Weaviate vs. Zilliz Cloud

Compara Weaviate vs. Zilliz Cloud para cargas de trabajo de búsqueda vectorial. Queremos que elijas la base de datos vectorial más adecuada para tu caso de uso, incluso si no somos nosotros.

Las bases de datos vectoriales se han convertido en una pieza fundamental de la infraestructura de las aplicaciones modernas de IA, como la generación aumentada por recuperación(RAG), los agentes de IA, la búsqueda multimodal y semántica y los sistemas de recomendación en una amplia gama de sectores. La elección de la base de datos vectorial adecuada puede afectar directamente al rendimiento, la escalabilidad, el coste y la fiabilidad de estas aplicaciones.

Esta comparativa entre Weaviate y Zilliz Cloud está dirigida a ingenieros y equipos técnicos que estén evaluando bases de datos vectoriales para cargas de trabajo de producción reales. Aunque Weaviate y Zilliz Cloud admiten la capacidad de búsqueda vectorial básica, difieren significativamente en áreas como la arquitectura, la escalabilidad, el rendimiento, la fiabilidad y muchas otras. Estas diferencias suelen ser sutiles durante los primeros experimentos, pero se vuelven cada vez más importantes a medida que aumenta el volumen de datos, se diversifican las cargas de trabajo y los sistemas pasan de prototipo a producción.

El objetivo de esta guía no es simplemente enumerar las características, sino ayudarle a determinar qué sistema se ajusta mejor a su caso de uso específico, limitaciones y etapa de crecimiento, incluso si nuestro producto(Milvus / Zilliz Cloud) no es la elección final.

Weaviate vs Zilliz Cloud de un vistazo

WeaviateZilliz Cloud
Positioning

Sí. Base de datos vectorial diseñada específicamente

Base de datos vectorial diseñada específicamente para aplicaciones nativas de IA: búsqueda de alto QPS y baja latencia a escala de miles de millones de vectores con costos predecibles

Open Source
✅

✅ vía Milvus (Apache 2.0) — sin dependencia de proveedor

License

Licencia BSD de 3 cláusulas

Propietario

Github Stars

16,933

N/A

Deployment

Local, Nube, Integrado

SaaS, BYOC

Billion-Scale Vector Support
✅
✅
Cloud Regions

--

AWS/GCP/Azure; 20+ global regions

Weaviate es una base de datos vectorial de código abierto que simplifica el desarrollo de aplicaciones de IA. Combina el almacenamiento de objetos y vectores, lo que permite búsquedas vectoriales eficientes con filtrado estructurado. Admite diversos tipos de medios, como texto e imágenes, y ofrece búsquedas rápidas y de baja latencia basadas en propiedades semánticas.

Zilliz Cloud es la base de datos vectorial gestionada de nivel empresarial construida sobre Milvus — la base de datos vectorial de código abierto más popular del mundo (Apache 2.0, más de 43 mil estrellas en GitHub, más de 100 millones de descargas). Ofrece búsqueda vectorial de baja latencia y alto QPS a escala de miles de millones de vectores con costos consistentemente predecibles, compatible con búsqueda semántica, búsqueda de texto completo, recuperación híbrida y búsqueda multimodal en una única plataforma unificada. Con implementación flexible en Serverless, Dedicated y BYOC — además de certificaciones empresariales que incluyen ISO 27001, SOC 2 Type II y HIPAA — Zilliz Cloud está creado para equipos que necesitan fiabilidad de nivel de producción sin dependencia de un proveedor. Comienza gratis y escala hasta miles de millones de vectores sin cambiar de plataforma.

Comparación de Weaviate y Zilliz Cloud Utilización de sus conjuntos de datos

No te fíes de la palabra de ningún vendedor: pruébalo tú mismo.

VectorDBBench es una herramienta de evaluación comparativa de código abierto creada específicamente para comparar bases de datos vectoriales en condiciones justas y reproducibles. Permite medir el rendimiento real ( latencia, rendimiento, recuperación, velocidad de indexación y comportamiento de escalado ) con las mismas cargas de trabajo en distintos sistemas o incluso con sus propios conjuntos de datos.

