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aem-GridColumn--default--12","image_1462611291":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","blog_text_1446103123":"aem-GridColumn aem-GridColumn--default--12","blog_text_1397254345":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,"appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"title_v2":{"id":"blog-title-0fa125efdd","propertiesId":"ai-finops","type":"heading4","lines":["엔터프라이즈 데이터 및 AI 플랫폼에서 FinOps 리포팅이 진화하는 방식"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"center"},"blog_text":{"id":"blog-text-8e35d3d1e0","text":"\u003Cp\u003ESnowflake는 오래전부터 Snowflake 플랫폼에서 자체 FinOps 운영을 수행해 왔습니다. 클라우드 비용, 사용량, 그리고 비즈니스 지표를 하나의 거버넌스 환경으로 통합함으로써 모든 이해관계자에게 일관성, 확장성 및 신뢰성을 제공할 수 있었습니다. Snowflake의 엔터프라이즈 데이터 및 AI 플랫폼은 이러한 데이터 세트를 컨텍스트에 맞게 연결해 실제 비즈니스 성과로 이어지도록 한다는 점에서 강점을 지닙니다.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca rel=\"nofollow noopener noreferrer\" href=\"https://medium.com/snowflake\"\u003ESnowflake 빌더 블로그\u003C/a\u003E의 여러 글([\u003Ca rel=\"nofollow noopener noreferrer\" href=\"https://medium.com/snowflake/finops-at-snowflake-dogfooding-multi-cloud-management-at-huge-scale-52b734e5de71\"\u003E1\u003C/a\u003E], [\u003Ca rel=\"nofollow noopener noreferrer\" href=\"https://medium.com/snowflake/forecasting-cloud-costs-to-drive-business-outcomes-e7faeb57b325\"\u003E2\u003C/a\u003E], [\u003Ca rel=\"nofollow noopener noreferrer\" href=\"https://medium.com/snowflake/bridging-the-gap-between-cloud-sprawl-and-cost-control-c7049e0ef6c6\"\u003E3\u003C/a\u003E])에 소개된 바와 같이, 이러한 큐레이션 기반 셀프서비스 모델을 통해 데이터 인사이트를 조직 전반에 확산하고 팀 간 분석을 공유할 수 있었습니다. 이 토대를 바탕으로 대규모 운영 효율을 유지하는 동시에, 스택 전체에 대한 정밀한 가시성을 확보했습니다.\u003C/p\u003E\r\n\u003Cp\u003E하지만 Snowflake의 비즈니스는 빠르게 변화하고 있으며, 그에 따라 더 신속한 반복과 개선이 요구되고 있습니다. Snowflake Cortex AI 기능, 새로운 웨어하우스 SKU, 그리고 빠르게 변화하는 고객 요구 사항은 클라우드 경제성과 마진을 측정하는 방식 자체의 재정립을 요구하고 있습니다. 변화의 속도는 점점 빨라지고 있으며, 그 속도에 맞춰 빠르게 반복 개선해 나가야 합니다.