PrivateGPT vs RAGFlow
PrivateGPT
privategpt.devRAGFlow
ragflow.ioSide by side
| Figure | PrivateGPT | RAGFlow |
|---|---|---|
| Price from | Free | $29/mo |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | Yes |
| Web | Yes | Yes |
| Windows | Yes | — |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 5 |
| Spec score · Retrieval-Augmented Generation Tools | 8.6 (#4) | 8.8 (#3) |
| Purpose | PrivateGPT provides an open-source API layer for building private AI applications on your own infrastructure. | RAGFlow describes itself as an open-source RAG engine and integrated agent platform for building a context layer for AI agents. |
Plans
RAGFlow
- FreeFree
- FreeFree
- Starter$29 a month
- Pro$129 a month
- EnterprisePriced on request
In their own words
PrivateGPT
PrivateGPT is a free, open-source API layer for building private AI applications on your own infrastructure. It does not run models itself: it connects to a separate OpenAI-compatible inference server, which can be local or remote. The Messages API supports streaming, asynchronous processing, and token counting. Document workflows include file and artifact ingestion, retrieval with citations, and tools for querying databases and analyzing tabular data. The documented capabilities also include web search and extraction, code execution, custom tools, and MCP connectors. A browser workbench at `/ui` can be used for demos and inspecting API requests, but it stores chats and settings in browser localStorage and has no access control; the documentation describes it as a local demonstrator. PrivateGPT can be installed as a Python package, from source, or with Docker, and runs as a local server. Package installation requires Python 3.11; Python 3.10 and 3.12 or later are unsupported. Platforms include Linux, macOS, Windows, web, API, and self-hosted environments.
RAGFlow
RAGFlow is an open-source retrieval-augmented generation engine and agent platform for building a context layer for AI agents. Its ingestion pipeline processes multiple data formats into semantic representations for retrieval. Search combines vector search and BM25 with custom scoring and re-ranking, while visual workflows connect retrieval, tools, and MCPs for agent orchestration. Users can connect online, local, and OpenAI-compatible model providers for knowledge bases, chats, search, and agents. Documented connectors include Notion, Google Drive, S3, Microsoft Teams, REST API, and Sitemap. RAGFlow provides HTTP and Python APIs, and a chat assistant can be embedded in another webpage using an iframe. It offers cloud and self-hosted deployment; Docker deployment has a stated minimum of 4 CPU cores, 16 GB RAM, and 50 GB disk. The free plan includes five apps, one team member, 0.1 GB of dataset storage, and 500 monthly credits, but no API key. Starter costs $29 a month and includes an API key.