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RAG Architecture Planner

Design Your RAG System

Get a practical RAG architecture based on your document types, scale, and accuracy requirements. Select components, understand trade-offs, and receive implementation guidance.

Plan Your RAG Architecture Estimate RAG Cost

A production RAG system consists of: document ingestion pipeline, chunking strategy, embedding model, vector store, retrieval layer, re-ranking, and LLM answer generation. The right architecture depends on your document volume, accuracy requirements, and permission model.

RAG Architecture Planner

Configure your requirements below to get a personalized estimate.

RAG Architecture Planner

Documents

Retrieval

Infrastructure

Document Volume

Document Format

How the RAG Planner Works

Define Document Sources

What types and volume of documents?

Specify Requirements

Accuracy, permissions, and response format needs.

Select Components

Choose chunking, embedding, retrieval, and generation components.

Get Architecture Plan

Receive component diagram, technology stack, and implementation plan.

Architecture Cost Implications

Chunking strategy

Fixed-size is cheap; semantic chunking with LLM is expensive but more accurate

Retrieval method

Vector-only is simple; hybrid search (vector + keyword) improves accuracy at higher cost

Re-ranking

Cross-encoder re-ranking improves quality but adds latency and cost

Permission model

Per-user or per-tenant retrieval adds metadata filtering and isolation complexity

Example Scenarios

Simple

Simple Knowledge Base

Basic document search and Q&A

Dev Cost

$20K–$50K

Timeline

6–12 weeks

Monthly

$200–$800

Medium

Customer Support RAG

Accurate answers with citations

Dev Cost

$40K–$100K

Timeline

10–20 weeks

Monthly

$500–$2,000

Complex

Multi-Tenant RAG

Isolated document retrieval per tenant

Dev Cost

$80K–$200K

Timeline

16–30 weeks

Monthly

$1,000–$4,000

MVP vs Production RAG Architecture

MVP Architecture

Fixed-size chunking
Single vector store
Basic similarity search
No re-ranking
Simple prompt templates
Manual evaluation

Production Architecture

Semantic chunking with overlap
Hybrid search (vector + BM25)
Cross-encoder re-ranking
Automated evaluation pipeline
Permission-aware retrieval
Citations and source tracking

Operating Cost Breakdown

Monthly costs for running your RAG system.

Cost Item	Range	Notes
Embedding	$50–$500/mo	Embedding model hosting or API costs
Vector store	$50–$500/mo	Database hosting and queries
LLM inference	$100–$2,000/mo	Answer generation from retrieved context
Document processing	$50–$300/mo	Ingestion pipeline for new documents

Frequently Asked Questions

What chunking strategy should I use?

Start with fixed-size chunks (500–1000 tokens with overlap). Upgrade to semantic chunking if retrieval accuracy is insufficient.

Do I need re-ranking?

Re-ranking significantly improves relevance for complex queries. Start without it, then add if users report poor retrieval quality.

How do I evaluate RAG quality?

Measure: retrieval precision, answer accuracy, citation correctness, and user satisfaction. Build an evaluation dataset from real queries.

Related Tools

Case Studies

EduPilotPro Knowledge System
RAG architecture for educational content

Need a Detailed RAG Architecture?

This planner provides a practical starting point. For a production architecture designed by experienced engineers, schedule a consultation.

Schedule Architecture Review Try AI Solution Architect

App Corp

Full-service software engineering

Engineering your experience…

Free Tool

RAG Architecture Planner

Design Your RAG System

Get a practical RAG architecture based on your document types, scale, and accuracy requirements. Select components, understand trade-offs, and receive implementation guidance.

Plan Your RAG Architecture Estimate RAG Cost

RAG Architecture Planner

Configure your requirements below to get a personalized estimate.

RAG Architecture Planner

Documents

Retrieval

Infrastructure

Document Volume

Document Format

How the RAG Planner Works

Define Document Sources

What types and volume of documents?

