Small business=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
0%from 0% in September 2026
Inside the floor by 11 pointsRank 6 of 29, unchanged
1 category · RankedLanceDB
31Judge labels
0First choices
4Negative labels
10 / 14Models named it
1Category
October 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-10.8, every buyer segment counted.
Best standing
0% in Vector databases for mid-market buyers
Rank 10 of 44 in the mid-market standingaccepted challenger
0 of 14 models made it the first choice on the direct prompt; 8% of its 12 labels there were negative.
What the models named instead of LanceDB →By buyer segmentRead the same way at every buyer size.
Small business6 of 36
0%11 labels
accepted challenger
Mid-market10 of 44
0%12 labels
accepted challenger
Enterprise34 of 45
0%8 labels
under the label threshold
In vector databases · each standing computed within its segment · bars are 0 to 100 · the accent bar is the product's own best reading
Standing by category
Every category where a model named LanceDB for a mid-market B2B company. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.
| Category | Function | Share | Rank | Negative rate | Labels | Quadrant | Since September 2026 |
|---|---|---|---|---|---|---|---|
| Vector databases | Data platform | 0% | 10 of 44 | 8% | 12 | accepted challenger | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked. |
Movement
Since September 2026, LanceDB held within the floor in its one category: no change cleared 11 points.
Best category · Vector databases · by segment
Mid-market=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
0%from 0% in September 2026
Inside the floor by 11 pointsRank 8 → 9 of 35
Enterprise=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
0%from 0% in September 2026
Inside the floor by 11 pointsRank 41 → 35 of 44
All categories · Mid-market
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| Vector databases | 0% | 0% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 8 → 10 of 44 |
Shares here are read over the models both editions asked, so they can differ by a point or two from the standing above, which counts every model in this edition.
The floor is 11 points of share, measured: how far the models move a leader on their own when the same questions are asked twice with nothing changed. A larger change is movement; a smaller one is noise, and both are shown. Movement is read over the twelve models both editions asked; GPT-6 Luna, Muse Glimmer 30B joined this edition and are in the standing but not yet in the comparison. How the floor is measured · The editions
By model
How each model treated LanceDB across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.ShowHide
By model
How each model treated LanceDB across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.
ShowHide| Model | First choice | Alternative | Mention | Negative | Labels |
|---|---|---|---|---|---|
| Claude Haiku 4.5 | 0 | 1 | 0 | 0 | 1 |
| GPT-5.4 mini | 0 | 0 | 0 | 0 | 0 |
| Gemini 3.5 Flash | 0 | 2 | 0 | 0 | 2 |
| Perplexity Sonar | 0 | 0 | 1 | 0 | 1 |
| Grok 4.20 | 0 | 0 | 1 | 1 | 2 |
| Mistral Small | 0 | 0 | 0 | 0 | 0 |
| DeepSeek V4 Flash | 0 | 0 | 0 | 0 | 0 |
| Llama 4 Maverick | 0 | 0 | 0 | 0 | 0 |
| Qwen 3.7 Flash | 0 | 1 | 0 | 0 | 1 |
| Kimi K2 | 0 | 1 | 0 | 0 | 1 |
| GLM 4.7 FlashX | 0 | 0 | 0 | 0 | 0 |
| MiniMax M2.5 | 0 | 1 | 1 | 0 | 2 |
| GPT-6 Luna | 0 | 0 | 0 | 0 | 0 |
| Muse Glimmer 30B | 0 | 0 | 2 | 0 | 2 |
By framing
Which of the six questions produced the naming. By model says how often; this says asked what. The first-choice count on the right carries the marks of the models that produced it.
FramingLabels by classFirst choices
Direct1 labelNone
Paraphrase3 labelsNone
Comparative13 labelsNone
Budget-constrained4 labelsNone
Scale-constrained3 labelsNone
Negative7 labelsNone
First choiceAlternativeMentionNegative31 labels in all, every segment counted; 0 of the 0 first choices count toward share, since the comparative and negative framings do not. The bar is one segment per label class, to scale within the framing.
What the models said for it
Verbatim evidence the judge attached to positive labels.
“LanceDB: Ideal if you are in the Python/AI data science space and want serverless, file-based vector storage” Qwen 3.7 Flash · Vector databases · negative prompt · alternative
“Best for edge computing, local-first applications, or multimodal datasets.” Gemini 3.5 Flash · Vector databases · comparative prompt · alternative
“LanceDB is for larger-than-memory datasets with disk-based indexing.” Claude Haiku 4.5 · Vector databases · comparative prompt · alternative
“LanceDB uniquely excels at storing vectors alongside raw media” Kimi K2 · Vector databases · comparative prompt · alternative
And against it
Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.
“Chroma/LanceDB great for local/MVPs but may falter in distributed prod (scalability complaints).” Grok 4.20 · Vector databases · negative prompt · soft negative
Named alongside
The products named in the same answers as LanceDB, over the 31 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and LanceDB was named but was not.ShowHide
Named alongside
The products named in the same answers as LanceDB, over the 31 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and LanceDB was named but was not.
