Vearch vs Weaviate
Vearch
vearch.github.ioWeaviate
weaviate.ioSide by side
| Figure | Vearch | Weaviate |
|---|---|---|
| Price from | Free | $45/mo |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| macOS | Yes | — |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 4 |
| Spec score · Vector Databases | 7.8 (#16) | 8.0 (#10) |
| Deployment | Documented deployment options include Kubernetes with Helm, Docker Compose, Docker, and source compilation. | Weaviate can run as a hosted service or a self-managed instance locally or with a cloud provider; deployment options include Docker and Kubernetes. |
| Integrations | The project lists integrations with LangChain, LlamaIndex, LangChainGo, and LangChain4j. | The integration ecosystem lists cloud and model integrations including AWS, Google Cloud, Azure, OpenAI, Anthropic, Cohere, Hugging Face, and Ollama. |
| Product | Vearch is a cloud-native distributed vector database for similarity search of embedding vectors in AI applications. | Weaviate is an open-source AI vector database for storing, indexing, and searching high-dimensional vectors, used as a foundation for search, RAG, and agents. |
| Support | The project directs users to GitHub Issues, a public email discussion list, and Slack for community support. | Free includes Basic Support; Flex lists Standard Support with next-business-day Sev 1 response, while Premium lists enterprise support as fast as 1-hour Sev 1. |
Plans
In their own words
Vearch
Vearch is a free, open-source, cloud-native distributed vector database for similarity search in AI applications. It searches embedding vectors and can combine vector queries with scalar filtering for metadata conditions. The project describes replication and elastic scale-out and says it can retrieve vectors among millions of objects in milliseconds. Its Router exposes REST operations for upserting, deleting, searching, and querying data. Client SDKs are listed for Python, Go, Java, and Rust, with integrations for LangChain, LlamaIndex, LangChainGo, and LangChain4j. Deployment options include Kubernetes through Helm, Docker Compose in standalone or cluster mode, Docker, and compilation from source. PartitionServer uses Raft-based replication for document partitions, while Gamma is a Faiss-based vector search engine. Vearch is licensed under Apache License 2.0 and supports Linux and macOS self-hosted deployments. Community support is available through GitHub Issues, an email discussion group, and Slack. The VisualSearch demo requires an image retrieval plugin for object detection and feature extraction.
Weaviate
Weaviate is an open-source AI vector database for storing, indexing and searching high-dimensional vectors, used as a foundation for search, retrieval-augmented generation (RAG) and agents. Its hybrid search combines vector retrieval with BM25 keyword search, alongside semantic and keyword search. Other listed capabilities include advanced filtering, vectorizer modules, configurable backups, native multi-tenancy, vector-index compression and tenant isolation. It can run as a hosted service or as a self-managed instance locally or with a cloud provider, with Docker and Kubernetes deployment options. Its integrations include AWS, Google Cloud, Azure, OpenAI, Anthropic, Cohere, Hugging Face and Ollama. Weaviate Cloud encrypts data in transit and at rest, supports role-based access controls and provides automated daily backups. The Free plan includes up to 100,000 objects, one collection and three tenants, but no backups or high-availability replication. Flex costs $45 a month; Premium costs $400 a month. Windows use is possible through containers such as Docker or WSL, but native Windows support is not offered. SDKs are listed for Python, TypeScript/JavaScript, Go, Java and C#.