
Zilliz: Vector Database built for enterprise-grade AI applications
Built for Performance, Priced for Scalability. Powered by open-source Milvus, Zilliz delivers the most performant and cost-effective vector database for AI at any scale.
About Zilliz
Helping organizations make sense of unstructured data. Powered by open-source Milvus, Zilliz delivers the most performant and cost-effective vector database for AI at any scale.
Zilliz Cloud, a managed vector database built on Milvus®
Zilliz Cloud is a highly performant, scalable, and fully managed vector database platform built on open-source Milvus. It's available on AWS, GCP, and Azure.
Milvus | Open-source Vector Database created by Zilliz
Milvus, built by the Zilliz team, is a high-performance open-source vector database designed to handle billions of vectors for AI applications.
Zilliz Cloud Signup
Sign up for Zilliz Cloud, a fully managed, cloud-native vector database that provides lightning-fast queries on any size data set.
Top Performing AI Models for Your GenAI Apps | Zilliz
Building GenAI applications using the right embedding and reranking models and Zilliz Cloud (the managed Milvus)
Top 5 AI Search Engines to Know in 2025 - Zilliz blog
Feb 8, 2025 · Discover the top AI-powered search engines of 2025, including OpenAI, Google AI, Bing, Perplexity, and Arc Search. Compare features, strengths, and limitations.
Zilliz Makes Real-Time AI a Reality with Confluent
Sep 26, 2023 · Zilliz and Confluent have joined forces in an innovative partnership to redefine real-time AI. Utilizing Confluent's Kafka producer and consumer APIs, this integration …
Customers | Zilliz & Milvus
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.
How AI Is Transforming Information Retrieval and What’s Next for …
Jan 20, 2025 · This blog will summarize the monumental changes AI brought to Information Retrieval (IR) in 2024, exploring how deep learning, LLMs, and vector databases redefined search, data analysis, and knowledge synthesis.