RAG Cost Calculator

How RAG Infrastructure Pricing Works in 2026

Building a Retrieval-Augmented Generation (RAG) system comes with hidden costs that scale aggressively with your user base. It’s not just about standard API queries anymore—you have to optimize three distinct layers of infrastructure:

1. Database Indexing & Embedding Costs

Before answering any questions, your system must process your raw knowledge base. Using models like OpenAI text-embedding-3-small ($0.02 per 1M tokens) or Cohere embed-english-v3.0 ($0.10 per 1M tokens), every document is chunked and stored. Smaller chunks mean better accuracy but significantly higher vector counts, increasing the eventual storage overhead.

2. Vector Storage Overheads (Pinecone)

A vector database like Pinecone Serverless charges based on GB stored ($0.33/GB per month) and Read/Write Operations ($2.00 per 1M reads). Crucially, you aren’t just storing raw text—you are storing high-dimensional float arrays alongside indexing structures like HNSW (Hierarchical Navigable Small World) graphs. This overhead typically balloons your index size to 1.5x – 2.0x the size of raw vector floats.

3. Dynamic LLM Synthesis

During a live search, the top K chunks are injected back into the LLM’s system prompt (e.g., GPT-4o-mini). If you inject 5 chunks of 512 tokens for 100,000 queries, you’re looking at 256 Million context tokens billed directly. Our calculator synthesizes all these variables into clear One-Time Startups vs Monthly recurring expenses to prevent post-launch bill shocks.