KDB.AI
Market Positioning
Vector Database
As of July 20, 2026, KDB.AI's positioning: The homepage headline "The Scalable Vector Database for AI" is a direct pitch to AI developers, and the sub-headline explicitly calls out contextual search, time series search, and mixing structured with unstructured data. That last point is the most specific claim on the page and is the one worth pressure-testing in a proof of concept.
High confidence · 4 dated facts
Corroborated across multiple dated, sourced facts.
Corroborated across multiple dated, sourced facts.
On their homepage
“The Scalable Vector Database for AI”
The vector database for contextual and time series search. Build AI apps, find patterns in your data, and mix structured with unstructured data.
Positioning Change Historydated events · values unlock with a key
Positioning changed: Target Segment
Positioning changed: Target Segment
Positioning changed: Target Segment
Positioning changed: Target Segment
Positioning changed: Target Segment
Every positioning fact on this pagekey · value · provenance · dated · sourced
| Fact | Value | Provenance | As of | Source |
|---|---|---|---|---|
| positioning.category | vector-database | company stated | 2026-07-20 | kdb.ai |
| positioning.h1 | The Scalable Vector Database for AI | company stated | 2026-07-20 | kdb.ai |
| positioning.tagline | The vector database for contextual and time series search. Build AI apps, find patterns in your data, and mix structured with unstructured data. | company stated | 2026-07-20 | kdb.ai |
| positioning.target_segment | ai-developers | company stated | 2026-07-20 | kdb.ai |
Positioning across Vector DatabasesKDB.AI ranked in place · tap through for each read
| Company | Positioning read | Facts |
|---|---|---|
| Pinecone | Vector Database | 7 |
| TopK | Search Engine | 7 |
| Vectara | AI Agent Platform | 7 |
| Zilliz | Vector Lakebase | 7 |
| LanceDB | Multimodal Lakehouse | 5 |
| Marqo | AI Ecommerce Search Platform | 5 |
| Turbopuffer | Vector And Full Text Search Database | 5 |
| Epsilla | AI Agent Platform | 4 |
| KDB.AI this record | Vector Database | 4 |
| Qdrant | Vector Search Engine | 4 |
| Vespa | AI Search Platform | 4 |
| Activeloop | AI Data Platform | 2 |
No observed positioning facts yet for Chroma, Milvus, MyScale and Weaviate.
Get positioning for kdb.ai via API / MCPevery field dated and sourced
RESTopen tier
GET https://api.bixel.com/v1/companies/kdb.ai/facts?dimension=positioning
{
"data": {
"facts": [
{
"key": "positioning.h1",
"value": "The Scalable Vector Database for AI",
"provenance": "company_stated",
"as_of": "2026-07-20",
"source_url": "kdb.ai"
},
{
"key": "positioning.tagline",
"value": "The vector database for contextual and time series search. Build AI apps, find patterns in your data, and mix structured with unstructured data.",
"provenance": "company_stated",
"as_of": "2026-07-20",
"source_url": "kdb.ai"
},
{
"key": "positioning.target_segment",
"value": "ai-developers",
"provenance": "company_stated",
"as_of": "2026-07-20",
"source_url": "kdb.ai"
},
{
"key": "positioning.category",
"value": "vector-database",
"provenance": "company_stated",
"as_of": "2026-07-20",
"source_url": "kdb.ai"
},
"…"
]
}
}MCPfor agents
# any MCP client (Claude, agents)
const record = await bixel.get_company_facts({ domain: "kdb.ai", dimension: "positioning" })
# returns the positioning record above,
# each value with its source_url + as_of,
# ready to reason overBuild on the company record. One key, REST + MCP, every signal dated and sourced back to the page it came from.
Public record, read from companies' own pages and boards. Every fact dated and sourced; provenance (observed vs company stated) shown inline.