4 posts tagged embeddings.
AI-native search is a real category now — Exa alone is a $700M business — and the open technical question is the same one we keep running into. The retrieval layer either finds the right document or it doesn't, and at scale most stacks settle for "approximately".
The standard paths to 100% vector recall are GPU brute-force and mmap-backed indexes. Both work; both have a property that ruled them out for the workloads we kept seeing. A look at the design space and where we landed.
Three production vector databases dominate the category. A neutral look at what each one does, what we do, and how the trade-offs compare.
text-embedding-3-large produces 3,072-dim vectors, and most vector pipelines truncate them to stay performant. Here's what happened when we benchmarked the full dimension end-to-end.