📄 中文摘要
许多企业最初使用大语言模型（LLM）API，如OpenAI、Anthropic和Google，因为其快速、有效且基础设施由他人管理。然而，随着使用的深入，法律部门开始关注数据隐私风险，财务部门质疑不可预测的API成本，工程团队希望在专有数据上微调模型却无法实现，所有操作都依赖于第三方。这时，私有LLM部署的概念便浮出水面。私有LLM是企业可以控制的大语言模型，运行在企业自有或管理的基础设施上，包括本地、私有云或虚拟私有云（VPC），确保数据不离开企业环境，第三方无法访问企业的提示、输出或训练数据。该指南详细介绍了部署私有LLM所需的基础设施选项、成本模型及合规性等内容。
📄 English Summary
Private LLM Deployment: A Practical Guide for Enterprise Teams (2026)
Many enterprises begin their journey with large language model (LLM) APIs from providers like OpenAI, Anthropic, and Google due to their speed, effectiveness, and managed infrastructure. However, as they delve deeper, legal teams raise concerns about data privacy risks, finance questions the unpredictable API costs, and engineering teams find themselves unable to fine-tune models on proprietary data, as everything is routed through a third party. This is where the concept of private LLM deployment comes into play. A private LLM is a large language model that organizations control, operating on infrastructure they own or manage—whether on-premises, in a private cloud, or within a virtual private cloud (VPC). This setup ensures that no data leaves the organization's environment and that no third party has access to prompts, outputs, or training data. The guide elaborates on the infrastructure options, cost models, and compliance requirements necessary for deploying a private LLM.