Private LLM
A private LLM is a language model deployment in which your data and prompts stay inside an environment you control or have contractually isolated, rather than a shared consumer service. It can mean an open-weight model you host yourself, or a commercial model accessed through your own cloud account with no training on your data and defined retention.
01why it matters for a business
The phrase gets used loosely, so pin it down before you buy. Some vendors call any enterprise plan private. The questions that matter are concrete: where is data processed, is it stored and for how long, is it ever used to train models, who at the vendor can access it, and which contract terms back that up. For many companies, an enterprise or API agreement through a major cloud provider answers these well without self-hosting anything.
Self-hosting gives maximum control at the cost of running the infrastructure and usually giving up some capability compared with the strongest hosted models. The right choice depends on the data's sensitivity, regulatory obligations such as HIPAA, volume, and the capability the task actually needs.
02what it looks like in practice
A wealth management firm wants advisors to query client meeting notes and portfolio commentary. Compliance will not allow client data in consumer AI tools. The firm accesses a commercial model through its existing cloud provider, within its own account, under terms that exclude training on its data, with logs kept in its own environment and access tied to its identity system. Advisors get a capable model; compliance gets documented data boundaries it can audit.
03common mistakes
- Accepting the word private without reading the data processing terms.
- Self-hosting by default when an enterprise agreement would meet the same requirements at lower cost.
- Securing the model but not the application around it. Logs, prompts, and retrieved documents also hold sensitive data.
- Ignoring access control inside the company. Private to the company is not the same as visible only to the right people.
04related terms
- On-premise AIOn-premise AI means running AI models and the systems around them on hardware in your own facilities or private data center, rather than calling a vendor's cloud service.
- Open-weight modelAn open-weight model is an AI model whose trained parameters, called weights, are published so anyone can download and run it on their own hardware or cloud, subject to its license.
- HIPAA and AIHIPAA and AI refers to how the US Health Insurance Portability and Accountability Act applies when AI systems create, receive, store, or transmit protected health information.
- SOC 2SOC 2 is an attestation report, defined by the American Institute of Certified Public Accountants (AICPA), in which an independent CPA firm examines a service organization's controls against the Trust Services Criteria: security, availability, processing integrity, confidentiality, and privacy.
- AI governanceAI governance is the set of policies, roles, processes, and controls a company uses to decide which AI systems it builds or buys, how they are approved, how risks are assessed, and how they are monitored once running.
05where insomnia club fits
Insomnia Club builds private AI deployments that match your real data obligations, whether that is a self-hosted open-weight model or a commercial model inside your own cloud account.
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