\u003C/p\u003E\r\n\u003Ch2\u003ECortex Code로 구현하는 업무 혁신\u003C/h2\u003E\r\n\u003Cp\u003E\u003Ca rel=\"nofollow noopener noreferrer\" href=\"https://www.snowflake.com/en/news/press-releases/snowflake-unveils-cortex-code-an-ai-coding-agent-that-drastically-increases-productivity-by-understanding-your-enterprise-data-context/\"\u003E2월\u003C/a\u003E에 출시된 \u003Ca rel=\"nofollow noopener noreferrer\" href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code\"\u003ESnowflake Cortex Code\u003C/a\u003E는 사실상 도입과 동시에 업무 방식의 패러다임을 근본적으로 변화시켰습니다.\u003C/p\u003E\r\n\u003Cp\u003ECortex Code를 통해 Snowflake에 이미 축적된 데이터를 기반으로, 각 팀원이 여러 명의 분석가와 개발자의 지원을 받는 듯한 환경이 마련되었습니다. 이제 팀에서는 다음과 같은 요청도 손쉽게 수행할 수 있습니다. “Google Cloud의 월별 비용을 리전별로 보여주고, 정의한 특정 지표에서 10% 이상 벗어나는 이상 징후를 표시하는 Streamlit 인터페이스를 만들어 주세요.”\u003C/p\u003E\r\n\u003Cp\u003ECortex Code는 이러한 의도를 해석하여 Snowflake에 이미 존재하는 거버넌스 데이터에 안전하게 연결되는 배포 가능한 애플리케이션 코드로 변환합니다. 전통적인 FinOps 도구와는 다르게 보일 수 있지만, 이 접근 방식의 핵심 가치는 비즈니스 맥락에 기반한 빠른 반복을 가능하게 한다는 데 있습니다. 이는 성공적인 FinOps 운영을 뒷받침하는 핵심 요소입니다.\u003C/p\u003E\r\n\u003Ch3\u003E사용 사례 1: 완전 자동화된 클라우드 예측\u003C/h3\u003E\r\n\u003Cp\u003E대부분의 팀과 마찬가지로, 과거의 예측 모델은 복잡한 스프레드시트 기반으로 운영되었습니다. 이 방식은 유효하지만 규모가 커질수록 취약해지고, 하위 도구와 직접적으로 연결되지 않으며, 매 사이클마다 업데이트에 많은 작업이 필요했습니다.\u003C/p\u003E\r\n\u003Cp\u003E현재 Snowflake는 Cortex Code를 활용해 내부 및 고객 대상 워크로드 전반의 클라우드 사용량은 물론, Snowflake 내부 기능 사용량과 서드 파티 SaaS 사용량까지 아우르며 예측 시스템을 재구축하고 있습니다. 이제 과거 비용 데이터뿐 아니라 예측 값 역시 구조화된 테이블에 저장되어 기존 도구와 바로 연결됩니다. 엔지니어링 팀과 프로덕트 팀은 재무 팀이 사용하는 동일한 숫자를 기반으로 목표를 설정하고 KPI를 추적할 수 있습니다. 더 이상 데이터 내보내기나 지표 간 격차가 발생하지 않으며, 단일 예측 체계가 유지됩니다.\u003C/p\u003E\r\n\u003Cp\u003E이 접근 방식을 통해 얻은 주요 성과는 다음과 같습니다.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E더 빨라진 재무 사이클: \u003C/b\u003E예측 데이터가 훨씬 더 자주 갱신되며(목표는 매일), 수작업도 크게 감소했습니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E더 빠른 편차 감지: \u003C/b\u003E이상 징후 보고를 확정된 예측 데이터와 직접 연결할 수 있습니다. 또한 편차가 발생할 경우 경영진에게 사전에 알림을 제공할 수 있어, 기업의 회계 기간 마감 시점에 사후 대응이 아닌 사전 대응이 가능해집니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E향상된 가시성: \u003C/b\u003E예측 로직이 투명하게 공개되어 주요 이해관계자가 직접 확인하고 의견을 낼 수 있습니다. 이러한 피드백 루프는 지속적인 개선에 매우 중요합니다. 데이터를 공유하면 책임 또한 명확해집니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ESnowflake의 목표는 수십억 달러 규모의 비용에 대한 예측 갱신에 소요되는 1주일 분량의 수작업을 몇 시간 수준으로 단축시키는 것입니다. 이는 단순한 이상적인 목표가 아니라 실제로 구현되고 있는 결과입니다.\u003C/p\u003E\r\n\u003Ch3\u003E사용 사례 2: AI 기반 주간 지표 리뷰와 세밀한 이상 감지\u003C/h3\u003E\r\n\u003Cp\u003E처음에는 주간 지표 리뷰를 도입해, 사업 전반의 작은 변화를 주 단위로 빠르게 조정하고자 했습니다. 