自托管大型语言模型指南：设置、工具与成本比较（2026）

Enterprise spending on large language models (LLMs) has surged, with model API costs reaching $8.4 billion in 2025, and 72% of companies planning to increase their AI budgets this year. However, a significant barrier to LLM adoption is data privacy and security, as highlighted in Kong's 2025 Enterprise AI report, where 44% of organizations cite these concerns. Every prompt sent to OpenAI, Anthropic, or Google involves external servers, posing a risk for companies handling sensitive data. Self-hosting LLMs addresses this issue by allowing organizations to run models on their own infrastructure, ensuring that data remains within their environment, thus avoiding third-party retention policies and compliance gray areas. While self-hosting introduces complexity, it offers enhanced data security for enterprises.