I benchmarked embedding APIs for speed, compared local vs hosted models, and tuned USearch for sub-millisecond retrieval on 143k chunks using only CPU. The post walks through the results, trade-offs, and what I learned about embedding API terms of service.
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I benchmarked embedding APIs for speed, compared local vs hosted models, and tuned USearch for sub-millisecond retrieval on 143k chunks using only CPU. The post walks through the results, trade-offs, and what I learned about embedding API terms of service.
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The main motivation for using USearch is that CPU compute is cheap and easy to scale.
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The main motivation for using USearch is that CPU compute is cheap and easy to scale.
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Blog post: 
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Blog post: 
https://huggingface.co/blog/adlumal/lightning-fast-vector-search-for-legal-documents
https://huggingface.co/blog/adlumal/lightning-fast-vector-search-for-legal-documents
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I benchmarked embedding APIs for speed, compared local vs hosted models, and tuned USearch for sub-millisecond retrieval on 143k chunks using only CPU. The post walks through the results, trade-offs, and what I learned about embedding API terms of service.
The main motivation for using USearch is that CPU compute is cheap and easy to scale.

Blog post: https://huggingface.co/blog/adlumal/lightning-fast-vector-search-for-legal-documents
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John6666 K-ai-Innovations NeuraAI2025 Xenobd
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umarbutler
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Maciej Piecko
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Adrian Lucas Malec
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