[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-cloud-infrastructure-spend-jumps-ai-demand-zh":3,"article-related-cloud-infrastructure-spend-jumps-ai-demand-zh":28,"series-industry-53332d20-ac19-4066-bbfd-4164923130f7":84},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":11,"views":25,"created_at":26,"published_at":27,"topic_cluster_id":11},"53332d20-ac19-4066-bbfd-4164923130f7","cloud-infrastructure-spend-jumps-ai-demand-zh","AI 需求把雲端支出推高 29%","\u003Cp>全球雲端基礎設施支出在 Q4 2025 衝到 \u003Cstrong>1109 億美元\u003C\u002Fstrong>。年增 \u003Cstrong>29%\u003C\u002Fstrong>。這不是小波動，是整個市場一起加碼。\u003C\u002Fp>\u003Cp>說白了，錢主要花在 AI。訓練、推論、資料處理，全部都在吃伺服器和 GPU。雲端大廠只能跟著擴容量，不然客戶一多就塞車。\u003C\u002Fp>\u003Cp>問題也很直接。企業一邊喊要上 AI，一邊又盯著成本。結果就是雲端支出往上跑，採購團隊卻還在算每個 Token 的成本。\u003C\u002Fp>\u003Ch2>為什麼帳單一直變大\u003C\u002Fh2>\u003Cp>這波支出成長，核心就是容量。\u003Ca href=\"https:\u002F\u002Faws.amazon.com\u002F\" target=\"_blank\" rel=\"noopener\">AWS\u003C\u002Fa>、\u003Ca href=\"https:\u002F\u002Fcloud.google.com\u002F\" target=\"_blank\" rel=\"noopener\">Google Cloud\u003C\u002Fa>、\u003Ca href=\"https:\u002F\u002Fazure.microsoft.com\u002F\" target=\"_blank\" rel=\"noopener\">Microsoft Azure\u003C\u002Fa> 這些 hy\u003Ca href=\"\u002Fnews\u002Fopenclaw-april-2026-update-xai-minimax-zh\">pe\u003C\u002Fa>rscaler，現在買的不只是機器，是整套 AI 基礎設施。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775200180978-1ufe.png\" alt=\"AI 需求把雲端支出推高 29%\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>AI 訓練很吃 GPU。推論也不便宜，尤其是高頻請求、長上下文、RAG 這些場景。你如果把模型放進產品主流程，雲端帳單通常會先嚇你一跳。\u003C\u002Fp>\u003Cp>另一個原因是企業工作負載沒有停。傳統 SaaS、資料倉儲、串流分析、備份與災難復原，這些都還在。AI 只是把原本就不低的雲端支出，再往上疊一層。\u003C\u002Fp>\u003Cul>\u003Cli>GPU 採購和機房建置同步增加\u003C\u002Fli>\u003Cli>推論流量把即時算力需求拉高\u003C\u002Fli>\u003Cli>企業把更多資料搬上雲\u003C\u002Fli>\u003Cli>大型模型服務吃掉更多網路與儲存資源\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>誰在付錢，誰在賺錢\u003C\u002Fh2>\u003Cp>這筆錢不是平均分。\u003Ca href=\"https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002F\" target=\"_blank\" rel=\"noopener\">Microsoft\u003C\u002Fa>、\u003Ca href=\"https:\u002F\u002Fwww.amazon.com\u002F\" target=\"_blank\" rel=\"noopener\">Amazon\u003C\u002Fa>、\u003Ca href=\"https:\u002F\u002Fwww.google.com\u002F\" target=\"_blank\" rel=\"noopener\">Google\u003C\u002Fa> 這類大廠，因為掌握雲端、AI 平台和銷售通路，最容易把需求轉成營收。\u003C\u002Fp>\u003Cp>但客戶端的感受通常沒那麼美好。很多團隊會先做 PoC，再發現正式上線後成本比預期高 2 倍，甚至 3 倍。這時候大家才開始認真看 cache、batching、量化和模型路由。\u003C\u002Fp>\u003Cp>\u003Ca href=\"https:\u002F\u002Fwww.nvidia.com\u002F\" target=\"_blank\" rel=\"noopener\">NVIDIA\u003C\u002Fa> 也在這波裡面吃到大量需求。因為雲端擴容的核心硬體，很多還是繞不開它的 GPU 生態。講白了，雲端大廠買卡，最後還是得看\u003Ca href=\"\u002Fnews\u002Ftrivy-docker-images-fresh-supply-chain-attack-zh\">供應鏈\u003C\u002Fa>。\u003C\u002Fp>\u003Cblockquote>“The AI boom is very real, and it’s very expensive.” — Satya Nadella\u003C\u002Fblockquote>\u003Ch2>跟其他雲端時期比，這次有什麼不同\u003C\u002Fh2>\u003Cp>以前雲端成長，主力是搬遷和數位轉型。現在不一樣，AI 是直接吃算力。差別在於，傳統雲端多半是穩定流量，AI 則是高峰明顯，而且單次請求成本高很多。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775200172132-jn93.png\" alt=\"AI 需求把雲端支出推高 29%\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>另一個差別是投資節奏。過去企業常常先省錢，再慢慢上雲。現在很多公司是先買算力，再想怎麼把產品做出來。