Low-Cost RAG Stack
This stack combines open-source and cost-efficient components to build a Retrieval-Augmented Generation (RAG) system with minimized operational expenses.
/ quick answer
To enable the construction and deployment of a Retrieval-Augmented Generation (RAG) system with a strong emphasis on minimizing operational costs through the strategic use of open-source and cost-efficient tooling. This stack combines open-source and cost-efficient components to build a Retrieval-Augmented Generation (RAG) system with minimized operational expenses.
- Sentence Transformers (for open-source embedding models)
- Qdrant / Chroma (for open-source vector database)
- LiteLLM / OpenRouter (for cost-optimized LLM routing)
- LangChain / LlamaIndex (for RAG orchestration and prompt engineering)
- Open-source LLMs (e.g., Llama 3, Mistral hosted on services like TogetherAI or run locally)
- MinIO / S3 (for low-cost document storage)
- Elasticsearch / OpenSearch (for keyword search fallback or hybrid search)
How do open-source embedding models save costs in RAG?
Open-source embedding models (like those from Hugging Face) save costs by allowing you to run them locally or on your own low-cost compute, avoiding per-call API charges from commercial providers. While they may require managing infrastructure, the operational cost per embedding is often significantly lower for high-volume use cases compared to proprietary alternatives.
What is the primary benefit of using LiteLLM/OpenRouter in a low-cost RAG stack?
The primary benefit of LiteLLM/OpenRouter is dynamic model routing. It allows the RAG system to intelligently send queries to the cheapest LLM that can adequately handle the task, switching between various open-source or commercial models based on current pricing and performance. This avoids being locked into a single expensive model and optimizes cost for every single LLM call within the RAG pipeline.