팀 전체가 플랫폼 영역, 고객, 워크로드 전반의 추세 변화를 검토하고, 분석가들이 수시간에 걸쳐 데이터를 분석해 원인을 파악한 뒤 이를 함께 논의했습니다. 하지만 회의가 끝날 즈음이면 분석은 완료되어 있었지만, 실제 행동으로 이어질 기회는 이미 줄어든 상태였습니다.\u003C/p\u003E\r\n\u003Cp\u003E팀은 이 프로세스를 자동화할 여지가 크다고 판단했고, Streamlit으로 이를 구현했습니다. 그러나 접근 방식의 속도에 진정한 변화가 나타난 계기는 FinOps를 위한 전용 Cortex Code 스킬을 개발하기 시작하면서부터였습니다.\u003C/p\u003E\r\n\u003Cp\u003E이제 1차 편차 분석, 이상 징후 탐지, 근본 원인 가이드까지 대부분 자동으로 확인할 수 있으며, 팀 리뷰 전에도 최소한의 개입만으로 검토할 수 있습니다. 대응까지 걸리는 시간이 크게 줄어들면서, 전체 환경에 걸쳐 훨씬 더 빠르게 문제를 분류하고 대처할 수 있게 되었습니다. 또한 주요 원인과 초안 코멘트를 자동으로 제공함으로써 재무 지침을 준비하는 시간 역시 크게 줄어들었습니다. 이는 고정된 분석 기능이 아니라 사용이 축적될수록 발전하는 ‘스킬’입니다.\u003C/p\u003E\r\n\u003Cp\u003E이러한 속도와 분석 역량이 제공되더라도, 이는 분석가의 전문적 판단을 대체하는 것이 아니라 판단의 근거를 강화합니다. 이제 팀은 데이터를 수집하는 대신 논의에 집중해 의사 결정을 내리고 결과를 도출할 수 있게 되었습니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-84fddc6268","alt":"Diagram showing the relationships between cloud tracking on Streamlit, investigation with Cortex Code and stakeholders taking action.","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--fbb4b425-5ccc-479f-96b0-dd8b2aa03fb1/finops-streamlit-cortexcode.jpg?quality=85&preferwebp=true","height":"1378","width":"1932","title":"자료 1: Cortex Code는 비용 및 워크로드 변화의 원인을 빠르게 진단해 인사이트 도출 속도를 높입니다.",":type":"snowflake-site/components/image"},"blog_text_1397254345":{"id":"blog-text-24f00f05b2","text":"\u003Ch3\u003E사용 사례 3: 회계 통제 프로세스 재구축\u003C/h3\u003E\r\n\u003Cp\u003ESnowflake는 매우 다양한 기능을 운영하고 있으며, 많은 서드 파티 서비스를 사용하고 있고, 비용을 정확히 인식하기 위한 내부 모델도 필요합니다. 이러한 이유로 회계 및 감사 팀과의 협력은 Snowflake 비즈니스의 핵심 요소입니다.\u003C/p\u003E\r\n\u003Cp\u003E감사 통제는 본질적으로 반복 수행이 가능하고, 검토와 설명이 명확하게 이루어져야 합니다. 그러나 Snowflake의 규모가 커지고 지속적으로 성장함에 따라, 일부 프로세스에는 상당한 수작업이 수반되고 있습니다. 이러한 작업은 체계적으로 자동화하고 간소화하기에 적합한 영역입니다. 현재 우리는 여러 프로세스와 감사 통제를 Snowflake 상에서 Cortex Code로 재구축하고 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E대표적인 예로, 매월 수행하는 클라우드 비용 통제 절차가 있습니다. 이는 계정별 클라우드 비용이 정확하게 인식되었는지를 확인하는 과정으로, Snowflake의 규모를 고려하면 상당히 번거로운 작업이 될 수 있습니다. 우리는 이 통제 절차를 Streamlit 애플리케이션으로 그대로 재구현하고, GitHub에 호스팅된 워크플로우와 작업 지침, 목적 설명까지 함께 포함했습니다. 승인 과정은 애플리케이션 내부에서 이루어지며, 자동으로 타임스탬프가 기록된 감사 추적 로그가 생성됩니다. 