Milvus® 기반 최고 성능의 벡터 데이터베이스, Zilliz Cloud

완전 관리형 벡터 데이터베이스 및 데이터 서비스로 AI 애플리케이션을 위한 비정형 데이터의 전체 잠재력을 발휘하세요.

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AWS, GCP 또는 Azure 마켓플레이스에서 구독하기

우수한 AI 기반 검색

AI 기반 AutoIndex 및 Cardinal 검색 엔진으로 최적의 검색 전략 제공, 수동 튜닝 불필요

Optimized Milvus

고성능 및 확장성

수요에 따른 확장성과 비용 효율적인 성장을 위한 분산 아키텍처의 클라우드 네이티브 데이터베이스

Built for Production Workloads

포괄적인 벡터 데이터베이스 기능 제품군으로 더 빠르게 구축하세요

  • 고성능 벡터 검색

    고성능 벡터 검색

    심층 신경망 및 기타 머신러닝(ML) 모델에서 생성된 대규모 임베딩 벡터 저장, 인덱싱 및 관리

  • 낮은 지연 시간과 높은 재현율

    낮은 지연 시간과 높은 재현율

    낮은 지연 시간과 높은 재현율로 데이터에 대한 신뢰를 심어주며 안정적이고 정확한 실시간 의사 결정 보장

  • 하이브리드 검색

    하이브리드 검색

    다중 모드, 희소-밀집 및 밀집-텍스트 조합을 지원하여 여러 벡터 필드에서 쿼리 가능, 더 정확한 결과 제공

    더 알아보기
  • 다양한 유사성 메트릭

    다양한 유사성 메트릭

    적절한 유사성 메트릭(코사인, 유클리드, IP 등) 선택으로 분류 및 클러스터링 성능 향상

    더 알아보기
  • 조정 가능한 일관성

    조정 가능한 일관성

    다양한 일관성 수준으로 유연성 확보, 고유한 애플리케이션 요구 사항에 맞춰 데이터 정확도와 성능 조정

    더 알아보기
  • 필요에 따른 확장

    필요에 따른 확장

    컴포넌트 기반 아키텍처로 수평적 확장 용이, 워크로드 변동에 관계없이 최고의 성능과 효율성 보장

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클라우드 네이티브 벡터 검색으로 TCO 절감

Milvus는 강력한 벡터 검색 기능을 제공하지만, 숙련된 엔지니어, 성능 튜닝 및 지속적인 유지보수에 상당한 투자가 필요합니다.

Zilliz는 혁신적인 Cardinal 검색 엔진과 관리 도구를 통해 이러한 비용을 제거합니다 - 총소유비용(TCO)을 최대 70% 절감하며 추천 시스템, RAG 애플리케이션 또는 이상 감지 시스템과 같은 사용 사례에 최적화되었습니다.

고성능 추천 시스템을 위해 구축

우리 플랫폼은 추천 시스템에 필요한 높은 재현율과 빠른 응답 시간을 제공하면서도 다음을 통해 비용을 절감합니다:

  • 데이터 인식 인덱싱 및 하드웨어 최적화로 검색 성능을 희생하지 않으면서 스토리지 및 컴퓨팅 비용 절감

  • 간소화된 API, 자동화된 배포 및 내장 기능으로 벡터 검색 구현에 일반적으로 필요한 수개월의 수동 개발 및 튜닝 제거

  • 자동화된 업그레이드, 확장 및 모니터링으로 시스템 안정성 보장하면서 운영 유지보수 제거

추천 시스템

2년간 TCO: Milvus 대 Zilliz Cloud

  • Zilliz 구독

  • 운영

  • 개발

  • 인프라

고성능 추천 시스템을 위해 구축

선호하는 도구로 AI 애플리케이션 구축

고객들의 평가 확인하기

“

We were running a multilingual RAG system at scale, indexing all of Wikipedia into tens of millions of high-dimensional vectors. Latency targets were tight. We needed a system that could handle real-time retrieval over millions of knowledge vectors without breaking under load. Zilliz gave us that. It freed up engineering cycles and let us focus on improving reasoning on the model side, not managing infrastructure. That reliability mattered because we were operating at 3,500+ dimensions per vector, across a fast-growing corpus, with sub-300ms latency requirements in production. "

Dr. Pratyush Kumar

Co-Founder of Sarvam

“

When we conducted our first test run (on Zilliz Cloud), the performance improvement was astonishing. Our search time went from eight seconds down to sub one second. We were nearly falling off our chairs with amazement at the speed."

