Small business▼−1Since September 2026: 46% → 45%, −1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.
45%from 46% in September 2026
Inside the floor by 10 pointsRank 1 of 29, unchanged
pgvector
180Judge labels
56First choices
16Negative labels
14 / 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
49% in Vector databases for small business buyers
Rank 1 of 44 in the mid-market standingendorsed leader
8 of 14 models made it the first choice on the direct prompt; 6% of its 66 labels there were negative.
What the models named instead of pgvector →By buyer segmentStrongest at small business.
Small business1 of 36
49%65 labels
endorsed leader
Mid-market1 of 44
35%66 labels
endorsed leader
Enterprise5 of 45
7%49 labels
accepted challenger
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 pgvector 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 | 35% | 1 of 44 | 6% | 66 | endorsed leader | ▲+1Since September 2026: 35% → 36%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked. |
Movement
Since September 2026, pgvector held within the floor in its one category: no change cleared 11 points.
Best category · Vector databases · by segment
Mid-market▲+1Since September 2026: 35% → 36%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.
36%from 35% in September 2026
Inside the floor by 10 pointsRank 2 → 1 of 35
Enterprise▼−3Since September 2026: 12% → 9%, −3 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
9%from 12% in September 2026
Inside the floor by 8 pointsRank 3 → 4 of 44
All categories · Mid-market
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| Vector databases | 35% | 36% | ▲+1Since September 2026: 35% → 36%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 2 → 1 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 pgvector 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 pgvector 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 | 2 | 1 | 1 | 0 | 4 |
| GPT-5.4 mini | 0 | 2 | 0 | 0 | 2 |
| Gemini 3.5 Flash | 3 | 2 | 0 | 1 | 6 |
| Perplexity Sonar | 2 | 2 | 1 | 0 | 5 |
| Grok 4.20 | 3 | 2 | 0 | 1 | 6 |
| Mistral Small | 3 | 0 | 1 | 0 | 4 |
| DeepSeek V4 Flash | 4 | 2 | 0 | 0 | 6 |
| Llama 4 Maverick | 2 | 1 | 0 | 0 | 3 |
| Qwen 3.7 Flash | 3 | 0 | 1 | 0 | 4 |
| Kimi K2 | 2 | 2 | 1 | 1 | 6 |
| GLM 4.7 FlashX | 2 | 3 | 1 | 0 | 6 |
| MiniMax M2.5 | 2 | 2 | 0 | 0 | 4 |
| GPT-6 Luna | 2 | 3 | 0 | 1 | 6 |
| Muse Glimmer 30B | 1 | 2 | 1 | 0 | 4 |
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
Direct31 labels18
Paraphrase26 labels10
Comparative34 labels6not counted in share
Budget-constrained31 labels18
Scale-constrained22 labels10
Negative36 labels19not counted in share
First choiceAlternativeMentionNegative180 labels in all, every segment counted; 56 of the 81 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.
“Unless you have 2-3 people who can own this infrastructure, lean managed or existing DB extension first.... Already run Postgres heavily, <10M vectors | pgvector” Kimi K2 · Vector databases · scale prompt · first choice
“The best vector database for a mid-market B2B company is pgvector, which is a PostgreSQL extension. It is the strongest default for mid-market enterprises.” Llama 4 Maverick · Vector databases · direct prompt · first choice
“evaluating pgvector might save you the overhead of learning a new technology, as it handles vector search well within a relational environment” Qwen 3.7 Flash · Vector databases · scale prompt · first choice
“Start with pgvector (or your existing Postgres) unless you have a clear reason not to... it's often the pragmatic default for most applications.” DeepSeek V4 Flash · Vector databases · comparative prompt · first choice
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.
“| Poor filtered search | pgvector, basic setups | ... | Reindexing locks | pgvector | Large HNSW rebuilds block reads/writes” Grok 4.20 · Vector databases · negative prompt · soft negative
“pgvector when you expect very high vector-search throughput or huge scale without testing” GPT-6 Luna · Vector databases · negative prompt · soft negative
“Be cautious at massive scale ... you must be cautious of its ceilings” Gemini 3.5 Flash · Vector databases · negative prompt · soft negative
“Be cautious with pgvector — convenient but hits walls at scale” Kimi K2 · Vector databases · negative prompt · soft negative
Named alongside
The products named in the same answers as pgvector, over the 179 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and pgvector was named but was not.ShowHide
Named alongside
The products named in the same answers as pgvector, over the 179 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and pgvector 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 pgvector. 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 170 of the 179 answers that named pgvector and are not a share of its labels.
Domains cited
dev.to84
iternal.ai72
atlan.com63
marktechpost.com60
pecollective.com54
g2.com53
firecrawl.dev52
cognee.ai46
encore.dev39
actian.com35
No domain is on file for pgvector, so its own site is not marked.
Pages cited
Pages are listed as the models cited them.
Search and answers
Where pgvector stands in Google search beside where it stands in the models' answers.ShowHide
Search and answers
Where pgvector stands in Google search beside where it stands in the models' answers.
ShowHideIn search
Google, US estimates- Searches for its name, Google
- 5,400 a month (“pgvector”)
- AI search demand for its name, est.
- 119 a month
- Its own site
- No site of its own on file, so no site figures
In answers
This editionShare of first choices
35%
rank 1 of 44 in vector databases
First choices
56
across its categories
- Named in
- 179 answers
- Its own site cited
- No site on file to match
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 pgvector
What the judge wrote, as written, with how often. The vendor table decides that these count as pgvector; a claim can dispute any of them.
PostgreSQL with pgvector 5PostgreSQL + pgvector 4pgvector (PostgreSQL) 3Postgres + pgvector 2PostgreSQL 1Postgres with pgvector 1pgvector (PostgreSQL extension) 1pgvector (on Postgres via Supabase, Neon, or Aiven) 1pgvector (on Postgres/Supabase) 1
Follow pgvector
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.
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 pgvector'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 pgvector, 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 pgvector by email, built from the raw record of the edition. It shows:
- where pgvector 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 pgvector, 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.
Badges
Wins only: a badge exists where pgvector is ranked in a category's top three this edition. Three sizes, one file each, light and dark; the code is under each.ShowHide
Badges
Wins only: a badge exists where pgvector is ranked in a category's top three this edition. Three sizes, one file each, light and dark; the code is under each.
ShowHideAI models recommend pgvector first in Vector databases this edition. Each badge says so in the buyer's words, names the edition, and links to the standing. The next edition issues a new badge; this one stays true as a record of October 2026.
Wide · 320 × 104 Website, beside other badges
Square · 240 × 240 LinkedIn, slides
Compact · 200 × 56 Email signature, footer
Badges read on light and dark pages. Alt text carries the claim, the category, the edition and the index, so it stays a citation where the image does not load.