Pathway vs Quivr

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	Pathway RAG	Quivr RAG
Tagline	Live data framework for production RAG and streaming ETL pipelines in Python.	Open-source RAG framework for building custom AI assistants over your own documents in a few lines of Python.
Category	RAG	RAG
Pricing	Freemium· Community free (BSL 1.1, 8GB/4 cores); Scale and Enterprise tiers with license key	Free· Open source (pip install quivr-core); pay only for LLM/vector-store usage
Model	Multi-model	Multi-model (OpenAI, Anthropic, Mistral, Gemma)
Editorial score	7.3 / 10	8.4 / 10
Use cases	live-ragstreaming-etldocument-indexingmultimodal-raganomaly-detection	document-qacustom-knowledge-baserag-pipelineinternal-assistantschat-with-pdf
Pros	Genuinely live indexing - documents update without rebuild jobs Self-hosted under BSL 1.1, no data leaves your infra Rich connector library (Kafka, S3, SharePoint, Postgres, Delta Lake) Same pipeline handles batch and streaming 20+ production-ready templates including multimodal and adaptive RAG	Genuinely open source and pip-installable, no vendor lock-in Model-agnostic: OpenAI, Anthropic, Mistral, and Gemma supported Minimal boilerplate to get a working RAG assistant running Pairs with Megaparse for tougher PDF and document ingestion Customizable pipeline with tools and web search when you need more
Cons	Steeper learning curve than prompt-chain frameworks BSL is not OSI-approved - commercial restrictions apply at scale Smaller community than LangChain/LlamaIndex Pricing for Scale/Enterprise tiers not transparent	Python library, not a hosted product or UI You manage infra, vector store, and evals yourself Documentation site is sparse compared to larger RAG frameworks LLM and embedding costs are on you
Website	pathway.com	core.quivr.com

Pick Pathway if

✅ Genuinely live indexing - documents update without rebuild jobs
✅ Self-hosted under BSL 1.1, no data leaves your infra
✅ Rich connector library (Kafka, S3, SharePoint, Postgres, Delta Lake)
✅ Same pipeline handles batch and streaming

Pick Quivr if

✅ Genuinely open source and pip-installable, no vendor lock-in
✅ Model-agnostic: OpenAI, Anthropic, Mistral, and Gemma supported
✅ Minimal boilerplate to get a working RAG assistant running
✅ Pairs with Megaparse for tougher PDF and document ingestion

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