RAG Cost Calculator

Retrieval-Augmented Generation (RAG) has become the standard architecture for building AI applications that need access to private or up-to-date information. By retrieving relevant documents and injecting them into the LLM context, RAG systems can answer questions about your specific data without expensive model fine-tuning. But the costs of a RAG pipeline are often underestimated, especially the ongoing LLM inference costs that scale with query volume.

A RAG pipeline has three main cost components: embedding (one-time), vector database hosting (monthly fixed), and LLM inference (monthly variable). Embedding costs are typically negligible — even large document collections cost only a few dollars to embed. Vector database costs depend on your provider and data volume but are usually $20-100/month for moderate workloads. The dominant cost is LLM inference, because each query sends both the user question and the retrieved context to the LLM.

Optimizing RAG Economics

The single most impactful optimization is reducing the number of tokens sent to the LLM per query. This means retrieving fewer but more relevant chunks (using rerankers), keeping chunk sizes small, and writing concise system prompts. A well-optimized pipeline retrieving 3 chunks at 300 tokens each costs roughly half as much as one retrieving 5 chunks at 500 tokens each — often with comparable answer quality. For building RAG systems that leverage competitive marketing data, Semrush provides structured data APIs ideal for RAG knowledge bases.

Choosing the Right LLM for RAG

Not every RAG query requires a flagship model. Simple factual lookups can be handled by budget models at 10-20x lower cost, while complex analytical queries benefit from more capable models. Implementing a query classifier that routes to the appropriate model tier can reduce LLM costs by 50-70% without meaningful quality degradation. Caching is another high-impact strategy — if 20% of your queries are repeated, caching alone cuts LLM costs by 20%.

When evaluating your RAG costs, compare against the typical benchmark of $0.02 per query. If your cost per query is significantly above this, focus on the cost breakdown chart to identify whether LLM input costs (driven by retrieved context size), LLM output costs (driven by response length), or vector DB costs are the primary driver. For organizations building marketing intelligence RAG systems, Semrush data feeds provide high-quality, structured content that improves retrieval precision and reduces chunk waste.