Esto hace que sea fácil ver cómo Weaviate y Zilliz Cloud se comportan realmente en la práctica, no sólo en los materiales de marketing. Todas las pruebas comparativas pueden reproducirse localmente, en su propio entorno, para que pueda validar los resultados importantes para su aplicación.

Echa un vistazo a la clasificación de VectorDBBench para conocer el rendimiento de las principales bases de datos vectoriales.

Por qué los equipos de más de 10.000 empresas se pasan a Milvus y Zilliz Cloud

La mayoría de las bases de datos vectoriales se ven bien en demostraciones o despliegues a pequeña escala, pero las lagunas aparecen en producción, cuando los conjuntos de datos crecen, las incrustaciones se actualizan con frecuencia y la latencia debe permanecer estable bajo tráfico real. Ahí es donde los equipos recurren a Milvus y Zilliz Cloud (servicio Milvus gestionado).

Milvus es una base de datos vectorial de código abierto y alto rendimiento utilizada por más de 10.000 equipos empresariales de todo el mundo y en la que confía una gran comunidad de código abierto con 46K+ estrellas de GitHub. Gestiona de decenas de millones a decenas de miles de millones de vectores, inserciones y eliminaciones frecuentes y búsquedas híbridas (vector + palabra clave + metadatos + reordenación) sin reindexación perjudicial ni ajustes frágiles. El rendimiento se mantiene predecible a medida que evolucionan el volumen de datos, los patrones de consulta y la incrustación de modelos. Esta es la razón por la que Milvus se despliega ampliamente para RAG empresariales, agentes de IA, búsqueda semántica y multimodal y sistemas de recomendación, cargas de trabajo en las que la inestabilidad se hace visible de inmediato.

Zilliz Cloud ofrece la misma arquitectura Milvus como servicio gestionado, con un motor vectorial avanzado(Cardinal) para un mayor rendimiento, además de escalado elástico, alta disponibilidad, seguridad y cumplimiento de nivel empresarial, y despliegue global. Los equipos obtienen una fiabilidad lista para la producción sin tener que operar o cuidar la base de datos.

“

"Uno de los mayores logros que hemos visto es un mejor manejo de las consultas que son semánticamente, pero no textualmente, relevantes. Las consultas más largas y conversacionales también han mejorado drásticamente.""

Heath Hohwald

Tech Leader and Search Manager, Consensus

“

Milvus has done an extraordinary job in revolutionizing Likee's video deduplication system, which significantly fueled the growth of BIGO's short-video business."

Xinyang Guo

Software Engineer at BIGO

“

Milvus has dramatically facilitated the MMU team in building various business systems and effectively supports our rapid business growth. Thanks to the Milvus team for developing such a fantastic vector database with stable vector search capabilities and rich functionalities."

The MMU team

Shopee

“

Our search system has been much more intelligent, stable, and reliable using Milvus. "

Rahul Yadav

Software Engineer at Tokopedia

“

We plan to expand the use of Milvus in different fields like content moderation and restriction and customized video services. BIGO and Milvus working together will benefit both businesses and I look forward to Milvus and its community to keep growing and prosper."

Xinyang Guo

Software Engineer at BIGO

“

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

“

We've gained so much from the Milvus community that we decided to contribute features like "hot reload," which have also benefited our internal operations."

Dennis Zhao

AI Infrastructure Lead at SmartNews

“

Milvus delivers unparalleled performance and flexibility, integrating seamlessly with leading vector index libraries like Faiss. Its intuitive API and robust solutions for high availability make it an indispensable tool in our APK security efforts."

Wei Huang

Senior Research Engineer

“

Milvus consistently outperformed Weaviate, emphasizing the indexing time for scenario S9, closely resembling the FARFETCH product catalog's dimensions."

PEDRO MOREIRA COSTA

Applied Scientist

“

When you're engineering a solution as complex as ours, you're not just ticking boxes—you're looking for that sweet spot where all your must-haves intersect. Think of it as an eight-circle Venn diagram; while many databases met one or two of our criteria, Milvus was the only one sitting right at the intersection of all eight. It checked every single box for us—something no other solution managed to do."

Jack Fischer

Co-founder & CTO at Credal AI

“

'wellSpent' is an intuitive mobile application designed to simplify and visualize spending habits. At its core, wellSpent displays a dynamic pie chart that offers a snapshot of expenditures. Users can access detailed transaction lists and explore predictive features like the travel planner, student debt planner, and other expense planners, all designed to optimize their financial journey. Built on Flutter and powered by Firebase, wellSpent stands as a testament to ensuring every penny is, indeed, well spent."