Alex Alexander

Co-founder & CEO of Picdmo Inc.

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“

Zilliz Cloud stood out for us with its comprehensive range of index types, automatically optimizing for the perfect balance between recall and performance. Its robust security and private networking features, combined with the advantage of a fully-managed database, have significantly lightened our operational load, enabling us to concentrate on driving innovation rather than managing backend complexities. "

Ben Kramer

Co-founder & CTO of Monterey AI

“

I was benchmarking different vector databases, and Milvus was at the top among all the popular options on the market."

Saumil Patel

the Senior Data Scientist from Ivy.ai

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“

Reliability, scalability, and performance are great. The fact that we can outsource all this kind of administration management and don't have to worry about servers breaking helps us put resources into product development and innovation, which truly differentiates our business. "

Michal Oglodek

CTO of Ivy.ai

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“

Zilliz Cloud perfectly aligns with MindStudio's vision. Its high-performance, secure platform and multi-tenancy simplifies data management and unlocks unprecedented productivity and innovation for our users’ AI applications. "

Sean Thielen

CTO @ MindStudio

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“

I appreciated using the open standard evaluation benchmarks for machine learning in general; this is also true for vector databases. The ones that Zilliz often publicizes have been beneficial, and the fact that they are open is significant. "

Sam Butler

Director of Machine Learning @ Dopple.AI

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“

Since transitioning from the open-source Milvus vector database to the fully managed Zilliz Cloud, we’ve experienced significant improvements in business performance. We’ve achieved lower operational costs, increased search speed, a more flexible system architecture, and a more stable user experience. Zilliz Cloud also provides expert support to resolve issues quickly and effectively. Overall, Zilliz Cloud has given us greater convenience and a competitive edge, and we are very pleased and optimistic about this change."

Shengyi Pan

CTO of Shulex

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“

When we identify a need for specific data, we can often find the required data in our database the same day using text or image search with Milvus. This greatly improves our data processing efficiency and has a positive effect on our business operations."

Mr. Zhang

BOSCH’s principal software engineer

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“

Zilliz is a very integral part of our workflow. If we just swapped Zilliz Cloud with something else, the kind of loops that we get might not make sense, which means the end composition might not sound very nice or might not be very accurate to the text prompt you had given."

Sangarshanan Veera

Senior Software Engineer at Beatoven.ai

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“

Working with Zilliz Cloud has been transformative for our AI agent architecture. The hybrid search capability alone delivered a 40% accuracy improvement, and the scalability means we never worry about performance, even during the highest traffic periods. It's been essential to our continued growth."

Sasidhar Janaki

Senior Software Engineer at Rexera

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“

We were facing latency issues and scaling challenges with our previous solutions. When traffic spiked with millions of customer requests, our self-hosted infrastructure couldn't keep up, and document retrieval was taking too long. "

Sasidhar Janaki

Senior Software Engineer at Rexera

Learn more
“

We were running a multilingual RAG system at scale, indexing all of Wikipedia into tens of millions of high-dimensional vectors. Latency targets were tight. We needed a system that could handle real-time retrieval over millions of knowledge vectors without breaking under load. Zilliz gave us that. It freed up engineering cycles and let us focus on improving reasoning on the model side, not managing infrastructure. That reliability mattered because we were operating at 3,500+ dimensions per vector, across a fast-growing corpus, with sub-300ms latency requirements in production. "

Dr. Pratyush Kumar

Co-Founder of Sarvam

“

When we conducted our first test run (on Zilliz Cloud), the performance improvement was astonishing. Our search time went from eight seconds down to sub one second. We were nearly falling off our chairs with amazement at the speed."

Alex Alexander

Co-founder & CEO of Picdmo Inc.

Learn more

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Pricing Plan

다양한 계층과 컴퓨팅 유형을 제공하는 확장 가능한 종량제 가격 정책으로 모든 예산과 워크로드 유형에 대응

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시장에서 최고의 성능-가격 비율을 발견하세요. 다른 벡터 데이터베이스 대비 최대 80%의 잠재적 비용 절감 효과 확인

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