KDB.AI

What KDB.AI is

KDB.AI is a vector database and search engine for AI applications using real-time data. It supports hybrid search across dense and sparse vectors, metadata filtering, and temporal similarity search. Its retrieval-augmented generation support works with unstructured text, video, audio, and image data. The cloud FAQ lists Flat, qFlat, IVF, IVFPQ, HSNW, and qHSNW indexes, while the product index list also includes HNSW, qHNSW, and BM25. Documented integrations include LangChain, Vector IO, and LlamaIndex, and native NVIDIA cuVS integration supports GPU-accelerated vector search. KDB.AI offers a managed Cloud Starter Edition and a self-managed Server Standard Edition. The free Cloud Starter plan provides 4 GB of RAM, 30 GB of storage, and a 10 MB query size. Its Cloud Starter agreement does not promise availability or uptime and provides the service without warranty. The cloud FAQ points users to its Slack community or [email protected] for help. The maker presents research assistants, portfolio management, real-time alpha and beta extraction, and image processing as use cases. KDB.AI was founded in 1993 and is headquartered in Newry, Northern Ireland.

Who it suits

It suits developers and teams building real-time AI applications that need vector search, metadata filtering, or retrieval-augmented generation. Deployment options include managed cloud and self-managed server editions.

Limits

The free Cloud Starter Edition provides 4 GB RAM, 30 GB storage, and a 10 MB query size. Its agreement makes no commitment to availability or uptime and provides the service without warranty.

Getting started

1. Visit the KDB.AI website.
2. Choose the managed Cloud Starter Edition or self-managed Server Standard Edition.
3. For Cloud Starter, begin with the free plan and its stated resource limits.
4. Use the API, web platform, or self-hosted deployment route.
5. Connect through a documented integration such as LangChain, Vector IO, or LlamaIndex.

Verdict

KDB.AI offers hybrid search and multimodal retrieval features, with managed cloud and self-managed deployment options. Users evaluating Cloud Starter should account for its stated resource limits and lack of availability commitment.