Zilliz
FreemiumLeverage predictive modeling, create custom visualizations, and utilize machine learning to optimize decisions.
About Zilliz
Zilliz is a comprehensive analytics platform designed to help businesses make informed decisions. Our intuitive and easy-to-use interface provides a comprehensive suite of tools to help you analyze, visualize, and interpret data from multiple sources. With Zilliz, you can quickly and accurately analyze your data and make decisions based on the insights gained. Our platform provides powerful analytics capabilities, including predictive modeling, forecasting, and machine learning, to help you make the best decisions. Additionally, we offer a wide range of customization options, allowing you to tailor the platform to your specific needs and goals. With Zilliz, you can unlock the power of your data and gain insights that will help you make informed decisions and drive your business forward.
Key Features
Pros & Cons
- Industry-leading performance: 3ms average latency and 1476 QPS on 768-dim vectors (Performance-Optimized)
- Scalable to over 100 billion vectors with predictable performance and tiered pricing
- Cost-effective: lake-native storage and on-demand compute cut costs by up to 90% vs. traditional vector databases
- Full-spectrum search support (vector, text, JSON, geospatial) with hybrid retrieval and reranking
- Managed service by creators of Milvus, with 8 years of production experience across 10,000+ enterprises
- Generous free tier (5GB storage, 2.5M vCUs/month) for learning and personal projects
- Robust security and compliance: SSO, RBAC, CMEK, HIPAA-eligibility, VPC peering, and global disaster recovery
- Complex pricing structure with multiple cluster types and tiers may require careful evaluation
- Dedicated cluster costs can add up for very large datasets (e.g., Performance-Optimized from $63/million vectors/month)
- Tiered-storage solution has higher latency (100-1000ms) unsuitable for real-time workloads
- Relatively newer product compared to traditional databases; ecosystem still maturing
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