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"
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