Why moving embedding inside turbopuffer drops search latency

Turbopuffer has introduced native embedding to relieve customers of managing external embedding pipelines and to improve semantic search performance through end-to-end system optimization. Previously, embeddings were generated externally and serialized with search execution, leaving the query planner unable to overlap that work with other tasks. With native embedding, turbopuffer can generate embeddings in parallel with vector-independent operations such as reading index metadata, reducing latency; beta users Linear and Readwise reported savings of 150 milliseconds and an eightfold reduction in median embedding latency, respectively. The service remains configurable for workloads where embeddings can be reused or should be handled externally, and planned caching aims to prevent redundant embedding requests. Turbopuffer also plans broader integrated capabilities, including asynchronous namespace re-embedding, model evaluation and migration, native reranking, improved parsing and chunking, and search agents, building on its search infrastructure for trillions of documents and high write and query volumes.

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