DeepSearcher vs Pathway

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

	DeepSearcher RAG	Pathway RAG
Tagline	Open-source agentic RAG framework for private enterprise data, built by the Zilliz/Milvus team.	Live data framework for production RAG and streaming ETL pipelines in Python.
Category	RAG	RAG
Pricing	Free· Free, Apache 2.0; bring your own LLM and vector DB costs	Freemium· Community free (BSL 1.1, 8GB/4 cores); Scale and Enterprise tiers with license key
Model	Multi-model (DeepSeek, OpenAI o1/o3-mini, Claude, Llama, others)	Multi-model
Editorial score	6.9 / 10	7.3 / 10
Use cases	enterprise-ragagentic-searchprivate-document-qaresearch-agentsknowledge-base-search	live-ragstreaming-etldocument-indexingmultimodal-raganomaly-detection
Pros	Apache 2.0, fully self-hostable for private data Agentic multi-step retrieval, not just one-shot RAG Pluggable LLMs and vector stores including Milvus Backed by Zilliz, the team behind Milvus	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
Cons	Library/CLI, no hosted product or managed API Web crawling and some loaders still in development Requires engineering effort to deploy and tune Best experience assumes you already run Milvus/Zilliz	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
Website	zilliztech.github.io	pathway.com

Pick DeepSearcher if

✅ Apache 2.0, fully self-hostable for private data
✅ Agentic multi-step retrieval, not just one-shot RAG
✅ Pluggable LLMs and vector stores including Milvus
✅ Backed by Zilliz, the team behind Milvus

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

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