Zilliz
Unify real-time vector search, iterative discovery, and batch analytics on a single source of truth. Built by Milvus creators. Scale to 100B+ entities.
Editor's Verdict
Key Takeaways
- Vector Lakebase for AI
- Real-time Serving
- Iterative Discovery
- Batch Analytics
In-Depth Review: What is Zilliz?
Zilliz Vector Lakebase is a fully managed platform that goes beyond vector databases. It combines real-time serving, iterative discovery, and batch analytics on a single source of truth, built for hundred-billion data scale. Powered by Milvus, it offers lake-native storage on S3 for 90% lower cost, tiered architecture for diverse workloads, massive multi-tenancy, and on-demand compute. Ideal for AI applications requiring high performance, scalability, and cost efficiency.
Core Features
Vector Lakebase for AI
Unifies real-time serving, iterative discovery, and batch analytics on a single source of truth at hundred-billion data scale.
Real-time Serving
Low-latency vector search for real-time AI applications with tiered architecture.
Iterative Discovery
Supports iterative querying and schema evolution without downtime.
Batch Analytics
Lake-scale analytics on vector data with on-demand compute for cost efficiency.
Full-Spectrum Search
Combines vector, text, JSON, and geospatial search with hybrid retrieval and reranking.
Lake-Native Storage
Unified storage on S3 using Vortex format for 10x faster random reads than Lance.
Tiered Architecture
Performance-optimized, capacity-optimized, and tiered-storage options for diverse workloads.
Massive Multi-Tenancy
Unlimited namespaces with hybrid search and hot-cold data serving for AI apps.
Global Cluster
Multi-region deployment with replication and failover for low-latency, high-availability access worldwide.
On-demand Compute
Pay-per-query model for lake-scale search and indexing jobs on external data.
Pricing
Free
- 5 GB storage
- 2.5M vCUs per month included
- Up to 5 collections
- Community support
Standard (Serverless)
- Fully managed vector databases with core APIs
- Backup, restore, and basic monitoring
- Built-in encryption for data in transit and at rest
- System-managed auto-scaling
- Single availability zone
Standard (Dedicated)
- Dedicated compute units (CUs)
- Manual scaling to 32 CUs
- Backup, restore, and basic monitoring
- Built-in encryption
- 99.95% uptime SLA (Enterprise only)
Enterprise
- 99.95% uptime SLA
- Audit logs, SSO (SAML 2.0), granular RBAC
- Multi-replica and elastic scaling
- Private endpoint and VPC peering
- On-demand compute support
- 24/7/365 support
Business Critical
- Global cluster with high-level availability and disaster recovery
- CMEK and full-path in-transit encryption
- HIPAA-eligible with enhanced data privacy
- Priority support and rapid incident response
- 99.99% uptime SLA (if multi-replica enabled)
On-demand Compute
- Zero-copy access to external data
- On-demand query jobs and system-managed index builds
- Pay only for active job runtime
BYOC (Bring Your Own Cloud)
- Deploy on your infrastructure of choice
- High-level control and security
- Same features and experience as SaaS Dedicated clusters
Pros and Cons
Pros
- Built for ReliabilityProduction-tested across 10,000+ enterprises over 8 years with deep understanding of large-scale vector database failure modes.
- Built for ScaleHandles 100B+ entities and 10K+ QPS with consistent latency and predictable performance.
- Lower CostAll data and indexes on S3 with hot cache and on-demand compute can cut costs by 90%.
- Full-Spectrum SearchSupports vector, text, JSON, and geospatial search with hybrid retrieval, filtering, and reranking.
- Lake-Native StorageUnified storage for serving and analytics using open Vortex format for up to 10x faster random reads.
Cons
- Complex Pricing StructureMultiple deployment options (Serverless, Dedicated, BYOC) and cluster types can be confusing to estimate costs.
- Limited Free TierFree tier has only 5 GB storage and 2.5M vCUs per month, which may not suffice for production testing.
- Learning CurveRequires understanding of Milvus concepts like CUs, vCUs, and cluster types, which may be steep for beginners.
- Vendor Lock-inProprietary Vortex format and tight integration with Zilliz Cloud may make migration challenging.
- On-demand Compute Cost UncertaintyPay-per-query model can lead to unpredictable costs for bursty workloads.
Use Cases & Recommended Professions
AI/ML Engineer→ View Toolkit
Needs a scalable vector database for real-time retrieval in RAG systems and LLM applications.
Data Scientist→ View Toolkit
Requires iterative discovery and batch analytics on large vector datasets for research and model evaluation.
Software Engineer (AI Apps)→ View Toolkit
Wants a managed service with full-text, vector, and hybrid search to build production AI applications.
DevOps/Infrastructure Engineer→ View Toolkit
Needs a vector database with multi-region replication, VPC peering, and compliance features for enterprise deployments.
Data Engineer→ View Toolkit
Seeks lake-native storage to unify serving and analytics without ETL, supporting large-scale data pipelines.
AI Startup CTO→ View Toolkit
Evaluates cost-effective vector search solutions with BYOC or Serverless to minimize infrastructure overhead.
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ℹ️ Curation Disclosure: The overview and features of Zilliz were synthesized using AI and fact-checked by our curation team to ensure accuracy.