Specify Requirements

Accuracy, permissions, and response format needs.

Select Components

Choose chunking, embedding, retrieval, and generation components.

Get Architecture Plan

Receive component diagram, technology stack, and implementation plan.

Architecture Cost Implications

Chunking strategy

Fixed-size is cheap; semantic chunking with LLM is expensive but more accurate

Retrieval method

Vector-only is simple; hybrid search (vector + keyword) improves accuracy at higher cost

Re-ranking

Cross-encoder re-ranking improves quality but adds latency and cost

Permission model

Per-user or per-tenant retrieval adds metadata filtering and isolation complexity

Example Scenarios

Simple

Simple Knowledge Base

Basic document search and Q&A

Dev Cost

$20K–$50K

Timeline

6–12 weeks

Monthly

$200–$800

Medium

Customer Support RAG

Accurate answers with citations

Dev Cost

$40K–$100K

Timeline

10–20 weeks

Monthly

$500–$2,000

Complex

Multi-Tenant RAG

Isolated document retrieval per tenant

Dev Cost

$80K–$200K

Timeline

16–30 weeks

Monthly

$1,000–$4,000

MVP vs Production RAG Architecture

MVP Architecture

Fixed-size chunking
Single vector store
Basic similarity search
No re-ranking
Simple prompt templates
Manual evaluation

Production Architecture

Semantic chunking with overlap
Hybrid search (vector + BM25)
Cross-encoder re-ranking
Automated evaluation pipeline
Permission-aware retrieval
Citations and source tracking

Operating Cost Breakdown

Monthly costs for running your RAG system.

Cost Item	Range	Notes
Embedding	$50–$500/mo	Embedding model hosting or API costs
Vector store	$50–$500/mo	Database hosting and queries
LLM inference	$100–$2,000/mo	Answer generation from retrieved context
Document processing	$50–$300/mo	Ingestion pipeline for new documents

Frequently Asked Questions

What chunking strategy should I use?

Start with fixed-size chunks (500–1000 tokens with overlap). Upgrade to semantic chunking if retrieval accuracy is insufficient.

Do I need re-ranking?

Re-ranking significantly improves relevance for complex queries. Start without it, then add if users report poor retrieval quality.

How do I evaluate RAG quality?

Measure: retrieval precision, answer accuracy, citation correctness, and user satisfaction. Build an evaluation dataset from real queries.

Related Tools

Case Studies

EduPilotPro Knowledge System
RAG architecture for educational content

Need a Detailed RAG Architecture?

This planner provides a practical starting point. For a production architecture designed by experienced engineers, schedule a consultation.

Schedule Architecture Review Try AI Solution Architect

RAG Architecture Planner

RAG Architecture Planner

RAG Architecture Planner

How the RAG Planner Works

Define Document Sources

Specify Requirements

Select Components

Get Architecture Plan

Architecture Cost Implications

Chunking strategy

Retrieval method

Re-ranking

Permission model

Example Scenarios

Simple Knowledge Base

Customer Support RAG

Multi-Tenant RAG

MVP vs Production RAG Architecture

MVP Architecture

Production Architecture

Operating Cost Breakdown

Frequently Asked Questions

Related Tools

Related Articles

Case Studies

Need a Detailed RAG Architecture?

RAG Architecture Planner

RAG Architecture Planner

RAG Architecture Planner

How the RAG Planner Works

Define Document Sources

Specify Requirements

Select Components

Get Architecture Plan

Architecture Cost Implications

Chunking strategy

Retrieval method

Re-ranking

Permission model

Example Scenarios

Simple Knowledge Base

Customer Support RAG

Multi-Tenant RAG

MVP vs Production RAG Architecture

MVP Architecture

Production Architecture

Operating Cost Breakdown

Frequently Asked Questions

Related Tools

Related Articles

Case Studies

Need a Detailed RAG Architecture?