The Hidden Cost of LangChain: Why My Simple RAG System Cost 2.7x More Than Expected

This content originally appeared on DEV Community and was authored by Himanjan

A developer’s journey from excitement to shock—and what I learned about LangChain’s true cost.

The Moment I Realized Something Was Wrong

Recently, I started deep diving into agentic AI, experimenting with LangChain for building a simple RAG (Retrieval-Augmented Generation) system. Everything seemed fine—until I noticed something strange.

Just two runs of my LangChain-based Python script consumed more than $0.038 in OpenAI API costs, and my credit balance dropped from around $5 to $4.93!

I’m on the Pay-As-You-Go plan — so I feel every API call.

That got me thinking: Is LangChain doing more under the hood than I realize?

I decided to compare it with manual GPT-4 API call using OpenAI’s SDK — and what I found might surprise you. It was not initially easy as I did not get much resource to trace the call directly or from any IDE based extensions.

The Investigation: LangChain vs Manual Implementation

The Task

Build a simple RAG system that:

Seems straightforward, right? Let me show you what happened when I built this two different ways.

Approach 1: The LangChain Way

Here’s my initial implementation using LangChain:

from langchain_community.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_community.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain_community.callbacks.manager import get_openai_callback
from langchain_core.callbacks import BaseCallbackHandler

# 🔹 LLM call counter
class CountingHandler(BaseCallbackHandler):
    def __init__(self):
        self.llm_calls = 0

    def on_llm_start(self, *args, **kwargs):
        self.llm_calls += 1
        print(f"🔍 LLM call #{self.llm_calls}")

# Load and split document
loader = TextLoader("myfile.txt")
docs = loader.load()

splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
chunks = splitter.split_documents(docs)

# Use OpenAI's embedding model (same for both examples)
embedding_model = OpenAIEmbeddings(model="text-embedding-ada-002")
vectorstore = FAISS.from_documents(chunks, embedding_model)
retriever = vectorstore.as_retriever()

# Setup GPT-4 LLM with counter
handler = CountingHandler()
llm = ChatOpenAI(model="gpt-4", temperature=0, callbacks=[handler])

qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=retriever,
    chain_type="refine"  # Change to "stuff" or "map_reduce" for testing
)

with get_openai_callback() as cb:
    response = qa_chain.run("What is the main idea of the document?")
    print("\n📌 Final Response:")
    print(response)

    print("\n📊 LangChain Usage:")
    print(f"Total LLM Calls: {handler.llm_calls}")
    print(f"Prompt Tokens: {cb.prompt_tokens}")
    print(f"Completion Tokens: {cb.completion_tokens}")
    print(f"Total Tokens: {cb.total_tokens}")
    print(f"Estimated Cost: ${cb.total_cost:.4f}")

Looks clean and simple, right?

Approach 2: The Manual Way

Here’s the same functionality using direct OpenAI SDK calls:

from openai import OpenAI
import faiss
import numpy as np
import os

client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

# 🔹 Load and split text
with open("myfile.txt") as f:
    text = f.read()

chunk_size = 500
chunk_overlap = 50
chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size - chunk_overlap)]

# 🔹 Get embeddings from OpenAI
def get_openai_embeddings(text_list):
    embeddings = []
    for text in text_list:
        response = client.embeddings.create(
            model="text-embedding-ada-002",
            input=text
        )
        embeddings.append(response.data[0].embedding)
    return embeddings

chunk_embeddings = get_openai_embeddings(chunks)

# 🔹 Store in FAISS
dimension = len(chunk_embeddings[0])
index = faiss.IndexFlatL2(dimension)
index.add(np.array(chunk_embeddings).astype("float32"))

# 🔹 Embed user query
query = "What is the main idea of the document?"
query_embedding = client.embeddings.create(
    model="text-embedding-ada-002",
    input=query
).data[0].embedding

# 🔹 Search top 3 chunks
D, I = index.search(np.array([query_embedding]).astype("float32"), k=3)
top_chunks = [chunks[i] for i in I[0]]

# 🔹 Build prompt and ask GPT-4
context = "\n\n".join(top_chunks)
prompt = f"""
You are a helpful assistant. Use the context below to answer the user's question.

Context:
{context}

Question: {query}
Answer:"""

response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": prompt}],
    temperature=0.2
)

# 🔹 Output
print("\n📌 Final Response:")
print(response.choices[0].message.content)

# 🔹 Token usage
usage = response.usage
print("\n📊 Manual GPT-4 Usage:")
print(f"Prompt Tokens: {usage.prompt_tokens}")
print(f"Completion Tokens: {usage.completion_tokens}")
print(f"Total Tokens: {usage.total_tokens}")
cost = usage.total_tokens / 1000 * 0.03  # Est. GPT-4 input/output token cost
print(f"Estimated Cost: ${cost:.4f}")

The Shocking Results

After running both implementations with identical documents

• “I added my blog which I have published in dev.to which compares RAG vs Prompt engineering vs fine tuning” which is here

https://dev.to/himanjan/rag-vs-fine-tuning-vs-prompt-engineering-the-complete-enterprise-guide-2jod

I saved this .md file contents directly to a .txt file which is myfile.txt in the code and run the code

The response comparison you can see below and both uses the same embedding model **text-embedding-ada-002** from opneAI

