Estimate monthly costs for a RAG pipeline including embeddings and LLM inference.
Embedding converts each chunk into a vector for similarity search. Cost depends on the number of chunks, tokens per chunk, and the embedding model's price per million tokens. Larger models like text-embedding-3-large cost more but provide better retrieval accuracy.
Each RAG query sends the retrieved chunks plus the user's question as context to the LLM. Both input tokens (chunks + question) and output tokens (generated answer) are billed. More retrieved chunks increase input token costs linearly.
Reduce costs by using smaller embedding models for retrieval, limiting chunks to 3-5 per query, using cheaper LLMs for simple queries, implementing caching for repeated queries, and batching embeddings during ingestion rather than per-query.