Onyx vs Pathway

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

	Onyx RAG	Pathway RAG
Tagline	Open-source AI chat connected to your docs, apps, and people	Live data framework for production RAG and streaming ETL pipelines in Python.
Category	RAG	RAG
Pricing	Freemium· Self-hosted open-source: free. Business Cloud: $20 per user/month (annual). Enterprise: custom pricing with SSO, on-prem, region deployments, and SLA.	Freemium· Community free (BSL 1.1, 8GB/4 cores); Scale and Enterprise tiers with license key
Model	LLM-agnostic — routes to OpenAI (GPT-4o/GPT-5), Anthropic Claude, Google Gemini, Azure OpenAI, AWS Bedrock, or local Ollama/vLLM models	Multi-model
Editorial score	—	7.3 / 10
Use cases	Internal knowledge-base chatbot over Confluence and Google DriveSupport-team assistant grounded in Zendesk tickets and help docsSales enablement over Salesforce, Gong, and pitch decksEngineering docs and codebase Q&A over GitHub and NotionSlack bot that answers questions in-thread with citationsDeep-research agent across web and internal sourcesOnboarding assistant for new hiresPermission-scoped RAG for regulated industries	live-ragstreaming-etldocument-indexingmultimodal-raganomaly-detection
Pros	Open-source (MIT-adjacent) with active development and 20k+ GitHub stars, so you can self-host and audit the retrieval pipeline 40+ pre-built connectors for common SaaS and file stores, saving weeks of custom ETL work Permission-aware retrieval that honors source-system ACLs, avoiding the classic RAG leak of exposing restricted docs LLM-agnostic: swap between GPT, Claude, Gemini, Bedrock, or a local Ollama/vLLM model without rewriting the stack Hybrid search plus re-ranking out of the box, rather than a naive top-k vector lookup Custom agent framework, code interpreter, and Slack bot ship in-product, not as separate SKUs Cloud tier gives a managed option with SOC 2 Type II, GDPR, SSO, and audit logs for enterprise buyers	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	Self-hosting is Docker/K8s-heavy and needs Postgres, Vespa/Vector store, and worker processes — not a one-click install for small teams Answer quality still depends heavily on your connector hygiene; stale or duplicated source docs produce confidently wrong answers Cloud pricing at $20/user/mo scales quickly for large orgs versus running the OSS build yourself Custom agent authoring is less mature than dedicated agent-builder tools like LangGraph or CrewAI Fine-grained observability (per-query latency, retrieval traces) is thinner than specialist LLMOps platforms	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	onyx.app	pathway.com

Pick Onyx if

✅ Open-source (MIT-adjacent) with active development and 20k+ GitHub stars, so you can self-host and audit the retrieval pipeline
✅ 40+ pre-built connectors for common SaaS and file stores, saving weeks of custom ETL work
✅ Permission-aware retrieval that honors source-system ACLs, avoiding the classic RAG leak of exposing restricted docs
✅ LLM-agnostic: swap between GPT, Claude, Gemini, Bedrock, or a local Ollama/vLLM model without rewriting the stack

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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