수작업 데이터 조작과 쿼리 집계가 더 이상 필요하지 않아 운영자 오류 위험의 대부분이 제거되었습니다. \u003C/p\u003E\r\n\u003Cp\u003E이 프로세스를 업데이트한 이후 매월 운영 작업량을 약 70% 줄일 수 있었습니다. 이를 통해 이러한 통제 시스템은 안정성, 감사 용이성, 확장성을 갖추게 되었으며, 취약하고 분산된 개별 워크플로우가 아닌 핵심 데이터 구조의 직접적인 확장으로 자리 잡도록 추가 프로세스 이전도 지속하고 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E이전에 이러한 통제 절차 유지에 며칠씩 투입되던 실무 전문가들은 이제 반복적인 단순 작업에서 벗어나 로직 고도화 등 고부가가치 업무에 집중하고 있습니다. Cortex Code를 통해 더 많은 시간을 확보할 수 있게 되었고, 이제 팀은 그 시간을 어디에 재투자할지 스스로 결정할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_1462611291":{"id":"image-7e3f04b7b2","alt":"FinOps accounting controls in Streamlit and Cortex Code.","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--c315dc0d-b877-40a5-b0f9-da572549a515/finops-cortexcodevsexcel.png?quality=85&preferwebp=true","height":"628","width":"1857","title":"자료 2: Streamlit과 Cortex Code 기반 회계 프로세스 개선을 통해 전문가들이 더 높은 가치의 업무에 집중할 수 있습니다.",":type":"snowflake-site/components/image"},"blog_text_1446103123":{"id":"blog-text-055eae395c","text":"\u003Ch2\u003E거버넌스와 신뢰: 데이터 전문가의 새로운 역할\u003C/h2\u003E\r\n\u003Cp\u003E재무 조직의 비즈니스 파트너로서, 분석 팀은 여전히 엔터프라이즈 FinOps 운영의 핵심 역할을 담당합니다. AI는 이러한 책임을 줄인 것이 아니라 오히려 더욱 강화했습니다. 분석 팀은 여전히 비즈니스 컨텍스트에 기반한 인사이트를 뒷받침하는 데이터의 건강성, 정확성, 그리고 그 근거를 명확히 설명하고 검증할 수 있는 상태를 책임지고 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E대시보드 구축이나 정기 분석과 같은 전통적인 BI 작업이 점점 자동화 되면서, Snowflake의 분석 팀 역할도 빠르게 변화하고 있습니다. Cortex Code가 기술적인 실행을 더 많이 담당하게 되면서, 수작업 데이터 처리에서 오케스트레이션과 제품화 중심 역할로 이동하고 있습니다. 이제 Snowflake는 에이전트를 지휘하고 결과를 큐레이션하며, 시맨틱 계층에 비즈니스 컨텍스트를 반영하는 오케스트레이터 역할을 수행하고 있습니다.\u003C/p\u003E\r\n\u003Cp\u003ECortex Code가 제공하는 속도를 제대로 활용하려면, 데이터 가드레일을 시스템 내부에 구축하는 것은 매우 중요합니다. 인사이트가 생성되기 전후에는 검증 절차가 수행되며, 결과물은 모두 로그로 남아 추적할 수 있고, 과거 결과도 계속 보존됩니다. 팀 전반에 걸쳐 피드백 루프를 활발히 운영하면서, 정성적 컨텍스트를 스킬과 시맨틱 모델에 반영하고 있으며, 이를 통해 모델의 성능을 지속적으로 고도화하고 있습니다. 우리는 이러한 도구를 분석가처럼 활용하며, 그 결과물 역시 높은 기준으로 평가합니다.\u003C/p\u003E\r\n\u003Cp\u003EAI가 워크플로우를 계속해서 가속화함에 따라, 팀 간 운영 모델 역시 점차 성숙해지고 있습니다. 각 팀은 점점 더 빠르게 설계, 리포팅 및 대시보드 구성을 수행하고, 분석 팀은 이를 총괄하며 피드백을 제공하고 AI 기반 고도화 기능을 더하고 있습니다. 이를 통해 컨텍스트, 품질, 그리고 Snowflake가 내부 및 외부에 공유할 수 있는 활용도 높은 워크플로우와 같은 더 높은 차원의 과제에 집중할 수 있게 되었습니다.