Zilliz

This Zilliz review covers the fully managed vector database cloud service built on the open-source Milvus project by the team that created it. Zilliz Cloud provides a managed Milvus experience with enterprise features including automated scaling, the proprietary Cardinal search engine, and multi-cloud deployment across AWS, Azure, and GCP. The platform supports billion-scale vector search and is trusted by over 10,000 enterprise users. Named a leader in the Forrester Wave for Vector Database Providers in Q3 2024, Zilliz delivers high-performance vector similarity search for AI applications including retrieval augmented generation, recommender systems, semantic search, and AI agents. We evaluate Zilliz's architecture, pricing tiers, use cases, and how it compares to Pinecone, Qdrant, ChromaDB, and the self-hosted Milvus option.

Overview

Zilliz is the commercial cloud offering built on top of Milvus, the open-source vector database with over 43,000 GitHub stars and 100 million+ downloads. Zilliz Cloud removes the operational overhead of running Milvus by providing a fully managed service with enterprise-grade features, while the underlying Milvus engine handles the vector indexing and search.

The platform targets AI engineering teams building applications that require vector similarity search at scale. Common workloads include retrieval augmented generation (RAG) for LLM applications, recommendation engines, semantic text search, image and audio similarity search, and multimodal retrieval. Zilliz claims 10x faster vector retrieval compared to self-hosted Milvus through its Cardinal search engine, which combines IVF and graph indexing techniques with hardware-level optimizations.

Zilliz Cloud operates across eight regions on AWS, Azure, and GCP, with both serverless and dedicated deployment options. The platform meets SOC2 Type II and ISO27001 security standards, supports role-based access control (RBAC), and provides 99.95% monthly uptime SLA on the Enterprise tier. Production customers include Beatoven.ai, Picdmo, Monterey AI, Ivy.ai, BOSCH, Shulex, Rexera, Sarvam, and MindStudio.

Key Features and Architecture

Zilliz's architecture layers managed cloud services on top of the Milvus vector database engine, adding performance optimizations and enterprise features.

Cardinal Search Engine is Zilliz's proprietary indexing technology that delivers 10x faster vector retrieval compared to standard Milvus. It combines advanced IVF and graph-based indexing with a smart query optimizer that automatically selects the optimal search algorithm for each dataset, along with end-to-end optimizations across algorithms, systems, and hardware.

Hybrid Search enables querying across multiple vector fields simultaneously, supporting multimodal, sparse-dense, and dense-text combinations for more accurate results. This is critical for RAG applications that need to combine semantic vector search with keyword-based filtering.

AutoIndex automatically optimizes index configurations to balance recall and performance, eliminating the need for manual tuning. Teams get optimal search quality without deep vector database expertise.

Built-in Embedding Pipelines convert unstructured data into searchable vector embeddings, handling the full workflow from data preparation through chunking, model selection, and transformation. This reduces the engineering effort needed to build a complete vector search pipeline.

Multi-Cloud Deployment supports AWS, Azure, and GCP across eight regions worldwide. Deployment options include fully managed clusters, serverless endpoints, and bring-your-own-cloud (BYOC) for organizations with strict data residency requirements.

Tiered Storage automatically moves data between hot and cold storage tiers to optimize cost and performance. Combined with elastic scaling, this allows clusters to handle workload fluctuations without over-provisioning resources.

Enterprise Security meets SOC2 Type II and ISO27001 standards, supports SAML 2.0-based SSO, granular RBAC, private endpoints, VPC peering, and CMEK encryption. The Business Critical tier adds HIPAA eligibility.

Ideal Use Cases

Zilliz is best suited for AI engineering teams building production RAG applications that need reliable, low-latency vector retrieval at scale. Teams running LLM-powered applications that retrieve context from millions of documents or knowledge base entries will benefit from Zilliz's managed infrastructure and Cardinal engine performance.

