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
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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.
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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.
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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.
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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
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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.
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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.
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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.
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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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