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May 15
Most AI teams do not start with a blank slate. They already have data in object storage, pipelines, logs, labels, evaluation sets, and production systems. Then vector search enters the picture. ๐—ง๐—ผ ๐˜€๐—ผ๐—น๐˜ƒ๐—ฒ ๐˜๐—ต๐—ถ๐˜€ ๐—ฑ๐—ฎ๐˜๐—ฎ ๐—ด๐—ฟ๐—ฎ๐˜ƒ๐—ถ๐˜๐˜† ๐—ฝ๐—ฟ๐—ผ๐—ฏ๐—น๐—ฒ๐—บ, ๐˜ƒ๐—ฒ๐—ฐ๐˜๐—ผ๐—ฟ ๐—ถ๐—ป๐—ณ๐—ฟ๐—ฎ๐˜€๐˜๐—ฟ๐˜‚๐—ฐ๐˜๐˜‚๐—ฟ๐—ฒ ๐—ต๐—ฎ๐˜€ ๐—ฒ๐˜ƒ๐—ผ๐—น๐˜ƒ๐—ฒ๐—ฑ ๐˜๐—ต๐—ฟ๐—ผ๐˜‚๐—ด๐—ต ๐˜๐—ต๐—ฟ๐—ฒ๐—ฒ ๐—ด๐—ฒ๐—ป๐—ฒ๐—ฟ๐—ฎ๐˜๐—ถ๐—ผ๐—ป๐˜€. ๐Ÿญ-๐—ฉ๐—ฒ๐—ฐ๐˜๐—ผ๐—ฟ ๐—ฑ๐—ฎ๐˜๐—ฎ๐—ฏ๐—ฎ๐˜€๐—ฒ๐˜€ solved the first problem: low-latency semantic retrieval for production AI applications. They are still critical for production RAG, agents, recommendation, search, and multimodal apps that need database-speed serving at high QPS. But as teams scale, more data already lives in lakes and lakehouses. Moving it into a separate serving system creates pipelines, sync jobs, and stale indexes. ๐Ÿฎ-๐—ฉ๐—ฒ๐—ฐ๐˜๐—ผ๐—ฟ ๐—น๐—ฎ๐—ธ๐—ฒ๐˜€ brought the search closer to the data. Useful, but incomplete. Search near the lake is not the same as production serving. It also does not cover the broader AI data lifecycle: embedding, evaluation, clustering, deduplication, and multimodal processing. ๐Ÿฏ-๐—ฉ๐—ฒ๐—ฐ๐˜๐—ผ๐—ฟ ๐—น๐—ฎ๐—ธ๐—ฒ๐—ฏ๐—ฎ๐˜€๐—ฒ takes one step further. It is a new AI-native and lake-native architecture evolved from vector database systems. It combines production-grade vector serving with lake-native storage and elastic compute, so online search and offline AI data operations can run on the same source of truth. That is the architecture behind Zilliz Vector Lakebase: ๐—”๐—œ ๐—ฑ๐—ฎ๐˜๐—ฎ ๐˜€๐—ต๐—ผ๐˜‚๐—น๐—ฑ ๐—น๐—ถ๐˜ƒ๐—ฒ ๐—ผ๐—ป๐—ฐ๐—ฒ, ๐—ฎ๐—ป๐—ฑ ๐˜„๐—ผ๐—ฟ๐—ธ ๐—ถ๐—ป ๐—บ๐˜‚๐—น๐˜๐—ถ๐—ฝ๐—น๐—ฒ ๐˜„๐—ฎ๐˜†๐˜€. No permanent tax from duplicate storage, sync pipelines, and stale data. --- ๐Ÿ‘‰ Follow @zilliz_universe for vector database and vector lakebase updates built for production AI. #VectorDatabase #VectorLakebase #ScaleWithZilliz #AIInfrastructure
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