Milvus vs. MongoDB Atlas

Comparez Milvus vs. MongoDB Atlas pour les charges de travail de recherche vectorielle. Nous voulons que vous choisissiez la base de données vectorielle la plus adaptée à votre cas d'utilisation, même si ce n'est pas nous.

Les bases de données vectorielles sont devenues un élément central de l'infrastructure des applications modernes d'intelligence artificielle, notamment la génération augmentée par récupération(RAG), les agents d'intelligence artificielle, la recherche multimodale et sémantique, et les systèmes de recommandation dans un large éventail de secteurs d'activité. Le choix de la bonne base de données vectorielle peut avoir une incidence directe sur les performances, l'évolutivité, le coût et la fiabilité de ces applications.

Cette comparaison entre Milvus et MongoDB Atlas est destinée aux ingénieurs et aux équipes techniques qui évaluent les bases de données vectorielles pour des charges de travail de production réelles. Bien que Milvus et MongoDB Atlas prennent tous deux en charge la capacité de recherche vectorielle de base, ils diffèrent considérablement dans des domaines tels que l'architecture, l'évolutivité, les performances, la fiabilité et bien d'autres encore. Ces différences restent souvent subtiles au cours des premières expériences, mais deviennent de plus en plus importantes à mesure que le volume de données augmente, que les charges de travail se diversifient et que les systèmes passent du stade du prototype à celui de la production.

L'objectif de ce guide n'est pas seulement d'énumérer des fonctionnalités, mais de vous aider à déterminer quel système est le mieux adapté à votre cas d'utilisation spécifique, à vos contraintes et à votre stade de croissance - même si notre produit(Milvus / Zilliz Cloud) n'est pas le choix final.

Comparaison entre Milvus et MongoDB Atlas en un coup d'œil

MilvusMongoDB Atlas
Positioning

Base de données vectorielle conçue spécifiquement pour les applications natives de l’IA : stockage vectoriel à grande échelle + recherche par similarité

Non. C'est un service de base de données NoSQL avec la recherche vectorielle en tant que module complémentaire.

Open Source

✅ Apache 2.0

❌
License

Apache 2.0

Licence propriétaire

Github Stars

46,519

N/A

Deployment

Sur site, intégré

Nuage

Billion-Scale Vector Support

✅

❌

Milvus est une base de données vectorielle open source, hautes performances et hautement évolutive, capable de stocker, d’indexer et de rechercher des données non structurées à l’échelle du milliard au moyen d’embeddings vectoriels de haute dimension. Elle est idéale pour créer des applications d’IA modernes telles que la génération augmentée par récupération (RAG), la recherche sémantique, la recherche multimodale et les systèmes de recommandation. Milvus fonctionne efficacement dans divers environnements, des ordinateurs portables aux systèmes distribués à grande échelle. Elle est disponible en tant que logiciel open source et service cloud.

MongoDB Atlas est un service de base de données cloud-native offrant le modèle de données flexible et orienté documents de MongoDB, avec une prise en charge intégrée de l’évolutivité et de la gestion opérationnelle. Ses capacités de recherche vectorielle, introduites via Atlas Search, permettent aux développeurs d’effectuer des requêtes de similarité parallèlement aux opérations sur des données structurées et non structurées, ce qui en fait une option polyvalente pour les applications modernes d’IA et de recherche.

Comparaison entre Milvus et MongoDB Atlas Utilisation de vos ensembles de données

Ne croyez pas les vendeurs sur parole, testez-les vous-même.

VectorDBBench est un outil de benchmarking open-source conçu spécifiquement pour comparer les bases de données vectorielles dans des conditions équitables et reproductibles. Il vous permet de mesurer les performances réelles - latence, débit, rappel, vitesse d'indexation et comportement de mise à l'échelle - en utilisant les mêmes charges de travail sur différents systèmes ou même en utilisant vos propres ensembles de données.

Il est ainsi facile de voir comment Milvus et MongoDB Atlas se comportent réellement dans la pratique, et pas seulement dans les documents marketing. Chaque benchmark peut être reproduit localement, dans votre propre environnement, afin que vous puissiez valider les résultats qui comptent pour votre application.

Consultez le tableau de bord VectorDBBench pour avoir un aperçu des performances des bases de données vectorielles courantes.

Pourquoi les équipes de plus de 10 000 entreprises adoptent-elles Milvus et Zilliz Cloud ?

La plupart des bases de données vectorielles sont parfaites dans les démonstrations ou les déploiements à petite échelle, mais les lacunes apparaissent en production, lorsque les ensembles de données augmentent, que les embeddings sont fréquemment actualisés et que la latence doit rester stable en cas de trafic réel. C'est là que les équipes se tournent vers Milvus et Zilliz Cloud (service géré Milvus).

Milvus est une base de données vectorielle open-source haute performance utilisée par plus de 10 000 équipes d'entreprise dans le monde entier et bénéficiant de la confiance d'une vaste communauté open-source avec 46K+ étoiles GitHub. Elle gère des dizaines de millions à des dizaines de milliards de vecteurs, des insertions et des suppressions fréquentes, et des recherches hybrides (vecteur + mot-clé + métadonnées + reranking) sans réindexation perturbatrice ou réglage fragile. Les performances restent prévisibles au fur et à mesure de l'évolution du volume de données, des schémas d'interrogation et de l'intégration des modèles. C'est pourquoi Milvus est largement déployé pour les RAG d'entreprise, les agents d'IA, la recherche sémantique et multimodale et les systèmes de recommandation - des charges de travail où l'instabilité devient immédiatement visible.