ShowHideProductSame answerTook the first choice insteadHead to head
A head-to-head page exists where both products are among a category's top eight. The other rows are the same fact without a page behind them, so they link to the product instead.
What carried it into the answer
The sites and pages cited by the answers that named LanceDB. A fact about retrieval, not a lever on the model.
Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 31 of the 31 answers that named LanceDB and are not a share of its labels.
Domains cited
iternal.ai22
atlan.com15
dev.to15
firecrawl.dev14
marktechpost.com13
g2.com10
redis.io10
cognee.ai8
actian.com7
alphacorp.ai7
121 of the 121 domain citations in answers naming LanceDB came from somebody else's page.
Pages cited
Pages are listed as the models cited them.
Search and answers
lancedb.com is not in Google's top ten for any of the category's searches. In the answers, LanceDB takes 0% of first choices and pgvector takes 35%.ShowHide
Search and answers
lancedb.com is not in Google's top ten for any of the category's searches. In the answers, LanceDB takes 0% of first choices and pgvector takes 35%.
ShowHideIn search
Google, US estimatesPosition for “vector database”
–
not in the top ten
Position for “vector databases”
–
not in the top ten
- Searches for its name, Google
- 2,900 a month (“lancedb”)
- AI search demand for its name, est.
- 30 a month
- Organic visits to its site
- about 1,213 a month
- Searches its site ranks for
- 384 · 29 in the top three
- Sites linking to it
- 1,340
- Paid Google search
- No ads found in the estimate; that does not mean it runs none
In answers
This editionShare of first choices
0%
rank 10 of 44 in vector databases
Segment leader
35%
pgvector
- First choices
- 0 across its categories
- Named in
- 31 answers
- Its own site cited
- in 31 of the answers that named it
Search figures are US estimates from DataForSEO, read September 28, 2026; AI search demand is its modeled, directional estimate, not a count of queries to any assistant. The answers are this edition's. Two measurements side by side: neither is read as the cause of the other.
What it publishes
ShowHide
What it publishes
ShowHideAddresses on lancedb.com
460
subdomains included
Content
153
counted in the table below
Documentation
230
Product · Integration
0 · 0
| Kind | Pages | Last 90 days | 2025-10 to 2026-09 | Latest | Categories named |
|---|---|---|---|---|---|
| Blog | 122 | 39 | 2026-09-28 | ||
| News or press | 15 | 13 | 2026-09-23 | ||
| Comparison | 11 | 0 | 2026-06-15 | Vector databases | |
| Case study | 4 | 1 | 2026-07-11 | ||
| Conference or event | 1 | 1 | 2026-09-27 |
Most recent
- 2026-09-28How jev compares to other rerankers blog
- 2026-09-27Announcing reverie summit 2026 conference or event
- 2026-09-2310 billion vector search blog
- 2026-09-23Make handwritten notes searchable optimizing an ocr pipeline with lancedb blog
- 2026-09-23Newsletter august 2026 news or press
- 2026-09-23Newsletter january 2026 news or press
How it is countedHide how it is counted
Every page lancedb.com exposes, subdomains included. Kind is read from the address and title. The last 90 days, the latest date and the twelve months count pages by when they were published, from the site's feeds, a date in the address, or the page's own publication date, read from up to a hundred of its most recently changed pages; a page that says only when it last changed is counted in its kind but not in when, so the recent counts are a floor, and a kind none of whose pages gives a publication date reads undated. An event counts as online when its address or title says so (webinar, on demand, virtual or online summit); a conference, summit, trade show, expo or roadshow that does not say so is counted as a conference or event, which on a vendor's site is mostly in person. Read September 29, 2026.
Follow LanceDB
An email the morning each edition publishes: where this product moved, where it held, and by how much against the noise floor. One address, confirmed by a click; a stop link in every email.
Already following? Everything you follow, with a stop for each.
The company
LanceDB is its own company.ShowHide
The company
LanceDB is its own company.
ShowHideIn its own words
Stated by the vendor, not checked- Positioning
- The AI-native Multimodal Lakehouse, the unified foundation to accelerate training dataset development.
- For
- teams building AI models
- Certifications
- GDPRHIPAASOC 2 Type 2
- Customers named
- MidjourneyRunwayCharacter.AIHarveyNetflixCodeRabbitContinueAnythingLLM
- Not stated on the pages read
- Price, starting price, free plan or trial, integrations, hosting
What LanceDB's own pages state, read September 28, 2026: lancedb.com, trust.lancedb.com, lancedb.com/customers. A claimed page can correct any of them.
Is this your product?
Claim this page
Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when LanceDB's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as LanceDB, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.
What a new claim receivesHide what a new claim receives
A new claim receives the current edition's vendor brief for LanceDB by email, built from the raw record of the edition. It shows:
- where LanceDB is named, by buyer and by framing, and which cells hold its first choices;
- the claims the models make when they name it, ranked, with the strongest and the weakest quoted;
- its vocabulary against the segment leader's, and the pages the models cited;
- who was chosen in the answers that did not name LanceDB, and every reason the record gives;
- a battlecard for each top rival: the head-to-head split, why they win, and the reservation quoted against them;
- one page of published figures cleared to show a buyer.