Stop Stitching Your RAG Stack: Why We Built seekdb

Hi — we're the seekdb team . We're building seekdb , an open-source AI-native hybrid search database. This is our first post here; in the ones that follow, we'll share our story with seekdb. If your RAG setup looks like this—MySQL for metadata, a vector DB for embeddings, Elasticsearch for full-text, and hundreds of lines of glue code to fuse multi-source retrieval— you're paying the "stitching tax." Industry surveys suggest that a large share of production AI applications still run on multiple databases—relational, vector, and full-text in separate systems—because of data diversity and legacy architecture. That pattern remains common even in large enterprises. This article is about why we built seekdb, and what you actually get when you stop stitching. 1. The Stitching Tax Is Real RAG, semantic search, and agents all need the same kinds of data: who, what, where (relational), what was said or written (full-text), and what it means semantically (vectors). In practice, that means MySQL/