The wellSpent Team

at HackNC

“

Milvus has not only streamlined but also remarkably expedited the retrieval of millions of semantic vectors, showcasing a nearly tenfold improvement compared to our previous experience with other vector similarity search engines."

Tingting Wang

NLP Algorithm Engineer at Sohu

“

Milvus searches tens of millions of vectors in milliseconds, providing optimal performance while keeping development costs low and resource consumption minimal. "

Yu Fang

AI scientist at Mozat

“

Creating a user-centric interface that's both intuitive and efficient tops our list. We're also immensely proud of the predictive features – like creating comprehensive tools that will tell you how you can save money even after a transaction, along with other tools like customer trip planner already built using your spending transactions and trends. Using vector databases was our first time, and we are very proud of our outcome."

The wellSpent Team

at HackNC

“

When it comes to vector databases, Milvus has impressed us with its performance and scalability, meeting our stringent criteria for handling our AI use case backlog."

Toby Yu

Team Lead of AI, ML, and Platform Solutions

“

Milvus-powered vector search has been running steadily in our recommendation systems, providing high performance and allowing us more flexibility in selecting models and algorithms."

VIPSHOP Search Service Team

VIPSHOP

“

During batch ingestion tests, Milvus demonstrated that it could complete an entire collection dump into the database at speeds 5-10 times faster than competitors."

Toby Yu

Team Lead of AI, ML, and Platform Solutions

“

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

“

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.

“

You can think of LlamaIndex as a black box around your data and LLM."

Jerry Liu

Co-founder and CEO of LlamaIndex

“

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

“

As our business has expanded, the demands on our vector database have increased. We need a solution that minimizes operational costs, offers elastic scaling capabilities to manage vast amounts of vector data and unexpected traffic surges, provides faster vector search speeds, and ensures a high service level agreement (SLA)."

Chenhui Li

Tech Lead at Shulex

“

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

“

I can set up Milvus for scale in an hour, not overthinking it, and have a clear architecture path forward to grow my use case much larger without having to redo everything."

Zen Yui

Co-founder & CTO at Troop

“

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

“

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

“

Even with numerous concurrent searches, we didn’t notice any slowdown in search speed with Milvus."

Mr. Zhang

BOSCH’s principal software engineer

“

We need an indexing technology that can handle complex search requirements and generative models, reduce training costs, improve update efficiency, and adapt flexibly to evolving data and query needs."

Mr. Zhang

BOSCH’s principal software engineer

“

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

“

I really like how Milvus' hybrid search allowed me to blend semantic and keyword search, which is crucial in a domain as technical and complex as EU policy."

Alessandro Saccoia

Co-Founder @ Veridien.ai

“

Milvus is renowned as one of the most advanced vector database platforms for AI applications. Rakuten Symphony engineers identified the Milvus Vector Database - an open source database which is horizontally scalable - as their platform of choice for LLM use and developing and maintaining AI applications."

Rakuten Symphony Engineering Team

“

Zilliz Cloud gave us the speed and scale we needed to power visual search at Leboncoin, meeting our sub-200ms latency target and making product discovery seamless for millions of users."

Yann Lemonnier

ML Engineer

“

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

“

The competitive advantage for meeting transcription products in the future will come from personalization – providing tailored insights and recommendations based on each user's specific meeting data and usage patterns. This is why performant vector search is imperative."

Dai Tongjie

CTO

“

Zilliz Cloud enabled us to scale search while dramatically reducing our costs. Its hybrid search capabilities helped us achieve higher relevance for both text and image queries."

Celine Lightfoot

CTO of Beni

“

By migrating to Zilliz Cloud, we've reduced our vector database infrastructure costs by approximately 70% compared to our self-hosted setup. This allows us to reinvest those savings into improving our core AI capabilities rather than managing database infrastructure."

Nguyễn Ngọc Hải Đăng_ Nguyễn Nhật Khoa

AI Engineer at CX Genie

“

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

“

Whenever we demonstrate our solution with Milvus, we’re effectively crushing the cloud vendor solution’s performance. It’s a great benchmark because most enterprise users already know the vendor solution, so the comparison is immediate."

