Description
Zilliz is the company behind Milvus, the world's most popular open-source vector database, and Zilliz Cloud, a fully managed Vector Lakebase for enterprise AI. It unifies real-time vector search, lake-scale discovery, and AI data operations, helping organizations make sense of unstructured data and build AI and RAG applications at any scale. It is available across AWS, Google Cloud, and Azure regions worldwide.
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Zilliz Cloud offers a range of plans from a free tier for learning and personal projects (5 GB storage, 2.5M vCUs per month, up to 5 collections) through Standard, Enterprise, and Business Critical tiers. Higher tiers add 99.95% uptime SLA, audit logs, SSO (SAML 2.0), granular RBAC, multi-replica and elastic scaling, private endpoints and VPC peering, CMEK encryption, HIPAA eligibility, and disaster recovery. On-Demand Compute runs lake-scale search and indexing jobs on zero-copy external data, and BYOC lets you deploy on your own cloud.
Dedicated clusters provide isolated, reserved environments with predictable performance and cluster types optimized for performance, capacity, or tiered storage, priced per million vectors per month. Zilliz also maintains open-source tools including VectorDBBench, GPTCache, Attu, and Feder.
Founded in 2017 with 10,000+ enterprise users, Zilliz starts free, with usage-based pricing from around $0.30 per GB per month.
- CategoryVector Databases & Retrieval
- Pricing ModelFreemium
- DeploymentCloud
- PlatformsWeb
Zilliz's Core Features
Fully managed vector database powered by Milvus
Billion-scale real-time vector search
Hybrid and lake-scale search and discovery
Vector storage for RAG and AI applications
Available on AWS, Google Cloud, and Azure
Enterprise security: SSO, RBAC, audit logs, CMEK
On-demand compute and BYOC deployment
Dedicated clusters optimized for performance or capacity
How to use Zilliz?
Start free: Create a Zilliz Cloud account and use the free tier.
Create a cluster: Choose a plan and deployment on AWS, Google Cloud, or Azure.
Load vectors: Ingest your embeddings using the core Milvus-compatible APIs.
Query: Run vector, hybrid, or lake-scale search for your AI or RAG app.
Scale up: Move to Enterprise or dedicated clusters for production workloads.
Zilliz's Use Cases
- RAG applications
- Semantic search
- Recommendation systems
- Enterprise AI at scale
- Lake-scale discovery
