IT AI Index

Index › Vendors › MongoDB Atlas Vector Search · September 2026 Edition

MongoDB · 1 category · Named, not ranked

MongoDB Atlas Vector Search

19Judge labels

0First choices

4Negative labels

8 of 12Models named it

1Category

September 2026 Edition. Every number here is derived from the raw labels under vendor table vv2026-09.2, every buyer segment counted.

Standing

7 labels, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. MongoDB Atlas Vector Search was named 7 times in Vector databases, where Qdrant led with 37%. The labels and the evidence are below, counted exactly.

By buyer segmentRead the same way at every buyer size.

Small business8 of 25

under the label threshold

Mid-market16 of 24

under the label threshold

Enterprise12 of 43

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
Vector databases	Data platform	0%	16 of 24	29%	7	under 10 labels · led by Qdrant at 37%

Movement

This is the first edition on this tier, so no move can be computed for MongoDB Atlas Vector Search yet. From the next edition this section shows, per buyer segment, whether its share moved by more than the measured noise floor.

By model

How each model treated MongoDB Atlas Vector Search across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.

Model	Alternative	Mention	Negative	Labels
Claude Haiku 4.5	0	0	0	0
GPT-5.4 mini	0	1	0	1
Gemini 3.5 Flash	0	1	0	1
Perplexity Sonar	0	0	1	1
Grok 4.1 Fast	0	0	0	0
Mistral Small	1	0	0	1
DeepSeek V4 Flash	0	1	1	2
Llama 4 Maverick	0	0	0	0
Qwen 3.7 Flash	0	0	0	0
Kimi K2	0	1	0	1
GLM 4.7 FlashX	0	0	0	0
MiniMax M2.5	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

Paraphrase2 labelsNone

Comparative4 labelsNone

Budget-constrained2 labelsNone

Scale-constrained4 labelsNone

Negative6 labelsNone

First choiceAlternativeMentionNegative19 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.

“If you're already using MongoDB, MongoDB Atlas Vector Search is the easiest path.” Mistral Small · Vector databases · paraphrase 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.

“These are general-purpose systems with vector features, so they may be less ideal than a purpose-built vector stack for certain retrieval patterns.” Perplexity Sonar · Vector databases · negative prompt · soft negative

“These added vector search but weren't designed for it from the ground up” DeepSeek V4 Flash · Vector databases · negative prompt · soft negative

Named alongside

The products named in the same answers as MongoDB Atlas Vector Search, over the 19 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.

ProductSame answerTook the first choice insteadHead to head

Pinecone18 of 194Not in the top three

Qdrant17 of 191Not in the top three

Weaviate17 of 190Not in the top three

pgvector16 of 199Not in the top three

Milvus14 of 191Not in the top three

Chroma8 of 190Not in the top three

Redis7 of 190Not in the top three

Elasticsearch6 of 190Not in the top three

Elasticsearch / OpenSearch5 of 190Not in the top three

Zilliz Cloud5 of 190Not in the top three

A head-to-head page exists where both products are in a category's top three. 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, four of the twelve in this edition, so these counts come from 13 of the 19 answers that named MongoDB Atlas Vector Search and are not a share of its labels.

Domains cited

firecrawl.dev7

encore.dev6

medium.com5

actian.com4

aiopsschool.com4

atlan.com4

iternal.ai4

dev.to3

g2.com3

pinecone.io3

Forty-three of the forty-three domain citations in answers naming MongoDB Atlas Vector Search came from somebody else's page.

Pages cited

firecrawl.dev/blog/best-vector-databases7

encore.dev/articles/best-vector-databases6

actian.com/blog/databases/how-to-evaluate-vector-databases-in-20264

aiopsschool.com/blog/top-10-vector-database-platforms-features-pros-cons-comparison4

atlan.com/know/top-vector-databases-enterprise-ai4

iternal.ai/insights/best-vector-databases-20264

g2.com/articles/best-vector-databases3

sesamedisk.com/when-not-to-use-vector-database-20263

Pages are listed as the models cited them.

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 4MongoDB Atlas 2

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.

It does not get any change to labels, shares or verdicts, any preview, or any say over which quotes appear. A verification link goes to your work email; an address at mongodb.com is approved on the spot, any other address is reviewed by hand.

Your name and company appear on the claimed page, or the company alone if you ask below. A title and a LinkedIn address appear there too if you give them, and are left off if you do not. Your email address is never published.