I Built a RAG Application from Scratch - Here's the Real Cost and Performance Data

Maheshnath09 — Sat, 07 Feb 2026 17:25:02 +0000

Last month, I spent three weeks building a RAG (Retrieval Augmented Generation) application for our company's internal documentation system. Everyone keeps telling you RAG is "simple" and "just works", but nobody talks about the real challenges, costs, and performance trade-offs.

So here's what actually happened when I built this thing from scratch.

The Problem I Was Solving

Our team had 5+ years of technical documentation scattered across Confluence, Google Docs, and random Markdown files. Engineers were wasting hours searching for information that already existed somewhere.

Classic RAG use case, right? Query the docs, get relevant chunks, feed them to an LLM, get an answer. Should be straightforward.

Spoiler: It wasn't.

The Tech Stack Decision

I tested two popular frameworks:

LangChain - The 800-pound gorilla everyone uses
LlamaIndex - The newer kid that's supposedly "better for RAG"

Here's what I actually found after implementing the same system in both.

LangChain Implementation

Pros:

Tons of examples and Stack Overflow answers
Great for complex chains and agents
Integrates with everything

Cons:

Abstract as hell - took me 2 days to understand what was happening under the hood
Breaking changes between minor versions (learned this the hard way)
Overkill for simple RAG

Real implementation time: 4 days including debugging

LlamaIndex Implementation

Pros:

Actually designed for RAG, not retrofitted
Cleaner API for document loading and indexing
Better defaults out of the box

Cons:

Smaller community means fewer answers when you're stuck
Less flexible for non-RAG use cases
Documentation has gaps

Real implementation time: 2 days

Winner for basic RAG: LlamaIndex. Fight me.

Vector Database Showdown

I tested three options: Pinecone, Weaviate, and Chroma.

Pinecone

Cost: $70/month for starter (1 pod, 100k vectors)
Setup time: 15 minutes
Performance: Fast. Really fast.
Pain points: You're locked into their pricing. No self-hosting option.

Weaviate

Cost: Free (self-hosted on AWS EC2 t3.medium)
Setup time: 3 hours (Docker, config, debugging)
Performance: Good, but slower than Pinecone on complex queries
Pain points: You're now managing infrastructure

Chroma

Cost: Free (embedded in your app)
Setup time: 5 minutes
Performance: Fine for <100k documents
Pain points: Not production-ready at scale. Memory issues with large datasets.

What I actually chose: Started with Chroma for development, moved to Pinecone for production. The performance difference was worth the cost once we hit 50k+ documents.

The Chunking Strategy That Actually Worked

Everyone talks about chunk size like there's a magic number. There isn't.

I tested:

256 tokens (too small, lost context)
512 tokens (sweet spot for most docs)
1024 tokens (too large, retrieval precision dropped)

But here's what REALLY mattered: overlap.

Without overlap between chunks:

Retrieval accuracy: 67%
Users complaining: Daily

With 50-token overlap:

Retrieval accuracy: 84%
Users complaining: Rarely

The overlap means important context isn't split awkwardly between chunks. Costs a bit more in storage, but totally worth it.

The Real Costs (Monthly)

Let me break down actual production costs for processing ~10k queries/month on 50k documents:

OpenAI API (GPT-4 Turbo):        $156
  - Embedding (ada-002):          $12
  - Completion calls:            $144

Pinecone:                         $70

Total Infrastructure:            $226/month

Cost per query: ~$0.02

If I'd gone with GPT-3.5-Turbo instead:

OpenAI API (GPT-3.5 Turbo):       $38
Pinecone:                         $70
Total:                           $108/month

Cost per query: ~$0.01

The catch? GPT-3.5's answers were noticeably worse for our technical docs. Users preferred GPT-4's responses 3:1 in blind tests.

Performance Benchmarks

Here's what really matters - how fast is it?

Average query latency:

Vector search (Pinecone): 45ms
LLM generation (GPT-4): 2.3s
Total end-to-end: ~2.4s

95th percentile:

Vector search: 120ms
LLM generation: 4.1s
Total: ~4.3s

The LLM is the bottleneck, not the vector search. Shocking, I know.

What I'd Do Differently

If I started over tomorrow:

Skip LangChain - Just use LlamaIndex for RAG. LangChain is great for complex agent workflows, but it's overkill here.
Start with GPT-3.5 - Test if it's good enough. You can always upgrade. We should've validated GPT-4 was necessary before committing.
Invest in evaluation metrics early - I spent week 2 building the RAG system and week 3 building eval tools. Should've been parallel.
Chunking strategy matters more than model choice - Seriously. I wasted time optimizing prompts when the real issue was how we were chunking documents.
Monitor everything - Set up logging for failed queries, low confidence scores, and user feedback from day one.

