RAG Cost Estimator

Understanding RAG System Costs

A RAG (Retrieval-Augmented Generation) system has four main cost components: embedding your documents, storing vectors, querying the vector database, and generating responses with an LLM. The relative weight of each depends on your corpus size and query volume.

Cost Breakdown by Component

• Embeddings (one-time + per-query): Converting documents and queries to vectors. One-time cost for corpus indexing, plus a small per-query cost. OpenAI text-embedding-3-small is $0.02/1M tokens — or use self-hosted models for free.
• Vector Database: Storing and retrieving vectors. Managed services (Pinecone, Qdrant Cloud) charge per-record storage plus per-query fees. Self-hosted (pgvector) has a fixed server cost.
• LLM Generation: Typically the largest cost component. The retrieved chunks are injected into the prompt, so higher top-K means more input tokens and higher LLM cost.

Managed vs Self-Hosted

Managed vector databases (Pinecone, Qdrant Cloud, Weaviate Cloud) offer zero-ops convenience with per-usage pricing. Self-hosted options (pgvector, ChromaDB) have a fixed infrastructure cost that becomes cheaper at scale. For most teams, managed services are the right choice until you exceed 10M+ vectors or have strict data residency requirements.

Cost Optimization Tips

• Use hybrid retrieval (keyword + semantic) to reduce top-K while maintaining quality
• Implement a re-ranking step — retrieve top-20 cheaply, re-rank to top-5 before LLM
• Cache frequent queries — many RAG systems see 30-40% cache hit rates
• Use smaller LLMs for simple factual queries, route complex ones to premium models
• Consider dimensionality reduction for embeddings to lower storage costs

Need a Production RAG Pipeline?

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