How GraphRAG Cut Our LLM Token Costs by 62% on Indian Pharma Data

Sachitha GS — Sun, 17 May 2026 18:02:09 +0000

How GraphRAG Cut Our LLM Token Costs by 62% on Indian Pharma Data

In the era of LLM-powered applications, token consumption is the hidden bill that only grows. Vector-based RAG helps, but it retrieves chunks, not relationships. For complex domains like pharmaceuticals, multi-hop reasoning across drugs, diseases, and manufacturers is the norm—and vector search often fails.

We built PharmaIntel, a three-pipeline benchmark, to prove that GraphRAG on TigerGraph can answer these questions with far fewer tokens and higher accuracy.

The Setup

2M+ tokens of medical articles and CDSCO-style Indian drug triples
Three pipelines: LLM-Only, Basic RAG (ChromaDB), GraphRAG (TigerGraph Savanna)
Metrics: tokens, latency, cost, LLM-Judge, BERTScore

Results

GraphRAG slashed token usage by 62% compared to Basic RAG and 79% compared to LLM-Only, while improving accuracy to 91% pass rate and 0.72 BERTScore F1. The cost dropped from $0.00126 per query to $0.00048.

The Secret: Multi-Hop Graph Traversal

While vector RAG retrieved loose chunks, GraphRAG walked explicit edges: "Aspirin → treats → Inflammation → symptom of → Arthritis". This focused context let the LLM produce precise answers without rambling.

Business Impact

At 100,000 queries/month, GraphRAG saves over $7,800 compared to LLM-Only. Our interactive ROI slider in the dashboard proves that at scale, graph-based inference isn't just smarter—it's a financial necessity.

Check out the full demo, code, and benchmark report in our GitHub repo. "https://github.com/sachithags/graphrag-inference-hackathon"

DEV Community: Sachitha GS

How GraphRAG Cut Our LLM Token Costs by 62% on Indian Pharma Data