Milvus vs Zilliz Cloud
Milvus
milvus.io
Zilliz Cloud
zilliz.comSide by side
| Figure | Milvus | Zilliz Cloud |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | Yes |
| Open source | — | — |
| Web | — | Yes |
| Linux | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | — |
| Plans published | 1 | 4 |
| Spec score · Ai Databases | 8.3 (#8) | 7.3 (#31) |
| Spec score · Vector Databases | 8.2 (#7) | 7.3 (#23) |
| Spec score · Search Databases | 8.2 (#11) | 7.3 (#26) |
| Spec score · Search & Vector Database Platforms | 8.2 (#9) | 7.3 (#16) |
| Access control | RBAC can control operations at collection, database, and instance levels. | The service uses role-based access control at organization, project, and cluster levels. |
| Deployment options | Milvus offers Lite as a Python library, Standalone as a single-machine server, and Distributed for Kubernetes clusters. | The service offers Free, Serverless, and Dedicated cluster deployment options. |
| Integrations | The integrations documentation lists tools including LangChain, LlamaIndex, Haystack, DSPy, Dify, Langflow, OpenAI, Cohere, and HuggingFace. | Documented integrations include Prometheus, Datadog, AWS S3, Azure Blob Storage, Google Cloud Storage, and external model providers. |
| Product | Milvus is an open-source vector database built for GenAI applications. | Zilliz Cloud is a fully managed Milvus service for deploying and scaling vector search applications. |
| Search | Milvus supports approximate nearest neighbor, filtering, range, hybrid, and full-text search, plus reranking, fetch, and query operations. | The service supports approximate nearest neighbor vector search, filtered search, hybrid search, and full-text search. |
| Security | Milvus documents user authentication, TLS encryption for gRPC and REST traffic, and role-based access control. | Zilliz Cloud documents TLS 1.2 or higher in transit and AES-256 encryption at rest. |
| Support | The Milvus site lists community channels including Slack, Discord, GitHub, and Milvus Office Hours. | Users can contact Zilliz Support to request access to features in private preview. |
Plans
Zilliz Cloud
- Free clusterFree
- Business CriticalPriced on request
- DedicatedPriced on request
- ServerlessPriced on request
In their own words
Milvus
Milvus is an open-source vector database from Zilliz for GenAI and scalable vector search. It supports approximate-nearest-neighbor, filtered, range, hybrid, and full-text search, along with reranking, fetch, and query operations. Metadata filtering, sparse vectors, and index types including HNSW, IVF, FLAT, DiskANN, and SCANN are documented. Deployment choices are Milvus Lite as a Python library, Standalone as a single-machine server, and Distributed on Kubernetes. Documentation recommends Lite for up to a few million vectors, Standalone up to 100 million, and Distributed for 100 million to tens of billions. Lite can run locally without a cloud service for private or sensitive data; an online upgrade from Standalone to a cluster is not currently supported. Official interfaces include REST plus Python, Go, Java, Node.js, and C++ APIs or SDKs. C# is contributed by Microsoft and Rust is under development. Security documentation covers authentication, TLS for gRPC and REST, and role-based access control at collection, database, and instance levels. The Apache 2.0 software is free, and Zilliz Cloud is offered as a managed option.
Zilliz Cloud
Zilliz Cloud is a managed Milvus service for deploying and scaling vector search applications. It supports approximate nearest neighbor, filtered, hybrid, and full-text search. Teams can choose Free, Serverless, or Dedicated cluster deployments; Serverless clusters scale automatically to zero. During ingestion, the service can create vector embeddings from raw text using a configured embedding function.
Documented integrations include Prometheus, Datadog, AWS S3, Azure Blob Storage, Google Cloud Storage, and external model providers. Migration sources include Pinecone, Qdrant, Elasticsearch, and PostgreSQL. The service supports sparse vectors, metadata filtering, up to 32768 dimensions, and a range of index types, including FLAT, HNSW, DISKANN, and AUTOINDEX. SDKs are listed for Python, Java, Go, Node.js, and C++. The Free cluster costs nothing and includes 5 GB storage, 2.5M vCUs/month, and up to 5 collections. Dedicated and Serverless pricing is pay as you go; storage charges apply to stored data and backup files even when a cluster is stopped.