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 21 → 23 of 29
FAISS
17Judge labels
0First choices
7Negative labels
9 / 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.
Standing
5 labels, too few to rank
A product needs 10 labels in a category before a share or quadrant is stated. FAISS was named 5 times in Vector databases, where pgvector led with 35%. The labels and the evidence are below, counted exactly.
By buyer segmentRead the same way at every buyer size.
Small business24 of 36
0%7 labels
under the label threshold
Mid-market15 of 44
0%5 labels
under the label threshold
Enterprise44 of 45
0%5 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 FAISS 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% | 15 of 44 | 0% | 5 | under 10 labels · led by pgvector at 35% | =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, FAISS 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 11 → 14 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 42 → 43 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 11 → 15 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 FAISS 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 FAISS 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 | 0 | 1 | 0 | 1 |
| GPT-5.4 mini | 0 | 0 | 0 | 0 | 0 |
| Gemini 3.5 Flash | 0 | 0 | 0 | 0 | 0 |
| Perplexity Sonar | 0 | 0 | 0 | 0 | 0 |
| Grok 4.20 | 0 | 0 | 1 | 0 | 1 |
| Mistral Small | 0 | 0 | 1 | 0 | 1 |
| DeepSeek V4 Flash | 0 | 0 | 0 | 0 | 0 |
| Llama 4 Maverick | 0 | 0 | 0 | 0 | 0 |
| Qwen 3.7 Flash | 0 | 0 | 1 | 0 | 1 |
| Kimi K2 | 0 | 0 | 0 | 0 | 0 |
| GLM 4.7 FlashX | 0 | 1 | 0 | 0 | 1 |
| MiniMax M2.5 | 0 | 0 | 0 | 0 | 0 |
| GPT-6 Luna | 0 | 0 | 0 | 0 | 0 |
| Muse Glimmer 30B | 0 | 0 | 0 | 0 | 0 |
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
Paraphrase0 labelsNone
Comparative2 labelsNone
Budget-constrained3 labelsNone
Scale-constrained0 labelsNone
Negative11 labelsNone
First choiceAlternativeMentionNegative17 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.
“Faiss (as an embedded library) | No network overhead, full control over indexing” GLM 4.7 FlashX · Vector databases · negative 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.
No model argued against it.
Named alongside
The products named in the same answers as FAISS, over the 17 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and FAISS was named but was not.ShowHide
Named alongside
The products named in the same answers as FAISS, over the 17 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and FAISS 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 FAISS. 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 16 of the 17 answers that named FAISS and are not a share of its labels.
Domains cited
dev.to9
marktechpost.com8
atlan.com7
actian.com6
linkedin.com6
firecrawl.dev5
iternal.ai5
pecollective.com5
altexsoft.com4
dupple.com4
Fifty-nine of the fifty-nine domain citations in answers naming FAISS came from somebody else's page.
Pages cited
Pages are listed as the models cited them.
Search and answers
faiss.ai is not in Google's top ten for any of the category's searches. In the answers, FAISS takes 0% of first choices and pgvector takes 35%.ShowHide
Search and answers
faiss.ai is not in Google's top ten for any of the category's searches. In the answers, FAISS 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
- 5,400 a month (“faiss”)
- AI search demand for its name, est.
- 234 a month
- Organic visits to its site
- about 1,679 a month
- Searches its site ranks for
- 82 · 29 in the top three
- Sites linking to it
- 849
- Paid Google search
- No ads found in the estimate; that does not mean it runs none
Ads on Google
None found in Google's ad transparency records for the US
In answers
This editionShare of first choices
0%
rank 15 of 44 in vector databases
Segment leader
35%
pgvector
- First choices
- 0 across its categories
- Named in
- 17 answers
- Its own site cited
- in 16 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.
Names read as FAISS
What the judge wrote, as written, with how often. The vendor table decides that these count as FAISS; a claim can dispute any of them.
Faiss 3Faiss (Facebook AI Similarity Search) 1
Follow FAISS
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
FAISS is its own company.ShowHide
The company
FAISS is its own company.
ShowHideIn its own words
Stated by the vendor, not checked- Positioning
- a library for efficient similarity search and clustering of dense vectors
- Not stated on the pages read
- Who it is for, price, starting price, free plan or trial, integrations, certifications, hosting, customers
What FAISS's own pages state, read September 28, 2026: faiss.ai. 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 FAISS'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 FAISS, 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 FAISS by email, built from the raw record of the edition. It shows:
- where FAISS 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 FAISS, 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.