Zilliz: Managed Vector Database for AI Applications
Zilliz helps software and enterprise companies manage unstructured data for AI workflows. It is designed for teams needing scalable infrastructure for RAG, semantic search, or recommender systems.
At a glance
- Category
- Software Development
- Best for
- Software companies, Enterprise AI teams, Developers building RAG applications
- Pricing
- Zilliz offers a free tier with 5 GB storage and 2.5M vCUs per month. Serverless plans start at $0 or $99/GB/month, and Dedicated Enterprise plans start from $155/month. Performance clusters are $65 per million vectors/month and capacity clusters are $20 per million vectors/month.
- Key use cases
- Retrieval Augmented Generation (RAG), Semantic and Text Search, Recommender Systems, Visual and Audio Similarity Search, AI Agent Knowledge Grounding
- Integrations
- LangChain, Cohere, Databricks, Confluent, Airbyte
- Official website
- zilliz.com
Zilliz is a cloud-native vector database service providing a managed version of the open-source Milvus project. It is designed for organizations building AI applications that require retrieval of embeddings from large datasets, supporting up to 100 billion vectors.
The service is intended for developers and enterprise teams integrating semantic search or memory into AI agents. It supports multiple data modalities, including text, images, audio, and video, and is available on AWS, Azure, and Google Cloud.
Buyers can choose between different cluster types—performance-optimized, capacity-optimized, and tiered-storage—to balance search speed and cost. The platform includes compliance options such as SOC2, ISO27001, and HIPAA eligibility.
Teams can opt for a fully managed SaaS experience or a 'Bring Your Own Cloud' (BYOC) deployment for more control over their infrastructure.
Key Features
A search engine designed for vector retrieval speed improvements over standard Milvus implementations.
Supports querying across different data modalities, including text, video, audio, and images.
Automatically optimizes indexing to help balance search recall and performance.
Supports scaling up to 500 compute units to manage large-scale vector datasets.
Supports converting unstructured data into searchable vector embeddings through chunking and transformation.
Includes RBAC, SSO (SAML 2.0), and encryption for data in transit and at rest.
Use Cases
Supports expanding LLM knowledge by incorporating external data sources.
Finding semantically similar text across large volumes of natural language documents.
Providing product or information recommendations based on user behavior and preferences.
Identifying similar images, audio clips, or video segments from libraries.
Supports AI agents with search and knowledge retrieval for decision-making.
Best For
Integrations
Pricing
Zilliz offers a free tier with 5 GB storage and 2.5M vCUs per month. Serverless plans start at $0 or $99/GB/month, and Dedicated Enterprise plans start from $155/month. Performance clusters are $65 per million vectors/month and capacity clusters are $20 per million vectors/month.
FAQ
Zilliz Cloud is a fully managed vector database service built on the open-source Milvus project, designed to store and search large-scale vector embeddings for AI applications.
Yes, Zilliz provides a free tier that includes 5 GB of storage and 2.5 million vCUs per month for learning and personal projects.
Zilliz is available on AWS, Google Cloud (GCP), and Microsoft Azure across eight global regions.
Zilliz is SOC 2 Type II and ISO/IEC 27001 compliant and is HIPAA-eligible. It supports RBAC and data encryption in transit and at rest.
Source category: Software Development
Source subcategory: Database
