{"id":28824,"date":"2025-08-12T05:13:05","date_gmt":"2025-08-12T12:13:05","guid":{"rendered":"https:\/\/www.pingcap.com\/?post_type=ebook-whitepaper&#038;p=28824"},"modified":"2026-09-17T14:46:12","modified_gmt":"2026-09-17T21:46:12","slug":"4-genai-use-cases-that-power-intelligent-scalable-and-context-awaresystems","status":"publish","type":"ebook-whitepaper","link":"https:\/\/www.pingcap.com\/ko\/ebook-whitepaper\/4-genai-use-cases-that-power-intelligent-scalable-and-context-awaresystems\/","title":{"rendered":"4 GenAI Use Cases That Power Intelligent, Scalable, and Context-Aware Systems"},"content":{"rendered":"<p class=\"wp-block-paragraph\">GenAI isn&#8217;t stuck because of models. Teams juggle an OLTP database, a separate vector store, caches, and sync jobs, building a &#8220;Frankenstack&#8221; that slows delivery and serves stale context. Now agents are creating their own tenants and mutating their own schemas in seconds, and that stack was never designed for it.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This ebook shows how four companies (Dify, Kimi, Manus, and Plaud) replaced that sprawl with a unified data layer that handles transactions, vectors, and agent state in one system. The result: simpler architectures, fresher context, and AI features that actually ship.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">You&#8217;ll discover:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>How Dify replaced nearly 500,000 database containers with one unified system and cut operational overhead by 90%.<\/li>\n\n\n\n<li>Why Kimi runs tens of millions of concurrent agent-created sites on a single cluster, provisioning each isolated database in under a second at zero idle cost.<\/li>\n\n\n\n<li>How Manus migrated in two weeks and reached close to one million database tenants in three months, with agents creating more than 90% of them.<\/li>\n\n\n\n<li>How Plaud cleared a schema freeze for 2 million users across 170 countries, hitting 10x QPS with P95 latency under 10 ms.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">If you&#8217;re scaling agentic AI, start with the data foundation. Read the ebook to see how a unified database turns GenAI from slideware into shipped software.<\/p>","protected":false},"excerpt":{"rendered":"<p>GenAI isn&#8217;t stuck because of models. Teams juggle an OLTP database, a separate vector store, caches, and sync jobs, building a &#8220;Frankenstack&#8221; that slows delivery and serves stale context. Now agents are creating their own tenants and mutating their own schemas in seconds, and that stack was never designed for it. This ebook shows how [&hellip;]<\/p>\n","protected":false},"featured_media":34941,"template":"","categories":[199],"tags":[147,9,31,297],"class_list":["post-28824","ebook-whitepaper","type-ebook-whitepaper","status-publish","has-post-thumbnail","hentry","category-customer-stories","tag-distributed-sql","tag-scalability","tag-tidb-cloud","tag-vector-search"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>GenAI at Scale: Unified SQL + Vector + Graph Database<\/title>\n<meta name=\"description\" content=\"Explore how Dify.AI, TOPRISM, and other companies ship GenAI at scale using TiDB as a unified data layer to cut costs and time to value.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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class=\"card-resource bg-white\" href=\"https:\/\/www.pingcap.com\/ko\/ebook-whitepaper\/4-genai-use-cases-that-power-intelligent-scalable-and-context-awaresystems\/\"><div class=\"card-resource__image-container\"><img class=\"card-resource__image\" alt=\"tidb_feature_1800x600\" src=\"https:\/\/static.pingcap.com\/files\/2025\/08\/17144604\/tidb_feature_1800x600.png\" loading=\"lazy\" width=3600 height=1200 \/><\/div><div class=\"card-resource__content-container\"><div class=\"card-resource__content-head\"><div class=\"card-resource__category\">Customer Story<\/div><\/div><h5 class=\"card-resource__title\">4 GenAI Use Cases That Power Intelligent, Scalable, and Context-Aware 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