Asymmetric Retrieval: Spend on Docs, Embed your Queries for Free

Asymmetric retrieval, a new approach supported by Vespa and Voyage AI's voyage-4 family, offers a cost-effective method for handling high-volume queries by embedding documents with a robust model while using a smaller, local model for queries. This method leverages the fact that document embedding is a one-time cost, allowing for the use of more resource-intensive models without latency concerns, whereas query embedding is continuous and cost-prohibitive at scale. By embedding documents with the highly accurate voyage-4-large model and queries with the lightweight voyage-4-nano model, companies can significantly reduce costs, as demonstrated by potential savings of $15,000 monthly at 10,000 queries per second. This strategy maintains retrieval quality through a shared vector space and phased ranking system, which combines the speed of binary search with the accuracy of full-precision reranking, making it feasible for large-scale production environments. Vespa's architecture, which separates query embedding from content storage, supports independent scaling and eliminates dependency on external APIs, ensuring stability even during API outages or traffic spikes.

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