Swapping your LLM edits a prompt. Swapping your embedding model re-embeds the corpus, rebuilds the index and invalidates every retrieval number you have — vectors from two models are not comparable, so there is no gradual migration. That makes this the one choice in a RAG stack you make under lock-in, and the deciding numbers are bytes per vector and who controls the model lifecycle, not a leaderboard rank.
Before you pick a local vector store, notice that Claude Code, Cursor and Codex deleted theirs — the leading coding agents retrieve with grep, not embeddings. If your corpus still needs an index, these four are not competing products but four different architectures: a search library with no storage, a SQLite extension, an embedded engine with a write-ahead log, and a columnar format on disk.
Four vector stores, four nearly identical feature lists — ANN, filters, hybrid search, all of it. The thing that actually decides which one survives the agentic-RAG stack at scale is invisible there: where the index sits relative to your primary data.