Hanlian Lyu

a Product Owner and BI Expert at Volvo Cars

“

We tested every mainstream vector database, and Milvus delivered the best overall performance."

Su Wei

CTO of Shining

“

Milvus’s real advantage was how easy and friendly it made things to understand and execute."

Hanlian Lyu

a Product Owner and BI Expert at Volvo Cars

“

With Zilliz Cloud, we've achieved query latencies as low as 5-10ms across our million-vector database. This represents performance that's twice as fast as our previous solution, which directly translates to more responsive chatbots for our customers."

Nguyễn Ngọc Hải Đăng_ Nguyễn Nhật Khoa

AI Engineer at CX Genie

“

After integrating Zilliz Cloud vector database service, our system performance has significantly improved. During implementation, the Zilliz Cloud expert team provided excellent support and assistance, giving our EviMed platform a strong competitive advantage in the industry."

Dr. Zeyuan Wang

CEO of EviMed

“

Respondemos en menos de 20 milisegundos entre cientos de millones de vectores, y todos los productos de IA que hemos construido — búsqueda, publicidad, RAG — se ejecutan en el mismo despliegue de Milvus. Eso es lo que nos permite seguir lanzando sin añadir infraestructura."

Nam Doan Ngoc Giang

Lead Data Scientist, Cốc Cốc

“

Thanks to the well-designed Python SDK and REST API, we were able to integrate Zilliz Cloud with our LangChain-based architecture in a matter of days. The schema-based collections perfectly aligned with how we structure our data, making the transition nearly seamless."

Nguyễn Ngọc Hải Đăng_ Nguyễn Nhật Khoa

AI Engineer at CX Genie

“

We have achieved a true consciousness of data... bringing the data together in the way that an individual doing their job needs to see it."

Nathan Morris

Co-Founder of Filevine

“

"Cuando Meituan se comprometió con la IA, necesitábamos una base de recuperación construida para vectores desde cero. Elegimos Milvus de código abierto y lo convertimos en una plataforma sobre la que cualquier equipo puede construir. Un año después, alberga más de 10 mil millones de vectores y ejecuta nuestras cargas de trabajo de IA de más rápido crecimiento, desde RAG hasta la memoria de agentes.""

The Meituan Database Team

“

When it comes to vector databases, Milvus has impressed us with its performance and scalability, meeting our stringent criteria for handling our AI use case backlog."

Team Lead

AI, ML, and Platform Solutions

“

During batch ingestion tests, Milvus demonstrated that it could complete an entire collection dump into the database at speeds 5–10 times faster than competitors."

Team Lead

AI, ML, and Platform Solutions

“

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

“

As a client of a law firm, at some point clients will demand that their law firms use AI because it's going to make them a better attorney to have the curation done versus some of the practices now where they just don't have complete info."

Brianna Connelly

AI Team Lead at Filevine

“

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

“

We have the full end-to-end data... law as a timeline. We have the incident, the start, everything that's happening, and the conclusion of the case. There's an enormous amount of power in having that all within the work system."

Brianna Connelly

AI Team Lead at Filevine

“

Milvus scaled really well with batches ranging from 1,000 to millions of records. That really impressed me."

Todor Voynikov

Data Engineer at TrialHub

“

What we wanted was to push intelligence to the user before they even asked. Milvus is what made that viable."

Rob Williams

Co-Founder and CTO at Read AI

“

It has saved us cost by at least 25 to 30% on what we were earlier incurring on the vector database, with potential savings reaching up to 40-50% during peak traffic periods. "

Rachit Jindal

Senior AI Engineer at Verbaflo.ai

“

La IA está cambiando cómo las personas descubren y disfrutan el contenido, y Kwai aspira a liderar ese cambio. Milvus nos brinda el rendimiento, la estabilidad y la apertura que necesitamos, y se ha convertido en una parte clave de nuestra infraestructura de IA."

Name

Title

“

We've got millions of monthly active users and all of the underlying data when we're trying to go find related conversations, find updates to an action item, find referenced documents...Milvus serves as the central repository and powers our information retrieval among billions of records."