這種順序，會讓支出先衝很快。\u003C\u002Fp>\u003Cp>如果把幾家主要\u003Ca href=\"\u002Fnews\u002Fsora-shutdown-ai-vendor-risk-zh\">供應商\u003C\u002Fa>放一起看，差距也很明顯。\u003Ca href=\"https:\u002F\u002Fwww.oracle.com\u002Fcloud\u002F\" target=\"_blank\" rel=\"noopener\">Oracle Cloud\u003C\u002Fa> 主打資料庫與企業客戶。\u003Ca href=\"https:\u002F\u002Fwww.digitalocean.com\u002F\" target=\"_blank\" rel=\"noopener\">DigitalOcean\u003C\u002Fa> 則偏中小團隊。\u003Ca href=\"https:\u002F\u002Fwww.ibm.com\u002Fcloud\" target=\"_blank\" rel=\"noopener\">IBM Cloud\u003C\u002Fa> 走的是既有企業關係。AI 時代的錢，還是更偏向能拿到大規模 GPU 與網路資源的人。\u003C\u002Fp>\u003Cul>\u003Cli>傳統雲端：搬遷、儲存、SaaS 為主\u003C\u002Fli>\u003Cli>AI 雲端：GPU、推論、向量資料庫為主\u003C\u002Fli>\u003Cli>傳統工作負載：成本相對可預測\u003C\u002Fli>\u003Cli>AI 工作負載：成本波動更大\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>台灣團隊該怎麼看\u003C\u002Fh2>\u003Cp>對台灣開發者來說，這代表兩件事。第一，AI 產品不只要看模型效果，也要看雲端成本。第二，架構能力會變重要，因為省 10% 成本，可能直接影響毛利。\u003C\u002Fp>\u003Cp>很多團隊現在都在改做法。有人把大模型請求拆成多段。有人先用小模型過濾，再丟大模型。也有人把熱門內容快取起來，避免每次都重算。這些都不是花招，是生存技能。\u003C\u002Fp>\u003Cp>如果你在做 SaaS、客服、搜尋或內容生成，我會建議你先算三個數字：每次請求成本、每月峰值流量、以及模型切換後的節省幅度。這三個數字，比 Demo 好不好看更重要。\u003C\u002Fp>\u003Ch2>接下來會怎麼走\u003C\u002Fh2>\u003Cp>我覺得接下來半年，雲端支出還會繼續偏高。原因很簡單，AI 需求沒有退燒，企業也還在試著把它塞進產品和內部流程。\u003C\u002Fp>\u003Cp>真正的變數是效率。如果模型更省 Token，或推論硬體更便宜，雲端帳單才可能慢下來。反過來說，只要大家還在拼命上 AI，雲端大廠的資本支出就很難停。\u003C\u002Fp>\u003Cp>對開發者來說，現在最實際的問題不是「要不要用 AI」，而是「怎麼用得不爆預算」。先把成本算清楚，再談規模，會比較像在做生意，不是在燒錢。\u003C\u002Fp>","Q4 2025 全球雲端基礎設施支出達 1109 億美元，年增 29%。AI 訓練、推論與雲端工作負載一起拉高 hyperscaler 投資。","www.ciodive.com","https:\u002F\u002Fwww.ciodive.com\u002Fnews\u002Fcloud-infrastructure-spend-rises\u002F816003\u002F",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775200180978-1ufe.png","industry","zh","642b6179-d79b-40f9-bba3-6cf0d0daffc8",[17,18,19,20,21,22,23,24],"雲端基礎設施","AI需求","hyperscaler","GPU","推論成本","AWS","Azure","Google Cloud",6,"2026-04-03T07:09:17.443915+00:00","2026-04-03T07:09:17.417+00:00",{"tags":29,"relatedLang":43,"relatedPosts":47},[30,32,34,35,36,38,40,42],{"name":20,"slug":31},"gpu",{"name":18,"slug":33},"ai需求",{"name":21,"slug":21},{"name":17,"slug":17},{"name":22,"slug":37},"aws",{"name":23,"slug":39},"azure",{"name":24,"slug":41},"google-cloud",{"name":19,"slug":19},{"id":15,"slug":44,"title":45,"language":46},"cloud-infrastructure-spend-jumps-ai-demand-en","Cloud infrastructure spend jumps 29% on AI demand","en",[48,54,60,66,72,78],{"id":49,"slug":50,"title":51,"cover_image":52,"image_url":52,"created_at":53,"category":13},"65ca7e37-1bf4-4e29-b7f8-cf6ae3182b72","congress-should-treat-fraud-cuts-as-tax-relief-zh","為什麼國會該把打擊詐領當成減稅，而不是殘酷","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780562880881-bpta.png","2026-06-04T08:47:27.829649+00:00",{"id":55,"slug":56,"title":57,"cover_image":58,"image_url":58,"created_at":59,"category":13},"f95cf6d8-0989-4ecd-88c4-c0ee6055b2ad","why-lisa-mcclain-committee-assignments-matter-zh","為什麼 Lisa McClain 的委員會席次比她的新聞標題更重要","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780561972248-a8m5.png","2026-06-04T08:32:20.773326+00:00",{"id":61,"slug":62,"title":63,"cover_image":64,"image_url":64,"created_at":65,"category":13},"76032ead-61f6-4f4f-a023-e20cb93a621b","why-the-clarity-act-is-here-to-stay-zh","為什麼 