Carbon (Perplexity) vs RAGFlow (2026)
Updated April 29, 2026
Overview
Carbon, acquired by Perplexity in December 2024, provided pre-built data connectors for ingesting unstructured data from 25+ sources into LLM applications. Its managed API was wound down in March 2025, with its technology now integrated into Perplexity's enterprise data connectivity stack. Carbon's connectors supported Google Drive, Notion, Slack, Confluence, and other popular data sources for RAG pipelines.
RAGFlow is Infiniflow's open-source RAG engine that fuses retrieval with agent capabilities. 78.3K+ GitHub stars. Deep document understanding (tables, images, multi-language), hybrid search (vector + BM25 + custom scoring + re-ranking), citation-backed answers, and visual workflow builder. April 2026 release added prebuilt ingestion pipelines, sandbox code execution, and chart generation.
Key features
Core capabilities each platform advertises.
- Data connectors
- Google Drive
- Notion
- Slack integration
- Deep document understanding — tables, images, multi-language
- Hybrid retrieval: vector + BM25 + custom scoring + re-ranking
- Citation-backed answers pointing to source chunks
- Visual agent workflow builder with RAG, tools, and MCP
- April 2026: prebuilt ingestion pipelines, sandbox code, chart generation
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Pre-built connectors for easy integration with multiple data sources
- AI-optimized data processing with automatic chunking, embedding, and cleaning
- Enterprise-grade security with SOC 2 Type II compliance and end-to-end encryption
- Streamlined RAG development with production-ready infrastructure
- Now backed by Perplexity AI with enhanced enterprise capabilities
Cons
- Company was acquired and original standalone service is winding down
- Pricing information not transparently available
- Limited documentation on migration paths after acquisition
- Uncertain future roadmap as integration with Perplexity progresses
Pros
- Best document parsing in the OSS RAG space — tables and OCR done right
- Citation-grade answers — every output traceable to source chunks
- Hybrid retrieval (vector + BM25 + re-ranking) out of the box
- Visual agent builder lowers the bar for non-engineers
- 78.3K stars and active 2026 development
Cons
- Heavier infra than minimal RAG libraries (LlamaIndex, Haystack)
- Enterprise pricing requires sales contact — opacity is a friction point
- Visual builder is helpful but bypasses code-first flexibility
- Best-fit team has dedicated RAG engineers, not casual builders
What people are saying
RAGFlow's parsing engine uses deep learning to understand document structure — recognizing tables, extracting text from images via OCR, preserving formatting.
Read full reviewCarbon (Perplexity) or RAGFlow — which should you choose?
Choose Carbon (Perplexity) if you wantChoose if you want
Choose RAGFlow if you wantChoose if you want
- Enterprise knowledge base with citation-grade answers
- Legal and healthcare document Q&A with compliance
- Multi-language document retrieval at scale
- Visual agent workflows that combine RAG and tools
- Research assistants with multi-source data analysis
Compare Carbon (Perplexity) and RAGFlow on your own traffic
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