Rob Williams

Co-Founder and CTO at Read AI

“

"Los creadores no hacen una imagen y se van. Construyen un personaje, un mundo, una historia, y todo lo que han generado se convierte en material para la siguiente escena. Zilliz Cloud nos permite tratar toda esa biblioteca como una única memoria creativa.""

John Qiao

Co-founder & CTO, OpenArt

“

The sub-10 millisecond latency for queries is quite the benchmark in the industry for vector databases. Combined with the cost savings and reliability, Zilliz Cloud has become a strategic enabler for our broader vision of transforming conversational AI across industries."

Rachit Jindal

Senior AI Engineer at Verbaflo.ai

“

Choosing Zilliz Cloud was one of our best early decisions. It enabled us to build the product we envisioned rather than the product our infrastructure limitations would allow. In AI applications, that difference often determines success or failure."

Ethan Zheng

Co-Founder & CTO of Jobright.ai

“

Our strongest competitive moat isn't our AI models—it's our ability to deploy those models at scale with an exceptional user experience. Zilliz Cloud gave us that capability."

Ethan Zheng

Co-Founder & CTO of Jobright.ai

“

"Nuestra búsqueda anterior tardaba 25 segundos en P99. Eso era inaceptable. Zilliz Cloud la redujo a menos de un tercio de segundo y nos permitió eliminar el código de recuperación que habíamos estado manteniendo nosotros mismos.""

Danny Xiong

Software Engineer, OpenArt

“

With Zilliz Cloud, we moved from operating at our limits to building with confidence. It gave us the scale, performance, and flexibility to protect music rights in real time—something we couldn’t achieve with traditional systems."

George Kastrinakis

Director of Data Science and AI Services at Orfium

“

Milvus has become the only technical choice for vector databases in our upcoming business expansion across the life sciences industry."

Xiaoming Zhang

VP of Technology at Biomap

“

Milvus transformed our ability to detect semantic plagiarism at scale. We can now process variable workloads ranging from 10 to 10,000+ documents daily while maintaining cost-effectiveness, which would have been impossible with traditional solutions."

Teis Petersen

Engineering Team Lead, UNIwise

“

If I were to choose again, I would still choose Milvus at this point. The scalability, documentation quality, and continuous innovation make it the right foundation for our plagiarism detection platform."

Teis Petersen

Engineering Team Lead, UNIwise

“

It was the best thing to offer—performance-wise, cost-wise, and ease-of-use-wise."

George Kastrinakis

Director of Data Science and AI Services at Orfium

“

La IA está pasando de responder preguntas puntuales a agentes que recuerdan, lo que convierte la memoria de IA en el corazón de los productos de IA de consumo. Zilliz Cloud nos brinda una base sólida en la que podemos confiar para la recuperación de memoria de agentes a gran escala, de modo que nuestro equipo puede dedicar su energía a la innovación del producto y a la experiencia del usuario, no a la fontanería subyacente."

Charles Liu

Co-founder & CTO, Plaud

“

Among Milvus's rich vector search capabilities, features such as support for multiple ANN index types, multi-vector support, and hybrid search have proven especially valuable in real-world service environments. As Milvus continues to evolve with new capabilities, NAVER expects even broader applications across its services."

NAVER Engineering Team

“

Elige por la escala a la que terminarás llegando, no por la que tienes ahora. Los datos de consumo crecen más rápido de lo que cualquiera planea, y una migración de ese tamaño es dolorosa. Entrega la infraestructura de recuperación a especialistas y dedica a tu propia gente al producto."

The engineering team, Plaud

“

From a system stability perspective, it's really quite good. Over the year-plus that we've been using it—from version 2.4.3 to now 2.5.8—I honestly haven't encountered many issues. The system can just run there for months, with new data being written every day and users searching every day, without any problems. I don't need to worry about it."

Team Lead

Senior Infrastructure Engineer

“

Milvus has become the bridge that connects our multi-modal foundation models with real-world applications. It's not just about performance – it's about enabling entirely new approaches to biological discovery that were previously impossible."

Xiaoming Zhang

VP of Technology at Biomap

“

From a system stability perspective, it's really quite good. Over the year-plus that we've been using it—from version 2.4.3 to now 2.5.8—I honestly haven't encountered many issues. The system can just run there for months, with new data being written every day and users searching every day, without any problems. I don't need to worry about it."