CLARITY Act 會留下來","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780561074594-hqmg.png","2026-06-04T08:17:26.885295+00:00",{"id":67,"slug":68,"title":69,"cover_image":70,"image_url":70,"created_at":71,"category":13},"381601ca-ac6d-41db-b8df-2711eadd0ed1","5-republican-quotes-on-federal-fraud-crackdowns-zh","5 個共和黨對聯邦反詐騙的說法","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780560172625-9ek9.png","2026-06-04T08:02:23.403684+00:00",{"id":73,"slug":74,"title":75,"cover_image":76,"image_url":76,"created_at":77,"category":13},"d73870f0-f463-413f-8f4e-0b859ca78c97","ai-fraud-blockchain-finance-defenses-zh","AI 詐騙跑太快，防線怎麼追","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780557487797-7fzf.png","2026-06-04T07:17:34.282107+00:00",{"id":79,"slug":80,"title":81,"cover_image":82,"image_url":82,"created_at":83,"category":13},"c64ecc12-d2bf-419c-938f-407b6ae2d74c","5-blockchain-ai-market-signals-for-buyers-zh","5 個區塊鏈 AI 市場訊號","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780556577201-iu8x.png","2026-06-04T07:02:25.523387+00:00",[85,90,95,100,105,110,115,120,125,130],{"id":86,"slug":87,"title":88,"created_at":89},"ee073da7-28b3-4752-a319-5a501459fb87","ai-in-2026-what-actually-matters-now-zh","2026 AI 真正重要的事","2026-03-26T07:09:12.008134+00:00",{"id":91,"slug":92,"title":93,"created_at":94},"83bd1795-8548-44c9-9a7e-de50a0923f71","trump-ai-framework-power-speech-state-preemption-zh","川普 AI 框架瞄準電力、言論與州權","2026-03-26T07:12:18.695466+00:00",{"id":96,"slug":97,"title":98,"created_at":99},"ea6be18b-c903-4e54-97b7-5f7447a612e0","nvidia-gtc-2026-big-ai-announcements-zh","NVIDIA GTC 2026 重點拆解","2026-03-26T07:14:26.62638+00:00",{"id":101,"slug":102,"title":103,"created_at":104},"4bcec76f-4c36-4daa-909f-54cd702f7c93","claude-users-spreading-out-and-getting-better-zh","Claude 用戶更分散，也更會用","2026-03-26T07:22:52.325888+00:00",{"id":106,"slug":107,"title":108,"created_at":109},"bd903b15-2473-4178-9789-b7557816e535","openclaw-raises-hard-question-for-ai-models-zh","OpenClaw 逼問 AI 模型價值","2026-03-26T07:24:54.707486+00:00",{"id":111,"slug":112,"title":113,"created_at":114},"eeac6b9e-ad9d-4831-8eec-8bba3f9bca6a","gap-google-gemini-checkout-fashion-search-zh","Gap 把結帳搬進 Gemini","2026-03-26T07:28:23.937768+00:00",{"id":116,"slug":117,"title":118,"created_at":119},"0740e53f-605d-4d57-8601-c10beb126f3c","google-pushes-gemini-transition-to-march-2026-zh","Google 把 Gemini 轉換延到 2026 年 3…","2026-03-26T07:30:12.825269+00:00",{"id":121,"slug":122,"title":123,"created_at":124},"e660d801-2421-4529-8fa9-86b82b066990","metas-llama-4-benchmark-scandal-gets-worse-zh","Meta Llama 4 分數風波又擴大","2026-03-26T07:34:21.156421+00:00",{"id":126,"slug":127,"title":128,"created_at":129},"183f9e7c-e143-40bb-a6d5-67ba84a3a8bc","accenture-mistral-ai-sovereign-enterprise-deal-zh","Accenture 攜手 Mistral AI 賣主權 AI","2026-03-26T07:38:14.818906+00:00",{"id":131,"slug":132,"title":133,"created_at":134},"191d9b1b-768a-478c-978c-dd7431a38149","mistral-ai-faces-its-hardest-year-yet-zh","Mistral AI 迎來最硬的一年","2026-03-26T07:40:23.716374+00:00"]