FastRAG vs RAGFlow
No leader: the top candidate RAGFlow has only 0.09 confidence (low), below the 0.35 needed to declare a winner. The attribute-by-attribute breakdown below, with a source and date on every value, is the honest way to compare them.
Capabilities
Feature-by-feature on the axes that matter for rag tools. “-” means undocumented, not absent.
What each one is
The product in its own terms, so the numbers below have context.
FastRAG
An open-source Python toolkit that combines document retrieval with large language models to create question-answering and generation systems. Provides optimized components and supports multiple inference backends including Intel accelerators.
RAGFlow
RAGFlow is an open-source RAG engine that streamlines data processing and integration for building production-ready AI systems. It supports multiple data formats and provides configurable LLM and embedding options with agent-based workflows.
Pricing
List pricing as published by each vendor, with the date we read it. Always verify at the source before you buy.
Platform & deployment
Where each product runs and how it can be hosted. A dash means undocumented, not unsupported.
Integrations
What each product connects to. Counts come from the vendor's own integration directory where one exists.
FastRAG
- Haystack
- HuggingFace
- Elastic
- Qdrant
- OpenVINO
- LlamaCpp
- ONNX Runtime
- Llama CPP
- Intel Gaudi
- Elasticsearch
RAGFlow
- Discord
- Feishu
- Telegram
- Line
- Confluence
- S3
- Notion
- Google Drive
- OpenAI
- DeepSeek
- Gemini
- OpenClaw
- OpenAI GPT-5
- Google Gemini 3 Pro
- DeepSeek v4
- MCP
FastRAG or RAGFlow: which one depends on you
A composite score cannot know your constraints. Describe them and both get re-weighted against what you actually need, with the evidence behind every position.
Free to run, no account needed to start. How the evaluation works
Comparison generated from independently-sourced facts. Every value links to its source and retrieval date. See the method.