whichdb
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KDB.AI vs Qdrant

KDB.AI: KX's vector database for time-aware AI search, with temporal similarity and hybrid retrieval. Qdrant: Rust vector search engine with rich payload filtering, quantization and a managed cloud.

key differences

  • Licensing: KDB.AI is proprietary (Proprietary (free and commercial licences)); Qdrant is open source (permissive) (Apache License 2.0).
  • Qdrant is built for larger datasets (≤ 1 PB) than KDB.AI (≤ 10 TB).
  • Horizontal scaling is none in KDB.AI and native in Qdrant.
  • Only Qdrant has secondary indexes, full-text search, geospatial, json, role-based access.

choose kdb-ai for

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

best at · Vector search, RAG, Time-series

choose qdrant for

  • +RAG retrieval with metadata filtering
  • +Recommendation and similarity search
  • +Image and multimodal search
  • +Multi-tenant semantic search

best at · Vector search, RAG

KDB.AIKX's vector database for time-aware AI search, with temporal similarity and hybrid retrieval.
QdrantRust vector search engine with rich payload filtering, quantization and a managed cloud.
overview
Primary model
Vector
Vector
Also supports
Time-Series
Document
Type
Database engine
Database engine
Status
Active
Active
Developer
KX
Qdrant Solutions GmbH
First release
2023
2021
Latest version
–
1.20.0 (Oct 22, 2026)
License
ProprietaryProprietary (free and commercial licences)
Open source (permissive)Apache License 2.0
Free tier
✓
✓
popularity
WhichDB Score
4.9
35.9
Rank
#393
#55
GitHub stars
–
35.3K
Stack Overflow questions
–
60
scale & guarantees
Comfortable data size
Medium (≤ 10 TB)
Large (≤ 1 PB)
Horizontal scaling
None
Native
Multi-region
None
None
ACID
None
None
Consistency
Strong
Tunable, Eventual
Schema
Strict
Schemaless
Architecture
Single node, Client server
Distributed, Client server
deployment
Self-hosted
✓
✓
Vendor-managed cloud
—
✓
Serverless
—
—
Embedded
—
—
Edge
—
—
Clouds
AWS, Google Cloud, Azure
AWS, Google Cloud, Azure
capabilities
Transactions
—
—
Joins
—
—
Secondary indexes
—
✓
Full-text search
—
✓
Vector search
✓
✓
Geospatial
—
✓
JSON
—
✓
Change data capture
—
—
Triggers
—
—
Stored procedures
—
—
Materialized views
—
—
TTL / expiry
—
—
Pub/Sub
—
—
Columnar storage
—
—
In-memory
—
—
Durable persistence
✓
✓
Compression
✓
✓
Encryption at rest
—
—
Role-based access
—
✓
Time travel
—
—
Branching
—
—
Online schema changes
—
—
Streaming ingest
—
—
Offline sync
—
—
Runs in WebAssembly
—
—
interfaces
Query languages
KDB.AI Python API, REST API
Qdrant Query API (JSON filters)
Client languages
Python, q
Python, JavaScript, Rust, Go, Java, .NET
Best for
Vector search, RAG, Time-series, Tick data
Vector search, RAG
Add more to this comparisonNot sure either fits? Try smart match

KDB.AI vs Qdrant: FAQ

What is the difference between KDB.AI and Qdrant?

KDB.AI: KX's vector database for time-aware AI search, with temporal similarity and hybrid retrieval. Qdrant: Rust vector search engine with rich payload filtering, quantization and a managed cloud. Licensing: KDB.AI is proprietary (Proprietary (free and commercial licences)); Qdrant is open source (permissive) (Apache License 2.0). Qdrant is built for larger datasets (≤ 1 PB) than KDB.AI (≤ 10 TB). Horizontal scaling is none in KDB.AI and native in Qdrant.

When should I choose KDB.AI over Qdrant?

Choose KDB.AI for pattern matching in market and sensor time series; RAG over financial documents with time constraints; and anomaly and outlier detection. Choose Qdrant for RAG retrieval with metadata filtering; recommendation and similarity search; and image and multimodal search.

Is KDB.AI or Qdrant more popular?

Qdrant ranks #55 on the WhichDB Score (35.9/100) and KDB.AI ranks #393 (4.9/100). Popularity is not fit: check the requirements that matter for your project.

Are KDB.AI and Qdrant open source?

No. KDB.AI is proprietary, closed-source software. Yes. Qdrant is open source. License: Apache License 2.0.