Bixel

LanceDB

Lancedb.com
Limited coverage

LanceDB's site sits behind a bot-verification wall that Bixel does not evade. Coverage is limited to signals observable off-site (job postings, DNS, subdomain surfaces) until BixelBot's verified-bot admission clears.

Company Overview

As of July 22, 2026: LanceDB is a multimodal vector database targeting AI/ML teams, with $38M raised and an enterprise-only pricing model that requires a sales conversation to get started.

The platform lists 23 stated capabilities including hybrid search, reranking, and RAG, alongside 17 integrations, making it a broad-surface tool for teams building production AI pipelines. LanceDB names OpenSearch directly as a competitor, which tells a buyer exactly where the company thinks the displacement opportunity sits.

High confidence · 67 dated facts
Corroborated across 6 dimensions of the record.
integrations
17
Jul 2026
technologies
8
observed
dated changes
66
Jul 2026
The record, by dimensionevery dimension is its own page →
PricingMedium confidence · 3 dated facts
Grounded in a smaller fact set; directionally reliable.
Enterprise, sales-led
The only listed tier is Enterprise at contact-sales, so there is no self-serve path and no public price to anchor a budget conversation before a sales call.
Full pricing read →
Features
Hybrid search platform
23 stated capabilities including hybrid search, reranking, and RAG, plus 17 integrations cover the core AI pipeline stack; a buyer should verify which capabilities are generally available versus roadmap before committing.
High confidence · 40 dated facts
Corroborated across multiple dated, sourced facts.Full read →
Stack
Modern web stack
The live site runs on Angular, Webflow, Cloudflare, and Google Analytics among 8 detected technologies, which is a standard marketing and delivery stack and carries no unusual vendor-lock risk for a buyer.
High confidence · 15 dated facts
Corroborated across multiple dated, sourced facts.Full read →
Positioning
Multimodal Lakehouse
LanceDB frames itself as a Multimodal Lakehouse for AI/ML teams and names OpenSearch directly, so buyers coming from search-infrastructure backgrounds are the explicit target audience.
Medium confidence · 3 dated facts
Grounded in a smaller fact set; directionally reliable.Full read →
Funding
$38M raised across 2 rounds
$38M across a Seed and a $30M Series A gives the company a reasonable runway for an early-stage infrastructure vendor, though no revenue figures are public to gauge burn against that capital.
High confidence · 5 dated facts
Corroborated across multiple dated, sourced facts.Full read →
Location
1 office stated
One office is listed in San Francisco with no stated headquarters or remote-work policy, which leaves support coverage and team distribution unclear for buyers with global operations.
Medium confidence · 1 dated fact
Grounded in a smaller fact set; directionally reliable.Full read →
Change History66 dated events on the record
Technology added: React
Feature removed: Blob Storage · high significance
Feature removed: Multimodal · high significance
Feature removed: Serverless · high significance
Feature removed: Zero Copy · high significance
Feature removed: LLM As Udf · high significance
Technology added: Webflow
Get this record via API / MCPevery field dated and sourced
RESTopen tier
GET https://api.bixel.com/v1/companies/lancedb.com

{
  "data": {
    "company": {
      "name": "LanceDB",
      "domain": "lancedb.com",
      "categories": [
        "vector-databases"
      ]
    },
    "dimensions": {
      "stack": {
        "facts": [
          {
            "key": "stack.tech.angular",
            "value": true,
            "provenance": "observed",
            "as_of": "2026-05-07",
            "source_url": "www.lancedb.com"
          }
        ]
      },
      "…": "…"
    }
  }
}
MCPfor agents
# any MCP client (Claude, agents)
const record = await bixel.get_company({ domain: "lancedb.com" })

# 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.