I've been evaluating both LangChain and LlamaIndex for a straightforward production use case: a document Q&A system for internal technical documentation. The requirements were simple: ingest a few hundred PDFs (mostly RFCs and architecture decision records), chunk them, embed them, and serve them via a REST API with low latency under concurrent load.
I was less interested in the "hello world" examples and more in how they handle real throughput. Here's what I found after load testing both setups on identical Kubernetes pods (4 cores, 8GB RAM) using the same `all-MiniLM-L6-v2` sentence transformer for embeddings and ChromaDB for the vector store.
**Throughput Test Setup:**
* 50 concurrent users (locust)
* 5000 unique questions from a curated set
* 3-second timeout per request
* 100ms p95 latency target
**Raw Numbers (avg over 5 runs):**
| Framework | Avg Latency (p50) | p95 Latency | Requests/sec | Timeouts (>3s) |
| :--- | :--- | :--- | :--- | :--- |
| LangChain (LCEL) | 412ms | 890ms | 112 | 0.2% |
| LlamaIndex (v0.10+) | 380ms | 1.2s | 98 | 1.1% |
**The Takeaway:**
LlamaIndex was slightly faster for simpler queries (better p50), but its p95 latency was significantly higher and more variable, causing a few more timeouts. LangChain's LCEL chains demonstrated more predictable performance under this specific load pattern.
**Why the difference?** Digging into the traces, LlamaIndex's query engine was doing extra work in its retrieval and response synthesis phases that wasn't always cached effectively. LangChain's more explicit chain composition let us optimize and prune steps more easily for this narrow task.
For our team, the decision came down to operational simplicity and consistency. We went with LangChain because:
* The performance was more predictable at scale.
* The abstraction matched our mental model for a "pipeline" better, making debugging easier.
* Integration with our existing FastAPI deployment was slightly more straightforward.
If your Q&A needs are highly dynamic or involve complex agent-like workflows, LlamaIndex's approach might be more suitable. But for a high-volume, simple retrieve-and-answer pipeline, LangChain's consistency won for us.
Here's the core LangChain chain we ended up with for reference:
```python
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
system_prompt = "Answer based only on the provided context."
prompt = ChatPromptTemplate.from_messages([
("system", system_prompt),
("human", "{input}"),
])
llm = ChatOpenAI(model="gpt-4-turbo-preview", temperature=0)
question_answer_chain = create_stuff_documents_chain(llm, prompt)
rag_chain = create_retrieval_chain(retriever, question_answer_chain)
```
This setup has been handling about 50k queries daily for three months now without issue.
I'm Sarah, senior analytics lead at a 350-person B2B SaaS company. We've had both frameworks in production for different document chatbot use cases over the last 18 months.
* **Abstraction tax:** LlamaIndex hides more, which you pay for when you need to debug. Tracing a slow query in LangChain (LCEL) is straightforward; in LlamaIndex, you're often digging through its "service context" layers. This added 15-20% to our dev time for any non-standard tuning.
* **Embedding & chunking control:** LangChain gives you direct, fine-grained control over the preprocessing pipeline. For your RFCs, we found LangChain's recursive text splitter with code-aware separators gave us 10-15% better retrieval accuracy over LlamaIndex's defaults, which mattered more than the raw latency.
* **Production tooling:** LangChain's LangSmith observability platform, while an added cost, was the only way we got to stable deployments. The tracing, monitoring, and prompt versioning saved us. LlamaIndex's tooling felt more geared toward prototyping.
* **Resource consumption:** Your numbers match our experience: LlamaIndex can be lighter for simple paths, but its memory footprint scales poorly with concurrent requests. Under sustained load, our LlamaIndex pods hit OOM kills at ~70% of the concurrent user load that identical LangChain pods handled.
I'd pick LangChain for your use case, specifically because you're in production and those RFCs have complex structure. The p95 latency win isn't worth the debugging black box. If you had a simpler doc set and a prototype-first mandate, I'd lean the other way. Tell me your team's Python expertise level and if you already have an APM tool in place, and I'd solidify that call.
Garbage in, garbage out.
Interesting you got those p95 numbers. I've seen something similar in our email support docs chatbot. LlamaIndex's p95 would spike whenever the query needed multi-hop retrieval, even with simple docs. LangChain's p50 was a bit slower but more consistent under load.
I wonder if part of that p95 difference is from LlamaIndex's default node parsing adding hidden overhead? I had to switch to their "global" metadata mode to get stable numbers, which isn't in the quickstart.
Did you keep the default chunking for both, or did you tune them separately? That's usually where I see the biggest swings.
You're spot on about the defaults being a trap! That p95 spike with multi-hop queries is exactly the kind of thing that derails a migration if you don't catch it in testing.
>Did you keep the default chunking for both, or did you tune them separately?
For a true comparison, I always tune them separately to their own "happy place" - it's only fair. With LlamaIndex, that meant ditching the default node parsing for the SimpleNodeParser and really adjusting the chunk size and overlap based on the document structure. Their defaults are optimized for a different type of document, I think, more like plain paragraphs.
Your point about global metadata mode is crucial. That hidden overhead from their default relationship building between nodes can really hammer performance under concurrency. It's one of those "quickstart vs. production" gaps that causes late-night fires. I found that extra metadata work added almost nothing to accuracy for our straightforward Q&A, so turning it off was a free win for latency.
migrate with care
Okay, the dev time overhead is a real concern. That 15-20% extra time is a silent killer on a startup budget.
>LangChain's LangSmith observability platform, while an added cost
This is the painful bit for me. You're saying the production tooling you needed for stability was locked behind another paid SaaS? Is LangSmith a requirement for going live with LangChain, or can you limp along with something else for monitoring? That extra monthly cost is scary when you're just trying to get something that works.
Your focus on p95 latency is critical for production. That 1.1% timeout rate for LlamaIndex, while seemingly small, would trigger our alert thresholds immediately in a user-facing system. Those timeouts often indicate underlying resource contention or garbage collection spikes that worsen with sustained load.
Did you monitor memory pressure during these tests? I've seen similar p95 degradation with LlamaIndex correlate with Python GC pauses, especially when its internal node relationships aren't managed strictly. The defaults can create reference cycles that aren't immediately obvious.
For internal docs, the 0.2% timeout rate with LangChain is far more acceptable. It suggests the LCEL composability provides more predictable resource utilization, even if the median latency is slightly higher. Consistency often trumps raw speed.
Security is a feature, not an afterthought.