RAG Vector Database Cost Calculator for Production AI Search
At meaningful query volume, embedding and vector DB cost routinely exceed LLM inference. Model it before you commit to a vendor β or watch re-embedding quietly dominate your bill.
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At meaningful query volume, embedding and vector DB cost routinely exceed LLM inference. Model it before you commit to a vendor β or watch re-embedding quietly dominate your bill.
LangChain started as an abstraction over LLM calls. Today it's the foundation of most serious agentic AI deployments. Here's the definitive guide from the team at SuperML.org.
The single most expensive AI mistake is picking the pattern first and the problem second. Here's how to choose between RAG, GraphRAG, fine-tuning, agentic, and hybrid β by task, not by brand.
The advertised context window is not the usable context window. Here's the math that decides whether your agent works in production β and the calculator that does it for you.
Most production RAG failures trace back to chunking β the upstream decision that gets the least architectural thought. Plan chunk size, overlap, and strategy before you embed 50GB the wrong way.