Recommendation system teams building personalized product, content, or media recommendations at scale represent a core Zilliz use case. The platform's dedicated compute and elastic scaling handle the high-throughput, low-latency requirements of production recommendation engines serving millions of users.

Companies building multimodal search across text, images, audio, and video will benefit from Zilliz's hybrid search capability, which supports querying across multiple vector field types in a single request. Rexera reported a 40% accuracy improvement using this feature.

Organizations that currently self-host Milvus and want to reduce operational overhead should evaluate Zilliz Cloud. Shulex reported lower operational costs, increased search speed, and a more stable user experience after migrating from self-hosted Milvus to Zilliz Cloud.

Zilliz is not the best fit for teams with small-scale vector search needs (under a few hundred thousand vectors) where the free tier of simpler solutions like ChromaDB would suffice. It is also overkill for prototyping and experimentation where a local Milvus instance provides sufficient performance.

Pricing and Licensing

Zilliz Cloud offers 4 pricing tiers with a permanent free tier and pay-as-you-go scaling.

Storage pricing was reduced 87% in January 2026, from $0.30/GB/month to $0.04/GB/month across all clusters. Serverless billing runs at $4 per million vCUs (virtual compute units), which unify resource consumption measurement for read and write operations. Dedicated clusters start at $99/month with a 30-day free trial.

Compared to competitors, Pinecone's paid plans start at $0.15/hour and scale up to $500/month. ChromaDB offers usage-based pricing starting at $5/month. Zilliz positions at the mid-market price point with its $155/month Enterprise tier.

Pros and Cons

Pros:

• Built on Milvus with 43,000+ GitHub stars and 100 million+ downloads, ensuring a mature and battle-tested vector engine
• Cardinal search engine delivers 10x faster retrieval with automatic index optimization via AutoIndex
• Permanent free tier with 5 GB storage and 2.5M vCUs/month for learning and prototyping
• Multi-cloud support across AWS, Azure, and GCP with 8 global regions
• Enterprise security with SOC2 Type II, ISO27001, RBAC, SSO, and HIPAA eligibility on Business Critical
• Built-in embedding pipelines reduce the engineering effort to build end-to-end vector search

Cons:

• Enterprise tier at $155/month represents a cost jump from the free Standard tier with no intermediate paid option
• Vendor lock-in risk despite being built on open-source Milvus, as Cardinal engine optimizations are proprietary
• Business Critical pricing requires custom quotes, making cost planning difficult for regulated organizations
• Limited to vector search workloads, requiring additional infrastructure for full application backends

Alternatives and How It Compares

Milvus (self-hosted) is the open-source foundation that Zilliz is built on. We recommend self-hosted Milvus for teams with strong infrastructure engineering capabilities that want full control and zero license costs. Choose Zilliz Cloud when you want managed operations, the Cardinal engine's 10x performance gains, and enterprise support without the operational burden.

Pinecone is a fully managed vector database with paid plans starting at $0.15/hour. We recommend Pinecone for teams that want the simplest possible developer experience with a pure serverless model. Zilliz offers more deployment flexibility (serverless, dedicated, BYOC) and lower entry pricing with its permanent free tier.

Qdrant is an open-source vector search engine written in Rust with a free tier. We recommend Qdrant for teams that prioritize Rust-native performance and a simpler operational model. Zilliz provides broader enterprise features including SSO, RBAC, and compliance certifications that Qdrant's managed offering does not match.

ChromaDB is an AI-native open-source embedding database with usage-based pricing starting at $5/month. We recommend ChromaDB for small-scale LLM applications and prototypes. Zilliz is the better choice for production workloads requiring billion-scale search, enterprise security, and multi-cloud deployment.

Marqo focuses on search conversion optimization using click-stream and purchase data. We recommend Marqo for e-commerce teams that need personalized search ranking. Zilliz serves a broader set of vector search use cases beyond e-commerce with deeper infrastructure capabilities.