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Use AI on Your Most Sensitive Data — Without Sending It Anywhere

Host AI models, RAG systems, and vector databases on infrastructure you control. palmiq designs private AI environments for regulated and sensitive workloads.

What Is Private AI Hosting?

Private AI hosting means running AI models on infrastructure your organization controls — in your data center, or in a hosted environment dedicated to you — instead of sending prompts and data to a public AI service. The model runs where your data already lives. Prompts, documents, and outputs never leave the boundary.

A typical private AI environment has four parts: an open-weight model running locally, a vector database storing your documents in a form the model can search, a RAG pipeline (retrieval-augmented generation — the mechanism that lets a model answer using your documents rather than its training data), and the infrastructure underneath it all.

The practical result: an assistant that can answer questions about your contracts, patient protocols, engineering standards, or student records — without any of that content being transmitted to a third party.

Why Organizations Choose Private AI

  • Some Data Legally Cannot Leave

    Patient records, criminal justice information, defense contract data, student records, privileged legal material. For these categories, public AI services aren't a policy question — they're prohibited. Private hosting is the only way those teams get AI at all.

  • Your Data Doesn't Train Someone Else's Model

    With a locally hosted model, the question of what a provider does with your prompts disappears entirely. Nothing was transmitted.

  • Predictable Cost at Volume

    Per-token pricing is attractive at pilot scale and unpleasant at production scale. Owned or hosted infrastructure converts a variable bill into a fixed one — which for steady, high-volume use often costs less.

  • Sovereignty and Contractual Control

    You choose where the infrastructure physically sits, which satisfies data residency requirements and the contractual commitments you've made to your own clients.

What palmiq Builds

  1. Use-Case Scoping

    • Which use cases genuinely require private hosting, and which are fine on a business-grade public service. Not everything needs the expensive answer.
    • Realistic expectations: what open-weight models do well today, and where they still lag the frontier services.
  2. The Infrastructure

    • Compute sized for inference and fine-tuning, storage for model weights and vector data, and the network fabric between them — see AI Infrastructure.
    • Hosted or on-premises. Platforms in our partner ecosystem offer infrastructure-as-a-service with backup, disaster recovery, and security built in — including options designed specifically for hosting local AI models, provisioned without you buying hardware.
    • Or on your own equipment, integrated with your private cloud and storage.
  3. The AI Stack

    • Model deployment and serving, vector database, and RAG pipeline connected to your document sources.
    • Access controls so the assistant respects the permissions your files already have — the single most-skipped step in private AI projects, and the one that causes incidents.
  4. Operations and Protection

    • Monitoring, patching, and capacity management by palmiq.
    • Backup and disaster recovery for the environment — an AI system that has ingested years of institutional knowledge is now business-critical data. See backup and disaster recovery.
    • Governance alignment with Securing AI Use and your compliance obligations.

Public AI Service vs. Private AI Hosting

Public AI service Private AI hosting
Where your data goes To the provider Nowhere — stays inside your boundary
Regulated data Often prohibited Workable with proper controls
Cost model Per token or per seat, variable Fixed infrastructure cost
Model capability Frontier models, always current Strong open models, you control versions
Setup effort Minutes A real project — palmiq's job

The honest recommendation for most organizations is both: a business-grade public service for general work, and private hosting for the narrow set of data that can't leave.

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Common questions

Do we need our own hardware for private AI?

No. Hosted infrastructure-as-a-service options in our partner ecosystem provision the compute, storage, and private networking for AI workloads in a dedicated environment — no CapEx, and your data stays under your control. Owning hardware is one path, not the only one.

Are private models as good as ChatGPT or Claude?

For general-purpose reasoning, frontier commercial models remain ahead. For focused business tasks — searching and summarizing your own documents, answering policy questions, drafting from your templates — well-chosen open models perform very well, especially with RAG grounding them in your content. Match the tool to the task.

What is RAG, in plain terms?

Retrieval-augmented generation. Rather than relying on what a model learned during training, the system searches *your* documents for relevant passages and gives them to the model as context. That's what lets a private assistant answer questions about your specific contracts or procedures — and it's why answers can cite a source document.

Can private AI meet HIPAA or CMMC requirements?

Private hosting removes the third-party disclosure problem that makes public AI services difficult under those frameworks — but compliance depends on the whole environment: access control, encryption, logging, and documentation. palmiq designs to the framework you're held to; see our [compliance services](/products-services/cmmc/cmmc-compliance-assessment).

How long does a private AI deployment take?

A scoped pilot on hosted infrastructure typically runs in weeks rather than months. On-premises builds depend on hardware lead times and your facilities. palmiq's usual advice: pilot hosted, prove the use case, then decide whether to own it.

Explore private AI for your sensitive data

A discovery call with palmiq: which of your AI use cases require private hosting, what infrastructure they need, and what it costs to run them.