Sizing Lakebase Search: lessons from a real app and 4 million passages
Summary
Sizing the Local File Cache to match the vector index keeps up to 98% of pages in memory and drops warm vector latency to single-digit milliseconds directly inside Lakebase Postgres. Reducing the search candidate pool to 200 cuts page reads by two-thirds while preserving 90.5% of top-10 results, with an always-on 0.5 CU floor providing an optimal production baseline for just $31 to $40 per month.
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