Vearch
6.4#12 in vector databasesFree planFree planAPILinuxmacOSSelf-hosted
VisitVearch vs Weaviate
What both publish
| Fact | Vearch | Weaviate |
|---|---|---|
| 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. |
Vearch plans
- VearchFree
Weaviate plans
- FreeFree
- Flex$45 a month
- PremiumPriced on request
Pick Vearch if…
Vearch suits teams building AI applications that need vector similarity search with metadata filtering. It is also relevant to teams that want to choose among documented deployment methods and use supported client SDKs or framework integrations.
Pick Weaviate if…
Weaviate is aimed at developers building search, RAG, or agent infrastructure. Its deployment options may suit teams ranging from startups to enterprises that want hosted or self-managed operation.