Zilliz
Enterprise-grade managed vector database built on Milvus for AI applications
Founded
2017
Starting Price
0
Category
AI & Machine Learning
Last updated March 12, 2026
About Zilliz
Zilliz Cloud is a fully managed vector database service built on the open-source Milvus engine, designed for enterprise-grade AI and machine learning workloads. It offers high-performance vector similarity search with features like AutoIndex, hybrid retrieval, tiered storage, and multi-cloud deployment across AWS, Azure, and GCP.
Pros & Cons
Pros
- 10x better performance than standard Milvus with the Cardinal search engine
- Generous free tier with $100 credits lowers barrier to entry
- Built on the widely adopted open-source Milvus project with strong community
- Intelligent AutoIndex eliminates need to understand complex index configurations
- Multi-cloud support across AWS, Azure, and GCP with standardized pricing
Key Features
AutoIndex & Cardinal Search Engine
AI-powered indexing that automatically selects optimal search strategies with zero manual tuning
Hybrid Retrieval
Combines vector similarity search with keyword/scalar filtering for more accurate, context-aware results
Tiered Storage
Multi-tier storage architecture delivering up to 87% storage cost reduction for infrequently accessed data
Multi-Cloud Deployment
Deploy across AWS, Azure, and Google Cloud with standardized pricing and cross-region backup support
Dynamic Schema
Flexible field insertion, JSON data type support, and RESTful API interface for easy integration
Multi-Tenant Partition Keys
Data isolation and separation for multi-tenant applications with partition key support
Serverless Clusters
Pay-per-operation serverless option that scales automatically with zero infrastructure management
Pricing
Free
0/month
- $100 in free credits (30 days)
- Serverless clusters
- Up to 2 free serverless clusters
- Community support
Serverless
0
Best For
RAG Applications
Power retrieval-augmented generation pipelines by storing and querying document embeddings for LLM context retrieval
Semantic Search
Build intelligent search systems that understand meaning and context rather than just keyword matching
Recommendation Systems
Create personalized recommendation engines using vector similarity to find related products, content, or users
Image & Video Search
Enable visual similarity search across large media libraries using image and video embeddings