Zilliz Cloud propose la même architecture Milvus en tant que service géré, avec un moteur vectoriel avancé(Cardinal) pour de meilleures performances, ainsi qu'une mise à l'échelle élastique, une haute disponibilité, une sécurité et une conformité de niveau entreprise, et un déploiement mondial. Les équipes bénéficient d'une fiabilité prête pour la production sans avoir à exploiter ou à surveiller la base de données.

“

Choisissez en fonction de l'échelle que vous atteindrez, pas celle que vous avez. Les données des consommateurs croissent plus vite que personne ne le prévoit, et une migration à cette échelle est douloureuse. Confiez l'infrastructure de recherche à des spécialistes, et consacrez vos propres équipes au produit."

The engineering team, Plaud

“

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

“

« Notre mission est de rendre les meilleures recherches accessibles à tous ceux qui utilisent Consensus. Zilliz Cloud offre à notre agent de recherche une récupération sémantique rapide et de haute qualité, élargissant directement le champ des preuves auxquelles il peut accéder. »"

Christian Salem

Co-founder & Chief Product Officer, Consensus

“

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

“

"L'un des plus grands gains que nous avons constatés est une meilleure gestion des requêtes pertinentes sur le plan sémantique mais pas textuel. Les requêtes plus longues et plus conversationnelles se sont également considérablement améliorées.""

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

“

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

“

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 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

“

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

“

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'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

“

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

“

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

“

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

“

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

“

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

Su Wei

CTO of Shining

“

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

“

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

“

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

“

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

“

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

“

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

“

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

“

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

“

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

“

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

“

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

“

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

“

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

Todor Voynikov

Data Engineer at TrialHub

“

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

“

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

“

« Lorsque Meituan s’est engagé dans l’IA, il nous fallait une fondation de recherche conçue pour les vecteurs dès le départ. Nous avons choisi Milvus en open source et l’avons transformé en une plateforme sur laquelle chaque équipe peut s’appuyer. Un an plus tard, elle contient plus de 10 milliards de vecteurs et fait tourner nos charges de travail IA qui connaissent la croissance la plus rapide, du RAG à la mémoire d’agent. »"

The Meituan Database Team

“

Nous répondons en moins de 20 millisecondes sur des centaines de millions de vecteurs, et chaque produit d'IA que nous avons construit — recherche, publicité, RAG — fonctionne sur le même déploiement Milvus. C'est ce qui nous permet de continuer à livrer sans ajouter d'infrastructure."

Nam Doan Ngoc Giang

Lead Data Scientist, Cốc Cốc

“

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

“

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

“

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

“

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

“

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

“

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

“

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

“

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

“

Les créateurs ne font pas une seule image puis s'en vont. Ils construisent un personnage, un monde, une histoire, et tout ce qu'ils ont jamais généré devient matière pour la scène suivante. Zilliz Cloud nous permet de traiter toute cette bibliothèque comme une seule mémoire créative."

John Qiao

Co-founder & CTO, OpenArt

“

« Notre ancien moteur de recherche mettait 25 secondes au P99. C'était inacceptable. Zilliz Cloud l'a ramené sous le tiers de seconde et nous a permis de supprimer le code de récupération que nous maintenions nous-mêmes. »"

Danny Xiong

Software Engineer, OpenArt

“

L'IA passe de la réponse à des questions ponctuelles à des agents dotés de mémoire, ce qui fait de la mémoire IA le cœur des produits grand public basés sur l'IA. Zilliz Cloud nous offre une base solide et fiable pour la récupération de mémoire agentique à très grande échelle, ce qui permet à notre équipe de concentrer son énergie sur l'innovation produit et l'expérience utilisateur, plutôt que sur l'infrastructure sous-jacente."

Charles Liu

Co-founder & CTO, Plaud

“

Choisissez en fonction de l'échelle que vous atteindrez, pas celle que vous avez. Les données des consommateurs croissent plus vite que personne ne le prévoit, et une migration à cette échelle est douloureuse. Confiez l'infrastructure de recherche à des spécialistes, et consacrez vos propres équipes au produit."

The engineering team, Plaud

“

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

Comment migrer vers Milvus/Zilliz ?

La migration vers Milvus ou Zilliz Cloud est simple. Vous pouvez importer des données depuis Qdrant, Weaviate, Pinecone, Elasticsearch, OpenSearch, Amazon S3 Vectors, PostgreSQL et bien d'autres encore à l'aide d'outils intégrés qui automatisent l'extraction et le chargement.

Pour les charges de travail de production, nous prenons en charge la migration sans temps d'arrêt avec synchronisation des données en direct. De nombreuses équipes ont réduit leurs coûts d'infrastructure vectorielle jusqu'à 50 % après le changement, tout en bénéficiant de performances plus rapides et d'une mise à l'échelle plus prévisible.

Commencer à migrer vers Milvus/Zilliz maintenant

Prêt à migrer vos données non structurées et vectorielles ? Qu'il s'agisse d'Elasticsearch, de Pinecone ou d'une autre base de données, Zilliz vous facilite la tâche.

forrester

Ne nous croyez pas sur parole

Zilliz est nommé leader dans le rapport Forrester Wave™ sur les bases de données vectorielles.

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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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