Jianping Wang

NVIDIA engineer

“

From a performance standpoint, Zilliz Cloud's retrieval speed far exceeds our existing system. We achieved approximately 70% reduction in retrieval latency, which translates to a 4-5x improvement in overall problem-solving time when we successfully match original questions. Whether measured by speed, cost, or overall value, Zilliz Cloud perfectly met our expectations."

Dr. Nick Yuan

CTO

“

We believe AI agents will become a fundamental interface for how people work, learn, and make decisions, and that only happens if those systems can access real-world information with speed, precision, and trust. That’s what we’re building at Exa. Aside from web search, Exa also operates entity search, and Zilliz Cloud has been an important part of that journey, giving us the retrieval performance and operational simplicity we need to scale our entity search product quickly and confidently."

Jeffrey Wang

Co-founder of Exa

“

The biggest immediate impact for the company would be the cost side of things. We were able to bring the estimated cost of our search cluster from above five digits a month to a significantly lower figure. That would be the biggest improvement for our company."

Su-Meng Yong

Engineering Team Lead

“

The migration was incredibly smooth. Using the built-in tools, we were able to import our data from Pinecone with essentially one click. The technical support has also been excellent — our questions get resolved almost instantly, and the documentation, demos, and examples are thorough and easy to work with."

Technical Team

“

The fully managed version really saves both my team and the developers a lot of time from having to deal with a lot of problems, a lot of self-managing of the cluster. And regarding latency — we went from an initial 100 milliseconds to now sub 30 to 50 milliseconds, a roughly 50% reduction while being able to maintain production throughput."

Su-Meng Yong

Engineering Team Lead

“

We don’t have any concern related to database operations anymore after adopting Zilliz Cloud. Before, we had a lot of troubles—memory suddenly wasn’t enough, we needed to scale up, we didn’t have auto-scaling. All of those things caused a lot of problems. The most obvious benefits for us are the management and the data ingestion. That’s very attractive."

Lixiang Li

the engineering team lead at JERA

“

We believe AI is becoming a meaningful support layer for physicians, but the experience has to feel trustworthy, reliable, and seamless. Building that kind of product requires a strong foundation behind the scenes. Zilliz Cloud has helped us create that foundation as we continue to grow and serve hundreds of thousands of clinicians."

Jagath Kumar

Head of Performance Engineering at OpenEvidence

“

"Nuestro trabajo es hacer que la mejor investigación sea localizable para cualquiera que use Consensus. Zilliz Cloud le brinda a nuestro agente de investigación una recuperación semántica rápida y de alta calidad, ampliando directamente la evidencia a la que puede acceder.""

Christian Salem

Co-founder & Chief Product Officer, Consensus

“

"Uno de los mayores logros que hemos visto es un mejor manejo de las consultas que son semánticamente, pero no textualmente, relevantes. Las consultas más largas y conversacionales también han mejorado drásticamente.""

Heath Hohwald

Tech Leader and Search Manager, Consensus

“

Milvus has done an extraordinary job in revolutionizing Likee's video deduplication system, which significantly fueled the growth of BIGO's short-video business."

Xinyang Guo

Software Engineer at BIGO

Cómo migrar a Milvus/Zilliz

Migrar a Milvus o Zilliz Cloud es sencillo. Puede importar datos de Qdrant, Weaviate, Pinecone, Elasticsearch, OpenSearch, Amazon S3 Vectors, PostgreSQL, etc. mediante herramientas integradas que automatizan la extracción y la carga.

Para las cargas de trabajo de producción, ofrecemos migración sin tiempo de inactividad con sincronización de datos en tiempo real. Muchos equipos reducen sus costes de infraestructura vectorial hasta en un 50 % tras el cambio, al tiempo que obtienen un rendimiento más rápido y un escalado más predecible.

Empezar a migrar a Milvus/Zilliz ahora

¿Listo para migrar tus datos no estructurados y vectoriales? Tanto si migras desde Elasticsearch, Pinecone u otra base de datos, Zilliz te lo pone fácil.

forrester

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Zilliz es nombrado líder en el informe Forrester Wave™ sobre bases de datos vectoriales

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The Definitive Guide to Choosing a Vector Database

Overwhelmed by all the options? Learn key features to look for & how to evaluate with your own data. Choose with confidence.

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