
KDB.AI
6.2#16 in vector databasesFree planFree planWebAPISelf-hosted
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| Fact | KDB.AI | Weaviate |
|---|---|---|
| Deployment | The maker documents both a managed cloud Starter Edition and a self-managed Server Standard Edition. | Weaviate can run as a hosted service or a self-managed instance locally or with a cloud provider; deployment options include Docker and Kubernetes. |
| Founded | 1993 | 2019 |
| Integrations | The documentation lists LangChain, Vector IO, and LlamaIndex integrations. | The integration ecosystem lists cloud and model integrations including AWS, Google Cloud, Azure, OpenAI, Anthropic, Cohere, Hugging Face, and Ollama. |
| Product | KDB.AI is a vector database and search engine for building AI applications with real-time data. | 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. |
| Search | It supports hybrid search combining dense and sparse vector search, metadata filtering, and temporal similarity search. | The database supports hybrid search that combines vector and BM25 keyword search, along with semantic and keyword search. |
| Support | The cloud FAQ directs users to the Slack community or [email protected] for assistance. | 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. |
KDB.AI is for developers building AI applications that need vector search over real-time or multimodal data. Its managed and self-managed options may suit teams choosing between cloud use and their own deployment.
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.