RAG vs GraphRAG: When to Use Each (With Benchmarks)

Everyone's adding RAG to their LLM applications. But basic RAG has limits—and GraphRAG promises to solve them. Is the added complexity worth it? We tested both architectures on 500,000 enterprise documents. Here's what the numbers actually show, and when each approach wins.

What is RAG (Retrieval-Augmented Generation)?

RAG embeds documents into vectors and retrieves the most semantically similar chunks to answer queries. It's the standard approach for grounding LLMs with external knowledge—simple, effective, and widely supported.

What is GraphRAG?

GraphRAG adds a knowledge graph layer to RAG. It extracts entities and relationships from documents, stores them in a graph database, and uses graph traversal alongside vector search. This enables multi-hop reasoning and relationship queries that basic RAG can't handle.

1. The Core Difference

The fundamental difference:

Visual Comparison

RAG Query Flow:

Query: "Who approved the Acme contract?"
    ↓
Vector Search: Find chunks with "Acme" + "approved" + "contract"
    ↓
Return: Top 5 similar chunks
    ↓
LLM: Generate answer from chunks

GraphRAG Query Flow:

Query: "Who approved the Acme contract?"
    ↓
Entity Extraction: "Acme", "contract", "approved"
    ↓
Graph Traversal: Acme → Contract → APPROVED_BY → ?
    ↓
Vector Search: Enrich with relevant context
    ↓
Fusion: Combine graph results + vector results
    ↓
LLM: Generate answer with evidence path

2. How Each Architecture Works

Basic RAG Architecture

GraphRAG Architecture

3. Benchmark Methodology

Test Dataset

Query Categories

Evaluation Metrics

• Accuracy: Did the answer correctly address the query?
• Completeness: Were all relevant facts included?
• Latency: Time from query to response
• Citation Quality: Were sources correctly identified?

Test Setup

• RAG: Pinecone + Claude 3 Sonnet, chunk size 512 tokens
• GraphRAG: Neo4j + Pinecone + Claude 3 Sonnet, same chunk size
• Hardware: AWS m6i.xlarge instances
• Queries: 1,000 per category (5,000 total)

4. Benchmark Results

Accuracy by Query Type

Latency Comparison

Completeness Score (0-100)

Key Finding

GraphRAG delivers 1.5x better accuracy overall, and 2x better on complex queries.

The tradeoff: 2.4x higher latency on average.

5. When RAG Wins

Scenario 1: Simple FAQ/Documentation

Query: "How do I reset my password?"
RAG Accuracy: 94%
GraphRAG Accuracy: 95%
Winner: RAG (nearly same accuracy, lower cost/latency)

Scenario 2: Speed-Critical Applications

Requirement: <1 second response time
RAG: 0.8s average ✅
GraphRAG: 2.2s average ❌
Winner: RAG

Scenario 3: Limited Budget

Monthly Infrastructure:
RAG: ~$300-500
GraphRAG: ~$800-1,500
Winner: RAG (if accuracy tradeoff is acceptable)

Scenario 4: Independent Documents

Document Type: Blog posts, articles, standalone guides
Relationships: Minimal
RAG Accuracy: 89%
GraphRAG Accuracy: 91%
Winner: RAG (graph adds little value)

RAG Sweet Spot

✅ Customer support bots ✅ Documentation search ✅ Simple Q&A applications ✅ Prototypes and MVPs ✅ Cost-constrained projects

6. When GraphRAG Wins

Scenario 1: Multi-Hop Reasoning

Query: "Find all contracts where the signatory also approved the related amendment"
RAG Accuracy: 34%
GraphRAG Accuracy: 91%
Winner: GraphRAG (by a mile)

Scenario 2: Relationship Queries

Query: "Who reports to the person who approved this budget?"
RAG Accuracy: 28%
GraphRAG Accuracy: 89%
Winner: GraphRAG

Scenario 3: Interconnected Documents

Document Type: Contracts referencing other contracts, policies linking regulations
RAG Accuracy: 52%
GraphRAG Accuracy: 88%
Winner: GraphRAG

Scenario 4: Compliance/Legal

Requirement: 95%+ accuracy with citation trails
RAG: Cannot guarantee citation accuracy
GraphRAG: Evidence-mapped retrieval with paths
Winner: GraphRAG

Scenario 5: Enterprise Knowledge Bases

Scale: 100K+ interconnected documents
Query Complexity: Mixed simple + complex
RAG: Fails on 40%+ of queries
GraphRAG: Handles all query types
Winner: GraphRAG

GraphRAG Sweet Spot

✅ Legal document analysis ✅ Contract management ✅ Compliance systems ✅ Enterprise knowledge bases ✅ Research databases ✅ Financial analysis

7. Cost Comparison

Monthly Infrastructure Costs (100K documents, 10K queries/month)

Cost Per Query

ROI Calculation

If GraphRAG improves accuracy from 57% to 86%:

Scenario: Customer support saving 10 minutes per escalation
Escalations avoided per month: (86% - 57%) × 10,000 queries = 2,900
Time saved: 2,900 × 10 min = 483 hours
Cost of human time: 483 × $50/hour = $24,150
GraphRAG extra cost: $315/month
ROI: 76x

For high-value use cases, GraphRAG pays for itself quickly.

8. Decision Framework

Quick Decision Tree

Scoring Matrix

Rate your use case (1-5) on each dimension:

Score > 3.5 → GraphRAG Score < 2.5 → RAG Score 2.5-3.5 → Start RAG, plan for GraphRAG

9. Migration Path: RAG to GraphRAG

If you start with RAG and need to migrate:

Phase 1: Baseline (Week 1-2)

• Identify query types failing in RAG
• Document accuracy gaps
• Estimate GraphRAG ROI

Phase 2: Entity Extraction (Week 3-4)

• Add entity extraction pipeline
• Store entities in graph DB
• Keep vector search running

Phase 3: Hybrid Search (Week 5-6)

• Implement graph traversal
• Add result fusion
• A/B test RAG vs GraphRAG

Phase 4: Full Migration (Week 7-8)

• Route complex queries to GraphRAG
• Keep simple queries on RAG (cost optimization)
• Monitor accuracy improvements

Code: Gradual Migration

def smart_retrieve(query: str) -> list:
    complexity = classify_query_complexity(query)
    
    if complexity == "simple":
        # RAG for simple queries (faster, cheaper)
        return vector_search(query)
    
    elif complexity == "relationship":
        # GraphRAG for relationship queries
        return graphrag_search(query)
    
    else:
        # Hybrid for medium complexity
        return hybrid_search(query, weights={"vector": 0.6, "graph": 0.4})

Conclusion: The Right Tool for the Job

RAG is not dead. It's the right choice for 60%+ of use cases.

GraphRAG is not hype. It delivers real accuracy improvements for complex queries.

The answer: Start with RAG. Monitor accuracy. Migrate to GraphRAG when you hit the ceiling.

Next Steps

1. GraphRAG Implementation Guide → Full architecture and code

2. Building Knowledge Graphs with Neo4j → Graph construction deep dive

3. Hybrid Search Implementation → Vector + Keyword + Graph fusion

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Want help deciding between RAG and GraphRAG?

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