Onyx vs RAGFlow
A side-by-side comparison of Onyx and RAGFlow, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Onyx | RAGFlow |
|---|---|---|
| Category (differs) | Assistant | Search |
| Pricing | FREEMIUM | FREEMIUM |
| License | Open core | Open core |
| Deployment (differs) | Self-host | Hybrid |
| Platforms | Web, API | Web, API |
| Model support (differs) | Multi-model | Model-agnostic |
| Vendor (differs) | Onyx | InfiniFlow Inc. |
| Capabilities (differs) |
|
|
The honest brief
Onyx
Formerly Danswer — MIT-core enterprise search/RAG over your own apps, self-hosted, optional features under open-core.
- Data stays on your own infrastructure
- Connects to company docs and apps
- Works with any LLM, incl. local
- MIT core, free
- Some features under separate license
- Self-host ops overhead
- Connectors need configuration
RAGFlow
DeepDoc parsing turns messy PDFs, tables, and scans into citation-backed chunks—grounding answers better than naive text-splitting RAG stacks.
- Apache-2.0, fully self-hostable
- Deep document, table, and scan parsing
- Hallucination-resistant grounded QA
- Hybrid vector + full-text search
- Built-in agent orchestration
- Heavier setup than hosted RAG APIs
- Cloud tiers cap apps and storage
- Resource-intensive to self-host
When to pick which
Both cover Chat with documents, RAG pipeline, and Cited answers.
Pick Onyx if you need Agent framework, Unified search, and Knowledge graph.
- Agent framework (secondary capability)
- Unified search (secondary capability)
- Knowledge graph (secondary capability)
Pick RAGFlow if you need Multi-agent orchestration, Vector search, and Document parsing (structured).
- Multi-agent orchestration (secondary capability)
- Vector search (secondary capability)
- Document parsing (structured) (primary capability)
Onyx leans on Cited answers as a headline capability; RAGFlow treats it as secondary.
- Cited answers (primary capability)
They also differ on:
- Deployment
- Self-host · Hybrid