LlamaParse 提供 5 种生产部署方式，按数据驻留位置选择
做文档解析 RAG 的团队可以看看这篇，LlamaParse 把部署方式拆成了 5 档，从全托管 API 到自己 GPU 跑模型都有，关键是先搞清你的文档能放哪。数据敏感的企业可以选本地部署方案，还能用现有推理商。
LlamaIndex 官方梳理了 LlamaParse 在生产环境的 5 种运行方式：托管 API、完整平台部署到自有云账户或数据中心、平台与存储都在本地但由官方跑模型、接入你现有的推理服务商、以及用自己的 GPU 跑支持的模型。选择的核心问题是文档数据被允许存在哪里。
官方承诺如果使用其推理服务，不会保留用户数据也不会用于训练。博客详细说明了每种方案中各组件运行位置、团队各自拥有哪些部分，以及如何做选型判断。
There are five ways to run LlamaParse in production. Which one fits usually comes down to one question: where are your documents allowed to live? 1/ Our managed API, where we run everything 2/ The full platform in your own cloud account or data center 3/ The platform and storage in your environment, with us running the models 4/ Your existing inference provider, connected to the platform 5/ Your own GPUs, running supported models yourself If you use our inference, we don’t retain your data or train on it. The blog walks through what runs where, what your team owns in each setup, and how to pick: llamaindex.ai/blog/how-to-fl…📊 3 ⚡
This report is categorized under AITOP's "How-to" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include LlamaParse, LlamaIndex, RAG, 文档解析.
Source attribution: curated from LlamaIndex (@llama_index).
