FinOps for RAG Systems
Overview
Retrieval-Augmented Generation (RAG) systems combine LLMs with external data sources, introducing unique cost considerations.
Cost Challenges
- Data retrieval costs
- LLM inference costs
- Storage and bandwidth
Architecture Approach
- Hybrid retrieval pipelines
- Query optimization
- Cost-aware routing
Optimization Techniques
- Caching frequent queries
- Reducing context window size
- Using open-source models where possible
Tools Used
- Haystack
- LangChain
- Vector databases (Pinecone, Weaviate)
Best Practices
- Monitor retrieval and inference costs separately
- Use cost dashboards
- Automate cost reporting