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 8 of 29, unchanged
MongoDB · 1 category · RankedMongoDB Atlas Vector Search
20Judge labels
1First choices
1Negative labels
13 / 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
2% in Vector databases for mid-market buyers
Rank 7 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 MongoDB Atlas Vector Search →By buyer segmentRead the same way at every buyer size.
Small business8 of 36
0%3 labels
under the label threshold
Mid-market7 of 44
2%12 labels
accepted challenger
Enterprise13 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 MongoDB Atlas Vector Search 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 | 2% | 7 of 44 | 8% | 12 | accepted challenger | ▲+2Since September 2026: 0% → 2%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked. |
Movement
Since September 2026, MongoDB Atlas Vector Search held within the floor in its one category: no change cleared 11 points.
Best category · Vector databases · by segment
Mid-market▲+2Since September 2026: 0% → 2%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
2%from 0% in September 2026
Inside the floor by 9 pointsRank 16 → 7 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 12 → 14 of 44
All categories · Mid-market
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| Vector databases | 0% | 2% | ▲+2Since September 2026: 0% → 2%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 16 → 7 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 MongoDB Atlas Vector Search 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 MongoDB Atlas Vector Search 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 | 0 | 0 | 0 |
| GPT-5.4 mini | 0 | 0 | 0 | 0 | 0 |
| Gemini 3.5 Flash | 0 | 0 | 1 | 0 | 1 |
| Perplexity Sonar | 0 | 0 | 1 | 0 | 1 |
| Grok 4.20 | 0 | 0 | 0 | 1 | 1 |
| 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 | 2 | 0 | 3 |
| Kimi K2 | 0 | 1 | 0 | 0 | 1 |
| GLM 4.7 FlashX | 1 | 0 | 1 | 0 | 2 |
| MiniMax M2.5 | 0 | 0 | 1 | 0 | 1 |
| 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
Direct2 labelsNone
Paraphrase3 labels1
Comparative6 labelsNone
Budget-constrained1 labelNone
Scale-constrained3 labelsNone
Negative5 labelsNone
First choiceAlternativeMentionNegative20 labels in all, every segment counted; 1 of the 1 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.
“the sweet spot today is MongoDB Atlas Vector Search (if your data already lives in MongoDB)” GLM 4.7 FlashX · Vector databases · paraphrase prompt · first choice
“Stick with MongoDB Atlas if your existing data pipeline is already built around MongoDB documents.” Qwen 3.7 Flash · Vector databases · direct prompt · alternative
“Use pgvector or Atlas Vector Search” Kimi K2 · Vector databases · scale 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.
“MongoDB Atlas gets pricing gripes in broader DB contexts” Grok 4.20 · Vector databases · negative prompt · soft negative
Named alongside
The products named in the same answers as MongoDB Atlas Vector Search, over the 20 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and MongoDB Atlas Vector Search was named but was not.ShowHide
Named alongside
The products named in the same answers as MongoDB Atlas Vector Search, over the 20 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and MongoDB Atlas Vector Search 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 MongoDB Atlas Vector Search. 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 19 of the 20 answers that named MongoDB Atlas Vector Search and are not a share of its labels.
Domains cited
firecrawl.dev8
iternal.ai8
dev.to7
g2.com7
atlan.com6
marktechpost.com6
cognee.ai5
qdrant.tech5
singlestore.com5
truefoundry.com5
Sixty-two of the sixty-two domain citations in answers naming MongoDB Atlas Vector Search came from somebody else's page.
Pages cited
Pages are listed as the models cited them.
Search and answers
mongodb.com is not in Google's top ten for any of the category's searches. In the answers, MongoDB Atlas Vector Search takes 2% of first choices and pgvector takes 35%.ShowHide
Search and answers
mongodb.com is not in Google's top ten for any of the category's searches. In the answers, MongoDB Atlas Vector Search takes 2% 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
- 260 a month (“mongodb atlas vector search”)
- AI search demand for its name, est.
- 2 a month
- Its company's site
- mongodb.com shared with MongoDB's other products; the figures are the site's
- Organic visits to its site
- about 342,632 a month
- Searches its site ranks for
- 46,940 · 4,155 in the top three
- Sites linking to it
- 45,167
- Paid Google search
- 20 searches its ads show for, about 365 clicks and $8,625 a month (estimate)
In answers
This editionShare of first choices
2%
rank 7 of 44 in vector databases
Segment leader
35%
pgvector
- First choices
- 1 across its categories
- Named in
- 20 answers
- Its own site cited
- in 19 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 MongoDB Atlas Vector Search
What the judge wrote, as written, with how often. The vendor table decides that these count as MongoDB Atlas Vector Search; a claim can dispute any of them.
MongoDB 1MongoDB Atlas 1
Follow MongoDB Atlas Vector Search
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
MongoDB is the company behind MongoDB Atlas Vector Search.ShowHide
The company
MongoDB is the company behind MongoDB Atlas Vector Search.
ShowHideOn record
Company records- Also from MongoDB
- MongoDB Atlas, MongoDB Atlas Vector
In its own words
Stated by the vendor, not checked- Positioning
- Build intelligent applications with vector search, hybrid search, and generative AI on the live operational data already in MongoDB.
- Free plan or trial
- Get started for free in minutes mongodb.com/products/platform/atlas-vector-search
- Hosting
- 125+ regions worldwide
- Customers named
- Novo Nordisk
- Not stated on the pages read
- Price, starting price, integrations, certifications
What MongoDB Atlas Vector Search's own pages state, read September 28, 2026: learn.mongodb.com/courses/mongodb-atlas-search, learn.mongodb.com/courses/managing-atlas-vector-search-indexes, learn.mongodb.com/courses/ds110-atlas-search-and-vector-search, learn.mongodb.com/pages/mongodb-vector-search-success-checklist, mongodb.com/products/platform/atlas-vector-search. 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 MongoDB Atlas Vector Search'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 MongoDB Atlas Vector Search, 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 MongoDB Atlas Vector Search by email, built from the raw record of the edition. It shows:
- where MongoDB Atlas Vector Search 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 MongoDB Atlas Vector Search, 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.