Response from Langchain version

Response from Manual OpenAPI invocation version

If you have already referred or skimmed through my blog you can understand how neat and precise summary the manual version produced with just 342 prompt tokens which is half of the Langchain used tokens. If you are getting a little confused that if prompt token is more then cost would go high. That is also a hidden game in Langchain. When we use refine chain type in Langchain which most of the production system we use it will break the things as below

• Call 1: Initial answer with first chunk

• Call 2: Refine answer with second chunk

• Call 3: Refine again with third chunk

• Call 4: Final refinement

Each call includes the full prompt + previous context, accumulating tokens.
Other chain types for RAG/QA are below

stuff – Puts all retrieved docs into a single prompt (most efficient)
refine – Iteratively refines answer with each document (what we used)
map_reduce – Processes each doc separately, then combines results
map_rerank – Scores each doc’s answer and returns the best one

Cost Comparison Summary:

• Manual approach: 487 tokens, $0.0146
• LangChain approach: 1,017 tokens, $0.0388 (2.7x more expensive!)

Let me break this down:

Why Is LangChain So Much More Expensive?

After digging deeper, I discovered several hidden costs in LangChain:

1. Suboptimal Batching

LangChain’s OpenAIEmbeddings defaults to batching 1,000 texts per API call, but OpenAI supports up to 2,048 inputs per request. This means:

• You’re making ~2x more API calls than necessary
• More calls = more latency + more rate limit exposure

2. Hidden Internal Calls

LangChain makes API calls you can’t see:

• Internal prompt formatting calls
• Retry logic that may duplicate requests
• Chain validation calls
• Memory management overhead

3. Inefficient Context Management

The framework often includes unnecessary context or makes redundant calls for:

• Document metadata processing
• Chain state management
• Output parsing validation

4. Broken Cost Tracking

Perhaps most troubling: get_openai_callback() often shows $0.00 when you’re actually being charged. I experienced this firsthand—the callback reported no costs while my OpenAI balance clearly decreased.

Then I explored further to read about this and found multiple blogs and some GitHub issues on this!!

The Broader Pattern: Companies Are Moving Away

My experience isn’t isolated. Research reveals a troubling pattern:

Real Company Migrations

Octomind used LangChain for a year to power AI agents that create and fix software tests. After growing frustrations with debugging and inflexibility, they removed LangChain entirely in 2024:

“Once we removed it… we could just code. No longer being constrained by LangChain made our team far more productive.”

Multiple development teams have documented similar experiences:

• 10+ months of LangChain code replaced with direct OpenAI implementations in just weeks
• Elimination of dependency conflicts and version incompatibilities
• Significant performance improvements and cost reductions

Technical Evidence from the Community

GitHub Issues document systematic problems:

• Issue #12994: get_openai_callback() showing $0.00 instead of actual $18.24 costs
• Issue #14952: Broken debug logs that merge messages incorrectly
• Widespread reports of AttributeError: module 'langchain' has no attribute 'debug'

Developer testimonials consistently report:

• Simple tasks requiring complex workarounds
• More time debugging LangChain than building features
• Inability to optimize for specific use cases due to abstraction layers

What This Means for Your Projects

When LangChain Might Be Worth It:

Rapid prototyping where cost isn’t a concern

Learning RAG concepts and experimentation

Demos and tutorials that need quick setup

Multi-provider scenarios requiring provider abstraction

When to Skip LangChain:

Production systems where cost and performance matter

Budget-conscious projects on pay-as-you-go plans

Applications requiring precise cost tracking

Performance-critical systems needing optimization

Projects requiring detailed debugging capabilities

The Bottom Line: Transparency Wins

My investigation revealed that what you can’t see can hurt you. LangChain’s abstractions, while convenient for learning, often hide:

• 2-3x higher token usage than optimal implementations
• Multiple hidden API calls that compound costs
• Suboptimal batching that wastes money and time
• Broken cost tracking that leaves you blind to expenses

For my pay-as-you-go budget, these hidden costs add up quickly. What should have been a $0.015 experiment became a $0.038 surprise—and that’s just for two simple runs.

Actionable Recommendations

For Learning and Prototyping:

• Use LangChain to understand RAG concepts quickly
• Expect 2-3x higher costs during development
• Don’t rely on get_openai_callback() for accurate tracking

For Production Systems:

• Start with direct API implementations for transparency
• Batch embeddings optimally (up to 2,048 inputs per OpenAI call)
• Track every token with precise cost calculation
• Profile your usage patterns before optimizing

Cost Optimization Strategy:

1. Implement precise token tracking from day one
2. Batch operations efficiently to minimize API calls
3. Cache embeddings to avoid repeated calculations
4. Monitor costs continuously with direct API usage metrics

Final Thoughts

LangChain serves an important purpose in the AI ecosystem—it helps developers learn and prototype quickly. But for production systems where every dollar counts, transparency and control are worth the extra development effort.

The 2.7x cost difference I discovered isn’t just about money—it’s about understanding what your code actually does. When you’re building AI applications that could scale to thousands of users, those hidden costs become hidden disasters.

My advice? Learn with LangChain, deploy with direct APIs.

Your wallet will thank you.

Have you experienced similar cost surprises with LangChain? Share your experience in the comments below.

Keywords: LangChain, OpenAI API, RAG, Cost Optimization, AI Development, Token Usage, Production AI

About the Author

A software developer exploring the practical challenges of building production AI systems. Currently investigating the gap between AI framework promises and real-world performance.

This content originally appeared on DEV Community and was authored by Himanjan