Zilliz is a technology company that provides a cloud-based vector database service built on the open-source Milvus project. The company focuses on delivering managed infrastructure for Artificial intelligence and Machine learning workloads, particularly those involving similarity search and retrieval of unstructured data such as images, text, and audio. Zilliz's platform is designed to support applications ranging from Generative AI systems to recommendation engines and anomaly detection.
The company was founded in 2017 by Charles Xie, who previously worked at Oracle and Yahoo. Zilliz is headquartered in San Francisco, California, with research and development operations in China. The name "Zilliz" is derived from the word "zillion," reflecting the company's ambition to handle billion-scale data operations.
Milvus and Vector Database Technology
Zilliz's core technology is Milvus, an open-source vector database first released in 2019. Milvus is designed to store, index, and manage massive vectors generated by Neural network models, such as embeddings from Large language models or image recognition systems. The database supports multiple indexing algorithms, including HNSW (Hierarchical Navigable Small World) and IVF (Inverted File), to enable fast approximate nearest neighbor search.
In 2021, Zilliz donated Milvus to the Linux Foundation's AI & Data Foundation, establishing it as a vendor-neutral open-source project. The company continues to lead development of the project while offering a fully managed cloud service called Zilliz Cloud, which provides enterprise features like high availability, data replication, and security controls.
Zilliz Cloud Platform
Zilliz Cloud is a fully managed vector database service available on major cloud providers, including Amazon Web Services, Google Cloud, and Microsoft Azure. The platform offers serverless and dedicated cluster options, allowing organizations to scale vector search workloads without managing underlying infrastructure. Key features include multi-tenancy, role-based access control, and integration with popular machine learning frameworks.
The service supports both CPU and GPU-accelerated computing, with GPU options leveraging AMD and NVIDIA hardware for high-throughput similarity search. Zilliz Cloud provides APIs compatible with Milvus, enabling seamless migration from self-hosted deployments. As of 2024, the platform processes billions of queries daily across various industries.
Applications and Use Cases
Zilliz's technology is used in a wide range of Deep learning applications. In Generative AI, vector databases serve as memory layers for Large language models, enabling retrieval-augmented generation (RAG) where models access external knowledge bases. Companies building chatbots, semantic search engines, and recommendation systems rely on Zilliz for efficient embedding storage and retrieval.
Other use cases include image and video similarity search, drug discovery, fraud detection, and personalized content delivery. The platform is also employed in autonomous driving systems for object recognition and in biotechnology for genomic sequence analysis. Zilliz reports customers across sectors including e-commerce, finance, healthcare, and media.
Funding and Growth
Zilliz has raised significant venture capital funding to support its growth. In 2022, the company completed a $60 million Series B round led by Temasek, with participation from existing investors including Yunqi Capital and 5Y Capital. The funding was directed toward expanding the cloud service, growing the engineering team, and advancing research in vector indexing algorithms.
By 2023, Zilliz reported over 1,000 enterprise customers and a community of more than 10,000 developers using Milvus. The company has established partnerships with cloud providers and AI infrastructure companies, including Coreweave and Cerebras, to offer optimized deployments for high-performance computing environments.
Competitive Landscape and Future Directions
Zilliz operates in the rapidly evolving vector database market, competing with other specialized providers and general-purpose databases adding vector support. The company differentiates through its focus on open-source development and performance optimization for billion-scale datasets. Zilliz continues to invest in research on approximate nearest neighbor algorithms and hybrid search capabilities that combine vector and scalar filtering.
Future development priorities include improving integration with Transformer (architecture)-based models, enhancing support for real-time streaming data, and expanding managed services to additional cloud regions. The company also explores partnerships with OpenAI and other AI model providers to streamline deployment of retrieval-augmented applications.
Community and Ecosystem
Zilliz maintains an active open-source community around Milvus, with regular releases and contributions from developers worldwide. The project has been adopted by academic institutions and research labs, including MIT CSAIL and BAIR (Berkeley AI Research), for experiments in large-scale similarity search. Zilliz also organizes conferences and meetups to foster knowledge sharing in the vector database and AI infrastructure space.
The company provides extensive documentation, SDKs in multiple programming languages, and tutorials to lower the barrier for adoption. Through its community programs, Zilliz aims to establish Milvus as the standard open-source vector database, similar to how relational databases became foundational for traditional data management.