputs trace every answer to its source data. Combined with our delivery practices, that produces the evidence model-risk, CISO, and examiner reviews need to approve a deployment.