LangChain vs LlamaIndex: Streaming Latency on 50K Docs

Why Streaming Latency Actually Matters

Streaming feels faster. That's the entire point. When you ask an LLM a question, you don't want to wait 8 seconds staring at a blank screen before the first token appears. You want something — even if it's just "Based on your documents..." — to show up within 500ms. That psychological threshold is what separates a responsive tool from one that feels broken.

But here's the thing: most RAG benchmarks measure total response time, not time-to-first-token (TTFT). They'll tell you LangChain took 3.2s and LlamaIndex took 3.4s to return a full answer, and call it a tie. Meanwhile, LangChain showed the first word at 800ms and LlamaIndex at 2.1s. One felt twice as fast.

I built the same RAG pipeline in both frameworks, pointed them at a 50,000-document corpus (about 120MB of text), and measured streaming performance under realistic conditions. The results weren't close.

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The Test Setup

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