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KDB.AI

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by KX · since 2023 · also known as KDBAI

KX's vector database for time-aware AI search, with temporal similarity and hybrid retrieval.

vectortime-seriesdatabase engineproprietaryemerging
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Overview

KDB.AI is a vector database from KX, the company behind the kdb+ time-series database used throughout capital markets. Its distinguishing feature is time-aware retrieval: Temporal Similarity Search finds similar patterns across time-series windows without precomputed embeddings, and searches can be constrained by time alongside metadata. It offers qFlat and qHNSW indexes (on-disk variants that cut memory), IVF-PQ, and hybrid dense plus sparse search, accessed via Python and REST. It makes most sense for teams in the KX/kdb ecosystem or with financial and sensor time-series; general RAG teams have broader options.

Vector searchRAG & AI memoryTime-series dataFinancial tick data

Strengths & trade-offs

Strengths

  • +Temporal Similarity Search over raw time series
  • +Time-windowed filtering combined with vector search
  • +On-disk qHNSW and qFlat indexes lower memory cost
  • +Fits naturally with existing kdb+ estates

Trade-offs

  • −Proprietary with a small developer community
  • −Python and REST are the only first-class interfaces
  • −Limited public pricing and roadmap information
  • −No built-in replication or sharding

Great for

  • +Pattern matching in market and sensor time series
  • +RAG over financial documents with time constraints
  • +Anomaly and outlier detection

Think twice for

  • −General-purpose RAG at large scale
  • −Open-source-only environments
  • −Full-text keyword search

Capabilities

Native or official first-party support · 3 of 25 tracked

✓Vector search
✓Durable persistence
✓Compression
–Transactions
–Joins
–Secondary indexes
–Full-text search
–Geospatial
–JSON
–Change data capture
–Triggers
–Stored procedures
–Materialized views
–TTL / expiry
–Pub/Sub
–Columnar storage
–In-memory
–Encryption at rest
–Role-based access
–Time travel
–Branching
–Online schema changes
–Streaming ingest
–Offline sync
–Runs in WebAssembly

Scale & consistency

Comfortable data size
Medium · ≤ 10 TB
Horizontal scaling
None
Multi-region
No
Transactions
None
Consistency
Strong
Schema
Strict
Architecture
Single node, Client server
Query languages
KDB.AI Python API, REST API

Server Edition runs as a Docker container; on-disk qHNSW/qFlat indexes reduce RAM needs.

Deployment

Self-hosted Vendor-managed cloud Serverless Embedded Edge Kubernetes operator

available on · AWS · Google Cloud · Azure

Ecosystem

apis & protocolsREST/JSON, Python
client languagesPython, q

Alternatives to KDB.AI

Compare all→
kdb+Time-Series31.8

Column-store time-series engine with the q language, a fixture of capital-markets tick data.

kdb-ai vs kdb-plus →
QdrantVector35.9

Rust vector search engine with rich payload filtering, quantization and a managed cloud.

kdb-ai vs qdrant →
MilvusVector45.9

Cloud-native, distributed open-source vector database built for billions of vectors.

kdb-ai vs milvus →
WeaviateVector33.4

Open-source AI-native vector database with hybrid search, modules for embeddings and multi-tenancy.

kdb-ai vs weaviate →
PineconeVector15.5

Fully managed serverless vector database with built-in embedding, reranking and assistants.

kdb-ai vs pinecone →
JaguarDBVector3.4

Distributed vector database from DataJaguar that also handles time series and geospatial data.

kdb-ai vs jaguar →

KDB.AI FAQ

What is KDB.AI?

KDB.AI is a vector database by KX, first released in 2023. KDB.AI is a vector database from KX, the company behind the kdb+ time-series database used throughout capital markets. Its distinguishing feature is time-aware retrieval: Temporal Similarity Search finds similar patterns across time-series windows without precomputed embeddings, and searches can be constrained by time alongside metadata. It offers qFlat and qHNSW indexes (on-disk variants that cut memory), IVF-PQ, and hybrid dense plus sparse search, accessed via Python and REST. It makes most sense for teams in the KX/kdb ecosystem or with financial and sensor time-series; general RAG teams have broader options.

Is KDB.AI open source?

No. KDB.AI is proprietary, closed-source software.

Does KDB.AI support ACID transactions?

No. KDB.AI does not provide ACID transactions; its consistency model is strong.

Does KDB.AI scale horizontally?

KDB.AI does not scale horizontally; it runs on a single node, so you scale it up rather than out. It is comfortable at medium scale (≤ 10 TB).

How can I run KDB.AI?

You can self-host it. It is available on AWS, Google Cloud and Azure.

What is KDB.AI best for?

KDB.AI is a strong fit for pattern matching in market and sensor time series; RAG over financial documents with time constraints; and anomaly and outlier detection. Think twice for general-purpose RAG at large scale.

What are the best alternatives to KDB.AI?

Common alternatives to KDB.AI include kdb+, Qdrant, Milvus, Weaviate and Pinecone. The right choice depends on your workload, scale and hosting; WhichDB's smart match ranks them against your requirements.

How popular is KDB.AI?

KDB.AI ranks #393 of 844 databases on the WhichDB Score (4.9/100), and #25 among vector databases. The score combines public adoption signals such as package downloads, GitHub activity, job posts and developer discussion.