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.
๐ง๐ผ ๐๐ผ๐น๐๐ฒ ๐๐ต๐ถ๐ ๐ฑ๐ฎ๐๐ฎ ๐ด๐ฟ๐ฎ๐๐ถ๐๐ ๐ฝ๐ฟ๐ผ๐ฏ๐น๐ฒ๐บ, ๐๐ฒ๐ฐ๐๐ผ๐ฟ ๐ถ๐ป๐ณ๐ฟ๐ฎ๐๐๐ฟ๐๐ฐ๐๐๐ฟ๐ฒ ๐ต๐ฎ๐ ๐ฒ๐๐ผ๐น๐๐ฒ๐ฑ ๐๐ต๐ฟ๐ผ๐๐ด๐ต ๐๐ต๐ฟ๐ฒ๐ฒ ๐ด๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ถ๐ผ๐ป๐.
๐ญ-๐ฉ๐ฒ๐ฐ๐๐ผ๐ฟ ๐ฑ๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ๐ 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.
๐ฎ-๐ฉ๐ฒ๐ฐ๐๐ผ๐ฟ ๐น๐ฎ๐ธ๐ฒ๐ 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.
๐ฏ-๐ฉ๐ฒ๐ฐ๐๐ผ๐ฟ ๐น๐ฎ๐ธ๐ฒ๐ฏ๐ฎ๐๐ฒ 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:
๐๐ ๐ฑ๐ฎ๐๐ฎ ๐๐ต๐ผ๐๐น๐ฑ ๐น๐ถ๐๐ฒ ๐ผ๐ป๐ฐ๐ฒ, ๐ฎ๐ป๐ฑ ๐๐ผ๐ฟ๐ธ ๐ถ๐ป ๐บ๐๐น๐๐ถ๐ฝ๐น๐ฒ ๐๐ฎ๐๐.
No permanent tax from duplicate storage, sync pipelines, and stale data.
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