Qdrant
Market Positioning
Vector Search Engine
As of October 30, 2026, Qdrant's positioning: Leads with "High-Performance Vector Search at Scale". Frames itself.
Backed by 3 dated facts
Every value derives from a dated, sourced capture — open any fact for its source.
Every value derives from a dated, sourced capture — open any fact for its source.
On their homepage
“High-Performance Vector Search at Scale”
Qdrant helps you build the AI retrieval you want. Ship high performance, full-feature vector search at any scale and with any deployment model.
Positioning Change Historydated events · values unlock with a key
Positioning changed: Target Segment
Positioning changed: Category
Positioning changed: Category
Positioning changed: Category
Positioning changed: Target Segment
Positioning changed: Target Segment
Positioning changed: Elasticsearch · high significance
Positioning changed: Target Segment
Every positioning fact on this pagekey · value · provenance · dated · sourced
| Fact | Value | Provenance | As of | Source |
|---|---|---|---|---|
| positioning.category | vector-search-engine | company stated | 2026-10-30 | qdrant.tech |
| positioning.h1 | High-Performance Vector Search at Scale | company stated | 2026-10-30 | qdrant.tech |
| positioning.tagline | Qdrant helps you build the AI retrieval you want. Ship high performance, full-feature vector search at any scale and with any deployment model. | company stated | 2026-10-30 | qdrant.tech |
Positioning across Vector DatabasesQdrant ranked in place · tap through for each read
| Company | Positioning read | Facts |
|---|---|---|
| Marqo | Product Discovery Platform | 5 |
| Epsilla | AI Platform | 4 |
| Pinecone | Vector Database | 4 |
| TopK | Retrieval Engine | 4 |
| Vectara | AI Platform | 4 |
| Vespa | AI Search Platform | 4 |
| Weaviate | AI Database | 4 |
| Zilliz | Vector Lakebase | 4 |
| Activeloop | GPU Database | 3 |
| Chroma | Search Infrastructure | 3 |
| LanceDB | Lakehouse | 3 |
| Qdrant this record | Vector Search Engine | 3 |
| Turbopuffer | Search Engine | 3 |
No observed positioning facts yet for KDB.AI, Milvus and MyScale.
IdentityBixel ID · free forever
Operated by
No registered company joined yet. Would resolve with: a registry identifier for the entity; a legal page on the site naming the registered company.
Registry identifiers
None on record.
Published identifiers
- YouTube channel/UC6ftm8PwH1RU_LM1jwG0LQA · inferred
Get positioning for qdrant.tech via API / MCPevery field dated and sourced
RESTopen tier
GET https://api.bixel.com/v1/companies/qdrant.tech/facts?dimension=positioning
{
"data": {
"facts": [
{
"key": "positioning.category",
"value": "vector-search-engine",
"provenance": "company_stated",
"as_of": "2026-10-30",
"source_url": "qdrant.tech"
},
{
"key": "positioning.h1",
"value": "High-Performance Vector Search at Scale",
"provenance": "company_stated",
"as_of": "2026-10-30",
"source_url": "qdrant.tech"
},
{
"key": "positioning.tagline",
"value": "Qdrant helps you build the AI retrieval you want. Ship high performance, full-feature vector search at any scale and with any deployment model.",
"provenance": "company_stated",
"as_of": "2026-10-30",
"source_url": "qdrant.tech"
},
"…"
]
}
}MCPfor agents
# any MCP client (Claude, agents)
const record = await bixel.get_company_facts({ domain: "qdrant.tech", 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.