How to reduce the computational cost of RAG

Reducing RAG computational cost is feasible through optimized retrieval strategies, lightweight components, and infrastructure choices.

Key principles involve minimizing the data processed by the expensive LLM: using metadata filters or smaller rerankers, implementing hybrid search (sparse+dense), and setting stricter relevance thresholds. Applying model quantization, pruning, or leveraging smaller LLMs for specific tasks further cuts cost. Infrastructure like optimized vector databases and hardware acceleration (GPUs/TPUs) boosts efficiency. Ensure reductions don't significantly compromise answer quality or require costly retraining. The focus is primarily on LLM inference and embedding generation costs.

Implementation steps include refining the retriever first—optimize indexing, apply selective filtering, and use tiered retrieval. Second, optimize the generator—downsize/quantize the LLM and experiment with caching or lightweight architectures. Third, optimize infrastructure—deploy on efficient hardware and benchmark continuously. This approach reduces latency, lowers resource demand, and cuts cloud costs significantly while maintaining application performance.