vs

KDB.AI vs Weaviate

KDB.AI: KX's vector database for time-aware AI search, with temporal similarity and hybrid retrieval. Weaviate: Open-source AI-native vector database with hybrid search, modules for embeddings and multi-tenancy.

key differences

  • Licensing: KDB.AI is proprietary (Proprietary (free and commercial licences)); Weaviate is open source (permissive) (BSD-3-Clause (core); enterprise features under the Weaviate License).
  • Weaviate is built for larger datasets (≤ 1 PB) than KDB.AI (≤ 10 TB).
  • Horizontal scaling is none in KDB.AI and native in Weaviate.
  • Only Weaviate 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 weaviate for

  • +RAG over documents with hybrid retrieval
  • +Multi-tenant SaaS semantic search
  • +Product and content recommendation
  • +Agent memory stores

best at · Vector search, RAG, Search

overview
Primary model
Vector
Vector
Also supports
Time-Series
Document, Search Engine
Type
Database engine
Database engine
Status
Active
Active
Developer
KX
Weaviate B.V.
First release
2023
2019
Latest version
–
1.40.2 (Oct 22, 2026)
License
ProprietaryProprietary (free and commercial licences)
Open source (permissive)BSD-3-Clause (core); enterprise features under the Weaviate License
Free tier
✓
✓
popularity
WhichDB Score
4.9
33.4
Rank
#393
#65
GitHub stars
–
16.9K
Stack Overflow questions
–
160
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
Flexible
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
GraphQL, Weaviate query API
Client languages
Python, q
Python, TypeScript, Go, Java, C#
Best for
Vector search, RAG, Time-series, Tick data
Vector search, RAG, Search

KDB.AI vs Weaviate: FAQ

What is the difference between KDB.AI and Weaviate?

KDB.AI: KX's vector database for time-aware AI search, with temporal similarity and hybrid retrieval. Weaviate: Open-source AI-native vector database with hybrid search, modules for embeddings and multi-tenancy. Licensing: KDB.AI is proprietary (Proprietary (free and commercial licences)); Weaviate is open source (permissive) (BSD-3-Clause (core); enterprise features under the Weaviate License). Weaviate is built for larger datasets (≤ 1 PB) than KDB.AI (≤ 10 TB). Horizontal scaling is none in KDB.AI and native in Weaviate.

When should I choose KDB.AI over Weaviate?

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 Weaviate for RAG over documents with hybrid retrieval; multi-tenant SaaS semantic search; and product and content recommendation.

Is KDB.AI or Weaviate more popular?

Weaviate ranks #65 on the WhichDB Score (33.4/100) and KDB.AI ranks #393 (4.9/100). Popularity is not fit: check the requirements that matter for your project.

Are KDB.AI and Weaviate open source?

No. KDB.AI is proprietary, closed-source software. Yes. Weaviate is open source. License: BSD-3-Clause (core); enterprise features under the Weaviate License.