\u003C/p\u003E\r\n\u003Ch2\u003ECortex Code로 가속되는 컨텍스트 기반 성과\u003C/h2\u003E\r\n\u003Cp\u003EFinOps Foundation의 \u003Ca rel=\"nofollow noopener noreferrer\" href=\"https://data.finops.org/\"\u003EState of FinOps 2026\u003C/a\u003E에서 조직의 사명은 “클라우드의 가치를 관리하는 사람들을 지원한다”에서 “기술의 가치를 관리하는 사람들을 지원한다”로 업데이트되었습니다. Snowflake의 플랫폼 재무 접근 방식은 이러한 방향성과 정확히 맞닿아 있습니다.\u003C/p\u003E\r\n\u003Cp\u003EFinOps 도구는 현대적인 클라우드 플랫폼 재무 관리의 틀을 형성해 왔지만, 이제 엔터프라이즈 컨텍스트가 결여된 비용 리포팅만으로는 충분하지 않습니다. 소비 기반 모델이 깊이와 범위 모두에서 확장됨에 따라, 조직은 핵심 회사 지표를 중심에 두고 투자를 성과와 비즈니스 가치에 직접 연결해야 합니다. Snowflake는 이미 수년 전부터 이를 실천해 왔으며, 이제 핵심 BI 및 데이터 파트너의 지원 아래 실무 전문가들이 솔루션을 더욱 빠르게 반복 개선할 수 있도록 하면서 그 속도를 한층 더 높이고 있습니다.\u003C/p\u003E\r\n\u003Cp\u003ESnowflake에서 Cortex Code를 활용하면 기술 사용자와 비기술 사용자 모두 실제 비즈니스 운영 방식에 맞는 FinOps 도구를 구축할 수 있습니다. 즉, 미리 정해진 모델에 억지로 맞추는 것이 아니라, 실제 요구에 맞춰 지표와 비용 요인, 워크플로우를 설계할 수 있다는 뜻입니다. 비용, 사용량, 비즈니스 데이터가 하나의 거버넌스 플랫폼 안에 함께 존재할 때, Snowflake와 Cortex Code는 사일로 안에 갇혀 개별적으로 작동하는 것이 아니라 도메인 전반을 넘나들며 추론할 수 있습니다. 그 결과 시간 절감 효과는 더욱 커지고, 엔터프라이즈 전반에서 인사이트를 도출하는 속도 역시 한층 빨라집니다.\u003C/p\u003E\r\n\u003Ch2\u003E핵심 요약\u003C/h2\u003E\r\n\u003Cp\u003E대규모로 클라우드 비용을 관리하는 조직이라면, 이제 Snowflake 플랫폼을 통해 엔터프라이즈 데이터가 존재하는 바로 그 환경에서 프로토타입을 구축할 수 있습니다. 또한 비즈니스 컨텍스트에서 클라우드 비용을 논의하면, 단순한 비용 데이터는 진정한 비즈니스 인사이트로 전환됩니다.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca rel=\"nofollow noopener noreferrer\" href=\"https://www.snowflake.com/ko/product/features/cortex-code/\"\u003ECortex Code\u003C/a\u003E를 시작하거나 \u003Ca href=\"https://signup.snowflake.com/cortex-code?utm_cta=cortex-code-custom-trial-en-www-homepage-hero/\" rel=\"nofollow noopener noreferrer\"\u003E무료 체험\u003C/a\u003E을 이용해 보세요.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["title_v2","blog_text","image","blog_text_1397254345","image_1462611291","blog_text_1446103123"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"premium_content_bann":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,"appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":items":{"premium_content_bann":{"id":"premium-content-banner-1aba9a1ec7","contentEyebrow":"무료 온디맨드 과정","contentHeadline":"FinOps for Snowflake","contentDescription":"사용량, 비용 및 기능을 FinOps 프레임워크에 맞게 조정하고 클라우드 지출을 최적화하는 방법을 확인해 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