Bixel

Qdrant

Qdrant.tech
Company Overview

As of July 21, 2026: Qdrant is a vector search engine targeting developers that has raised $85.5M across three rounds and offers a free tier to start.

The platform states 42 capabilities including hybrid search and RAG, with 8 integrations and 11 security controls including SSO, encryption at rest, and encryption in transit. Pricing starts at $0/mo on the Free Tier, with Standard, Premium, Hybrid Cloud, and Private Cloud tiers above it, though only the Free Tier has a published price.

High confidence · 98 dated facts
Corroborated across 7 dimensions of the record.
integrations
8
Jul 2026
technologies
4
observed
dated changes
250
Jul 2026
The record, by dimensionevery dimension is its own page →
PricingHigh confidence · 11 dated facts
Corroborated across multiple dated, sourced facts.
Self-serve, free tier
The Free Tier is $0/mo and gives developers a real on-ramp, but every tier above Free has either an unstated price or a contact-sales gate, so total cost of ownership is opaque until you talk to someone.
Full pricing read →
Features
Hybrid search platform
42 stated capabilities anchored on hybrid search and RAG, plus 8 integrations, cover the core developer use cases for AI retrieval; buyers should verify which capabilities are available on which tier before building.
High confidence · 50 dated facts
Corroborated across multiple dated, sourced facts.Full read →
Security
Baseline security
11 controls including SSO, encryption at rest, and encryption in transit satisfy a baseline enterprise checklist, but data residency is listed as Global with no regional isolation option stated, which matters for regulated workloads.
Medium confidence · 12 dated facts
Grounded in a smaller fact set; directionally reliable.Full read →
Stack
8 detected
Netlify, HubSpot, Datadog, and YouTube were detected on the live site; Datadog on their own infrastructure is a reasonable indicator of production monitoring practice, though nothing here confirms SLA or uptime history.
High confidence · 8 dated facts
Corroborated across multiple dated, sourced facts.Full read →
Positioning
Vector Search Engine
The homepage headline is "High-Performance Vector Search at Scale" aimed squarely at developers, so enterprise procurement teams evaluating vendor stability will need to look past the developer-first framing to assess support and contractual terms.
High confidence · 4 dated facts
Corroborated across multiple dated, sourced facts.Full read →
Funding
$85.5M raised across 3 rounds
A $50M Series B closed in March 2026 and led by AVP brings total funding to $85.5M across three rounds, giving the company runway to sustain development, though burn rate and revenue are not disclosed.
High confidence · 12 dated facts
Corroborated across multiple dated, sourced facts.Full read →
Location
1 office stated
San Francisco is listed as an office but headquarters is not formally stated, which is a minor gap for buyers who need a legal entity address for contracts or compliance documentation.
Medium confidence · 1 dated fact
Grounded in a smaller fact set; directionally reliable.Full read →
Change History250 dated events on the record
Feature added: Datadog
Feature added: Token Revocation
Feature removed: Private Cloud · high significance
Feature removed: Tracing · high significance
Feature added: Auto Scaling
Feature added: Graph RAG
Feature added: High Availability
Get this record via API / MCPevery field dated and sourced
RESTopen tier
GET https://api.bixel.com/v1/companies/qdrant.tech

{
  "data": {
    "company": {
      "name": "Qdrant",
      "domain": "qdrant.tech",
      "categories": [
        "vector-databases"
      ]
    },
    "dimensions": {
      "stack": {
        "facts": [
          {
            "key": "stack.tech.netlify",
            "value": true,
            "provenance": "observed",
            "as_of": "2026-07-20",
            "source_url": "qdrant.tech"
          }
        ]
      },
      "…": "…"
    }
  }
}
MCPfor agents
# any MCP client (Claude, agents)
const record = await bixel.get_company({ domain: "qdrant.tech" })

# returns the whole record above,
# each value with its source_url + as_of,
# ready to reason over
Build on the company record. One key, REST + MCP, every signal dated and sourced back to the page it came from.
How Bixel reads this

Every read above is derived from public signals, each sourced and dated, and kept honest about provenance: what the company states on its own pages and job posts (pricing, security, careers, positioning, its backend stack) versus what Bixel independently detects (technologies, infrastructure). Where a stated claim is also detected we mark it verified; where we only have the claim, we say so. Bixel infers posture: how it monetizes, how mature it is, where it's heading. It does notclaim private financials it can't observe. Where signals are thin, the confidence says so.

Public record, read from companies' own pages and boards. Every fact dated and sourced; provenance (observed vs company stated) shown inline.