Practical writing for the engineers who build AI search and the leaders who pay for it: vector storage costs, hybrid retrieval, GraphRAG and running search where your data has to live.
Compression is the headline in vector search. Where vector storage cost really sits, what compression can mean, and three questions to ask any provider about who keeps the savings.
Read the article →Running a graph database beside a vector database means two writes and no shared transaction. What that costs in production GraphRAG, when a separate graph still makes sense, and a checklist before you scale.
Read the article →Exact match, meaning or relationships? A practical way to route RAG retrieval between keyword, vector and graph search, with examples you can test on your own data.
Read the article →Embeddings, indexes and relationship data are built from your contracts, tickets and policies. Five questions for risk, data and platform leads about where AI search data really lives.
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