LlamaIndex vs Onyx

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

	LlamaIndex RAG	Onyx RAG
Tagline	Data framework for connecting LLMs to your data.	Open-source AI chat connected to your docs, apps, and people
Category	RAG	RAG
Pricing	Freemium· Free open-source; LlamaCloud paid	Freemium· Business: $20 · Enterprise: Contact us
Model	BYO (Claude / GPT / open)	LLM-agnostic — routes to OpenAI (GPT-4o/GPT-5), Anthropic Claude, Google Gemini, Azure OpenAI, AWS Bedrock, or local Ollama/vLLM models
Editorial score	8.7 / 10	—
Use cases	RAGdata ingestionindexing	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
Pros	Focused on retrieval (not general agent stuff) Many ingestion connectors Strong production patterns LlamaCloud for managed ingestion	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
Cons	API surface is large Documentation can be hard to navigate	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
Website	www.llamaindex.ai	onyx.app

Pick LlamaIndex if

✅ Focused on retrieval (not general agent stuff)
✅ Many ingestion connectors
✅ Strong production patterns
✅ LlamaCloud for managed ingestion

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

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