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KDB.AI

Freemium#20 of 27 in Vector DatabasesVector Database HostingAi Databases

KDB.AI: A broad, integration-rich vector database for teams managing their own deployment. Ranked #20 of 27 in Vector Databases by our editors (7.4/10); pricing: Free plan; best for integration-heavy self-hosted vector deployments.

7.4/10Editor score
Visit KDB.AI
KDB.AI7.4 Visit KDB.AI

At a glance

  • Editor score
    7.4 / 10
  • Pricing
    Free plan
  • Best for
    Integration-heavy self-hosted vector deployments
  • Free plan
    Yes
  • Deployment
    Both
  • Founded
    1993 · Newry, Northern Ireland, UK
  • Facts checked
    22 Sep 2026
KDB.AI screenshot

  • Where it wins

    • Supports dense, sparse, hybrid, filtered, multimodal, and time-series search
    • Offers Flat, qFlat, IVF, IVFPQ, HNSW, and qHNSW indexes
    • Integrates with LangChain, LlamaIndex, OpenAI, and NVIDIA cuVS

  • Where it doesn't

    • Self-hosted deployment puts infrastructure responsibility on the customer
    • Contact-sales pricing makes cost comparison less direct
    • A managed cloud deployment is not the current deployment focus

Our verdict on KDB.AI

KDB.AI is a vector database and search engine for AI applications working with real-time and historical data. It suits development teams that need more than dense semantic retrieval, including keyword search, hybrid retrieval, metadata filtering, multimodal data, and time-series similarity search. KDB.AI Server is designed for self-managed deployment on-premises or in a customer-owned cloud environment, with access through Python, q, and REST interfaces. Its positioning fits organizations that want control over deployment and a broad search feature set.

Search depth is a central strength. KDB.AI supports dense vector similarity search alongside sparse retrieval using BM25, with hybrid search for combining semantic and keyword results. Metadata filtering can use pre-filtering or post-filtering, while supported index types include Flat, qFlat, IVF, IVFPQ, HNSW, and qHNSW. The platform also covers time-series similarity search and multimodal retrieval across text, video, audio, and images. OAuth 2.0 authentication and authorization add an access-control layer for deployments that need it.

Its ecosystem is broad, spanning Azure AI, Cohere, Hugging Face, Jina AI, kdb+, LangChain, LlamaIndex, Model Context Protocol, NVIDIA cuVS, OpenAI, Unstructured, Vector IO, and Voyage AI. This makes KDB.AI a practical fit for teams assembling AI workflows from multiple model, orchestration, and data tools. Pricing follows a contact-sales model, so organizations should expect a sales-led evaluation rather than a simple self-serve comparison. Choose KDB.AI when self-hosting, integration breadth, and search flexibility matter most; choose an alternative when a managed cloud vector database or straightforward public pricing is the priority.

Your product? Our editors can test KDB.AI in depth and publish a review, labelled as paid · from $149/mo →

KDB.AI pricing

Plans Free planFreemium A usable free plan; paid tiers unlock the limits above. Prices checked 22 Sep 2026.
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All 2 KDB.AI plans and prices →

KDB.AI fact sheet

Free planYes
Paid fromNot verified
DeploymentBoth
Hybrid searchYes
Metadata filteringYes
Sparse vectorsYes
Maximum dimensionsNot verified
Index typesFlat, qFlat, IVF, IVFPQ, HNSW, qHNSW, BM25
SDK languagesPython, q
DeploymentCloud, Self-hosted
SupportEmail, Community, Docs
Built forSmall business, Mid-market, Enterprise (editorial estimate)
Integrations4 integrations: LangChain, LlamaIndex, Vector IO, kdb+
PricingFree plan (source)
Websitekdb.ai
Facts checked22 Sep 2026

KDB.AI integrations

KDB.AI lists 4 integrations on its own site.

  • LangChain
  • LlamaIndex
  • Vector IO
  • kdb+

Alternatives to KDB.AI

See all KDB.AI alternatives →

Also Ranked In

KDB.AI is ranked in 3 of our directories.

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Featured on iTechGuides

Featured on iTechGuides — KDB.AI 7.4/10

KDB.AI is listed in our Vector Databases directory. Add the badge to your site — it links back to this page.

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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

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