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 September 3, 2026: LanceDB is a multimodal vector database positioning itself against OpenSearch, with $38M raised and a fully free model that currently lists no paid tiers.

The platform states 16 capabilities including hybrid search and reranking, backed by 9 integrations, making it a credible evaluation target for teams building search or AI retrieval pipelines. A $30M Series A closed in June 2025 suggests the free-only pricing is a growth phase choice, not a permanent business model, so buyers should expect monetization changes.

Backed by 49 dated facts
Drawn from 6 dimensions of the record, each dated and sourced.
integrations
9
Jul 2026
technologies
7
observed
dated changes
74
Aug 2026
The record, by dimensionevery dimension is its own page →
PricingBacked by 1 dated fact
Every value derives from a dated, sourced capture — open any fact for its source.
Free
There are no paid tiers listed today, which removes procurement friction for early evaluation but gives buyers no contractual SLA to anchor on.
Full pricing read →
Features
Hybrid search platform
16 stated capabilities anchored by hybrid search and reranking, plus 9 integrations, cover the core retrieval stack a production AI application needs, though stated capability counts are self-reported and should be tested against your specific query patterns.
Backed by 25 dated facts
Every value derives from a dated, sourced capture — open any fact for its source.Full read →
Stack
14 detected
14 technologies detected on the live site, including Angular, Cloudflare, Google Analytics, and Google Tag Manager; the web infrastructure is standard, and nothing detected suggests unusual operational complexity for an integration team.
Backed by 14 dated facts
Every value derives from a dated, sourced capture — open any fact for its source.Full read →
Positioning
Multimodal Lakehouse
LanceDB names OpenSearch directly as the frame of reference, which tells buyers this is pitched as a replacement for keyword-plus-vector hybrid workloads rather than a pure vector-only niche tool.
Backed by 2 dated facts
Every value derives from a dated, sourced capture — open any fact for its source.Full read →
Funding
$38M raised across 2 rounds
The $30M Series A in June 2025 follows an earlier Seed round for a $38M total raised across 2 rounds; that runway is meaningful, but investors will eventually require a revenue model, so free access today carries some longevity risk.
Backed by 6 dated facts
Every value derives from a dated, sourced capture — open any fact for its source.Full read →
Location
1 office stated
One office listed in San Francisco with no stated headquarters or remote work policy, which leaves support coverage and hiring geography unclear for enterprise buyers who care about time-zone alignment.
Backed by 1 dated fact
Every value derives from a dated, sourced capture — open any fact for its source.Full read →
Change History74 dated events on the record
Funding details changed: Round Series A Announced On · high significance
Pricing changed: Model · high significance
Pricing changed: Enterprise tier contact-sales flag · high significance
Pricing tier removed: Enterprise tier name · high significance
Funding details changed: Round Series A Announced On
Funding details changed: Round Series A Announced On · high significance
Funding details changed: Round Series A Announced On
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 record says so.

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