The Code (Simplified)

Here's a basic implementation using LlamaIndex and Pinecone:

from llama_index import VectorStoreIndex, ServiceContext
from llama_index.vector_stores import PineconeVectorStore
from llama_index.embeddings import OpenAIEmbedding
import pinecone

# Initialize Pinecone
pinecone.init(api_key="your-key", environment="us-west1-gcp")
pinecone_index = pinecone.Index("docs")

# Create vector store
vector_store = PineconeVectorStore(pinecone_index=pinecone_index)

# Set up service context with chunking params
service_context = ServiceContext.from_defaults(
    chunk_size=512,
    chunk_overlap=50,
    embed_model=OpenAIEmbedding()
)

# Build index
index = VectorStoreIndex.from_vector_store(
    vector_store=vector_store,
    service_context=service_context
)

# Query
query_engine = index.as_query_engine()
response = query_engine.query("How do I deploy to production?")
print(response)

Final Thoughts

RAG isn't magic. It's a solid approach for grounding LLMs in your own data, but it requires real engineering work.

The hype makes it sound like you can spin this up in an afternoon. In reality, getting it production-ready with good accuracy and reasonable costs took me three weeks of full-time work.

But was it worth it? Absolutely. Our engineers are finding answers in seconds instead of hours. The system paid for itself in saved time within the first month.

Just don't believe anyone who tells you it's trivial.

Want to build your own RAG system? The hardest parts are evaluation (knowing if your answers are good) and chunking strategy (breaking documents intelligently). Start simple, measure everything, and iterate.

Feel free to drop questions in the comments. I'll share more specific implementation details if there's interest.

FastAPI vs Flask: Which Python Framework Should You Choose?

Maheshnath09 — Sat, 17 Jan 2026 19:55:17 +0000

I've shipped production apps with both Flask and FastAPI, and here's what I've learned: they're both great, but for different reasons.

Flask: The Reliable Classic

Flask has been around since 2010, and it's still popular for good reason.

What works:

Dead simple to learn. Minimal boilerplate, just Python functions
Massive ecosystem with extensions for everything
Complete flexibility—build your app however you want
Battle-tested in production by thousands of companies

The challenges:

Manual data validation gets repetitive fast
No automatic API documentation (need extra libraries)
Async support exists but feels tacked on
More code to achieve the same results as FastAPI

Quick example:

from flask import Flask, request

app = Flask(__name__)

@app.route('/users', methods=['POST'])
def create_user():
    data = request.get_json()
    # Manual validation needed
    if not data or 'email' not in data:
        return {'error': 'Invalid data'}, 400
    return {'id': 123}

FastAPI: The Modern Powerhouse

Launched in 2018, FastAPI takes advantage of modern Python features.

What works:

Automatic validation using type hints—seriously reduces bugs
Free interactive API docs at /docs (Swagger UI)
Built for async from the ground up
Noticeably faster performance (2-3x in my tests)
Less code, more features

The challenges:

Smaller ecosystem (though growing fast)
Learning curve if your team doesn't know type hints or async
The "magic" can be confusing when debugging

Same example in FastAPI:

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class User(BaseModel):
    email: str
    name: str

@app.post('/users')
def create_user(user: User):
    return {'id': 123}  # Validation automatic!

When I Use Flask

Quick internal tools and admin panels
Projects where Flask extensions solve my exact problem
Teams new to Python web development
"If it ain't broke" maintenance mode apps

When I Use FastAPI

Public APIs where performance matters
High-throughput microservices
ML model deployment
Any project with complex data validation
Modern teams comfortable with type hints

Bottom Line

New API project? → Start with FastAPI. The productivity boost is real.

Simple CRUD app or internal tool? → Flask gets you there faster.

Already using Flask and it works? → Don't rewrite unless you're hitting actual limitations.

Both are excellent choices. FastAPI is my default now for APIs, but Flask still earns its place on plenty of my projects.

What's your go-to? Let me know in the comments! 👇

DEV Community: Maheshnath09

I Built a RAG Application from Scratch - Here's the Real Cost and Performance Data

The Problem I Was Solving

The Tech Stack Decision

LangChain Implementation

LlamaIndex Implementation

Vector Database Showdown

Pinecone

Weaviate

Chroma

The Chunking Strategy That Actually Worked

The Real Costs (Monthly)

Performance Benchmarks

What I'd Do Differently

The Code (Simplified)

Final Thoughts

FastAPI vs Flask: Which Python Framework Should You Choose?

Flask: The Reliable Classic

What works:

The challenges:

Quick example:

FastAPI: The Modern Powerhouse

What works:

The challenges:

Same example in FastAPI:

When I Use Flask

When I Use FastAPI

Bottom Line