LangFlow (8.2/10) vs LlamaIndex (8.4/10) — extremely close. LangFlow stands out with visual drag-and-drop flow builder and 100+ integrations. LlamaIndex wins on advanced data indexing with LlamaIndex is cheaper to self-host. Your choice depends on whether you prioritize approach.
| Feature | LangFlow | LlamaIndex |
|---|---|---|
| Primary Use Case | Visual framework for building multi-agent and RAG applications with a Python-based drag-and-drop UI | Data framework for connecting custom data sources to LLMs with advanced indexing, retrieval, and query engine capabilities |
| GitHub Stars | 35,000+ | 36,000+ |
| Integrations | 100+ | 300+ |
| Language | Python | Python/TypeScript |
| License | MIT | MIT |
| Min RAM | 1GB | 512MB |
| Docker Image Size | ~650MB | ~120MB |
| Self-Host Cost | $10/mo | $5/mo |
| Cloud Pricing | N/A (self-host only) | $35/mo |
| Ease of Use | 8.5/10 | 6.5/10 |
| AI Capabilities | 8.5/10 | 9/10 |
| Community Score | 8/10 | 8.5/10 |
| Contributors | 200+ | 1200+ |
Both tools are free to self-host. The real cost is infrastructure. Here's what you'll actually pay:
Skip the DevOps entirely. Deploy LangFlow and LlamaIndex with one click, persistent storage included, auto-SSL, and subdomain routing — starting at $9.99/month.
Python developers wanting a visual way to build and iterate on LangChain-based LLM applications. Key features include visual drag-and-drop flow builder, python-native components, multi-agent orchestration, making it the go-to choice for AI development workflows.
Teams building data-intensive RAG applications who need advanced indexing and retrieval beyond simple vector search. It stands out with advanced data indexing, query engines (semantic, keyword, hybrid), rag pipeline optimization, making it ideal for LLM-powered applications.
You need the strengths of both. LangFlow handles visual drag-and-drop flow builder while LlamaIndex provides advanced data indexing. Many production teams combine them via APIs for a complete AI stack.
Persistent storage. Auto-SSL. Subdomain routing. No DevOps required.
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