Zilliz Cloud, la base de datos vectorial más eficiente, construida sobre Milvus®

Un servicio de base de datos vectorial y datos completamente gestionado, que te permite desbloquear todo el potencial de los datos no estructurados para tus aplicaciones de IA.

Empieza Gratis

Suscríbete en AWS, GCP o Azure Marketplace

Búsqueda Superior con IA

Estrategias de búsqueda óptimas y cero ajuste manual gracias al Motor de Búsqueda Cardinal y AutoIndex potenciado por IA

Optimized Milvus

Alto Rendimiento y Escalabilidad

Una base de datos nativa de la nube con arquitectura distribuida para escalabilidad bajo demanda y crecimiento rentable

Built for Production Workloads

Construye más rápido con un conjunto completo de funciones de base de datos vectoriales

  • Búsqueda Vectorial de Alto Rendimiento

    Búsqueda Vectorial de Alto Rendimiento

    Almacena, indexa y gestiona enormes vectores de incrustación generados por redes neuronales profundas y otros modelos de aprendizaje automático (ML).

  • Baja Latencia con Alta Recuperación

    Baja Latencia con Alta Recuperación

    Infunde confianza en tus datos con baja latencia y alta recuperación, asegurando una toma de decisiones en tiempo real confiable y precisa.

  • Búsqueda Híbrida

    Búsqueda Híbrida

    Permite consultar a través de múltiples campos vectoriales, soportando combinaciones multimodales, dispersas-densas y densas-texto para resultados más precisos.

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  • Varias Métricas de Similitud

    Varias Métricas de Similitud

    Elige la métrica de similitud correcta (Coseno, Euclidiana, IP, etc.) para mejorar el rendimiento de la clasificación y agrupación.

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  • Consistencia Ajustable

    Consistencia Ajustable

    Obtén flexibilidad con múltiples niveles de consistencia para alinear la precisión de los datos y el rendimiento con las necesidades únicas de tu aplicación.

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  • Escala según sea Necesario

    Escala según sea Necesario

    Escala horizontalmente sin esfuerzo con una arquitectura basada en componentes, garantizando un rendimiento máximo y eficiencia, independientemente de las fluctuaciones de carga de trabajo.

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Reduzca el TCO con Búsqueda Vectorial Nativa de la Nube

Aunque Milvus ofrece capacidades potentes de búsqueda vectorial, requiere una inversión significativa en ingenieros calificados, ajuste de rendimiento y mantenimiento continuo.

Zilliz elimina estos costos a través de nuestro innovador motor de búsqueda Cardinal y herramientas de gestión—reduciendo el costo total de propiedad hasta en un 70% y optimizado para sus casos de uso: Sistemas de Recomendación, Aplicaciones RAG, o Sistemas de Detección de Anomalías.

Construido para Recomendaciones de Alto Rendimiento

Nuestra plataforma ofrece la alta recuperación y los tiempos de respuesta rápidos que tus sistemas de recomendación necesitan, mientras reduce costos a través de:

  • La indexación consciente de los datos y las optimizaciones de hardware reducen los costos de almacenamiento y computación sin sacrificar el rendimiento de búsqueda

  • APIs simplificadas, despliegue automatizado y características integradas eliminan meses de desarrollo y ajuste manual típicamente necesarios para la búsqueda vectorial

  • Las actualizaciones automatizadas, el escalado y el monitoreo garantizan la confiabilidad del sistema mientras eliminan el mantenimiento operativo

Sistema de Recomendación

TCO de 2 años: Milvus vs. Zilliz Cloud

  • Suscripción de Zilliz

  • Operaciones

  • Desarrollo

  • Infraestructura

Construido para Recomendaciones de Alto Rendimiento

Construye Aplicaciones de IA con tus Herramientas Favoritas

Mira lo que nuestros clientes están diciendo

“

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

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“

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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Ofreciendo múltiples niveles y tipos de computación, nuestros precios escalables y de pago por uso se ajustan a cualquier presupuesto y acomodan todo tipo de cargas de trabajo

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